From 5388acf3ec8d2426d2b810e96a4256a8473080ec Mon Sep 17 00:00:00 2001 From: ladroid Date: Mon, 9 Nov 2026 14:45:31 +0100 Subject: [PATCH 1/2] Splits sparql_database.rs. Declares the Python environment in pyproject.toml and uv.lock. 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zIEB;r6kp*yzQuRAh@bHre#alUEJ2zjl}n1WOj<6jknWW3k~T`6(srp!N=V((khDj# zC07~~i;#-UONw@}lnzmC(tbkBqwj&7dD6SrXI|ui_RSqzrVsNj%;8*{U%6o6qUh}_ zSFc%jZ}S8k1%Qi~y_yRi{I4O62Y(M8lW|L|3@=rdMS6%I`hhEK6znW=RW0L8kU3z( zw<@uSI)fM_ns+EGBI-y-_PT1w;; z&HI$NqB6c;h;~b)EuwO&{bP{7ze2!0*7&gApbmGz{_|Qui1#~`bI$;txZrM}1q|m*YZ` Zh0?$NAzs - +The script derives the path from the interpreter pyo3 uses, so do not write a path +down anywhere. -- bullets for multi-part changes, one per logical change +## Workflow - -``` +1. Branch from `main`. +2. Make one logical change per commit (see [Commit messages](#commit-messages)). +3. Run the checks in [Before you commit](#before-you-commit). +4. Open a pull request. It is squash-merged, with a short summary as the message. -**Subject** — third person present, ≤72 characters, no trailing period. -Matching existing history: `Adds …`, `Fixes …`, `Solves …`, `Removes …`. -Not `Add`, not `Added`. +## Before you commit -**Body** — required for anything non-trivial. Wrap at 72 characters, blank line -after the subject. +1. **`scripts/verify.sh`** runs everything CI blocks on and prints a summary: + tests, both feature configurations, benches compiled with `--no-run`, `cargo doc` + with links denied, the module-graph cycle check, the README examples, the + examples run end to end, and the Python checks. Compare the test count with the + known-good baseline, and name any failure you are not fixing. +2. **`scripts/warning_report.py`**: the warning count must not go up. +3. **`changes.txt`**: add a line if users will notice the change. + +Do not run `cargo test --workspace --all-targets`. It pulls the heavy benchmark +sweeps into a debug test run that does not finish. `verify.sh` runs the tests +without it and compiles the benches separately. + +### What CI blocks on + +| Check | Command | +|---|---| +| Tests, default and `--no-default-features` | `cargo test` | +| Minimum Rust version | build with 1.85 | +| Documentation | `cargo doc` with `-D rustdoc::broken_intra_doc_links` | +| Rust formatting | `cargo fmt --check` | +| Lints held at zero | `scripts/check_lint.sh ` | +| Module graph has no cycles | `scripts/module_graph.py --check` | +| README examples compile | `scripts/readme_examples.py` | +| `uv.lock` matches `pyproject.toml` | `uv lock --check` | +| Python lint | `uv run ruff check` | +| Python formatting | `uv run ruff format --check` | +| Python tests | `uv run pytest` | + +### Examples + +`scripts/smoke_examples.py` runs every example that can finish on its own: -Rules of thumb that have paid off here: +```bash +scripts/smoke_examples.py --list # what runs, what is skipped and why +scripts/smoke_examples.py # run them; non-zero names each failure +``` -- **Explain why, not only what.** The diff already shows what changed. -- **Record decisions a future reader would otherwise re-litigate** — why a CI - gate is advisory, why a dependency version is pinned, why an approach was - rejected. These are the facts that get rediscovered expensively. -- **Include evidence.** Test pass/fail counts against the known baseline, flake - rates, benchmark deltas. A claim without a number cannot be checked later. -- **Say what you deliberately did not do, and why.** Absence of work is - otherwise indistinguishable from oversight. -- **Never bundle an unrelated fix into a refactor commit.** It destroys - bisectability. Split them, even when the fix is one line. +New examples are picked up automatically. An example that cannot finish unattended +(it needs a broker, or serves until stopped) goes in `SKIP` with a reason. -### Example +### Intermittent failures +```bash +scripts/flakehunt.sh 40 # 40 passes over every test binary +scripts/flakehunt.sh 40 ml # only binaries matching "ml" ``` -Fixes order-dependent flake in scan conflict test -scan_rejects_repeated_variable_conflicts_before_extending_rows failed -roughly 20% of the time (5 of 25 isolated runs). The code under test was -fine; the test asserted a positional property of a result that has no -defined order. +It runs the test binaries directly and reports the name of each failing test, with +full logs. Prefer it to looping `cargo test`. -DatasetIndex::query_graph returns hash order, and Rust re-seeds -RandomState per process, so the fixture quads come back differently each -run. The precondition now checks the fixture by content, and the result is -compared as a multiset via the module's existing sorted() helper. +## Python tooling -0 failures in 30 runs, from 5 in 25. +Cargo builds all Rust code. uv manages everything Python: the environment in +`.venv`, the dependencies declared in `pyproject.toml`, and the exact versions in +`uv.lock`. Ruff lints and formats; pytest runs the tests. -Eight other tests in this file compare Bindings with assert_eq! on a Vec -and are latently exposed to the same variation. +| Rust | Python | +|---|---| +| `cargo fmt` | `uv run ruff format` | +| `cargo clippy` | `uv run ruff check` | +| `cargo test` | `uv run pytest` | + +```bash +uv run ruff check --fix # lint, applying safe fixes +uv run ruff format # format +uv run pytest # Python tests ``` -## Before you commit +- **Run anything built with `ml` through `uv run`**, for example + `uv run cargo test -p kolibrie --features ml`. The embedded interpreter then + loads packages from `.venv` instead of whatever is installed for the user. +- **Dependencies live in groups:** `ml` for the runtime the `ml` feature needs, + `dev` for tools. Add one with `uv add --group ml ` or + `uv add --group dev `. +- **Commit `pyproject.toml` and `uv.lock` together.** Never edit `uv.lock` by hand; + run `uv lock` instead. +- **Ruff's settings live in `pyproject.toml`**, with the same 100-column limit as + `rustfmt.toml`. -1. `scripts/verify.sh` — runs tests, `cargo check --all-targets`, both feature - configurations, the benches with `--no-run`, `cargo doc` with the same deny flag - the blocking CI job uses, `scripts/module_graph.py --check` so the module graph - stays acyclic, and the examples both compiled and *run*, then prints a summary. Compare the test count against the known-good baseline rather than - against zero, and name any pre-existing failure you are not fixing. -2. `python3 scripts/warning_report.py` — the warning count is a tracked metric - and should not go up. -3. Update `changes.txt`. - -Do not use `cargo test --workspace --all-targets`: `--all-targets` pulls the four -heavy benchmark sweeps into the test run in debug mode, where -`citybench_cross_window_compare` does not finish — it ran over 36 minutes without -completing. `verify.sh` runs the tests without `--all-targets` and compile-checks -the benches separately with `--no-run`. - -Summarise test output only while also keeping it. A pipeline that counted passes -through `awk` once reported "642 passed, 1 failed" and discarded the failing -test's name, which cost an afternoon to recover. `verify.sh` writes full logs. - -### Examples are run, not just compiled - -`scripts/smoke_examples.py` runs every example that can finish on its own, and -`verify.sh` calls it. It exists because compiling an example proves very little: -three were broken for months while compiling cleanly — one used a rule syntax the -parser does not accept, one read an absolute path into a personal directory, and one -read a dataset that `.gitignore` excludes. +## Commit messages -```bash -scripts/smoke_examples.py --list # what runs, what is skipped and why -scripts/smoke_examples.py # run them; non-zero names each failure ``` +s -Three are skipped, listed in `SKIP` in that script with a reason each: two need an -MQTT broker and one serves HTTP until interrupted. -The eleven behind the `ml` feature are not run either — they need a trusted-local -training directory, a model allowlist, or the MNIST corpus, so running them here -would report environment setup as failure. + -Adds about a minute, almost all of it two streaming demos sleeping through timed -windows. If you add an example, it is picked up automatically: the list comes from -`cargo metadata`, not from a list in the script. An example that cannot finish -unattended belongs in `SKIP` with a reason, not left to time out. +- one bullet per logical change, for multi-part commits -### When a test fails intermittently + +``` -`scripts/flakehunt.sh [passes] [name filters]` stresses the prebuilt test binaries -directly and reports the name of anything that fails, with full logs. +- **Subject:** third person present (`Adds …`, `Fixes …`, `Removes …`), at most + 72 characters, no trailing period. +- **Body:** required for anything non-trivial. Wrap at 72 characters. +- **Explain why.** The diff already shows what. +- **No measurements or failed attempts:** no timings, speedups, benchmark results + or approaches that were tried and lost. They belong in internal notes. +- **Describe user impact in words**, such as "lower memory use" or "a new error". +- **Keep unrelated fixes out of refactor commits**, even one-line ones. +- **Squash merges** get a short summary of the pull request, not the collected + commit messages. + +Example: -```bash -scripts/flakehunt.sh 40 # 40 passes over every test binary -scripts/flakehunt.sh 40 ml # only binaries matching "ml" ``` +Caches parsed prefixes per query -Prefer it over looping `cargo test`: a full workspace run takes ~3 minutes, and -eight of them once failed to reproduce a 1-in-25 flake that this found on its -first pass. Exits non-zero if anything failed. +Queries that repeat the same PREFIX declarations parsed them every +time they were used. They are now parsed once per query, which +shortens parsing for long queries. The public API is unchanged. +``` -Anything built with the `ml` feature needs Python's shared library on the loader's -path. Source the helper rather than writing a path down: it derives the directory -from the interpreter pyo3 resolves, and sets whichever variable the running platform -reads, so it works on Linux and in CI as well as on macOS. +## Changelog -```bash -. scripts/python_env.sh -``` +`changes.txt` lists what changed for people who use Kolibrie, in the spirit of +[Keep a Changelog](https://keepachangelog.com/en/1.1.0/). + +- One line per user-visible change, under the current version. +- Grouped as Added, Changed, Deprecated, Removed, Fixed or Security. +- Say what changed for a user, not how or why. +- Internal refactors need no entry. +- Newest version first. Open a new heading only for a new version. -`verify.sh`, `check_lint.sh` and `flakehunt.sh` already do this. The default suite -does not need it at all -- pyo3 bakes an absolute rpath into the binaries it links, -and all 773 tests pass with nothing set. - -## Writing documentation - -`missing_docs` is being burned down per crate, and each crate that reaches zero gets -a blocking gate (see `scripts/check_lint.sh`). Two rules are worth knowing before you -start, because both have cost real debugging time: - -- **A module with its own `//!` must not also carry an outer `///` on its `pub mod` - declaration.** The two concatenate, and rustdoc then resolves the module file's - bare intra-doc links in `lib.rs`'s scope, where that module's own items are not - visible. It looks like the links are wrong; they are not. Put the summary in one - place — the module file — and rustdoc uses its first line. - **In practice:** when you add a `//!` to a module, delete the outer `///` from its - `pub mod` line in the same change. Doing one without the other is what breaks it, - and it has happened repeatedly in this burn-down — including twice after this - warning was written down. `verify.sh` now runs `cargo doc`, so it is caught before - a commit rather than in CI; `cargo check` cannot see it, because it does not - resolve doc links at all. -- **Bracketed notation in a doc comment is read as a link.** `Box[a,b]` and friends - need backticks, or `cargo doc` fails. The `docs` CI job is blocking and runs with - `-D rustdoc::broken_intra_doc_links`. - -A doc comment that restates the item's name passes the lint and tells a reader -nothing, which is worse than the warning because it looks like documentation. Prefer -writing down the invariant, the unit, the panic, or the thing a caller would -otherwise discover by experiment. - -## Scripted edits must preserve line endings - -The tree has mixed line endings — 15 CRLF `.rs` files against 221 LF, with -`kolibrie/src/rsp/simple_r2r.rs` mixed *within* the file — and `.gitattributes` -carries no `text` or `eol` rule, so nothing normalises them. - -A script that reads a file as text and writes it back will silently convert the -whole file, because Python's `read_text()` and most equivalents apply universal -newlines on the way in and a single ending on the way out. The result compiles, -passes tests, and shows up as every line deleted and re-added, which buries the -real change and destroys `git blame` on the file. - -This has happened twice, to these same two files. Read bytes, and either leave -existing lines untouched or re-apply each line's original ending: - -```python -raw = path.read_bytes() -lines = raw.splitlines(keepends=True) # keeps \r\n per line -# insert whole lines; do not round-trip the rest through str ``` +0.4.0 +Added +- What a user can now do +Fixed +- What no longer goes wrong for them +``` + +## Comments and documentation -Check it before committing: `git diff --numstat` on a comment-only change should -show additions and no deletions. If it shows the whole file rewritten, the endings -moved. +Comments are short and say what something is or does. One line gives the main +idea. -Normalising the tree to one ending is a reasonable thing to want, but as its own -commit with a `.gitattributes` rule, not as a side effect. +**Doc comments (`///`, `//!`)** -## Public API surface is not tracked automatically +- One sentence saying what the item does, including a module's `//!`. +- No period at the end. +- No measurements, timings, known weaknesses, history or rejected approaches. +- Public fields and enum variants get one short line (`missing_docs` is blocking). + Private fields get none. +- A tested code example may follow the sentence after an empty `///` line. -There was a `public-api` CI job that diffed each crate's public surface against a -checked-in `api-baseline/`. It is gone, and the baseline was never created, so it -never reported anything. +**Comments inside code (`//`)** -It was dropped for one reason: `cargo-public-api` works by asking rustdoc for JSON, -which is unstable and nightly-only. That made nightly a requirement for the job and -for anyone refreshing a baseline, and the format shifting between nightly releases -would have produced diffs that are not API changes. The repository now has no -reference to nightly at all — `rust-toolchain.toml` pins 1.96.0 and the MSRV job -enforces 1.85. +- None by default; the code and its doc comment should be enough. +- One short line only where a reader would otherwise get it wrong. +- `// SAFETY:` is required above every `unsafe` block or impl. -What this costs: nothing warns when a `pub` item is added, removed or changed shape. -That matters most when crates are split or merged, since a lot of surface moves at -once. Until something replaces it, a change to the public API is something a reviewer -has to notice, so say so in the commit message when you change one — the `unreachable_pub` -gate catches a module becoming reachable, but nothing catches a signature changing. +**Python:** one-line docstrings without a period; no `#` comments except the +licence header and tool pragmas. Layout is whatever `ruff format` produces. -If this is picked up again, the thing to check first is whether rustdoc JSON has -stabilised, which removes the only real objection. +**Every file** keeps its MPL licence header. -## Known gaps +```rust +/// Parses N-Triples and adds them +pub fn parse_ntriples_and_add(&mut self, ntriples_data: &str) { +``` -One thing is deliberately left undone, recorded so it is not rediscovered as a -surprise. +**Two rules that break `cargo doc`:** -**The HTTP status mapping exists twice.** `kolibrie::sparql_protocol::query_result` -and `kolibrie-http-server`'s `ml_error_response` both map a `QueryError` to a status -and a wire code, in two crates, and they have to be changed together. Each carries a -comment naming the other. Deduplicating means `kolibrie-http-server` growing a -`lib.rs` — it is binary-only today, which is also why nothing can reuse its request -handling. +- A module with its own `//!` must not also have a `///` on its `pub mod` line. + When you add one, delete the other. +- Bracketed notation such as `Box[a,b]` is read as a link. Put it in backticks. -The ten `redundant explicit link target` warnings that used to be listed here are -gone, fixed in one mechanical pass once they were the only thing left. +## Code conventions -If you add an entry, say how to re-derive its numbers. That one went stale twice: it -was still quoted as eleven warnings across five crates after one had been fixed in -passing, and then as ten after the rest were. +- **No `unsafe` in library crates.** Where it is unavoidable outside them, add + `// SAFETY:` and a scoped `#[allow(unsafe_code)]`. +- **The module graph has no cycles.** Run `scripts/module_graph.py` when adding a + module. +- **Public API changes are not tracked automatically.** Say so in the commit + message when you add, remove or change a `pub` item. +- **Scripted edits preserve line endings.** Read and write bytes, not text, so a + file's CRLF or LF endings stay as they were. A comment-only change should show + no whole-file rewrite in `git diff --numstat`. ## Notes for AI coding assistants -- Do not commit, push, or create branches unless explicitly asked. -- Do not weaken a test to make it pass. If a test is wrong, say so and explain - why before changing it. -- Report outcomes faithfully: if tests fail, show the output; if a step was - skipped, say so. -- Prefer reusing an existing helper over adding a parallel one — check the - surrounding module first. +Everything above applies to you. In addition: + +**How you work** + +- Do not commit, push or create branches unless asked. +- Read the module you are changing and its callers before writing code. +- Ask before changing anything the task did not mention. +- Report outcomes faithfully: show failing output, and say when a step was skipped. +- Do not call a change done until `scripts/verify.sh` passes. Compiling is not + enough. +- Run Python and `ml` builds through `uv run`. Do not use `pip install`, and do not + install anything globally. + +**How you write code** + +- **Stay in scope.** Change only what the task needs: no drive-by refactors, + renames, reformatting or reordering of code you did not otherwise touch. +- **Follow the surrounding code.** Match its naming, error handling and structure + rather than introducing a new style. +- **Reuse before adding.** Search for an existing helper, type or trait before + writing a parallel one. +- **Do not guess APIs.** Check that a function, method or field exists before you + call it. +- **Return errors, do not panic,** on anything a caller or input controls. No new + `unwrap()` or `expect()` on those paths. +- **No new dependencies** without asking, in `Cargo.toml` or `pyproject.toml`, + including new features of existing ones. When one is approved, add it with + `cargo add` or `uv add` so the lock file follows. +- **Keep the public API stable.** Do not add, remove or change a `pub` item unless + the task asks for it. +- **Fix warnings, do not silence them.** No new `#[allow(...)]` except the scoped + `unsafe_code` case above, and no new `# noqa` or ruff ignores. +- **Test what you change.** A behaviour change comes with a test; a bug fix comes + with a test that fails without the fix. +- **Do not weaken a test to make it pass.** If a test is wrong, say why first. +- **Leave no traces.** No commented-out code, no `TODO`s, no `println!`, `dbg!` or + `print()` left behind, no comments describing your own edit. +- **Do not edit generated files by hand.** Regenerate `uv.lock` with `uv lock`. diff --git a/Dockerfile b/Dockerfile index 76f37944..42b6f7ac 100644 --- a/Dockerfile +++ b/Dockerfile @@ -37,15 +37,22 @@ ENV RUSTUP_HOME=/usr/local/rustup \ CARGO_HOME=/usr/local/cargo \ PATH=/usr/local/cargo/bin:$PATH -# ML runtimes are installed only for explicitly enabled images. +COPY --from=ghcr.io/astral-sh/uv:0.13.5 /uv /usr/local/bin/uv + +# ML runtimes are installed only for explicitly enabled images, from uv.lock +ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ + UV_PYTHON_DOWNLOADS=never \ + UV_COMPILE_BYTECODE=1 \ + UV_LINK_MODE=copy RUN if [ "$ENABLE_ML" = "true" ]; then \ - apt-get update && apt-get install -y python3 python3-pip python3-dev && \ - pip3 install --no-cache-dir numpy scikit-learn rdflib pandas packaging psutil && \ - pip3 install --no-cache-dir -e /app/ml/src && \ + apt-get update && apt-get install -y python3 python3-dev && \ + uv sync --locked --no-dev --no-cache && \ rm -rf /var/lib/apt/lists/*; \ fi -ENV PYTHONPATH=/app/ml/src:/app/python/target/release:${PYTHONPATH} +# The embedded interpreter finds the environment through the python3 on PATH +ENV PATH=/app/.venv/bin:$PATH \ + PYTHONPATH=/app/ml/src:/app/python/target/release:${PYTHONPATH} ARG ENABLE_WEB_UI RUN if [ "$ENABLE_ML" = "true" ]; then \ diff --git a/README.md b/README.md index d1c994a4..fe626559 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ For more information about our research and ongoing projects, please visit the [ ### Native Installation -Ensure you have [Rust](https://www.rust-lang.org/tools/install) installed (version 1.60 or higher). +Ensure you have [Rust](https://www.rust-lang.org/tools/install) installed (version 1.85 or higher). Clone the repository: @@ -60,7 +60,7 @@ cargo build --release Then, include it in your project: ```rust -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; ``` ### WebUI @@ -72,6 +72,24 @@ cargo run --bin kolibrie-http-server After that in the browser type `localhost:8080` or `0.0.0.0:8080` +### Python and Machine Learning + +The `ml` feature runs Python models inside Kolibrie. Its Python packages are +declared in `pyproject.toml` and pinned in `uv.lock`, and are installed with +[uv](https://docs.astral.sh/uv/getting-started/installation/) and Python 3.10 or +newer: + +```bash +uv sync +``` + +Then run anything built with the `ml` feature through `uv run`, so the embedded +interpreter loads the packages uv installed: + +```bash +uv run cargo run --bin kolibrie-http-server --features ml +``` + ### Docker Installation **Kolibrie** provides Docker support for running the web UI or opening a development shell. The Docker setup automatically handles Rust and Python ML dependencies which are fully integrated into Kolibrie. @@ -152,23 +170,23 @@ docker run -it \ ### Initializing the Database -Create a new instance of the `SparqlDatabase`: +Create a new instance of the `KolibrieDB`: ```rust -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; fn main() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); // Your code here } ``` The snippets that follow assume that `db`, and the imports above, are in scope. @@ -340,7 +358,7 @@ Supported forms are `INSERT DATA`, `DELETE DATA`, `INSERT ... WHERE`, `DELETE ... WHERE`, combined `DELETE/INSERT ... WHERE`, and `DELETE WHERE`. The `WHERE` pattern is evaluated once before changes are applied, with deletes performed before inserts. Python exposes the same behavior as -`SparqlDatabase.update()`. Other SPARQL 1.1 Update forms such as `WITH`, +`KolibrieDB.update()`. Other SPARQL 1.1 Update forms such as `WITH`, `USING`, `LOAD`, and graph-management operations are not part of this release. #### Query with FILTER @@ -524,7 +542,7 @@ for (subject, predicate, object) in results { #### Query with Joins ```rust -let other_db = SparqlDatabase::new(); +let other_db = KolibrieDB::new(); // ... populate other_db ... let results = db.query() @@ -559,10 +577,10 @@ The **Volcano Optimizer** is integrated within **Kolibrie** to optimize query ex ```rust use kolibrie::execute_query::*; -use kolibrie::sparql_database::*; +use kolibrie::database::*; fn main() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); // Parse N-Triples data let ntriples_data = r#" @@ -607,7 +625,7 @@ than a cast. ```rust -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; use shared::rdf_term::{kind_of, numeric_value, spell_literal, TermKind}; fn main() { @@ -634,7 +652,7 @@ fn main() { assert_eq!(kind_of("http://example.org/alice"), TermKind::Iri); // Which is what lets the engine aggregate typed data. - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_ntriples_and_add(&format!( " \"30\"^^<{XSD_INTEGER}> .\n \"12\"^^<{XSD_INTEGER}> .\n" )); @@ -655,10 +673,10 @@ readers scope it. Publishing a graph for another system to merge means replacing blank nodes with IRIs, which RDF 1.1 §3.5 calls skolemisation: ```rust -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; fn main() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_ntriples_and_add("_:b \"1\" .\n"); // Document-local, and sound only inside this store: a second instance counts @@ -675,10 +693,10 @@ fn main() { ### Working with the Reasoner @@ -761,28 +779,12 @@ Alice is ancestor of Charlie ## API documentation -Two generated references, neither written by hand: - -- **[`cargo doc --workspace --open`]** is the authority. It carries the types, the - bounds, the panics and the prose, and it cannot disagree with the code because it - *is* the code. -- **[`docs/api.md`](docs/api.md)** is the index: every public item, by crate and - file, generated by `scripts/api_surface.py`. It exists so a change to the public - surface shows up in a diff. - -How the workspace fits together — the crate graph, the modules of `kolibrie` -arranged into layers, and what depends on what — is in -[`docs/architecture.md`](docs/architecture.md), generated by -`scripts/module_graph.py`. +The API reference is generated from the source by +[`cargo doc`](https://doc.rust-lang.org/cargo/commands/cargo-doc.html): -There used to be 371 lines here restating method signatures by hand, and they -drifted: three methods that no longer existed were documented for a while after they -were removed, here and in five translations that are gone for the same reason -- -nothing checked any of them. `scripts/verify.sh` now checks that `docs/api.md` -matches the source and that every example in this README still compiles, so neither -can go stale quietly again. - -[`cargo doc --workspace --open`]: https://doc.rust-lang.org/cargo/commands/cargo-doc.html +```bash +cargo doc --workspace --open +``` ## How to Contribute diff --git a/changes.txt b/changes.txt index 3c16adda..f6aad25a 100644 --- a/changes.txt +++ b/changes.txt @@ -1,711 +1,69 @@ 0.3.0 -1. Add workspace-level package metadata ([workspace.package]: version, edition, license, rust-version, authors, repository) inherited by all 8 crates; shared, python, cli and kolibrie-http-server previously had no license field despite MPL headers in every source file -2. Add [workspace.lints] with all lints at warn, opted into by every crate; establishes the burn-down baseline of 3022 warnings (2713 missing_docs, 144 unreachable_pub, 80 missing_debug_implementations, 50 unused_qualifications, 27 deprecated, 7 unsafe_code) -3. Declare MSRV 1.85 (the floor imposed by hashbrown, indexmap and toml_edit) and enforce it with a dedicated CI job rather than only declaring it; pin 1.96 in rust-toolchain.toml for development -4. Add GitHub Actions CI: test (default and --no-default-features), MSRV, rustdoc and the ml/python build are blocking; rustfmt, clippy, cargo-deny and cargo-public-api are advisory until the surface shrinks -5. Add scripts/warning_report.py so the warning count is reproducible locally and not only in CI -6. Add rustfmt.toml and deny.toml (licences and duplicate versions; the advisories database is deliberately not a blocking gate yet) -7. Include datalogmtl and ml in default-members; a bare cargo build never checked them before -8. Move rumqttc and chrono to dev-dependencies: both are used only by examples, so every consumer of kolibrie was pulling in tokio and rustls -9. Declare pyo3 features per consumer instead of workspace-wide: extension-module is correct for the python cdylib but breaks ml, which embeds Python, by suppressing libpython linking; ml and kolibrie now use auto-initialize -10. Align pyo3-build-config to 0.25 to match pyo3; Cargo.lock previously carried both 0.23 and 0.25, so the build script configured linkage with a different pyo3 generation than the one being compiled -11. Remove datalog's dev-dependency on shared, which duplicated its normal dependency, and switch kolibrie-http-server to the workspace serde entries -12. Fix an order-dependent flake in scan_rejects_repeated_variable_conflicts_before_extending_rows: it asserted a positional property of a hash-ordered query result and failed about 20% of the time; the fixture is now asserted by content and the result compared as a multiset -13. Add CONTRIBUTING.md defining the commit message structure and the requirement that every commit updates changes.txt, with CLAUDE.md pointing to it -14. Add 5 microbenchmarks (micro_dictionary, micro_pattern, micro_scan, micro_join, micro_field_access) isolating the mechanisms the architecture refactor changes: term interning, cross-crate pattern matching, scan dispatch, binding representation and field access -15. Add scripts/perf_bench.sh with routine/heavy/census tiers; cargo bench --workspace is not usable because four parameter sweeps (49-85 cases, up to 3.9s per iteration) dominate it, with citybench_cross_window_compare exceeding 36 minutes without finishing, while the 8-bench routine tier covering 32 cases completes in ~350s -16. Add scripts/perf_report.py (collect, baseline, compare, noise) and perf-baseline/baseline.json so benchmark results survive cargo clean and have a reference point in history; compare applies per-benchmark noise-aware thresholds because measured noise ranges from 0.1% to 10.7% across the suite, so a single global threshold is either blind to the quiet benchmarks or cries wolf on the noisy ones -17. Measure and document two distinct variances: within-session repeatability (median 1.5%, max 8.7%) and cross-session reproducibility (median 3.0%, max 12.5%); micro_pattern/all_variables read 1.244ms and 1.119ms in consecutive sessions on identical source, so the authoritative procedure is before/after in one session and the committed baseline is a gross-drift reference only -18. Verify the harness detects regressions rather than assuming it: an injected pessimisation in matches_rule_pattern was reported as +44.5% and +30.0% with a non-zero exit under --fail-on-regression, and the signal disappeared on revert -19. Settle three architecture design questions with measurements instead of argument: schema+tuple bindings join 4.7x faster than Vec> (11.4ms vs 53.1ms), trait dispatch is free whether static or dynamic (all within noise) while a per-row Box costs 19-27%, and accessor calls are indistinguishable from direct field reads, so sealing SparqlDatabase's 23 fields carries no performance cost -20. Remove datalog::reasoning_experimental (307 lines) and its only two consumers, the hierarchy_reasoning and hierarchy_reasoning_advanced examples, along with their [[example]] registrations -21. Remove the vestigial #[allow(dead_code)] from the impl SparqlDatabase block: it masked nothing, since pub items in a library crate are never dead code, so the whole 23-field impl was carrying a suppression that could only ever have hidden private helpers -22. Remove QueryEngineConfig and QueryEngine::with_config: the config was a field-less unit struct and with_config ignored its argument and delegated to new(), so the pair documented a configurability that did not exist -23. Remove StorageMode::Hybrid, which nothing ever constructed; explain() only ever yields Static or Streaming, so the Static | Hybrid arm was unreachable and callers were matching on a state they could never observe -24. Remove IntervalFactStore::coalesce and holds_at_all from the DatalogMTL store; holds_at_all additionally carried a stale doc comment claiming it was used for Box evaluation, which the interval-native evaluator superseded -25. Remove CostEstimator::count_bound_variables together with its three unit tests and the create_test_stats helper they orphaned: the cost model never called it, so the tests only demonstrated that unreachable code worked (646 tests, down from 649) -26. Mark the s2r Consumer and ConsumerInner test harness #[cfg(test)] instead of #[allow(dead_code)]: they are used by a CSPARQLWindow test, so they are test infrastructure rather than dead code, and the suppression had hidden that distinction -27. Remove the unused dictionary field from datalogmtl's ShapeIngester and the parameter from its constructor, updating six call sites across kolibrie-http-server, the drone demo and the stream tests; events arrive already encoded, so ingestion never interned anything -28. Narrow the dead-code suppression in shared/src/sdd.rs from the whole VTreeNode enum to the single unread Leaf::var field, keeping the field deliberately: var_to_vtree holds the same mapping in the direction lookups use, but a vtree leaf that does not record its variable is not independently interpretable -29. Record that dead-code analysis must use cargo check --all-targets: without it, test-only usage is invisible and the compiler reports items as dead that tests rely on, which initially mis-flagged the s2r Consumer harness and count_bound_variables -30. Record that the per-benchmark noise figures in perf-baseline/baseline.json understate session-scale variance for sub-100ns benchmarks: micro_scan_selective_subject/dyn_trait and dyn_boxed_iter ranged 92.4-102.9ns and 116.0-126.1ns across five runs on untouched code, so their 1.7% and 2.0% back-to-back figures are roughly 5x too tight and both were flagged against step 3 despite that code path never being modified -31. Remove kolibrie::query_engine (207 lines, 3 tests): the module had no consumer inside or outside the crate, so demoting it would only have converted it to dead code; this also removes QueryEngine, QueryExplanation, QueryEngineStats and StorageMode from the public API, and the facade will be written deliberately when the facade crate lands (643 tests, down from 646) -32. Demote five zero-consumer kolibrie modules to pub(crate): error_handler, ml_feature_loader, term_order, ml_predict_candle and ml_predict_runtime, each with no reference outside the crate but at least one inside it -33. Demote four datalog materialisation submodules to pub(crate): my_naive, semi_naive, semi_naive_parallel and semi_naive_with_repairs; this corrects the architecture document, which scoped six, and records that it does not hide their methods, because inherent impl blocks are not namespaced by their module and callers reach them through Reasoner (43 external call sites for semi_naive alone, against zero references to its module path) -34. Gate ml_feature_loader's query_training_rows and build_feature_vec behind the ml feature, together with the four imports they alone require: only ml-gated modules call them, which the pub(crate) demotion surfaced as dead code, while their sibling rdf_term_to_f64 stays ungated because program.rs and execute_query.rs use it in the default build -35. Remove the imports left unused by the step-3 deletions: shared::dictionary::Dictionary, Arc and RwLock in datalogmtl's stream.rs, and gate s2r's Arc, Mutex and thread behind cfg(test) now that only the test-only Consumer harness uses them; these were missed in step 3 because only the total warning count was checked, and it fell -36. Add scripts/verify.sh, which prints the standard per-step verification summary while retaining full logs: an ad-hoc pipeline reported "642 passed, 1 failed" through awk and discarded the failing test's name, leaving a genuine flake unidentifiable and unreproducible in eight subsequent runs -37. Enable serde_json's float_roundtrip feature workspace-wide, fixing lossy float persistence: serialisation uses ryu and is already exact, but the default parser takes a fast path that can land 1 ULP off, so every save-then-load cycle could silently perturb stored f64 values, which for the ml crate means model weights changed on load -38. Fix the flaky ml test byte_loading_checks_all_layer_dimensions, which failed about 1 run in 25: it compared forward outputs of an original and a reloaded model for exact float equality, and the reloaded model genuinely had 1-ULP-different weights because of the serde_json parsing issue above; the test now asserts byte-level round-trip exactness, which is both deterministic and a stronger claim, and compares forward outputs within 1e-12 -39. Record that the byte-level round-trip assertion added above is the regression guard for the float_roundtrip feature: without the feature it fails in roughly 63% of runs, against the 4% rate of the original symptom, so an accidental removal is caught promptly rather than appearing as a rare unexplained flake -40. Add scripts/flakehunt.sh, which stresses the prebuilt test binaries directly and names the tests that fail: a full workspace run takes about three minutes and eight of them failed to reproduce the 1-in-25 flake above, while this runs each binary in 1-4 seconds and caught it on the first pass; verified to detect that flake (4 of 6 passes) and to exit zero on a clean tree -41. Correct the pre-commit checklist in CONTRIBUTING.md, which told contributors to run cargo test --workspace --all-targets: that pulls the four heavy benchmark sweeps into the test run in debug mode, where citybench_cross_window_compare exceeds 36 minutes without finishing; the checklist now calls scripts/verify.sh, which tests without --all-targets and compile-checks the benches separately -42. Demote 24 unreachable pub items to pub(crate), taking the unreachable_pub count to zero from 144 at the step-1 baseline: 22 in library code, mostly cascading from the modules made private in step 4a, plus two helper functions in example and test support files -43. Scope unreachable_pub out of the four test and example modules where it cannot be satisfied: pub is required there for visibility from a test binary or example main, but nothing can import from those crate roots, so every item is reported unreachable by construction; each allow carries the reason -44. Add scripts/check_unreachable_pub.sh and a blocking CI job that keeps the lint at zero under three feature configurations; the lint itself stays at warn in [workspace.lints] rather than deny because making a module private is exactly what produces the warning, and a hard local gate would break the build midway through the module moves steps 5 and 6 require -45. Record that lint checks must run per feature configuration: eight unreachable pub items were invisible to a default-features check because they sit behind #[cfg(feature = "ml")], and two more were missed because a measurement loop suppressed cargo's stderr, so a failed invocation produced empty output that parsed as a count of zero -46. Add kolibrie::prelude with three re-exports (SparqlDatabase, parse_combined_query and the query entry point) and replace all 91 glob imports of kolibrie modules across 55 files, so no module's entire contents is part of the API by accident -47. Choose prelude membership by measured breadth of use rather than by frequency: the three included symbols are each needed across about twenty directories, while every other candidate clusters into one or two, so all six probabilistic-program symbols stay explicit imports because the same two test files need all of them, as do the volcano optimizer types and the datalog rule helpers -48. Re-export the query entry point under its existing name execute_query_rayon_parallel2_volcano rather than introducing a cleaner alias, leaving the rename to the facade step that defines the single entry point -49. Record what the prelude does and does not buy, since it is easy to overstate: it removes dependence on module paths, so moving the query entry point to another crate now edits one line instead of 47 files, measured by simulating the move and observing that only five files inside kolibrie/src broke and none of the 47 prelude consumers did; renaming the function itself still touches all 61 call sites that spell it out and will need a deprecated alias -50. Replace the glob imports in the optimizer examples with the three real paths they depended on, which makes the dependency on operators::logical, optimizer and types visible at the import site -51. Remove the unused TemporalStore import in datalogmtl's box_ test, the last of the seven imports left unused by the step-3 deletions and missed in step 4a because only the total warning count was checked -52. Move matches_rule_pattern and construct_triple from datalog::reasoning::rules into the new shared::pattern module: both relate a triple pattern to a fact through a variable binding and contain no reasoning, so no engine should need the Datalog reasoner to reach them -53. Drop datalogmtl's dependency on datalog, which those two functions were the entirety of; cargo tree now shows datalogmtl depending only on shared, making the temporal and Datalog reasoners siblings over a shared vocabulary rather than one layered on the other -54. Keep datalog::reasoning::{matches_rule_pattern, construct_triple} working as re-exports of the shared definitions, so the move breaks no existing consumer -55. Document the pre-existing conventions carried over with the move rather than silently inheriting them: bindings are all-or-nothing so a late failure leaves none applied, unbound variables and quoted triples resolve to term id 0 instead of reporting an unbound conclusion, and construct_triple's dictionary parameter is unused but retained because the planned parse/prepare/execute split gives it a TermEncoder -56. Measure the cross-crate cost this step was expected to carry, before and after in one session: all six relevant benchmarks moved between -1.7% and -2.9%, inside the measured noise floor, so thin LTO preserves the inlining and the move costs nothing detectable -57. Record that micro_pattern alone could not have answered that question: its call site was already cross-crate as kolibrie to datalog and is now kolibrie to shared, so the new datalog to shared boundary inside the reasoner's own loops needed a separate probe, for which family_tree_cross_window_compare was used because it drives cross_window_incremental through the Reasoner -58. Seal the eight SparqlDatabase fields that had no consumer outside kolibrie/src, taking the struct from 22 public fields to 14: cached_stats, exact_stats_generation, planning_stats, stats_epoch and stats_rebuild_nanos were used only inside sparql_database.rs and are now fully private, while udfs, ml_predict_materialized_triples and implicit_neural_materialization cross a module boundary and are reached through accessors -59. Give those three fields purposeful accessors rather than &mut getters: udf() performs the lookup the optimizer actually needs, set_ml_materialized and take_ml_materialized name the record-and-undo operations the ML predict runtime performs, and set_implicit_neural_materialization returns the previous value so program.rs can save and restore it in one call; handing out &mut HashMap would have sealed the fields in name while still freezing their representation -60. Verify externally that the seal holds rather than assuming it: a probe referencing four of the sealed fields from an example was rejected with four E0616 private-field errors, one per field -61. Gate set_ml_materialized and take_ml_materialized behind the ml feature to match their only caller, since the default build reported both dead; this is the same blind spot the step-4a demotions surfaced for ml_feature_loader, now encountered a second time -62. Remove the normalized_predicate and relation fields from CandleDispatch, which was constructed with both and consumed for only predictions, probabilities and output_kind, together with the now-orphaned binding that computed one of them; this was pre-existing dead data that step 4b's demotion of the struct to pub(crate) revealed, because while the struct was public its fields counted as reachable -63. Record that the perf baseline understates variance badly enough to produce false positives: filter_numeric is recorded at 0.22% noise but spans 2.056 to 2.242 ms across four runs of one unchanged binary, a 9% spread, so a single before-and-after pair flagged a 5.7% regression that four runs in each state showed to be absent, with the unsealed state measuring slower if anything -64. Make perf_report.py baseline accept N runs instead of exactly two and record noise as the full observed range rather than the gap between two samples, which cannot estimate a spread at all; it warns below four runs and stores the per-benchmark run count -65. Rebuild perf-baseline/baseline.json from four routine-tier passes, which revealed that 25 of 32 benchmarks had understated noise, several by more than an order of magnitude: micro_dictionary/decode from 0.95% to 38.3%, micro_field_access/outlined_accessor from 0.03% to 11.4%, micro_join/schema_plus_tuples from 1.2% to 11.8% -66. Make compare warn when the baseline carries no noise data at all, which is the case for a collect summary and is how the filter_numeric false positive got through, and annotate flagged results with thin baseline or within 1.5x of the limit so a borderline delta is not presented as a finding -67. Correct the micro_scan table in the architecture document: the boxed-iterator penalty on the broad scan was reported as a significant +18.9% but measures +12.5% against that benchmark's own 19.6% spread, so it is not significant; the conclusion is unchanged and better supported, since per-row boxing is a fixed cost per call that dominates a scan returning 8 quads (+22.4%) and disappears into one returning 40,000 -68. Record the residual limitation rather than claiming the harness is fixed: noise depends on how a benchmark is invoked, not only on the benchmark, with filter_numeric spreading 1.9% across full-tier passes and 9.0% when run as a filtered single case after a rebuild, so two of the three historical false positives would still clear their limits against a full-tier baseline and like-for-like invocation remains necessary -69. Document the benchmarks that cannot currently detect a regression because their own spread exceeds the effects being hunted, led by micro_dictionary/decode at 38.3% where three runs cluster near 70 microseconds and one sits at 97; they still answer the design questions they were written for, where the effects are large, and the fix is to make them quieter rather than to narrow thresholds until they fire -70. Take twelve more SparqlDatabase fields out of the public API, leaving only dictionary and dataset_index: ml_context, prefixes, planning_stats_policy, quoted_triple_store, model_decls, neural_relation_decls, neural_model_artifacts, neural_materialized_triples, probability_seeds, stats_rebuild_count, rule_map and train_neural_relation_decls are now pub(crate) behind public accessors, which removes them from every consumer's reach while leaving a representation change a within-crate refactor -71. Return guards rather than the handle for the quoted-triple store, through the new QuotedTriplesRef and QuotedTriplesMut types that deref to QuotedTripleStore: P5 forbids Arc> in public signatures, and a getter handing back the Arc would have sealed the field while freezing both the representation and the locking strategy; call sites read db.quoted_triples().len() where they read db.quoted_triple_store.read().unwrap().len(), and sites holding one lock across several operations keep working because the guard lives as long as the binding -72. Add shares_quoted_triples_with in place of Arc::ptr_eq on the raw field, which states the property the one caller actually tested, namely whether two databases are isolated or share a store -73. Add extend_prefixes and add_prefix for the prefix map, take_neural_materialized for the retraction path, and add_rule_mapping for rule-name expansion, so the mutating operations are named rather than exposed as mutable map access -74. Record that grep over-counted the work by up to twice: model_decls, probability_seeds, neural_relation_decls and several others are also fields on CombinedQuery, Rule, CompiledProgram and Reasoner, so field-name matching counted sites belonging to other types; sealing the fields and reading the compiler's E0616 list gave the true figure of 79 external sites in 20 files, against a grep estimate of 100 that was wrong per field in both directions, with planning_stats_policy at 15 rather than 0 and prefixes at 11 rather than 22 -75. Record that a line-targeted rewrite is still not safe enough when two types share a field name: rewriting the line reasoner.probability_seeds = db.probability_seeds.clone() changed both sides and broke the Reasoner field access, which the compiler caught only because the result was syntactically invalid; a same-named method on the other type would have compiled silently, so every rewritten line was audited by receiver afterwards -76. Seal SparqlDatabase::dictionary behind DictionaryRef and DictionaryMut guards, leaving dataset_index as the only public field; call sites read db.dictionary().decode(id) where they read db.dictionary.read().unwrap().decode(id), and the 80 sites that hold one lock across several operations keep working because the guard lives as long as the binding -77. Add dictionary_handle and set_dictionary_handle as the one deliberate, documented place where Arc> still reaches the public API: sharing one term encoding between a database and a Reasoner is a real capability used by the HTTP server and two examples, Reasoner::dictionary is itself typed that way in another crate, and confining the P5 violation to two methods leaves one place to change when encoding moves behind TermEncoder instead of 96 call sites -78. Add shares_dictionary_with for the two isolation assertions that used Arc::ptr_eq on the raw field -79. Rewrite micro_field_access, whose original three-way comparison of a direct field read against inline and outlined accessors is no longer expressible now that the field is private; it measured the three within 1.3% before sealing, which is what justified proceeding, and it now guards the two paths callers actually have, the guard accessor and the handle escape hatch, with an outlined arm as the pessimistic bound -80. Record that grep over-counted this step by 3.25 times, reporting 312 sites against a true 96: dictionary is also a field on Reasoner, RSPEngine and DatalogMTLEvaluator, and on local variables of the same name; the compiler's E0616 list under a pub(crate) probe is the only reliable count, and even that needs re-collecting after each fix round because cascading failures hide sites in targets that never compile -81. Record that a blanket string rewrite silently rewrote other types: reasoner.dictionary.read().unwrap() and session.evaluator.dictionary.read().unwrap() became method calls on Reasoner and DatalogMTLEvaluator, and two of the five such sites were invisible to a default-features check because they sit in required-features ml examples; all were reverted and every introduced call was then audited by receiver -82. Confirm the dictionary guard costs nothing on the hot path by repeating execution_hot_paths/join_two_patterns four times in each state with identical filtered invocation: unsealed spans 11.474 to 11.758 ms with a median of 11.59, sealed spans 11.626 to 11.759 with a median of 11.67, so the medians differ by 0.7% and each range contains the other's median -83. Record that the full-tier comparison flagged five regressions that were all noise, and that checking each against the rebuilt baseline's recorded noise dismissed every one: all four micro_scan_broad_all arms moved together by 8 to 11 percent although that benchmark never touches the dictionary, which is the signature of a whole-benchmark shift rather than a code effect -84. Seal SparqlDatabase::dataset_index, leaving the struct with no public fields at all, behind eight named operations rather than a dataset_index() getter: default_triple_count, quad_count, all_quads, graphs, named_graphs, create_graph, insert_triple and clear_dataset, which is roughly the QuadSource surface section 4 wants, so callers depend on that shape instead of on a borrow of DatasetIndex that would freeze the representation the storage trait is meant to abstract -85. Record that 33 of the 46 external call sites only wanted a count of triples in the default graph, which is why the replacement is mostly thin wrappers rather than a redesign -86. Move exact_statistics_notice_a_write_that_bypassed_the_helpers and a_replacement_index_with_a_lower_generation_is_not_mistaken_for_fresh into a new test module inside sparql_database.rs: both deliberately write to dataset_index without going through the public helpers, which is no longer possible from outside the crate, and both assert that statistics detect a change they were never told about through DatasetIndex::generation; rewriting them to use replace_dataset_index would have kept them compiling while silently ceasing to test that, because that method invalidates the cache itself -87. Do not invalidate the statistics cache from the new mutating methods, matching add_triple, add_quad and the delete methods, which do not either: statistics detect a changed index through its generation rather than through notification, so invalidating would force a rebuild the generation check avoids and would change the behaviour the rewritten call sites previously had; three such calls were added and then removed once the convention was checked -88. Correct the claim that .dataset_index is unambiguous: Reasoner has a public dataset_index field too, so a blanket rewrite turned four Reasoner accesses in combined_rules_example into calls to methods it does not have; the evidence was already visible from the earlier dictionary work and was asserted away in a comment, so all introduced calls were audited by receiver again afterwards -89. Record that grep over-counted this field by 74 percent, reporting 80 sites against a true 46 -90. Document 13 of the shared crate's 15 modules, taking its missing_docs from 495 items to 349 and the workspace total from 2559 to 2273; only query.rs and hybrid.rs remain, which hold 349 of the original 495 between them and are each their own step -91. Give the shared crate a root document explaining the one fact every consumer needs first, that RDF terms are interned to u32 ids rather than stored as strings, with the id space split at 0x8000_0000 between ordinary terms and quoted triples, and that an id is therefore meaningless without the dictionary that produced it -92. Document the invariants rather than restating the field names, which is the only reason these comments are worth reading: that Triple's derived Ord sorts by integer id and has no relation to lexical order, that Dictionary::encode panics rather than colliding with the quoted-triple range, that Dictionary::merge keeps existing entries and so silently resolves ids to the wrong terms when two independently built dictionaries are merged, that DatasetIndex::query_graph returns hash order which varies per process, that get_matching_triples does not enforce repeated variables, and that RuleIndex::query_candidate_rules over-approximates -93. Record on Bindings that it is a transparent alias nobody can build against, which is why every engine grew its own variant, and that the measured replacement joins the same data about 4.7 times faster -94. Fix a pre-existing unresolved intra-doc link in tag_store, where encode_as_rdf_star was referenced by bare name from a more specialised impl block than the one defining it -95. Document all 184 items in shared's query.rs, leaving hybrid.rs as the only undocumented file in the crate and taking the workspace missing_docs count from 2273 to 1899 -96. Give query.rs a module document stating what the type names do not: that every term keeps its lexical spelling, including sigils, brackets, quotes and unexpanded prefixes, because a term only becomes a Term once bound to a particular dictionary, and a rule interned against the wrong vocabulary parses and validates and then silently matches nothing -97. Record that CombinedQuery is a union rather than a choice, so one request may carry standard SPARQL together with rule, RSP-QL and ML extensions, and that sparql is None only for an extension-only request -98. Document the positional tuple shapes that the types alone do not explain: projections are (kind, variable, alias), and both RSPQLSelectQuery::where_clause and CombinedRule::body are five-tuples of triple patterns, filters, VALUES, BIND clauses and subqueries -99. Capture the semantics a caller can get wrong rather than restating field names: that Value::Undef leaves a variable unbound rather than binding it and so does not constrain the join, that window bounds are half-open so RANGE n holds n timestamps, that SyncPolicy only matters with more than one window and trades latency against mixing cycles, that Fallback::Steal emits data from different cycles, that SELECT is bag semantics unless distinct is set, that LIMIT without ORDER BY leaves the surviving rows unspecified, and that a declared save_path is not itself authority to write there -100. Record that a module carrying its own //! documentation must not also have an outer /// on its pub mod declaration: the two concatenate and rustdoc then resolves the module file's bare intra-doc links in lib.rs's scope, which left eight links in query.rs unresolved until the duplicate outer docs on query, hybrid and pattern were removed -101. Document all 165 remaining items in hybrid.rs, taking the shared crate to zero missing_docs and the workspace total from 1899 to 1569 -102. Write down the escalation policy the type names only hint at: that AlertDecision::Indeterminate is not NoAlert and a caller conflating them turns "do not know" into "no", that LowerBound is sound only because monotone lineage means unenumerated proofs can only raise the probability, that UnsafeApproximation has no guaranteed direction of error under negation or exclusive groups and is a diagnostic rather than an answer, and that evaluate_hybrid always returns a result so a spent budget is reported as NeedsExact with whatever bounds were reached -103. Record why seed identity is the point of SeedRegistry: one arrival becomes one seed even when fanned out to overlapping windows, because counting the same uncertainty twice inflates every derived probability, which is also why EventKey carries a sequence number and register_occurrence is idempotent per key while register_exclusive deliberately is not -104. Generalise scripts/check_unreachable_pub.sh into scripts/check_lint.sh, which takes a lint name and an optional path prefix, and gate missing_docs at zero for shared/src alongside the three existing unreachable_pub configurations; the per-crate prefix is what lets a lint still burning down elsewhere be held at zero where it has been finished -105. Keep missing_docs at warn in [workspace.lints] rather than switching shared to deny, matching the decision already taken for unreachable_pub: adding a public item mid-change is exactly what trips the lint, so the gate belongs at PR time rather than halfway through an edit -106. Exclude the Python cdylib from rustdoc with doc = false: its lib must be named kolibrie so Python imports it under that name, which collided with the kolibrie crate's documentation output and made cargo doc --workspace fail outright, so the blocking docs CI job could never have passed -107. Fix sixteen broken intra-doc links that the collision had been hiding, all pre-existing: thirteen were MTL operator notation such as Box[a,b] in doc comments, which rustdoc reads as a link and which is now backticked, one referenced strip_window_prefix from the wrong module and now carries its real path, and one linked reset from exec_stats where that item exists only under the exec-stats feature and is now named as code -108. Document the kolibrie crate root and its entry-point modules, taking the crate from 540 undocumented items to 470; the remainder divides cleanly into four subsystems, namely the optimizer at 147, stream processing at 130, program.rs at 110 and parser.rs at 46, plus 37 across the ML modules, each of which is its own step -109. Give the crate root an overview rather than a list: how a query flows from text through the lexical syntax tree to a costed physical plan, a table of what each subsystem is for, and the fact that reasoning lives outside this crate in datalog and datalogmtl as siblings over the shared vocabulary rather than as layers on this engine -110. Explain on SparqlDatabase why it has no public fields, which is that term interning, index maintenance and generation-based statistics caching are invariants a caller writing to a field would break, and that encoded ids mean nothing without this store's dictionary -111. Document the behaviour a caller would otherwise discover by experiment: that parse_rdf skips malformed input rather than reporting it, so a partial parse looks like a smaller successful one; that parse_ntriples parses without adding while parse_ntriples_and_add is the usual entry point; that union returns a third store because the two dictionaries assigned ids independently and other's must be translated; that PlanningStatsPolicy::Bounded scales its tolerance with store size rather than using a fixed count; and that utils::run_server is a single-connection demonstration that panics on any I/O error, not the kolibrie-http-server binary -112. Record on TripleFilter::Custom that cloning it panics, because a closure cannot be cloned and the existing impl chose to panic rather than silently drop the filter -113. Document all 46 items in kolibrie's parser.rs, taking the crate from 470 undocumented items to 424; the optimizer at 147, stream processing at 130, program.rs at 110 and the ML modules at 37 remain -114. Point the parser module document at the five entry points a caller actually wants and record that everything else there is a grammar fragment, public only because tests/parser_test.rs is an integration test and therefore outside the crate, so exercising a combinator directly requires exporting it; moving those cases to unit tests inside the module would let the fragments become pub(crate) -115. Record that nom::IResult appears in every public signature in parser.rs, so a nom major version bump is a breaking change for any consumer naming one of those types, which violates P5 and is resolved in section 4 by giving the parser its own error type and keeping the combinators internal -116. Explain on convert_combined_rule that it is the prepare step, so its result is only meaningful against the dictionary it interned against, and that a rule lowered against the wrong one parses and validates but matches nothing -117. Restore the information carried by four comments that a mechanical rewrite had replaced: the BIND syntax example, the note that the PREFIX and LIMIT primitives are backed by the same parsers the request path uses, and the relationship between the two ORDER BY condition parsers -118. Add a documentation section to CONTRIBUTING.md recording the two rules that have each cost debugging time, namely that a module with its own //! must not also carry an outer /// on its pub mod declaration because the two concatenate and misplace intra-doc link resolution, and that bracketed notation in a doc comment is read as a link and needs backticks, together with the point that a comment restating an item's name passes the lint while telling a reader nothing -119. Document all 110 items in kolibrie's program.rs, taking the crate from 424 undocumented items to 314 and the workspace total from 1337 to 1113; the optimizer at 147, stream processing at 130 and the ML modules at 37 remain -120. Explain in the program module document why compiling and executing are separate, which is that a CompiledProgram holds its terms as text rather than dictionary ids and interns them only when it runs, so one compiled program can be executed against several stores -121. Record the three properties of this API that a caller can get wrong: that frozen_predictions exists so a run is reproducible and that a snapshot carries the artifact hash it came from, so replaying it against a changed model is detected rather than silently accepted; that an uncovered required choice is a gap in the answer rather than a zero probability; and that ProgramError::Resource says nothing about correctness, since the same program may succeed with a larger budget -122. Document the distinction between fact_probability returning None and returning Some(0.0), the first meaning the fact is absent from the result and the second that it was derived and found impossible, and that answer_probability collapses absence to zero only when coverage was confirmed, erroring otherwise rather than reporting a question the run never decided as a certainty that it will not happen -123. Record that ProgramError's variants render with stable PROGRAM_ prefixes that reach callers over the HTTP API, so those strings are part of the contract rather than diagnostics, and that ProgramTimings phases need not sum to total -124. Make the CONTRIBUTING rule about module documentation actionable rather than merely true, by stating that the outer /// must be deleted in the same change that adds the //!; adding one without the other is what breaks link resolution and it has now happened three times during this burn-down -125. Document all 132 items across the eight RSP modules, taking kolibrie/src from 314 undocumented items to 184 and the workspace total from 1113 to 849; only the optimizer at 147 and the ML modules at 37 remain in that crate -126. Give rsp/mod.rs an overview of the three-stage pipeline, namely that a continuous query cannot evaluate over an unbounded stream so S2R windows it, R2R evaluates over the window, and R2S decides which solutions are published, with a table mapping each stage to its module -127. Record the two coordination facts that govern when results appear: that a coordinator is needed only when results must be joined across sources, meaning more than one window or static patterns alongside a window, and that with a single window and no static data each firing is independently complete; and that under SingleThread nothing is delivered until stop is called, so an engine that is never stopped appears to produce nothing -128. Record that ReportStrategy conditions combine with AND rather than OR, so each strategy added makes a window report less often, that a Report with no strategies reports unconditionally, and that Report::report is not a pure predicate because OnContentChange records what it saw -129. Record that flush ignores the report strategies and merges every active window, so it publishes content that had not met its reporting condition and mixes windows that would otherwise have fired separately, and that stop closes the channel but does not stop a registered callback -130. Record why ProbabilisticOccurrence carries its seed id, which is that one arrival may land in several overlapping windows and must remain one seed, since counting the same uncertainty twice inflates every probability derived from it -131. Record that R2ROperator requires Send because a multi-threaded engine moves each window's operator to its own thread, and AsAnyMut because a caller needing implementation-specific state has to downcast the trait object, together with the downcast idiom its loose output type requires -132. Record that WindowResult and WindowTriple carry decoded text rather than dictionary ids, because a result crossing between windows cannot assume one dictionary -133. Verify after each mechanical edit that every derive set still matches HEAD rather than trusting that the crate compiles: removing a derive line that sat above a doc comment deleted WindowTriple's five derives, which produced 305 errors, and the inverse mistake of leaving a duplicate derive would have compiled had the original carried none -134. Document all 147 items across the fourteen optimizer files, leaving kolibrie/src with 37 undocumented items, all of them in the two ML modules, and taking the workspace total from 849 to 555 -135. Give the optimizer a subsystem document explaining why it exists, which is that a query has many execution strategies with wildly different costs and the written order is not usually the cheapest, with a table mapping each module to its role in the logical-to-physical translation -136. Record that the logical-to-physical mapping is one-to-many and that this is the entire point: one logical Join becomes a hash, nested-loop, star or bind join depending on cardinalities the query text cannot express -137. Record that the cost model reads cached statistics which may lag the data under the store's default bounded policy, so a plan can be costed against slightly stale cardinalities, and that this trades planning accuracy for not rebuilding statistics on every write -138. Record that COST_PER_ROW_NESTED_LOOP is deliberately five times COST_PER_ROW_JOIN, which is what makes the optimizer prefer hashing unless the inner side is tiny, and that an unmeasured graph is costed at a non-zero default so a newly created or empty graph does not make a plan look free -139. Record that TableScan and IndexScan dispatch to the same executor and are distinguished only for costing, that StarJoin avoids materialising an intermediate result per pair when several patterns share one variable, and that BindJoin re-enters its right side once per left solution with those bindings pushed into its index probes -140. Record that a graph present in the store but absent from a query's DatasetView is invisible to that query, that FROM replaces the default graph so default_graphs is not always just GraphId::Default, and that a subquery's LIMIT applies before the join, which is what distinguishes it from limiting the outer query -141. Record that Streamertail::memo keys costed plans by a canonical form of the logical subplan so a repeated subexpression is not re-optimised, and that with_cached_stats is preferred because collecting statistics scans the store and a per-query rebuild would cost more than the planning it informs -142. Document the ML modules in kolibrie, bringing kolibrie/src to zero missing_docs in all three feature configurations: ml_policy (32 items), execute_ml_train (32), neural_relations (10), execute_ml (8), neural_relations_disabled (5) and the two ml-gated module declarations in lib.rs -143. Present ml_policy as the authority boundary it is, rather than a settings struct: the module doc tabulates the three modes and records that trusted-local authority is not reachable over HTTP, that verified_bytes refuses bytes not matching the pinned SHA-256 so replacing a model file disables the model instead of silently changing its behaviour, and that MlError renders as stable ML_* strings which are therefore API contract rather than prose -144. Write down the reasons behind the policy refusals, which are the facts a caller would otherwise have to rediscover: allow_pickle defaults off because unpickling executes arbitrary code, ApprovedModel uses deny_unknown_fields so a registry typo errors rather than leaving a model less constrained than intended, trusted_local refuses an allowlist inside the output directory because training could otherwise rewrite the registry deciding what is approved, local_artifact refuses a pre-existing symlink, and validate_artifact_filename rejects path separators and Windows reserved device names because artifact filenames are attacker-influenced -145. Record the argmax-versus-distribution split at the neural_relations entry points, since the limitation is not visible from the signatures: these bind the single most likely label, and a distribution over competing labels needs the mutually-exclusive seed groups in program; DISTRIBUTION_REQUIRES_PROGRAM is documented as the message that says so -146. Document materialize_neural_relations_for_patterns as the implicit path, where querying a neural predicate runs its model without the query asking, and note that execute_program deliberately switches it off so a program's own declarations are the only thing invoking a model -147. Document MLPredictTiming field by field and state why the breakdown exists: the Rust-to-Python crossing and marshalling often cost more than the inference on small batches, which only a split measurement shows; print_breakdown's omission of the model name and input is recorded as deliberate, because a breakdown may be logged where the data it came from should not be -148. Note on execute_ml_training_owned that its randomness is unseeded, so two runs of the same clause differ, and point to the seeded variant; and on OwnedNeuralTrainingClause::save_path that a path in a declaration is a request rather than permission, because writing an artifact requires trusted-local authority from the ML context -149. Record that default_model_artifact_path maps every non-alphanumeric character to an underscore, so two model names differing only in punctuation collide on the same artifact file -150. Check both feature configurations before starting rather than after, which is what the previous six misses in this burn-down failed to do: the default build showed 37 items for this step while the real figure was 89, since 52 live behind #[cfg(feature = "ml")] -151. Gate missing_docs on kolibrie/src in CI across the same three feature configurations as unreachable_pub, making it the second crate scope held at zero after shared; the gate is scoped to /src on purpose, because the remaining ~130 items outside src are one crate-level doc comment per test, example and bench, which are not part of the public surface -152. Reduce workspace missing_docs to 481 of the original 2713, with the remainder concentrated in datalogmtl/src (170), datalog/src (110) and ml/src (86); total warnings 641 -153. Restore the original line endings in kolibrie/src/rsp/builder.rs and simple_r2r.rs, which the documentation commit c8b04a3 flipped from CRLF to LF across 374 and 127 lines while changing nothing else on them; the content is byte-identical, so the only effect was to make git blame on both files attribute every line to that commit. Restored per line rather than by converting the file, because simple_r2r.rs was already mixed at 127 CRLF to 93 LF and a blanket conversion would have changed lines the commit never touched -154. Document datalog/src to zero missing_docs: 21 module docs, the Reasoner struct and its 6 fields, 6 materialisation entry points, 2 traits, and 10 free functions; datalog declares no Cargo features, so unlike kolibrie this is a single configuration and the gate needs one line rather than three -155. Give materialisation.rs a table of the eight strategies against their entry points and what each records per fact, because the modules are the only place that distinction was written down and four of them are pub(crate) with their methods reachable only through Reasoner, which makes the module list an unreliable index of what exists -156. Record the semantic differences a caller would otherwise hit by experiment: rule filters are not SPARQL filters, since evaluate_filters skips a filter on an unbound variable rather than failing it and parses a non-numeric operand as 0.0, so a rule relying on either does not translate to a SPARQL FILTER unchanged -157. Record that backward_chaining is bounded by a hard-coded depth of 10 and that exceeding it yields no bindings, which is indistinguishable from the goal having no proof; a recursive rule set deeper than that silently answers nothing, so forward chaining is the safer choice when the rules are known to recurse -158. Record that the negative stratum in provenance_semi_naive is a single pass and not a fixpoint, so a fact derived through negation cannot feed another negated premise, and that semi_naive_with_initial_tags_and_delta drops negative rules entirely rather than running them as a second stratum, which is not visible from either signature -159. Record that seed ids in provenance_semi_naive are positions in the triple-sorted order of Reasoner::probability_seeds and that TagStore::seed_triples preserves that order, making the pairing part of the contract: a tag is only interpretable alongside the seed_triples it was built with -160. Document the provenance driver loop's termination condition and why it differs from the plain one: deriving no new fact is insufficient because a round can leave the fact set unchanged while improving a tag, since re-deriving a known fact is a second proof the tag has to absorb, so a strategy reporting tag_changed as false too early leaves tags short of their fixpoint -161. Document the half-open window bound in cross_window_sds, where a fact is alive at current_time only while event_time + alpha > current_time, so a fact expiring exactly now has already gone; this matches the RSP window semantics in kolibrie rather than restating the code -162. Document the cheap-materialise/expensive-evaluate split in hybrid_materialisation and point infer_new_facts_with_hybrid's callers at materialize_lineage plus per-fact evaluate, since the convenience path compiles an SDD for every derived fact and the cost is the compilation -163. Note that convert_string_binding_to_u32 silently drops variables whose value is absent from the dictionary, so its result can be shorter than its input, and that get_readable_binding panics on an id the dictionary does not hold -164. Fill the empty /// stub above get_id_from_term rather than leaving it: an empty doc comment satisfies nothing and reads as documentation, and the function's real behaviour is that an unbound variable and a quoted triple both resolve to term id 0, which is a legitimate dictionary id, so the stderr warning is the only signal that a conclusion was built from one -165. Add brief module docs to the four pub(crate) strategy modules as well, which missing_docs does not require: their methods are the public entry points, so a reader navigating by file needs to know which strategy is which, and semi_naive_parallel in particular holds the dictionary write lock for the whole fixpoint and still has no callers -166. Gate missing_docs on datalog/src in CI, making it the third crate scope held at zero after shared and kolibrie/src -167. Reduce workspace missing_docs to 371 of the original 2713, with the remainder in datalogmtl/src (170) and ml/src (86) plus ~110 crate-level comments on tests, examples and benches; total warnings 531 -168. Document datalogmtl/src to zero missing_docs: 85 items across 11 files, being 7 module docs plus the crate doc, the 21 interval and operator fields in syntax.rs, the 16 tick counters in metrics.rs, the 6 TemporalStore trait methods and 8 store items, and the remainder in stream, evaluator, parser and the three automata modules; like datalog it declares no Cargo features, so one configuration and one gate line -169. Give the crate doc a table contrasting the two evaluation strategies, because the choice between them is the first thing a reader needs and was written down nowhere: the tick evaluator answers over integer points and streams, the interval-native automata engine answers over the real line, supports future operators and unbounded time via omega-materialisation but needs the whole timeline, and the two do not agree in general without either being wrong -170. Record the time-unit trap in the evaluator module doc, which has cost real debugging time twice: Box steps one integer at a time across its window, so with millisecond timestamps and a one-second sampling period Box[0,5000] asks for a fact at 5001 consecutive milliseconds, finds one at every thousandth, and derives nothing, which is also what a correctly unsatisfied rule does; the fix is to express time in units of the sampling period so consecutive integers are consecutive observations -171. Record that the same stepping makes a rule's cost proportional to its window width in whatever unit the timestamps use rather than to the number of facts in the window, and that box_evals times the window width is therefore the useful cost figure rather than box_evals alone -172. Record the eviction rule precisely: each tick drops everything older than t minus w_max, where nesting adds so Box[0,5]Diamond[0,3] reaches back 8, while separate rules take the maximum rather than the sum because each reaches back independently; this is the distinction a past max-versus-sum bug in compute_w_max got wrong -173. Record why Box and the phi side of Since do not make a variable safe, which is the reason validate rejects rather than returning nothing: a universal operator asserts something about every point of a window instead of finding a point, so it has no witness to take a value from, and a rule whose head variable occurs only there would derive nothing at every tick -174. Document the two stores as differing in representation rather than in answers, with the interval store's memory growing in contiguous segments rather than distinct timestamps because a sampled reading repeats until it changes, and note on the trait that a time with no facts and a time already evicted are indistinguishable, both answering with nothing -175. Document the step-function model in stream.rs and why on_expiry exists: a reading holds until its successor or until max_gap_ms passes, and a rule cannot observe the absence of an event, so without expiry facts a source that has stopped reporting looks exactly like one reporting normally; only ChannelKey variables may appear there because the key binding is all that remains known about an expired channel -176. Note that BindingRelation::vars defines the tuple layout of rows so the two fields are only meaningful together, that Database::facts is canonical only after coalesce_all because add_raw deliberately leaves it unmerged, and that mutating DatalogMTLEvaluator::rules after construction bypasses validation and leaves w_max stale, so eviction can then drop facts a rule still needs -177. Expand the five single-line temporal variants in syntax.rs to multi-line form so their interval and inner fields can carry documentation; the fields and their types are unchanged -178. Gate missing_docs on datalogmtl/src in CI, the fourth crate scope held at zero after shared, kolibrie/src and datalog/src; only ml remains before the gate can cover the whole workspace -179. Record that the warning count counts instances per compiled target, not distinct items: a crate whose lib carries inline #[cfg(test)] modules is compiled twice under --all-targets and its items reported twice, which is why datalogmtl measured 170 workspace-wide against 85 distinct items, and datalog 110 against 55; the figure is consistent with the 2713 baseline, which was taken the same way, but it is not a count of things to write -180. Reduce workspace missing_docs to 201 of the original 2713, with the remainder in ml/src (86 instances, 44 distinct), ~93 crate-level comments on kolibrie's examples, tests and benches, and 6 elsewhere; total warnings 361 -181. Document ml/src to zero missing_docs: 43 items across lib.rs and candle_model.rs, completing the burn-down for all five library crates; ml declares no Cargo features either, and the one #[cfg(windows)] block in candle_model.rs guards statements inside a private function rather than an item, so 43 is the whole scope on every platform -182. State in the crate doc that ml grants no authority: nothing in it checks whether a model may run, loading by path loads whatever the path points at, and the trust decisions all live in kolibrie's ml_policy, so the entry points here are the mechanism and not the gate -183. Document the forward/backward queue contract in candle_model, which is a runtime obligation with no type to enforce it: forward_with_grads queues one activation set and surrogate_backward pops the oldest, gradients accumulate until zero_grads clears them, predict caches nothing and so cannot be followed by a backward pass, and zero_grads also discards queued forwards because that is how an aborted batch is recovered from -184. Record that MLHandler::predict ignores its model_name argument whenever best_model is set, which compare_models sets as a side effect and nothing clears, so a handler that has compared models always predicts with the winner regardless of what the caller asks for -185. Record that compare_models scores on resource use and not accuracy (0.5 CPU percent, 0.4 memory MB, 0.1 prediction time, with no accuracy term), that its two dominant terms are in different units so the weights do not transfer across machines, and that a model whose schema failed to parse is cached with default all-zero metrics and therefore always wins the comparison -186. Record the naming and path rules a caller cannot see: load_model_with_schema derives the schema path by replacing every occurrence of .pkl with .ttl, so a directory containing .pkl is rewritten too, and discover_and_load_models only picks up _predictor.pkl files and names each model after the text before the first underscore, so two files sharing a prefix collide on one name -187. Record why load_model validates its model and module names as ASCII identifiers of at most 64 characters: the model directory's parent is prepended to sys.path and the names are interpolated into a Python import, so an unchecked name would choose what gets imported -188. Document MlpNeuralPredicate::varmap as unused: a candle VarMap is constructed and never read or written, the parameters live in a private Vec>, and the arithmetic does not go through candle at all, so the field is public API and nothing else; left in place rather than removed, since dropping a public field is an API change and does not belong in a documentation commit -189. Note on to_bytes that the float round-trip is bit-exact only because serde_json is built with float_roundtrip, whose absence made model persistence lossy and produced a 1-in-25 flake, so the guarantee depends on a Cargo feature rather than on this code -190. Note that optimizer_step divides by example_count, which must therefore be the number of samples that actually contributed since the last zero_grads and not the batch size, because surrogate_backward skips samples whose gradients are all zero -191. Gate missing_docs on ml/src in CI, the fifth and last library crate scope; the five lines stay separate rather than collapsing into one workspace check because cli, kolibrie-http-server and python still hold six items between them, and because the per-crate form names which crate regressed -192. Keep build scripts outside the gates deliberately: the scopes are /src, and ml/build.rs and python/build.rs are each one crate-level comment on code that is not part of any public surface -193. Reduce workspace missing_docs to 115 of the original 2713. Of those, 95 are one crate-level comment per kolibrie example, test and bench plus 2 datalog tests, 6 are in cli, kolibrie-http-server and python, 2 are build scripts, and 12 come from criterion's own macros expanding inside our benches and cannot be fixed here, so the floor is 12 rather than 0 while the benches use criterion_group!; total warnings 275 -194. Document cli, kolibrie-http-server and kolibrie-python to zero missing_docs, which is three crate-level comments rather than the six the workspace count implied: the figure was two instances each, one for the binary and one for its test harness -195. Document ml/build.rs and python/build.rs as well, so every build script in the workspace is covered; this goes slightly beyond the three crates asked for, but it is two comments and leaving them would have stranded a one-line follow-up commit -196. Record in the http server's crate doc what it does not do, since none of it is visible from the handlers: it binds 0.0.0.0 with no authentication of any kind, resolves the ML context once at startup and exits rather than starting and refusing requests later, and over HTTP never holds trusted-local authority, so it does not train, write artifacts or accept model declarations from a query -197. Note that the CLI's --version string is hard-coded in its clap attributes rather than read from the workspace version; it matches today, and nothing keeps it matching, so it will drift silently at the next bump -198. Record both reasons python/src sets doc = false: the library must be named kolibrie for Python to import it under that name, which collides with the kolibrie crate's rustdoc output, and the documentation that matters for that crate is its Python API rather than the Rust shim -199. Record why ml/build.rs is as long as it is, which is that this crate embeds Python rather than being loaded by it: the interpreter must be linked rather than deferred, and macOS sysconfig can report a framework path through LDLIBRARY even when the prefix does not look like one, hence the fallback chain -200. Add a --crate-src mode to scripts/check_lint.sh that scopes a lint to /src/** across every crate at once, and replace the six per-crate missing_docs gate lines with it; a per-crate list silently fails to cover a crate added later, while this covers one the day it lands, and the failure output still names every offending path so it says which crate regressed -201. Verify the new mode rather than assuming it, since a gate that cannot fail is worse than none: an undocumented pub item added to shared/src was reported with its path and exited non-zero, and the same item added to kolibrie/tests was correctly ignored -202. Supersede the rationale recorded one commit earlier for keeping the gate lines per crate: the blocker named there was the six remaining items in cli, kolibrie-http-server and python, and those are now documented -203. Reduce workspace missing_docs to 107 of the original 2713. Every crate's src and every build script is now at zero, so the whole remainder is one crate-level comment per kolibrie example (55), test (31) and bench (7) plus 2 datalog tests, and 12 from criterion's own macros that cannot be fixed here; total warnings 267 -204. Remove MlpNeuralPredicate::varmap, a public field holding a candle VarMap that was constructed in the one place and never read or written anywhere in the workspace; the parameters live in a private Vec> and the arithmetic never went through candle, so the field was public API and nothing else. Found while documenting ml/src, where writing down what it did made clear it did nothing -205. Drop the candle-nn dependency with it, since VarMap was its only use; candle-core stays for the Tensor the forward pass returns. Cargo.lock loses the candle-nn entry and nothing else, so its transitive dependencies were all reachable through candle-core already -206. Record that this unblocks a Debug derive on MlpNeuralPredicate, which VarMap had prevented: adding one now compiles. Left for the missing_debug_implementations burn-down rather than taken here, so that axis stays one change rather than a scattering -207. Note in the struct's documentation that it now has no public fields, so the parameter representation is a private matter and can change without an API break -208. Take missing_debug_implementations to zero under every crate's src: 38 types, not the 76 the warning count showed, which was again two instances each for the lib and its test harness. 16 take a plain derive, 22 get hand-written impls -209. Write summarising impls rather than deriving wherever the type holds a dataset, which is 16 of the 22: SimpleR2R and PySparqlDatabase embed a whole SparqlDatabase, CompiledSdd an SddManager, MlpNeuralPredicate the weight matrices, and the datalogmtl stores their entire fact base, so a derived {:?} would print the data rather than describe the value. Each prints counts and configuration instead, with finish_non_exhaustive -210. Record on each of those impls why it is not a derive, so the next person does not replace a deliberate summary with #[derive(Debug)] and silently turn every log line into a data dump -211. Make the six impls that cannot derive at all explicit about what they omit: ResultConsumer and QueryBuilder hold dyn Fn, CSPARQLWindow a dyn FnMut, RSPBuilder and RSPEngine trait objects, and SddOperationBudget a &mut dyn FnMut. None of them calls the closure it holds, since invoking the deadline check would spend the budget being reported on and invoking a result consumer would publish a solution -212. Use try_borrow and try_lock rather than borrow and lock in every impl that needs interior state: a Debug impl that blocks can deadlock against the thread already holding the lock, which for a value being inspected mid-run is the likely case, and one that panics is worse than no impl at all. RSPEngine takes no lock whatsoever and reports only unlocked fields -213. Note on QueryBuilder's impl that it reports whether a custom filter and sort key are present rather than omitting them, because Clone drops both and a cloned builder therefore behaves differently from the original -214. Verify the impls by running them rather than by compiling them: a temporary example printed QueryBuilder, Streamertail and SimpleR2R over a three-quad fixture, confirming the output is one readable line each and that the count paths work. The probe also caught that the fixture was empty on the first attempt, because parse_ntriples parses without adding and parse_ntriples_and_add is the inserting one -215. Correct a self-inflicted regression before committing: qualifying the trait as std::fmt::Debug in the five files that already carry use std::fmt::Debug pushed unused_qualifications from 50 to 60. The trait is now unqualified in those files while Formatter and Result stay qualified, since those are not in scope, and the count is back to its pre-existing 50 -216. Gate missing_debug_implementations on /src in CI across the same three feature configurations as missing_docs -217. Add a CONTRIBUTING section on line endings, which this step broke for the second time in the same two files: reading a file as text and writing it back converts the whole file, because universal newlines apply on the way in and one ending on the way out. The result compiles, passes tests, and shows up as every line deleted and re-added. simple_r2r.rs is mixed within the file, so even matching the dominant ending is wrong; the check is that a comment-only change shows additions and no deletions in git diff --numstat -218. Reduce total warnings to 191 of the original 3022, with missing_debug_implementations eliminated entirely; what remains is 107 missing_docs outside every crate's src, 50 unused_qualifications, 27 deprecated and 7 unsafe_code -219. Take unused_qualifications to zero across the whole workspace: 29 sites, not the 50 the warning count showed, which was two instances each for the lib and its test harness. Every one is a redundant module prefix on a name already in scope, so the fix is a prefix deletion and nothing else; the diff contains no semantic change and no line was added or removed -220. Apply rustc's own suggestion spans rather than matching paths by hand, since the lint reports the exact byte range of the prefix to delete and whether a name is in scope is the compiler's judgement, not something grep can answer; 18 of the 29 are in parser.rs, where `use std::time::Duration` and `use shared::query::*` make the qualifications redundant -221. Check all three feature configurations before starting, which found 3 sites invisible to the default build, two of them in the fraud_detection_system example; the ml-feature configuration is checked in the gate for that reason -222. Gate unused_qualifications on the whole workspace rather than on /src, unlike missing_docs: the lint had nothing to say about tests, examples or benches that was not worth fixing, so there is no reason to scope it -223. Reduce total warnings to 141 of the original 3022, leaving 107 missing_docs outside every crate's src, 27 deprecated and 7 unsafe_code; four lints are now held at zero by CI, being unreachable_pub, missing_docs, missing_debug_implementations and unused_qualifications -224. Format the whole workspace with cargo fmt --all: 183 Rust files, ~1,578 hunks, which is the one-off reformat the rustfmt CI job had been waiting on since step 1. The tree had never been formatted, so this was always going to be one large commit; it carries no change other than formatting -225. Make the rustfmt CI job blocking now that the tree matches it exactly, which its own comment had asked for in the same commit as the reformat; drift from here is a real finding rather than the backlog -226. Add *.rs text eol=lf to .gitattributes, because rustfmt rewrites whatever it touches to LF regardless of what the file had: without the rule the endings drift back into the diff on every formatter run, which now happens as a blocking check. Verified by restoring one CRLF file and running cargo fmt -p datalog over it, which converted it again -227. Convert shared/src/terms.rs to LF by hand, the one Rust file the reformat left alone because it needed no formatting changes; it was the last CRLF .rs file in the tree -228. Leave the six .py, six .md, LICENSE and .dockerignore files that still use CRLF untouched: nothing formats or complains about them, so normalising them would be churn without a reason, and the .gitattributes rule is scoped to *.rs for the same reason -229. Verify the reformat changed nothing but layout rather than assuming it: 643 tests passed with 0 failures, all five lint-gate invocations stayed at zero, rustdoc stayed clean under -D rustdoc::broken_intra_doc_links, and the warning count held at 141. The working tree was also stashed and restored byte-identically to confirm the diff was purely formatter output with nothing hand-edited mixed in -230. Take deprecated to zero: all 27 sites were in one example, real_scenario.rs, and all were pre-0.9 rand names that the crate itself renamed, being gen_range to random_range, gen_bool to random_bool and thread_rng to rng. The rest of the tree had already moved, so this file was the last holdout and the change is a rename with no semantic difference -231. Correct an earlier prediction: this was expected to need a decision per call site, since a deprecation usually does, and it did not. Measuring first showed one file and three renames -232. Accept rustfmt's expansion of three inline conditionals rather than restructuring to keep them on one line: random_bool is four characters longer than gen_bool, which pushes `if rng.random_bool(0.5) { "High" } else { "Medium" },` one character past the width budget. Twelve lines in an example is cheaper than hoisting expressions to fight the formatter in a rename commit -233. Gate deprecated on the whole workspace, with the difference from the other gates recorded in the job: this one can fire from a dependency update rather than from a change here, because what counts as deprecated is upstream's decision, so a red build after cargo update is that and not a regression -234. Record a pre-existing runtime failure found while checking the rename: the real_scenario example panics on `Failed to parse combined query` for a RULE ... => ... form, verified identical at HEAD without the change. It compiles, so neither verify.sh nor CI catches it, and it is not in the pre-existing-failures list, which lives in the gitignored CLAUDE.md and is therefore invisible to everyone else -235. Reduce total warnings to 114 of the original 3022, leaving 107 missing_docs outside every crate's src and 7 unsafe_code; five lints are now held at zero by CI -236. Take unsafe_code to zero, in a different sense from the other five lints: the library unsafe is removed outright, and the unsafe that has to stay carries a scoped #[allow(unsafe_code)] with a SAFETY comment rather than being silenced wholesale. The gate therefore catches new unsafe, and the allow is what makes adding any a deliberate reviewed act -237. Remove both unsafe blocks from utils::get_num_cpus by using std::thread::available_parallelism, which has been stable since 1.59 and is well inside the declared MSRV of 1.85; this deletes the only unsafe in library code across every platform, since the function had an FFI branch for unix and another for windows -238. Note that available_parallelism is not the same number the old code produced: libc::sysconf(_SC_NPROCESSORS_ONLN) counts online processors, while available_parallelism accounts for CPU affinity and container quotas, so a process limited to two cores on a 64-core host is now told two. For sizing the worker pool that parse_rdf starts, the smaller figure is the correct one -239. Move libc and winapi to dev-dependencies now that only the repro_sensor_path_benchmark example uses them; as normal target dependencies they made every consumer of kolibrie pull a C binding it never called, which is the same problem entry 8 fixed for rumqttc and chrono -240. Write SAFETY comments on the four unsafe sites that remain, all outside library code: the GlobalAlloc impl in the allocation_profile test, where unsafe is unavoidable because the trait and its methods are unsafe, and three platform-specific resident-set-size readers in one example, where std has no equivalent to reach for -241. Record while documenting those readers that they do not measure the same thing: macOS uses getrusage's ru_maxrss, which is peak resident size and already in bytes, while /proc/self/statm and the Windows WorkingSetSize are current. The figure that example prints on macOS is therefore a high-water mark that never falls back down -242. Add the allows at source level so they cover the blocks a given runner never compiles: the Windows and Linux readers sit behind #[cfg], so a macOS check sees neither, and a gate verified only on macOS would have left them to fail on another platform -243. Record that parse_rdf, the only caller of get_num_cpus, has no automated test at all: the only users are examples, the CLI and the HTTP server. The threaded parse was therefore checked by hand against a three-description fixture, which returned the expected three quads, and by running the simple_select and select_all examples -244. Reduce total warnings to 107 of the original 3022, all of which are missing_docs outside every crate's src: 95 are one crate-level comment per test, example and bench, and 12 come from criterion's own macros and cannot be fixed here. Six lints are now held at zero by CI, being unreachable_pub, missing_docs, missing_debug_implementations, unused_qualifications, deprecated and unsafe_code -245. Keep the workspace lints at warn rather than deny despite all six reaching zero, for the reason recorded when the gates were introduced: making a module private or adding a public item mid-change is exactly what trips them, and a local deny stops the build halfway through an edit, so the enforcement belongs at PR time -246. Remove the public-api CI job rather than finally creating the api-baseline it compared against. The job had never reported anything, because the baseline was never generated, so it printed "no baseline for $c yet" on every run since step 1 -247. Record the reason, which is the nightly requirement and not the job's value: cargo-public-api works by asking rustdoc for JSON, which is unstable and nightly-only, so it made nightly a prerequisite both for CI and for anyone refreshing a baseline, and a format shift between nightly releases would show diffs that are not API changes. The repository now contains no reference to nightly at all, with rust-toolchain.toml pinned to 1.96.0 and the MSRV job enforcing 1.85 -248. Record what this costs in CONTRIBUTING rather than leaving it implicit: nothing now warns when a pub item is added, removed or changes shape, which matters most when crates are split and a lot of surface moves at once. The unreachable_pub gate catches a module becoming reachable; nothing catches a signature changing, so an API change is something a reviewer has to notice and a commit message has to mention -249. Note the condition under which this is worth revisiting: rustdoc JSON stabilising, which removes the only real objection to the tool -250. Refresh a stale figure in the advisory job's comment, which still said ~115 warnings where the count is 107, and state what the number is for: six lints are held at zero by the blocking job, so anything the advisory job reports beyond those 107 is new -251. Add execute(sparql, &mut database) -> Result>, QueryError> as the query entry point to prefer, and put it in the prelude. The recommended path until now was execute_query_rayon_parallel2_volcano, which reports failure by returning an empty vector, so a typo was indistinguishable from a query that correctly matched nothing; measured against a fixture, the old path answers 0 rows both for a misspelled SELECT and for a pattern matching nothing, while execute answers Err for the first and Ok(0 rows) for the second -252. Add QueryError with Parse, Unsupported, Ml and Execution variants, classified in execute_request at the stage that failed rather than stringified and re-sniffed afterwards; the old wrapper had to match the error text against five ML code literals to decide what to print -253. Follow the shape ProgramError already set rather than inventing a second vocabulary for the same idea: the same variant names where they apply, and a Display of CODE: message with a stable QUERY_* prefix. The program module had the right design already and the oldest, most-used path had never caught up -254. Keep execute_sparql_query and execute_sparql_with_ml_context returning exactly the strings they returned before, through a pub(crate) QueryError::legacy_string, because the HTTP server passes those to clients as the documented stable codes from entry 134. The same refusal therefore reads as QUERY_POLICY: ML_FORBIDDEN through execute and as ML_FORBIDDEN through those two, which is recorded in the type's documentation as deliberate and temporary -255. Change no call sites: the old entry point becomes a thin wrapper over execute with byte-identical behaviour, including the eprintln that loses the real message. Its documentation now says that is why it is superseded, and that it cannot be marked #[deprecated] while its callers exist, since that lint fires at the use site and is held at zero by a blocking gate, so marking it would turn the build red at 115 places at once -256. Correct the call-site figure the earlier prelude comment recorded: there are 138 references to the old name outside its own module, being 23 import statements and 115 calls in bodies, spread over 48 files that reach it through the prelude glob and 25 that import it explicitly. Only 3 of the 138 are outside the kolibrie package, in cli and python, so the migration is almost entirely our own tests, examples and benches -257. Fix format_parse_error emitting ANSI escape sequences, which Renderer::styled produced unconditionally: a one-token parse failure carried 26 control characters across 6 lines. All four callers put the string into an error value rather than printing it, so the escapes were travelling into logs, JSON responses and test comparisons; Renderer::plain keeps the annotated snippet and the line and column without them. This also fixes the already-public ProgramError::Parse, which had carried it since it was introduced -258. Remove the deliberate leading newline from both parse-error renderings, which made sense when the output went to a terminal and not when it goes into an error value whose Display should not begin with a blank line; no test depended on it, since every test matches on the variant rather than the text -259. Document that a QueryError::Parse message is multi-line, carrying the snippet that points at the offending token, so a caller formatting it into a single-line log knows to expect that -260. Note the duplication this step did not address: execute_sparql_query repeats execute_request's pipeline almost line for line, parse, single_rule, validate_request, ml_predict and dispatch, which is a fifth place for the five entry points to drift apart -261. Add Solutions and Solution, and make execute return Solutions instead of Vec>; the change is free because execute is one commit old and had no call sites outside the prelude -262. Recover the variable names the engine already knew and threw away: finalize_select computed projection_columns, used it to order the columns, and then discarded it, so every caller had to re-derive the column order from the query text. Solutions::variables now carries them with the leading ? or $ stripped, and Solution::get accepts a name with or without the sigil -263. Distinguish unbound from empty, which Vec> cannot express: finalize_select mapped a missing binding through unwrap_or_default, so a variable the solution never bound and a variable bound to the empty string were both "". A value is now None when unbound, verified against a fixture where one row reads name=Some("") alongside absent=None and the flattened form collapses both to "" -264. Separate "not projected" from "projected but unbound" with Solutions::has_variable, since Solution::get returns None for both; the probe shows has_variable(absent) true while get(absent) is None -265. Name the columns on the ML prediction path too rather than leaving that one blank: MLPredictClause keeps the input query's projection and WHERE patterns, so predict_input_columns mirrors projection_columns over them, expanding * from the pattern triples, and the prediction itself takes the name of the variable the clause binds it to -266. Keep every published signature unchanged. execute_sparql_query and execute_sparql_with_ml_context still return Vec>, and the compatibility entry point still returns it, all through From for Vec> which reproduces the old flattening including its loss of the unbound distinction; the conversion carries a comment saying so, so it is not mistaken for a neutral adapter -267. Verify the type by running it rather than by reading it, against a fixture carrying both an empty-string literal and a projected-but-unbound variable; 643 tests passed with 0 failures and all seven lint gates stayed at zero -268. Fix the real_scenario example, which panicked on its first rule with a parse failure: all six of its rules were written as RULE :Name :- WHERE { ... } => { ... } with a rule invocation :Name(?var) in a trailing SELECT, and the parser supports neither. parse_construct_clause requires the literal CONSTRUCT keyword, and the only rule-call parser takes RULE(:Predicate, ?var) rather than :Predicate(?var), so the form was either removed long ago or never worked -269. Rewrite those six rules into the supported RULE :Name :- CONSTRUCT { conclusions } WHERE { premises } form, keeping each rule's name, premises and conclusion exactly, and drop the trailing SELECT blocks, which the example never used: its loop only calls single_rule to extract the rule and add it to the reasoner -270. Correct parse_rule's own documentation, which advertised RULE :Name(?room) :- WHERE { ... } => { ... } as a supported form; it comes from 0be0f9a and has been wrong since, which is presumably where the example's syntax came from -271. Note that the last two rules in that example are byte-identical, both named :MarkAllEventsUnauthorized with the same premises and conclusion, although the second is commented as overriding to authorized when an event falls within an allowed time slot; the behaviour is kept as it was rather than inventing the intended logic -272. Establish that the engine was not at fault before changing the example, since a filter that silently never fires would have looked the same from outside: both FILTER directions were probed in isolation and each derived its fact, two rules concluding the same predicate with different objects both fired, and the RDF/XML parse was checked to store "72" rather than a whitespace-padded value that would have parsed as 0.0 -273. Fix the deep_taxonomy example, which panicked reading $HOME/eye/reasoning/deep-taxonomy/test-dl.ttl, a checkout of the EYE reasoner's corpus that nobody else has; it now resolves a corpus directory from the first argument, then KOLIBRIE_DEEP_TAXONOMY_DIR, then that original path, and generates an equivalent chain when none of them holds both files -274. Generate the fallback corpus in the same shape as the original rather than something merely loadable: a single 1000-level subClassOf chain plus one instance of its root, so the transitive closure is the whole taxonomy, which is what makes this a reasoning benchmark rather than a data-volume one. The run derives 999 facts from 999 taxonomy triples, which is the complete closure -275. Stop the example panicking on a named-but-unreadable corpus path: a directory that does not hold both files now falls through to the generated corpus, and a file that exists but cannot be read reports which path and why -276. Raise the examples that run unattended from 48 of 55 to 50. The five that remain are four long-runners by design, being the drone demo which is a server, http_check which needs that server, and two that need an MQTT broker, plus repro_sensor_path_benchmark which reads ../benchmark_dataset/repro-data.nt from outside the repository and is the same class of problem as deep_taxonomy was -277. Record that verify.sh compiles examples but has never run them, which is how two examples stayed broken: one panicked on a rule syntax the parser does not accept and one on an absolute path into a personal directory. Neither is visible to a compile check -278. Fix the repro_sensor_path_benchmark example, which failed with a bare No such file or directory: it reads ../benchmark_dataset/repro-data.nt relative to the kolibrie manifest, which resolves inside the repository but is excluded by .gitignore, so a fresh checkout never has it. It now resolves the file from the first argument, then KOLIBRIE_REPRO_DATASET, then that original path -279. Generate a stand-in corpus when no file is found, in the shape the query needs rather than merely parseable: it is the Train Benchmark six-segment pattern, so the generator emits chains of base:connectsTo segments all base:monitoredBy one base:Sensor with everything typed, 2000 chains of 10 segments giving 59,500 triples -280. Leave one chain in four unmatchable by withholding monitoredBy from its middle segment, because a corpus where every window matches makes the six-way self-join cheaper than the one this example was written to measure; the break sits in every window of its chain, so those chains contribute nothing -281. Make the generated row count predictable so the corpus can be checked rather than trusted: 1500 unbroken chains of 10 segments yield 5 six-segment windows each, and the run returns exactly 7500 rows. A handwritten 7-segment chain returns exactly 2 -282. State in the example's own output that a generated run is a stand-in and that its timings are comparable with each other and with nothing else, since this is a benchmark and the obvious hazard is someone comparing a generated number against one from the real corpus -283. Raise the examples that run unattended to 51 of 55. The four that remain cannot pass unattended by nature rather than being broken: the drone demo is a server, http_check needs that server running, and two need an MQTT broker -284. Add scripts/smoke_examples.py, which runs every example that can finish on its own, and call it from verify.sh. Compiling an example proves very little: three were broken for months while compiling cleanly, one using a rule syntax the parser does not accept, one reading an absolute path into a personal directory, and one reading a dataset .gitignore excludes -285. Take the example list from cargo metadata rather than keeping one in the script, so an example added later is covered without anyone remembering to add it; the four that cannot finish unattended are named in a SKIP map with a reason each, being two that need an MQTT broker, one that serves HTTP until interrupted, and one that expects that server running -286. Leave the eleven examples behind the ml feature out of the smoke run deliberately: they need a trusted-local training directory, a model allowlist or the MNIST corpus, so running them would report environment setup as failure rather than a defect -287. Verify the smoke run fails when an example does, since a check that cannot fail is worse than none: a panic injected into simple_select was reported by name with its exit code and log path, the run exited non-zero, and the panic message was kept in the log -288. Record the cost rather than leaving it to be discovered: the run adds about 55 seconds, of which 45 are two streaming demos sleeping through timed windows, continious_stream at 24s and automate_policy at 21s, against 2.4s for the other 43 together. It is waiting rather than computing, so the cost is wall-clock and not CPU -289. Document in CONTRIBUTING that examples are run and not only compiled, with the two commands, the skip list and its reasons, and the rule that an example which cannot finish unattended belongs in SKIP with a reason rather than being left to time out -290. Migrate every call site off execute_query_rayon_parallel2_volcano to execute: 108 calls rewritten mechanically to execute(..).expect("query should run").into_rows(), plus 12 single and 11 grouped imports, across 59 files. Downstream code is untouched, since into_rows reproduces the old Vec> exactly -291. Add Solutions::into_rows for that migration rather than relying on Into: 50 of the sites are `let x = CALL;` with no annotation, where .into() has nothing to infer a target from, and an explicit method needs no turbofish and says at the call site that the conversion is lossy -292. Make QueryError::Debug delegate to Display instead of deriving it, because expect and unwrap print the Debug form: a derived one escapes the newlines in a parse snippet, turning the annotated message that points at the offending token into an unreadable single line. The QUERY_* prefix carries the variant name, so nothing is lost -293. Find three examples that had been printing empty results for as long as the old entry point has existed, which the migration exposed immediately because expect panics where the old path returned no rows: min.rs and sum.rs wrote GROUPBY without a space, and values.rs placed VALUES after the WHERE clause. All three now produce output, with MIN, SUM and the VALUES binding respectively -294. Record that a trailing VALUES clause is valid SPARQL 1.1 that this parser does not accept: VALUES is parsed only inside a group pattern, so values.rs now places it there. The example was not wrong about SPARQL, only about this implementation -295. Give the cli a non-zero exit and an error message on a failed query, where it printed Results: [] and exited 0, which is also what a query matching nothing prints -296. Make the Python binding's exec_query raise rather than return an empty table, matching the convention its neighbouring update method already documented: an empty list now means the query matched nothing, where it previously also meant the query was rejected or the database lock was poisoned. This is a visible change for Python callers -297. Deprecate execute_query_rayon_parallel2_volcano rather than deleting it, so code outside this repository keeps compiling with a warning, and remove it from the prelude: a glob import should not be how anyone reaches a deprecated entry point, and re-exporting one there would put a deprecation warning on every file that globs the module. The deprecated gate stays at zero because no call site remains -298. Note that the example smoke run added one commit earlier is what caught the three broken examples, within a minute of the migration landing; a compile check passed all three before and after -299. Collapse the duplication between execute_sparql_query and execute_request, which repeated the same pipeline line for line: parse, single_rule, validate_request, the ML.PREDICT branch and the operation dispatch. execute_sparql_query is now four lines delegating to the one implementation, down from twenty-nine -300. Replace execute_request's allow_data_aliases flag with a RequestKind of Any or QueryOnly, which names the whole of what the two entry points disagreed about: whether an Update is accepted, and whether the standalone INSERT/DELETE aliases parse. Both followed from the same distinction and were previously expressed in two unrelated places, a boolean argument and a match arm -301. Put the query-only refusal after the ML.PREDICT branch and before prepare_extensions, which is exactly where it took effect before. prepare_extensions registers prefixes and neural declarations on the database, so a refused request must not reach it; and an ML.PREDICT request still takes the predict branch, as it did when the check lived in the dispatch match below -302. Verify that ordering rather than assume it, since it is the kind of thing a refactor silently loses: a refused Update carrying PREFIX zz: leaves no zz registered on the database, while a successful SELECT carrying PREFIX ok: does register ok. The Update refusal returns the identical message, execute still accepts an Update, and the alias form is still refused by the query-only path and accepted by execute -303. Record what the collapse left behind rather than implying the duplication is gone: execute_update_request is still a second pipeline beginning with the same parse and single_rule, and validate_query_policy a third, but they are not trivially mergeable, since one returns UpdateSummary and the other only validates -304. Record that validate_request runs twice for a query request, once in execute_request and again inside prepare_extensions. The inner call is redundant there and load-bearing for the update path, which has no validation of its own, so removing it would silently drop the ML policy check on every Update -305. Move the HTTP server onto QueryError: validate_query_policy and execute_sparql_with_ml_context now return it, and the server's four call sites go through a new query_error_response that matches on the variant instead of string-comparing the message -306. Keep query_error_response delegating to ml_error_response rather than matching variants against a second status table: three other callers still pass bare strings, including the test that checks an error body does not echo the request, so the table has to stay there and two copies would drift -307. Stop validate_query_policy flattening every parse failure to the literal "SPARQL_PARSE_ERROR", which bought nothing: the string appeared in exactly one place in the repository, the line producing it, and the one caller that reported it mapped every non-ML error to QUERY_EXECUTION_FAILED regardless. The real parse message, naming the offending token, is now carried instead -308. Pin the wire format with a test rather than trusting the refactor: every QueryError variant is asserted to produce byte-identical output to the code the string-matching version produced, including MlError::ExecutionFailed landing on the default arm because it was never in the status table -309. Add a test that a parse failure does not leak the request, which became a real concern only with this change: the error now carries the offending query text where it used to be flattened away, so query_error_response is asserted not to put it in the response body -310. Record in the server that the mapping is deliberately the one that was already in place: every non-ML failure including a syntax error reports as QUERY_EXECUTION_FAILED with 400, which is misleading for a parse error, and the variants now make a QUERY_PARSE_ERROR possible but adding one changes what clients receive -311. Fix a regression this caused before committing it, caught by the ml_security_test that asserts a rule rejection starts with ML_: mapping validate_query_policy's error with to_string in process_rule_definition and execute_neural_program turned the bare ML_FEATURE_DISABLED into QUERY_POLICY: ML_FEATURE_DISABLED. Both now use legacy_string, so their error strings are unchanged; the test was right and the code was wrong -312. Correct legacy_string's documentation accordingly: three callers rather than one, all returning Result<_, String> themselves, with the reason the last two cannot use Display recorded where someone would otherwise simplify it away -313. Remove QueryError::legacy_string by migrating its three callers off String errors: execute_sparql_query, process_rule_definition and execute_neural_program now return QueryError. Nothing in the repository renders an error two ways any more -314. Measure the cost before committing to it rather than guessing from the reference count: process_rule_definition has 75 references across 21 files, which suggested a large migration, and flipping its return type produced 15 errors all inside its own body. None of the 75 call sites broke, because they use unwrap, expect or an Ok pattern rather than naming the error type -315. Classify each of those 15 by what actually failed instead of mapping them all to one variant: a rule form this entry point does not handle is Unsupported, a failure to parse is Parse, and everything downstream of the plan is Execution -316. Replace the magic string "ML_FEATURE_DISABLED" returned by process_rule_definition when the ml feature is off with QueryError::Ml(MlError::Disabled), so the policy refusal is a value rather than text that happens to match a code elsewhere -317. Strengthen the ml_security_test assertions instead of adapting them: two tests asserted the error string starts with "ML_", which is now matches!(error, QueryError::Ml(_)) — a statement about the type rather than about a rendering, and a stronger one -318. Convert sparql_database's handle_http_sparql_query to match on the variant too, which revealed a second copy of the HTTP status table living inside the library alongside the server's ml_error_response; both are now variant-matched and the comment in each says the other exists -319. Record that the ml feature configuration had 10 errors the default build could not see, found only because verify.sh checks it: the migration was verified against cargo check --workspace --all-targets, which is default features, and the ml-gated bodies of execute_neural_program and process_rule_definition propagate their own String errors. This is the same blind spot recorded six times earlier in this rollout -320. Note a test failure observed once and not reproduced: rsp_ql_multi_window_integration failed a non-empty assertion during one full-workspace run, then passed 12 isolated runs, 6 suite-level runs and 4 further full-workspace runs. The assertion is on the success path, about window results arriving in time under CPU contention, and nothing in this change touches it; it is recorded rather than attributed -321. Fix the demo server reporting every failure as HTTP 200. handle_http_sparql_query returned a bare body on success but a complete HTTP response on an ML refusal, and handle_connection wrapped whatever came back in another 200 OK, so a refusal reached the client as a success whose body was a second HTTP response, headers and all. Demonstrated before changing anything: a 101-byte 200 OK containing HTTP/1.1 503 Service Unavailable -322. Split the two renderers into a status line and a body, as http_query_result and http_update_result, so handle_http_request composes exactly one response and handle_connection writes it verbatim; a query failure is now 400, an ML refusal 403 or 503, an unknown method 400, and a successful query or update 200 -323. Fix update failures reporting success as well, which the same wrapping hid: handle_update returned the plain text "Update Failed" and the caller paired it with 200 OK. handle_update keeps returning body text, since 17 references expect that, and the HTTP layer now supplies the status -324. Change handle_http_request's contract deliberately, from a bare body to a complete response, and update the seven assertions that depended on the old shape. They are stronger for it: each now checks the status as well as the body, which the previous form could not express at all -325. Drop the "Query Failed: " prefix from the query-failure body in favour of the status code, so the body is the bare QUERY_EXECUTION_FAILED that the real server already returns -326. Verify every path on the wire rather than by reading the code: success answers 200 with the row, a syntax error and an Update on the query endpoint answer 400 with QUERY_EXECUTION_FAILED, an ML refusal answers 503 with ML_FEATURE_DISABLED, an unknown method answers 400, and no response body contains a nested HTTP response -327. Record what this does not address: an HTTP request handler still lives on SparqlDatabase, parsing requests and composing responses inside the store crate, duplicating the kolibrie-http-server crate's job and its status table. Retiring it means moving the drone demo and the http_check example onto the real server, which is why it was not done here -328. Correct the premise recorded in entry 327 before acting on it: the HTTP handler on SparqlDatabase was described as duplicating kolibrie-http-server, and it does not. It implements the W3C SPARQL 1.1 Protocol, being GET with ?query=, POST of application/sparql-query or application/sparql-update, and form-encoded query= and update= parameters, while the server crate serves a JSON API of its own and accepts none of those four content types. They share only a mapping from failures to status codes, so retiring the handler would have deleted the project's only standards-compliant SPARQL endpoint -329. Correct two further claims made while sizing that work: the drone demo does not use kolibrie::utils::handle_connection but defines its own function of the same name, and utils::handle_connection therefore had no consumers at all; utils::run_server had exactly one, a 23-line example whose whole body was a call to it -330. Move the protocol handling out of SparqlDatabase into a new kolibrie::sparql_protocol module, as a free answer(&mut database, request) taking request text and returning response text. A store should not be parsing HTTP requests and composing responses, and the three request forms, the status mapping and the form decoding now live together in one place rather than as four methods on the store plus two free helpers -331. Delete utils::run_server and utils::handle_connection with the http_test/http_check example that was their only caller, so the library no longer owns a TcpListener or a serving loop; the transport, the threading and the port are the caller's choice now, which is what the new module's documentation says -332. Keep SparqlDatabase::handle_update, which is not HTTP despite its name and has seventeen references expecting plain text; it now delegates to sparql_protocol::update_result so the status and the body come from one implementation rather than two -333. Convert the eight tests that exercised the protocol from a method on the store to the free function, which is the whole of the change at the call sites; the GET, direct-POST and form-encoded coverage is unchanged -334. Correct the smoke run's skip list, which claimed http_check "expects kolibrie-http-server to be running" when in fact it started a server of its own and that is why it timed out; the example is gone, so three examples are skipped rather than four and the runnable count stays at 51 -335. Record what remains duplicated: the mapping from a QueryError to an HTTP status and code exists in both sparql_protocol::query_result and the server crate's ml_error_response, in two crates, and the comment in each names the other -336. Note twelve rustdoc redundant-explicit-link-target warnings across five crates, left over from the documentation burn-down where a link was written as [`Name`](path::to::Name) and the text already resolves on its own. The docs job denies only broken links, so these do not fail it; one in the HTTP server's module doc is fixed here because this change touched that line, and the other eleven are left for a pass of their own -337. Add a Known gaps section to CONTRIBUTING for the two things left undone, so they live somewhere a reader looks rather than only in a commit message: the QueryError-to-status mapping exists in both sparql_protocol and the server crate's ml_error_response and has to be changed in both, which cannot be deduplicated until kolibrie-http-server grows a lib.rs since it is binary-only today; and eleven rustdoc redundant-link warnings remain across five crates from the documentation burn-down -338. Repair a doc link broken by the previous commit: removing the SparqlDatabase import from utils.rs as unused also removed what a doc link on ClonableFn resolved through, because rustc does not count a doc link as a use while rustdoc does. The link now carries the full path, which is the right form for a type referenced only from documentation. The blocking docs job would have failed; it was missed because verify.sh does not run cargo doc and the rustdoc check that reported zero had been run before the import was removed -339. Note the sequencing lesson rather than only the fix: a measurement taken before the last edit is not a measurement of the result. Run cargo doc after the final change, not before it, or add it to verify.sh -340. Move DatabaseStats and its term maps out of streamertail_optimizer into a new top-level stats module, severing the one reference that made the store and the optimizer mutually dependent. sparql_database imported DatabaseStats from streamertail_optimizer while streamertail_optimizer reaches into SparqlDatabase six times, so the two could never become separate crates; the type is now in a layer below both and neither direction is a cycle. kolibrie::streamertail_optimizer::DatabaseStats still resolves, as a re-export, so no public path changed -341. Change DatabaseStats::gather_stats_fast to take &DatasetIndex and a quoted-triple count instead of &SparqlDatabase. It read exactly two things from the store, both of them shared types, so the whole stats module now depends on nothing in kolibrie: the cost model's statistics are computable from a dataset index alone. SparqlDatabase::gather_stats is the new store-side entry point and is public, since gathering a fresh uncached snapshot was previously only reachable through the type -342. Delete execute_query::aggregate_value, a one-line delegate to aggregate::value whose only external caller was the optimizer's execution engine. The delegate made the optimizer depend on execute_query for a function that lives two layers down; both call sites now use crate::aggregate::value directly -343. Correct the module-coupling measurement: counting crate:: paths without excluding /// and //! lines treats a documentation link as a dependency, which inflated the reported cycle from its true size. On the commit before this one the figure was 15 modules and 22974 lines, not the 17 modules and 25533 lines first reported, and two of the modules named as irreducible were held there by doc links alone. Doc-stripped, this commit takes the cycle to 15 modules and 22392 lines, 84% of the crate, with 54 internal edges of which 45 are one or two references -344. Take parsing out of the ML policy: MlExecutionContext::validate_request no longer calls the parser or touches SparqlDatabase. It took &SparqlDatabase to read one field, which is now passed as a has_registered_relations bool, and it parsed each ML.PREDICT input itself to validate the query nested inside. The caller parses now and hands the result over as a PredictInputs, so ml_policy sits below parser instead of beside it. ml_policy has no code reference to anything in kolibrie at all: 575 lines that were held inside the module cycle by exactly two references are now a leaf over shared -345. Shape PredictInputs as a tree rather than a flat list, because validation interleaves: a clause's model name is checked, then its nested query is walked in full, and only then is the next clause's name checked. A flat list cannot express that. The order is load-bearing beyond tidiness. With the ml feature off, authority is refused before any nested input is examined, so the request is ML_FEATURE_DISABLED (503); a version of this change that validated the parsed inputs up front would report an unparseable input as ML_FORBIDDEN (403) instead. The parse outcome is therefore consulted only at the point the old code parsed -346. Record that the policy's re-parse was redundant work. No input could be constructed that reaches its parse-failure branch through a public entry point: the outer parser already requires an ML.PREDICT INPUT block to be a complete well-formed query, so re-parsing input_raw inside the policy always succeeded. The Err case in PredictInputs is kept as a defensive case and documented as one, because a policy check that is correct only because another layer happens to be strict is the kind of coupling that breaks quietly later -347. Module cycle after this change: 14 modules and 21862 lines, 82% of the crate, 47 internal edges, down from 15 modules, 22392 lines, 84% and 54 edges. Against the corrected pre-refactor baseline of 15 modules and 22974 lines the two steps together have moved ml_policy and the statistics out and removed 9 edges -348. Note a near-miss: the first version of the new builder wrote crate::parser::parse_combined_query in a file that already imports parse_combined_query, which fired unused_qualifications, one of the six lints gated at zero. It was caught by reading compiler output during an unrelated test run rather than by the gate, because the gate is a PR step and not a local pre-commit hook -349. Split ml_feature_loader so that it depends on nothing in the crate: rdf_term_to_f64 and build_feature_vec are pure conversion from an RDF term to a number, while query_training_rows has to run a query, which is a layer above. The latter moves to execute_query and the module becomes a leaf. Its three importers (neural_relations, execute_ml_train, ml_predict_runtime) now take it from execute_query, which is the direction the dependency should run -350. Drop a second parse of every training query. query_training_rows parsed select_query once to recover the projected variable names and then executed it, which parsed it again; Solutions::variables already carries those names, so the explicit parse is gone. This also removes a latent misalignment: the old code zipped names taken from one derivation against row columns taken from another, so if the two ever disagreed in order or length the resulting rows would have been keyed wrongly and silently. Solutions builds both from projection_columns, so they cannot disagree -351. Note a panic left in place rather than fixed here, to keep this commit to one subject: query_training_rows calls execute(...).expect("query should run") on a query that comes from a TRAIN clause, so a malformed training query aborts the process instead of returning the Err the function's own signature already allows. Pre-existing, now visible in execute_query -352. Module cycle after this change: 13 modules and 21756 lines, 82% of the crate, 41 internal edges, from 14 modules, 21863 lines and 47 edges. Three steps have taken the cycle from the corrected baseline of 15 modules, 22974 lines and 56 edges, moving stats, ml_policy and ml_feature_loader out -353. Repair a documentation regression from the previous commit: the doc on PredictInputs wrote [`crate::execute_query`](crate::execute_query), the redundant-explicit-link-target form that CONTRIBUTING's Known gaps section counts, taking kolibrie from seven of those to eight and the repository from eleven to twelve. The same sentence also named the constructor `build` when the function is `predict_inputs`. Both fixed, and kolibrie is back to seven -354. Extract neural_registry from neural_relations: recording a model, neural relation or training declaration on a store needs the store, the ML policy and the declaration types, and none of the machinery that later runs a model. parser, execute_query and program all register declarations and none of them should have to reach into the ML runtime to do it. neural_relations drops from 965 to 812 lines -355. Make registration ungated. The real implementation already produces the disabled behaviour on its own: with the ml feature off require_local refuses with ML_FEATURE_DISABLED, and a request declaring nothing never reaches it, which is exactly what the stub in neural_relations_disabled did by hand. One implementation now serves both builds and 27 lines of mirrored stub are gone. kolibrie::neural_relations::register_neural_declarations stays a working path, as a re-export from both the real module and the disabled one -356. Record that this step made the cycle metric worse rather than better, which is the honest result: 14 modules, 21798 lines and 45 edges, up from 13 modules, 21756 lines and 41 edges. neural_registry depends on sparql_database and is called by execute_query and parser, so it joins the cycle instead of escaping it. The extraction is still worth having, both for the removed duplication and because registration cannot escape while it is embedded in the ML runtime, but it does not pay off on its own -357. Measure where the cycle actually breaks, which this step clarified by accident. sparql_database has four references out to modules above it (two to execute_query, one each to query_builder and sparql_protocol). Removing sparql_database -> execute_query alone frees nothing; removing all four at once frees five modules and 4275 lines, taking the cycle from 14 modules and 21798 lines to 9 modules and 17523 lines, roughly 82% of the crate down to 66%. Those five are sparql_database itself, query_builder, ml_predict_candle, sparql_protocol and neural_registry. No other available cut is close -358. Note what neural_relations itself still needs, which is not more code movement. parser and execute_query reach into it for three genuine runtime operations (execute_train_decl, materialize_neural_relations_for_patterns and execute_neural_program), all of which really do need the ML runtime. Those edges invert rather than move: the ML layer has to register a hook the query layer calls through. That is a design step, not a mechanical one -359. Take the three upward delegates off SparqlDatabase, which is what was holding a quarter of the crate inside the module cycle. query() returned a QueryBuilder, execute_update() called execute_query and handle_update() called sparql_protocol, so the store named three modules that all sit above it. None of the three did any work of its own; each forwarded to the layer that implements it -360. Replace SparqlDatabase::query() with a Queryable trait in query_builder, implemented for SparqlDatabase and re-exported from the prelude. An inherent method would have had to live on the store and name the builder; a trait lets the builder claim the store instead, which is the direction that survives the two becoming separate crates. db.query() still compiles unchanged for the two examples, which already import the prelude, and integration_test gained one use line -361. Remove SparqlDatabase::execute_update and SparqlDatabase::handle_update. Their callers now use execute_query::execute_sparql_update and sparql_protocol::update_result, the functions the methods forwarded to. update_result becomes public for that reason. handle_update was documented as being for the demo server, which no longer exists, and its only remaining callers were eight assertions in rdf_star_test, now going through a local helper that keeps the same strings -362. Module cycle after this change: 9 modules and 17523 lines, 66% of the crate, 21 internal edges, from 14 modules, 21798 lines and 45 edges. Five modules leave at once, exactly as the measurement in the previous commit predicted: sparql_database itself at 2685 lines, query_builder at 931, ml_predict_candle at 271, sparql_protocol at 198 and neural_registry at 189. The crate's central type is no longer part of any dependency cycle -363. Note what this cost in public API, since it is the first step in this sequence to remove public items rather than move them: SparqlDatabase loses query, execute_update and handle_update; query_builder gains the Queryable trait; sparql_protocol::update_result becomes public. The one call site outside the kolibrie crate was in the python bindings, which refer to the crate as kolibrie_core -364. Move the RSP builder from rsp to rsp_engine, where the engine it builds lives. rsp::builder constructed an RSPEngine while rsp_engine used six of rsp's modules, so the two were mutually dependent; a module that builds a thing belongs with it rather than below it. Every external caller already reached RSPBuilder through kolibrie::rsp_engine, which re-exported it, so no public path changed -365. Move QueryExecutionMode from rsp_engine down to rsp::simple_r2r, the R2R operator that carries it as a field and the only thing in rsp that needed it. It is re-exported from rsp_engine, so kolibrie::rsp_engine::QueryExecutionMode still resolves. With the builder moved too, rsp no longer references rsp_engine at all -366. Module cycle after this change: 8 modules and 15721 lines, 59% of the crate, 16 internal edges, from 9 modules, 17523 lines and 21 edges. rsp_engine leaves at 1809 lines. Nothing inside the cycle points at it any more: the only two references were the builder and the execution mode, both of which were in rsp -367. Note why rsp itself stays. The single inbound edge is parser -> rsp, and it is not a stray type: process_rule_definition builds CSPARQLWindows, wires a Relation2StreamOperator and runs the content through them, so roughly 200 lines of stream execution live inside a 375-line parser function. Freeing rsp means moving that out of the parser, which is a refactor of execution code rather than a move of types, and is left for a step of its own -368. Split the execution tail out of parser into a new rule_execution module. process_rule_definition, process_retrieve_clause and their helpers were the last 513 lines of a 4181-line file, and they were the only part of it that reached into the rest of the crate: between them they validated the ML policy, registered declarations, ran training, drove the Datalog reasoner and built and fired CSPARQLWindows. Everything above them was, and is, pure parsing. parser now references nothing in the crate at all -369. Accept the public path change this forces. kolibrie::parser::process_rule_definition becomes kolibrie::rule_execution::process_rule_definition, updating 20 files: 12 examples, 4 test files, 3 modules and the HTTP server binary. A re-export from parser would have kept the old path and recreated the edge the split exists to remove, so there is none -370. Module cycle after this change: 5 modules and 4147 lines, 16% of the crate, 9 internal edges, from 8 modules, 15721 lines and 16 edges. parser leaves at 3647 lines, rsp at 1410 and streamertail_optimizer at 6442. What remains is one cluster, and it is the ML dispatch that has been flagged since the neural_registry step: execute_query, neural_relations, execute_ml_train, ml_predict_runtime and rule_execution. Nothing else in the crate is part of a cycle -371. Fix a bug in the cycle measurement that nearly produced a false result. The script reported the largest strongly-connected component by line count, so once the real cycle fell to 4147 lines the 6442-line singleton streamertail_optimizer outranked it and the output read as though the graph were acyclic. It now reports the largest component with more than one module. Earlier figures in this sequence are unaffected, since every cycle reported so far was larger than any single module -372. Invert the ML dispatch, removing the crate's last dependency cycle. execute_query and rule_execution called up into neural_relations and ml_predict_runtime for four operations that genuinely need the ML runtime: materializing a neural relation's facts before matching, running a TRAIN declaration, running a top-level ML.PREDICT, and running an ML.PREDICT inside a rule. They now call through the MlRuntime trait in the new ml_runtime module, which names only SparqlDatabase, QueryError and the declaration types. The module graph is acyclic: 28 modules, 88 edges, zero strongly-connected components with more than one module -373. Move QueryError out of execute_query into its own query_error module, because ml_runtime names it in a trait that execute_query then calls through, and an error type several layers return has to sit below all of them. kolibrie::execute_query::QueryError still resolves, as a re-export -374. Keep the error variants across the inversion rather than flattening them to strings. execute_program and execute_predict_clause return QueryError, not String, so that a refusal with the ml feature off stays a QueryError::Ml and still renders QUERY_POLICY: ML_FEATURE_DISABLED. Routing those two through a String would have turned a 503 into a QUERY_EXECUTION_FAILED, which is what the HTTP status table and ml_security_test both read -375. Let the crate root choose the implementation. lib.rs is the one place that may name both the MlRuntime trait and a concrete implementation, which is what keeps execute_query and rule_execution off the ML layer. No cfg is needed in the selector: neural_relations is already either the real module or the #[path]-substituted disabled one, so naming its NeuralRuntime picks the right one for the build. Nothing registers anything, so no behaviour changed and no caller has to install a runtime. At crate-split time this selection moves to the facade crate and the object is threaded through the store instead -376. Drop a cfg block that the inversion made redundant: the in-rule ML.PREDICT site in rule_execution had a #[cfg(not(feature = "ml"))] arm returning MlError::Disabled and a #[cfg(feature = "ml")] arm calling the runtime. The build's own runtime now refuses it with the same code, so the call site is one unconditional expression -377. Identify the cause of a flaky ML test rather than attributing it to this change. rerun_cleans_stale_predictions in ml_predict_candle_runtime failed once in a full ml run, and failed 3 times in 20 runs of its own test binary while passing in isolation. It also fails at the previous commit, once in 42 runs, so it is not a regression: MlpNeuralPredicate::new initialises weights from rand::rng() unseeded, as its own documentation states, and the test asserts one exact predicted label. A bad initialisation leaves a sample misclassified after 80 epochs on three features. The fix is new_seeded, which already exists and is used by the ml crate's own tests -378. Walk into the ///-on-pub-mod trap that CONTRIBUTING documents, for the second time in this sequence. Five module declarations in lib.rs were given an outer /// summary while the module file already carried a //! header; the two merge and the merged text resolves in the parent module's scope, so a link to an item of the module itself does not resolve. Two broken links in ml_runtime's header came from this. The outer comments are gone -379. Give the declarative training path a seed, which it never had. SparqlDatabase gains set_training_seed and training_seed, and execute_train_decl uses execute_ml_training_owned_seeded when one is set. The programmatic path through program already took a training_seed through its options, so a TRAIN NEURAL RELATION run issued as a query was the only one that could not be repeated; weight initialisation and batch shuffling both derive from the seed, so the same declaration over the same data now trains the same model -380. Fix the flaky rerun_cleans_stale_predictions by seeding it rather than by loosening its assertion. The test asserts one exact predicted label, which is the heart of what it checks -- that a prediction is recomputed from changed features rather than left stale -- so weakening it to a count would have removed the thing it exists to catch. It failed 3 times in 20 runs of its test binary before this and 0 in 22 after -381. Seed top_level_ml_predict_materializes_after_training too. It asserts exact labels for two samples and had not been observed to fail, but it is flaky by construction rather than by luck, and the next unlucky initialisation would have been debugged from scratch -382. Add cargo doc to verify.sh, running exactly what the blocking rustdoc CI job runs, including RUSTDOCFLAGS=-D rustdoc::broken_intra_doc_links. It is a separate step rather than another entry in the check loop because the flag contains a space and would not survive the loop's word splitting. Verified to fire by injecting an unresolved link into the stats module doc: 2 errors, naming the file and line -383. Record why this was the gap worth closing: four broken doc links reached commits during one refactoring session while every other check passed. cargo check does not resolve doc links at all, so nothing else in verify.sh could see them. Two came from the ///-on-pub-mod trap that CONTRIBUTING already documented, and one from removing an import that only a doc link still used -384. Correct the redundant-explicit-link-target count in CONTRIBUTING's Known gaps, which had been stale since one of them was fixed in passing: ten remain, not eleven, across four crates rather than five -- five in kolibrie, three in datalog, one each in datalogmtl and shared. The entry now says to re-count before quoting the number -385. Stop query_training_rows aborting the process on a malformed training query. It called execute(...).expect("query should run") while its own signature already returned Result, and the query is caller-supplied: a TRAIN declaration's QUERY { ... } block is captured by the parser as raw text and never parsed, so anything at all can reach here. The QueryError now propagates, which Box accepts directly -386. Add malformed_training_query_is_an_error_not_a_panic, verified in both directions: it passes with the fix and panics at execute_query.rs:189 with the expect restored. Three inputs that reach the old panic -- text that is not a query, a truncated WHERE clause, and the empty string -387. Repair a doc comment split in two by an earlier commit in this sequence. Inserting predict_inputs used an anchor that fell inside validate_query_policy's own doc, between "if the policy" and "refuses it", so the first half ended up documenting query_training_rows and validate_query_policy -- which is public -- rendered starting mid-sentence at "refuses it.". rustdoc never complained because a doc comment followed by any item is valid; it just documented the wrong thing, which is a failure mode neither the docs job nor the new verify.sh step can catch -388. Note the lesson for scripted edits: an insertion anchor has to be an item boundary, not any unique string. Both halves of a split doc comment still compile, both still render, and nothing reports it -389. Declare 0.3.0. [workspace.package] still said 0.1.1, which all eight crates inherit, so every crate in the workspace reported a version three changelog sections out of date: changes.txt has had a 0.2.0 section as well as this 0.3.0 one, and the only tag in the repository is v0.1.1. The changelog is the authoritative record, so the declared version follows it -390. Stop the CLI hardcoding its version. cli/src/main.rs carried version = "0.1.1" as a clap attribute, which had already drifted and would have drifted again at the next bump. Given no value, clap reads CARGO_PKG_VERSION, so `cli --version` now prints 0.3.0 and tracks the workspace automatically -391. Correct the deprecation marker on estimate_operator_selectivity, which claimed since = "0.1.1". git log -S dates the attribute to a532b85, which is not an ancestor of the v0.1.1 tag, so the function was not deprecated in that release and the marker named a version it never shipped in. It first ships deprecated in 0.3.0 -392. Move the vendored mlschema Python package to 0.3.0 as well. It is a separate distributable with its own name, but its version has tracked the workspace so far and leaving it behind would have started a second, silent drift of the kind entry 390 describes -393. Add docs/architecture.md: the workspace crate graph, and the kolibrie crate's 28 modules arranged into 7 layers by longest path, with a table giving every module's direct dependencies. Generated from the source rather than drawn, so it states what the code does and not what anyone intended it to do -394. Add scripts/module_graph.py, which produces all of it and carries the cycle check. Three modes: a layer listing, --mermaid for the diagram in the document, and --check for CI or a pre-commit hook. The document's diagram is byte-identical to the script's output, verified by diff, so the claim that the script reproduces the document is true rather than aspirational -395. Draw the transitive reduction, 40 of the 88 edges, with those implied by a longer path removed. Reachability is unchanged and the full set is in the document's table; all 88 drawn at once is unreadable -396. Report the largest multi-module component on a cycle, never the largest component. This writes the measurement bug from entry 371 into the tool so it cannot recur, proven with a synthetic tree where a 5000-line singleton sits beside a two-module cycle: the two-module cycle is still what gets named -397. Fix the diagram's direction before publishing it. It was written as graph BT, which renders a dependency above its dependent and so contradicted the document's own statement that layer 0 is the floor. graph TD, with edges running from a module to what it depends on, puts layer 0 at the bottom -398. Add shared::quad_source::QuadSource: four methods for reading quads, against DatasetIndex's 33. The surface is what consumers were measured to need rather than what the index offers -- datalog and datalogmtl between them call two of those 33 -- so an array, a window's content or a file-backed dataset can implement it without implementing a store. Writing is deliberately absent: a read-only source implements this and nothing else -399. Take a visitor rather than return a boxed iterator, on the evidence already in perf-baseline: benches/micro_scan measured a boxed iterator at +22% on a scan returning 8 quads and +25% at a few thousand, disappearing only around 40,000, because the cost is per call. That falls hardest on the short-lived small sources the trait exists for. The same benchmark found dynamic dispatch free -- &dyn measured within noise of direct field access at every selectivity -- so consumers take &dyn and avoid monomorphisation -400. Record the two limits in the trait's own documentation rather than discovering them later. Term ids are dictionary-relative, so a source holding its own term space has to intern through the consumer's Dictionary, which is the gap a TermEncoder would close. And nothing is fallible, matching DatasetIndex; a source that can fail mid-scan has nowhere to report it, and adding Result afterwards would touch every consumer, so it is a decision to take before the first fallible implementation -401. Add Reasoner::infer_with_strategy_over and infer_new_facts_semi_naive_over, which read the base facts from a QuadSource as well as from the reasoner's own index. This is what removes the copy: infer_with_strategy already flattened the index into a Vec as its first act, so a caller that inserted a whole store into a fresh Reasoner was building three sorted orders in order to enumerate them once and drop them -402. Port the RSP operator's semi-naive path to read its store directly instead of copying it into the reasoner. Covered by rsp_ql_reasoning_derives_types, which was verified to cover it: feeding an empty source in place of the store makes that test fail, so a green run means the facts are genuinely arriving -403. Keep the copy in the RSP operator's hybrid branch, confined to that branch and documented there. infer_new_facts_with_hybrid goes through lineage materialisation, which reads the reasoner's own index rather than a source. Removing the copy for both branches at once silently emptied the base for the hybrid one, and the test suite did not catch it: only parser_test mentions hybrid at all, so that path has no coverage here -404. Note what the other two copy sites need, neither of which is the trait. rule_execution has ten inference calls reading kg's index -- two semi-naive, seven provenance across five provenance types, and one through a per-window kg.clone() -- so it needs an _over variant for the generic provenance entry point, and the clone needs an owned source rather than a borrow because it is moved into a closure. execute_ml_train's build_ground_reasoner_from_db hands a prebuilt Reasoner to execute_ml_training_impl as a fact base, so porting it changes that function's signature rather than one call -405. Drop a redundant mut that the new entry point made dead: infer_with_strategy now only delegates to infer_with_strategy_over, so its by-value strategy is moved rather than mutated. Caught by the warning count rising from 109 to 110 in verify.sh, which is what that metric is for -406. Gate model_hidden_layers on the ml feature, closing a dead-code warning introduced when it moved into the ungated neural_registry: every caller is ml-gated, so a default build had no use for it. Its sibling model_output_type was already gated, because it names a type from the ml crate -407. Note that the raw warning count in verify.sh is only meaningful after a build that actually recompiles the crate in question. cargo re-emits warnings on compilation, not from cache, so the number moves between runs for reasons unrelated to the code; the two warnings above were confirmed gone by touching their files to force recompilation rather than by reading the total -408. Port the plain rule path in rule_execution to read the store through QuadSource instead of a copy. The copy is now conditional: a windowed rule still needs its own facts, because it clones kg into a 'static callback per window that cannot borrow the store, and the provenance paths still need theirs, because the machinery behind infer_new_facts_with_provenance does not take a source. The plain path, which is what an unannotated non-windowed rule takes, copies nothing -409. Add plain_rule_derives_from_the_stores_facts, because nothing covered that path. Withholding the base facts entirely left all 646 tests green and all 51 examples passing, so the whole of verify.sh would have accepted a plain rule that derived nothing: the other process_rule_definition tests use the annotated path, the ml ones assert on predictions rather than derivation, and the examples print results rather than asserting on them while the smoke run only checks exit status -410. Record the technique, since it is cheap and worth repeating for the remaining sites: replace the source with DatasetIndex::new() and see whether anything fails. If nothing does, the port is not verified, whatever the suite reports. That is how both this gap and the earlier hybrid regression were found -411. Note why the provenance plumbing was not attempted here: base facts are read in four places across provenance_infer_generic and provenance_semi_naive, including two public free functions and a negative-stratum pass, so threading a source through touches six signatures in two files. Worth doing deliberately rather than at the end of another change -412. Thread an optional QuadSource through the provenance machinery, the six signatures entry 411 named: infer_with_provenance_strategy and _and_rules in provenance_infer_generic, semi_naive_with_initial_tags and _and_delta, run_negative_stratum_pass, and a new public infer_new_facts_with_provenance_over. The original infer_new_facts_with_provenance delegates with None, so its 25 call sites are untouched. The nine internal callers in hybrid_materialisation, sdd_seed_materialise and cross_window_incremental pass None and keep reading only the reasoner's index, which is what they did before -413. Port rule_execution's six provenance calls to read the store, and drop prob_annotation from the copy condition. Only a windowed rule now copies the store into kg, because it clones kg into a 'static callback per window and a boxed callback cannot borrow the store. The hybrid annotation copies in its own branch instead, since lineage materialisation has no source parameter -414. Add annotated_rule_derives_from_the_stores_facts, because rule_execution's annotated path was uncovered in the same way its plain path was. Withholding the base inside datalog's provenance driver fails 32 tests, which is why the path looked covered, but those reach it through program or through datalog directly: passing None at rule_execution's own six call sites left all 647 green. The new test fails when the base is withheld, deriving 0 where it expects 1 -415. Record that the empty-source check has now found a defect or a coverage hole at every one of the four sites it has been run on: a hybrid regression introduced by the first port, and uncovered plain and annotated paths in rule_execution. Running it before trusting a port has been worth more than the ports themselves -416. Thread an optional QuadSource through materialize_lineage, which turned out to cost one parameter: it already called semi_naive_with_initial_tags with the base argument added in the previous commit, passing None. Six call sites updated, four of them datalog's own tests. infer_new_facts_with_hybrid delegates with None and gains an infer_new_facts_with_hybrid_over sibling -417. Remove the local copies from both hybrid branches, in rule_execution and in the RSP operator, which the previous two commits had to leave in place because lineage materialisation had no source parameter. Both now read their store directly -418. Add hybrid_rule_matches_an_untagged_fact_from_the_store, and record why the three existing provenance=hybrid tests could not catch a missing base: materialize_lineage inserts the snapshot's seed triples into the reasoner itself, and those tests supply every fact through add_tagged_triple, so their rule bodies are satisfied by seeds alone and derive correctly with no base at all. The new test needs one tagged fact and one untagged one, so the base is load bearing; it derives 0 instead of 1 when the base is withheld -419. Leave site 3 unported, deliberately, and pass None at its materialize_lineage call to preserve exactly what it does today. Withholding the base in build_ground_reasoner_from_db leaves all 421 ml tests passing, so the path is unverifiable, and it is the one where a silent change does the most damage: the base feeds materialize_lineage once per row per batch per epoch, so a wrong answer corrupts a trained model rather than failing -420. Note two things about site 3 for whoever takes it. The per-row base_reasoner.clone() is the most expensive copy of the four, a full index clone per row per batch per epoch, so that is where the performance is. And build_ground_reasoner_from_db cannot simply stop seeding: provenance_feedback_loop.rs reasons with the returned reasoner directly, so the training path needs an unseeded sibling rather than a changed contract -421. The empty-source check is now six for six. Three consecutive sites -- rule_execution's plain path, its annotated path, and its hybrid path -- looked covered and were not, each for a different reason: no test at all, tests reaching the same function through a different caller, and tests whose facts arrive as seeds rather than as base facts -422. Gate the acyclic module graph, in verify.sh and in the lint-gates CI job. Nothing protected it: a module cycle costs nothing at runtime, so one use crate:: in the wrong direction is invisible until someone tries to split the crate. The check reads source only, needs no build and takes under a second -423. Demonstrate how fragile that property is rather than assert it. Adding a single reference from parser, which is layer 0, to rule_execution puts five modules and 13373 lines back into one cycle: parser, execute_query, rsp, rule_execution and streamertail_optimizer. The gate names all five and exits 1 -424. Clear the last ten rustdoc redundant-explicit-link-target warnings, five in kolibrie and three in datalog, one each in datalogmtl and shared. Each was [`Name`](path::to::Name) where the text already resolves, which is what made it redundant; dropping the explicit target leaves zero redundant and zero unresolved, so every bare link still resolves -425. Correct the Known gaps section, which this emptied by half. It said two things were left undone and now says one, the duplicated QueryError-to-status mapping, which is still true. A first attempt at that edit claimed both entries were resolved while leaving the status-mapping entry in place directly above, which would have put a false statement in the contributor documentation -426. Measure the TermEncoder surface before designing it, as was done for QuadSource. Dictionary has 7 public methods and two of them are 538 of the 546 calls: encode at 308 and decode at 230, spread across all six crates. decode_term and decode_triple are 3 each, merge is 2, decode_triple_star is 0. So the trait is two methods, and unlike QuadSource the difficulty is adoption rather than surface: 125 call sites hold one dictionary guard across 1 to 9 calls, plus 38 that lock and call in a single expression -427. Extend micro_dictionary with a micro_term_encoder group of 8 benchmarks, because micro_scan had already settled dispatch for QuadSource and nothing had settled the equivalent question here. Nine benchmarks separating three costs that were being argued together: the lock, the vtable, and the allocation. The ninth, a guard-held dyn decode, was added because the first eight left a trait method unbenched and the dead-code warning caught it -- it turned out to be the shape that isolates dispatch on decode -428. Result over four runs: locking itself is modest, +6% on encode and +17% on decode, both against a concrete receiver so no vtable is involved. The two guard-held shapes could not be measured at all: both carry about 21% noise across runs, the worst in the suite, and their medians move enough between runs to reverse their order. A single run had suggested a vtable costs 22% on decode; four runs put the dyn variant below the concrete one, so that claim is withdrawn and no conclusion about batching or dispatch can be drawn from those two rows -429. Result: the cost is entirely in what decode returns. An owned String per call, which interior locking forces because a borrow cannot outlive the guard, costs 299% over direct decode -- three times the decode itself. Lending through a visitor recovers most of it at +55%, the same answer QuadSource::for_each_quad reached for the same reason -430. Note what the measurement changed. The owned-String shape was the obvious reading of `encode(&self)` and would have been recommended on the strength of the encode numbers alone; the visitor variant was added only after the owned one measured badly. There is no shape that is both free and guard-less, so decode is a deliberate trade and should be documented as one rather than presented as a free abstraction -431. Add micro_term_encoder_contended: the same 10000 terms divided among 2, 4 and 8 threads, with an independent-dictionary control at each count so contention is separable from threading overhead. This is the number that was missing, because every other figure here is single-threaded and therefore uncontended, which is the cheap case -432. Result, and it reverses entry 428. Per-call locking on a shared dictionary is slower than a single thread at two threads and 4 to 12 times worse at eight: shared encode goes 1.6x, 2.2x and 3.9x slower at 2, 4 and 8 threads, shared decode 3.3x, 5.4x and 12.3x, while independent dictionaries scale about 2x. So the cost is the sharing, not the threading. Reads are worse than writes because a read acquisition still does an atomic read-modify-write on one cache line and the acquisitions ping-pong it between cores -433. Withdraw the recommendation that followed from the uncontended numbers. encode(&self) with interior per-call locking is the +8% shape single-threaded and a 4x regression at eight threads, and rsp_engine spawns a thread per window, so that is the access pattern it would meet. P5 cannot be closed by interior locking alone: the abstraction has to admit a guard-holding form or thread-local encoders -434. Withdraw the dismissal of batching as buying 3%. That figure was uncontended. The 125 call sites holding one guard across several calls are what keeps the current design workable, and an abstraction forbidding them would be a concurrency regression presented as a cleanup -435. Note that the contended group was added at the suggestion of someone asking whether a single-threaded and a multi-threaded mode would be better, which the uncontended data had been used to argue against. The data answering that question did not exist until it was asked for -436. Move the contended group out of micro_dictionary into its own benches/contended_dictionary.rs and into the heavy tier. Tiers are keyed by bench target, not by group, so it needed its own target. It answers a design question rather than guarding a refactor, which is what the heavy tier is for, and it costs about 45s per routine run that no longer has to be paid -437. That also makes the regenerated baseline correct as it stands: it was built from four runs predating the contended group, so it holds exactly the routine tier's 41 benchmarks and none of the 9 contended ones, which is what a routine baseline should hold. No second regeneration is needed -438. Document a harness gotcha found while checking that: criterion accumulates results in target/criterion and perf_report.py collect summarises everything present rather than the last run, despite what its help says. A heavy-only run collected 50 benchmarks, 41 left over from an earlier routine pass. Wipe target/criterion when switching tiers, or a comparison silently mixes stale numbers from whenever the other tier last ran -439. Add shared::term_encoder with the two modes: LocalTerms owns a dictionary outright and takes no lock, for one thread; SharedTerms wraps the Arc> the workspace already passes around, for the case rsp_engine creates when it spawns a thread per window plus a coordinator. Only SharedTerms is Sync, which is the point rather than a limitation -- a field that crosses threads will not accept the local one, so the choice is checked by the compiler instead of being a convention -440. Offer with_terms, which lends the dictionary for a closure and acquires at most once, and deliberately offer no per-call encode method. A per-call method is the obvious thing to reach for and the measurement says it is the wrong one, so it is absent and the module documentation says why -441. Measure what the scoped shape recovers: encode_scoped against encode_shared at 2, 4 and 8 threads gives 256 against 299, 283 against 643, and 330 against 697 microseconds. Scoped access roughly halves the contended cost at 4 and 8 threads and stops the degradation compounding, 1.4x to 1.8x slower than one thread rather than 1.6x to 3.8x -442. Record that it does not make threading profitable, which is the limit of this design. with_terms holds an exclusive lock for the whole chunk, so threads serialise and the result is single-threaded throughput minus handoff: at 4 threads scoped is still 3x worse than independent dictionaries and 1.5x worse than one thread. Parallel speedup on encoding needs a sharded dictionary or a thread-local cache in front of a shared one -443. Record that the contended figures are directional and not thresholds. encode_shared/4 measured 413 and 643 microseconds on two runs of identical code, a 56% swing, because contended timing depends on scheduling. The ordering has been stable across every run, so the shape is trustworthy and the multipliers are not -444. Walk into the ///-on-pub-mod trap a third time, on shared/src/lib.rs this time: the insertion anchor was `pub mod terms;`, which looks like an item boundary but had a doc comment attached above it, so that comment ended up documenting term_encoder. Entry 388 said an anchor has to be an item boundary; the refinement is that the boundary sits above any doc comment, not on the item line -445. Correct the count of manual-sharing sites. Of the 13, only 4 are cross-component: three in kolibrie and the HTTP server that hand a store's dictionary to a reasoner, plus one that hands it back. The other nine are datalog wiring its own reasoners, where the field is in-crate and a SharedTerms would add verbosity for nothing. Those nine are left alone -446. Add Reasoner::adopt_terms and SparqlDatabase::terms, so a caller sharing a term space never names Arc>. Only SharedTerms is Sync, so a single-threaded encoder cannot be passed to a reasoner by mistake. The four cross-component sites now use them -447. Delete a no-op in kolibrie-http-server. It assigned the database's dictionary handle to a reasoner, inferred, then called set_dictionary_handle with kg.dictionary.clone() under the comment "sync the enriched dictionary back so SPARQL can decode the new terms". kg.dictionary was never reassigned in between, so that handed the store the handle it already held, and the comment described copy semantics the type does not have. The terms were decodable the moment they were interned -448. Add tests/shared_term_space_test.rs rather than trust that reading. Three tests: a term interned on the reasoner's side decodes through the store with no sync step, the two mint the same id for the same term, and -- as a negative control, so the first two are not trivially true -- a reasoner with its own dictionary mints a different id -449. Note that P5's last violation in kolibrie is now nearly closed. set_dictionary_handle has no callers left at all, and dictionary_handle has exactly one: benches/micro_field_access.rs, which measures that escape hatch on purpose. Removing both would close it, but that is a public API removal plus a change to a baselined benchmark, so it wants its own commit -450. Close P5 for SparqlDatabase: dictionary_handle and set_dictionary_handle are gone, and Arc> no longer appears anywhere in its public API. Sharing a term encoding, which was the one case that needed it, goes through terms() and returns an opaque SharedTerms. The struct documentation no longer claims an exception, because there is none -451. Move the micro_field_access handle arm onto the replacement path: handle_then_lock becomes terms_then_scope, measuring terms() plus with_terms, which does the same two things the removed method did. The baseline carries the old name, so a comparison will report one removed and one new until it is regenerated -452. Record a caveat on that arm rather than a win. It measures 5.09us against the removed arm's baselined 8.53us, but also slightly under guard_accessor at 5.41us despite doing an Arc clone and taking a write lock where that arm takes a read lock. That is the clone and its matching drop being cancelled because the SharedTerms provably does not escape the loop, which a real call site holding the encoder would not get. Useful as a floor, not as a figure to quote -453. Note what remains: two Arc> signatures in shared::term_encoder, SharedTerms::from_handle and SharedTerms::handle. They are the bridge to datalog's Reasoner::dictionary, which is still a public field of that type, so closing them means changing datalog's API rather than kolibrie's -454. Regenerate perf-baseline/baseline.json from four fresh runs, after wiping target/criterion so the heavy-tier contended results were not folded in -- the accumulation gotcha from entry 438, now avoided rather than described. Nothing drifted: 40 of 40 comparable benchmarks within 5%, with exactly one entry swapped, handle_then_lock out and terms_then_scope in. 41 entries, median noise 3.3% -455. Find that the decode reference is unusable as an anchor. micro_dictionary/decode measured 4.2% noise in the previous baseline and 31.2% in this one on identical code. encode_warm is at 8.4%. Both are the references every earlier percentage was expressed against -456. Find the pattern behind it: every shape that locks per call measures stably at 0.5 to 2.1%, and every direct or guard-held shape is noisy at 8 to 31%. Per-call locking dominates its own measurement and swamps scheduling variance, while the tight loops are at the mercy of it. That is a property of the harness worth knowing beyond this bench -457. Restate the encoder conclusions against stable anchors only. An owned String return costs 2.55x a visitor, 278 against 109 microseconds. A visitor costs 31% over per-call locking that lends, 109 against 83. A vtable costs about 4% on encode, 200 against 193 -- the only dispatch figure here worth quoting, after an earlier claim of 22% on decode turned out to compare two noisy rows -458. State what is no longer claimed: any overhead figure against direct access. The direct rows are too unstable, so these benchmarks measure the differences between candidate shapes and not the absolute cost of the abstraction. The design conclusion is unchanged -- interior locking is affordable on encode, an owned decode is not, a visitor is -- only the precision of the numbers -459. Measure the conversion cost at the boundary before migrating anything onto a schema+tuple shape, and find that direction decides everything. Converting a 20000-row input to Relation costs 0.85ms; converting the 200000-row join result back to maps costs 31.3ms, 38 times more, because a join multiplies rows and every output row needs its own map with five string keys -460. So the incremental path is viable in one direction only. Joining in Relation form and handing the map shape back measures 43.5ms against today's 54.5ms, keeping 1.23x of the 4.7x. Converting inputs instead and joining natively measures 13.4ms, keeping 4.1x. Anything adopting this type must make the edges that return bindings native and may leave the edges that supply them converting -461. Classify the 33 production boundaries by direction, since that is now the migration order: 14 return the shape, 17 take it, and 2 do both -- join_solution_sequences and hash_join_solution_sequences, the joins themselves, which have to be native. Of the 14 returns, 9 are in datalogmtl but are sources rather than joins, so their cardinality is input-scale and they can keep returning maps; the 5 expensive ones are all in kolibrie -462. Add shared::relation::Relation. The API is shaped by what those boundaries need rather than by the bench stand-in: schema and rows accessors, column() resolving a name to an index once, push, len, shared_columns for what a join keys on, and the two conversions with their measured costs in the documentation so nobody reaches for to_bindings in a hot path -463. Offer no method that takes a row and a variable name together, for the same reason TermEncoder offers no per-call encode: that is the map shape's access pattern and the thing this type exists to stop. A caller resolves a column once and indexes rows with the result -464. Record a safety property traded away rather than a free win. Every row must hold exactly schema().len() values. A HashMap per row was self-describing and could not be the wrong width; a tuple can. push and with_rows check it with debug_assert, free in release and caught in tests, and one test asserts the panic fires -465. Verify the real type keeps the stand-in's win, rather than assuming encapsulation is free: 11.88ms against the stand-in's 11.46ms, 3.6% apart and inside this bench's noise, with an assertion that both join to the same cardinality. 4.59x against today's 54.5ms -466. Name it Relation only while both shapes exist. Bindings is the better name -- it says what the thing is rather than how it is stored -- but it currently means the map shape at 55 production sites, and reusing it now would silently change the meaning of every one. The rename happens when the old alias is deleted -467. Stop the engine migration on finding Relation cannot represent what the engine produces. A SPARQL solution mapping is a partial function: OPTIONAL and VALUES with UNDEF both yield sequences whose rows bind different variable sets, and the engine's join already branches on it. A schema plus equal-width tuples has no way to say a particular row binds nothing in a particular column -468. Fix the first half with a reserved value. Relation::UNBOUND is u32::MAX, from_bindings writes it where a row does not bind a variable, and to_bindings omits those cells rather than emitting them as term ids, so a partial sequence survives the round trip unchanged. Rows stay Vec, so there is no per-cell cost for this -469. Record that from_bindings previously wrote 0 there, which was a latent correctness bug. Dictionary::next_id starts at 0, so the first term ever interned has id 0 and the conversion conflated it with unbound. It was unused and therefore harmless, and is fixed rather than built on -470. Fix a second bug in the same function: the schema was taken from the first row's keys. Rows being partial, a variable may be bound only in a later row, so that silently dropped it along with its column. The schema is now the union of every row's variables, in sorted first-seen order so it does not depend on HashMap iteration order -471. Find that the sentinel was sound only by accident, and make it sound by construction. Dictionary::encode asserts next_id stays below QUOTED_TRIPLE_ID_BIT, so an ordinary term id can never reach u32::MAX. Quoted triple ids climb from that bit upwards and had no upper bound at all, and a variable can be bound to a quoted triple. QuotedTripleStore::encode now asserts next_qt_id < u32::MAX, which also closes an unguarded increment that would have wrapped silently in release -472. Catch a test that passed for the wrong reason, by deleting each fix in turn and checking the matching test fails. Three bit immediately. The fourth did not: asserting only that minting the reserved id panics is satisfied by the debug-only arithmetic overflow check, so the test passed with the new guard removed, while a release build would have wrapped around and returned u32::MAX as a real id. It now matches the panic message instead -473. Pre-size the maps to_bindings builds. Filtering unbound cells turns the iterator's exact size hint into an upper bound, so collect could no longer size the map and rehashed its way up, costing 58ms against 36ms on the 200000-row result. Building with_capacity and inserting recovers it -474. Restate the boundary numbers now that both conversions are correct, because the margin narrowed. Input conversion is 2.3ms rather than 0.85ms, the union schema needing a pass over every row. Converting inputs and joining natively now keeps 3.2x of 4.3x rather than 4.1x of 4.7x; joining and converting results back keeps 1.18x. The conclusion is unchanged -- convert inputs, never results -- but the earlier figures were measured against a first-row schema that cannot represent the engine's bindings -475. Note what the join figure still does not measure. micro_join binds every variable in every row, so the unbound paths are work it never exercises -- including an unkeyed row joining against every row on the other side, which is what SPARQL compatibility requires and what the engine already does. 4.3x is an upper bound measured on uniform data, and the engine migration needs a partial-data measurement before it is worth committing to -476. Add a control arm to micro_join that puts the map shape behind a struct with accessors, keeping one HashMap per row, because the question of whether wrapping a HashMap is what costs anything deserved a measurement rather than an argument. It measures 51.11ms against the raw shape's 51.01ms, 0.2% apart. Wrapping is free; the earlier real-type arm had already shown the same for the other representation at 2.4% -477. Record what the win is therefore made of, since neither figure should be credited to encapsulation. The map join hashes a string per key variable per row and allocates 200000 output maps with five cloned String keys; Relation keys on row[i] and pushes a Vec. That is also why to_bindings costs around 40ms: building those maps is most of what the map join spends its 54ms on, so converting a result back re-does the work the representation avoids rather than adding anything -478. Delete Streamertail::execute_plan_with_ids_and_dataset, one of the return boundaries counted in entry 461, on finding it has no callers at all, is not re-exported and is not in the prelude. It was a pub delegate to an ExecutionEngine method that is reached directly everywhere else, so the migration has one fewer boundary to carry rather than one more to convert -479. Profile the five end-to-end queries in optimizer_hotpath before migrating the engine onto Relation, because micro_join measures a join in isolation and the question was what a real query spends its time on. 75.3% of working time is hashing, allocation and copying -- but only 12.8% of it is in the engine's row handling, which is what a Relation migration would fix. 54.5% is in the output path. The engine migration's ceiling is therefore about 1.15x for 21 functions of partial-row join semantics, so it is not the place to start -480. Find the output path building a shape it immediately takes apart. decode_bindings produced Vec> and finalize_select flattened it into Solutions, which is already a schema and rows. Per result cell that cost three String allocations -- the variable name, the decoded value, then a second copy to read it back -- and two hashes, where one allocation and no hashes suffice -481. Find decode_any taking the dictionary read lock per call, with decode_bindings calling it per cell: 60,000 sequential acquisitions for a 20,000 row three column result. The chunked pattern that fixes it already existed in ExecutionEngine::execute_with_dataset and simply was not used here -482. Replace the intermediate map with StringTable, a schema and rows matching what Solutions holds, and make the four private functions that consumed the old shape work positionally: finalize_select resolves each projected column once, apply_order_by resolves its sort keys once rather than per comparison, and aggregate_rows keys groups by index. No public API changes; StringBinding was private to this file -483. Decode in parallel chunks with one lock per chunk and one decode per distinct id, so a term repeated down a joined column is decoded once. Add an identity fast path so a projection that already matches the schema moves its rows instead of rebuilding them a cell at a time -484. Measure 1.22x on graph_variable_scan, 1.21x on join_two_patterns, 1.16x on union_branches, 1.12x on graph_fixed_scan and no change on filter_numeric. Less than the profiled 54.5% suggests: what is left is irreducible at this layer, since every cell still has to become an owned String for Solutions to hold. What went away was the redundant work around the decoding -485. Correct a first reading of those numbers that claimed 1.71x on join_two_patterns and 1.45x on union_branches. That baseline was one run taken straight after a clean build and was slow; two stable runs per side give the figures above. The lesson is the one from entry 455, applied to a different bench: a single run is not a measurement -486. Check the rewrite by deleting each behaviour in turn rather than trusting a green suite. Eight probes bite -- COUNT(*) off by one, ORDER BY skipped, DISTINCT not deduplicating, the identity fast path forced on, a first-row-only schema, unbound collapsed into a value, the decode cache keyed wrong, and an unprojectable column reading column 0. The ninth, removing the schema sort, changes nothing, because the output column order comes from projection_columns rather than the table: that sort is defensive determinism and is documented as such rather than claimed to be tested -487. Add low_cardinality_projection to optimizer_hotpath: 20,000 solutions over 50 distinct departments, the counterpart to join_two_patterns whose columns are a distinct IRI and a near-distinct literal per row. Grouping columns, rdf:type and graph names all have that shape, and it is the case where storing each value once could pay -488. Build the interned result, so Solutions held each distinct string once and rows held indices, and measure that which representation wins depends on how the result is read. Through into_rows it is 10.65ms against 9.53ms on join_two_patterns, so owning wins by 1.12x. Through iter and Solution::get it is 9.11ms against 10.09ms, so interning wins by 1.11x. into_rows allocates a String per cell whatever the result holds, so it pays interning's indirection and gets nothing back -489. Correct the first explanation of that, which said a small String clone is cheaper than a hash lookup. Measured directly, a u32 hash lookup is 5.6ns against 24.4ns to clone a short IRI, so hashing is the cheaper half. The actual cause is parallelism: the owned decode does its per-cell work inside a parallel chunked region across 12 cores, and the interned build adds a sequential pass over every cell to assign indices. Making the owned decode sequential costs 3.7ms on the same query, which is more than interning was ever going to save -490. Keep the owned representation, on the grounds of what reads results rather than which is faster in the abstract. Every consumer in the workspace materialises -- the CLI, the Python bindings, the HTTP server, the examples, and the crate's own doc example all call into_rows or want Vec>. Nothing uses iter. Interning would slow all of them by 1.12x to speed up a pattern that has no callers yet -491. Add both read paths to optimizer_hotpath rather than only the materialising one, since the original arms all ended in into_rows and so could not show a representation difference at all. The borrowed_read arms are what made the trade-off visible, and they stay as the measurement to re-run when a borrowing consumer appears -492. Record the trade-off on Solutions itself, with both figures and the reason, so the question is answered from measurements next time instead of from first principles -- which got it backwards twice here -493. Parallelise the index assignment, which is what the owned representation was winning on rather than anything about strings. Entry 490 kept owning because interning measured slower through into_rows; entry 489 had already found the cause was a sequential pass over every cell, so the pass is the thing to remove -494. Reject sorting the ids as the way to remove it. It does away with the hashing, but pays an n log n sort and a cache-missing binary search per cell, and measured worse than the global map on every arm -- 6.10ms against 5.33ms on low_cardinality_projection. A chunk's map stays in cache; sorting 60,000 ids to find 50 distinct does not -495. Intern per chunk instead. Each chunk interns into its own table under one dictionary lock, exactly where the owned path cloned; a prefix sum over the per-chunk counts gives each chunk an offset, and one parallel integer add per cell makes the indices global. Nothing runs sequentially over cells. A term shared by two chunks is held twice, trading a little memory to keep every step parallel -496. Measure that against owning back to back in one session, two runs a side, since entry 485 had already been burned by a stale baseline: 8.26ms against 10.15ms reading through iter and Solution::get, so 1.23x, and 9.50ms against 9.83ms through into_rows, so 1.03x. Faster on all four arms -497. Reverse entry 490 on that basis. Interning is kept, and the reason it was rejected there no longer holds: it is no longer slower for a caller that materialises, so the consumers that all call into_rows lose nothing and a caller that borrows gains 1.23x. Solution::values changes from &[Option] to an iterator of Option<&str>, which has no callers in the workspace; no other public signature moves -498. Retract the 2.4% into_rows regression reported for the per-chunk version before that A/B was run. Baseline staleness for the third time in this work, after entry 485 and the first interning verdict, and the reason comparisons here are now run back to back rather than against whatever criterion last stored -499. Leave From sequential, having tried it in parallel. Interning moves a cost there -- owning a String per cell let that conversion move them out, sharing them means a copy per cell -- but collecting nested Vecs in parallel measured 5.38ms against 5.14ms on low_cardinality_projection, so allocator contention costs more than the parallelism buys -500. Probe the interned rewrite and find its new mechanism entirely untested. Deleting the prefix-sum shift, and chunking the shift differently from the interning, both left the whole suite green: every existing query test returns fewer rows than one decode chunk holds, so nothing had ever crossed a chunk boundary. The manual PartialEq was untested too. Three of five probes finding nothing is the coverage hole, not a clean bill -501. Add solution_interning_test with 3,000 rows, deliberately above the 1,024 row chunk, covering all three: values past the first chunk, the two read paths agreeing on a multi-chunk result, and two equal results built with different term tables -502. Rewrite the first of those tests after probing it, because it passed with the shift deleted. It compared a row's two columns against each other, and both shift by the same amount when the prefix sum is wrong -- so a second-chunk row reads urn:s0 with urn:o0, self-consistent and entirely wrong. It now asserts the pairs read back are the pairs stored and that all 3,000 are distinct, which is what catches a chunk reading another chunk's terms -503. Start the engine migration with the thing it cannot be done without: PhysicalOperator::output_variables, naming the variables a plan's solutions can bind given what its input binds. The engine never needed this, because it builds rows a variable at a time and a HashMap per row does not have to know its own width in advance. A schema and tuples does, and resolving a name to a column once per plan instead of hashing it once per row is the whole point of the change -504. State the direction of the approximation rather than leaving it implied. Over-approximating is fine, since a column no solution binds is simply unbound in every row, which is representable. Under-approximating has nowhere to put the variable, so it is the failure to guard against -505. Guard it with a debug_assert in execute_with_ids_and_input that no produced row binds a variable the function did not declare, which puts the tests that reach the engine behind it rather than a few unit tests. Measured that reach rather than assuming it, by making the engine panic unconditionally and counting what failed: 161 of 668 -506. Check the guard can actually fire, since an assertion that never trips proves nothing. Deleting a scan's pattern variables trips it, as does dropping a hash or nested-loop join's right side, and as does dropping a BIND's output variable -507. Find that cargo test --workspace stops at the first failing binary, so every ad-hoc probe count in this session's work was an undercount -- the first attempt at the reach measurement reported 6 rather than 161. The conclusions stand, since a probe that failed at all still bites and a probe that failed nowhere still ran everything, but the magnitudes did not. scripts/verify.sh already passes --no-fail-fast, and its header comment says it exists because an earlier ad-hoc cargo test pipeline reported wrong numbers, which is exactly the trap walked into here -508. Measure the engine rewrite before committing to it, and choose against it for now. The row-handling functions are 1,092 of engine.rs's 1,187 production lines, the dispatcher alone 349, for a profiled ceiling of about 1.15x. Naming the type is what §4 actually asks for and costs nothing, so it goes first and the layout change becomes a separate measured step behind it -509. Make Bindings a type instead of a transparent alias for Vec>, which was spelled out at 46 boundaries where nothing could be said about it. Deref hands out a slice, FromIterator and IntoIterator and rayon's IntoParallelIterator and FromParallelIterator are implemented, and the owning operations are inherent, so nearly every call site compiled unchanged -510. Name the row too: Binding is one solution mapping, documented as the partial function SPARQL requires rather than left as a bare HashMap in every signature -511. Give the two empty sequences names, because the difference is load-bearing and the old spellings hid it. Bindings::empty() is a pattern that matched nothing; Bindings::unit() is one row binding no variables, which was written vec![HashMap::new()] at the sites that needed it and read as an implementation detail rather than as the identity of a join -512. Convert the 15 remaining raw-shape signatures outside the engine as well: neural_relations, ml_predict_runtime, datalogmtl's store trait and evaluator, and datalog's repairs and rules. Vec> now appears nowhere in the workspace outside the definition in shared::terms, including the stale comment in the Python bindings that described it -513. Confirm the wrapping is free on the real engine rather than trusting the earlier bench control arm, with two back-to-back A/B rounds against HEAD. Every difference is within 2% and the sign flips between rounds on three of the four arms, while the run-to-run spread inside one configuration is just as large -- 9.42ms then 9.70ms for the same HEAD build. No measurable cost, which is what the control arm's 0.2% predicted -514. State plainly what this does not do. Binding is still a HashMap and Deref hands out a slice of them, so the collection is hidden and the row is not; code treating a row as a map still compiles and will still need changing when the layout does. What the layout change will not need is the 46 boundaries, which is the point -515. Park the filter-evaluation conversion rather than land it here. Condition::evaluate_with_ids was rewritten to take a row and a schema and resolve a variable by scanning the schema instead of hashing a name per row, which is correct and compiles, but it only means something once rows are tuples. It belongs with the layout change -516. Start the engine migration with the thing it cannot be done without: PhysicalOperator::output_variables, naming the variables a plan's solutions can bind given what its input binds. The engine never needed this, because it builds rows a variable at a time and a HashMap per row does not have to know its own width. A schema does, and resolving a name to a column once per plan instead of hashing it once per row is the whole point -517. Guard it with a debug_assert in execute_with_ids_and_input that no produced row binds a variable the plan did not declare. Over-approximating is fine, since a column no solution binds is unbound in every row; under-approximating has nowhere to put the variable. Measured the reach rather than assuming it, by making the engine panic and counting what failed: 161 of 668 tests, plus all 51 examples. Deleting a scan's variables, a join's right side, or a BIND's output each trips it -518. Find that cargo test --workspace stops at the first failing binary, so every ad-hoc probe count in this work was an undercount -- the first reach measurement said 6 rather than 161. scripts/verify.sh already passes --no-fail-fast, and its header says it exists because an earlier ad-hoc cargo test pipeline reported wrong numbers, which is exactly the trap walked into here -519. Measure the engine rewrite before committing to it: the row-handling functions are 1,092 of engine.rs's 1,187 production lines, the dispatcher alone 349, against a profiled ceiling of about 1.15x. Name the type first, since that is what §4 asks for and it costs nothing, and leave the layout as a separate measured step -520. Make Bindings a type instead of a transparent alias for Vec>, spelled out at 46 boundaries where nothing could be said about it. Name the row as Binding, and give the two empty sequences names: Bindings::empty() is a pattern that matched nothing, Bindings::unit() is one row binding no variables, which was written vec![HashMap::new()] and read as an implementation detail rather than as the identity of a join -521. Convert the 15 remaining raw-shape signatures outside the engine too -- neural_relations, ml_predict_runtime, datalogmtl's store trait and evaluator, datalog's repairs and rules -- so Vec> appears nowhere in the workspace outside its definition, down to a stale comment in the Python bindings -522. Confirm the wrapping is free on the real engine rather than trusting the earlier bench control arm, with two back-to-back A/B rounds: every difference within 2%, the sign flipping between rounds on three of four arms, and the run-to-run spread inside one configuration just as large -523. Then change the layout: Bindings holds one schema and a row of u32 per solution, with UNBOUND for a variable a row does not bind. Rows are still constructed as maps at the door, because a scan does not know what it will bind until it binds it, and push widens the schema as new variables appear -524. Measure that and find a 1.5x to 2.5x regression, not the predicted gain, because keeping map-based construction made the work strictly additive: every row built a map as before and then paid to become a tuple. The compatibility shims that made it compile were exactly the cost the layout was meant to remove -525. Convert the producers, which is what actually pays. Filter and Projection work on cells, so a filter no longer rebuilds a map per row to ask the condition about one; Condition::evaluate_with_ids takes a row and a schema and resolves a variable by scanning the schema rather than hashing a name -526. Rewrite the scan on cells, which is where the profile put the engine's cost. ScanLayout resolves each of a pattern's positions to a column once per scan, where the map form hashed ?s, ?p and ?o again for every candidate quad. The output schema is the incoming schema followed by what the pattern adds, so a base row is the incoming row padded and needs no remapping -527. Replace FreshBindings, PendingMatch and RowExtender with that layout. They existed to delay emitting a row so the last match could move its map rather than clone it; copying a Vec is cheap enough that the lookahead is not worth its complexity -528. Add Bindings::append for sequence concatenation, because extend takes maps and so rebuilt a HashMap per row and took it apart again. Going through maps cost union_branches about twice its runtime. A fast path moves the rows untouched when both schemas already agree, which is the common case, and the slow path adds every missing column in one pass rather than padding all the rows once per column -529. Measure the result: filter_numeric is 3.0x faster, 0.70ms against 2.09ms, and the two join arms are at parity or slightly better. The scans are 4 to 7% slower and union_branches 18% slower -530. Record why union is the worst of them, since it is a property of the shape rather than a defect. A tuple row carries a column for every variable any branch binds, where a map carried only what that row bound, so a union of branches binding different variables makes every row wider and the output path then walks the unbound columns too -531. Profile what is left and find the engine is no longer the cost. Of the remaining hashing 42% attributes to execute_select and none to the engine's row handling; of the allocation, 40% is execute_select against 8.8% for Bindings::column_for, 4.0% for append and 2.7% for match_quad; all the string comparison is in the output path -532. Note one unresolved flake. rsp_ql_multi_window_integration failed once in roughly six full-suite runs under verify.sh's parallel load, and did not reproduce in five isolated runs or five further full-suite runs; HEAD passes in isolation too. The test turns on a fixed two-second window, so it is load-sensitive, but one occurrence is not enough to call it pre-existing and it is recorded here rather than dismissed -533. Collect the seam saving the layout change was supposed to make possible, which entry 529 left on the table. decode_bindings still recovered the schema by scanning every row's keys, still called into_iter on the sequence -- which rebuilt a HashMap with owned String keys per row -- and then looked each variable's column up again per entry. A solution sequence already is a schema and a row of ids, so all of that was redundant twice over. It now takes the sequence apart with into_parts -534. Retract entry 530. It said union was the worst arm because a tuple row carries a column for every variable any branch binds, so the output path walks unbound columns. That is true of the shape and was not the cause. The seam's cost scales with row count rather than width, and union produces 60,000 rows against the other queries' 20,000, so it simply paid the map rebuild three times over. The shape was never the problem -535. Measure every arm against the map shape after that, three runs a side: graph_variable_scan 1.97x, graph_fixed_scan 1.74x, union_branches 1.62x, filter_numeric 2.99x, join_two_patterns 1.82x, low_cardinality_projection 1.87x, and the two borrowed-read arms 2.21x and 2.26x. Nothing is slower, and union is no longer the exception but merely the smallest gain, which supersedes entry 529 -536. Record the external benchmark either side of this. The layout change alone measured 1.20x; with the seam it measures 1.49x. So the layout on its own left most of its gain unclaimed, because a sequence that already carried its schema was still taken apart into maps at the boundary. 1.49x is the figure to quote -537. Note why the project's own bench pointed the wrong way throughout. Every optimizer_hotpath query returns about 20,000 rows, so output materialisation dominates and dilutes an engine-side win, and union_branches is the worst case for the shape rather than a representative one. filter_numeric, the arm closest to engine-dominated, was the one reading correctly at 3x and was treated as an outlier instead of the signal -538. Delete shared::relation. Relation was the prototype of the schema-and-tuples shape, built under its own name so that adopting it would not silently change the meaning of Bindings at 46 boundaries. Bindings now has that layout, so Relation was a second public type with twelve methods saying the same thing, used by nothing but one benchmark arm. §4 asks for fewer and better defined public types, and this is one of them going -539. Give UNBOUND a home in shared::terms rather than a re-export from the deleted module, keeping the whole argument for why u32::MAX is the safe sentinel and 0 is not -540. Find that Bindings had no direct unit tests at all, which is worth stating plainly: it was rewritten from a transparent alias into a schema and tuples, the entire engine was moved onto it, and the 668 tests covered it only through the engine. push widening the schema, both of append's paths, bind_column, retain, sort_by and every BindingRef accessor were untested in isolation -541. Add fifteen, adapting the ten the deleted Relation carried -- partial rows reading as absent, term id zero not being unbound, the unit sequence against the empty one, the id-space sentinel being unmintable by either half -- and covering what Relation never had: schema widening on push, append with matching and with differing schemas, append onto an empty sequence, bind_column with and without a value per row, retain and sort_by seeing values rather than cells -542. Probe all six behaviours they claim rather than trusting that they pass. Stopping push from widening trips ten, making append ignore a schema mismatch trips two, mapping append's columns positionally trips two, letting get report UNBOUND as a term trips six, and ignoring bind_column's per-row values trips three -543. Rewrite the equality test after probing found it vacuous. It built both sides with push, which sorts a row's keys, so both came out with the same schema order and comparing the fields directly would have agreed anyway. It now lays one side out reversed and another padded with a column no row binds, which is what equality has to see past -544. Rename the benchmark's local alias for the old shape from Bindings to MapRows, since Bindings now means the type the benchmark was written to argue for. The comparison still reads the same: 52.4ms for the map shape against 12.2ms for the real type, which is the 4.3x this started from -545. Close P5 for the parser. Its entry points returned nom's IResult, which put nom::Err> in this crate's public API at 50 signatures: a caller had to name a third-party type to use the crate, and the crate could not change parser library without a breaking release. kolibrie::parse_error::ParseError replaces it, with offending, is_incomplete, expected and offset_in, and nom now appears in exactly one place outside the parser's own body -- the private conversion that builds it -546. Add Expected as the crate's own vocabulary for what the parser wanted, because the alternative leaked nom's ErrorKind through error_handler. Those kinds name combinators -- Tag, Alt, TakeWhile1, Many0 -- which describe how the grammar happens to be written rather than anything a reader of the query would recognise -547. Take nom out of error_handler, which was the last module outside the parser to name it. format_parse_error takes ParseError now, so the chain from a failed parse to a QueryError mentions nom nowhere -548. Fix the one consumer that reached into the error. A test matched nom::Err::Error against nom::Err::Failure and nom::Err::Incomplete to find which lexeme was rejected; it asks error.offending() now, which tests the behaviour rather than nom's representation of it -549. Move parser_test.rs into the crate as parser_tests.rs. Twenty-six combinators were public only because an integration test can reach nothing else, and that is what put IResult in the API. Testing internals from inside is what lets the surface stay small -550. Measure the result: the parser's public surface goes from 55 functions to 14, and public items naming nom from 50 to zero -551. Find dead code the pub kept hidden. Demoting the combinators let the compiler see that nothing called eighteen of them; nine were unreferenced everywhere and are deleted, and four more became unreferenced once those nine went. One of the four is aggregate::star_count, orphaned by the layout change in entry 529, which replaced its only caller with an inline COUNT(*) -552. Promote five of the eighteen instead of deleting them -- parse_prefix, parse_comparison, parse_group_by, parse_order_by and parse_limit -- since the grammar never calls them but tests do, and parsing a fragment is a reasonable thing to offer. They are public with ParseResult, which is both honest about their status and free of nom -553. Correct a classification that mixed two build targets. An aggregate grep over cargo check output reported all eighteen as dead, because the lib target cannot see cfg(test) code; five of them are called from the tests and would have been deleted on that reading. Counting references per file separated them -554. Add six tests for ParseError, having just found that Bindings had none. One of them caught a bug in offset_in written minutes earlier: it subtracted the rejected text's length from the input's, which assumes the parser hands back the whole unconsumed suffix. It does not always -- for one of the fixtures it narrows the error to ?illegal, which starts at byte 30 while the subtraction says 32 -- and the error messages built on that were quietly off by the width of whatever followed. It uses the slice's position now -555. Keep three of those tests with the parser's rather than beside ParseError, because importing parse_combined_query there made parse_error depend on parser and module_graph.py refused the cycle. The gate added in the untangling earned its keep on code written minutes before -556. Note that the rsp_ql_multi_window_integration flake of entry 532 recurred, a second time under verify.sh's parallel load and never on an isolated or sequential run. Two occurrences in roughly ten verify runs, against a fixed two-second window, so it is load sensitivity rather than anything this work touched -- but it is now a known intermittent rather than a one-off, and worth fixing on its own -557. Replace the widest positional tuple in the parser with a named struct. parse_where returned seven values as an anonymous tuple -- patterns, filters, a VALUES table, binds, subqueries, window blocks and negated patterns -- which every caller destructured and then named itself. shared::query::WhereClause names them once -558. Note what the tuple was costing at the call sites rather than only in the signature. Three of the four callers wanted one or two of the seven parts and spelled the rest as underscores: one read `let (_, (patterns, _, _, _, _, _, _))`, which says nothing about what the other six are. They read as struct patterns with `..` now -559. Use the named aliases the crate already had while converting. The tuple's first and last elements were raw (&str, &str, &str) and its fourth a raw (&str, Vec<&str>, &str); they are LexicalTriplePattern and BindClause, which shared::query has defined all along -560. Let Default replace a fallback that spelled out an empty seven-tuple. One caller recovered from a failed parse with (vec![], vec![], None, vec![], vec![], vec![], vec![]), which is WhereClause::default() -561. Give WhereClause no PartialEq, and say why in its documentation rather than deriving around it. WindowBlock has none, and adding one to make the containing struct comparable would be deciding when two window declarations are the same question on behalf of a type that has not answered it -562. Leave the three-element aliases alone. TriplePattern, LexicalTriplePattern, BindClause and UnresolvedTriple are tuples, but they are named tuples and subject-predicate-object is the shape RDF actually has, so a struct would add ceremony without adding information. §4's complaint is about wide anonymous tuples, and after this there are none in a public signature -563. Add kolibrie-api, the facade §4 asked for: one crate for a consumer to depend on. Its surface is what the consumers in this repository actually reach for, measured rather than guessed -- kolibrie 121 times, shared 41, datalog 15, ml 6, datalogmtl 6 -- and the shared items are concentrated, with Term 12, Triple 10 and Rule 8 accounting for most of it. A reader had to know that a query runs through kolibrie but a Term lives in shared, which is a fact about this repository rather than about RDF -564. Keep it empty of logic. Every item is a re-export, so there is one definition of each and the facade cannot drift from what it fronts, and the four crates are re-exported whole so anything the prelude leaves out is still reachable without a second dependency entry -565. State what it leaves out and why: the optimizer's operators, the execution engine, the parser's combinators, the window and stream machinery. Depending on kolibrie directly is right for work on the engine and wrong for using it, and keeping those out is what lets them change without breaking a consumer -566. Find that a facade has to forward features, not only types. The CLI's ml feature pointed at kolibrie/ml, which stops resolving the moment kolibrie is no longer a direct dependency; kolibrie-api forwards it, so a consumer can reach the feature without depending on the crate underneath -- which is the thing the facade exists to avoid -567. Port the CLI as the proof, since a facade nothing uses proves nothing. It needed exactly SparqlDatabase, execute and into_rows, all of which the prelude covers, and its two crate imports become one -568. Leave the HTTP server where it is, deliberately. It reaches into datalogmtl's automata, evaluator, parser, rdf_parser, store, stream and syntax, and kolibrie's rsp_engine and rule_execution: it is engine-integrated rather than a consumer, and routing it through a facade that excludes those on purpose would mean widening the facade until it excluded nothing -569. Cover both claims with doctests rather than prose. One runs a query through the prelude alone; the other reaches a type the prelude omits through the re-exported crate. Writing the second found two things: BooleanProvenance has no Default, and SparqlDatabase has no public triples field -- the second being P5 holding, since that field was public before this work -570. Close the last Arc> that mattered. datalog's Reasoner carried it as a public field, which is P5 breached in the plainest way: a caller had to name the locking strategy to intern a term, and a dozen places shared a term space by assigning the field. It is private SharedTerms now, reached through terms, adopt_terms, with_terms, encode_term, decode_term, encode_triple, encode_pattern and dictionary_snapshot -571. Give SparqlDatabase the matching adopt_terms, so two components agree on one term space the same way whichever is adopting. It had terms() and no counterpart, which is why several places assigned its dictionary field instead -- something only possible from inside the crate and silent about intent -572. Name the shapes the call sites actually had rather than exposing a lock: four callers encoded a triple's three terms under one lock, one encoded a pattern's optional positions, and many encoded a single term. Those are encode_triple, encode_pattern and encode_term, each one lock acquisition exactly as before -573. Add dictionary_snapshot for the read-only dumps, and say what it trades. A scoped encoder will not hand its lock out, so a caller that decodes every term it prints either takes the lock per term or copies once; to_dot and the Python bindings copy, and they cannot hold the lock while they format -574. Delete the sync-back in rule_execution. It assigned the store's dictionary from the reasoner's after interning, which was a no-op because they are the same term space -- the thing shared_term_space_test was written to pin. The assignment is gone and the test still passes -575. Record the defect in semi_naive_parallel plainly instead of carrying it quietly. It took a write lock on the shared dictionary and then cloned it per inference, because a &mut Dictionary cannot cross rayon's workers, so every term it interned went into a copy and never reached the shared space. The strategy has no callers; the comment now says so rather than reading like working code -576. Note the method that caused the most damage here, so it is not repeated. Converting call sites with blanket regexes introduced the very bug described above in seven more places -- write locks became snapshots, so interned terms were discarded -- and two mnist tests caught it. A later blanket removal of drop(dict) then broke files that had never been converted at all. The sites that needed judgement were done by reading them -577. Find two latent ml-gated breakages from earlier commits in this run: the parser demotion had made three window parsers private while an ml-only example imported them, and the layout change had left LogicalOperator::buffer taking Bindings where an ml-only example passed a Vec. Both were invisible to cargo check on default features, which is what verify.sh's ml stage exists for and what was not run between those commits -578. State what is left rather than claiming P5 closed. Three signatures remain in shared::term_encoder -- SharedTerms::from_handle, handle and the tuple struct itself -- and two in datalogmtl, its evaluator's public dictionary field and parse_data's parameter. The term_encoder three are now the bridge for datalogmtl and the RSP engine's cross-window dictionary rather than for datalog, and closing them means threading SharedTerms through RSPEngine's own field, which is its own change -579. Convert the cross-window chain entry 578 named, so the handle is no longer spelled along it. datalog's five public cross-window functions take &SharedTerms, and RSPEngine's cross_window_dictionary field holds Option, which is where every one of those calls started -580. Give SharedTerms the conveniences the call sites wanted, so a caller never reaches for the handle to encode or decode: encode, decode and encode_triple, with the scope taken and released around each. Reasoner::encode_triple delegates to it rather than keeping a second copy of the same code -581. Replace SharedTerms::from_handle(Arc::clone(&db.dictionary)) with db.terms() wherever a store's own term space was being rebuilt from its handle, which is what terms() is for. simple_r2r had two, and the file no longer imports Arc or SharedTerms at all -582. Reduce from_handle from a general sharing mechanism to the three places that genuinely need it: SparqlDatabase::terms, which is the one component still typed in terms of the handle; term_encoder's own test of the bridge; and the contended benchmark, which seeds an encoder from a warm Dictionary. Everything that merely wanted to share a term space now asks for one -583. Say plainly that P5 is not closed for the handle. Three signatures remain in shared::term_encoder and two in datalogmtl -- its evaluator's public dictionary field and parse_data's parameter. Deleting the term_encoder three needs SparqlDatabase's own field converted, which 67 call sites read directly, and the datalogmtl two are that crate's own P5 work -584. Copy, where a read-only walk decodes many terms. build_cross_window_sds and three comparison benchmarks decode every triple they print; a scoped encoder will not hold its lock across a walk, so each takes one copy and says so, rather than taking the lock per term -585. Audit the copies entry 584 introduced, after the question was raised whether a component writing to its own copy breaks synchronisation. It does, and worse than losing terms: a copy's next_id starts wherever the shared space's stood, so a term interned into a copy can be given a number the shared space later assigns to a different term. An id minted from a copy is fiction, and a fact built from one decodes to someone else's term -586. Find eight of the nine copies read-only and the ninth already known: semi_naive_parallel. Correct what that comment claimed, which was that its ids never reach the shared space. They do -- line 191 inserts the facts into dataset_index and returns them -- so it fabricates ids and stores them as if real. Two rayon workers in one run also mint the same id for different terms, so its output is not even internally consistent -587. Make the hazard unexpressible rather than documented. shared::term_encoder::TermView is a read-only copy with decode and nothing else; SharedTerms::view and Reasoner::term_view return it in place of an owned Dictionary, whose encode was sitting right there. Documentation had not stopped semi_naive_parallel, so the type does -588. Take the staleness half seriously too, since a view cannot prevent it. A term interned after a view is taken is missing from it, so decoding that id yields None -- sound rather than wrong, a blank instead of someone else's term. Every caller here is safe for one reason, now written down: the ids it decodes come from facts already stored, so they predate the view -589. Pin both halves with tests rather than asserting them. One shows two dictionaries from a common starting point minting the same id for different terms, which is why the view exists; one shows a view blind to a later term. The collision test had to be rewritten against Dictionary directly, because after the change it could no longer be written through the reasoner's API -- which is the clearest evidence the hazard is gone -590. Route the five remaining hand-rolled copies through view as well: build_cross_window_sds twice and the three comparison benchmarks were each doing Dictionary::new plus a clone inside with_terms, which produced exactly the mutable copy this removes -591. Close the handle out of datalogmtl, which entry 583 named as that crate's own P5 work. Its evaluator's public dictionary field and its eleven parser and rdf_parser parameters take SharedTerms now, so Arc> appears in no public signature of kolibrie, shared, datalog or datalogmtl -- only in the three bridge items of shared::term_encoder, which are documented as such -592. Add SharedTerms::try_with_terms, because three datalogmtl parsers reported a poisoned lock as a recoverable error and the HTTP server turns that into a response. with_terms unwraps, which would have turned a request-level error into a panicked handler; the fallible form keeps the contract -593. Let TermView deref to &Dictionary, which keeps the hazard closed while sparing the churn. Dictionary::encode takes &mut self and a Deref only yields &, so interning through a view stays unexpressible, and the read-only helpers that already take &Dictionary -- meteor_fmt's formatting, label lookups, comparison output -- need no change -594. Record that the view earned its keep immediately. Converting the HTTP server, a blanket substitution turned four interning sites into views, and the compiler refused all four: tokenize_and_encode_ntriples, the static rules parse and two fact parses all intern, and a view has no encode. Under the old shape those would have compiled and silently interned into a copy, which is the corruption of entry 585 -595. Add SharedTerms::with_terms_read, the read scope the API was missing. Converting the store's dictionary field needed a scope that several threads can hold at once: the execution engine takes a read lock per chunk inside a rayon iterator, so with_terms would have serialised the chunks and destroyed the parallelism behind entry 578's 1.49x, while view would have copied a 40,000-term dictionary about twenty times per query. Pinned by a nested-borrow test, which deadlocks rather than fails if the lock is ever made exclusive -596. Add SharedTerms::read and write, which hand out TermsRef and TermsMut: opaque borrows that deref to Dictionary and name no lock in their signature. The closure scopes cannot serve a borrow held across a match, compared against a second borrow, or opened mid-function, which is the shape of most of the 58 call sites converted in entry 597; the two guard types are #[inline] because they sit in the engine's per-row decode loop and a non-generic cross-crate function is not inlined without it -597. Convert SparqlDatabase's dictionary field from Arc> to SharedTerms, which is what entry 591's three remaining bridge items were waiting on. 58 guard sites across 12 files, plus the constructors, the handle cloned into parse_rdf's worker threads and a Vec type annotation; kolibrie's DictionaryRef and DictionaryMut become aliases for TermsRef and TermsMut, so one guard type covers the term space instead of two that have to be kept in step -598. Delete SharedTerms::from_handle and handle, the last two signatures in the workspace naming Arc>, and replace them with from_dictionary (shares a space that already has terms in it) and shares_space_with (whether two components are isolated, which is the property Arc::ptr_eq was being used to test). Cloning the encoder already expressed everything from_handle did. The handle now appears only as the private field of the newtype: zero signatures in shared, kolibrie, datalog or datalogmtl, closing P5 for the term space -599. Record what the measurement of entry 597 nearly concluded. The first run reported 18-28% regressions on four execution_hot_paths arms and 35% on micro_term_encoder/decode_dyn_guard; a second run of the identical tree moved those same arms by -11% to -20%, so the first was a cold run and the settled figures are -2.1% to +4.5%. The five arms micro_dictionary's own module doc marks usable as anchors moved +0.8% to +3.4%, and decode_dyn_guard -- which the same doc records at 8% noise and marks unusable -- operates a raw handle declared inside the benchmark, untouched by this change. Performance-neutral -600. Make RSPEngine::stop wait for the results it flushed. Under MultiThread it flushed each window and returned, but flushing only queues content for that window's thread, which forwards a result to the coordinator, which joins and calls the consumer: two handoffs nobody awaited. The window and coordinator threads were spawned with their handles dropped, so there was nothing to join; stop now keeps both and joins them in order, and drops the engine's own result sender in between so the coordinator sees the channel close. A channel yields what is queued before reporting disconnection, so neither join can lose a result -601. Emit the coordinator's final state on shutdown instead of discarding it. With SyncPolicy::Wait the cycle completes as soon as every window has fired once, using whatever each window's latest result is at that instant -- and on a loaded machine the coordinator is often not scheduled until stop has already flushed every window, so it drains a queue in which one window's first result completes the cycle while that window's real results arrive afterwards with nothing left to pair against. The engine was holding a complete, non-empty set for every window and threw it away. The shutdown path now publishes it, guarded on a window having fired since the last emission so an idle shutdown does not republish the same relation -602. Delete the six two-second sleeps in rsp_engine_test that stood in for the guarantee entry 600 adds. They were not slack in a passing test: rsp_ql_multi_window_integration failed about one run in 24 with eight test threads despite them, because the defect in entry 601 is not a matter of waiting longer. The file now asserts immediately after stop and runs in 0.02s rather than 12s, so the assertions are strictly stronger than before -603. Record how entry 601 was found, because two plausible diagnoses were wrong. The test passed 800 times in isolation and 300 times under twelve spinning background processes, failing only alongside the other 25 tests in its binary, which pointed at shared state; there is none. Printing the coordinator's decisions perturbed the race enough to hide it (0 failures in 250 runs with eprintln), so the trace was buffered in memory instead and printed only on the failing assertion. The first such trace was unreadable because the sibling test rsp_ql_joining_multi_window_integration registers the same :wind and :wind2 IRIs and both engines wrote to one buffer; keying the buffer by engine made the cause plain in a single capture -604. Add shared::rdf_term, which decides how an RDF term is spelled. The Dictionary keys on a term's text, so the spelling is the identity: two terms sharing a spelling share an id and can never be told apart again, which makes the choice a correctness question rather than a formatting one. Bare where it can be -- an IRI stays http://example/x and a plain literal stays 25, so the overwhelming majority of terms and the results built from them are byte-identical -- and quoted where it must be, so a typed literal is "25"^^ and a language-tagged one is "hello"@en. A well-formed IRI cannot begin with a quote, so no quoted form can collide with one. An xsd:string literal is spelled as the plain literal it is, per RDF 1.1 section 3.3 -605. Stop the N-Triples reader discarding a literal's datatype. clean_ntriples_term decoded the literal and then returned the lexical form alone, so "25"^^xsd:integer, "25"^^xsd:string and plain "25" all interned as 25 and the datatype could not be recovered -- data loss, not imprecision, since the graph could no longer be written back out. A language tag fared differently and no better: it was glued onto the text as hello@en, which is also how a plain literal of those eight characters was stored -606. Fix the same loss a second time in encode_term_star, which re-parsed the reader's output and kept only the lexical form, stripping the datatype back off whatever the reader had preserved. Both now call one canonicaliser, which is idempotent precisely so it can sit on a path that may already have run; two functions each dropping the datatype is how fixing one of them achieves nothing -607. Give the Turtle reader the same spelling as the N-Triples one. clean_turtle_term tested for a literal with starts_with('"') && ends_with('"'), which a suffixed literal fails, so "30"^^xsd:integer fell through to trim_matches('"') and became 30"^^xsd:integer: a stray quote, and a prefix never expanded, because the only branch that could expand it split the term on its first colon into the prefix "30"^^xsd and reported a failure. Literals are now handed on whole and spelled canonically in resolve_query_term, which is the one place holding the prefix table -608. Unify the fifth spelling, in resolve_term, which emitted "25"^^http://... with no angle brackets around the datatype. Counting it, one term had five spellings across the codebase: the N-Triples reader's, this one's, Turtle's mangled one, the correct one that tag_store and hybrid already wrote directly, and RDF/XML's, which reads neither rdf:datatype nor xml:lang and so has no datatype at all. The first four now agree, which also means a probability written as "0.8"^^<...#double> can match a 0.8 read from a file; it never could before, because the writer and the reader minted different ids for the one term -609. Read a term's number through its lexical form, at the seven places that parsed the spelling. Keeping the datatype is only half of it: the number lives in the lexical form, so SUM over typed data returned no answer at all and ORDER BY sorted "7"^^<...> after "21"^^<...>, both silently. Fixed in aggregate (SUM, AVG), term_order (ORDER BY, MIN, MAX), the four arithmetic resolvers in the optimizer's filter evaluation, and the engine's ML feature path. The lexical comparison path already read through the literal via normalize_lexical, which is why arithmetic and comparison disagreed about whether a typed literal was a number -610. Delete decode_ntriples_literal, whose callers all went through the canonicaliser, after porting the escapes it understood and shared::rdf_term did not: \\uXXXX, \\UXXXXXXXX, \\b, \\f and \\'. Deleting it first would have turned a valid literal into a different one with no error, so the escape set is pinned by tests, including that an ill-formed \\uZZZZ is refused rather than read as the letter u -611. Record what the test suite could not tell us. All 710 tests passed with the datatype preserved and before any of entry 609's fixes, because typed literals appear 12 times in the whole corpus and never in an aggregate, an ORDER BY or a FILTER. The suite passing meant the path was untested, not that it worked; the two tests written next both failed, SUM returning empty and ORDER BY returning 21, 30, 7. Nine tests in rdf_term_identity_test now pin term identity, value reading and reader agreement -612. Teach the RDF/XML reader the datatype it had always ignored, closing the fifth of the five spellings entry 608 counted. The literal's text arrives as its own event, after the property element that declares what it means, so rdf:datatype is remembered when the element opens and spent when the text arrives. It is an absolute IRI by definition and needs no prefix expansion. The datatype does not carry to the next property element, which is pinned by its own test because element-scoped state is the obvious way to get this wrong -613. Give xml:lang the scope XML gives it. It applies to an element and everything inside it (XML 1.0 section 2.12), and documents routinely declare the language once on the root, so the tag in force is a stack rather than a field: each start element pushes its own tag or the one it inherits, each end element pops. A field would have handled only a tag written directly on the property element -- which is the case that matters least. Popping an empty stack is a no-op, so malformed nesting cannot panic, and Event::Empty never pushes so it must not pop -614. Pin all three ways the language scope can be got wrong, since one test would have passed on two broken implementations: a tag inherited from an enclosing element, an inner tag overriding an outer one with the outer resuming afterwards, and a tag not outliving the element it was written on. The last of these passed before the stack existed, because resetting on every element satisfies it trivially -- which is exactly why the other two were needed to say anything -615. Scope blank node labels to the document they were read from. A label identifies a node only within its document (RDF 1.1 section 3.4), so two files each saying _:b describe two different nodes; the readers interned the label itself, so the two became one node and merging the files silently fused unrelated statements. The one defect in this family that invents a fact rather than losing one. Relabelling is forced rather than chosen: the dictionary keys on a term's text, so two distinct nodes must have two distinct spellings. One scope is taken per read, before the parallel chunks fan out so that every chunk relabels alike, and the form matches what SPARQL updates already minted for the same reason -616. Scope all four readers that can carry a label -- N-Triples, Turtle, N-Quads including its graph name, and N3 -- and recurse through quoted triples, since scoping only the outermost term would leave a nested label bare and two documents would still share the node a quoted triple asserts about. RDF/XML is untouched because it never reads rdf:nodeID, so it produces no blank nodes at all -617. Replace a previous scope instead of nesting inside it. Reading a graph back from its own serialisation is a new document and scopes it again, so a label would otherwise collect one prefix per round trip and grow without bound. The node is still a new node each time, which is what RDF says; only the spelling stays a sane length. A label a user genuinely wrote as kolibrie-doc-5-x is indistinguishable from one this produced and is treated as the latter -618. Re-aim an assertion in nquads_round_trip_keeps_default_and_named_quads that pinned the _:kolibrie-update- prefix after a serialise-and-re-read. It asserted a blank node label's lexical form across a boundary RDF makes insignificant, and which now changes because the reader scopes what it reads. The test's intent, readable from the sibling assertion that the serialised form contains no <_:, is that the node survives as a node rather than becoming an IRI; that is what it checks now, together with the written label still being legible -619. Fix a silent data loss in parse_n3 that the scoping tests surfaced and that has nothing to do with blank nodes. Each parallel chunk parsed into its own SparqlDatabase, and its triples were then inserted carrying that database's ids while only the dictionaries were merged -- so the ids meant nothing in the target. With an empty target both numbered terms in the same order, which is why the first read looked correct; a second read reinterpreted its ids as the first document's terms and stored a copy of the first document in place of the second. Two reads of two unrelated IRI-only documents left one triple. Ids are translated through reencode_term_id now, which also carries the chunk's quoted triples across, as the merge never did -620. Give a stream one blank-node scope instead of one per element, which entry 615 had got wrong for streaming. Each arriving element is its own document in RDF's terms, so scoping per element is the correct reading, but the dictionary never forgets a term: a fresh scope per element minted a term per element and grew it without bound. Measured at 204 terms over 200 elements naming one blank node, against 5 for the same stream without one. Dictionary::encode asserts rather than wraps when ids reach the quoted-triple range, so a long-running stream would not merely leak but eventually panic -621. Add parse_ntriples_in_scope and parse_and_encode_ntriples_in_scope, so a caller whose document boundary is not one call can say so, and give SimpleR2R one scope for its whole life. Bounded within a stream, and still separate between streams and from every file -- which is also what the engine did before scoping existed, so stream semantics do not change, only what the labels are spelled as. The per-request path in the HTTP server and add_static_ntriples keep a fresh scope per call, which is right: each is a document, and neither accumulates -622. Pin both halves, since the bounded one is a regression guard rather than a feature: 200 elements naming one blank node intern no more terms than the same stream without one, and two streams reading the same label get two nodes -623. Add the rdf_terms example, a guided tour of the term model: what an IRI, a blank node and each kind of literal are spelled as, which spellings RDF says denote one term and which two, how to read a value back out of a term rather than its spelling, and what a blank node scope does across two documents. It ends by putting the same terms through a store and summing two xsd:integer ages to 42, which is the property that broke when the datatype was first kept and the engine still parsed spellings -624. Add rdf_term::spell_blank_node, which writing the example made the case for. Building a literal had spell_literal, while building a blank node meant knowing both halves of a convention -- that the term carries a `_:` prefix, and that a bare label collides with every other document's until it is scoped. One function does both, from the label as written. There is deliberately no equivalent for an IRI: an IRI is spelled as itself, so a constructor would be the identity function and would only suggest that something is being done to it -625. Add rdf_term::to_ntriples_term, the writer the term model never had. A canonical spelling is deliberately not a wire form -- an IRI is stored bare and a plain literal unquoted, which is what keeps identity cheap and left every existing term untouched -- so writing that text out directly was wrong in four ways at once. Every serialiser goes through it now -626. Stop the serialisers demoting a typed literal to a plain one. generate_nquads and generate_ntriples quoted the canonical spelling a second time, emitting "\"30\"^^<...#integer>" : a plain string literal whose lexical form is the forty-eight characters of a typed one. Valid N-Triples, and read as a string by every other RDF tool. It went unnoticed because these readers re-canonicalise what they read, so a round trip through Kolibrie recovered the datatype; only a second implementation would have seen it, which is the case that matters for exchanging data between systems. Introduced by entry 605, which kept the datatype without teaching the writers about it -627. Fix three pre-existing faults in generate_ntriples that the same writer closes: a blank node subject was written <_:b>, which is an IRI and not a blank node; an IRI whose scheme was not http or https was written "urn:a", which is a literal; and a literal was not escaped at all, so an embedded quote produced output that does not parse. generate_nquads had the first two right and the third; generate_turtle had all three wrong -628. Delete escape_ntriples_literal and looks_like_absolute_iri, superseded by the writer. Checked before removing rather than after, as entry 610 required: escape_lexical is a strict superset, adding \\b and \\f, and the IRI test is the same scheme check under a different name -629. Assert the emitted text rather than what re-reading it gives back. Six tests now read the wire form directly -- a datatype present, a language tag present, an IRI in brackets whatever its scheme, a blank node not in brackets, an embedded quote escaped and recoverable, and every kind of term surviving a round trip -- because asserting on a re-read is what let entry 626 hide: the reader repaired the writer's output on the way back in -630. Add skolemisation, so data can be exchanged between systems without fusing their blank nodes. A label means nothing outside its document and the scope counter is process-local, so two instances both mint _:kolibrie-doc-1-b for their first document and merging their output makes one node of two -- the collision entry 615 removed, reappearing at the distribution boundary. RDF 1.1 section 3.5 answers it by replacing blank nodes with IRIs, which are global: rdf_term::skolemise mints one under the /.well-known/genid/ path RFC 8615 reserves for exactly this, and deskolemise recovers the node for a consumer that would rather have it, recognising the IRI by its path so it works on any publisher's -631. Require the base authority rather than defaulting it. A Skolem IRI is unique because the authority minting it is, and this crate has no identity of its own to offer; a default would mint the same IRI on two machines and put the collision straight back. Two bases giving one label two IRIs is pinned, as is the merge of two authorities' output arriving as two nodes -632. Add generate_ntriples_skolemised and generate_nquads_skolemised rather than changing the plain ones, because skolemisation is a choice made at an exchange boundary and not a change to how terms are stored: a blank node is still a blank node inside one store, and both forms are valid RDF. The plain serialisers now say in their documentation that their labels are document-local and point at the skolemised form -633. Skolemise per term rather than by rewriting the serialised text. The first attempt rewrote the written form, which is shorter and wrong: a literal reading "see _:b here" contains a label that is not a term, and the rewrite would have replaced it. Pinned by a test with exactly that literal -634. Add shared::rdf_term::TermTriple, a triple of canonical term spellings: what a triple is before it is interned and after it is decoded. Ids are only meaningful against the dictionary that minted them, so anything crossing out of a store carries text instead, and two places had worked that out independently -- the parsers' return type and the windows' item type -- naming it differently or not at all. It carries Hash and Ord so it can serve as a window's item, and to_ntriples writes a line through the term writer -635. Replace WindowTriple's own three fields with TermTriple, keeping the name as an alias because a window's item type reads better for it. Fifteen field accesses renamed from s/p/o, done by fully-qualified text rather than a regex on `.s`, which would have matched far more than these -636. Name what parse_ntriples returns. Its signature, and parse_ntriples_in_scope's and encode_triples', said Vec> -- three strings in a particular order, with nothing saying which order or that they are terms rather than a pattern. They say TermTriple now -637. Correct an overstatement from the session that proposed this. Five representations were claimed to be the same thing; two are. WindowResult's results are solution mappings, a row rather than a triple. CompiledProgram holds rules and declarations, not triples. WindowedTriple's predicate is documented as a local name under its window IRI and not a full IRI, and it carries a timestamp, so it is an SDS fact rather than a triple of terms. Of the thirty-one places naming (String, String, String), most are triple patterns holding variables, filter comparisons of the form (lhs, op, rhs), or diagnostics; folding those in would have conflated a variable with a term named ?s, which is the class of blanket change this work has got wrong before -638. Add shared::terms::TextBindings, the unencoded sibling of Bindings and the last place carrying terms in an unnamed shape. WindowResult's solutions were Vec>, which is exactly the shape Bindings replaced on the encoded side after measuring a 4.7x difference on the join, and the costs are worse here because the keys are strings rather than u32: a variable was hashed once per row per column instead of being resolved to a column once for the sequence. A cell is Option because a solution mapping is partial and neither of the other tricks is available -- a map says so by leaving a key out, Bindings says so with a sentinel id, and the empty string is a perfectly good literal -639. Convert the coordinator's join path: WindowResult.results, both last_materialized maps, join_window_results, emit_results and the cross-window materialiser. join_window_results no longer clones every row of every window before joining any of them, which it did to build an owned Vec it could drain. natural_join is deleted; TextBindings::join carries the semantics and resolves the shared columns once from the two schemas rather than looking a variable up per row -640. Keep the join comparing rather than hashing, deliberately. An unbound cell is compatible with any value, because two solution mappings are compatible where both are defined (SPARQL 1.1 section 18.5), and a hash join on the shared columns cannot express that -- it would silently drop or invent solutions. Pinned by a test where one row leaves the shared variable unbound and so joins every row of the other side, and the merged row takes the bound side's value. A hash join with the unbound rows handled separately is a later change, and one to measure -641. Preserve the single-thread path's append semantics rather than quietly changing them. It accumulates each window's results with extend while the multi-thread coordinator replaces them; that asymmetry is pre-existing and not this change's to settle, so TextBindings::extend concatenates, widening the schema and leaving the rows that predate a column unbound. Three tests hold it, including that cells follow their variable's name and not its position when the two schemas are given in a different order -642. Expose the term model to Python, which the term work had left behind. Its signatures were unaffected -- the bindings call parse_ntriples_and_add, whose signature did not change -- but its behaviour was: a query now hands back a typed literal as its canonical spelling, and Python could read a value out of it only by parsing that string by hand, which is the thing the Rust side stopped doing. Eleven functions now mirror shared::rdf_term: term_kind, lexical_value, numeric_value, literal_datatype, literal_language, make_literal, new_document_scope, make_blank_node, to_ntriples_term, skolemise, deskolemise, plus the XSD_STRING and SKOLEM_PATH constants -643. Add scripts/smoke_python.py, which builds the cdylib and then imports it. CI built kolibrie-python and stopped there, so nothing in this repository's automation had ever executed a line of Python -- the same gap smoke_examples.py exists to close on the Rust side, where it found three examples that compiled and did not run. It stages the module as kolibrie.so beside the examples so their relative paths resolve, runs each one, and is now the CI step in place of the bare build. All five examples pass, the four that predate this included -644. Add python/examples/rdf_terms.py, which asserts rather than prints: a typed literal keeps its datatype, a plain literal stays bare, a language tag is not glued to the text, an IRI is an IRI whatever its scheme, a prefixed name is expanded, and the engine's SUM agrees with Python's own arithmetic over the same terms -- the property that breaks silently if either side reads a spelling instead of a value -645. Stop clean_turtle_term stripping an IRI's angle brackets. They are the only thing distinguishing an absolute IRI from a prefixed name once the text is bare, and resolve_query_term is the one that needs to tell them apart: a bare urn:a reaching it was read as the prefix urn, found undeclared, and reported as KOLIBRIE_OPERATION_FAILED. Every non-http IRI in a Turtle document logged one for its subject and one for its predicate, two per triple, while parsing correctly regardless -- noise that would mask a real failure. Found by running the Python API rather than compiling it -646. Add load_ntriples to the Python database, the counterpart to parse_turtle and the format data usually arrives in over a wire. load_file could already read N-Triples from disk, so text held in memory had to be written to a file first. Blank node labels are scoped to the call, which is one document, so two calls naming _:b describe two nodes -647. Add to_ntriples, to_nquads and their skolemised forms, so Python can publish as well as read. The skolemised ones take the base authority with no default, for the reason the Rust ones do: a Skolem IRI is globally unique because its authority is. All five raise on a poisoned lock rather than silently doing nothing, matching exec_query rather than parse_turtle -648. Extend the Python example to the exchange itself: two stores publishing the same document skolemised under different authorities, merged into a third, and a round trip that keeps every datatype and language tag. One assertion there had to be weakened to be honest -- merging alice's and bob's output gives two nodes in a single process whether or not skolemisation happened, because they drew consecutive scopes from one counter, so what carries the weight is the separate assertion that one label under two authorities gives two IRIs, which is what two machines would actually rest on -649. Put every lock in the Python bindings through one helper. Eighteen of them had been handled three ways: two different messages for the same condition, and in ten places no handling at all. A swallowed lock did nothing and said nothing -- count answered zero, the four accessors answered an empty list, and add_stream_triple returned success having stored nothing. None of those is distinguishable by a caller from a query that matched nothing, which is the ambiguity exec_query was changed to avoid, and add_stream_triple reporting success is worse than either -650. Change seven signatures to PyResult so the failure has somewhere to go: add_triple, parse_turtle, get_decoded_triples, get_subjects, get_predicates, get_objects and count, and the three group_by methods that call the first of those. Nothing changes for a Python caller -- pyo3 unwraps Ok transparently, so count() still answers 2 and add_triple() still answers None -- the only visible difference is that a poisoned lock raises instead of lying -651. Expose the real RSP engine to Python as RspEngine. What Python had was not it: QueryBuilder::as_stream builds a StreamingQuery that accumulates triples in a list and applies limit, offset and distinct, with no windows, no RSP-QL and no joining across streams -- a filter with a stream-shaped name, which is why the windowing example was outdated rather than merely dated. RspEngine takes an RSP-QL query, opens a stream per window, and joins across them as the windows fire -652. Collect the engine's results into a buffer rather than calling a Python callable. The Rust consumer is invoked from the engine's own threads, so handing it a Python function would mean taking the GIL on a thread Python does not know about, once per solution. The buffer is the shape StreamingQuery already had, and results are complete when stop returns, which entry 600 made true: no sleeping -653. Replace the Python examples with five that are each asserted rather than printed: 01_terms (building and reading terms), 02_parsing (the four readers, the two writers, and that every reader spells a term the same way), 03_sparql (SELECT, joins, FILTER, aggregates, ORDER BY, the query builder, INSERT and DELETE), 04_streaming (one window, two windows joined, and that stop makes the results final), 05_datalog (one rule, rules feeding each other to a fixpoint, and backward chaining). contradictions.py went with them: it demonstrated semi_naive_with_repairs, which is no longer what this should show. kolibrie_usage.py never touched the query API and test_windowing.py exercised the shim -654. Fix the second RDF/XML reader, found by the parsing example rather than by compiling. parse_rdf_from_file is an independent copy of parse_rdf -- its own event loop, 157 lines -- so entry 612 taught one of the two about rdf:datatype and xml:lang and left the other discarding both. The same document read from a file and from a string produced different terms, which is exactly what the example asserts cannot happen. Left as two readers rather than made to delegate: a benchmark reads a file through it, and turning a streaming reader into one that slurps is a change to measure, not to slip into a correctness fix -655. Add KnowledgeGraph::decode_term and name the fields of the Term variants. backward_chaining answers in the graph's own terms, so its bindings read Term.Constant(0) and there was no inverse of encode_term to make sense of them; and the fields were pyo3's unnamed _0 rather than id and name. The example now prints {'who': 'alice', 'whose': 'carol'} instead of a dictionary of integers and internal solver variables -656. Remove the repairs API from the Python bindings: query_with_repairs, infer_new_facts_semi_naive_with_repairs and add_constraint. The Rust side keeps them, since nothing asked for that and a materialisation strategy is not a binding's to retire -657. Let Python parse rules instead of assembling them. KnowledgeGraph::add_n3_rules takes an N3 document -- { premise } => { conclusion } under @prefix declarations -- and SparqlDatabase::add_rule takes a Kolibrie RULE block, runs it, and answers the triples it derived. Both intern against the store they are added to, so a parsed rule matches the facts already there. Building the same rule through Rule, TriplePattern and Term means naming every constant by the id encode_term gave it, which is reasonable for a rule a program generates and not for one a person wrote -658. Make parse_n3_document accept the statement terminator every N3 statement carries. parse_unresolved_rule stops at the conclusion's closing brace, and the document parser requires the whole input to be consumed, so a trailing dot failed with Eof -- which no test caught because the single-rule parser leaves leftover input behind and looked correct. The conventional spelling of an N3 document was rejected outright -659. Rewrite 05_datalog around the text syntaxes: one N3 rule, two sharing a prefix block with the second consuming what the first derives, a CONSTRUCT rule run against a store and then queried, backward chaining from a built goal, and last the assembled form, kept because a generated rule is easier to build than to render -660. Remove the repairs API from the Rust side as well. Two modules go -- reasoning::repairs and materialisation::semi_naive_with_repairs, 145 lines between them -- along with Reasoner's constraints field, add_constraint, and the two private helpers only they used, violates_constraints and compute_repairs. Nothing else in the workspace reached any of it, and no test covered a line: inconsistency-tolerant reasoning was carried for several releases without a single assertion about what it answers -661. Remove what documented it: the contradictions example and its [[example]] registration, the inconsistency function in the knowledge_graph example, the bullet in datalog's crate docs, which was an intra-doc link that would have broken, and the three method sections in README.md and the Dutch, Spanish and German translations -662. Correct the materialisation module's own documentation, which this invalidated twice over. Its table listed the repairing strategy, and its prose said the strategies differ in cost and in what they record "and the repair variant additionally alters the fact base before it starts, so it is the one that can disagree about the result" -- with it gone, every remaining strategy agrees about which facts follow, which is worth stating positively. The count in "the first four live in pub(crate) modules" is now three -663. Add scripts/readme_examples.py, which compiles every Rust block in the README. Sixteen of twenty-three did not compile when it was first run, and the reasons are a catalogue of how a README rots: execute_query after it became execute, indexing a Solutions as though it were a Vec, Reasoner.dictionary and SparqlDatabase.dictionary after both became private in this same session's work, a Rule built without its negative_premise, and datalog::knowledge_graph under a name it no longer has. Compile-only on purpose; whether an example runs is what smoke_examples.py answers about kolibrie/examples -664. Give the README a hidden preamble directive rather than demand every block be a whole program. Most are fragments shown mid-narrative -- db.parse_turtle(..) with the db from two paragraphs up -- which is how a README should read and not something a compiler accepts, so a section declares setup in an HTML comment and every block after it is compiled with that prepended, the way rustdoc hides setup behind # lines -665. Replace the README's hand-written API documentation, 371 lines of restated signatures, with pointers to two generated references: cargo doc, which is the authority, and docs/api.md, which is the index. It had drifted exactly as such a list does -- three removed methods documented after the fact, in this file and four translations -- and nothing could have noticed -666. Add scripts/api_surface.py, generating docs/api.md: 1,132 public items across six crates. Read lexically from the source rather than from rustdoc JSON or cargo public-api, both of which want nightly, which this project does not take. What that costs is stated in the file: no type resolution, no macro expansion, a re-export listed where it is written, and a pub item in a private module listed although nothing can reach it. It is the index; cargo doc is the reference -667. Add both to verify.sh and to CI, which is the whole point: a generated listing nobody regenerates is a listing nobody can trust, and --check fails when the surface moved without it -668. Update the README for the public API it never mentioned: typed terms with their datatypes and language tags, blank node scoping, skolemisation for exchange, the kolibrie-api facade, and what the Python bindings now cover. Two new sections carry worked examples, which the new check holds honest -669. Keep the Volcano optimizer section, contrary to the request that prompted this. It reads as internal and it is not: streamertail_optimizer is pub mod, Streamertail is publicly re-exported, and two examples construct one the way a consumer would. The condition given was to remove it if it were not public API, and it is -670. Remove the five translated READMEs -- Dutch, German, Spanish, French and Japanese -- rather than repair them. They carried the same hand-written API sections entry 665 deleted from the English one, documenting the same three methods that no longer exist, plus their own copies of examples that stopped compiling; nothing checked any of it, and the drift was invisible because no reader of the English README would see it. Keeping them would mean five more copies of every example for scripts/readme_examples.py to compile and five more surfaces to update per API change -671. Remove what pointed at them: the language bar at the top of the README, and the sentence about the drift, which cited four translations and now says five and why they went. Every local markdown link in the repository resolves -672. Group the ML implementation into kolibrie/src/ml/. Seven of the eleven ml_ and neural_ prefixed files move there and lose the prefix -- ml_runtime is ml::runtime, execute_ml is ml::execute -- which is a module doing by language what a prefix was doing by hand. The eight #[cfg(feature = "ml")] attributes scattered through lib.rs's module list are gathered in one place, which is most of the value: it took reading all eight to see which half of the ML layer was conditional, and ml-gated code is invisible to a default cargo check, which has twice let a breakage reach a commit -673. Keep ml_policy, ml_feature_loader and neural_registry in the crate root, because they are the ML contract and not its implementation: the engine depends on them and they reach back no further than the store, while everything inside ml depends on the engine. lib.rs already stated this intent -- the crate root is the one place that may name both the abstraction and the implementation -- and this makes the file layout say it -674. Record how that line was found, because the first attempt moved all ten and broke the acyclicity gate. module_graph.py counts a directory as one node, by design and consistently with rsp and streamertail_optimizer, so a leaf the engine depends on and a consumer that depends on the engine became the same node: seven modules collapsed into one strongly-connected component. Measuring each submodule's direction, with doc links excluded as the script excludes them, showed exactly three with no edge into the engine. Moving those three out left the graph acyclic at 23 modules and 66 edges, down from 30 and 94 -- the grouping made the graph smaller rather than only rearranging it -675. Note the hazard the module name creates. kolibrie depends on a crate called ml, and five files inside kolibrie::ml write use ml::... for it. That resolves today, but it is a name standing for two things in one scope, and the module documents it so the next reader does not have to work it out -676. Do not consolidate rsp and rsp_engine, having proposed it. The measurement refuses it: rsp_engine names rule_execution::process_rule_definition, and rule_execution names crate::rsp::r2s and crate::rsp::s2r, so merging the two makes rsp -> rule_execution -> rsp, a two-module cycle that the acyclicity gate would reject. The split is not an accident -- rsp is the RSP-QL operator library, S2R, R2R and R2S, and rsp_engine is the engine that orchestrates them and reaches into rule execution -- and the layout already says so. Measured before moving anything this time, rather than after, which is the lesson of entry 674 -677. Delete kolibrie/src/rsp/lib.rs, which was never compiled. Only the crate root's lib.rs is special, so a lib.rs inside a module directory is an ordinary file nothing includes; rsp/mod.rs is the real module root and declares all five submodules. The dead copy declared four, one of them `pub mod stream;` for a stream.rs that does not exist -- it would not have compiled had anything included it, which is the proof it never did -678. Rename rsp_engine.rs to rsp_engine/mod.rs, so all four directory modules -- ml, rsp, rsp_engine and streamertail_optimizer -- use the same convention. No path changes, no API change, no graph change; it removes the inconsistency that made the RSP layer look split three ways when it is two layers and a stray file -679. Retune the dev profile for the edit-build-test cycle it is actually used for. It was opt-level 2 with thin LTO and incremental compilation off -- a release-grade build for every cargo test -- which cost 436 seconds to rebuild and run the suite after a one-line edit to sparql_database.rs. At opt-level 1 with incremental on and no LTO the same edit costs 149 seconds, 2.9 times faster, with all 773 tests still passing. debug-assertions and overflow-checks stay on: they are why a dev build is worth running -680. Reject opt-level 0, measured rather than assumed. It is slower both ways -- 162 seconds for the same edit against 149, and 328 cold against 295 -- because the tests run slowly enough unoptimised to outweigh what the codegen saves. The number is recorded beside the setting so nobody tries it again on intuition -681. Stop automate_policy sleeping for no reason: twenty-one seconds, in a loop that already timestamps its stream with a counter and a window of [RANGE 10 STEP 5] that has no TICK clause, so the firing is driven by those timestamps and not by wall time. The trailing sleep after stop was the obsolete pattern entry 600 removed from the tests. Four seconds now, with byte-identical output: thirty-nine lines, thirty-nine window firings -682. Scale continious_stream from twenty-four seconds to eight. It builds no RSPEngine and calls add_to_stream nowhere -- the TICK TIME_DRIVEN in its rule text is parsed and the rule runs, but nothing fires on a clock -- so the pacing is narrative and the same eight batches at half a second show the same thing -683. Make the README check incremental. It deleted and recreated its generated crate on every run, so every run compiled twenty-five blocks cold; keeping the target directory and clearing only the examples takes a repeat run from twenty-seven seconds to four. Stale readme_line_N.rs files are removed each time, so a block that moves line leaves nothing behind -684. Add per-phase timing to verify.sh and per-example timing to smoke_examples.py, because none of the above was findable without them. The first attempt to speed this up timed each cargo command standing alone, summed to forty-two seconds, and missed most of the run. The first attempt at the timer itself used an EXIT trap, which fires in every subshell and printed thousands of lines -685. Record what the example smoke run actually costs, since three earlier readings of it -- 221, 172 and 15 seconds -- looked contradictory. Run twice with no rebuild between, it takes 124 seconds and then 16: the cost is one-time per freshly linked binary and is cached afterwards, so it falls on whichever run follows a build, which is the run anyone doing work will make. The two sleeping examples were a real forty-five seconds of it and are fixed; the rest is fifty-one process launches and is not addressable by changing what the examples do. Entries 686 to 688 identify what that per-launch cost is and close the question; the guess recorded here first, that binary size was the lever, was wrong -686. Reject debug = 1 in the dev profile, measured. It does shrink the example binaries by a third, 4.5MB mean to 3.0MB, but it bought no time anywhere that matters: a one-line edit plus the test suite cost 160 seconds against 149, a cold rebuild 282 against 295, and the example smoke run 182 seconds against 124. Two of the three moved the wrong way, so full debug info stays and the debugger is not traded for nothing. The number is recorded beside the setting, as with opt-level 0 in entry 680 -687. Identify the per-launch cost that entry 685 could only measure: it is macOS Gatekeeper, not this repository. Over ten freshly linked binaries the wall time was 36 seconds, during which syspolicyd burned 30.1 seconds of CPU and XprotectService 9.5, while the example processes themselves reported 0.00 user and 0.00 sys -- blocked, not computing. It is the assessment of an executable the system has never seen, which is why every rebuild pays it again, why a byte-identical copy of an already-assessed binary does not (0.18 seconds), and why debug = 1 did not help: the assessment is keyed on content, not proportional to the debug sections dyld never loads. Of a 185-second run only about 16 seconds is the examples doing anything, 13 of that in three of them, and the fastest example costs 2.2 seconds on its first execution against 0.0 on its second. There is no slow example to find. The fix is a machine setting -- exempting the terminal that runs cargo under Privacy and Security, Developer Tools -- so it is documented in smoke_examples.py rather than worked around -688. Reject running the examples concurrently, having implemented it. Sixteen jobs took 170 seconds against 185 serially, because the cost in entry 687 is charged by a daemon that serialises: every example's own duration rose to about 53 seconds as all fifty-one queued behind it, and fifty-one times 3.3 seconds is the 170. All fifty-one still passed, but the eight percent is not worth what contention does to the streaming examples -- continious_stream went from 7.4 seconds to 42 and replay_profile from 6.4 to 56 -- because their RSP windows are wall-clock, and passing once at six times the duration is not evidence they stay correct there. The reasoning is recorded in the script so the next person does not reimplement it -689. Report the median and the floor from smoke_examples.py, not only the slowest eight, and add --dump-timings for the full distribution. A top-N list alone is what made this hard to diagnose: it ranks noise, since below the fourth entry the examples differ by tenths and swap places between runs, and it hides the fact that the fastest example also pays seconds. The median against the floor is what shows a flat per-binary cost rather than a few slow examples -690. Derive the Python library path instead of hardcoding it, in a sourced scripts/python_env.sh that verify.sh, check_lint.sh and flakehunt.sh all use. The three carried the same literal string, naming an exact Homebrew patch version (python@3.11/3.11.11) and $HOME/miniconda3, which breaks on brew upgrade, on any differently laid out machine, and on Linux, where the loader does not read DYLD_LIBRARY_PATH at all -- so the CI runner this is headed for silently got nothing. The helper asks the interpreter pyo3 resolves for sysconfig's LIBDIR and exports the variable the running platform reads, DYLD_LIBRARY_PATH on Darwin and LD_LIBRARY_PATH elsewhere, appending rather than replacing so an existing value still wins -691. Record that the hardcoded value was wrong in a way that worked by luck: it named Homebrew's python@3.11 first, while the binaries on the machine it was written for link against miniconda's 3.11 -- otool -l shows the rpath pyo3 baked in pointing at $HOME/miniconda3/lib. DYLD_LIBRARY_PATH takes precedence over rpath, so it was shadowing the libpython the build had chosen with a different copy that merely shared its ABI -692. Record the actual scope of that requirement, which neither script nor document stated. The default suite does not need a library path at all: cargo test --workspace passes all 773 tests with the variable unset, because pyo3's build script bakes an absolute rpath into the binaries it links. The ml feature does need one, because that rpath is not emitted transitively -- without it cargo test -p kolibrie --features ml fails 34 targets with "Library not loaded: @rpath/libpython3.11.dylib" and kolibrie-http-server exits 127. CONTRIBUTING.md documented the path as unconditionally required and now says which builds need it and points at the helper; CLAUDE.md carried the same claim and is corrected too, though it is gitignored and so not part of this commit -693. Resolve check_lint.sh's script directory before its cd to the repository root rather than after. BASH_SOURCE is relative to the caller's directory, so sourcing the helper afterwards looked for it in the repository root and failed for any relative invocation from inside scripts/, which is how the first version of this broke -694. Measure what the macOS Gatekeeper floor costs on Linux, since the suite is headed for a GitHub Action: nothing. The example smoke run takes 18 seconds there against 185, and takes 18 again on a second pass where macOS needed a first run of 185 and a second of 16, because there is no per-binary assessment to cache. A control confirms the mechanism rather than the correlation: twenty freshly compiled 5.4MB binaries with full debug info cost 8 milliseconds in total to launch on Linux, against 3,300 milliseconds each on macOS. In a 4-CPU container matching ubuntu-latest, a cold cargo build --workspace --all-targets is 137 seconds and cargo test --workspace is 4 seconds once built, so compilation dominates CI and the smoke run is cheap to add -695. Record that cargo test --workspace --all-targets, which the CI test job runs, fails. --all-targets includes the thirteen harness = false bench targets, and criterion runs them in test mode: citybench_cross_window_compare was SIGKILLed in a container under 7.7GB and reached 12.3GB resident after ten minutes on a 48GB host while still growing, against the 16GB of an ubuntu-latest runner. The job needs cargo test --workspace for the tests and a separate cargo build --workspace --all-targets for the compile check, which is the split already used locally and for the same reason -696. Record that a clean Linux build leaves 12GB in target/debug, 4.4GB of it examples, against the roughly 14GB documented for an ubuntu-latest runner. This is where debug = 1 is worth setting after all, for disk rather than for the speed entry 686 rejected it for, and it belongs in the workflow as CARGO_PROFILE_DEV_DEBUG rather than in Cargo.toml, so the local profile keeps full debug info -697. Drop --all-targets from the CI test job's test step, keeping it on the build step. It was pulling the thirteen harness = false bench targets into a test run, where criterion runs them in test mode and citybench_cross_window_compare does not survive it, per entry 695. The build step already compile-checks every target, so no coverage is lost, and doc tests are gained: --all-targets excludes them, so six doc-test targets had never run in CI -698. Run the examples in CI, not only compile them. The workflow compiled them for months while three were broken anyway -- a rule syntax the parser rejects, an absolute path into a personal directory, and a gitignored dataset -- none of which cargo build can see. It reuses the build already done in the same job, costs about 17 seconds, and is gated to the default leg because the script skips every example carrying required-features and the no-default leg has not built them -699. Print each failing example's captured output in CI rather than only its name and a path. smoke_examples.py writes per-example logs under the output directory and reports the path, which on a runner is discarded when the job ends; without this step a CI failure would name an example and lose the evidence, which is the exact mistake scripts/verify.sh was written to prevent. Verified both ways, since a step guarded by failure() otherwise ships untested: it groups each log when one exists and exits 0 when none do -700. Verify the reworked job by running both matrix legs as defined, in a 4-CPU Linux container. Default: build 120s, test 4s with 773 passing across six doc-test targets, examples 51 of 51 in 17 seconds. No-default: build 8s, test 4s, 773 passing -- the same count because kolibrie's default feature set is empty, so --no-default-features is not a narrower configuration for it -701. Format the workspace: 48 files, 148 hunks, no behaviour change. The drift accumulated across this branch's own work -- chiefly the files the term model added or rewrote, rdf_term.rs, terms.rs, term_encoder.rs, sparql_database.rs, rsp_engine/mod.rs and py_rsp.rs -- because fmt was never run as those landed, and cargo fmt --all --check is a blocking CI gate, so the merge would have failed on it -702. Regenerate docs/api.md, which the formatting invalidated. Four public signatures are recorded by scripts/api_surface.py as they are written in source, and rustfmt rewrapped them: three collapsed onto one line and sort_by expanded onto several. The gate that compares the file to the source is blocking too, so it would have failed the same merge for a purely cosmetic reason. No item was added, removed or changed -703. Spell five types in neural_relations.rs by the names already imported, taking unused_qualifications under the ml feature back to zero. The MlRuntime impl wrote std::collections::HashMap twice, shared::query::TrainNeuralRelationDecl, shared::query::MLPredictClause and shared::triple::Triple in full, while HashMap, TrainNeuralRelationDecl, MLPredictClause and Triple are all imported at the top of the file. CI gates this lint in three feature configurations and the ml one was the only failure, so the merge would have stopped there. crate::query_error::QueryError and shared::query::CombinedRule keep their paths, being genuinely not in scope -- the lint flags only the five it can shorten -704. Run every gate CI runs rather than the one that failed, after fixing it one at a time twice over. All eighteen check_lint.sh invocations across workspace, ml and no-default, plus the module graph, api surface and README example checks: 21 of 21 pass. The rustdoc job passes too, at exit 0 with zero errors; its one remaining warning is private_intra_doc_links, that ml's public documentation links to a private item named syntax, which -D rustdoc::broken_intra_doc_links does not deny, so it is latent rather than breaking -705. Drop three dependencies nothing uses: serde_json from shared, and serde and rayon from datalogmtl. cargo machete reported them and a search confirmed it rather than taking its word, since its usual false positive is a crate reached only through a derive macro -- but neither crate mentions these anywhere, not in src, not in tests, not in the examples or comparison directories, and not as a Serialize or Deserialize derive. Removing them changes nothing about serde_json's float_roundtrip guarantee from entry 37: that feature is declared on the workspace dependency, and ml, which holds the test guarding it, declares serde_json for itself -706. Record that the supply-chain CI job carrying cargo machete and cargo deny is continue-on-error, so this was advisory and would not have blocked the merge, unlike the rustfmt and lint gates fixed in entries 701 to 704. Fixed anyway, because an unused dependency is real cruft and three of them were cheap to remove -707. Record a repeat of the zsh word-splitting mistake from entry 684, this time in a verification loop rather than a timing one: `cargo check $cfg` with an unquoted variable holding several flags passes them as one argument, so all three feature configurations reported failure while the build was fine. The first reading contradicted a passing 773-test run in the same output, which is what gave it away; `eval` fixed it and all three pass +Added +- DatalogMTL reasoning over RDF streams (`datalogmtl` crate): `Box`, `Diamond`, `Since`, and stream shapes with channel staleness and expiry +- Interval-based DatalogMTL engine with future operators and unbounded (periodic) time, alongside the streaming tick engine +- MeTeoR-syntax parser for DatalogMTL programs +- DatalogMTL endpoints in the HTTP server: `/datalogmtl/register`, `/datalogmtl/push`, `/datalogmtl/static` and `/datalogmtl/events/` +- Drone geofencing demo with a visual clock in the web UI, and an updated traffic demo +- `kolibrie-api`: one crate that re-exports the whole workspace +- `kolibrie::prelude` with the common entry points +- `execute()` as the query entry point to prefer, returning `Solutions` or a `QueryError` that names the failing stage +- `Solutions` and `Solution`: results with named variables; `Solutions::into_rows` for the old row shape +- `ParseError` with `offending`, `offset_in` and `is_incomplete`, so callers no longer name `nom` types +- RDF term model in `shared::rdf_term`: canonical spellings, `to_ntriples_term`, `spell_blank_node`, `TermTriple` +- Skolemisation for exchanging data between systems: `generate_ntriples_skolemised` and `generate_nquads_skolemised` +- `parse_ntriples_in_scope` and `parse_and_encode_ntriples_in_scope` for streams that share one blank-node scope +- `QuadSource` for reading quads from any source, with reasoner entry points that read from it +- `TermEncoder` with `LocalTerms` and `SharedTerms`, plus `KolibrieDB::terms`, `adopt_terms` and read-only `TermView` +- `TextBindings` for decoded results, used by cross-window joins +- `KolibrieDB::set_training_seed`, so declarative training is reproducible +- Python: `RspEngine`, `load_ntriples`, `to_ntriples`, `to_nquads` and their skolemised forms +- Python: `KnowledgeGraph.add_n3_rules` and `KnowledgeGraph.decode_term`, and reading a value out of a term +- Python examples for terms, parsing, SPARQL, streaming and Datalog, and the Rust `rdf_terms` example + +Changed +- Builds with any stable Rust 1.85 or newer; no toolchain version is pinned +- The Python environment for the `ml` feature is declared in `pyproject.toml` and locked in `uv.lock`; `uv sync` installs it, and the ML Docker image installs it the same way +- `mlschema` is installed from `ml/src/pyproject.toml` instead of `setup.py`, and needs Python 3.10 or newer +- All crates share the workspace version, edition, licence and minimum Rust version +- `SparqlDatabase` is renamed `KolibrieDB` and moves from `kolibrie::sparql_database` to `kolibrie::database`, one file per concern; in Python it is `kolibrie.KolibrieDB` +- `KolibrieDB` has no public fields; its state is reached through methods +- Query results hold one schema and interned values instead of a map per row +- The HTTP server answers failed queries and updates with real HTTP status codes +- The CLI exits non-zero with a message when a query fails, and `--version` follows the workspace version +- Python `exec_query` raises on a failed query instead of returning an empty list +- `RSPEngine::stop` returns only after every flushed result has been delivered +- `datalogmtl` no longer depends on `datalog` +- `rumqttc` and `chrono` are dev-dependencies, so depending on `kolibrie` no longer pulls in `tokio` and `rustls` +- API documentation is one sentence per item +- The README's examples compile, and its hand-written API section points to `cargo doc` + +Deprecated +- `execute_query_rayon_parallel2_volcano`: use `execute` +- `estimate_operator_selectivity`: use `CostEstimator::estimate_cost` + +Removed +- Inconsistency-tolerant reasoning, including the Python repairs API +- `datalog::reasoning_experimental` and the two hierarchy reasoning examples +- Unused APIs: `StorageMode::Hybrid`, `IntervalFactStore::coalesce`, `holds_at_all` and `MlpNeuralPredicate::varmap` +- Unused dependencies: `candle-nn`, `serde_json` from `shared`, `serde` and `rayon` from `datalogmtl` +- The five translated READMEs +- The MeTeoR comparison harness and the benchmark tier and baseline scripts + +Fixed +- The N-Triples reader dropping a literal's datatype +- RDF/XML `rdf:datatype` and `xml:lang` being ignored +- Serialisers writing typed literals as plain ones and blank-node subjects as IRIs +- The Turtle reader stripping the angle brackets of an absolute IRI +- Reading a second N3 document into a store storing a copy of the first +- Blank-node labels from different documents being treated as the same node +- Lossy float round-trip when saving and loading a neural model +- A malformed training query aborting the process instead of returning an error +- Python bindings hiding a poisoned lock +- The `real_scenario` and `repro_sensor_path_benchmark` examples panicking + +Security +- The library crates no longer contain `unsafe` code 0.2.0 1. Add support multiple window + static data for RSP engine diff --git a/cli/src/main.rs b/cli/src/main.rs index 74a80538..fb7bc43f 100644 --- a/cli/src/main.rs +++ b/cli/src/main.rs @@ -8,22 +8,9 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! `kolibrie-cli`: load one RDF file, run one SPARQL query, print the results. -//! -//! Deliberately minimal — a smoke test for the library and a way to run a query -//! without writing a program. It parses the file into a fresh in-memory store, so -//! nothing persists between invocations and a large file is re-parsed every time. -//! -//! ```text -//! kolibrie-cli --file data.rdf --query 'SELECT ?s WHERE { ?s ?p ?o }' -//! ``` -//! -//! The `--version` string is hard-coded in the `clap` attributes rather than read -//! from the workspace version. It matches today; nothing keeps it matching, so it -//! will drift silently at the next version bump. +//! `kolibrie-cli`: load one RDF file, run one SPARQL query, print the results use clap::Parser; -// One dependency, not two crates and a guess about which holds what. use kolibrie_api::prelude::*; #[derive(Parser)] @@ -46,11 +33,9 @@ struct Args { fn main() { let args = Args::parse(); - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf_from_file(&args.file); - // A failed query exits non-zero and says why. It used to print `Results: []` - // and exit 0, which is also what a query matching nothing prints. match execute(&args.query, &mut database) { Ok(solutions) => println!("Results: {:?}", solutions.into_rows()), Err(error) => { diff --git a/datalog/src/cross_window_sds.rs b/datalog/src/cross_window_sds.rs index 70fe85c4..c5e4cc1d 100644 --- a/datalog/src/cross_window_sds.rs +++ b/datalog/src/cross_window_sds.rs @@ -8,22 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! An RSP-QL streaming dataset (SDS), and its translation to and from plain Datalog. -//! -//! Datalog has no notion of a window, so a rule spanning several windows is -//! evaluated by flattening the SDS into ordinary triples with the originating window -//! encoded into the predicate as `window_iri + local_name` — see -//! [`annotate_predicate`]. [`strip_window_prefix`] reverses it, and -//! [`all_component_iris`] sorts longest-first because one component IRI can be a -//! prefix of another, where the shorter one would otherwise match first and strip -//! too little. -//! -//! # Time -//! -//! A fact carries the timestamp it arrived at and a window carries a width `alpha` -//! in the same unit. A fact is alive at `current_time` when -//! `event_time + alpha > current_time`, so the interval is half-open and a fact -//! expiring exactly now has already gone. +//! An RSP-QL streaming dataset (SDS), and its translation to and from plain Datalog use shared::term_encoder::SharedTerms; use shared::triple::Triple; @@ -50,11 +35,11 @@ pub fn strip_window_prefix<'a, 'b>( /// A single RDF triple from a streaming window, carrying its stream-level timestamp #[derive(Clone, Debug, PartialEq, Eq)] pub struct WindowedTriple { - /// Subject IRI, as a string: not yet interned. + /// Subject IRI, as a string: not yet interned pub subject: String, - /// Local predicate name under the owning window IRI. NOT a full IRI + /// Local predicate name under the owning window IRI pub predicate: String, - /// Object IRI or literal, as a string. + /// Object IRI or literal, as a string pub object: String, /// `t_d`: the timestamp at which this triple arrived in the stream pub event_time: u64, @@ -65,25 +50,24 @@ pub struct WindowedTriple { pub struct WindowData { /// Window width α (in the same time unit as event_time) pub alpha: u64, - /// Contents of the window, including triples that have already expired: - /// liveness is decided per fact at translation time, not on arrival. + /// Contents of the window, including triples that have already expired: liveness is decided per + /// fact at translation time, not on arrival pub triples: Vec, } /// An RSP-QL Streaming Dataset (SDS) at a point in time #[derive(Clone, Debug, Default)] pub struct Sds { - /// Sliding windows, by window IRI. + /// Sliding windows, by window IRI pub windows: HashMap, - /// Non-streaming graphs, by graph IRI. These never expire. + /// Non-streaming graphs, by graph IRI pub static_graphs: HashMap>, - /// IRIs that only ever appear in rule conclusions. They hold no input facts - /// but their prefixes still have to be strippable, so they are listed. + /// IRIs that only ever appear in rule conclusions pub output_iris: HashSet, } impl Sds { - /// An empty streaming dataset: no windows, no static graphs, no outputs. + /// An empty streaming dataset: no windows, no static graphs, no outputs pub fn new() -> Self { Self::default() } @@ -102,8 +86,8 @@ pub fn all_component_iris(sds: &Sds) -> Vec { iris } -/// Translate the alive facts of an SDS at `current_time` into annotated -/// datalog triples with per-fact expiration times +/// Translate the alive facts of an SDS at `current_time` into annotated datalog triples with +/// per-fact expiration times pub fn translate_sds_to_datalog( sds: &Sds, dict: &SharedTerms, @@ -148,8 +132,8 @@ pub fn translate_sds_to_datalog( result } -/// External / user-facing translation: strip window-IRI prefixes from -/// annotated predicates and route each triple to its component bucket +/// External / user-facing translation: strip window-IRI prefixes from annotated predicates and +/// route each triple to its component bucket pub fn translate_datalog_back( facts: &[Triple], dict: &SharedTerms, @@ -252,7 +236,6 @@ mod tests { let short = "http://w/"; let long = "http://w/longer/"; let annotated = "http://w/longer/pred"; - // sorted longest-first let iris = vec![long.to_string(), short.to_string()]; let result = strip_window_prefix(annotated, &iris); assert_eq!(result, Some((long, "pred"))); @@ -261,9 +244,8 @@ mod tests { #[test] fn translate_filters_expired() { let dict = make_dict(); - let sds = make_sds(); // event_time=5, alpha=10 → expiry=15 - let translated = translate_sds_to_datalog(&sds, &dict, 15); // current_time=15 - // expiry=15 is NOT > 15, so filtered out + let sds = make_sds(); + let translated = translate_sds_to_datalog(&sds, &dict, 15); assert!( translated.is_empty(), "fact with expiry=15 at current_time=15 should be filtered" @@ -273,8 +255,8 @@ mod tests { #[test] fn translate_includes_alive() { let dict = make_dict(); - let sds = make_sds(); // expiry=15 - let translated = translate_sds_to_datalog(&sds, &dict, 14); // current_time=14 + let sds = make_sds(); + let translated = translate_sds_to_datalog(&sds, &dict, 14); assert_eq!(translated.len(), 1); assert_eq!(translated[0].1, 15); } @@ -308,7 +290,6 @@ mod tests { assert!(back.contains_key("http://sensor/")); let triples = &back["http://sensor/"]; assert_eq!(triples.len(), 1); - // predicate should be stripped to "reading" let pred_str = dict.decode(triples[0].predicate).unwrap().to_string(); assert_eq!(pred_str, "reading"); } diff --git a/datalog/src/lib.rs b/datalog/src/lib.rs index b18b09aa..fdc1d8e6 100644 --- a/datalog/src/lib.rs +++ b/datalog/src/lib.rs @@ -8,21 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! A Datalog reasoner over dictionary-encoded RDF triples. -//! -//! Rules are evaluated to a fixpoint over a [`shared::dataset_index::DatasetIndex`], -//! with terms interned as `u32` ids by a [`shared::dictionary::Dictionary`] that the -//! reasoner shares with its caller rather than owning. -//! -//! Three things separate it from a textbook Datalog engine: -//! -//! - **Provenance.** A materialisation can carry a semiring tag per derived fact, -//! which is what lets a probability or an expiry time be computed for a -//! conclusion instead of only a truth value. See -//! [`reasoning::materialisation::provenance_semi_naive`]. -//! - **Streaming.** [`cross_window_sds`] represents an RSP-QL streaming dataset, so -//! a rule can span several sliding windows; facts carry expiry times and leave the -//! materialisation as their window moves past them. +//! A Datalog reasoner over dictionary-encoded RDF triples pub mod cross_window_sds; pub mod parser_n3_logic; diff --git a/datalog/src/parser_n3_logic.rs b/datalog/src/parser_n3_logic.rs index 4d15d281..29b89793 100644 --- a/datalog/src/parser_n3_logic.rs +++ b/datalog/src/parser_n3_logic.rs @@ -8,16 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Parser for N3 logic rules, including the window annotations RSP-QL needs. -//! -//! Reads `{ premise } => { conclusion }` under `@prefix` declarations and produces -//! [`shared::rule::Rule`] values whose terms are already interned, which is why -//! parsing needs a dictionary rather than only a string. -//! -//! [`parse_n3_rules_for_sds`] additionally returns a [`WindowContext`]. A rule -//! mentioning two windows is indistinguishable from one mentioning two predicates -//! unless the window each predicate came from is recorded, and the cross-window -//! materialisation needs exactly that to decide when a derived fact expires. +//! Parser for N3 logic rules, including the window annotations RSP-QL needs use crate::reasoning::*; use nom::combinator::{map, opt}; @@ -41,12 +32,11 @@ pub struct WindowContext { pub predicate_to_window: HashMap, /// Maps window IRI → window width α (in the same time unit as event timestamps) pub window_widths: HashMap, - /// All component IRIs found in the rules (windows + output-only components), - /// sorted longest-first for [`crate::cross_window_sds::strip_window_prefix`] + /// All component IRIs in the rules, longest first, for + /// [`crate::cross_window_sds::strip_window_prefix`] pub all_component_iris: Vec, } -// Parsing prefix statements fn parse_prefix(input: &str) -> IResult<&str, (&str, &str)> { let (input, _) = tag("@prefix").parse(input)?; let (input, _) = multispace1.parse(input)?; @@ -112,21 +102,16 @@ fn parse_nested_unresolved_rule(input: &str) -> IResult<&str, UnresolvedTriple> fn parse_unresolved_clause_block(input: &str) -> IResult<&str, Vec> { separated_list1( multispace0, - alt(( - parse_nested_unresolved_rule, // if it's a nested rule - parse_unresolved_triple, // if it's just a triple - )), + alt((parse_nested_unresolved_rule, parse_unresolved_triple)), ) .parse(input) } -// Updated to parse multiple conclusion triples fn parse_unresolved_rule( input: &str, ) -> IResult<&str, (Vec, Vec)> { let (input, _) = multispace0.parse(input)?; - // Parse premise let (input, premise_triples) = delimited( tag("{"), parse_unresolved_clause_block, @@ -138,10 +123,9 @@ fn parse_unresolved_rule( let (input, _) = tag("=>").parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse multiple conclusions let (input, conclusion_triples) = delimited( tag("{"), - parse_unresolved_clause_block, // Using the same parser for multiple triples + parse_unresolved_clause_block, preceded(multispace0, tag("}")), ) .parse(input)?; @@ -156,16 +140,13 @@ pub fn parse_n3_rule<'a>( ) -> IResult<&'a str, (Vec<(&'a str, &'a str)>, Rule)> { let (input, prefixes) = many0(preceded(multispace0, parse_prefix)).parse(input)?; - // Build a HashMap for prefix expansion let prefix_map: HashMap = prefixes .iter() .map(|(prefix, uri)| (prefix.to_string(), uri.to_string())) .collect(); - // Parse to an intermediate unresolved form with multiple conclusions let (input, (premise_triples, conclusion_triples)) = parse_unresolved_rule(input)?; - // Convert from unresolved string-based data to final `Term` with dictionary encoding let premise_parsed: Vec = premise_triples .into_iter() .map(|(s, p, o)| { @@ -177,7 +158,6 @@ pub fn parse_n3_rule<'a>( }) .collect(); - // Convert all conclusions let conclusions_parsed: Vec = conclusion_triples .into_iter() .map(|(s, p, o)| { @@ -204,7 +184,6 @@ fn to_term(ut: UnresolvedTerm, graph: &mut Reasoner, prefix_map: &HashMap Term::Variable(v), UnresolvedTerm::Prefixed(s) => { - // Expand the prefix! let expanded = expand_prefix(&s, prefix_map); Term::Constant(graph.encode_term(&expanded)) } @@ -219,21 +198,16 @@ fn expand_prefix(prefixed: &str, prefix_map: &HashMap) -> String let local = &prefixed[colon_pos + 1..]; if let Some(base_uri) = prefix_map.get(prefix) { - // Expand: prefix + local part format!("{}{}", base_uri, local) } else { - // No prefix found, return as-is prefixed.to_string() } } else { - // No colon, return as-is prefixed.to_string() } } -/// Parse a complete N3 document: one shared prefix block followed by any -/// number of rules. All rules share the same prefix map, avoiding context -/// loss between rules when the document is parsed in one shot +/// Parse a complete N3 document: one shared prefix block followed by any number of rules pub fn parse_n3_document<'a>( input: &'a str, graph: &mut Reasoner, @@ -245,12 +219,6 @@ pub fn parse_n3_document<'a>( .map(|(k, v)| (k.to_string(), v.to_string())) .collect(); - // A rule may be terminated by `.`, as every N3 statement is, and - // `parse_unresolved_rule` stops at the conclusion's closing brace. Accepting the - // terminator here is what lets a document be written the conventional way: this - // function requires the whole input to be consumed, so a trailing dot used to - // make it fail with `Eof` while the single-rule parser silently left the dot - // behind and looked fine. let terminated = |input| { let (input, rule) = preceded(multispace0, parse_unresolved_rule).parse(input)?; let (input, _) = @@ -301,8 +269,7 @@ pub fn parse_n3_document<'a>( Ok((input, (prefix_map, rules))) } -/// Parse an N3 rule document while associating predicate constants with their -/// owning SDS windows +/// Parse an N3 rule document while associating predicate constants with their owning SDS windows pub fn parse_n3_rules_for_sds( input: &str, graph: &mut Reasoner, @@ -311,7 +278,6 @@ pub fn parse_n3_rules_for_sds( let (_, (prefix_map, rules)) = parse_n3_document(input, graph).map_err(|e| format!("N3 parse error: {:?}", e))?; - // Sort window IRIs longest-first for correct prefix matching. let mut sorted_window_iris: Vec<&String> = window_widths.keys().collect(); sorted_window_iris.sort_by(|a, b| b.len().cmp(&a.len())); @@ -319,10 +285,6 @@ pub fn parse_n3_rules_for_sds( let mut output_iris: Vec = Vec::new(); { - // Walk predicate positions only. Each decode takes the lock for its own - // call, which is what a scoped encoder gives up in exchange for not handing - // the lock out; the walk is over rule predicates, not facts. - // for rule in &rules { let preds = rule .premise @@ -333,7 +295,6 @@ pub fn parse_n3_rules_for_sds( for term in preds { if let Term::Constant(id) = term { if let Some(iri) = graph.decode_term(*id) { - // Check against known window IRIs (longest-first) let matched = sorted_window_iris .iter() .find(|w| iri.starts_with(w.as_str())) @@ -342,8 +303,6 @@ pub fn parse_n3_rules_for_sds( if let Some(window_iri) = matched { predicate_to_window.insert(*id, window_iri); } else { - // Unknown window → look for a matching prefix_map value - // (these are output / result component IRIs) for comp_iri in prefix_map.values() { if iri.starts_with(comp_iri.as_str()) && !output_iris.contains(comp_iri) @@ -360,7 +319,6 @@ pub fn parse_n3_rules_for_sds( } } - // Build all_component_iris: windows + output, sorted longest-first, deduped let mut all_component_iris: Vec = window_widths .keys() .cloned() diff --git a/datalog/src/reasoning.rs b/datalog/src/reasoning.rs index 73797172..76b5b46d 100644 --- a/datalog/src/reasoning.rs +++ b/datalog/src/reasoning.rs @@ -7,17 +7,7 @@ * License, v. 2.0. If a copy of the MPL was not distributed with this file, * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The [`Reasoner`]: rules, facts, and the strategies that derive one from the other. -//! -//! A [`Reasoner`] owns its rule set and a [`DatasetIndex`] of facts, and shares its -//! [`Dictionary`] with whoever created it — an `Arc>`, not a private copy, -//! because the ids in its facts have to mean the same thing to the caller. -//! -//! Forward chaining lives in [`materialisation`] and is chosen by calling the -//! matching method on the reasoner rather than by naming a strategy type: several of -//! those submodules are `pub(crate)`, and their methods are reachable through -//! [`Reasoner`] regardless. [`backward_chaining`] proves a single goal without -//! materialising anything, and [`to_dot`] renders the whole thing for inspection. +//! The [`Reasoner`]: rules, facts, and the strategies that derive one from the other pub mod backward_chaining; pub mod helpers; @@ -37,48 +27,22 @@ use shared::term_encoder::{SharedTerms, TermEncoder, TermView}; use shared::triple::Triple; use std::collections::{BTreeMap, HashMap}; -// Logic part: Knowledge Graph - -/// A rule set, a fact base, and the dictionary the two are encoded against. -/// -/// The fields are public, which means a caller can insert facts directly into -/// [`dataset_index`](Self::dataset_index) or swap the dictionary for a shared one -/// without going through a method, and the cross-window strategies do exactly that. -/// Nothing here is cached or derived from anything else, so there is no invariant -/// between the fields to break — except that every id in `dataset_index` and -/// `rules` must be meaningful in `dictionary`, which is why replacing the -/// dictionary wholesale is only safe before any facts are inserted. +/// A rule set, a fact base, and the dictionary the two are encoded against #[derive(Debug, Clone)] -// Are there RDF connections here or not? pub struct Reasoner { - /// Term interning, shared with the caller rather than owned: a fact inserted - /// here has to decode to the same string on the other side. - /// - /// Private, and reached through [`terms`](Self::terms) or - /// [`with_terms`](Self::with_terms). It was a public `Arc>`, - /// which is P5's "nothing leaks" breached in the plainest way: a caller had to - /// name the locking strategy to intern a term, and six places shared a dictionary - /// by assigning the field rather than by asking. terms: SharedTerms, - /// Rules to evaluate. Order does not affect the fixpoint. - pub rules: Vec, // List of dynamic rules + /// Rules to evaluate + pub rules: Vec, - /// The fact base, base and derived facts alike. Materialisation inserts into - /// this as it goes, so after a run it holds both. + /// The fact base, base and derived facts alike pub dataset_index: DatasetIndex, - /// Predicate-to-rule lookup, so a fact only tries rules that could match it. + /// Predicate-to-rule lookup, so a fact only tries rules that could match it pub rule_index: RuleIndex, - /// Input probabilities for provenance seeding. Only the provenance strategies - /// read this; the plain ones ignore it entirely. - pub probability_seeds: HashMap, // Input probabilities for provenance seeding + /// Input probabilities for provenance seeding + pub probability_seeds: HashMap, } -/// Interns a string binding against an existing dictionary, without extending it. -/// -/// A variable whose value is not already in the dictionary is **dropped**, so the -/// result can be smaller than the input. That is deliberate — a value absent from -/// the dictionary cannot appear in any fact, so a binding mentioning it cannot -/// match — but it means the result's length is not a count of the input's. +/// Interns a string binding against an existing dictionary, without extending it pub fn convert_string_binding_to_u32( binding: &BTreeMap, dict: &Dictionary, @@ -93,68 +57,39 @@ pub fn convert_string_binding_to_u32( } impl Reasoner { - /// Adopts `terms` as this reasoner's term space. - /// - /// The point of taking a [`SharedTerms`] rather than the handle is that the - /// caller does not have to name `Arc>`, and cannot pass - /// something single-threaded by mistake: only the shared encoder is `Sync`. - /// - /// Sharing rather than copying, so a term interned here afterwards is - /// immediately visible to everything else holding the same `SharedTerms`. Code - /// that assigns the handle and then assigns it back to "sync" the result is - /// doing nothing; see the note this replaced in `kolibrie-http-server`. + /// Adopts `terms` as this reasoner's term space pub fn adopt_terms(&mut self, terms: SharedTerms) { self.terms = terms; } - /// The term space this reasoner is using, to share with something else. - /// - /// The counterpart to [`adopt_terms`](Self::adopt_terms): two components agree on - /// a term space by one handing its own over, without either naming how it is - /// held. + /// The term space this reasoner is using, to share with something else pub fn terms(&self) -> SharedTerms { self.terms.clone() } - /// Runs `visit` against the dictionary, for the duration of the call only. - /// - /// The scoped form: the lock is taken and released around `visit`, so a caller - /// cannot hold it open across unrelated work, which is what reaching into a - /// public handle allowed. + /// Runs `visit` against the dictionary, for the duration of the call only pub fn with_terms(&self, visit: &mut dyn FnMut(&mut Dictionary)) { TermEncoder::with_terms(&self.terms, visit); } - /// Interns `text` in this reasoner's term space and returns its id. - /// - /// The common case by far, and worth a name: most callers took a write lock to - /// encode one term and dropped it again. + /// Interns `text` in this reasoner's term space and returns its id pub fn encode_term(&self, text: &str) -> u32 { let mut id = 0; self.with_terms(&mut |dictionary| id = dictionary.encode(text)); id } - /// A read-only copy of the term space, for decoding many ids at once. - /// - /// The scoped encoder will not hand its lock out, so code that decodes every - /// term it prints either takes the lock per term or copies once. This copies, and - /// returns a [`TermView`] rather than a `Dictionary` so that interning into the - /// copy is not expressible -- see that type for why it would be wrong and not - /// merely useless. + /// A read-only copy of the term space, for decoding many ids at once pub fn term_view(&self) -> TermView { self.terms.view() } - /// Interns a triple's three terms under one lock. - /// - /// Four callers took a write lock, encoded subject, predicate and object, and - /// dropped it. One lock acquisition, as before, but the caller no longer says so. + /// Interns a triple's three terms under one lock pub fn encode_triple(&self, subject: &str, predicate: &str, object: &str) -> (u32, u32, u32) { self.terms.encode_triple(subject, predicate, object) } - /// Interns whichever of a pattern's positions are given. + /// Interns whichever of a pattern's positions are given pub fn encode_pattern( &self, subject: Option<&str>, @@ -172,7 +107,7 @@ impl Reasoner { ids } - /// The string `id` stands for, if this term space knows it. + /// The string `id` stands for, if this term space knows it pub fn decode_term(&self, id: u32) -> Option { let mut text = None; self.with_terms(&mut |dictionary| { @@ -181,10 +116,7 @@ impl Reasoner { text } - /// An empty reasoner with a fresh dictionary of its own. - /// - /// To share a caller's term space instead, call - /// [`adopt_terms`](Self::adopt_terms) before inserting anything. + /// An empty reasoner with a fresh dictionary of its own pub fn new() -> Self { Self { terms: SharedTerms::new(), @@ -195,8 +127,7 @@ impl Reasoner { } } - /// Add a triple with an associated probability value. - /// The triple is added to the index; the probability is stored for provenance seeding. + /// Add a triple with an associated probability value pub fn add_tagged_triple( &mut self, subject: &str, @@ -215,8 +146,7 @@ impl Reasoner { self.probability_seeds.insert(triple, probability); } - /// Materialize provenance tags as RDF-star triples so they are queryable via SPARQL-star. - /// Generates triples of the form: << s p o >> "0.7"^^xsd:double + /// Materialize provenance tags as RDF-star triples so they are queryable via SPARQL-star pub fn materialize_tags_as_rdf_star(&mut self, tag_store: &TagStore

) { let mut qt_store = shared::quoted_triple_store::QuotedTripleStore::new(); let mut rdf_star_triples = Vec::new(); @@ -231,7 +161,7 @@ impl Reasoner { /// Add an ABox triple (instance-level information) pub fn add_abox_triple(&mut self, subject: &str, predicate: &str, object: &str) { - let (s, p, o) = self.encode_triple(subject, predicate, object); // Release lock early + let (s, p, o) = self.encode_triple(subject, predicate, object); self.dataset_index.insert(&Triple { subject: s, @@ -240,7 +170,7 @@ impl Reasoner { }); } - /// Insert an already-ground triple directly into the fact index. + /// Insert an already-ground triple directly into the fact index pub fn insert_ground_triple(&mut self, triple: Triple) { self.dataset_index.insert(&triple); } @@ -252,7 +182,7 @@ impl Reasoner { predicate: Option<&str>, object: Option<&str>, ) -> Vec { - let (s, p, o) = self.encode_pattern(subject, predicate, object); // Release lock early + let (s, p, o) = self.encode_pattern(subject, predicate, object); self.dataset_index.query(s, p, o) } @@ -264,7 +194,6 @@ mod term_space_tests { #[test] fn two_components_adopting_one_space_agree_on_ids() { - // The invariant the scoped API exists to provide. let left = Reasoner::new(); let mut right = Reasoner::new(); right.adopt_terms(left.terms()); @@ -276,21 +205,12 @@ mod term_space_tests { "one term space mints one id per term" ); - // And a term interned on one side is visible from the other, with no copying - // back: that is what sharing means, and what the removed sync-back assumed. let late = right.encode_term("urn:late"); assert_eq!(left.decode_term(late).as_deref(), Some("urn:late")); } #[test] fn two_dictionaries_from_one_starting_point_mint_colliding_ids() { - // Why `term_view` hands out a read-only view instead of a `Dictionary`. - // - // This is the underlying fact, shown against `Dictionary` directly, because - // the hazard it used to allow is no longer expressible through the reasoner: - // a `TermView` has no `encode`. Were it still a copy, a fact built from an id - // minted in it would decode, through the shared space, to someone else's - // term -- corrupting the fact base rather than losing the term. let reasoner = Reasoner::new(); reasoner.encode_term("urn:already-here"); @@ -313,10 +233,6 @@ mod term_space_tests { #[test] fn a_view_does_not_see_terms_interned_after_it() { - // The staleness half, which `TermView` does not and cannot prevent. It is - // sound rather than wrong -- a blank, not someone else's term -- and every - // caller is safe because the ids it decodes come from facts already stored, - // so they predate the view. let reasoner = Reasoner::new(); let before = reasoner.encode_term("urn:before"); let view = reasoner.term_view(); diff --git a/datalog/src/reasoning/backward_chaining.rs b/datalog/src/reasoning/backward_chaining.rs index 11349dca..2837fe0d 100644 --- a/datalog/src/reasoning/backward_chaining.rs +++ b/datalog/src/reasoning/backward_chaining.rs @@ -1,15 +1,4 @@ -//! Goal-directed evaluation: proving one query without materialising everything. -//! -//! Unification works over [`Term`] rather than over dictionary -//! ids alone, because a goal can carry unbound variables where a fact never does. -//! Quoted triples unify component-wise. -//! -//! # Depth limit -//! -//! There is no loop check. Recursion is bounded by a hard-coded depth of 10, and -//! exceeding it yields no bindings, which is indistinguishable from the goal having -//! no proof. A recursive rule set deeper than that silently answers nothing, so -//! forward chaining is the safer choice when the rules are known to recurse. +//! Goal-directed evaluation: proving one query without materialising everything use crate::reasoning::Reasoner; use shared::rule::Rule; @@ -53,7 +42,6 @@ fn unify_terms(term1: &Term, term2: &Term, bindings: &mut HashMap) } true } - // QuotedTriple terms unify if their components unify (Term::QuotedTriple(qt1), Term::QuotedTriple(qt2)) => { unify_terms(&qt1.0, &qt2.0, bindings) && unify_terms(&qt1.1, &qt2.1, bindings) @@ -68,12 +56,7 @@ fn unify_terms(term1: &Term, term2: &Term, bindings: &mut HashMap) } } -/// Follows a variable through the binding chain to whatever it ultimately stands for. -/// -/// Chains, because unification can bind one variable to another: resolving `?x` -/// where `?x` is bound to `?y` and `?y` to a constant yields the constant. An -/// unbound variable resolves to itself, so the result is a `Term` and not an -/// `Option`. +/// Follows a variable through the binding chain to whatever it ultimately stands for pub fn resolve_term<'a>(term: &'a Term, bindings: &'a HashMap) -> Term { match term { Term::Variable(v) => { @@ -122,7 +105,6 @@ fn rename_rule_variables(rule: &Rule, counter: &mut usize) -> Rule { ) -> Term { match term { Term::Variable(v) => { - // If a variable mapping already exists (e.g. ?person -> v0) then just clone that name if let Some(new_v) = var_map.get(v) { Term::Variable(new_v.clone()) } else { @@ -166,14 +148,14 @@ fn rename_rule_variables(rule: &Rule, counter: &mut usize) -> Rule { } impl Reasoner { - /// Returns all variable bindings that satisfy `query` via backward chaining. + /// Returns all variable bindings that satisfy `query` via backward chaining pub fn backward_chaining(&self, query: &TriplePattern) -> Vec> { let bindings = HashMap::new(); let mut variable_counter = 0; self.backward_chaining_helper(query, &bindings, 0, &mut variable_counter) } - /// Recursive helper for backward chaining. Depth-limited to prevent infinite loops. + /// Recursive helper for backward chaining fn backward_chaining_helper( &self, query: &TriplePattern, @@ -197,11 +179,9 @@ impl Reasoner { } } - // Match with rules for rule in &self.rules { let renamed_rule = rename_rule_variables(rule, variable_counter); - // Try to unify with each conclusion in the rule for conclusion in &renamed_rule.conclusion { if let Some(rb) = unify_patterns(conclusion, &substituted, bindings) { let mut premise_results = vec![rb.clone()]; diff --git a/datalog/src/reasoning/helpers.rs b/datalog/src/reasoning/helpers.rs index 4884dfaf..1cf12253 100644 --- a/datalog/src/reasoning/helpers.rs +++ b/datalog/src/reasoning/helpers.rs @@ -1,17 +1,9 @@ -//! Decoding bindings back to strings, for debugging and error messages. -//! -//! Nothing in evaluation needs this: rules are evaluated over `u32` ids throughout. -//! It exists so a binding can be printed. +//! Decoding bindings back to strings, for debugging and error messages use shared::dictionary::Dictionary; use std::collections::HashMap; -/// Decodes a binding's ids back to their strings. -/// -/// # Panics -/// -/// If any id is absent from the dictionary. Only ids that came out of this -/// dictionary should be passed. +/// Decodes a binding's ids back to their strings pub fn get_readable_binding( bindings: &HashMap, dictionary: &Dictionary, diff --git a/datalog/src/reasoning/materialisation.rs b/datalog/src/reasoning/materialisation.rs index d1033942..f432ceb1 100644 --- a/datalog/src/reasoning/materialisation.rs +++ b/datalog/src/reasoning/materialisation.rs @@ -1,33 +1,10 @@ -//! Forward chaining: deriving every fact the rules entail. -//! -//! All of these run to a fixpoint. They differ in what they cost and in what they -//! record on the way, so every one of them agrees about which facts follow. -//! -//! | strategy | entry point | records per fact | -//! |---|---|---| -//! | naive | `Reasoner::infer_new_facts_naive` | — | -//! | semi-naive | `Reasoner::infer_new_facts_semi_naive` | — | -//! | semi-naive, parallel | `Reasoner::infer_new_facts_semi_naive_parallel` | — | -//! | provenance | [`provenance_semi_naive`] | a semiring tag | -//! | lineage | [`hybrid_materialisation`] | a Boolean lineage formula | -//! | SDD-seeded | [`sdd_seed_materialise`] | an SDD node | -//! | cross-window | [`cross_window_naive`], [`cross_window_incremental`] | an expiry time | -//! -//! The first three live in `pub(crate)` modules on purpose. Their methods are -//! inherent on [`Reasoner`](crate::reasoning::Reasoner) and so remain reachable -//! through the type; it is the module path, not the method, that is not API. +//! Forward chaining: deriving every fact the rules entail use shared::dictionary::Dictionary; use shared::terms::{Term, TriplePattern}; use shared::triple::Triple; use std::collections::HashMap; -// These four contain only `impl Reasoner` blocks. Inherent impls are not namespaced -// by their module, so demoting the module does NOT hide the methods — callers reach -// `reasoner.infer_new_facts_semi_naive()` through the type, and 43 external call sites -// keep working. What this does buy: nothing outside can couple to these paths, and the -// modules leave the documentation ledger. Anything exporting a type or free function -// by path stays `pub` below. pub(crate) mod my_naive; pub(crate) mod semi_naive; pub(crate) mod semi_naive_parallel; @@ -40,15 +17,9 @@ pub mod provenance_infer_generic; pub mod provenance_semi_naive; pub mod sdd_seed_materialise; -/// The id a conclusion term stands for, given the bindings that satisfied the premises. -/// -/// Returns 0 for an unbound variable (after warning on stderr) and for a quoted -/// triple, which is not resolved here. Term id 0 is a real dictionary id, so a -/// conclusion built from one is indistinguishable from a legitimately derived fact — -/// the warning is the only signal. +/// The id a conclusion term stands for, given the bindings that satisfied the premises fn get_id_from_term(term: &Term, vars: &HashMap) -> u32 { match term { - // If term is a variable, then get the value bound to the variable Term::Variable(v) => vars.get(v).copied().unwrap_or_else(|| { eprintln!( "Warning: Variable '{}' not found in bindings. Available variables: {:?}", @@ -71,14 +42,11 @@ pub fn replace_variables_with_bound_values( let subject = get_id_from_term(&conclusion.0, vars); let predicate = get_id_from_term(&conclusion.1, vars); - // Also some kind of get_id_from_term, but uses ml_output_placeholder or something as well. let object = match &conclusion.2 { Term::Variable(v) => { - // Check if this variable is bound in the current context if let Some(&bound_value) = vars.get(v) { bound_value } else { - // If not bound, create a new placeholder in the dictionary dict.encode(&format!("ml_output_placeholder_{}", v)) } } diff --git a/datalog/src/reasoning/materialisation/cross_window_incremental.rs b/datalog/src/reasoning/materialisation/cross_window_incremental.rs index aeab74e3..670e9f36 100644 --- a/datalog/src/reasoning/materialisation/cross_window_incremental.rs +++ b/datalog/src/reasoning/materialisation/cross_window_incremental.rs @@ -8,20 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Maintaining a materialised streaming dataset across ticks instead of rebuilding it. -//! -//! Carries the previous result forward as [`SdsWithExpiry`] and derives only from -//! the facts that arrived since, which is what makes the cost proportional to the -//! delta rather than to the window contents. -//! -//! Expiry is what makes this possible without recomputing: every derived fact is -//! tagged with an [`ExpirationProvenance`] -//! tag giving the time it stops holding, which is the minimum over the facts it was -//! derived from, so a fact leaving a window retracts its consequences without any -//! search for them. -//! -//! [`cross_window_naive`](super::cross_window_naive) computes the same thing the -//! slow way and is the oracle this is tested against. +//! Maintaining a materialised streaming dataset across ticks instead of rebuilding it use crate::cross_window_sds::{ all_component_iris, strip_window_prefix, translate_sds_to_datalog, Sds, @@ -82,10 +69,8 @@ pub fn incremental_sds_plus( reasoner.dataset_index.insert(t); } - // Seed TagStore with expiry values from D_old ∪ D_new. let mut initial_tags = TagStore::new(ExpirationProvenance); for (t, e) in d_old.iter().chain(d_new.iter()) { - // set_tag skips u64::MAX (= one()), so static facts are implicitly ∞. if *e < u64::MAX { initial_tags.set_tag(t, *e); } diff --git a/datalog/src/reasoning/materialisation/cross_window_naive.rs b/datalog/src/reasoning/materialisation/cross_window_naive.rs index bb858c3f..68381f5a 100644 --- a/datalog/src/reasoning/materialisation/cross_window_naive.rs +++ b/datalog/src/reasoning/materialisation/cross_window_naive.rs @@ -8,12 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Recomputing a materialised streaming dataset from scratch at each time point. -//! -//! The reference implementation for cross-window reasoning: flatten the SDS, derive -//! everything, translate back. Correct by construction and the oracle the -//! incremental version in [`cross_window_incremental`](super::cross_window_incremental) -//! is checked against, but it repeats all the work on every tick. +//! Recomputing a materialised streaming dataset from scratch at each time point use crate::cross_window_sds::{translate_datalog_back, translate_sds_to_datalog, Sds}; use crate::reasoning::Reasoner; diff --git a/datalog/src/reasoning/materialisation/hybrid_materialisation.rs b/datalog/src/reasoning/materialisation/hybrid_materialisation.rs index 43b8c8d3..e3654c2f 100644 --- a/datalog/src/reasoning/materialisation/hybrid_materialisation.rs +++ b/datalog/src/reasoning/materialisation/hybrid_materialisation.rs @@ -7,19 +7,7 @@ * License, v. 2.0. */ -//! Materialisation that records each fact's lineage and leaves evaluation to the caller. -//! -//! Deriving the lineage of every fact is cheap; turning one into a probability means -//! compiling it to an SDD, which is not. Splitting the two lets a caller materialise -//! once and then compile only the hypotheses a decision actually rests on — see -//! [`LineageMaterialization`]. -//! -//! # Rules this rejects -//! -//! [`materialize_lineage`] validates the rule set first and refuses two things a -//! lineage formula cannot represent: a variable in predicate position, which makes -//! the dependency graph unknowable before evaluation, and a cycle in that graph, -//! because a recursive derivation has no finite Boolean lineage. +//! Materialisation that records each fact's lineage and leaves evaluation to the caller use crate::reasoning::materialisation::provenance_semi_naive::semi_naive_with_initial_tags; use crate::reasoning::Reasoner; @@ -35,17 +23,14 @@ use shared::triple::Triple; use std::collections::{HashMap, HashSet}; use std::sync::Arc; -/// Full lineage result. Evaluation is intentionally separate so callers can -/// compile only the hypotheses that matter to a decision. +/// Full lineage result pub struct LineageMaterialization { - /// Facts derived by this run, in derivation order. Base facts are not included. + /// Facts derived by this run, in derivation order pub new_facts: Vec, - /// Lineage formula per fact, as a handle into the shared formula store. + /// Lineage formula per fact, as a handle into the shared formula store pub tags: TagStore, } -// Summarised rather than derived: a materialisation holds every derived fact and a -// tag per fact, so the derived form would print the whole result set. impl std::fmt::Debug for LineageMaterialization { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("LineageMaterialization") @@ -56,18 +41,13 @@ impl std::fmt::Debug for LineageMaterialization { } impl LineageMaterialization { - /// The lineage of a fact: a handle to the Boolean formula over seed variables - /// that says how it could have been derived. + /// The lineage of a fact: a handle to the Boolean formula over seed variables that says how it + /// could have been derived pub fn lineage(&self, triple: &Triple) -> LineageId { self.tags.get_tag(triple) } - /// Turns one fact's lineage into a probability, under the escalation policy in - /// `config`. - /// - /// Always answers, which is the point: when exact compilation would exceed the - /// configured budget the result carries bounds and the reason instead of - /// failing, so a caller gets something to decide on either way. + /// Turns one fact's lineage into a probability, under the escalation policy in `config` pub fn evaluate(&self, triple: &Triple, config: &HybridConfig) -> HybridProbabilityResult { let provenance = self.tags.provenance(); evaluate_hybrid( @@ -78,11 +58,8 @@ impl LineageMaterialization { ) } - /// Compiles one fact's lineage to an SDD, for a caller that needs the diagram - /// rather than a number. - /// - /// Unlike [`evaluate`](Self::evaluate), this reports a budget overrun as an - /// error rather than degrading to bounds. + /// Compiles one fact's lineage to an SDD, for a caller that needs the diagram rather than a + /// number pub fn compile_exact( &self, triple: &Triple, @@ -115,8 +92,7 @@ fn constant_predicate(term: &Term) -> Result { } } -/// Reject predicate dependency cycles before lineage materialisation. This is -/// deliberately conservative: even a data-bounded recursive rule is rejected. +/// Reject predicate dependency cycles before lineage materialisation pub fn validate_hybrid_rules(rules: &[Rule]) -> Result<(), HybridError> { let mut graph: HashMap> = HashMap::new(); for rule in rules { @@ -174,16 +150,7 @@ pub fn validate_hybrid_rules(rules: &[Rule]) -> Result<(), HybridError> { Ok(()) } -/// Derives every fact the rules entail, recording each one's lineage. -/// -/// Inserts the snapshot's seed facts into `reasoner` as it goes, so the reasoner is -/// left holding both the seeds and the derived facts. -/// -/// # Errors -/// -/// Rejects the rule set before deriving anything if it has a variable in predicate -/// position or a cycle in its predicate dependency graph, neither of which has a -/// finite Boolean lineage. +/// Derives every fact the rules entail, recording each one's lineage pub fn materialize_lineage( reasoner: &mut Reasoner, base: Option<&dyn QuadSource>, @@ -226,16 +193,7 @@ pub fn materialize_lineage( } impl Reasoner { - /// Materialises with lineage and evaluates **every** derived fact. - /// - /// The convenient path, and the expensive one: compiling one lineage is the - /// costly step, and this does it once per fact. Prefer - /// [`materialize_lineage`] plus - /// [`LineageMaterialization::evaluate`] on the facts that matter when the - /// answer is only needed for a few of them. - /// - /// The returned [`LineageMaterialization`] is the same one the probabilities - /// came from, so further facts can be evaluated without materialising again. + /// Materialises with lineage and evaluates **every** derived fact pub fn infer_new_facts_with_hybrid( &mut self, snapshot: SeedSnapshot, @@ -251,11 +209,8 @@ impl Reasoner { self.infer_new_facts_with_hybrid_over(None, snapshot, config) } - /// As [`infer_new_facts_with_hybrid`](Self::infer_new_facts_with_hybrid), reading - /// the base facts from `base` as well as from this reasoner's own index. - /// - /// The seed triples a snapshot names are still inserted here, since they are the - /// subject of the lineage rather than part of the store's facts. + /// As [`infer_new_facts_with_hybrid`](Self::infer_new_facts_with_hybrid), also reading base + /// facts from `base` pub fn infer_new_facts_with_hybrid_over( &mut self, base: Option<&dyn QuadSource>, diff --git a/datalog/src/reasoning/materialisation/infer_generic.rs b/datalog/src/reasoning/materialisation/infer_generic.rs index 05a0b6c9..a47be630 100644 --- a/datalog/src/reasoning/materialisation/infer_generic.rs +++ b/datalog/src/reasoning/materialisation/infer_generic.rs @@ -1,9 +1,4 @@ -//! The driver loop shared by the forward-chaining strategies that carry no provenance. -//! -//! A strategy supplies one round ([`InferenceStrategy::infer_round`]) and the driver -//! repeats it until a round derives nothing new, inserting each round's facts into -//! the reasoner as it goes. The strategies themselves are `pub(crate)`; the loop is -//! public because it is also what a new strategy would plug into. +//! The driver loop shared by the forward-chaining strategies that carry no provenance use crate::reasoning::Reasoner; use shared::dictionary::Dictionary; @@ -12,18 +7,12 @@ use shared::rule::Rule; use shared::triple::Triple; use std::collections::{HashMap, HashSet}; -/// One way of satisfying a rule's premises: variable name to term id. +/// One way of satisfying a rule's premises: variable name to term id pub type SolutionMapping = HashMap; -/// One round of forward chaining, which [`Reasoner::infer_with_strategy`] repeats. -/// -/// A strategy decides which facts a round considers; it does not decide when to -/// stop, since the driver stops as soon as a round derives nothing new. +/// One round of forward chaining, which [`Reasoner::infer_with_strategy`] repeats pub trait InferenceStrategy { - /// Applies a single round of inference within the materialisation algorithm. - /// `param` all_facts: all facts currently in the knowledge base (base facts + those derived in previous rounds) - /// `known_facts`: same facts but in HashSet for quick membership checks - /// `returns`: facts that were inferred in this round + /// Applies a single round of inference within the materialisation algorithm fn infer_round( &mut self, dictionary: &mut Dictionary, @@ -34,38 +23,25 @@ pub trait InferenceStrategy { } impl Reasoner { - /// Generic function that infers all derivable facts using a given strategy, e.g. SemiNaive, or Naive + /// Generic function that infers all derivable facts using a given strategy, e.g. SemiNaive, or + /// Naive pub fn infer_with_strategy(&mut self, strat: S) -> Vec { self.infer_with_strategy_over(None, strat) } - /// As [`infer_with_strategy`](Self::infer_with_strategy), reading the base facts - /// from `base` as well as from this reasoner's own index. - /// - /// Lets a caller reason over a store it does not own without copying it in. The - /// three call sites that did copy were each building a second full index — three - /// sorted orders over facts that this function immediately flattens back into a - /// `Vec` — so the index was constructed to be enumerated once and then dropped. - /// - /// Facts already held here are still included, so a reasoner seeded with a few - /// facts of its own and pointed at a larger base sees the union. Duplicates - /// between the two are dropped, since `known_facts` dedupes. - /// - /// Derived facts land in [`dataset_index`](Reasoner::dataset_index) as before. - /// Base facts read from `base` do not, which is the saving; a caller that needs - /// them there should insert them itself. + /// As [`infer_with_strategy`](Self::infer_with_strategy), reading the base facts from `base` as + /// well as from this reasoner's own index pub fn infer_with_strategy_over( &mut self, base: Option<&dyn QuadSource>, mut strat: S, ) -> Vec { - // In each iteration, facts are added to this list. Use vector to preserve index for initial facts let mut all_facts: Vec = self.dataset_index.query(None, None, None); if let Some(base) = base { all_facts.extend(base.query_default(None, None, None)); } let mut known_facts: HashSet = all_facts.iter().cloned().collect(); - let idx_before_inference = all_facts.len(); // Used to keep track of which facts are inferred by the algorithm + let idx_before_inference = all_facts.len(); loop { let mut inferred_facts_this_round = HashSet::new(); @@ -79,9 +55,8 @@ impl Reasoner { } for fact in inferred_facts_this_round.drain() { - // Insert into known_facts first; if it was not present, also store it. if !known_facts.contains(&fact) { - known_facts.insert(fact.clone()); // Necessary clone apparently + known_facts.insert(fact.clone()); self.dataset_index.insert(&fact); all_facts.push(fact); } diff --git a/datalog/src/reasoning/materialisation/my_naive.rs b/datalog/src/reasoning/materialisation/my_naive.rs index 0f5af083..eba258ec 100644 --- a/datalog/src/reasoning/materialisation/my_naive.rs +++ b/datalog/src/reasoning/materialisation/my_naive.rs @@ -1,8 +1,4 @@ -//! Naive forward chaining: every rule against every fact, every round. -//! -//! Re-derives everything it already knows on each pass, so it is the slowest -//! strategy and the one whose result is easiest to trust. Kept as a reference for -//! the others. +//! Naive forward chaining: every rule against every fact, every round use crate::reasoning::materialisation::infer_generic::{InferenceStrategy, SolutionMapping}; use crate::reasoning::materialisation::replace_variables_with_bound_values; @@ -17,7 +13,6 @@ pub(crate) struct NaiveStrategy; impl NaiveStrategy { /// Convert rule evaluation to use the optimized hash join - /// Optimised hash-join: return early when build map is empty (see for loop) fn find_premise_solutions( &mut self, dict: &Dictionary, @@ -28,10 +23,8 @@ impl NaiveStrategy { return Vec::new(); } - // Initialise current bindings to an empty vector let mut cur_binding_set = vec![BTreeMap::new()]; - // Process each premise with the build hash-table using the optimized join for premise in &rule.premise { cur_binding_set = join_premise_with_hash_join(premise, all_facts, cur_binding_set, dict); @@ -40,7 +33,6 @@ impl NaiveStrategy { } } - // Convert results back to HashMap cur_binding_set .into_iter() .map(|binding| convert_string_binding_to_u32(&binding, dict)) @@ -56,19 +48,13 @@ impl InferenceStrategy for NaiveStrategy { all_facts: &Vec, known_facts: &HashSet, ) -> HashSet { - // Also HashSet to prevent duplicate triples from being added this round let mut inferred_facts_this_round: HashSet = HashSet::new(); - // Loop over each rule for rule in rules { - // These are all bindings such that the premise is satisfied for the given rule let binding_sets = self.find_premise_solutions(dictionary, rule, all_facts); - // For each binding that satisfies the premises of the rule, get to the conclusion and apply bindings for binding_set in &binding_sets { if evaluate_filters(&binding_set, &rule.filters, dictionary) { - // Loop over each conclusion of the rule, since for the current binding, - // the conclusions of the rule can be inferred (because premises are met) for conclusion in &rule.conclusion { let inferred_fact = replace_variables_with_bound_values( conclusion, @@ -89,11 +75,8 @@ impl InferenceStrategy for NaiveStrategy { } impl Reasoner { - /// Derives every entailed fact by naive forward chaining, returning the new - /// ones and leaving them in [`dataset_index`](Reasoner::dataset_index). - /// - /// Prefer [`infer_new_facts_semi_naive`](Reasoner::infer_new_facts_semi_naive) - /// unless the point is to check it against something. + /// Derives every entailed fact by naive forward chaining, returning the new ones and leaving + /// them in [`dataset_index`](Reasoner::dataset_index) pub fn infer_new_facts_naive(&mut self) -> Vec { self.infer_with_strategy(NaiveStrategy) } diff --git a/datalog/src/reasoning/materialisation/provenance_infer_generic.rs b/datalog/src/reasoning/materialisation/provenance_infer_generic.rs index 07c791ae..34e12fbb 100644 --- a/datalog/src/reasoning/materialisation/provenance_infer_generic.rs +++ b/datalog/src/reasoning/materialisation/provenance_infer_generic.rs @@ -8,13 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The driver loop for strategies that carry a provenance tag per fact. -//! -//! Separate from [`infer_generic`](super::infer_generic) because of its termination -//! condition. Deriving no new fact is not enough: a round can leave the set of facts -//! unchanged while improving the tag on one of them — a second derivation of a known -//! fact is a second way of proving it, which the tag has to absorb — so the loop -//! runs again whenever either the facts or a tag changed. +//! The driver loop for strategies that carry a provenance tag per fact use crate::reasoning::Reasoner; use shared::dictionary::Dictionary; @@ -25,25 +19,18 @@ use shared::tag_store::TagStore; use shared::triple::Triple; use std::collections::HashSet; -/// Result of a single provenance inference round. +/// Result of a single provenance inference round #[derive(Debug)] pub struct ProvenanceInferResult { - /// Newly derived triples (not previously in known_facts). + /// Newly derived triples (not previously in known_facts) pub new_facts: HashSet, - /// Whether any existing triple's tag was updated this round. + /// Whether any existing triple's tag was updated this round pub tag_changed: bool, } -/// Strategy trait for provenance-based materialisation. -/// -/// Implementors define how a single inference round works (e.g. naive, semi-naive). -/// The driver loop calls `infer_round` repeatedly until fixpoint. +/// Strategy trait for provenance-based materialisation pub trait ProvenanceInferenceStrategy { - /// Runs one round, updating `tag_store` in place for facts it derives or - /// re-derives, and reporting through - /// [`ProvenanceInferResult::tag_changed`] whether any tag moved. Reporting - /// that wrongly as `false` terminates the loop early and leaves tags that are - /// not at their fixpoint. + /// Runs one round, updating `tag_store` and reporting whether any tag changed fn infer_round( &mut self, dictionary: &mut Dictionary, @@ -55,7 +42,7 @@ pub trait ProvenanceInferenceStrategy { } impl Reasoner { - /// Generic driver for provenance-based materialisation. Loops until fixpoint. + /// Generic driver for provenance-based materialisation pub fn infer_with_provenance_strategy( &mut self, base: Option<&dyn QuadSource>, @@ -70,8 +57,7 @@ impl Reasoner { self.infer_with_provenance_strategy_and_rules(base, strat, tag_store, &rules) } - /// Same as `infer_with_provenance_strategy` but with an explicit rule slice. - /// Used for stratified evaluation where stratum-0 uses only positive rules. + /// Same as `infer_with_provenance_strategy` but with an explicit rule slice pub fn infer_with_provenance_strategy_and_rules( &mut self, base: Option<&dyn QuadSource>, @@ -107,7 +93,6 @@ impl Reasoner { } } - // Terminate when no new facts AND no tag updates if !has_new_facts && !result.tag_changed { break; } diff --git a/datalog/src/reasoning/materialisation/provenance_semi_naive.rs b/datalog/src/reasoning/materialisation/provenance_semi_naive.rs index 3d5cf914..13a81ddd 100644 --- a/datalog/src/reasoning/materialisation/provenance_semi_naive.rs +++ b/datalog/src/reasoning/materialisation/provenance_semi_naive.rs @@ -8,23 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Semi-naive materialisation that tags every derived fact with its provenance. -//! -//! # Stratification -//! -//! Rules with a negative premise are evaluated in a second stratum, after the -//! positive rules have reached their fixpoint, and that stratum is a *single pass* -//! rather than a fixpoint. Negation is therefore not recursive here: a fact derived -//! by a negative rule cannot feed another negative rule. -//! -//! # Seed identity -//! -//! Seed probabilities are assigned ids by position in the triple-sorted order of -//! [`Reasoner::probability_seeds`](crate::reasoning::Reasoner), and -//! [`TagStore::seed_triples`](shared::tag_store::TagStore) records that order so an -//! explanation can map an id back to the triple it came from. The ordering is part -//! of the contract between the two: a tag is only interpretable alongside the -//! `seed_triples` it was built with. +//! Semi-naive materialisation that tags every derived fact with its provenance use crate::reasoning::convert_string_binding_to_u32; use crate::reasoning::materialisation::provenance_infer_generic::{ @@ -41,20 +25,16 @@ use shared::tag_store::TagStore; use shared::triple::Triple; use std::collections::{BTreeMap, BTreeSet, HashSet}; -/// Semi-naive materialisation strategy parameterized by a provenance semiring. +/// Semi-naive materialisation strategy parameterized by a provenance semiring struct ProvenanceSemiNaiveStrategy { start_idx_for_delta: usize, - /// Facts whose tags improved last round — re-enter as delta triggers. delta_improved: Vec, - /// If Some, used as the delta for the very first round instead of the - /// `start_idx_for_delta` slice. After the first round this is drained to None. explicit_initial_delta: Option>, first_round: bool, } impl ProvenanceSemiNaiveStrategy { - /// Find all bindings where at least one premise is matched by delta facts. - /// Returns bindings along with the matched premise triples (for tag lookup). + /// Find all bindings where at least one premise is matched by delta facts fn find_premise_solutions_with_triples( &mut self, dict: &Dictionary, @@ -69,11 +49,9 @@ impl ProvenanceSemiNaiveStrategy { for i in 0..nr_premises { let mut current_bindings = vec![BTreeMap::new()]; - // At least one premise matched by delta facts current_bindings = join_premise_with_hash_join(&rule.premise[i], delta_facts, current_bindings, dict); - // Join remaining premises with all facts for j in 0..nr_premises { if j == i { continue; @@ -90,7 +68,6 @@ impl ProvenanceSemiNaiveStrategy { } for binding in current_bindings { - // Reconstruct matched premise triples from the binding let u32_binding = convert_string_binding_to_u32(&binding, dict); let matched_triples = resolve_premise_triples(&rule.premise, &u32_binding); if seen_derivations.insert((binding.clone(), matched_triples.clone())) { @@ -103,7 +80,7 @@ impl ProvenanceSemiNaiveStrategy { } } -/// Resolve premise patterns to concrete triples using the current binding. +/// Resolve premise patterns to concrete triples using the current binding fn resolve_premise_triples( premises: &[shared::terms::TriplePattern], bindings: &std::collections::HashMap, @@ -155,7 +132,6 @@ impl ProvenanceInferenceStrategy

for ProvenanceSemiNaiveStrate let end_idx = all_facts.len(); - // Build effective delta for this round let effective_delta: Vec = if self.first_round { self.first_round = false; self.start_idx_for_delta = end_idx; @@ -191,7 +167,6 @@ impl ProvenanceInferenceStrategy

for ProvenanceSemiNaiveStrate continue; } - // Combine premise tags via conjunction (⊗) let conclusion_tag = matched_triples .iter() .map(|t| tag_store.get_tag(t)) @@ -215,7 +190,6 @@ impl ProvenanceInferenceStrategy

for ProvenanceSemiNaiveStrate } else { if tag_store.update_disjunction(&inferred_fact, &conclusion_tag) { if !is_new { - // Tag improved on an existing fact → re-enter as delta tag_changed = true; improved_this_round.push(inferred_fact); } @@ -235,9 +209,7 @@ impl ProvenanceInferenceStrategy

for ProvenanceSemiNaiveStrate } impl Reasoner { - /// Run provenance-based semi-naive materialisation with single-stratum NAF support. - /// - /// Positive rules run to fixpoint first (stratum 0), then NAF rules run once (stratum 1). + /// Run provenance-based semi-naive materialisation with single-stratum NAF support pub fn infer_new_facts_with_provenance( &mut self, provenance: P, @@ -245,16 +217,8 @@ impl Reasoner { self.infer_new_facts_with_provenance_over(None, provenance) } - /// As [`infer_new_facts_with_provenance`](Self::infer_new_facts_with_provenance), - /// reading the base facts from `base` as well as from this reasoner's own index. - /// - /// For a caller reasoning over a store it does not own. Nothing is copied in but - /// the facts derived, as with - /// [`infer_new_facts_semi_naive_over`](Self::infer_new_facts_semi_naive_over). - /// - /// The seeded tags come from `probability_seeds` either way: a source supplies - /// facts, not probabilities, so a caller wanting seeded base facts still sets - /// them on the reasoner. + /// As [`infer_new_facts_with_provenance`](Self::infer_new_facts_with_provenance), also reading + /// base facts from `base` pub fn infer_new_facts_with_provenance_over( &mut self, base: Option<&dyn QuadSource>, @@ -262,10 +226,6 @@ impl Reasoner { ) -> (Vec, TagStore

) { let mut tag_store = TagStore::new(provenance.clone()); - // Seed tag store from probability seeds. - // Sort by triple for deterministic ID assignment (needed by TopKProofs which - // uses the ID as a variable index in the probability table). - // Zero cost for other provenances — their tag_from_probability_with_id ignores id. let mut seeds: Vec<(&Triple, f64)> = self .probability_seeds .iter() @@ -276,7 +236,6 @@ impl Reasoner { let tag = provenance.tag_from_probability_with_id(*prob, id); tag_store.set_tag(triple, tag); } - // Record sorted seed triples so encode_as_rdf_star_with_explanation can map IDs -> triples. tag_store.seed_triples = seeds.iter().map(|(t, _)| (*t).clone()).collect(); let new_facts = semi_naive_with_initial_tags(self, base, provenance, tag_store.clone()); @@ -284,21 +243,13 @@ impl Reasoner { } } -/// Runs the two strata to a fixpoint, starting from tags the caller has already set. -/// -/// Positive rules first, to a fixpoint, then one pass of the rules with a negative -/// premise. The negative stratum is a single pass and not a fixpoint, so a fact -/// derived through negation cannot feed another negated premise. -/// -/// Returns the derived facts and the tag store, which the caller needs to keep: a -/// tag is a handle into that store and is meaningless without it. +/// Runs the two strata to a fixpoint, starting from tags the caller has already set pub fn semi_naive_with_initial_tags( reasoner: &mut Reasoner, base: Option<&dyn QuadSource>, provenance: P, mut initial_tags: TagStore

, ) -> (Vec, TagStore

) { - // Split rules into positive (stratum 0) and negated (stratum 1). let positive_rules: Vec = reasoner .rules .iter() @@ -312,7 +263,6 @@ pub fn semi_naive_with_initial_tags( .cloned() .collect(); - // Stratum 0: run positive fixpoint. let mut new_facts = reasoner.infer_with_provenance_strategy_and_rules( base, ProvenanceSemiNaiveStrategy { @@ -325,7 +275,6 @@ pub fn semi_naive_with_initial_tags( &positive_rules, ); - // Stratum 1: single negative pass (if any NAF rules exist). if !negative_rules.is_empty() { let neg_new = run_negative_stratum_pass( reasoner, @@ -340,15 +289,7 @@ pub fn semi_naive_with_initial_tags( (new_facts, initial_tags) } -/// As [`semi_naive_with_initial_tags`], seeded with an explicit delta for the first -/// round, so a run can continue from a previous one instead of rediscovering it. -/// -/// Two differences from that function, both of which matter to a caller: -/// **negative rules are ignored entirely** rather than run as a second stratum, and -/// `_provenance` is unused — the tags come from `initial_tags`, which carries its -/// own provenance. This is the entry point -/// [`cross_window_incremental`](super::cross_window_incremental) uses, where rules -/// are positive and the delta is the facts that arrived this tick. +/// As [`semi_naive_with_initial_tags`], seeded with an explicit delta for the first round pub fn semi_naive_with_initial_tags_and_delta( reasoner: &mut Reasoner, base: Option<&dyn QuadSource>, @@ -378,7 +319,7 @@ pub fn semi_naive_with_initial_tags_and_delta( (new_facts, initial_tags) } -/// Single forward pass for NAF rules (stratum 1) over the stratum-0 closure. +/// Single forward pass for NAF rules (stratum 1) over the stratum-0 closure fn run_negative_stratum_pass( reasoner: &mut Reasoner, base: Option<&dyn QuadSource>, @@ -393,13 +334,9 @@ fn run_negative_stratum_pass( let all_facts_set: HashSet = all_facts.iter().cloned().collect(); let mut new_derived: Vec = Vec::new(); - // Scoped around the whole pass, because `replace_variables_with_bound_values` - // interns new terms and a copy would throw them away. Taken out of `reasoner` - // first so the closure can still insert into its fact base. let terms = reasoner.terms(); shared::term_encoder::TermEncoder::with_terms(&terms, &mut |dict| { for rule in rules { - // Join all positive premises to get candidate bindings. let mut bindings: Vec> = vec![BTreeMap::new()]; for prem in &rule.premise { bindings = join_premise_with_hash_join(prem, &all_facts, bindings, &dict); @@ -415,7 +352,6 @@ fn run_negative_stratum_pass( continue; } - // Conjunction of positive premise tags (⊗). let pos_triples = resolve_premise_triples(&rule.premise, &u32_binding); let pos_tag = pos_triples .iter() @@ -428,7 +364,6 @@ fn run_negative_stratum_pass( continue; } - // Conjunction of NAF contributions for each negated atom (⊗). let mut neg_tag = provenance.one(); for neg_pat in &rule.negative_premise { let s = resolve_term(&neg_pat.0, &u32_binding); @@ -443,15 +378,11 @@ fn run_negative_stratum_pass( object: o, }; if all_facts_set.contains(&neg_triple) { - // Fact present -> negate its provenance tag. provenance.negate(&tag_store.get_tag(&neg_triple)) } else { - // Fact absent -> NOT absent = certainly true = one(). provenance.one() } } - // Unbound variable in negated atom — safety check should have - // caught this; treat as zero (cannot fire). _ => provenance.zero(), }; @@ -466,7 +397,6 @@ fn run_negative_stratum_pass( continue; } - // Derive conclusions. for conclusion in &rule.conclusion { let inferred = replace_variables_with_bound_values(conclusion, &u32_binding, dict); diff --git a/datalog/src/reasoning/materialisation/sdd_seed_materialise.rs b/datalog/src/reasoning/materialisation/sdd_seed_materialise.rs index af0683dc..89203713 100644 --- a/datalog/src/reasoning/materialisation/sdd_seed_materialise.rs +++ b/datalog/src/reasoning/materialisation/sdd_seed_materialise.rs @@ -8,17 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Materialisation whose seeds are compiled into an SDD as they are inserted. -//! -//! The reason to build the diagram up front rather than from a lineage formula -//! afterwards is mutual exclusion. An exclusive group — one of these choices holds, -//! not several — is expressed as an `exactly_one` constraint conjoined onto each -//! choice's tag, and that constraint has to be in the diagram before derivation -//! starts for the derived facts to inherit it. -//! -//! [`try_infer_new_facts_with_sdd_seed_specs`] is the same thing under an -//! [`SddLimits`] budget, and reports exhaustion as an error -//! rather than running until it finishes. +//! Materialisation whose seeds are compiled into an SDD as they are inserted use shared::sdd::{BoolOp, SddBudgetError, SddLimits, SddProvenance, VarKind}; use shared::seed_spec::SeedSpec; @@ -36,17 +26,7 @@ fn zero_triple() -> Triple { } } -/// Materialises from SDD-compiled seeds, with no budget. -/// -/// Inserts each seed fact into `reasoner` and tags it with its SDD node, so an -/// exclusive group's `exactly_one` constraint is already in the diagram before -/// derivation starts and every fact derived from a choice inherits it. -/// -/// # Panics -/// -/// If the provenance manager's lock is poisoned. Use -/// [`try_infer_new_facts_with_sdd_seed_specs`] where compilation may blow up: this -/// one has no budget and will keep going. +/// Materialises from SDD-compiled seeds, with no budget pub fn infer_new_facts_with_sdd_seed_specs( reasoner: &mut Reasoner, seeds: Vec, @@ -114,12 +94,7 @@ fn record_seed_triple(tags: &mut TagStore, variable: u32, triple: tags.seed_triples[variable as usize] = triple.clone(); } -/// As [`infer_new_facts_with_sdd_seed_specs`], under an explicit budget. -/// -/// # Errors -/// -/// Reports budget exhaustion instead of continuing. Nothing is rolled back: the -/// reasoner may already hold the seed facts inserted before the budget ran out. +/// As [`infer_new_facts_with_sdd_seed_specs`], under an explicit budget pub fn try_infer_new_facts_with_sdd_seed_specs( reasoner: &mut Reasoner, seeds: Vec, diff --git a/datalog/src/reasoning/materialisation/semi_naive.rs b/datalog/src/reasoning/materialisation/semi_naive.rs index faeccb31..fd206132 100644 --- a/datalog/src/reasoning/materialisation/semi_naive.rs +++ b/datalog/src/reasoning/materialisation/semi_naive.rs @@ -1,8 +1,4 @@ -//! Semi-naive forward chaining: each round joins against only what the last one derived. -//! -//! A derivation is new only if it uses at least one fact from the previous round, so -//! for each rule this tries each premise position in turn against the delta and -//! joins the remaining premises against everything. The default strategy. +//! Semi-naive forward chaining: each round joins against only what the last one derived use crate::reasoning::materialisation::infer_generic::{InferenceStrategy, SolutionMapping}; use crate::reasoning::materialisation::replace_variables_with_bound_values; @@ -32,11 +28,9 @@ impl SemiNaiveStrategy { for i in 0..nr_premises { let mut current_bindings = vec![BTreeMap::new()]; - // At least one premise should be satisfied by facts derived from last round (if not, then you simply derive the same things) current_bindings = join_premise_with_hash_join(&rule.premise[i], &delta_facts, current_bindings, dict); - // Join remaining premises with all facts (includes delta facts as well) for j in 0..nr_premises { if j == i { continue; @@ -52,7 +46,6 @@ impl SemiNaiveStrategy { } } - // Convert and add results for binding in current_bindings { let u32_binding = convert_string_binding_to_u32(&binding, dict); results.push(u32_binding); @@ -71,24 +64,18 @@ impl InferenceStrategy for SemiNaiveStrategy { all_facts: &Vec, known_facts: &HashSet, ) -> HashSet { - // Also HashSet to prevent duplicate triples from being added this round let mut inferred_facts_this_round: HashSet = HashSet::new(); let end_idx_for_delta = all_facts.len(); - let delta_facts = &all_facts[self.start_idx_for_delta..end_idx_for_delta]; // Take derived facts from last round and use as delta - self.start_idx_for_delta = end_idx_for_delta; // Update the pointer for the next round + let delta_facts = &all_facts[self.start_idx_for_delta..end_idx_for_delta]; + self.start_idx_for_delta = end_idx_for_delta; - // Loop over each rule for rule in rules { - // These are all bindings such that the premise is satisfied for the given rule let binding_sets = self.find_premise_solutions(dictionary, rule, all_facts, delta_facts); - // For each binding that satisfies the premises of the rule, get to the conclusion and apply bindings for binding_set in &binding_sets { if evaluate_filters(&binding_set, &rule.filters, dictionary) { - // Loop over each conclusion of the rule, since for the current binding, - // the conclusions of the rule can be inferred (because premises are met) for conclusion in &rule.conclusion { let inferred_fact = replace_variables_with_bound_values( conclusion, @@ -109,24 +96,15 @@ impl InferenceStrategy for SemiNaiveStrategy { } impl Reasoner { - /// Derives every entailed fact by semi-naive forward chaining, returning the - /// new ones and leaving them in [`dataset_index`](Reasoner::dataset_index). - /// - /// The default choice: same fixpoint as - /// [`infer_new_facts_naive`](Reasoner::infer_new_facts_naive) without - /// re-deriving what it already knows. + /// Derives every entailed fact by semi-naive forward chaining and returns the new ones pub fn infer_new_facts_semi_naive(&mut self) -> Vec { self.infer_with_strategy(SemiNaiveStrategy { start_idx_for_delta: 0, }) } - /// As [`infer_new_facts_semi_naive`](Self::infer_new_facts_semi_naive), reading - /// the base facts from `base` instead of requiring them to have been inserted - /// here first. - /// - /// For a caller that wants to reason over a store it does not own. Nothing is - /// copied into this reasoner's index but the facts it derives. + /// As [`infer_new_facts_semi_naive`](Self::infer_new_facts_semi_naive), reading base facts from + /// `base` pub fn infer_new_facts_semi_naive_over( &mut self, base: &dyn shared::quad_source::QuadSource, diff --git a/datalog/src/reasoning/materialisation/semi_naive_parallel.rs b/datalog/src/reasoning/materialisation/semi_naive_parallel.rs index d73b5230..fcb76bc0 100644 --- a/datalog/src/reasoning/materialisation/semi_naive_parallel.rs +++ b/datalog/src/reasoning/materialisation/semi_naive_parallel.rs @@ -1,9 +1,4 @@ -//! Semi-naive forward chaining with the delta scanned in parallel. -//! -//! Unfinished, and currently has no callers; retained because the approach is worth -//! keeping rather than because it is ready. It takes the dictionary write lock for -//! the whole fixpoint, which serialises anything else holding the same dictionary, -//! so the parallelism it adds inside the loop is paid for outside it. +//! Semi-naive forward chaining with the delta scanned in parallel use crate::reasoning::materialisation::replace_variables_with_bound_values; use crate::reasoning::rules::matches_rule_pattern; @@ -14,56 +9,25 @@ use std::collections::{HashMap, HashSet}; use std::sync::Arc; impl Reasoner { - /// Semi-naive forward chaining with each round's delta scanned across rayon's - /// thread pool. - /// - /// Unfinished and currently unused. It holds the dictionary write lock for the - /// whole fixpoint, so no other holder of that dictionary makes progress while - /// it runs. + /// Semi-naive forward chaining with each round's delta scanned across rayon's thread pool pub fn infer_new_facts_semi_naive_parallel(&mut self) -> Vec { - // Collect all known facts let all_initial = self.dataset_index.query(None, None, None); let mut all_facts: HashSet = all_initial.into_iter().collect(); - // Delta = all the initial facts let mut delta = all_facts.clone(); - // Keep track of newly inferred facts so we can return them later let mut inferred_so_far = Vec::new(); - // This strategy is unsound, and the term handling is why. It has no callers. - // - // A `&mut Dictionary` cannot cross rayon's workers, so the loop below clones - // this copy per inference and interns into the clone. Those ids are minted - // against a private counter that starts wherever the shared space happened to - // be, so: - // - // - the shared space can later assign the same number to a different term, - // and then a fact stored here decodes to the wrong string; - // - two workers in one run mint the same id for different terms, so the - // results are not even internally consistent. - // - // And the facts are not discarded: the loop inserts them into - // `self.dataset_index` and returns them. So this fabricates term ids and - // stores them as if they were real, rather than merely losing them, which is - // what an earlier version of this comment said. - // - // Fixing it means keeping the parallel section free of interning -- deriving - // conclusions as unresolved terms and interning them sequentially afterwards - // -- which is a rewrite of the strategy rather than a change to this line. let mut dict = shared::dictionary::Dictionary::new(); self.with_terms(&mut |shared| dict = shared.clone()); - // Repeat until no new facts are inferred loop { - // Wrap all_facts in an Arc for shared read-only access in parallel let all_facts_arc = Arc::new(all_facts.clone()); let new_facts: HashSet = delta .par_iter() .fold( || HashSet::new(), |mut local_set, triple1| { - // Use only the predicate for candidate rule lookup let candidate_rule_ids = self.rule_index.query_candidate_rules( None, Some(triple1.predicate), @@ -73,14 +37,12 @@ impl Reasoner { let rule = &self.rules[rule_id]; match rule.premise.len() { 1 => { - // Single-premise rule let mut variable_bindings = HashMap::new(); if matches_rule_pattern( &rule.premise[0], triple1, &mut variable_bindings, ) { - // Process each conclusion for conclusion in &rule.conclusion { let inferred = replace_variables_with_bound_values( conclusion, @@ -95,14 +57,12 @@ impl Reasoner { } 2 => { - // Two-premise rule let mut variable_bindings_1 = HashMap::new(); if matches_rule_pattern( &rule.premise[0], triple1, &mut variable_bindings_1, ) { - // Process join in parallel over all_facts let local_new: HashSet = all_facts_arc .par_iter() .flat_map(|triple2| { @@ -113,7 +73,6 @@ impl Reasoner { triple2, &mut variable_bindings_2, ) { - // Process each conclusion rule.conclusion .iter() .filter_map(|conclusion| { @@ -138,7 +97,6 @@ impl Reasoner { local_set.extend(local_new); } - // Option 2: Assume triple1 matches the second premise let mut variable_bindings_1b = HashMap::new(); if matches_rule_pattern( &rule.premise[1], @@ -155,7 +113,6 @@ impl Reasoner { triple2, &mut variable_bindings_2b, ) { - // Process each conclusion rule.conclusion .iter() .filter_map(|conclusion| { @@ -195,7 +152,6 @@ impl Reasoner { }, ); - // If no new facts were found, we've reached a fixpoint if new_facts.is_empty() { break; } else { diff --git a/datalog/src/reasoning/rules.rs b/datalog/src/reasoning/rules.rs index eca37c98..d24e9e05 100644 --- a/datalog/src/reasoning/rules.rs +++ b/datalog/src/reasoning/rules.rs @@ -1,19 +1,8 @@ -//! Matching rule premises against facts, and joining the bindings that result. -//! -//! This is the inner loop every forward-chaining strategy shares. Premises are -//! joined one at a time, left to right, through -//! [`shared::join_algorithm::perform_hash_join_for_rules`]; an empty intermediate -//! result ends the rule early. -//! -//! Rule filters are not SPARQL filters: see [`evaluate_filters`] for where the two -//! semantics part company. +//! Matching rule premises against facts, and joining the bindings that result use crate::reasoning::Reasoner; use shared::dictionary::Dictionary; use shared::join_algorithm::perform_hash_join_for_rules; -// Moved to `shared` in step 6: these are vocabulary operations, and they were the -// only thing `datalogmtl` needed from this crate. Re-exported below so -// `datalog::reasoning::{matches_rule_pattern, construct_triple}` keeps working. pub use shared::pattern::{construct_triple, matches_rule_pattern}; use shared::rule::{FilterCondition, Rule}; use shared::terms::{Bindings, TriplePattern}; @@ -25,14 +14,10 @@ pub fn join_rule(rule: &Rule, all_facts: &HashSet, delta: &HashSet 5` on the value `"abc"` is false while `?x < 5` is true. -/// -/// A rule relying on either behaviour will not translate to a SPARQL FILTER -/// unchanged. +/// Tests a candidate binding against a rule's filters pub fn evaluate_filters( bindings: &HashMap, filters: &Vec, @@ -97,7 +65,6 @@ pub fn evaluate_filters( ) -> bool { for filter in filters { if let Some(&lhs_code) = bindings.get(&filter.variable) { - // If the filter value is itself a bound variable, compare by dictionary ID if let Some(&rhs_code) = bindings.get(&filter.value) { match filter.operator.as_str() { "!=" if lhs_code == rhs_code => return false, @@ -105,7 +72,6 @@ pub fn evaluate_filters( _ => {} } } else { - // Compare bound variable against a numeric constant let value_str = dict.decode(lhs_code).unwrap_or(""); let bound_num: f64 = value_str.parse().unwrap_or(0.0); let filter_num: f64 = filter.value.parse().unwrap_or(0.0); @@ -124,11 +90,7 @@ pub fn evaluate_filters( true } -/// Extends each binding with the ways one premise can match the facts. -/// -/// Returns the empty vector when the premise matches nothing, which the callers -/// treat as the rule having failed and stop early rather than continuing with -/// nothing to join against. +/// Extends each binding with the ways one premise can match the facts pub fn join_premise_with_hash_join( premise: &TriplePattern, all_facts: &[Triple], @@ -139,19 +101,12 @@ pub fn join_premise_with_hash_join( } impl Reasoner { - /// Add a dynamic rule to the graph. - /// - /// Panics if the rule has unsafe negation (a variable in `negative_premise` - /// that is not bound by `premise`). For a non-panicking version use [`try_add_rule`]. - /// - /// [`try_add_rule`]: Self::try_add_rule + /// Add a dynamic rule to the graph pub fn add_rule(&mut self, rule: Rule) { self.try_add_rule(rule).expect("rule safety check failed"); } - /// Add a dynamic rule to the graph, returning `Err` if it violates safety. - /// - /// Safety requirement: every variable in `negative_premise` must appear in `premise`. + /// Add a dynamic rule to the graph, returning `Err` if it violates safety pub fn try_add_rule(&mut self, rule: Rule) -> Result<(), String> { shared::rule::check_rule_safety(&rule)?; let rule_id = self.rules.len(); diff --git a/datalog/src/reasoning/to_dot.rs b/datalog/src/reasoning/to_dot.rs index f8d58964..71e7f5cf 100644 --- a/datalog/src/reasoning/to_dot.rs +++ b/datalog/src/reasoning/to_dot.rs @@ -1,10 +1,4 @@ -//! Rendering a reasoner as a DOT graph, for looking at rather than querying. -//! -//! Facts become edges between subject and object nodes labelled by predicate, and -//! each rule becomes a premise node pointing at a conclusion node, so a rule that -//! has not fired is still visible. Everything is decoded back to strings, which -//! makes this the one place where a dictionary id missing from the dictionary -//! panics rather than degrading. +//! Rendering a reasoner as a DOT graph, for looking at rather than querying use crate::reasoning::Reasoner; use shared::term_encoder::TermView; @@ -13,9 +7,8 @@ use shared::triple::Triple; use std::collections::HashSet; /// Contains a method to convert a specific data structure into a representation in the DOT language -/// For better visualisation pub trait ToDot { - /// Renders as a complete DOT `digraph`, ready to hand to Graphviz. + /// Renders as a complete DOT `digraph`, ready to hand to Graphviz fn to_dot(&self) -> String; } @@ -54,16 +47,15 @@ fn get_subject_object_iterator(triple: &Triple) -> impl Iterator { fn create_nodes(kg: &Reasoner, out: &mut String) { let dict = kg.term_view(); - // These ids represent nodes in the knowledge graph (one id for each subject or object) let mut all_subject_object_ids: Vec = kg .dataset_index .query(None, None, None) .iter() .flat_map(|t| get_subject_object_iterator(&t)) - .collect::>() // Collect into HashSet to remove duplicates + .collect::>() .into_iter() .collect(); - all_subject_object_ids.sort(); // Sorted ids (in order of creation) + all_subject_object_ids.sort(); for id in all_subject_object_ids { let name = dict.decode(id).unwrap(); @@ -101,7 +93,7 @@ fn create_edges(kg: &Reasoner, out: &mut String) { } } -/// Outputs a graph for the reasoner, making it easy to see what it consists of. +/// Outputs a graph for the reasoner, making it easy to see what it consists of impl ToDot for Reasoner { fn to_dot(&self) -> String { let mut out = String::new(); @@ -109,7 +101,7 @@ impl ToDot for Reasoner { create_nodes(self, &mut out); - out.push_str("\n"); // Whitespace between nodes and relations + out.push_str("\n"); create_edges(self, &mut out); diff --git a/datalog/tests/cross_window_tests.rs b/datalog/tests/cross_window_tests.rs index 28beb6eb..e6bd02c2 100644 --- a/datalog/tests/cross_window_tests.rs +++ b/datalog/tests/cross_window_tests.rs @@ -117,13 +117,11 @@ fn test_translate_filters_expired() { let dict = make_dict(); let sds = make_sds(); let translated = translate_sds_to_datalog(&sds, &dict, 15); - // Sensor fact (expiry=15) must be filtered at current_time=15 let expiry15_present = translated.iter().any(|(_, e)| *e == 15); assert!( !expiry15_present, "expired fact (expiry=15 at current_time=15) should be filtered" ); - // Map fact (expiry=23) should still be present let alive = translated.iter().any(|(_, e)| *e == 23); assert!(alive, "alive map fact (expiry=23) should remain"); } @@ -216,7 +214,6 @@ fn test_naive_incremental_agree() { let component_iris = all_component_iris(&sds); let incr_result = sds_with_expiry_to_external(&incr_internal, &dict, &component_iris); - // Compare triple sets (predicate strings) per component let naive_preds = pred_strings(&naive_result, "http://result/", &dict); let incr_preds = pred_strings(&incr_result, "http://result/", &dict); assert_eq!( diff --git a/datalog/tests/reasoning_tests.rs b/datalog/tests/reasoning_tests.rs index 28d2327f..a0f8b2de 100644 --- a/datalog/tests/reasoning_tests.rs +++ b/datalog/tests/reasoning_tests.rs @@ -41,8 +41,6 @@ fn bc_has(results: &[HashMap], var: &str, val: u32) -> bool { .any(|b| b.get(var) == Some(&Term::Constant(val))) } -// Forward chaining - #[test] fn fc_1hop_base() { let mut r = Reasoner::new(); @@ -78,7 +76,6 @@ fn fc_2hop_transitive() { let parent = enc(&r, "parent"); let ancestor = enc(&r, "ancestor"); - // base: parent -> ancestor r.add_rule(rule( vec![( Term::Variable("X".into()), @@ -91,7 +88,6 @@ fn fc_2hop_transitive() { Term::Variable("Y".into()), )], )); - // transitive: ancestor + ancestor -> ancestor r.add_rule(rule( vec![ ( @@ -324,7 +320,6 @@ fn fc_no_spurious() { #[test] fn fc_sibling_three_children() { - // A, B, C all share parent P -> 6 directional pairs, no self-sibling let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "P"); r.add_abox_triple("B", "parent", "P"); @@ -384,7 +379,6 @@ fn fc_sibling_three_children() { #[test] fn fc_multi_conclusion() { - // One rule head produces two distinct conclusions let mut r = Reasoner::new(); r.add_abox_triple("A", "marriedTo", "B"); @@ -420,7 +414,6 @@ fn fc_multi_conclusion() { #[test] fn fc_diamond_ancestor() { - // Diamond: A->B->D and A->C->D; A should be ancestor of D via both paths let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "B"); r.add_abox_triple("A", "parent", "C"); @@ -482,7 +475,6 @@ fn fc_diamond_ancestor() { #[test] fn fc_disconnected_graphs() { - // Two separate parent chains must not cross-contaminate let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "B"); r.add_abox_triple("X", "parent", "Y"); @@ -519,7 +511,6 @@ fn fc_disconnected_graphs() { #[test] fn fc_no_matching_facts() { - // Rules that match no facts should produce no inferred triples let mut r = Reasoner::new(); r.add_abox_triple("A", "likes", "B"); @@ -549,7 +540,6 @@ fn fc_no_matching_facts() { #[test] fn fc_idempotent() { - // Running inference a second time must not add duplicate triples let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "B"); @@ -586,8 +576,6 @@ fn fc_idempotent() { #[test] fn fc_uncle_derived() { - // Two-stage: sibling derived first, then uncle derived from sibling + parent - // Setup: A and B share parent P; C's parent is A -> B is uncle of C let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "P"); r.add_abox_triple("B", "parent", "P"); @@ -597,7 +585,6 @@ fn fc_uncle_derived() { let sibling = enc(&r, "sibling"); let uncle = enc(&r, "uncle"); - // sibling(X, Y) :- parent(X, Z), parent(Y, Z), X != Y r.add_rule(Rule { premise: vec![ ( @@ -624,7 +611,6 @@ fn fc_uncle_derived() { }], }); - // uncle(U, N) :- sibling(U, Par), parent(N, Par) r.add_rule(rule( vec![ ( @@ -659,7 +645,6 @@ fn fc_uncle_derived() { inferred(&mut r, "B", "uncle", "C"), "B should be uncle of C" ); - // A is the parent of C, not uncle assert!( !inferred(&mut r, "A", "uncle", "C"), "A (parent) should not also be uncle of C" @@ -668,7 +653,6 @@ fn fc_uncle_derived() { #[test] fn fc_constant_object_premise_filters() { - // A constant-object premise must not match every triple with the predicate let mut r = Reasoner::new(); r.add_abox_triple("v1", "inZone", "RestrictedZone"); r.add_abox_triple("v2", "inZone", "SafeZone"); @@ -679,7 +663,6 @@ fn fc_constant_object_premise_filters() { let alert = enc(&r, "alert"); let flagged = enc(&r, "Flagged"); - // alert(V, Flagged) :- inZone(V, RestrictedZone) r.add_rule(rule( vec![( Term::Variable("V".into()), @@ -711,7 +694,6 @@ fn fc_constant_object_premise_filters() { #[test] fn fc_constant_subject_premise_filters() { - // A constant-subject premise must only match that subject let mut r = Reasoner::new(); r.add_abox_triple("v1", "inZone", "RestrictedZone"); r.add_abox_triple("v2", "inZone", "SafeZone"); @@ -721,7 +703,6 @@ fn fc_constant_subject_premise_filters() { let observed = enc(&r, "observedZone"); let sensor = enc(&r, "sensor"); - // observedZone(sensor, Z) :- inZone(v1, Z) r.add_rule(rule( vec![( Term::Constant(v1), @@ -749,7 +730,6 @@ fn fc_constant_subject_premise_filters() { #[test] fn fc_constant_object_premise_in_join() { - // Constant-object filtering must also hold when joining across premises let mut r = Reasoner::new(); r.add_abox_triple("v1", "inZone", "RestrictedZone"); r.add_abox_triple("v2", "inZone", "SafeZone"); @@ -763,7 +743,6 @@ fn fc_constant_object_premise_in_join() { let alert = enc(&r, "alert"); let flagged = enc(&r, "Flagged"); - // alert(V, Flagged) :- type(V, Vehicle), inZone(V, RestrictedZone) r.add_rule(rule( vec![ ( @@ -798,14 +777,12 @@ fn fc_constant_object_premise_in_join() { #[test] fn fc_variable_predicate_premise() { - // A variable-predicate premise must match all triples and bind ?p let mut r = Reasoner::new(); r.add_abox_triple("A", "likes", "B"); r.add_abox_triple("A", "knows", "C"); let linked_from = enc(&r, "linkedFrom"); - // linkedFrom(O, S) :- any(S, P, O) r.add_rule(rule( vec![( Term::Variable("S".into()), @@ -837,14 +814,12 @@ fn fc_variable_predicate_premise() { #[test] fn fc_variable_predicate_join() { - // RDFS-subproperty style: ?p bound in one premise, reused in join and conclusion let mut r = Reasoner::new(); r.add_abox_triple("A", "worksFor", "B"); r.add_abox_triple("worksFor", "subPropertyOf", "affiliatedWith"); let sub_property_of = enc(&r, "subPropertyOf"); - // Q(X, Y) :- P(X, Y), subPropertyOf(P, Q) r.add_rule(rule( vec![ ( @@ -879,7 +854,6 @@ fn fc_variable_predicate_join() { #[test] fn fc_shared_predicate_variable() { - // A bound predicate variable must filter later premises, not rebind let mut r = Reasoner::new(); r.add_abox_triple("A", "rel1", "X"); r.add_abox_triple("C", "rel2", "Z"); @@ -890,7 +864,6 @@ fn fc_shared_predicate_variable() { let d = enc(&r, "D"); let shares = enc(&r, "sharesRelWith"); - // sharesRelWith(A, C) :- P(A, _), P(C, _) -- A and C share no predicate r.add_rule(rule( vec![ ( @@ -906,7 +879,6 @@ fn fc_shared_predicate_variable() { ], vec![(Term::Constant(a), Term::Constant(shares), Term::Constant(c))], )); - // sharesRelWith(A, D) :- P(A, _), P(D, _) -- A and D both use rel1 r.add_rule(rule( vec![ ( @@ -937,7 +909,6 @@ fn fc_shared_predicate_variable() { #[test] fn fc_repeated_variable_premise() { - // rel(V, V) must only match triples with subject == object let mut r = Reasoner::new(); r.add_abox_triple("a", "rel", "a"); r.add_abox_triple("a", "rel", "b"); @@ -946,7 +917,6 @@ fn fc_repeated_variable_premise() { let self_linked = enc(&r, "selfLinked"); let true_c = enc(&r, "True"); - // selfLinked(V, True) :- rel(V, V) r.add_rule(rule( vec![( Term::Variable("V".into()), @@ -972,8 +942,6 @@ fn fc_repeated_variable_premise() { ); } -// Backward chaining - #[test] fn bc_direct_fact() { let mut r = Reasoner::new(); @@ -1274,7 +1242,6 @@ fn bc_multi_rule_chain() { #[test] fn bc_sibling_join() { - // BC should find a sibling via a 2-premise join rule let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "P"); r.add_abox_triple("B", "parent", "P"); @@ -1319,7 +1286,6 @@ fn bc_sibling_join() { #[test] fn bc_full_scan() { - // Fully variable query should return bindings for all matching base facts let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "B"); r.add_abox_triple("C", "parent", "D"); @@ -1345,7 +1311,6 @@ fn bc_full_scan() { #[test] fn bc_no_spurious_negative() { - // BC must not return results for a predicate with no facts or applicable rules let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "B"); @@ -1366,9 +1331,6 @@ fn bc_no_spurious_negative() { #[test] fn prov_transitive_addmult_combination() { - // A related B (0.8), B related C (0.7) - // Rule: ?X related ?Y, ?Y related ?Z => ?X related ?Z - // AddMultProbability: ⊗ = multiply, so 0.8 * 0.7 = 0.56 let mut r = Reasoner::new(); r.add_tagged_triple("A", "related", "B", 0.8); r.add_tagged_triple("B", "related", "C", 0.7); @@ -1419,11 +1381,6 @@ fn prov_transitive_addmult_combination() { #[test] fn prov_addmult_multiple_paths() { - // A related B (0.6), A related C (0.9), B related D (0.8), C related D (0.5) - // Rule: ?X related ?Y, ?Y related ?Z => ?X related ?Z - // Path 1: A->B->D = 0.6*0.8 = 0.48 - // Path 2: A->C->D = 0.9*0.5 = 0.45 - // AddMultProbability ⊕ = noisy-OR: 0.48 + 0.45 - 0.48*0.45 ≈ 0.714 let mut r = Reasoner::new(); r.add_tagged_triple("A", "related", "B", 0.6); r.add_tagged_triple("A", "related", "C", 0.9); @@ -1462,7 +1419,6 @@ fn prov_addmult_multiple_paths() { object: d, }; let prob = AddMultProbability.recover_probability(&tag_store.get_tag(&triple)); - // noisy-OR: 0.48 + 0.45 - 0.48*0.45 = 0.93 - 0.216 = 0.714 assert!( (prob - 0.714).abs() < 1e-6, "AddMult disjunction should be ~0.714, got {}", @@ -1472,8 +1428,6 @@ fn prov_addmult_multiple_paths() { #[test] fn prov_minmax_conjunction() { - // A knows B (0.9), B trusts C (0.6) - // MinMaxProbability: ⊗ = min, so min(0.9, 0.6) = 0.6 let mut r = Reasoner::new(); r.add_tagged_triple("A", "knows", "B", 0.9); r.add_tagged_triple("B", "trusts", "C", 0.6); @@ -1523,11 +1477,6 @@ fn prov_minmax_conjunction() { #[test] fn prov_minmax_multiple_paths() { - // A related B (0.6), A related C (0.9), B related D (0.8), C related D (0.5) - // MinMaxProbability: ⊗ = min, ⊕ = max - // Path 1: A->B->D = min(0.6, 0.8) = 0.6 - // Path 2: A->C->D = min(0.9, 0.5) = 0.5 - // Disjunction: max(0.6, 0.5) = 0.6 let mut r = Reasoner::new(); r.add_tagged_triple("A", "related", "B", 0.6); r.add_tagged_triple("A", "related", "C", 0.9); @@ -1575,7 +1524,6 @@ fn prov_minmax_multiple_paths() { #[test] fn prov_boolean_matches_classical() { - // BooleanProvenance should produce the same facts as classical semi-naive let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "B"); r.add_abox_triple("B", "parent", "C"); @@ -1617,7 +1565,6 @@ fn prov_boolean_matches_classical() { let (inferred, tag_store) = r.infer_new_facts_with_provenance(BooleanProvenance); - // Should infer A ancestor B, B ancestor C, A ancestor C let a = enc(&r, "A"); let b = enc(&r, "B"); let c = enc(&r, "C"); @@ -1631,7 +1578,6 @@ fn prov_boolean_matches_classical() { .iter() .any(|t| t.subject == a && t.predicate == ancestor && t.object == c)); - // All tags should be true (one()) for t in &inferred { assert_eq!(tag_store.get_tag(t), true); } @@ -1639,7 +1585,6 @@ fn prov_boolean_matches_classical() { #[test] fn prov_classical_rules_still_work() { - // Regression test: classical (non-provenance) rules should still work let mut r = Reasoner::new(); r.add_abox_triple("A", "parent", "B"); @@ -1665,10 +1610,8 @@ fn prov_classical_rules_still_work() { #[test] fn prov_zero_tag_pruning() { - // If a fact has probability 0.0, its tag is zero() and derivations through it - // should be pruned (zero is annihilator for ⊗) let mut r = Reasoner::new(); - r.add_tagged_triple("A", "related", "B", 0.0); // zero tag + r.add_tagged_triple("A", "related", "B", 0.0); r.add_tagged_triple("B", "related", "C", 0.9); let related = enc(&r, "related"); @@ -1695,7 +1638,6 @@ fn prov_zero_tag_pruning() { let (inferred, _tag_store) = r.infer_new_facts_with_provenance(AddMultProbability); - // A related C should NOT be inferred (zero ⊗ anything = zero, pruned) let a = enc(&r, "A"); let c = enc(&r, "C"); let a_related_c = inferred @@ -1709,17 +1651,10 @@ fn prov_zero_tag_pruning() { #[test] fn topk_wmc_overlap_vs_noisy_or() { - // Canonical overlap test: directly build the tag and verify WMC. - // proof1 = {0, 1}: uses x (0.8) and y (0.6) -> score 0.48 - // proof2 = {0, 2}: uses x (0.8) and z (0.5) -> score 0.40 (shares x!) - // - // Noisy-OR of 0.48 and 0.40 = 0.688 (overcounts x) - // Exact WMC via inclusion-exclusion: - // P(x∧y) + P(x∧z) - P(x∧y∧z) = 0.48 + 0.40 - 0.24 = 0.64 let p = TopKProofs::new(5); - p.tag_from_probability_with_id(0.8, 0); // x - p.tag_from_probability_with_id(0.6, 1); // y - p.tag_from_probability_with_id(0.5, 2); // z + p.tag_from_probability_with_id(0.8, 0); + p.tag_from_probability_with_id(0.6, 1); + p.tag_from_probability_with_id(0.5, 2); let mut proof1: std::collections::BTreeSet = Default::default(); proof1.insert(0); @@ -1739,12 +1674,6 @@ fn topk_wmc_overlap_vs_noisy_or() { #[test] fn topk_transitive_chain() { - // A related B (0.9), B related C (0.8) - // Rule: ?X related ?Y, ?Y related ?Z => ?X related ?Z - // TopKProofs ⊗ = Cartesian product of proof sets - // tag(A->B) = [{0}], tag(B->C) = [{1}] - // conjunction: [{0}] ⊗ [{1}] = [{0,1}] - // WMC of [{0,1}] = P(A->B) * P(B->C) = 0.9 * 0.8 = 0.72 let mut r = Reasoner::new(); r.add_tagged_triple("A", "related", "B", 0.9); r.add_tagged_triple("B", "related", "C", 0.8); @@ -1788,7 +1717,6 @@ fn topk_transitive_chain() { let prob = tag_store .provenance() .recover_probability(&tag_store.get_tag(&triple)); - // Both seeds are distinct variables, so WMC = 0.9 * 0.8 = 0.72 assert!( (prob - 0.72).abs() < 1e-6, "TopK chain probability should be 0.72, got {}", @@ -1798,24 +1726,6 @@ fn topk_transitive_chain() { #[test] fn topk_overlap_two_paths_share_base_fact() { - // TopKProofs as the overlap-aware replacement for noisy-OR. - // - // Two rule firings produce the same conclusion triple but share a base fact (seed x). - // Noisy-OR (AddMultProbability) would treat the paths as independent and overcount x, - // giving 0.688. TopKProofs tracks proof sets and applies exact WMC via - // inclusion-exclusion, giving the correct 0.64. - // - // Facts: - // A is_active yes (seed x, prob 0.8) ← appears in both proofs - // A knows B (seed y, prob 0.6) - // A knows C (seed z, prob 0.5) - // - // Rule: ?X is_active ?_, ?X knows ?Y => ?X is_socially_active yes - // - // Firing 1: X=A, Y=B -> proof {x, y} = seeds {0, 1}, score 0.8 * 0.6 = 0.48 - // Firing 2: X=A, Y=C -> proof {x, z} = seeds {0, 2}, score 0.8 * 0.5 = 0.40 - // - // Exact WMC: P(x∧y) + P(x∧z) − P(x∧y∧z) = 0.48 + 0.40 − 0.24 = 0.64 let mut r = Reasoner::new(); r.add_tagged_triple("A", "is_active", "yes", 0.8); r.add_tagged_triple("A", "knows", "B", 0.6); @@ -1874,16 +1784,6 @@ fn topk_overlap_two_paths_share_base_fact() { #[test] fn wmc_overlap_exact_inference() { - // Same overlap scenario as topk_overlap_two_paths_share_base_fact, but using - // WmcProvenance (exact symbolic, no truncation). - // - // Two proof paths to "A is_socially_active yes" share seed x (is_active): - // Path 1: {x, y} = seeds {0, 1} -> P(x)*P(y) = 0.8*0.6 = 0.48 - // Path 2: {x, z} = seeds {0, 2} -> P(x)*P(z) = 0.8*0.5 = 0.40 - // - // Shannon expansion (exact WMC): - // Condition on x: P(x)*WMC({y}∨{z}) + P(¬x)*WMC(∅) - // = 0.8*(0.6+0.5-0.30) + 0 = 0.8*0.80 = 0.64 let mut r = Reasoner::new(); r.add_tagged_triple("A", "is_active", "yes", 0.8); r.add_tagged_triple("A", "knows", "B", 0.6); @@ -1942,17 +1842,6 @@ fn wmc_overlap_exact_inference() { #[test] fn wmc_no_truncation_three_paths() { - // WmcProvenance keeps all proof paths; TopKProofs(k=2) would truncate the lowest. - // - // Three proof paths to "A is_socially_active yes", all sharing seed x (is_active): - // Path 1: {x, y} = seeds {0,1}, P = 0.9*0.8 = 0.72 - // Path 2: {x, z} = seeds {0,2}, P = 0.9*0.7 = 0.63 - // Path 3: {x, w} = seeds {0,3}, P = 0.9*0.6 = 0.54 ← dropped by TopKProofs(k=2) - // - // Exact WMC (all 3 paths via Shannon / inclusion-exclusion): - // Condition on x: P(x)*WMC({y}∨{z}∨{w}) + P(¬x)*0 - // WMC({y}∨{z}∨{w}) = 1-(1-0.8)(1-0.7)(1-0.6) = 1-0.2*0.3*0.4 = 1-0.024 = 0.976 - // WMC = 0.9 * 0.976 = 0.8784 let mut r = Reasoner::new(); r.add_tagged_triple("A", "is_active", "yes", 0.9); r.add_tagged_triple("A", "knows", "B", 0.8); @@ -2012,12 +1901,6 @@ fn wmc_no_truncation_three_paths() { #[test] fn wmc_explanation_encoding() { - // Same 3-seed overlap setup as wmc_overlap_exact_inference: - // seed0 = A is_active yes (P=0.8) - // seed1 = A knows B (P=0.6) - // seed2 = A knows C (P=0.5) - // Rule fires for Y=B -> proof {0,1} and Y=C -> proof {0,2}, - // both deriving the same conclusion: A is_socially_active yes. let mut r = Reasoner::new(); r.add_tagged_triple("A", "is_active", "yes", 0.8); r.add_tagged_triple("A", "knows", "B", 0.6); @@ -2050,7 +1933,6 @@ fn wmc_explanation_encoding() { let (inferred, tag_store) = r.infer_new_facts_with_provenance(WmcProvenance::new()); - // seed_triples should record the 3 seeds in sorted order assert_eq!(tag_store.seed_triples.len(), 3, "three probability seeds"); let a = enc(&r, "A"); @@ -2061,7 +1943,6 @@ fn wmc_explanation_encoding() { "Should infer A is_socially_active yes" ); - // formula for the derived triple should have 2 proof paths let derived = shared::triple::Triple { subject: a, predicate: is_socially_active, @@ -2070,7 +1951,6 @@ fn wmc_explanation_encoding() { let formula = tag_store.get_tag(&derived); assert_eq!(formula.len(), 2, "two proof paths: {{0,1}} and {{0,2}}"); - // encode_as_rdf_star_with_explanation should produce the explanation triples let mut dict = shared::dictionary::Dictionary::new(); let mut qt = shared::quoted_triple_store::QuotedTripleStore::new(); let triples = tag_store.encode_as_rdf_star_with_explanation(&mut dict, &mut qt); @@ -2079,17 +1959,6 @@ fn wmc_explanation_encoding() { let has_proof_pred = dict.encode("http://www.w3.org/ns/prob#hasProof"); let has_seed_pred = dict.encode("http://www.w3.org/ns/prob#hasSeed"); - // The tag store holds all 4 tagged triples (3 seeds + 1 derived fact), - // so annotation counts span all of them: - // - // Triple | formula | proofCount | hasProof | hasSeed - // A is_active yes | {{0}} | 1 | 1 | 1 - // A knows B | {{1}} | 1 | 1 | 1 - // A knows C | {{2}} | 1 | 1 | 1 - // A is_socially_active yes | {{0,1},{0,2}} | 2 | 2 | 4 - // ──────────────────────────────────────────────────────────────────────────── - // Total | 4 | 5 | 7 - let proof_count_triples: Vec<_> = triples .iter() .filter(|t| t.predicate == proof_count_pred) @@ -2104,7 +1973,6 @@ fn wmc_explanation_encoding() { .iter() .filter(|t| t.predicate == has_proof_pred) .collect(); - // seeds have 1 proof each (3×1=3), derived has 2 proof paths -> total 5 assert_eq!( has_proof_triples.len(), 5, @@ -2115,7 +1983,6 @@ fn wmc_explanation_encoding() { .iter() .filter(|t| t.predicate == has_seed_pred) .collect(); - // seeds: 1+1+1=3; derived: proof {0,1}->2, proof {0,2}->2 = 4 -> total 7 assert_eq!( has_seed_triples.len(), 7, @@ -2123,9 +1990,8 @@ fn wmc_explanation_encoding() { ); } -// ─── Phase 1 NAF tests ──────────────────────────────────────────────────────── - -/// BooleanProvenance: exact NAF — blocked fact IS present -> NOT blocked = false -> effective not derived. +/// BooleanProvenance: exact NAF — blocked fact IS present -> NOT blocked = false -> effective not +/// derived #[test] fn boolean_naf_blocked_present() { let mut r = Reasoner::new(); @@ -2137,7 +2003,6 @@ fn boolean_naf_blocked_present() { let effective = enc(&r, "effective"); let yes = enc(&r, "yes"); - // RULE: active(?x, yes), NOT blocked(?x, yes) -> effective(?x, yes) r.add_rule(naf_rule( vec![( Term::Variable("x".into()), @@ -2158,7 +2023,6 @@ fn boolean_naf_blocked_present() { let (new_facts, _tag_store) = r.infer_new_facts_with_provenance(BooleanProvenance); - // blocked IS present -> NOT true = false -> effective must NOT be derived assert!( !inferred(&mut r, "P", "effective", "yes"), "effective must not be derived when blocked is present" @@ -2176,12 +2040,11 @@ fn boolean_naf_blocked_present() { ); } -/// BooleanProvenance: exact NAF — blocked fact is ABSENT -> NOT blocked = true -> effective derived. +/// BooleanProvenance: exact NAF — blocked fact is ABSENT -> NOT blocked = true -> effective derived #[test] fn boolean_naf_absent_negated() { let mut r = Reasoner::new(); r.add_abox_triple("P", "active", "yes"); - // "blocked" triple intentionally NOT added let active = enc(&r, "active"); let blocked = enc(&r, "blocked"); @@ -2230,9 +2093,7 @@ fn boolean_naf_absent_negated() { ); } -/// AddMultProbability (approximate): active(0.8) AND NOT blocked(0.3) -> P ≈ 0.8 * 0.7 = 0.56. -/// -/// This is approximate — exact only if seeds are independent. Labelled as such. +/// AddMultProbability (approximate): active(0.8) AND NOT blocked(0.3) -> P ≈ 0.8 * 0.7 = 0.56 #[test] fn addmult_naf_approximate() { let mut r = Reasoner::new(); @@ -2272,8 +2133,6 @@ fn addmult_naf_approximate() { }; let prob = tag_store.get_tag(&eff_t); - // P(effective) ≈ P(active) * (1 - P(blocked)) = 0.8 * 0.7 = 0.56 - // (approximate: exact only for independent seeds) assert!( (prob - 0.56).abs() < 1e-9, "AddMultProbability NAF: expected ~0.56, got {}", @@ -2281,12 +2140,11 @@ fn addmult_naf_approximate() { ); } -/// AddMultProbability: blocked absent -> NOT blocked = one() -> P(effective) = P(active) = 0.8. +/// AddMultProbability: blocked absent -> NOT blocked = one() -> P(effective) = P(active) = 0.8 #[test] fn addmult_naf_absent_negated() { let mut r = Reasoner::new(); r.add_tagged_triple("P", "active", "yes", 0.8); - // blocked NOT added let active = enc(&r, "active"); let blocked = enc(&r, "blocked"); @@ -2321,7 +2179,6 @@ fn addmult_naf_absent_negated() { }; let prob = tag_store.get_tag(&eff_t); - // blocked absent -> NOT blocked = one() -> P = 0.8 * 1.0 = 0.8 assert!( (prob - 0.8).abs() < 1e-9, "AddMultProbability NAF (absent): expected 0.8, got {}", @@ -2329,7 +2186,7 @@ fn addmult_naf_absent_negated() { ); } -/// try_add_rule rejects rules where a negated variable is unbound by positive premises. +/// try_add_rule rejects rules where a negated variable is unbound by positive premises #[test] fn unsafe_negation_rejected() { let mut r = Reasoner::new(); @@ -2339,21 +2196,17 @@ fn unsafe_negation_rejected() { let effective = enc(&r, "effective"); let yes = enc(&r, "yes"); - // UNSAFE: ?y appears in NOT-body but is NOT bound by premise (?x is bound, ?y is not) let bad_rule = Rule { premise: vec![( Term::Variable("x".into()), Term::Constant(active), Term::Constant(yes), )], - negative_premise: vec![ - // ?y is unbound — not in any positive premise - ( - Term::Variable("y".into()), - Term::Constant(blocked), - Term::Constant(yes), - ), - ], + negative_premise: vec![( + Term::Variable("y".into()), + Term::Constant(blocked), + Term::Constant(yes), + )], conclusion: vec![( Term::Variable("x".into()), Term::Constant(effective), @@ -2373,12 +2226,7 @@ fn unsafe_negation_rejected() { ); } -// ─── Phase 2 WMC NAF integration tests ────────────────────────────────────── - -/// WmcProvenance: independent seeds, active(P=0.8) AND NOT blocked(P=0.3). -/// -/// Exact result: P(active=T AND blocked=F) = 0.8 * 0.7 = 0.56 -/// (exact because seeds are independent — no shared variable) +/// WmcProvenance: independent seeds, active(P=0.8) AND NOT blocked(P=0.3) #[test] fn wmc_naf_independent_seeds_integration() { let mut r = Reasoner::new(); @@ -2427,23 +2275,14 @@ fn wmc_naf_independent_seeds_integration() { ); } -/// WmcProvenance: same triple used for both active and blocked (shared seed). -/// -/// active tag = {{(0,T)}}, blocked tag = {{(0,T)}} (same seed 0) -/// NOT blocked = {{(0,F)}} -/// conjunction = {{(0,T),(0,F)}} = contradictory -> zero -> P = 0.0 +/// WmcProvenance: same triple used for both active and blocked (shared seed) #[test] fn wmc_naf_shared_seed_integration() { let mut r = Reasoner::new(); - // Add the SAME triple for both active and blocked at the same probability. - // Since it's the same triple, both get the same seed ID in the tag store. r.add_tagged_triple("P", "status", "active", 0.8); - // Use a different triple to represent blocked so we can control the seed. - // To force shared-seed: use the same triple for both predicates is not possible - // in the normal API. So we test the algebraic property directly via provenance: let p = WmcProvenance::new(); let active_tag = p.tag_from_probability_with_id(0.8, 0); - let blocked_tag = p.tag_from_probability_with_id(0.8, 0); // same ID = shared seed + let blocked_tag = p.tag_from_probability_with_id(0.8, 0); let not_blocked = p.negate(&blocked_tag); let effective_tag = p.conjunction(&active_tag, ¬_blocked); diff --git a/datalogmtl/comparison/README.md b/datalogmtl/comparison/README.md deleted file mode 100644 index 761f25b9..00000000 --- a/datalogmtl/comparison/README.md +++ /dev/null @@ -1,103 +0,0 @@ -# datalogmtl ↔ MeTeoR correctness harness - -Uses the [MeTeoR](https://github.com/wdimmy/MeTeoR) reasoner as a **ground-truth -baseline** to validate the `datalogmtl` engine on a shared corpus of small -programs, authored once in MeTeoR's native syntax and fed to both engines. - -## Supported fragment - -The corpus is restricted to the fragment both engines share: - -- **Past operators only**: `Boxminus[a,b]`, `Diamondminus[a,b]`, `L Since[a,b] R` - (operators may stack, e.g. `Boxminus[1,2]Diamondminus[0,1]A(X)`). -- **Plain (operator-free) rule heads.** -- **Closed integer intervals** `[a,b]` (or a point `[a]` / `@t`). -- **Predicate arity ≤ 2**, mapped to RDF triples: - - unary `A(x)` ↔ `(x, rdf:type, A)` - - binary `C(x,y)` ↔ `(x, C, y)` - - arity 0 (propositional) and arity ≥ 3 are rejected by the parser. - -Out-of-fragment input (future operators `Boxplus`/`Diamondplus`/`Until`, open -interval bounds, head operators, non-integer times) produces a descriptive error. - -## How comparison works - -MeTeoR is batch + continuous-interval; datalogmtl is streaming + discrete point -time. They are reconciled on the integer grid `[0, T]`: - -- `T = max fact endpoint + max operator interval width` (computed identically on - both sides in `compare.py`). -- Interval facts `A(a)@[l,r]` are **densified** — inserted at every integer tick - in `[l,r]` — for the Rust engine, which is advanced over ticks `0..=T`. -- Both outputs are normalized to the set of `(atom, integer t)` that hold, then - re-coalesced into closed integer intervals `Pred(args)@[l,r]` and diffed. - -## Running - -```bash -# Build the Rust driver once -DYLD_LIBRARY_PATH="/opt/homebrew/.../python@3.11/.../lib" \ - cargo build -p datalogmtl --example meteor_compare - -# Run all cases (MeTeoR imported from $METEOR_HOME, default the local checkout) -python3 compare.py # snapshot store (Phase 1) -python3 compare.py --store interval # interval store (Phase 2) -python3 compare.py --case diamond_basic -v # one case, show output -``` - -Environment: -- `METEOR_HOME` — path to the MeTeoR checkout (default - `/Users/u0164257/Documents/Github/MeTeoR`). -- `DYLD_LIBRARY_PATH` — Python 3.11 dylib (see repo `CLAUDE.md`). - -## Layout - -| File | Role | -|---|---| -| `cases//{program.txt,data.txt}` | one test case in MeTeoR syntax | -| `run_meteor.py` | runs MeTeoR, emits normalized integer-interval lines | -| `compare.py` | computes `T`, runs both engines, diffs, reports PASS/MISMATCH | -| `../examples/meteor_compare.rs` | Rust driver (parses corpus, runs engine) | -| `../src/parser.rs` | MeTeoR-syntax → `DatalogMTLRule` + facts | -| `../src/meteor_fmt.rs` | RDF triples → MeTeoR atom text + coalescing | - -## Cases - -| Case | Exercises | -|---|---| -| `diamond_basic` | `Diamondminus` (existential lookback) | -| `boxminus_basic` | `Boxminus` (universal window) — regression for the leading-boundary fix | -| `since_basic` | `Since` (reset + continuation) | -| `reach` | non-temporal join + recursion (transitive closure) | -| `chain_box_diamond` | two rules: `Box` feeding `Diamond` | -| `nested_box_diamond` | nested operators in one atom (`Boxminus[..]Diamondminus[..]A`) | -| `diamond_conj` | conjunction of two `Diamond`s joined on a variable | -| `temporal_binary_join` | binary predicates joined, one side under `Diamond` (wasNear-style) | -| `since_derived` | `Since` whose continuation is a derived predicate (guarded) | -| `recursive_temporal_path` | temporal transitive closure (recursion + `Diamond`) | -| `multi_op_conj` | `Box` and `Diamond` conjoined in one body | - -## Status - -All corpus cases pass against MeTeoR under both store backends (Phase 1 snapshot -and Phase 2 interval). - -### Rule-safety divergence (corpus convention, not a bug) - -MeTeoR will not bind a head variable that occurs **only** inside a binary -`Since`/`Until` literal — such a variable needs a *guard* (an ordinary body atom -binding it), otherwise MeTeoR derives nothing. datalogmtl is more permissive and -binds the variable from within `Since`. Corpus rules therefore guard such -variables (e.g. `Alarm(X):-Warn(X),Warn(X)Since[1,8]Spike(X)`) to stay in the -common fragment. Found via `since_derived`. - -### Bugs found & fixed via this harness - -- **`Box` leading-boundary over-derivation** (found by `boxminus_basic`). - For `Boxminus[1,2]A(X)` with `A(a)@[0,10]`, MeTeoR yields `B(a)@[2,11]` but - datalogmtl originally yielded `B(a)@[1,11]`. At `t=1` the required window - `[t-2,t-1]=[-1,0]` extends before time 0; `Interval::absolute_range`'s - `saturating_sub` clamped the lower bound to 0, so `Box` saw only `A@0` and - wrongly succeeded. Fixed by guarding `eval_box` on `t < interval.end` (the - whole universal window must lie within observable time `[0, t]`). - See `evaluator.rs:eval_box`. diff --git a/datalogmtl/comparison/cases/boxminus_basic/data.txt b/datalogmtl/comparison/cases/boxminus_basic/data.txt deleted file mode 100644 index 1266b92b..00000000 --- a/datalogmtl/comparison/cases/boxminus_basic/data.txt +++ /dev/null @@ -1 +0,0 @@ -A(a)@[0,10] diff --git a/datalogmtl/comparison/cases/boxminus_basic/program.txt b/datalogmtl/comparison/cases/boxminus_basic/program.txt deleted file mode 100644 index 2c08e752..00000000 --- a/datalogmtl/comparison/cases/boxminus_basic/program.txt +++ /dev/null @@ -1 +0,0 @@ -B(X):-Boxminus[1,2]A(X) diff --git a/datalogmtl/comparison/cases/chain_box_diamond/data.txt b/datalogmtl/comparison/cases/chain_box_diamond/data.txt deleted file mode 100644 index 6b70a6fa..00000000 --- a/datalogmtl/comparison/cases/chain_box_diamond/data.txt +++ /dev/null @@ -1 +0,0 @@ -Raw(a)@[0,12] diff --git a/datalogmtl/comparison/cases/chain_box_diamond/program.txt b/datalogmtl/comparison/cases/chain_box_diamond/program.txt deleted file mode 100644 index e4c984d3..00000000 --- a/datalogmtl/comparison/cases/chain_box_diamond/program.txt +++ /dev/null @@ -1,2 +0,0 @@ -Cand(X):-Boxminus[1,3]Raw(X) -Flag(X):-Diamondminus[0,2]Cand(X) diff --git a/datalogmtl/comparison/cases/diamond_basic/data.txt b/datalogmtl/comparison/cases/diamond_basic/data.txt deleted file mode 100644 index 80f2ca7f..00000000 --- a/datalogmtl/comparison/cases/diamond_basic/data.txt +++ /dev/null @@ -1 +0,0 @@ -A(a)@[0,0] diff --git a/datalogmtl/comparison/cases/diamond_basic/program.txt b/datalogmtl/comparison/cases/diamond_basic/program.txt deleted file mode 100644 index 008baee8..00000000 --- a/datalogmtl/comparison/cases/diamond_basic/program.txt +++ /dev/null @@ -1 +0,0 @@ -B(X):-Diamondminus[1,5]A(X) diff --git a/datalogmtl/comparison/cases/diamond_conj/data.txt b/datalogmtl/comparison/cases/diamond_conj/data.txt deleted file mode 100644 index 91c19db8..00000000 --- a/datalogmtl/comparison/cases/diamond_conj/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -P(a)@[0,0] -Q(a)@[5,5] diff --git a/datalogmtl/comparison/cases/diamond_conj/program.txt b/datalogmtl/comparison/cases/diamond_conj/program.txt deleted file mode 100644 index 7887f72e..00000000 --- a/datalogmtl/comparison/cases/diamond_conj/program.txt +++ /dev/null @@ -1 +0,0 @@ -R(X):-Diamondminus[0,2]P(X),Diamondminus[1,4]Q(X) diff --git a/datalogmtl/comparison/cases/multi_op_conj/data.txt b/datalogmtl/comparison/cases/multi_op_conj/data.txt deleted file mode 100644 index 1d7d3fc4..00000000 --- a/datalogmtl/comparison/cases/multi_op_conj/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -Sensor(a)@[0,10] -Trigger(a)@[3,3] diff --git a/datalogmtl/comparison/cases/multi_op_conj/program.txt b/datalogmtl/comparison/cases/multi_op_conj/program.txt deleted file mode 100644 index 9406a25f..00000000 --- a/datalogmtl/comparison/cases/multi_op_conj/program.txt +++ /dev/null @@ -1 +0,0 @@ -Alert(X):-Boxminus[1,2]Sensor(X),Diamondminus[0,5]Trigger(X) diff --git a/datalogmtl/comparison/cases/nested_box_diamond/data.txt b/datalogmtl/comparison/cases/nested_box_diamond/data.txt deleted file mode 100644 index 1266b92b..00000000 --- a/datalogmtl/comparison/cases/nested_box_diamond/data.txt +++ /dev/null @@ -1 +0,0 @@ -A(a)@[0,10] diff --git a/datalogmtl/comparison/cases/nested_box_diamond/program.txt b/datalogmtl/comparison/cases/nested_box_diamond/program.txt deleted file mode 100644 index 68ce86f4..00000000 --- a/datalogmtl/comparison/cases/nested_box_diamond/program.txt +++ /dev/null @@ -1 +0,0 @@ -B(X):-Boxminus[1,2]Diamondminus[0,1]A(X) diff --git a/datalogmtl/comparison/cases/reach/data.txt b/datalogmtl/comparison/cases/reach/data.txt deleted file mode 100644 index 7d55206a..00000000 --- a/datalogmtl/comparison/cases/reach/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -Edge(a,b)@[0,5] -Edge(b,c)@[0,5] diff --git a/datalogmtl/comparison/cases/reach/program.txt b/datalogmtl/comparison/cases/reach/program.txt deleted file mode 100644 index a8935997..00000000 --- a/datalogmtl/comparison/cases/reach/program.txt +++ /dev/null @@ -1,2 +0,0 @@ -Reach(X,Y):-Edge(X,Y) -Reach(X,Z):-Edge(X,Y),Reach(Y,Z) diff --git a/datalogmtl/comparison/cases/recursive_temporal_path/data.txt b/datalogmtl/comparison/cases/recursive_temporal_path/data.txt deleted file mode 100644 index 22aef0b3..00000000 --- a/datalogmtl/comparison/cases/recursive_temporal_path/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -Link(a,b)@[0,8] -Link(b,c)@[0,8] diff --git a/datalogmtl/comparison/cases/recursive_temporal_path/program.txt b/datalogmtl/comparison/cases/recursive_temporal_path/program.txt deleted file mode 100644 index 6029050c..00000000 --- a/datalogmtl/comparison/cases/recursive_temporal_path/program.txt +++ /dev/null @@ -1,2 +0,0 @@ -Path(X,Y):-Link(X,Y) -Path(X,Z):-Link(X,Y),Diamondminus[0,2]Path(Y,Z) diff --git a/datalogmtl/comparison/cases/since_basic/data.txt b/datalogmtl/comparison/cases/since_basic/data.txt deleted file mode 100644 index 898df94c..00000000 --- a/datalogmtl/comparison/cases/since_basic/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -A(a)@[0,20] -B(a)@[3,3] diff --git a/datalogmtl/comparison/cases/since_basic/program.txt b/datalogmtl/comparison/cases/since_basic/program.txt deleted file mode 100644 index b8e48635..00000000 --- a/datalogmtl/comparison/cases/since_basic/program.txt +++ /dev/null @@ -1 +0,0 @@ -C(X):-A(X)Since[1,5]B(X) diff --git a/datalogmtl/comparison/cases/since_derived/data.txt b/datalogmtl/comparison/cases/since_derived/data.txt deleted file mode 100644 index b61919f1..00000000 --- a/datalogmtl/comparison/cases/since_derived/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -Hot(a)@[0,15] -Spike(a)@[2,2] diff --git a/datalogmtl/comparison/cases/since_derived/program.txt b/datalogmtl/comparison/cases/since_derived/program.txt deleted file mode 100644 index 27093e6a..00000000 --- a/datalogmtl/comparison/cases/since_derived/program.txt +++ /dev/null @@ -1,2 +0,0 @@ -Warn(X):-Hot(X) -Alarm(X):-Warn(X),Warn(X)Since[1,8]Spike(X) diff --git a/datalogmtl/comparison/cases/temporal_binary_join/data.txt b/datalogmtl/comparison/cases/temporal_binary_join/data.txt deleted file mode 100644 index 681fe9e6..00000000 --- a/datalogmtl/comparison/cases/temporal_binary_join/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -Loc(a,r1)@[3,3] -Loc(b,r1)@[0,0] diff --git a/datalogmtl/comparison/cases/temporal_binary_join/program.txt b/datalogmtl/comparison/cases/temporal_binary_join/program.txt deleted file mode 100644 index eb940bdc..00000000 --- a/datalogmtl/comparison/cases/temporal_binary_join/program.txt +++ /dev/null @@ -1 +0,0 @@ -Near(X,Y):-Loc(X,L),Diamondminus[1,5]Loc(Y,L) diff --git a/datalogmtl/comparison/cases_future/README.md b/datalogmtl/comparison/cases_future/README.md deleted file mode 100644 index 2e2c8281..00000000 --- a/datalogmtl/comparison/cases_future/README.md +++ /dev/null @@ -1,26 +0,0 @@ -# Future-operator cases (static mode) - -Future operators (`Diamondplus`, `Boxplus`, `Until`) look into not-yet-arrived time, -so they are valid **only on static (whole-timeline) data**, never on a stream. They -require `--mode static`, which uses the interval (static) engine — the tick engine is -streaming/forward-time and cannot evaluate them. - -```bash -cargo build --release -p datalogmtl --example meteor_compare -# validate these cases against MeTeoR: -cd datalogmtl/comparison -python3 compare.py cases_future --mode static --strategy interval -``` - -Mode gating: -- `--mode streaming` (default) + a future operator → parse error. -- `--mode static --strategy tick` → error (tick engine can't do future ops). -- `--mode static` with no `--strategy` → auto-selects the interval engine. - -Cases (all parity-checked vs MeTeoR): -- `diamondplus_basic`: `B(X):-Diamondplus[1,5]A(X)`, `A(a)@[10,10]` → `B(a)@[5,9]`. -- `boxplus_basic`: `B(X):-Boxplus[1,2]A(X)`, `A(a)@[10,20]` → `B(a)@[9,18]`. -- `until_basic`: `C(X):-A(X)Until[1,5]B(X)`, `A(a)@[0,20]`,`B(a)@[15,15]` → `C(a)@[10,14]`. - -Scope: finite static materialization. Unbounded-time + future operators (left/past -periods in the ω-automaton) is a deferred follow-up. diff --git a/datalogmtl/comparison/cases_future/boxplus_basic/data.txt b/datalogmtl/comparison/cases_future/boxplus_basic/data.txt deleted file mode 100644 index e4959b4f..00000000 --- a/datalogmtl/comparison/cases_future/boxplus_basic/data.txt +++ /dev/null @@ -1 +0,0 @@ -A(a)@[10,20] diff --git a/datalogmtl/comparison/cases_future/boxplus_basic/program.txt b/datalogmtl/comparison/cases_future/boxplus_basic/program.txt deleted file mode 100644 index aaf8711b..00000000 --- a/datalogmtl/comparison/cases_future/boxplus_basic/program.txt +++ /dev/null @@ -1 +0,0 @@ -B(X):-Boxplus[1,2]A(X) diff --git a/datalogmtl/comparison/cases_future/diamondplus_basic/data.txt b/datalogmtl/comparison/cases_future/diamondplus_basic/data.txt deleted file mode 100644 index 48025517..00000000 --- a/datalogmtl/comparison/cases_future/diamondplus_basic/data.txt +++ /dev/null @@ -1 +0,0 @@ -A(a)@[10,10] diff --git a/datalogmtl/comparison/cases_future/diamondplus_basic/program.txt b/datalogmtl/comparison/cases_future/diamondplus_basic/program.txt deleted file mode 100644 index 6b74ebe0..00000000 --- a/datalogmtl/comparison/cases_future/diamondplus_basic/program.txt +++ /dev/null @@ -1 +0,0 @@ -B(X):-Diamondplus[1,5]A(X) diff --git a/datalogmtl/comparison/cases_future/mixed_past_future/data.txt b/datalogmtl/comparison/cases_future/mixed_past_future/data.txt deleted file mode 100644 index 842ece7b..00000000 --- a/datalogmtl/comparison/cases_future/mixed_past_future/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -A(a)@[10,20] -C(a)@[15,15] diff --git a/datalogmtl/comparison/cases_future/mixed_past_future/program.txt b/datalogmtl/comparison/cases_future/mixed_past_future/program.txt deleted file mode 100644 index ec2c87f8..00000000 --- a/datalogmtl/comparison/cases_future/mixed_past_future/program.txt +++ /dev/null @@ -1 +0,0 @@ -B(X):-Boxminus[0,2]A(X),Diamondplus[0,3]C(X) diff --git a/datalogmtl/comparison/cases_future/stacked_future/data.txt b/datalogmtl/comparison/cases_future/stacked_future/data.txt deleted file mode 100644 index e4959b4f..00000000 --- a/datalogmtl/comparison/cases_future/stacked_future/data.txt +++ /dev/null @@ -1 +0,0 @@ -A(a)@[10,20] diff --git a/datalogmtl/comparison/cases_future/stacked_future/program.txt b/datalogmtl/comparison/cases_future/stacked_future/program.txt deleted file mode 100644 index 9b559d72..00000000 --- a/datalogmtl/comparison/cases_future/stacked_future/program.txt +++ /dev/null @@ -1 +0,0 @@ -B(X):-Diamondplus[0,2]Boxplus[0,1]A(X) diff --git a/datalogmtl/comparison/cases_future/until_basic/data.txt b/datalogmtl/comparison/cases_future/until_basic/data.txt deleted file mode 100644 index 07475d9a..00000000 --- a/datalogmtl/comparison/cases_future/until_basic/data.txt +++ /dev/null @@ -1,2 +0,0 @@ -A(a)@[0,20] -B(a)@[15,15] diff --git a/datalogmtl/comparison/cases_future/until_basic/program.txt b/datalogmtl/comparison/cases_future/until_basic/program.txt deleted file mode 100644 index 6f16d1a3..00000000 --- a/datalogmtl/comparison/cases_future/until_basic/program.txt +++ /dev/null @@ -1 +0,0 @@ -C(X):-A(X)Until[1,5]B(X) diff --git a/datalogmtl/comparison/compare.py b/datalogmtl/comparison/compare.py deleted file mode 100644 index f45cde6b..00000000 --- a/datalogmtl/comparison/compare.py +++ /dev/null @@ -1,162 +0,0 @@ -#!/usr/bin/env python3 -""" -Correctness harness: run every case under cases/ through both the MeTeoR -reference reasoner (baseline / ground truth) and the datalogmtl Rust engine, -then diff their normalized outputs. - -Each case directory contains `program.txt` and `data.txt` in MeTeoR's past-only, -integer, arity-<=2 syntax. The comparison window [0, T] is derived identically -for both engines: T = max fact endpoint + max operator interval width. - -Usage: - PYTHONPATH is set automatically to $METEOR_HOME (default - /Users/u0164257/Documents/Github/MeTeoR). - - python compare.py [cases_dir] [--case NAME] [--store snapshot|interval] [-v] -""" -import argparse -import difflib -import os -import re -import subprocess -import sys - -HERE = os.path.dirname(os.path.abspath(__file__)) -REPO_ROOT = os.path.abspath(os.path.join(HERE, "..", "..")) -METEOR_HOME = os.environ.get( - "METEOR_HOME", "/Users/u0164257/Documents/Github/MeTeoR" -) -DYLD = os.environ.get( - "DYLD_LIBRARY_PATH", - "/opt/homebrew/Cellar/python@3.11/3.11.11/Frameworks/Python.framework/Versions/3.11/lib", -) - -INTERVAL_RE = re.compile(r"\[\s*(-?\d+)(?:\s*,\s*(-?\d+))?\s*\]") - - -def max_interval_end(text): - """Largest integer interval endpoint appearing in bracketed intervals.""" - best = 0 - for m in INTERVAL_RE.finditer(text): - end = int(m.group(2) if m.group(2) is not None else m.group(1)) - best = max(best, end) - return best - - -def max_fact_end(data_text): - """Largest fact validity endpoint across the data file.""" - best = 0 - for line in data_text.splitlines(): - line = line.split("#", 1)[0].strip().replace(" ", "") - if not line or "@" not in line: - continue - span = line.rsplit("@", 1)[1] - m = INTERVAL_RE.search(span) - if m: - best = max(best, int(m.group(2) if m.group(2) is not None else m.group(1))) - else: - try: - best = max(best, int(span)) - except ValueError: - pass - return best - - -def horizon_for(program_text, data_text): - return max_fact_end(data_text) + max_interval_end(program_text) - - -def normalize(output): - return sorted(l.strip() for l in output.splitlines() if l.strip()) - - -def rust_binary(): - path = os.path.join(REPO_ROOT, "target", "debug", "examples", "meteor_compare") - return path if os.path.exists(path) else None - - -def run_rust(program, data, horizon, store, strategy="tick", mode="streaming"): - args = ["--program", program, "--data", data, - "--horizon", str(horizon), "--store", store, "--strategy", strategy, - "--mode", mode] - env = dict(os.environ, DYLD_LIBRARY_PATH=DYLD) - binary = rust_binary() - if binary: - cmd = [binary] + args - else: - cmd = ["cargo", "run", "-q", "-p", "datalogmtl", - "--example", "meteor_compare", "--"] + args - proc = subprocess.run(cmd, cwd=REPO_ROOT, env=env, - capture_output=True, text=True) - if proc.returncode != 0: - raise RuntimeError("rust engine failed:\n" + proc.stderr) - return proc.stdout - - -def run_meteor(program, data, horizon, mode="seminaive"): - env = dict(os.environ, PYTHONPATH=METEOR_HOME) - cmd = [sys.executable, os.path.join(HERE, "run_meteor.py"), - "--program", program, "--data", data, "--horizon", str(horizon), "--mode", mode] - proc = subprocess.run(cmd, env=env, capture_output=True, text=True) - if proc.returncode != 0: - raise RuntimeError("meteor reasoner failed:\n" + proc.stderr) - return proc.stdout - - -def run_case(case_dir, store, verbose, strategy="tick", meteor_mode="seminaive", mode="streaming"): - program = os.path.join(case_dir, "program.txt") - data = os.path.join(case_dir, "data.txt") - program_text = open(program).read() - data_text = open(data).read() - horizon = horizon_for(program_text, data_text) - - meteor = normalize(run_meteor(program, data, horizon, meteor_mode)) - rust = normalize(run_rust(program, data, horizon, store, strategy, mode)) - - ok = meteor == rust - name = os.path.basename(case_dir) - status = "PASS" if ok else "MISMATCH" - print("[{}] {} (T={})".format(status, name, horizon)) - if not ok or verbose: - diff = difflib.unified_diff(meteor, rust, fromfile="meteor", tofile="rust", lineterm="") - for line in diff: - print(" " + line) - return ok - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("cases_dir", nargs="?", default=os.path.join(HERE, "cases")) - ap.add_argument("--case", help="run only this case name") - ap.add_argument("--store", default="snapshot", choices=["snapshot", "interval"]) - ap.add_argument("--strategy", default="tick", choices=["tick", "interval"]) - ap.add_argument("--meteor-mode", default="seminaive", choices=["seminaive", "naive"]) - ap.add_argument("--mode", default="streaming", choices=["streaming", "static"]) - ap.add_argument("-v", "--verbose", action="store_true") - args = ap.parse_args() - args.cases_dir = os.path.abspath(args.cases_dir) # Rust binary runs from repo root - - cases = sorted( - d for d in os.listdir(args.cases_dir) - if os.path.isdir(os.path.join(args.cases_dir, d)) - and (args.case is None or d == args.case) - ) - if not cases: - print("no cases found", file=sys.stderr) - return 1 - - failures = 0 - for name in cases: - try: - if not run_case(os.path.join(args.cases_dir, name), args.store, args.verbose, args.strategy, args.meteor_mode, args.mode): - failures += 1 - except Exception as e: # noqa: BLE001 - print("[ERROR] {}: {}".format(name, e)) - failures += 1 - - print("\n{}/{} cases passed".format(len(cases) - failures, len(cases))) - return 1 if failures else 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/datalogmtl/comparison/omega/README.md b/datalogmtl/comparison/omega/README.md deleted file mode 100644 index 4299e839..00000000 --- a/datalogmtl/comparison/omega/README.md +++ /dev/null @@ -1,73 +0,0 @@ -# ω-automaton phase — unbounded-time reasoning - -The finite interval evaluator caps at `K` iterations, so recursive-in-time programs -(whose minimal model is **infinite**) get truncated/wrong answers. This phase adds -unbounded-time materialization + entailment queries, validated against MeTeoR's -**canonical model** (its ground truth for infinite / eventually-periodic models). - -## What ships — full lasso (prefix + repeating period) - -`materialize_omega` returns a `PeriodicModel` = finite prefix + period, the -automata-theoretic canonical model. - -- **Infinity-aware intervals** (`automata/interval.rs`): `NEG_INF`/`POS_INF` - sentinels, MeTeoR's inf-openness rules, inf-guarded metric shifts, `contains_point`, - `shifted`. -- **Bidirectional materialization + acceleration** (`automata/omega.rs`): past - operators propagate **forward** (a *right* period toward +∞); future operators - propagate **backward** (a *left* period toward −∞). Beyond `R = max_end + reach` - and before `L = min_start − reach` the model is periodic with period dividing - `P = lcm` of the metric constants. So: - 1. materialize a prefix clipping intervals to `[Lb, H]` (`H=R+3P`, `Lb=L−3P`); - 2. **eventually-always** — a fact contiguous over `[R,R+P]` → `[l,+∞)`, or over - `[L−P,L]` → `(−∞,e]` (represented exactly); - 3. **genuinely periodic (gappy)** — verify each tail is `P`-periodic (two - consecutive period windows agree) and keep it. -- **Entailment** (`automata::entails`, driver `--strategy omega --mode static - --entail`): answers `Pred(args)@[l,r]` at *any* time (far future OR far past) in - O(1) — reduce the query modulo `P` into the materialized right/left period window - and check **real-interval inclusion** (a query spanning a gap fails; continuous - saturation via `[l,+∞)` / `(−∞,e]`). - -### MeTeoR `fact_entailment` limitations found - -MeTeoR's canonical `fact_entailment` is the oracle, but two limitations surfaced -(our engine answers these correctly): -- **Wide queries in the *left* (past) periodic tail** return False even when the - fact holds continuously there (e.g. `Q=(−∞,100]`, `Q@[10,20]` → MeTeoR False, ours - True). Point queries are handled correctly, so the cross-check uses point queries. -- (Right-tail wide queries *are* handled by MeTeoR — the asymmetry is left-only.) - -## Soundness note (ℤ vs ℝ) - -Saturation to `[l,+∞)` requires the recursion to produce **overlapping** copies -(operator interval including 0, e.g. `Diamondminus[0,1]`). `Diamondminus[1,1]` of a -point yields disjoint points (`[5,5]`,`[6,6]` have a real gap) — that is genuinely -gappy under real semantics and is correctly refused, matching MeTeoR. - -## Running / validating - -```bash -cargo build --release -p datalogmtl --example meteor_compare -# Entailment (far-future answered instantly): -./target/release/examples/meteor_compare \ - --program comparison/omega/program.txt --data comparison/omega/data.txt \ - --strategy omega --entail comparison/omega/queries.txt -# Cross-check against MeTeoR's canonical model: -cd comparison/omega && python3 validate_omega.py # -> ALL MATCH -``` - -Five validated cases (`validate_omega.py ` → **ALL MATCH** vs MeTeoR canonical): -- **.** (right always): `Alarm:-Diamondminus[0,1]Alarm` → `[5,+∞)`; entailed at `t=1e6`. -- **periodic** (right gappy): `P:-Diamondminus[2,2]P` → even ticks forever (+∞ side). -- **left_periodic** (future recursion): `P:-Diamondplus[2,2]P`, `Seed@[100,100]` → even - ticks toward −∞; `P(a)@[10,10]`=true, `@[11,11]`=false. -- **both_periodic**: past + future recursion (`Diamondminus[2,2]` and `Diamondplus[2,2]`) - → period-2 both directions; far-past `[10,10]` and far-future `[10000,10000]` true. -- **past_always**: `Q:-Diamondplus[0,1]Q` → `(−∞,100]` (continuous past saturation). - -## Next sub-milestones - -1. **Multi-constant period tightening** — `P = lcm` is a safe period; the true period - may be a divisor (MeTeoR's gcd-aligned detection finds the minimal one). -2. **Rational (sub-integer) time** — currently integer endpoints; MeTeoR uses `Decimal`. diff --git a/datalogmtl/comparison/omega/both_periodic/data.txt b/datalogmtl/comparison/omega/both_periodic/data.txt deleted file mode 100644 index 7c1b39c5..00000000 --- a/datalogmtl/comparison/omega/both_periodic/data.txt +++ /dev/null @@ -1 +0,0 @@ -Seed(a)@[100,100] diff --git a/datalogmtl/comparison/omega/both_periodic/program.txt b/datalogmtl/comparison/omega/both_periodic/program.txt deleted file mode 100644 index 356f6d19..00000000 --- a/datalogmtl/comparison/omega/both_periodic/program.txt +++ /dev/null @@ -1,3 +0,0 @@ -R(X):-Seed(X) -R(X):-Diamondminus[2,2]R(X) -R(X):-Diamondplus[2,2]R(X) diff --git a/datalogmtl/comparison/omega/both_periodic/queries.txt b/datalogmtl/comparison/omega/both_periodic/queries.txt deleted file mode 100644 index ffddb82c..00000000 --- a/datalogmtl/comparison/omega/both_periodic/queries.txt +++ /dev/null @@ -1,6 +0,0 @@ -R(a)@[100,100] -R(a)@[10,10] -R(a)@[10000,10000] -R(a)@[9999,9999] -R(a)@[10001,10001] -R(a)@[10,11] diff --git a/datalogmtl/comparison/omega/data.txt b/datalogmtl/comparison/omega/data.txt deleted file mode 100644 index 00542f66..00000000 --- a/datalogmtl/comparison/omega/data.txt +++ /dev/null @@ -1 +0,0 @@ -Trigger(a)@[5,5] diff --git a/datalogmtl/comparison/omega/left_periodic/data.txt b/datalogmtl/comparison/omega/left_periodic/data.txt deleted file mode 100644 index 7c1b39c5..00000000 --- a/datalogmtl/comparison/omega/left_periodic/data.txt +++ /dev/null @@ -1 +0,0 @@ -Seed(a)@[100,100] diff --git a/datalogmtl/comparison/omega/left_periodic/program.txt b/datalogmtl/comparison/omega/left_periodic/program.txt deleted file mode 100644 index 3c35cadd..00000000 --- a/datalogmtl/comparison/omega/left_periodic/program.txt +++ /dev/null @@ -1,2 +0,0 @@ -P(X):-Seed(X) -P(X):-Diamondplus[2,2]P(X) diff --git a/datalogmtl/comparison/omega/left_periodic/queries.txt b/datalogmtl/comparison/omega/left_periodic/queries.txt deleted file mode 100644 index 20250db0..00000000 --- a/datalogmtl/comparison/omega/left_periodic/queries.txt +++ /dev/null @@ -1,6 +0,0 @@ -P(a)@[100,100] -P(a)@[98,98] -P(a)@[99,99] -P(a)@[10,10] -P(a)@[11,11] -P(a)@[10,11] diff --git a/datalogmtl/comparison/omega/past_always/data.txt b/datalogmtl/comparison/omega/past_always/data.txt deleted file mode 100644 index 7c1b39c5..00000000 --- a/datalogmtl/comparison/omega/past_always/data.txt +++ /dev/null @@ -1 +0,0 @@ -Seed(a)@[100,100] diff --git a/datalogmtl/comparison/omega/past_always/program.txt b/datalogmtl/comparison/omega/past_always/program.txt deleted file mode 100644 index f90d4eb6..00000000 --- a/datalogmtl/comparison/omega/past_always/program.txt +++ /dev/null @@ -1,2 +0,0 @@ -Q(X):-Seed(X) -Q(X):-Diamondplus[0,1]Q(X) diff --git a/datalogmtl/comparison/omega/past_always/queries.txt b/datalogmtl/comparison/omega/past_always/queries.txt deleted file mode 100644 index 7e0af365..00000000 --- a/datalogmtl/comparison/omega/past_always/queries.txt +++ /dev/null @@ -1,5 +0,0 @@ -Q(a)@[100,100] -Q(a)@[80,80] -Q(a)@[10,10] -Q(a)@[101,101] -Q(a)@[200,200] diff --git a/datalogmtl/comparison/omega/periodic/data.txt b/datalogmtl/comparison/omega/periodic/data.txt deleted file mode 100644 index 8b3fa458..00000000 --- a/datalogmtl/comparison/omega/periodic/data.txt +++ /dev/null @@ -1 +0,0 @@ -Seed(a)@[0,0] diff --git a/datalogmtl/comparison/omega/periodic/program.txt b/datalogmtl/comparison/omega/periodic/program.txt deleted file mode 100644 index 276c7640..00000000 --- a/datalogmtl/comparison/omega/periodic/program.txt +++ /dev/null @@ -1,2 +0,0 @@ -P(X):-Seed(X) -P(X):-Diamondminus[2,2]P(X) diff --git a/datalogmtl/comparison/omega/periodic/queries.txt b/datalogmtl/comparison/omega/periodic/queries.txt deleted file mode 100644 index efd2cc4b..00000000 --- a/datalogmtl/comparison/omega/periodic/queries.txt +++ /dev/null @@ -1,6 +0,0 @@ -P(a)@[0,0] -P(a)@[1,1] -P(a)@[2,2] -P(a)@[1000,1000] -P(a)@[1001,1001] -P(a)@[1000,1001] diff --git a/datalogmtl/comparison/omega/program.txt b/datalogmtl/comparison/omega/program.txt deleted file mode 100644 index 74f7d5d0..00000000 --- a/datalogmtl/comparison/omega/program.txt +++ /dev/null @@ -1,2 +0,0 @@ -Alarm(X):-Trigger(X) -Alarm(X):-Diamondminus[0,1]Alarm(X) diff --git a/datalogmtl/comparison/omega/queries.txt b/datalogmtl/comparison/omega/queries.txt deleted file mode 100644 index dc3d7b9c..00000000 --- a/datalogmtl/comparison/omega/queries.txt +++ /dev/null @@ -1,5 +0,0 @@ -Alarm(a)@[4,4] -Alarm(a)@[5,5] -Alarm(a)@[1000,1000] -Alarm(a)@[5,1000] -Alarm(a)@[1000000,1000000] diff --git a/datalogmtl/comparison/omega/validate_omega.py b/datalogmtl/comparison/omega/validate_omega.py deleted file mode 100644 index 27044040..00000000 --- a/datalogmtl/comparison/omega/validate_omega.py +++ /dev/null @@ -1,105 +0,0 @@ -#!/usr/bin/env python3 -""" -Validate the datalogmtl ω-materialization's entailment answers against MeTeoR's -**canonical model** (its ground truth for infinite/eventually-periodic models). - -For a program + data + a list of query facts `Pred(args)@[l,r]`: - - datalogmtl: meteor_compare --strategy omega --entail queries.txt - - MeTeoR: CanonicalRepresentation + find_periods + fact_entailment -Both must agree on every query (including far-future ones), which the finite -materializers cannot answer. - -Usage: - PYTHONPATH set to $METEOR_HOME automatically. - python validate_omega.py [case_dir] (default: this directory) -""" -import contextlib -import os -import subprocess -import sys - -HERE = os.path.dirname(os.path.abspath(__file__)) -REPO_ROOT = os.path.abspath(os.path.join(HERE, "..", "..", "..")) -METEOR_HOME = os.environ.get("METEOR_HOME", "/Users/u0164257/Documents/Github/MeTeoR") -DYLD = os.environ.get( - "DYLD_LIBRARY_PATH", - "/opt/homebrew/Cellar/python@3.11/3.11.11/Frameworks/Python.framework/Versions/3.11/lib", -) - - -def rust_binary(): - for p in ("release", "debug"): - b = os.path.join(REPO_ROOT, "target", p, "examples", "meteor_compare") - if os.path.exists(b): - return b - sys.exit("build first: cargo build -p datalogmtl --example meteor_compare") - - -def run_rust(program, data, queries): - env = dict(os.environ, DYLD_LIBRARY_PATH=DYLD) - out = subprocess.run( - [rust_binary(), "--program", program, "--data", data, - "--strategy", "omega", "--mode", "static", "--entail", queries], - env=env, cwd=REPO_ROOT, capture_output=True, text=True, - ) - if out.returncode != 0: - sys.exit("rust omega failed:\n" + out.stderr) - ans = {} - for line in out.stdout.splitlines(): - fact, val = line.rsplit("\t", 1) - ans[fact.strip()] = (val.strip() == "true") - return ans - - -def run_meteor(program, data, query_lines): - sys.path.insert(0, METEOR_HOME) - from meteor_reasoner.utils.loader import load_program, load_dataset - from meteor_reasoner.materialization.coalesce import coalescing_d - from meteor_reasoner.materialization.index_build import build_index - from meteor_reasoner.canonical.canonical_representation import CanonicalRepresentation - from meteor_reasoner.canonical.utils import find_periods, fact_entailment - from meteor_reasoner.utils.parser import parse_str_fact - from meteor_reasoner.classes.atom import Atom - - prog = load_program([l for l in open(program).read().splitlines() if l.strip()]) - D = load_dataset([l for l in open(data).read().splitlines() if l.strip()]) - with open(os.devnull, "w") as dn, contextlib.redirect_stdout(dn): - coalescing_d(D) - build_index(D) - cr = CanonicalRepresentation(D, prog) - cr.initilization() - d1, common, vl, lp, ll, vr, rp, rl = find_periods(cr) - - ans = {} - for q in query_lines: - pred, ent, itv = parse_str_fact(q) - with open(os.devnull, "w") as dn, contextlib.redirect_stdout(dn): - ans[q] = bool(fact_entailment(d1, Atom(pred, ent, itv), common, lp, ll, rp, rl)) - return ans - - -def main(): - case = os.path.abspath(sys.argv[1]) if len(sys.argv) > 1 else HERE - program = os.path.join(case, "program.txt") - data = os.path.join(case, "data.txt") - queries = os.path.join(case, "queries.txt") - query_lines = [l.strip() for l in open(queries) if l.strip()] - - rust = run_rust(program, data, queries) - meteor = run_meteor(program, data, query_lines) - - ok = True - print("{:<32} {:>8} {:>8}".format("query", "meteor", "omega")) - for q in query_lines: - m = meteor.get(q) - r = rust.get(q) - mark = "OK" if m == r else "MISMATCH" - if m != r: - ok = False - print("{:<32} {:>8} {:>8} {}".format(q, str(m), str(r), mark)) - print("\n{}".format("ALL MATCH" if ok else "MISMATCH")) - return 0 if ok else 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/datalogmtl/comparison/perf/README.md b/datalogmtl/comparison/perf/README.md deleted file mode 100644 index b9b04871..00000000 --- a/datalogmtl/comparison/perf/README.md +++ /dev/null @@ -1,212 +0,0 @@ -# Performance harness (datalogmtl vs MeTeoR) - -Mirrors MeTeoR's LUBM methodology — LUBM-style entities/relations with several -random temporal intervals per atom — but with a **scalable synthetic generator** -so we can grow the dataset arbitrarily and stay inside the shared past-fragment. - -## Workload - -- Program: `programs/lubm_past.txt` — the past-fragment subset of MeTeoR's - `Π¹_L` (`p1.txt`): `Boxminus`/`Diamondminus` rules + one binary temporal join, - plain heads, arity ≤ 2 (drops the `Boxplus` rule, which is future-only). -- Data: `gen_lubm.py --scale N` emits `UndergraduateStudent`, `GraduateStudent`, - and `publicationAuthor` atoms for `N` entities, each atom with - `--intervals k` random closed integer intervals in `[0, --horizon H]`. -- Both engines read the identical generated file. - -## Running - -```bash -cargo build --release -p datalogmtl --example meteor_compare -python3 perf.py --scales 100,500,1000,2000 [--horizon 20] [--intervals 3] - [--store snapshot|interval] [--repeats 3] [--verify] -``` - -`perf.py` times only the reasoning step on each side (MeTeoR `materialize`; the -Rust tick loop), not parsing/loading. - -## Results (horizon=20, intervals/atom=3, release build) - -After the hot-path optimization (indexed joins — see below): - -| scale | facts | meteor_ms | rust_ms | rust/mtr | -|------:|------:|----------:|--------:|---------:| -| 100 | 900 | 19.8 | 20.1 | 1.02 | -| 200 | 1800 | 39.1 | 33.1 | 0.84 | -| 400 | 3600 | 81.2 | 67.3 | 0.83 | -| 800 | 7200 | 149.7 | 120.7 | 0.81 | -| 1600 | 14400 | 303.2 | 264.2 | 0.87 | -| 3200 | 28800 | 601.1 | 590.7 | 0.98 | - -- Both engines now scale **≈ linearly** in dataset size. -- datalogmtl is **at par with MeTeoR (~0.8–1.0×)**, occasionally slightly faster. - -### The optimization - -The original engine was `O(facts²)` per tick and ~20–160× slower (e.g. scale 400: -**13648 ms → 67 ms, ~200× faster**). Three fixes, all in the base-atom seeding / -`Base`-atom evaluation path: - -1. **Indexed `query_at`** (`store.rs`) — use `UnifiedIndex::get_matching_triples` - (constant-position SPO/POS/OSP lookup) instead of scanning every fact at `t`. -2. **Indexed base join** (`evaluator.rs::seed_base_atoms`) — join a rule's `Base` - atoms by substituting the running binding into each next pattern and probing - the index, instead of a nested loop over all current facts (`join_base_atoms`, - removed). -3. **Specialized inner `Base` queries** (`evaluator.rs::eval_atom_at`) — substitute - already-bound variables into the pattern before querying, so `Diamond`/`Box` - inner lookups are constrained/indexed too. - -## Deeper evaluation: two workloads, larger scale, more dimensions - -Two programs (select with `perf.py --program`): -- **`past`** (`programs/lubm_past.txt`, 4 rules) — `Boxminus`/`Diamondminus` + a binary join. -- **`deep`** (`programs/lubm_deep.txt`, 8 rules) — a longer derivation chain - (`UndergraduateStudent → RAC → RA → ExperiencedRA → Eligible`), multi-way temporal - joins (`advisor` ⋈ `Diamondminus takesCourse`), and a guarded `Since`. `gen_lubm.py - --rich` emits the extra base predicates (`teachingAssistant`/`takesCourse`/`advisor`). - -**MeTeoR baseline mode matters.** MeTeoR's default **seminaive** mode is *incorrect* -for the `deep` program: a `Since` whose trigger (`RAC`) is a *derived* predicate is -missed (e.g. `DedicatedTA` is never derived), whereas MeTeoR **naive** derives it and -**matches our interval engine**. So the correct baseline for `deep` is `--meteor-mode -naive`; `perf.py`/`compare.py` expose `--meteor-mode`. This is a MeTeoR limitation, not -ours — our naive interval fixpoint is correct (parity OK vs MeTeoR naive). - -### Scale (release, horizon=20, intervals/atom=3) - -`deep` — interval vs MeTeoR **naive** (correct baseline, capped, it is slow): - -| scale | facts | meteor_ms | interval_ms | interval/mtr | -|------:|--------:|----------:|------------:|-------------:| -| 500 | 9,000 | 723 | 13.5 | 0.019 | -| 2,000 | 36,000 | 2,941 | 55.4 | 0.019 | -| 20,000| 360,000 | — | 673.6 | — | -| 50,000| 900,000 | — | 2,161.7 | — | - -`past` — interval vs MeTeoR **seminaive** (correct & fast here): - -| scale | facts | meteor_ms | interval_ms | interval/mtr | -|------:|--------:|----------:|------------:|-------------:| -| 1,000 | 9,000 | 188 | 7.9 | 0.042 | -|10,000 | 90,000 | 1,951 | 76.5 | 0.039 | -|50,000 | 450,000 | — | 501.6 | — | - -Interval is **linear** in dataset size (~2–3 µs/fact) to ~1M facts in a couple of -seconds, and **~25× (past) / ~50× (deep) faster than MeTeoR**. - -### Fact density and horizon (`deep`, scale=2,000) - -| intervals/atom | facts | meteor_ms | interval_ms | | horizon | interval_ms | -|---------------:|--------:|----------:|------------:|-----|--------:|------------:| -| 1 | 12,000 | 1,960 | 37.7 | | 20 | 55.0 | -| 3 | 36,000 | 2,941 | 55.0 | | 100 | 62.4 | -| 6 | 72,000 | 3,194 | 59.8 | | 500 | 63.6 | -| 10 | 120,000 | 3,186 | 59.6 | | 2,000 | 74.4 | - -- **Near-flat in fact density**: coalescing bounds the number of *distinct* intervals, - so 10× the raw intervals barely moves interval time (37→60 ms) while facts grow 10×. -- **Near-flat in horizon** (55→74 ms over 100× horizon) — event-complexity confirmed. - -## Future operators (`--program future`, static mode) - -`programs/lubm_future.txt` (4 rules) mirrors `lubm_past` but with **future** -operators (`Diamondplus`/`Boxplus`) over a derived chain + a binary join. These -require static data, so `perf.py --program future` runs `--mode static` on the -interval engine. Parity vs MeTeoR is **OK** (unary future ops over derived are -seminaive-correct). Same profile as the past ops: - -| scale | facts | meteor_ms | interval_ms | interval/mtr | -|------:|--------:|----------:|------------:|-------------:| -| 500 | 4,500 | 104 | 4.5 | 0.043 | -| 2,000 | 18,000 | 421 | 17.0 | 0.040 | -| 5,000 | 45,000 | 1,086 | 40.4 | 0.037 | -|50,000 | 450,000 | — | 601.2 | — | - -- **Linear** to 450k facts (0.6 s); **~25× faster** than MeTeoR. -- **Density-flat** (1→10 intervals/atom: 13.8→16.5 ms) and **horizon-flat** - (H 20→2000: 16.7→17.7 ms) — same event-complexity as the past fragment. - -Correctness: `comparison/cases_future/` — 5 cases (diamondplus, boxplus, until, -mixed past+future, stacked future) all parity-checked vs MeTeoR. - -## Interval-native "automata" strategy — `--strategy interval` (recommended) - -A second evaluation strategy (`datalogmtl/src/automata/`) replaces the per-tick loop -with **interval-arithmetic transducers** (a Rust port of MeTeoR's operator arithmetic) -over a **semi-naive interval fixpoint**. Facts stay as intervals (no densification); -temporal operators are endpoint transforms (`Diamond`=dilate, `Box`=erode, `Since`= -anchor-intersect+shift). Cost scales with interval **endpoints**, not horizon or width. - -Because it uses MeTeoR's real-line interval semantics, it is an **exact** MeTeoR match — -`perf.py --strategy interval --verify` reports **parity OK** on the LUBM workload (the -ℤ-vs-ℝ artifact is gone), and it is **horizon-independent**: - -Horizon sweep @ scale=400 (`reason_ms`): - -| horizon | meteor | tick engine | interval (automata) | -|--------:|-------:|------------:|--------------------:| -| 20 | 77 | 60 | **3.3** | -| 100 | 86 | 315 | **3.6** | -| 500 | 88 | 1650 | **3.7** | -| 2000 | 89 | 6839 | **3.8** | - -Scale sweep @ horizon=20: interval is **~0.04–0.09× MeTeoR** (11–25× faster) and -~20× faster than the tick engine, with near-flat scaling. Milestone-1 limits: finite -(non-periodic) programs only (iteration cap `K=1000`); `Prev` and future operators are -out of the ported fragment. Unbounded/periodic programs are the ω-automaton phase 2. - -## Tick engine (historical) — horizon-dependence + idle-tick skipping - -The engine advances tick-by-tick, so a naive loop over `0..=H` pays `O(H)` even -when the data occupies a tiny slice of the timeline. The driver now **skips -provably-empty ticks**: a tick is evaluated only if some base fact lies within a -rule's temporal *reach* of it (`reach = num_rules * w_max`, a safe bound on how -far a fact propagates forward through the rule chain; recursive programs fall -back to every tick). Skipped ticks have no active base fact and none in any -operator's lookback, so they derive nothing — output is bit-identical (`--no-skip` -disables it for A/B). - -This makes reasoning **independent of idle horizon**. With `N=5` narrow-interval -entities and a growing horizon: - -| horizon | `--no-skip` | skip (default) | -|--------:|------------:|---------------:| -| 2,000 | 2.8 ms | 1.55 ms | -| 20,000 | 12.1 ms | 1.62 ms | -| 200,000 | 123.0 ms | 1.84 ms | - -no-skip is linear in `H`; skip is flat. (Extreme case: `H=200k` with ~27 facts, -**125 ms → 1 ms**.) - -### Remaining cost: fact density, not idle time - -Skipping helps only when the horizon has genuine *gaps*. When facts densely cover -the timeline (wide intervals), there is little to skip and cost is proportional to -the facts' actual temporal extent × entities — real work, not idle horizon. After -the indexing fix, empty ticks are already nearly free (one indexed miss per rule), -so this residual is *not* removable by skipping. Collapsing it further would need -interval-native / delta-based (semi-naive) evaluation that reasons over interval -endpoints instead of per tick — a larger redesign. - -## Correctness on this workload — integer time vs real time - -Box/Since now use **dense integer semantics** (every integer point in the window), -which matches MeTeoR on data whose intervals are not integer-adjacent. The hand- -crafted correctness suite (`../cases/`, adjacency-free) passes exactly, and -`gen_lubm.py` coalesces each atom's *base* intervals (merging gaps ≤ 1). - -A residual divergence remains and is **fundamental**, not an engine bug: -datalogmtl reasons over integer time (ℤ, `u64` ms ticks) while MeTeoR reasons over -real/rational time (ℝ). A temporal operator can expand two real-separated -intervals into integer-adjacency. Example (`g28`): `Diamond[0,2]` over -`GS@[2,17]` and `GS@[20,20]` gives `RAC = [2,19] ∪ [20,22]`. MeTeoR keeps the real -gap `(19,20)`, so `Box[0,5]RAC → RA=[7,19]`; datalogmtl has `RAC` at ticks 19 and -20 with nothing between, so it is contiguous `[2,22]` and `Box[0,5]RAC → RA=[7,22]`. - -Consequently `perf.py --verify` reports boundary-only MISMATCHes on the generated -LUBM workload. These are the ℤ-vs-ℝ artifact, confined to interval edges, and do -not affect the timing conclusion (both engines do comparable work). Exact output -parity between an integer-time and a real-time reasoner is not achievable on -arbitrary chained-operator programs; the adjacency-free `../cases/` suite is the -exact-correctness oracle, and MeTeoR here is the performance baseline. diff --git a/datalogmtl/comparison/perf/gen_lubm.py b/datalogmtl/comparison/perf/gen_lubm.py deleted file mode 100644 index 5017ab23..00000000 --- a/datalogmtl/comparison/perf/gen_lubm.py +++ /dev/null @@ -1,95 +0,0 @@ -#!/usr/bin/env python3 -""" -Generate a scalable temporal-LUBM dataset for the past-fragment program -`perf/programs/lubm_past.txt`, mirroring MeTeoR's methodology of taking LUBM-style -entities/relations and assigning each atom several random temporal intervals. - -Base predicates produced (arity <= 2, matching lubm_past.txt): - - UndergraduateStudent(u_i) - - GraduateStudent(g_i) - - publicationAuthor(pub_i, u_i) # publication authored by student u_i - -Scaling is by `--scale N` (entity count). Each atom gets `--intervals k` random -closed integer intervals within `[0, --horizon H]`. Output is MeTeoR fact syntax -(`Pred(args)@[l,r]`), the same format consumed by the correctness harness and by -the Rust `meteor_compare` driver, so both engines read identical input. - -Usage: - python gen_lubm.py --scale 500 --horizon 20 --intervals 3 --seed 42 --out data.txt -""" -import argparse -import random -import sys - - -def random_intervals(rng, k, horizon, max_width=None): - """k random closed integer intervals within [0, horizon], coalesced. - - `max_width` caps each interval's width (default: unbounded, i.e. up to the - horizon). Small widths over a large horizon model bursty/sparse event data. - - Overlapping OR integer-adjacent intervals (gap <= 1) are merged. This keeps - the workload unambiguous between integer-time (datalogmtl) and real-time - (MeTeoR): without it, two integer-adjacent intervals like [3,12] and [13,15] - look contiguous on the integer grid but have a real gap (12,13) for MeTeoR, - making universal operators (Box) disagree. - """ - raw = [] - for _ in range(k): - a = rng.randint(0, horizon) - hi = horizon if max_width is None else min(horizon, a + max_width) - b = rng.randint(a, hi) - raw.append((a, b)) - raw.sort() - merged = [] - for (a, b) in raw: - if merged and a <= merged[-1][1] + 1: - merged[-1] = (merged[-1][0], max(merged[-1][1], b)) - else: - merged.append((a, b)) - return merged - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--scale", type=int, required=True, help="entity count N") - ap.add_argument("--horizon", type=int, default=20) - ap.add_argument("--intervals", type=int, default=3, help="intervals per atom") - ap.add_argument("--max-width", type=int, default=None, - help="cap each interval's width (default: up to horizon)") - ap.add_argument("--rich", action="store_true", - help="also emit teachingAssistant/takesCourse/advisor (for lubm_deep)") - ap.add_argument("--seed", type=int, default=42) - ap.add_argument("--out", default="-", help="output file ('-' for stdout)") - args = ap.parse_args() - - rng = random.Random(args.seed) - lines = [] - - def emit(atom): - for (a, b) in random_intervals(rng, args.intervals, args.horizon, args.max_width): - lines.append("{}@[{},{}]".format(atom, a, b)) - - n = args.scale - for i in range(n): - emit("UndergraduateStudent(u{})".format(i)) - emit("GraduateStudent(g{})".format(i)) - emit("publicationAuthor(pub{},u{})".format(i, i)) - if args.rich: - emit("takesCourse(u{},c{})".format(i, i)) - emit("advisor(u{},p{})".format(i, i)) - if i % 2 == 0: - emit("teachingAssistant(u{})".format(i)) - - text = "\n".join(lines) + "\n" - if args.out == "-": - sys.stdout.write(text) - else: - with open(args.out, "w") as f: - f.write(text) - print("wrote {} facts ({} atoms x {} intervals) to {}".format( - len(lines), 3 * n, args.intervals, args.out), file=sys.stderr) - - -if __name__ == "__main__": - main() diff --git a/datalogmtl/comparison/perf/perf.py b/datalogmtl/comparison/perf/perf.py deleted file mode 100644 index 268b0bbb..00000000 --- a/datalogmtl/comparison/perf/perf.py +++ /dev/null @@ -1,166 +0,0 @@ -#!/usr/bin/env python3 -""" -Performance harness: generate temporal-LUBM datasets at increasing scale, run the -past-fragment program through both MeTeoR (baseline) and the datalogmtl Rust -engine, and tabulate reasoning wall-clock time. - -For each scale N: - 1. gen_lubm.py writes a dataset (identical file feeds both engines). - 2. run_meteor_perf.py times MeTeoR's materialize(). - 3. meteor_compare --timing times the Rust engine's tick loop. - -Optionally (--verify) the smallest scale is also run through the correctness -comparator to confirm both engines still agree on the generated workload. - -Usage: - python perf.py [--scales 100,500,1000,2000] [--horizon 20] [--intervals 3] - [--store snapshot|interval] [--repeats 1] [--verify] -""" -import argparse -import json -import os -import subprocess -import sys -import tempfile - -HERE = os.path.dirname(os.path.abspath(__file__)) -COMP = os.path.dirname(HERE) # comparison/ -REPO_ROOT = os.path.abspath(os.path.join(COMP, "..", "..")) -PROGRAMS = { - "past": os.path.join(HERE, "programs", "lubm_past.txt"), # 4 rules, past ops - "deep": os.path.join(HERE, "programs", "lubm_deep.txt"), # 8 rules, deeper chain + joins - "future": os.path.join(HERE, "programs", "lubm_future.txt"), # 4 rules, future ops (static) -} -METEOR_HOME = os.environ.get("METEOR_HOME", "/Users/u0164257/Documents/Github/MeTeoR") -DYLD = os.environ.get( - "DYLD_LIBRARY_PATH", - "/opt/homebrew/Cellar/python@3.11/3.11.11/Frameworks/Python.framework/Versions/3.11/lib", -) -RUST_BIN_RELEASE = os.path.join(REPO_ROOT, "target", "release", "examples", "meteor_compare") -RUST_BIN_DEBUG = os.path.join(REPO_ROOT, "target", "debug", "examples", "meteor_compare") -RUST_BIN = RUST_BIN_RELEASE if os.path.exists(RUST_BIN_RELEASE) else RUST_BIN_DEBUG - - -def gen_data(scale, horizon, intervals, seed, out, max_width=None, rich=False): - cmd = [sys.executable, os.path.join(HERE, "gen_lubm.py"), - "--scale", str(scale), "--horizon", str(horizon), - "--intervals", str(intervals), "--seed", str(seed), "--out", out] - if max_width is not None: - cmd += ["--max-width", str(max_width)] - if rich: - cmd += ["--rich"] - subprocess.run(cmd, check=True, capture_output=True, text=True) - - -def run_rust(program, data, store, strategy="tick", mode="streaming"): - env = dict(os.environ, DYLD_LIBRARY_PATH=DYLD) - cmd = [RUST_BIN, "--program", program, "--data", data, - "--store", store, "--strategy", strategy, "--mode", mode, "--timing"] - proc = subprocess.run(cmd, cwd=REPO_ROOT, env=env, capture_output=True, text=True) - if proc.returncode != 0: - raise RuntimeError("rust engine failed:\n" + proc.stderr) - return json.loads(proc.stdout.strip().splitlines()[-1]) - - -def run_meteor(program, data, mode="seminaive"): - env = dict(os.environ, PYTHONPATH=METEOR_HOME) - cmd = [sys.executable, os.path.join(HERE, "run_meteor_perf.py"), - "--program", program, "--data", data, "--mode", mode] - proc = subprocess.run(cmd, env=env, capture_output=True, text=True) - if proc.returncode != 0: - raise RuntimeError("meteor reasoner failed:\n" + proc.stderr) - return json.loads(proc.stdout.strip().splitlines()[-1]) - - -def best_of(fn, repeats): - return min(fn()["reason_ms"] for _ in range(repeats)) - - -def verify(program, scale, horizon, intervals, seed, store, strategy="tick", - max_width=None, rich=False, meteor_mode="seminaive", mode="streaming"): - """Confirm both engines still agree on a generated dataset via compare.py.""" - with tempfile.TemporaryDirectory() as tmp: - cases = os.path.join(tmp, "cases") - case = os.path.join(cases, "gen") - os.makedirs(case) - subprocess.run(["cp", program, os.path.join(case, "program.txt")], check=True) - gen_data(scale, horizon, intervals, seed, os.path.join(case, "data.txt"), max_width, rich) - env = dict(os.environ, DYLD_LIBRARY_PATH=DYLD, METEOR_HOME=METEOR_HOME) - proc = subprocess.run( - [sys.executable, os.path.join(COMP, "compare.py"), cases, - "--store", store, "--strategy", strategy, "--meteor-mode", meteor_mode, - "--mode", mode], - env=env, capture_output=True, text=True, - ) - print(proc.stdout.strip()) - return proc.returncode == 0 - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--scales", default="100,500,1000,2000") - ap.add_argument("--program", default="past", choices=list(PROGRAMS), - help="workload: past (4 rules) or deep (8 rules, deeper chain+joins)") - ap.add_argument("--horizon", type=int, default=20) - ap.add_argument("--intervals", type=int, default=3) - ap.add_argument("--seed", type=int, default=42) - ap.add_argument("--store", default="snapshot", choices=["snapshot", "interval"]) - ap.add_argument("--strategy", default="tick", choices=["tick", "interval"]) - ap.add_argument("--max-width", type=int, default=None) - ap.add_argument("--repeats", type=int, default=1, help="take best of N runs") - ap.add_argument("--meteor-cap", type=int, default=None, - help="skip MeTeoR above this scale (it is the slow baseline)") - ap.add_argument("--meteor-mode", default="seminaive", choices=["seminaive", "naive"], - help="MeTeoR materialization mode (naive = ground truth for Since-over-derived)") - ap.add_argument("--verify", action="store_true") - args = ap.parse_args() - - if not os.path.exists(RUST_BIN): - print("Rust driver not built. Run:\n cargo build -p datalogmtl --example meteor_compare", - file=sys.stderr) - return 1 - - program = PROGRAMS[args.program] - rich = args.program == "deep" - scales = [int(s) for s in args.scales.split(",") if s.strip()] - per_entity = 6 if rich else 3 # base atoms emitted per entity - - # Future operators need static data + the interval engine. - mode = "static" if args.program == "future" else "streaming" - strategy = args.strategy - if mode == "static" and strategy == "tick": - strategy = "interval" - - if args.verify: - print("== parity check on generated data (scale={}) ==".format(scales[0])) - ok = verify(program, scales[0], args.horizon, args.intervals, args.seed, - args.store, strategy, args.max_width, rich, args.meteor_mode, mode) - print("parity: {}\n".format("OK" if ok else "MISMATCH")) - - print("program={} mode={} horizon={} intervals/atom={} store={} strategy={} repeats={}".format( - args.program, mode, args.horizon, args.intervals, args.store, strategy, args.repeats)) - print("{:>8} {:>11} {:>13} {:>13} {:>9}".format( - "scale", "facts", "meteor_ms", "rust_ms", "rust/mtr")) - print("-" * 58) - - with tempfile.TemporaryDirectory() as tmp: - for n in scales: - data = os.path.join(tmp, "lubm_{}.txt".format(n)) - gen_data(n, args.horizon, args.intervals, args.seed, data, args.max_width, rich) - facts = per_entity * n * args.intervals - - skip_meteor = args.meteor_cap is not None and n > args.meteor_cap - m_ms = None if skip_meteor else best_of(lambda: run_meteor(program, data, args.meteor_mode), args.repeats) - r_ms = min(run_rust(program, data, args.store, strategy, mode)["reason_ms"] - for _ in range(args.repeats)) - ratio = (r_ms / m_ms) if m_ms else float("nan") - m_str = "-" if m_ms is None else "{:.2f}".format(m_ms) - ratio_str = "-" if m_ms is None else "{:.3f}".format(ratio) - print("{:>8} {:>11} {:>13} {:>13.2f} {:>9}".format( - n, facts, m_str, r_ms, ratio_str)) - - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/datalogmtl/comparison/perf/programs/lubm_deep.txt b/datalogmtl/comparison/perf/programs/lubm_deep.txt deleted file mode 100644 index bf619c9f..00000000 --- a/datalogmtl/comparison/perf/programs/lubm_deep.txt +++ /dev/null @@ -1,8 +0,0 @@ -ResearchAssistantCandidate(X):-Boxminus[0,5]UndergraduateStudent(X) -ResearchAssistantCandidate(X):-Diamondminus[0,2]GraduateStudent(X) -ResearchAssistant(X):-Boxminus[0,5]ResearchAssistantCandidate(X) -ResearchAssistant(X):-publicationAuthor(Y,X),Boxminus[0,1]ResearchAssistantCandidate(X) -ExperiencedRA(X):-Boxminus[0,3]ResearchAssistant(X) -Mentored(X):-advisor(X,Y),Diamondminus[0,4]takesCourse(X,Z) -DedicatedTA(X):-teachingAssistant(X),teachingAssistant(X)Since[1,6]ResearchAssistantCandidate(X) -Eligible(X):-ExperiencedRA(X),Mentored(X) diff --git a/datalogmtl/comparison/perf/programs/lubm_future.txt b/datalogmtl/comparison/perf/programs/lubm_future.txt deleted file mode 100644 index 00f47da4..00000000 --- a/datalogmtl/comparison/perf/programs/lubm_future.txt +++ /dev/null @@ -1,4 +0,0 @@ -ProspectiveRA(X):-Diamondplus[0,5]UndergraduateStudent(X) -ProspectiveRA(X):-Boxplus[0,2]GraduateStudent(X) -FutureLead(X):-Diamondplus[0,3]ProspectiveRA(X) -FutureLead(X):-publicationAuthor(Y,X),Boxplus[0,1]ProspectiveRA(X) diff --git a/datalogmtl/comparison/perf/programs/lubm_past.txt b/datalogmtl/comparison/perf/programs/lubm_past.txt deleted file mode 100644 index 7b730256..00000000 --- a/datalogmtl/comparison/perf/programs/lubm_past.txt +++ /dev/null @@ -1,4 +0,0 @@ -ResearchAssistantCandidate(X):-Boxminus[0,5]UndergraduateStudent(X) -ResearchAssistantCandidate(X):-Diamondminus[0,2]GraduateStudent(X) -ResearchAssistant(X):-Boxminus[0,5]ResearchAssistantCandidate(X) -ResearchAssistant(X):-publicationAuthor(Y,X),Boxminus[0,1]ResearchAssistantCandidate(X) diff --git a/datalogmtl/comparison/perf/run_meteor_perf.py b/datalogmtl/comparison/perf/run_meteor_perf.py deleted file mode 100644 index 7cd0af86..00000000 --- a/datalogmtl/comparison/perf/run_meteor_perf.py +++ /dev/null @@ -1,59 +0,0 @@ -#!/usr/bin/env python3 -""" -Time MeTeoR's materialization on a program + data file and print a JSON summary: - - {"reason_ms": , "native_facts": } - -Only the reasoning (`materialize`) is timed, to match the Rust driver, which times -its tick loop and not parsing. `native_facts` counts the coalesced (predicate, -entity, interval) rows MeTeoR holds after materialization. - -Usage: - PYTHONPATH=$METEOR_HOME python run_meteor_perf.py --program P --data D [--K 1000] -""" -import argparse -import contextlib -import os -import time - -from meteor_reasoner.utils.loader import load_program, load_dataset -from meteor_reasoner.materialization.materialize import materialize -from meteor_reasoner.materialization.coalesce import coalescing_d - - -def count_facts(D): - total = 0 - for predicate in D: - entries = D[predicate] - if isinstance(entries, list): - total += len(entries) - else: - for _entity, intervals in entries.items(): - total += len(intervals) - return total - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--program", required=True) - ap.add_argument("--data", required=True) - ap.add_argument("--K", type=int, default=1000) - ap.add_argument("--mode", default="seminaive", choices=["seminaive", "naive"]) - args = ap.parse_args() - - with open(os.devnull, "w") as devnull, contextlib.redirect_stdout(devnull): - rules = load_program(args.program) - D = load_dataset(args.data) - - start = time.perf_counter() - materialize(D, rules, mode=args.mode, K=args.K) - coalescing_d(D) - elapsed_ms = (time.perf_counter() - start) * 1000.0 - - facts = count_facts(D) - - print('{{"reason_ms": {:.3f}, "native_facts": {}}}'.format(elapsed_ms, facts)) - - -if __name__ == "__main__": - main() diff --git a/datalogmtl/comparison/run_meteor.py b/datalogmtl/comparison/run_meteor.py deleted file mode 100644 index 3af69914..00000000 --- a/datalogmtl/comparison/run_meteor.py +++ /dev/null @@ -1,126 +0,0 @@ -#!/usr/bin/env python3 -""" -Run the MeTeoR reference reasoner on a program + data file and print the full -materialization as normalized, coalesced integer-interval lines: - - Pred(args)@[l,r] - -Each MeTeoR (possibly open) interval is expanded to the integer points it covers -within the comparison window [0, T], then re-coalesced into closed integer -intervals. This matches the point-grid form emitted by the Rust `meteor_compare` -example so the two can be diffed line-for-line. - -Usage: - PYTHONPATH=/path/to/MeTeoR python run_meteor.py --program P --data D --horizon T -""" -import argparse -import contextlib -import os -import sys -from collections import defaultdict - -from meteor_reasoner.utils.loader import load_program, load_dataset -from meteor_reasoner.materialization.materialize import materialize -from meteor_reasoner.materialization.coalesce import coalescing_d - - -def interval_int_points(interval, horizon): - """Integer points in [0, horizon] covered by a MeTeoR interval.""" - lo = interval.left_value - hi = interval.right_value - - # Clamp infinities / out-of-window bounds to the comparison window. - try: - lo_f = float(lo) - except (ValueError, OverflowError): - lo_f = float("-inf") - try: - hi_f = float(hi) - except (ValueError, OverflowError): - hi_f = float("inf") - - if lo_f == float("-inf") or lo_f < 0: - first = 0 - else: - first = int(lo) - if interval.left_open and first == lo: - first += 1 - elif first < lo: # non-integer closed lower bound - first += 1 - - if hi_f == float("inf") or hi_f > horizon: - last = horizon - else: - last = int(hi) - if interval.right_open and last == hi: - last -= 1 - # closed upper bound with fractional value: int() already floors - - if last < first: - return [] - first = max(first, 0) - last = min(last, horizon) - return list(range(first, last + 1)) - - -def coalesce(points): - """Sorted set of ints -> list of maximal closed (lo, hi) intervals.""" - out = [] - for t in sorted(points): - if out and out[-1][1] + 1 == t: - out[-1] = (out[-1][0], t) - elif out and out[-1][1] == t: - pass - else: - out.append((t, t)) - return out - - -def atom_string(predicate, entity): - """MeTeoR atom text: propositional P, or P(a,b).""" - names = [term.name for term in entity if term.name != "nan"] - if not names: - return predicate - return "{}({})".format(predicate, ",".join(names)) - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--program", required=True) - ap.add_argument("--data", required=True) - ap.add_argument("--horizon", type=int, required=True) - ap.add_argument("--mode", default="seminaive") - ap.add_argument("--K", type=int, default=1000) - args = ap.parse_args() - - # MeTeoR's materialize() prints progress to stdout; keep our stdout clean. - with open(os.devnull, "w") as devnull, contextlib.redirect_stdout(devnull): - rules = load_program(args.program) - D = load_dataset(args.data) - materialize(D, rules, mode=args.mode, K=args.K) - coalescing_d(D) - - # atom string -> set of integer timepoints - points = defaultdict(set) - for predicate in D: - entries = D[predicate] - if isinstance(entries, list): # propositional predicate - atom = predicate - for interval in entries: - points[atom].update(interval_int_points(interval, args.horizon)) - else: - for entity, intervals in entries.items(): - atom = atom_string(predicate, entity) - for interval in intervals: - points[atom].update(interval_int_points(interval, args.horizon)) - - lines = [] - for atom, pts in points.items(): - for lo, hi in coalesce(pts): - lines.append("{}@[{},{}]".format(atom, lo, hi)) - lines.sort() - print("\n".join(lines)) - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/datalogmtl/examples/meteor_compare.rs b/datalogmtl/examples/meteor_compare.rs index 6da1f0aa..b6f1e51a 100644 --- a/datalogmtl/examples/meteor_compare.rs +++ b/datalogmtl/examples/meteor_compare.rs @@ -8,16 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Drive the datalogmtl engine from a MeTeoR-syntax program + data file and -//! print the full materialization as coalesced `Pred(args)@[l,r]` lines, so the -//! output can be diffed against the MeTeoR reference reasoner. -//! -//! Usage: -//! meteor_compare --program --data [--horizon T] [--store snapshot|interval] -//! -//! Facts with interval `[l,r]` are densified onto the integer grid (inserted at -//! every tick in `[l,r]`). The engine is advanced over ticks `0..=T`; at each -//! tick every fact holding in the store is recorded, then coalesced on output. +//! Runs a MeTeoR-syntax program and prints its materialisation as `Pred(args)@[l,r]` lines use std::collections::{BTreeSet, HashMap}; use std::time::Instant; @@ -64,29 +55,23 @@ fn main() { store_kind = args.get(i + 1).cloned().unwrap_or_default(); i += 2; } - // Evaluation strategy: tick (per-tick engine) or interval (automata). "--strategy" => { strategy = args.get(i + 1).cloned().unwrap_or_default(); strategy_set = true; i += 2; } - // Data contract: streaming (past-only) or static (past + future operators). "--mode" => { mode_str = args.get(i + 1).cloned().unwrap_or_default(); i += 2; } - // Print timing + fact/derivation counts as JSON instead of the full - // materialization (for the performance harness). "--timing" => { timing = true; i += 1; } - // Disable skipping provably-empty ticks (for A/B comparison). "--no-skip" => { skip_empty = false; i += 1; } - // Answer entailment for the query facts in (with --strategy omega). "--entail" => { entail_file = args.get(i + 1).cloned(); i += 2; @@ -109,8 +94,6 @@ fn main() { other )), }; - // Future operators require the static (interval) engine; the tick engine - // cannot see the future. In static mode default the strategy to interval. if mode == Mode::Static { if strategy_set && strategy == "tick" { fail("--mode static requires the interval engine; drop '--strategy tick'"); @@ -166,7 +149,6 @@ fn main() { }; if timing { - // Minimal JSON so the perf harness can parse without a JSON dep. println!( "{{\"reason_ms\": {:.3}, \"atoms\": {}, \"intervals\": {}, \"horizon\": {}}}", out.reason_micros as f64 / 1000.0, @@ -187,14 +169,7 @@ struct RunOutput { atom_count: usize, } -/// Run the engine over the relevant ticks in `0..=horizon`, timing the loop. -/// -/// A tick is *relevant* only if some base fact lies within a rule's temporal -/// reach of it; other ticks provably derive nothing (no base fact active and -/// none within any operator's lookback), so they are skipped. Reach is -/// `num_rules * w_max` for non-recursive programs (a safe bound on how far a -/// fact can propagate forward through the rule chain); recursive programs fall -/// back to evaluating every tick. +/// Run the engine over the relevant ticks in `0..=horizon`, timing the loop fn run( store: S, rules: Vec, @@ -208,7 +183,6 @@ fn run( let mut eval = DatalogMTLEvaluator::new(rules, store, dict.clone()) .unwrap_or_else(|e| fail(&format!("invalid program: {}", e))); - // Densify facts: tick -> triples valid at that tick. let mut by_tick: HashMap> = HashMap::new(); for f in facts { for t in f.start..=f.end.min(horizon) { @@ -231,7 +205,6 @@ fn run( let ingest = by_tick.remove(&t).unwrap_or_default(); eval.advance(t, ingest); - // Record every fact (base + derived) active at this tick. for b in eval.store.query_at(&wildcard, t) { if let (Some(&s), Some(&p), Some(&o)) = (b.get("_s"), b.get("_p"), b.get("_o")) { holds @@ -256,9 +229,8 @@ fn run( } } -/// Interval-native ("automata") strategy: materialize with interval transducers -/// (no densification, no per-tick loop), then expand to integer points clipped to -/// `[0, horizon]` and reuse the same coalesced output path as the tick strategy. +/// Interval-native strategy: materialises with interval transducers and prints like the tick +/// strategy fn run_interval( rules: Vec, facts: &[TemporalFact], @@ -299,10 +271,7 @@ fn run_interval( } } -/// ω-strategy: unbounded-time materialization (`materialize_omega`). With -/// `--entail ` it answers entailment for each query fact `Pred(args)@[l,r]` -/// (works for arbitrarily far-future times); otherwise it prints the model -/// clipped to `[0, horizon]` like `--strategy interval`. +/// ω-strategy: unbounded-time materialization (`materialize_omega`) fn run_omega( rules: Vec, facts: &[TemporalFact], @@ -377,9 +346,8 @@ fn run_omega( } } -/// Mark each tick in `0..=horizon` relevant if some base fact lies within the -/// program's temporal reach of it. Recursive programs (or `skip_empty=false`) -/// mark every tick relevant. +/// Mark each tick in `0..=horizon` relevant if some base fact lies within the program's temporal +/// reach of it fn relevant_ticks( rules: &[DatalogMTLRule], facts: &[TemporalFact], @@ -397,7 +365,6 @@ fn relevant_ticks( if reach >= horizon { return vec![true; n]; } - // Difference array over [start, end+reach] for each base fact: O(facts + H). let mut diff = vec![0i64; n + 1]; for f in facts { let lo = f.start.min(horizon) as usize; @@ -414,9 +381,8 @@ fn relevant_ticks( relevant } -/// The identifying predicate of an atom: for a `?s rdf:type Class` triple it is -/// `Class` (object); otherwise the predicate-position constant. `None` if that -/// position is not a constant. +/// The identifying predicate of an atom: for a `?s rdf:type Class` triple it is `Class` (object); +/// otherwise the predicate-position constant fn atom_pred_id(pat: &TriplePattern, rdf_type: u32) -> Option { let pred = match &pat.1 { Term::Constant(c) => *c, @@ -456,8 +422,8 @@ fn collect_body_preds(atom: &TemporalAtom, rdf_type: u32, out: &mut Vec) { } } -/// True if the rule set has a predicate reachable from itself (a cycle in the -/// body-predicate -> head-predicate dependency graph). +/// True if the rule set has a predicate reachable from itself (a cycle in the body-predicate -> +/// head-predicate dependency graph) fn is_recursive(rules: &[DatalogMTLRule], rdf_type: u32) -> bool { let mut edges: HashMap> = HashMap::new(); for r in rules { diff --git a/datalogmtl/src/automata/eval.rs b/datalogmtl/src/automata/eval.rs index 25b5cc94..8ba3cef3 100644 --- a/datalogmtl/src/automata/eval.rs +++ b/datalogmtl/src/automata/eval.rs @@ -8,11 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Interval-native materialization: a naive fixpoint over interval-valued facts. -//! Each rule body is folded by conjunctive join; the head projection unions new -//! intervals into the database; iterate until nothing changes (or `max_iters`). -//! This is event-complexity — cost scales with interval endpoints, not the -//! horizon or interval widths. +//! Interval-native materialization: a naive fixpoint over interval-valued facts use super::interval::TInterval; use super::relation::{conjoin, eval_atom, Database}; @@ -21,9 +17,7 @@ use shared::terms::{Term, TriplePattern}; use shared::triple::Triple; use std::collections::HashMap; -/// Materialize `rules` over interval-valued `facts`. `max_iters` caps the -/// fixpoint for programs that are not finitely materializable (their minimal -/// model is infinite/periodic — handled by the ω-automaton layer, future work). +/// Materialize `rules` over interval-valued `facts` pub fn materialize( rules: &[DatalogMTLRule], facts: Vec<(Triple, TInterval)>, @@ -53,8 +47,8 @@ pub fn materialize( db } -/// Evaluate one rule against the current database, returning head facts with -/// the intervals over which the (conjunctive) body holds. +/// Evaluate one rule against the current database, returning head facts with the intervals over +/// which the (conjunctive) body holds pub fn eval_rule(rule: &DatalogMTLRule, db: &Database) -> Vec<(Triple, Vec)> { let Some((first, rest)) = rule.body.split_first() else { return Vec::new(); diff --git a/datalogmtl/src/automata/interval.rs b/datalogmtl/src/automata/interval.rs index ff2a473e..d8c3e5ff 100644 --- a/datalogmtl/src/automata/interval.rs +++ b/datalogmtl/src/automata/interval.rs @@ -8,41 +8,33 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Rational/real-line intervals with open/closed bounds — a faithful Rust port -//! of MeTeoR's `meteor_reasoner/classes/interval.py`. Endpoints are `i64` (data -//! and operator bounds are integer); ±infinity is out of scope for the finite -//! materialization milestone. -//! -//! The key difference from the tick engine is **real-line** merging: two -//! integer-adjacent-but-separated intervals like `[2,19]` and `[20,22]` are NOT -//! coalesced (there is a real gap `(19,20)`), which is exactly what makes this -//! evaluator agree with MeTeoR on chained operators. +//! Rational/real-line intervals with open/closed bounds — a faithful Rust port of MeTeoR's +//! `meteor_reasoner/classes/interval.py` -/// Negative infinity sentinel (used only as a left endpoint, always open). +/// Negative infinity sentinel (used only as a left endpoint, always open) pub const NEG_INF: i64 = i64::MIN; -/// Positive infinity sentinel (used only as a right endpoint, always open). +/// Positive infinity sentinel (used only as a right endpoint, always open) pub const POS_INF: i64 = i64::MAX; fn is_inf(v: i64) -> bool { v == NEG_INF || v == POS_INF } -/// A closed/open interval `[start, end]` over the rationals (integer endpoints), -/// with `NEG_INF`/`POS_INF` sentinels for unbounded tails (ω-materialization). +/// A closed or open interval `[start, end]` with integer endpoints and `NEG_INF`/`POS_INF` tails #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)] pub struct TInterval { - /// Left endpoint, or [`NEG_INF`]. + /// Left endpoint, or [`NEG_INF`] pub start: i64, - /// Right endpoint, or [`POS_INF`]. + /// Right endpoint, or [`POS_INF`] pub end: i64, - /// Whether the left endpoint is excluded. Always true for [`NEG_INF`]. + /// Whether the left endpoint is excluded pub start_open: bool, - /// Whether the right endpoint is excluded. Always true for [`POS_INF`]. + /// Whether the right endpoint is excluded pub end_open: bool, } impl TInterval { - /// A closed interval `[start, end]`. + /// A closed interval `[start, end]` pub fn closed(start: i64, end: i64) -> Self { TInterval { start, @@ -52,7 +44,7 @@ impl TInterval { } } - /// The unbounded-future interval `[start, +∞)`. + /// The unbounded-future interval `[start, +∞)` pub fn from_to_inf(start: i64, start_open: bool) -> Self { TInterval { start, @@ -62,7 +54,7 @@ impl TInterval { } } - /// The unbounded-past interval `(-∞, end]`. + /// The unbounded-past interval `(-∞, end]` pub fn from_neg_inf(end: i64, end_open: bool) -> Self { TInterval { start: NEG_INF, @@ -72,12 +64,12 @@ impl TInterval { } } - /// Whether both endpoints are real values rather than infinity sentinels. + /// Whether both endpoints are real values rather than infinity sentinels pub fn is_finite(&self) -> bool { !is_inf(self.start) && !is_inf(self.end) } - /// Whether integer point `t` lies inside this interval (respecting open bounds). + /// Whether integer point `t` lies inside this interval (respecting open bounds) pub fn contains_point(&self, t: i64) -> bool { let after_start = if self.start_open { t > self.start @@ -92,7 +84,7 @@ impl TInterval { after_start && before_end } - /// This interval shifted along the timeline by `delta` (finite endpoints only). + /// This interval shifted along the timeline by `delta` (finite endpoints only) pub fn shifted(&self, delta: i64) -> TInterval { TInterval { start: if is_inf(self.start) { @@ -110,7 +102,7 @@ impl TInterval { } } - /// Whether the four fields form a valid non-empty interval (MeTeoR `is_valid_interval`). + /// Whether the four fields form a valid non-empty interval (MeTeoR `is_valid_interval`) pub fn is_valid(start: i64, end: i64, start_open: bool, end_open: bool) -> bool { if start == end && start_open && end_open { return false; @@ -118,7 +110,6 @@ impl TInterval { if start > end { return false; } - // Infinite endpoints must be open. if is_inf(start) && !start_open { return false; } @@ -144,8 +135,8 @@ impl TInterval { } } - /// The finite integer points covered by this interval, clamped to - /// `[0, cap]` (an unbounded `+∞` end is treated as `cap`). + /// The finite integer points covered by this interval, clamped to `[0, cap]` (an unbounded `+∞` + /// end is treated as `cap`) pub fn integer_points_upto(&self, cap: u64) -> impl Iterator { let lo = if is_inf(self.start) { 0 @@ -166,10 +157,7 @@ impl TInterval { (lo..=hi).filter(|v| *v >= 0).map(|v| v as u64) } - // ── Metric shifts (MeTeoR Interval.add / sub / circle_add / circle_sub) ── - // `v2` is always the finite operator bound; only `v1`'s endpoints may be ±∞. - - /// `v1- + v2-`, `v1+ + v2+` — Diamondminus dilation. + /// `v1- + v2-`, `v1+ + v2+` — Diamondminus dilation pub fn add(v1: TInterval, v2: TInterval) -> TInterval { let start = if is_inf(v1.start) { v1.start @@ -189,7 +177,7 @@ impl TInterval { } } - /// `v1- - v2+`, `v1+ - v2-` — Diamondplus. + /// `v1- - v2+`, `v1+ - v2-` — Diamondplus pub fn sub(v1: TInterval, v2: TInterval) -> TInterval { let start = if is_inf(v1.start) { v1.start @@ -209,7 +197,7 @@ impl TInterval { } } - /// `v1- + v2+`, `v1+ + v2-` — Boxminus erosion (may be invalid → `None`). + /// `v1- + v2+`, `v1+ + v2-` — Boxminus erosion (may be invalid → `None`) pub fn circle_add(v1: TInterval, v2: TInterval) -> Option { let start = if is_inf(v1.start) { v1.start @@ -229,7 +217,7 @@ impl TInterval { ) } - /// `v1- - v2-`, `v1+ - v2+` — Boxplus. + /// `v1- - v2-`, `v1+ - v2+` — Boxplus pub fn circle_sub(v1: TInterval, v2: TInterval) -> Option { let start = if is_inf(v1.start) { v1.start @@ -249,9 +237,7 @@ impl TInterval { ) } - // ── Set operations (MeTeoR intersection / union / inclusion) ── - - /// Intersection, or `None` if disjoint. + /// Intersection, or `None` if disjoint pub fn intersection(v1: TInterval, v2: TInterval) -> Option { let (start, start_open) = if v1.start > v2.start { (v1.start, v1.start_open) @@ -284,7 +270,7 @@ impl TInterval { Self::make(start, end, start_open, end_open) } - /// Union of overlapping/touching intervals, or `None` if there is a real gap. + /// Union of overlapping/touching intervals, or `None` if there is a real gap pub fn union(v1: TInterval, v2: TInterval) -> Option { let (v1, v2) = if v1.start > v2.start { (v2, v1) @@ -307,7 +293,7 @@ impl TInterval { }); } if v2.start == v1.end && v2.start_open && v1.end_open { - return None; // touch only at an excluded point + return None; } if v2.start <= v1.end { let (end, end_open) = Self::merge_right(v1, v2); @@ -322,7 +308,7 @@ impl TInterval { } } - /// Right endpoint of a union (v1.start <= v2.start already established). + /// Right endpoint of a union (v1.start <= v2.start already established) fn merge_right(v1: TInterval, v2: TInterval) -> (i64, bool) { let end = v1.end.max(v2.end); let end_open = if v1.end == v2.end && v1.end_open != v2.end_open { @@ -350,8 +336,8 @@ impl TInterval { } } -/// Merge a list of intervals into sorted, disjoint canonical form (MeTeoR -/// `coalescing`): sort by `(start, start_open)`, then fold with `union`. +/// Merge a list of intervals into sorted, disjoint canonical form (MeTeoR `coalescing`): sort by +/// `(start, start_open)`, then fold with `union` pub fn coalesce(mut intervals: Vec) -> Vec { if intervals.is_empty() { return intervals; @@ -372,7 +358,7 @@ pub fn coalesce(mut intervals: Vec) -> Vec { out } -/// Intersect two interval lists: all pairwise intersections, coalesced. +/// Intersect two interval lists: all pairwise intersections, coalesced pub fn intersect_lists(a: &[TInterval], b: &[TInterval]) -> Vec { let mut out = Vec::new(); for &x in a { diff --git a/datalogmtl/src/automata/mod.rs b/datalogmtl/src/automata/mod.rs index 32072de9..7d94ffd0 100644 --- a/datalogmtl/src/automata/mod.rs +++ b/datalogmtl/src/automata/mod.rs @@ -8,14 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Interval-native ("automata") evaluation strategy for DatalogMTL. -//! -//! Temporal operators are interval-arithmetic **transducers** (the batch I/O of -//! each operator's automaton) over rational/real-line intervals ported from -//! MeTeoR, and a **semi-naive-ready** interval fixpoint materializes the program. -//! Cost scales with the number of interval endpoints, independent of the time -//! horizon and interval widths — and it matches MeTeoR's semantics exactly -//! (dissolving the ℤ-vs-ℝ artifact of the tick engine). +//! Interval-native ("automata") evaluation strategy for DatalogMTL pub mod eval; pub mod interval; diff --git a/datalogmtl/src/automata/omega.rs b/datalogmtl/src/automata/omega.rs index efb6e45b..5adcdfde 100644 --- a/datalogmtl/src/automata/omega.rs +++ b/datalogmtl/src/automata/omega.rs @@ -8,22 +8,8 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! ω-materialization: unbounded-time reasoning via a finite **lasso** (prefix + -//! repeating period), the automata-theoretic canonical model. -//! -//! DatalogMTL minimal models are ultimately periodic in **both** directions. -//! Past operators propagate forward (a *right* period toward +∞); future -//! operators propagate backward (a *left* period toward −∞). Beyond -//! `R = max_data_end + reach` (right) and before `L = min_data_start − reach` -//! (left) the model repeats with a period dividing `P = lcm` of the metric -//! constants. We: -//! 1. materialize a finite prefix clipping intervals to `[Lb, H]` -//! (`H = R+3P`, `Lb = L−3P`); -//! 2. widen contiguous saturation to `[l, +∞)` (right) or `(−∞, e]` (left); -//! 3. verify each side is `P`-periodic and keep it. -//! -//! Entailment for arbitrarily far times reduces the query modulo `P` into the -//! materialized period window (right or left) and checks real-interval inclusion. +//! ω-materialization: unbounded-time reasoning via a finite **lasso** (prefix + repeating period), +//! the automata-theoretic canonical model use super::eval::eval_rule; use super::interval::{coalesce, TInterval, NEG_INF, POS_INF}; @@ -32,31 +18,27 @@ use crate::evaluator::compute_w_max; use crate::syntax::{DatalogMTLRule, TemporalAtom}; use shared::triple::Triple; -/// A finitely-represented (possibly infinite, ultimately periodic) model. +/// A finitely-represented (possibly infinite, ultimately periodic) model pub struct PeriodicModel { - /// Facts materialized over the prefix `[Lb, H]`, plus `[l,+∞)` / `(−∞,e]`. + /// Facts materialized over the prefix `[Lb, H]`, plus `[l,+∞)` / `(−∞,e]` pub db: Database, - /// Period `P` — the pattern repeats every `P` beyond each tail. + /// Period `P` — the pattern repeats every `P` beyond each tail pub period: i64, - /// Right (future) regime start `R`: beyond this the model is `P`-periodic. + /// Right (future) regime start `R`: beyond this the model is `P`-periodic pub right_start: i64, - /// Materialization bound `H = R+3P`. Three periods, so periodicity can be - /// checked by comparing repetitions rather than assumed. + /// Materialization bound `H = R+3P` pub right_horizon: i64, - /// A genuinely gappy right period was observed. + /// A genuinely gappy right period was observed pub right_periodic: bool, - /// Left (past) regime start `L`: before this the model is `P`-periodic. + /// Left (past) regime start `L`: before this the model is `P`-periodic pub left_start: i64, /// Materialization bound `Lb = L−3P`, the past-facing counterpart of - /// [`right_horizon`](Self::right_horizon). + /// [`right_horizon`](Self::right_horizon) pub left_horizon: i64, - /// A genuinely gappy left period was observed. + /// A genuinely gappy left period was observed pub left_periodic: bool, } -// Summarised rather than derived: the embedded `Database` is the whole materialised -// prefix. The regime bounds are what a reader is usually checking, so those print in -// full. impl std::fmt::Debug for PeriodicModel { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("PeriodicModel") @@ -74,7 +56,7 @@ impl std::fmt::Debug for PeriodicModel { } } -/// Build the ω-model of `rules` over `facts`. +/// Build the ω-model of `rules` over `facts` pub fn materialize_omega( rules: &[DatalogMTLRule], facts: Vec<(Triple, TInterval)>, @@ -99,11 +81,10 @@ pub fn materialize_omega( .unwrap_or(0); let r = max_x + reach; - let h = r + 3 * period; // right materialization bound + let h = r + 3 * period; let l = min_x - reach; - let lb = l - 3 * period; // left materialization bound + let lb = l - 3 * period; - // 1. Finite fixpoint, clipping interval ends/starts to `[lb, h]`. let mut db = Database::new(); for (t, iv) in facts { db.add_raw(t, iv); @@ -111,7 +92,6 @@ pub fn materialize_omega( db.coalesce_all(); fixpoint(&mut db, rules, lb, h); - // 2. Widen contiguous saturation on each side. let right_win = TInterval::closed(r, r + period); let left_win = TInterval::closed(l - period, l); let mut widened = false; @@ -136,10 +116,9 @@ pub fn materialize_omega( } } if widened { - fixpoint(&mut db, rules, lb, h); // propagate ±∞ (still clipped; ∞ is absorbing) + fixpoint(&mut db, rules, lb, h); } - // 3. Verify P-periodicity of each tail. let right_periodic = verify_side(&db, r + period, period, Dir::Right)?; let left_periodic = verify_side(&db, l - period, period, Dir::Left)?; @@ -161,7 +140,6 @@ pub fn entails(model: &PeriodicModel, triple: &Triple, query: TInterval) -> bool return false; }; - // Covered directly (prefix, or an unbounded `[l,+∞)` / `(−∞,e]` interval). if ivs.iter().any(|iv| TInterval::inclusion(query, *iv)) { return true; } @@ -171,23 +149,21 @@ pub fn entails(model: &PeriodicModel, triple: &Triple, query: TInterval) -> bool i64::MAX }; - // Far future: reduce modulo P into the materialized right period window. if model.right_periodic && query.is_finite() && query.start > model.right_horizon { if width >= model.period { return false; } let base = model.right_start + model.period; - let k = (query.start - base) / model.period; // >= 1 + let k = (query.start - base) / model.period; let reduced = query.shifted(-k * model.period); return ivs.iter().any(|iv| TInterval::inclusion(reduced, *iv)); } - // Far past: reduce modulo P into the materialized left period window. if model.left_periodic && query.is_finite() && query.end < model.left_horizon { if width >= model.period { return false; } let base = model.left_start - model.period; - let k = (base - query.end) / model.period; // >= 1 + let k = (base - query.end) / model.period; let reduced = query.shifted(k * model.period); return ivs.iter().any(|iv| TInterval::inclusion(reduced, *iv)); } @@ -200,8 +176,8 @@ enum Dir { Right, } -/// Confirm a tail is `P`-periodic (two consecutive period windows agree) and -/// report whether a genuinely gappy (finite) tail exists on that side. +/// Confirm a tail is `P`-periodic (two consecutive period windows agree) and report whether a +/// genuinely gappy (finite) tail exists on that side fn verify_side(db: &Database, base: i64, p: i64, dir: Dir) -> Result { let mut has_tail = false; for ivs in db.facts.values() { @@ -236,7 +212,7 @@ fn holds_at(ivs: &[TInterval], t: i64) -> bool { ivs.iter().any(|iv| iv.contains_point(t)) } -/// Naive interval fixpoint with derived interval endpoints clamped to `[lb, h]`. +/// Naive interval fixpoint with derived interval endpoints clamped to `[lb, h]` fn fixpoint(db: &mut Database, rules: &[DatalogMTLRule], lb: i64, h: i64) { const MAX_ITERS: usize = 1_000_000; for _ in 0..MAX_ITERS { @@ -260,7 +236,6 @@ fn fixpoint(db: &mut Database, rules: &[DatalogMTLRule], lb: i64, h: i64) { fn clip_intervals(ints: Vec, lb: i64, h: i64) -> Vec { ints.into_iter() .filter_map(|mut iv| { - // Drop intervals entirely outside the materialized window. if (iv.start != NEG_INF && iv.start > h) || (iv.end != POS_INF && iv.end < lb) { return None; } @@ -277,7 +252,7 @@ fn clip_intervals(ints: Vec, lb: i64, h: i64) -> Vec { .collect() } -/// All operator interval end-values appearing in rule bodies. +/// All operator interval end-values appearing in rule bodies fn op_ends(rules: &[DatalogMTLRule]) -> Vec { fn walk(atom: &TemporalAtom, out: &mut Vec) { match atom { diff --git a/datalogmtl/src/automata/relation.rs b/datalogmtl/src/automata/relation.rs index a4aac568..6900faf0 100644 --- a/datalogmtl/src/automata/relation.rs +++ b/datalogmtl/src/automata/relation.rs @@ -8,10 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Timed binding-relations and the relational join that carries interval -//! payloads. An atom evaluates to a `BindingRelation` (variable tuple -> -//! interval list); conjunction is a natural join with per-binding interval -//! **intersection**; temporal operators transduce the interval lists. +//! Timed binding-relations and the relational join that carries interval payloads use super::interval::{coalesce, intersect_lists, TInterval}; use super::transducer; @@ -21,18 +18,14 @@ use shared::terms::{Term, TriplePattern}; use shared::triple::Triple; use std::collections::HashMap; -/// The interval-valued fact base, indexed for pattern matching. +/// The interval-valued fact base, indexed for pattern matching pub struct Database { - /// When each fact holds. Canonical — non-overlapping and ascending — only - /// after [`coalesce_all`](Self::coalesce_all); [`add_raw`](Self::add_raw) - /// deliberately leaves it unmerged. + /// When each fact holds pub facts: HashMap>, by_pred: HashMap>, by_pred_obj: HashMap<(u32, u32), Vec>, } -// Summarised rather than derived: this is the fact base, so the derived form would -// print every fact and both indexes over them. impl std::fmt::Debug for Database { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { let intervals: usize = self.facts.values().map(Vec::len).sum(); @@ -44,7 +37,7 @@ impl std::fmt::Debug for Database { } impl Database { - /// An empty fact base. + /// An empty fact base pub fn new() -> Self { Database { facts: HashMap::new(), @@ -61,7 +54,7 @@ impl Database { .push(t.clone()); } - /// Append one interval to a fact (indexes the triple if new). Does not coalesce. + /// Append one interval to a fact (indexes the triple if new) pub fn add_raw(&mut self, t: Triple, iv: TInterval) { if !self.facts.contains_key(&t) { self.index_triple(&t); @@ -69,14 +62,14 @@ impl Database { self.facts.entry(t).or_default().push(iv); } - /// Coalesce every fact's interval list into canonical form. + /// Coalesce every fact's interval list into canonical form pub fn coalesce_all(&mut self) { for ivs in self.facts.values_mut() { *ivs = coalesce(std::mem::take(ivs)); } } - /// Union `new_ivs` into fact `t`, coalescing. Returns true if it changed. + /// Union `new_ivs` into fact `t`, coalescing pub fn add_intervals(&mut self, t: Triple, new_ivs: &[TInterval]) -> bool { let is_new = !self.facts.contains_key(&t); if is_new { @@ -90,7 +83,7 @@ impl Database { *entry != old } - /// Candidate ground triples that could match a pattern, using the indexes. + /// Candidate ground triples that could match a pattern, using the indexes fn candidates(&self, pattern: &TriplePattern) -> Vec { let pred = as_const(&pattern.1); let obj = as_const(&pattern.2); @@ -109,16 +102,14 @@ fn as_const(term: &Term) -> Option { } } -/// A relation over some variables with an interval list per binding tuple. +/// A relation over some variables with an interval list per binding tuple #[derive(Debug)] pub struct BindingRelation { - /// The variables this relation binds, sorted and distinct. The order defines - /// the tuple layout in [`rows`](Self::rows), so the two must be read together. - pub vars: Vec, // sorted, distinct - /// One entry per binding tuple, positionally aligned to - /// [`vars`](Self::vars), mapped to the intervals over which that tuple - /// holds. - pub rows: HashMap, Vec>, // values aligned to `vars` + /// The variables this relation binds, sorted and distinct + pub vars: Vec, + /// One entry per binding tuple, positionally aligned to [`vars`](Self::vars), mapped to the + /// intervals over which that tuple holds + pub rows: HashMap, Vec>, } impl BindingRelation { @@ -130,7 +121,7 @@ impl BindingRelation { } } -/// Distinct variable names appearing in a pattern, sorted. +/// Distinct variable names appearing in a pattern, sorted fn pattern_vars(pattern: &TriplePattern) -> Vec { let mut vs = Vec::new(); for term in [&pattern.0, &pattern.1, &pattern.2] { @@ -144,7 +135,7 @@ fn pattern_vars(pattern: &TriplePattern) -> Vec { vs } -/// Evaluate a (possibly nested) temporal atom to a timed binding-relation. +/// Evaluate a (possibly nested) temporal atom to a timed binding-relation pub fn eval_atom(atom: &TemporalAtom, db: &Database) -> BindingRelation { match atom { TemporalAtom::Base(pattern) => eval_base(pattern, db), @@ -159,8 +150,6 @@ pub fn eval_atom(atom: &TemporalAtom, db: &Database) -> BindingRelation { TemporalAtom::Prev { .. } => { panic!("Prev has no MeTeoR analogue and is unsupported by the interval strategy"); } - // All conjuncts hold at the same instants, so the result holds exactly - // where their validity intervals overlap. TemporalAtom::Conj(atoms) => { let mut iter = atoms.iter(); let Some(first) = iter.next() else { @@ -216,7 +205,7 @@ fn eval_base(pattern: &TriplePattern, db: &Database) -> BindingRelation { BindingRelation { vars, rows } } -/// Apply an interval-list transform to every row, dropping rows that become empty. +/// Apply an interval-list transform to every row, dropping rows that become empty fn map_transduce( r: BindingRelation, f: impl Fn(&[TInterval]) -> Vec, @@ -231,9 +220,7 @@ fn map_transduce( BindingRelation { vars: r.vars, rows } } -/// Natural join of two relations on shared variables, combining the interval -/// lists per matched binding with `combine` (intersection for conjunction, -/// `since` for the Since operator). +/// Natural join of two relations on shared variables, combining interval lists with `combine` pub fn join_with( a: &BindingRelation, b: &BindingRelation, @@ -265,7 +252,6 @@ pub fn join_with( .map(|v| b.vars.iter().position(|x| x == v).unwrap()) .collect(); - // Hash b by its shared-variable values. let mut b_index: HashMap, Vec<(&Vec, &Vec)>> = HashMap::new(); for (bk, bints) in &b.rows { let key: Vec = b_shared.iter().map(|&i| bk[i]).collect(); @@ -303,7 +289,7 @@ pub fn join_with( } } -/// Conjunctive join (interval intersection) — used to fold a rule body. +/// Conjunctive join (interval intersection) — used to fold a rule body pub fn conjoin(a: &BindingRelation, b: &BindingRelation) -> BindingRelation { join_with(a, b, |x, y| intersect_lists(x, y)) } diff --git a/datalogmtl/src/automata/transducer.rs b/datalogmtl/src/automata/transducer.rs index cecb7925..5a1583ec 100644 --- a/datalogmtl/src/automata/transducer.rs +++ b/datalogmtl/src/automata/transducer.rs @@ -8,26 +8,24 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Per-operator interval-list transducers — the batch I/O behaviour of each -//! temporal operator's automaton. Faithful port of MeTeoR -//! `meteor_reasoner/materialization/apply.py`. Each takes the inner atom's -//! interval list and returns the operator's output interval list (coalesced). +//! Per-operator interval-list transducers — the batch I/O behaviour of each temporal operator's +//! automaton use super::interval::{coalesce, TInterval}; use crate::syntax::Interval as OpInterval; -/// The metric bound `[a,b]` of an operator as a closed rational interval. +/// The metric bound `[a,b]` of an operator as a closed rational interval fn bound(op: &OpInterval) -> TInterval { TInterval::closed(op.start as i64, op.end as i64) } -/// `Diamondminus[a,b]`: dilate each interval `[s,e] -> [s+a, e+b]`. +/// `Diamondminus[a,b]`: dilate each interval `[s,e] -> [s+a, e+b]` pub fn diamond(inner: &[TInterval], op: &OpInterval) -> Vec { let b = bound(op); coalesce(inner.iter().map(|&iv| TInterval::add(iv, b)).collect()) } -/// `Boxminus[a,b]`: erode each interval `[s,e] -> [s+b, e+a]`, dropping invalid. +/// `Boxminus[a,b]`: erode each interval `[s,e] -> [s+b, e+a]`, dropping invalid pub fn box_(inner: &[TInterval], op: &OpInterval) -> Vec { let b = bound(op); coalesce( @@ -38,7 +36,7 @@ pub fn box_(inner: &[TInterval], op: &OpInterval) -> Vec { ) } -/// phi `Since[a,b]` psi (MeTeoR `since_deduce`), combined over all interval pairs. +/// phi `Since[a,b]` psi (MeTeoR `since_deduce`), combined over all interval pairs pub fn since(phi: &[TInterval], psi: &[TInterval], op: &OpInterval) -> Vec { let b = bound(op); let mut out = Vec::new(); @@ -52,9 +50,8 @@ pub fn since(phi: &[TInterval], psi: &[TInterval], op: &OpInterval) -> Vec Option { - // Special case S_[0,b]: MeTeoR returns psi shifted by the operator bound. if !op.start_open && op.start == 0 { return Some(TInterval::add(psi, *op)); } @@ -64,15 +61,13 @@ fn since_deduce(op: &TInterval, phi: TInterval, psi: TInterval) -> Option [s-b, e-a]`. +/// `Diamondplus[a,b]`: `[s,e] -> [s-b, e-a]` pub fn diamond_plus(inner: &[TInterval], op: &OpInterval) -> Vec { let b = bound(op); coalesce(inner.iter().map(|&iv| TInterval::sub(iv, b)).collect()) } -/// `Boxplus[a,b]`: `[s,e] -> [s-a, e-b]`, dropping invalid (future erosion). +/// `Boxplus[a,b]`: `[s,e] -> [s-a, e-b]`, dropping invalid (future erosion) pub fn box_plus(inner: &[TInterval], op: &OpInterval) -> Vec { let b = bound(op); coalesce( @@ -83,7 +78,7 @@ pub fn box_plus(inner: &[TInterval], op: &OpInterval) -> Vec { ) } -/// phi `Until[a,b]` psi (MeTeoR `until_deduce`), combined over all interval pairs. +/// phi `Until[a,b]` psi (MeTeoR `until_deduce`), combined over all interval pairs pub fn until(phi: &[TInterval], psi: &[TInterval], op: &OpInterval) -> Vec { let b = bound(op); let mut out = Vec::new(); @@ -97,7 +92,7 @@ pub fn until(phi: &[TInterval], psi: &[TInterval], op: &OpInterval) -> Vec Option { if !op.start_open && op.start == 0 { return Some(TInterval::sub(psi, *op)); diff --git a/datalogmtl/src/evaluator.rs b/datalogmtl/src/evaluator.rs index 393e8770..6db7f2a7 100644 --- a/datalogmtl/src/evaluator.rs +++ b/datalogmtl/src/evaluator.rs @@ -8,35 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The tick evaluator: one bottom-up fixpoint per time point, over a stream. -//! -//! [`DatalogMTLEvaluator::advance`] ingests the facts arriving at `t`, derives -//! everything the rules entail at `t`, and evicts what can no longer matter. -//! -//! # The time unit is not a free choice -//! -//! `Box` is decided by stepping one integer at a time across its window, so a -//! point in that window with no supporting fact fails it. Two consequences, both -//! of which have cost real debugging time: -//! -//! - Cost is proportional to the window's width *in whatever unit the timestamps -//! use*, not to the number of facts in it. -//! - With millisecond timestamps and a sampling period of one second, -//! `Box[0,5000]` asks for a fact at 5001 consecutive milliseconds and finds one -//! at every thousandth, so it derives nothing — silently, because deriving -//! nothing is also what a correctly unsatisfied rule does. -//! -//! Express time in units of the sampling period, so consecutive integers are -//! consecutive observations. The interval engine in -//! [`automata`](crate::automata) does not have this property at all, because it -//! reasons over real intervals instead of integer points. -//! -//! # Eviction -//! -//! Each tick drops everything older than `t - w_max`, where `w_max` is the -//! furthest back any single body atom can reach. Nesting adds, so -//! `Box[0,5]Diamond[0,3]` reaches back 8; separate rules take the maximum rather -//! than the sum, since each reaches back independently of the others. +//! The tick evaluator: one bottom-up fixpoint per time point, over a stream use crate::metrics::TickMetrics; use crate::store::TemporalStore; @@ -49,28 +21,18 @@ use shared::triple::Triple; use std::collections::HashSet; use std::time::Instant; -/// A validated rule set together with the store it reads and writes. -/// -/// Generic over the store so the representation can be chosen per workload -/// without the evaluator knowing which it got. +/// A validated rule set together with the store it reads and writes pub struct DatalogMTLEvaluator { - /// Rules, validated at construction. Mutating this afterwards bypasses that - /// check and leaves `w_max` stale, so eviction may then drop facts a rule - /// still needs; rebuild the evaluator instead. + /// Rules, validated at construction pub rules: Vec, - /// Facts, both ingested and derived. + /// Facts, both ingested and derived pub store: S, - /// Term interning, shared with the caller: ids in the rules and the store - /// have to mean the same thing outside. + /// Term interning, shared with the caller: ids in the rules and the store have to mean the same + /// thing outside pub dictionary: SharedTerms, - /// Maximum interval width across all rules. Used for eviction cutoff. w_max: u64, } -// Summarised rather than derived, and deliberately not bounded on `S: Debug`: the -// store is the fact base, and requiring it to be printable would push that -// obligation onto every store implementation for no benefit. `snapshot_count` is the -// cheap question to ask one. impl std::fmt::Debug for DatalogMTLEvaluator { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("DatalogMTLEvaluator") @@ -82,12 +44,7 @@ impl std::fmt::Debug for DatalogMTLEvaluator { } impl DatalogMTLEvaluator { - /// Validates `rules` and computes the eviction horizon from them. - /// - /// # Errors - /// - /// The message from [`validate_rules`]: - /// an interval with `start > end`, or a head variable no body atom can bind. + /// Validates `rules` and computes the eviction horizon from them pub fn new( rules: Vec, store: S, @@ -103,18 +60,15 @@ impl DatalogMTLEvaluator { }) } - /// Advance to time t, ingesting new_triples into the store. - /// Returns all newly derived triples and per-tick metrics. + /// Advance to time t, ingesting new_triples into the store pub fn advance(&mut self, t: u64, new_triples: Vec) -> (Vec, TickMetrics) { let start = Instant::now(); let mut metrics = TickMetrics::new(t); - // 1. Ingest stream triples at t. for triple in &new_triples { self.store.insert(triple, t); } - // 2. Bottom-up fixpoint: derive new facts at t. let mut all_derived: HashSet = HashSet::new(); loop { let mut new_this_round = Vec::new(); @@ -141,7 +95,6 @@ impl DatalogMTLEvaluator { metrics.new_triples += new_this_round.len(); } - // 3. Evict snapshots older than w_max. let cutoff = t.saturating_sub(self.w_max); self.store.evict(cutoff); @@ -152,10 +105,8 @@ impl DatalogMTLEvaluator { (all_derived.into_iter().collect(), metrics) } - /// Evaluate one rule at time t. Returns all satisfying variable bindings. + /// Evaluate one rule at time t fn evaluate_rule(&self, rule: &DatalogMTLRule, t: u64, metrics: &mut TickMetrics) -> Bindings { - // Step 1: seed bindings from all Base atoms at the current snapshot, - // using indexed store lookups and indexed joins (no full-fact scan). let base_atoms: Vec<&TriplePattern> = rule .body .iter() @@ -170,10 +121,9 @@ impl DatalogMTLEvaluator { let mut bindings = self.seed_base_atoms(&base_atoms, t); - // Step 2: for each temporal atom, filter/extend bindings. for atom in &rule.body { match atom { - TemporalAtom::Base(_) => {} // already handled above + TemporalAtom::Base(_) => {} TemporalAtom::Diamond { interval, inner } => { metrics.diamond_evals += 1; bindings = self.eval_diamond(interval, inner, t, &bindings); @@ -191,13 +141,9 @@ impl DatalogMTLEvaluator { bindings = new_bindings; metrics.since_scan_depth += depth; } - // A top-level conjunction is just its conjuncts at time t. TemporalAtom::Conj(_) => { bindings = self.eval_atom_at(atom, t, &bindings); } - // Future operators require static data; the streaming tick engine - // cannot evaluate them. Unreachable — the parser blocks future ops - // in Streaming mode, and Static mode uses the interval engine. TemporalAtom::DiamondPlus { .. } | TemporalAtom::BoxPlus { .. } | TemporalAtom::Until { .. } => { @@ -211,10 +157,7 @@ impl DatalogMTLEvaluator { bindings } - /// Seed bindings by joining the rule's Base atoms at time `t` using indexed - /// store lookups. Each subsequent atom is queried with the running binding - /// substituted in, turning the join into indexed probes rather than an - /// O(facts^k) nested-loop scan. + /// Seed bindings by joining the rule's Base atoms at time `t` using indexed store lookups fn seed_base_atoms(&self, base_atoms: &[&TriplePattern], t: u64) -> Bindings { let Some((first, rest)) = base_atoms.split_first() else { return Bindings::unit(); @@ -229,8 +172,6 @@ impl DatalogMTLEvaluator { let spec = substitute_pattern(pattern, binding); let candidates = self.store.query_at(&spec, t); for candidate in candidates.iter() { - // Owned, because a merge adds variables the row did not have and - // a row of a sequence is a view into one schema. let mut merged = binding.to_binding(); let mut consistent = true; for (var, val) in candidate.iter() { @@ -253,9 +194,7 @@ impl DatalogMTLEvaluator { bindings } - // --- Temporal operator implementations --- - - /// Diamond[a,b]: phi must hold at SOME t' in [t-b, t-a]. + /// Diamond[a,b]: phi must hold at SOME t' in [t-b, t-a] fn eval_diamond( &self, interval: &Interval, @@ -279,12 +218,7 @@ impl DatalogMTLEvaluator { results } - /// Box[a,b]: phi must hold at EVERY integer point t' in [t-b, t-a] - /// (dense semantics, matching the DatalogMTL/MeTeoR reference: an integer in - /// the window with no supporting fact makes Box fail — it is not skipped). - /// Collects candidate bindings from the first point, then filters them - /// against every subsequent point — so variables introduced solely inside - /// Box (e.g. Box[0,5](?x :sensor ?v)) are still grounded. + /// Box[a,b]: phi must hold at every integer point t' in [t-b, t-a] fn eval_box( &self, interval: &Interval, @@ -292,25 +226,18 @@ impl DatalogMTLEvaluator { t: u64, bindings: &Bindings, ) -> Bindings { - // Universal operator: the whole window [t-end, t-start] must lie within - // observable time [0, t]. If t < end the window extends before time 0, - // where nothing holds, so Box fails. (Guarding on interval.start would - // let absolute_range's saturating_sub clamp the lower bound to 0 and - // spuriously satisfy Box at the leading boundary.) if t < interval.end { return Bindings::empty(); } - let (lo, hi) = interval.absolute_range(t); // lo <= hi since end >= start + let (lo, hi) = interval.absolute_range(t); let mut results = Bindings::empty(); 'outer: for binding in bindings { - // Seed candidates from the first integer point. let mut candidates = self.eval_atom_at(inner, lo, &Bindings::single(binding.to_binding())); if candidates.is_empty() { continue 'outer; } - // Require every subsequent integer point in the window to satisfy inner. for t_prime in (lo + 1)..=hi { let mut surviving = Bindings::empty(); for candidate in candidates { @@ -330,7 +257,7 @@ impl DatalogMTLEvaluator { results } - /// Prev[a,b]: phi holds at the MOST RECENT t' in [t-b, t-a]. + /// Prev[a,b]: phi holds at the MOST RECENT t' in [t-b, t-a] fn eval_prev( &self, interval: &Interval, @@ -355,10 +282,8 @@ impl DatalogMTLEvaluator { results } - /// phi Since[a,b] psi: - /// EXISTS t' in [t-b, t-a]: psi holds at t' - /// AND FORALL t'' in (t', t]: phi holds at t'' - /// Returns (bindings, total_scan_depth). + /// phi Since[a,b] psi: EXISTS t' in [t-b, t-a]: psi holds at t' AND FORALL t'' in (t', t]: phi + /// holds at t'' fn eval_since( &self, interval: &Interval, @@ -368,8 +293,6 @@ impl DatalogMTLEvaluator { bindings: &Bindings, ) -> (Bindings, usize) { let (lo, hi) = interval.absolute_range(t); - // Reset candidates: fact-bearing points in the window where psi may hold - // (psi only holds where a fact exists, so scanning timestamps is complete). let since_timestamps = self.store.timestamps_in(lo, hi); let mut results = Bindings::empty(); let mut scan_depth = 0; @@ -383,8 +306,6 @@ impl DatalogMTLEvaluator { continue; } - // FORALL integer t'' in (t', t]: phi must hold (dense semantics — - // an uncovered integer point makes the continuation fail). for t_pp in (t_prime + 1)..=t { scan_depth += 1; let phi_results = @@ -394,21 +315,19 @@ impl DatalogMTLEvaluator { } } results.extend(psi_results); - break 'reset; // existential: first valid reset point is enough + break 'reset; } } (results, scan_depth) } - /// Evaluate a single TemporalAtom at a fixed time point t_prime, - /// starting from the given partial bindings. + /// Evaluate a single TemporalAtom at a fixed time point t_prime, starting from the given + /// partial bindings fn eval_atom_at(&self, atom: &TemporalAtom, t_prime: u64, bindings: &Bindings) -> Bindings { match atom { TemporalAtom::Base(pattern) => { let mut results = Bindings::empty(); for binding in bindings { - // Specialize the pattern with already-bound variables so the - // store query is as constrained (and indexed) as possible. let spec = substitute_pattern(pattern, binding); let candidates = self.store.query_at(&spec, t_prime); for candidate in candidates.iter() { @@ -444,8 +363,6 @@ impl DatalogMTLEvaluator { let (b, _) = self.eval_since(interval, phi, psi, t_prime, bindings); b } - // All conjuncts hold at the SAME time point: thread bindings through - // them left to right, so each is constrained by the previous. TemporalAtom::Conj(atoms) => { let mut current = bindings.clone(); for a in atoms { @@ -456,8 +373,6 @@ impl DatalogMTLEvaluator { } current } - // Future operators are unsupported by the streaming tick engine - // (blocked upstream by the parser/mode); yield no bindings. TemporalAtom::DiamondPlus { .. } | TemporalAtom::BoxPlus { .. } | TemporalAtom::Until { .. } => Bindings::empty(), @@ -465,8 +380,7 @@ impl DatalogMTLEvaluator { } } -/// Substitute a pattern's variables that are already bound in `binding` with -/// their constant values, so the resulting query is maximally constrained. +/// Replaces a pattern's variables already bound in `binding` with their values fn substitute_pattern(pattern: &TriplePattern, binding: BindingRef<'_>) -> TriplePattern { let resolve = |term: &Term| -> Term { match term { @@ -484,15 +398,7 @@ fn substitute_pattern(pattern: &TriplePattern, binding: BindingRef<'_>) -> Tripl ) } -/// How far back in time the rules can read. Drives store eviction. -/// -/// Nesting is **additive**, not a max: `Diamond[6,20](Box[0,5] X)` evaluates the -/// Box at some t' in `[t-20, t-6]`, and the Box then reads `[t'-5, t']` — so the -/// earliest point touched is `t-25`. Taking the max here would evict `t-25..t-21` -/// while a rule still needed it, and the rule would silently never fire. -/// -/// A conjunction is the exception: its atoms all sit at the same time point, so -/// its reach is the widest of them, not their sum. +/// How far back in time the rules can read pub fn compute_w_max(rules: &[DatalogMTLRule]) -> u64 { fn atom_max(atom: &TemporalAtom) -> u64 { match atom { diff --git a/datalogmtl/src/lib.rs b/datalogmtl/src/lib.rs index 24dc5154..a4b55f24 100644 --- a/datalogmtl/src/lib.rs +++ b/datalogmtl/src/lib.rs @@ -8,31 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! DatalogMTL over RDF: Datalog whose rule bodies can say *when* a pattern held. -//! -//! `Box[0,10]` for throughout the last 10 time units, `Diamond[0,10]` for -//! somewhere in them, `Since` for continuously since a trigger. [`syntax`] defines -//! the operators, [`parser`] reads them from text, and [`validate`] rejects the -//! programs that would derive nothing. -//! -//! # Two evaluation strategies -//! -//! | | [`evaluator`] (tick) | [`automata`] (interval-native) | -//! |---|---|---| -//! | time | integer points, one tick at a time | real-line intervals | -//! | cost grows with | window width | number of interval endpoints | -//! | streaming | yes | no, needs the whole timeline | -//! | future operators | no | yes | -//! | unbounded time | no | yes, via [`automata::omega`] | -//! -//! The two do not agree in general, and neither is wrong: the tick engine decides -//! `Box` by checking every integer point in its window, so it answers over ℤ, -//! while the interval engine answers over ℝ and matches the MeTeoR reference -//! reasoner exactly. Choose the tick engine for a stream and the interval engine -//! for a fixed corpus. -//! -//! This crate does not depend on `datalog`, deliberately — the two reasoners are -//! siblings over [`shared`]'s vocabulary rather than one layered on the other. +//! DatalogMTL over RDF: Datalog whose rule bodies can say *when* a pattern held pub mod automata; pub mod evaluator; @@ -45,9 +21,6 @@ pub mod stream; pub mod syntax; pub mod validate; -// The test modules share fixtures via `pub`, which `unreachable_pub` flags because -// `tests` itself is private. That is the lint working correctly on code where it has -// nothing useful to say. #[cfg(test)] #[allow(unreachable_pub)] mod tests; diff --git a/datalogmtl/src/meteor_fmt.rs b/datalogmtl/src/meteor_fmt.rs index ab6d4c1b..07849044 100644 --- a/datalogmtl/src/meteor_fmt.rs +++ b/datalogmtl/src/meteor_fmt.rs @@ -8,17 +8,15 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Reverse of [`crate::parser`]: render derived RDF triples back into MeTeoR's -//! textual atom form and coalesce per-atom integer tick-sets into closed -//! intervals, so datalogmtl output can be diffed line-for-line against MeTeoR. +//! Renders derived triples back into MeTeoR's textual form, coalesced into closed intervals use crate::parser::RDF_TYPE; use shared::dictionary::Dictionary; use shared::triple::Triple; use std::collections::{BTreeSet, HashMap}; -/// Render a triple as a MeTeoR atom string (no interval): -/// `(x, rdf:type, A)` → `A(x)`; `(x, C, y)` → `C(x,y)`. +/// Render a triple as a MeTeoR atom string (no interval): `(x, rdf:type, A)` → `A(x)`; `(x, C, y)` +/// → `C(x,y)` pub fn atom_string(triple: &Triple, dict: &Dictionary) -> String { let s = dict.decode(triple.subject).unwrap_or("?"); let p = dict.decode(triple.predicate).unwrap_or("?"); @@ -30,8 +28,7 @@ pub fn atom_string(triple: &Triple, dict: &Dictionary) -> String { } } -/// Coalesce a per-triple set of integer timepoints into MeTeoR-format lines -/// `Pred(args)@[l,r]`, one line per contiguous interval, globally sorted. +/// Coalesces a triple's integer time points into sorted `Pred(args)@[l,r]` lines pub fn format_tick_sets(ticks: &HashMap>, dict: &Dictionary) -> Vec { let mut lines = Vec::new(); for (triple, points) in ticks { @@ -44,7 +41,7 @@ pub fn format_tick_sets(ticks: &HashMap>, dict: &Dictionar lines } -/// Merge a sorted set of integer points into maximal contiguous closed intervals. +/// Merge a sorted set of integer points into maximal contiguous closed intervals fn coalesce(points: &BTreeSet) -> Vec<(u64, u64)> { let mut out: Vec<(u64, u64)> = Vec::new(); for &t in points { diff --git a/datalogmtl/src/metrics.rs b/datalogmtl/src/metrics.rs index 676636e2..c31045f2 100644 --- a/datalogmtl/src/metrics.rs +++ b/datalogmtl/src/metrics.rs @@ -8,49 +8,37 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Per-tick counters, for seeing where the tick evaluator's time goes. -//! -//! One [`TickMetrics`] per [`advance`](crate::evaluator::DatalogMTLEvaluator::advance) -//! call, counting operator evaluations rather than timing them individually, so -//! the overhead is an increment per operator. -//! -//! [`TickMetrics::csv_header`] and [`TickMetrics::to_csv_row`] are a pair: they -//! are written out separately and nothing checks that their column orders agree, -//! so a field added to one belongs in both. +//! Per-tick counters, for seeing where the tick evaluator's time goes #[derive(Debug, Default, Clone)] -/// What one tick cost, and what it did. +/// What one tick cost, and what it did pub struct TickMetrics { - /// The time point this tick evaluated. + /// The time point this tick evaluated pub timestamp: u64, - /// Rounds the bottom-up fixpoint took. More than one means a rule derived - /// something another rule could then use. + /// Rounds the bottom-up fixpoint took pub fixpoint_iterations: usize, - /// Rule evaluations, counted once per rule per round rather than per rule. + /// Rule evaluations, counted once per rule per round rather than per rule pub rules_fired: usize, - /// Facts derived at this tick, not counting those ingested. + /// Facts derived at this tick, not counting those ingested pub new_triples: usize, - /// Distinct timestamps the store holds after eviction. + /// Distinct timestamps the store holds after eviction pub snapshot_count: usize, - /// Facts the store holds in total, across every timestamp. + /// Facts the store holds in total, across every timestamp pub total_triples_in_store: usize, - /// Wall-clock time for the whole tick, in microseconds. + /// Wall-clock time for the whole tick, in microseconds pub eval_time_us: u64, - /// `Diamond` evaluations. Each scans its window for one witness. + /// `Diamond` evaluations pub diamond_evals: usize, - /// `Box` evaluations. Each steps over every integer point in its window, so - /// this count times the window width is the better guide to cost. + /// `Box` evaluations pub box_evals: usize, - /// `Since` evaluations. + /// `Since` evaluations pub since_evals: usize, - /// Total points scanned by those `Since` evaluations, summed. Divided by - /// `since_evals` it gives the average backward scan, which is what grows - /// when a trigger stops occurring. + /// Total points scanned by those `Since` evaluations, summed pub since_scan_depth: usize, } impl TickMetrics { - /// Zeroed counters for a tick at `timestamp`. + /// Zeroed counters for a tick at `timestamp` pub fn new(timestamp: u64) -> Self { Self { timestamp, @@ -58,17 +46,14 @@ impl TickMetrics { } } - /// Column names for [`to_csv_row`](Self::to_csv_row), without a trailing - /// newline. The two are written independently and nothing checks that their - /// orders agree, so a new field belongs in both. + /// Column names for [`to_csv_row`](Self::to_csv_row), without a trailing newline pub fn csv_header() -> &'static str { "timestamp,fixpoint_iterations,rules_fired,new_triples,snapshot_count,\ total_triples_in_store,eval_time_us,diamond_evals,box_evals,\ since_evals,since_scan_depth" } - /// One row in the order [`csv_header`](Self::csv_header) declares, without a - /// trailing newline. + /// One row in the order [`csv_header`](Self::csv_header) declares, without a trailing newline pub fn to_csv_row(&self) -> String { format!( "{},{},{},{},{},{},{},{},{},{},{}", @@ -87,7 +72,7 @@ impl TickMetrics { } } -/// Writes a header and one row per tick to `path`, truncating any existing file. +/// Writes a header and one row per tick to `path`, truncating any existing file pub fn write_metrics_csv(path: &str, metrics: &[TickMetrics]) -> std::io::Result<()> { use std::io::Write; let mut f = std::fs::File::create(path)?; diff --git a/datalogmtl/src/parser.rs b/datalogmtl/src/parser.rs index 9b2832db..d712c7ed 100644 --- a/datalogmtl/src/parser.rs +++ b/datalogmtl/src/parser.rs @@ -9,50 +9,27 @@ */ //! Parser for the **past-only, integer, arity-≤2** fragment of MeTeoR's textual -//! DatalogMTL syntax. Used to drive the datalogmtl engine from the same corpus -//! files that feed the MeTeoR reference reasoner, so results can be diffed. -//! -//! Supported fragment: -//! - Operators (past): `Boxminus[a,b]`, `Diamondminus[a,b]`, `L Since[a,b] R`. -//! Operators may stack (`Boxminus[1,2]Diamondminus[0,1]A(X)`). -//! - Plain (operator-free) rule heads only. -//! - Closed integer intervals `[a,b]` (and single point `[a]`); open bounds rejected. -//! - Predicate arity 1 or 2. Arity 0 (propositional) and arity ≥3 rejected. -//! -//! RDF mapping (arity ≤2): -//! - unary `A(x)` ↔ triple `(x, rdf:type, A)` -//! - binary `C(x,y)` ↔ triple `(x, C, y)` -//! -//! Future operators (`Boxplus`/`Diamondplus`/`Until`) and openness are outside the -//! fragment and produce a descriptive error. use crate::syntax::{DatalogMTLRule, Interval, Mode, TemporalAtom}; use shared::term_encoder::SharedTerms; use shared::terms::{Term, TriplePattern}; use shared::triple::Triple; -/// Full IRI for the `rdf:type` predicate used to encode unary atoms. +/// Full IRI for the `rdf:type` predicate used to encode unary atoms pub const RDF_TYPE: &str = "http://www.w3.org/1999/02/22-rdf-syntax-ns#type"; -/// A grounded temporal fact with a closed integer validity interval `[start, end]`. +/// A grounded temporal fact with a closed integer validity interval `[start, end]` #[derive(Debug, Clone, PartialEq, Eq)] pub struct TemporalFact { - /// The fact, with terms already interned. + /// The fact, with terms already interned pub triple: Triple, - /// First time it holds, inclusive. + /// First time it holds, inclusive pub start: u64, - /// Last time it holds, inclusive. Equal to `start` for a single-point fact, - /// which is how the surface syntax's `[a]` form parses. + /// Last time it holds, inclusive pub end: u64, } -// ──────────────────────────────────────────────────────────────── -// Public entry points -// ──────────────────────────────────────────────────────────────── - -/// Parse a whole program (one rule per non-empty, non-comment line). In `Static` -/// mode future operators (`Boxplus`/`Diamondplus`/`Until`) are accepted; in -/// `Streaming` mode they are rejected (they require the whole timeline). +/// Parse a whole program (one rule per non-empty, non-comment line) pub fn parse_program( text: &str, dict: &SharedTerms, @@ -71,7 +48,7 @@ pub fn parse_program( Ok(rules) } -/// Parse a data file (one fact per non-empty, non-comment line). +/// Parse a data file (one fact per non-empty, non-comment line) pub fn parse_data(text: &str, dict: &SharedTerms) -> Result, String> { let mut facts = Vec::new(); for (i, raw) in text.lines().enumerate() { @@ -86,10 +63,6 @@ pub fn parse_data(text: &str, dict: &SharedTerms) -> Result, S Ok(facts) } -// ──────────────────────────────────────────────────────────────── -// Rule parsing -// ──────────────────────────────────────────────────────────────── - fn parse_rule( line: &str, _lineno: usize, @@ -103,7 +76,6 @@ fn parse_rule( return Err("negation ('||') is outside the supported fragment".into()); } - // Head must be a plain atom (no temporal operator), in either mode. if any_operator_prefix(head_str) || find_kw_depth0(head_str, "Since").is_some() || find_kw_depth0(head_str, "Until").is_some() @@ -130,14 +102,12 @@ fn parse_rule( }) } -/// Parse a single body literal into a (possibly nested) `TemporalAtom`. +/// Parse a single body literal into a (possibly nested) `TemporalAtom` fn parse_literal(s: &str, dict: &SharedTerms, mode: Mode) -> Result { - // Binary Since (past): `L Since[a,b] R` (whitespace already stripped). if let Some(idx) = find_kw_depth0(s, "Since") { let (interval, phi, psi) = parse_binary(s, idx, "Since", dict, mode)?; return Ok(TemporalAtom::Since { interval, phi, psi }); } - // Binary Until (future, static only). if let Some(idx) = find_kw_depth0(s, "Until") { if mode != Mode::Static { return Err("Until (future operator) requires static mode".into()); @@ -146,7 +116,6 @@ fn parse_literal(s: &str, dict: &SharedTerms, mode: Mode) -> Result = Vec::new(); let mut rest = s; loop { @@ -178,7 +147,7 @@ fn parse_literal(s: &str, dict: &SharedTerms, mode: Mode) -> Result[a,b] R` at `idx` (start of `kw`) into (interval, phi, psi). +/// Split `L [a,b] R` at `idx` (start of `kw`) into (interval, phi, psi) fn parse_binary( s: &str, idx: usize, @@ -215,7 +184,7 @@ impl OpKind { } } -/// A supported leading operator keyword (past always; future only in `Static`). +/// A supported leading operator keyword (past always; future only in `Static`) fn leading_operator(s: &str, mode: Mode) -> Option { if s.starts_with("Boxminus") { Some(OpKind::Box) @@ -230,8 +199,8 @@ fn leading_operator(s: &str, mode: Mode) -> Option { } } -/// A leading operator that is NOT allowed here: the `SOMETIME`/`ALWAYS` aliases -/// (unsupported), and future operators when in `Streaming` mode. +/// A leading operator not allowed here: the `SOMETIME`/`ALWAYS` aliases, and future operators in +/// `Streaming` mode fn disallowed_operator(s: &str, mode: Mode) -> Option<&'static str> { for kw in ["SOMETIME", "ALWAYS"] { if s.starts_with(kw) { @@ -248,7 +217,7 @@ fn disallowed_operator(s: &str, mode: Mode) -> Option<&'static str> { None } -/// Whether `s` starts with any temporal operator keyword (mode-agnostic; for head check). +/// Whether `s` starts with any temporal operator keyword (mode-agnostic; for head check) fn any_operator_prefix(s: &str) -> bool { [ "Boxminus", @@ -262,10 +231,6 @@ fn any_operator_prefix(s: &str) -> bool { .any(|kw| s.starts_with(kw)) } -// ──────────────────────────────────────────────────────────────── -// Fact parsing -// ──────────────────────────────────────────────────────────────── - fn parse_fact(line: &str, dict: &SharedTerms) -> Result { let Some((atom_str, span)) = line.rsplit_once('@') else { return Err("fact missing '@'".into()); @@ -275,7 +240,7 @@ fn parse_fact(line: &str, dict: &SharedTerms) -> Result { Ok(TemporalFact { triple, start, end }) } -/// Parse a fact interval: `t`, `[t]`, or `[l,r]`. Closed integers only. +/// Parse a fact interval: `t`, `[t]`, or `[l,r]` fn parse_span(span: &str) -> Result<(u64, u64), String> { let span = span.trim(); if span.starts_with('[') || span.starts_with('(') { @@ -307,15 +272,11 @@ fn parse_span(span: &str) -> Result<(u64, u64), String> { } } -// ──────────────────────────────────────────────────────────────── -// Atom ↔ RDF triple mapping -// ──────────────────────────────────────────────────────────────── - -/// Split an atom string `Pred(a,b)` into (functor, args). Nullary => empty args. +/// Split an atom string `Pred(a,b)` into (functor, args) fn split_atom(s: &str) -> Result<(&str, Vec<&str>), String> { let s = s.trim(); let Some(open) = s.find('(') else { - return Ok((s, Vec::new())); // no parens: propositional (arity 0) + return Ok((s, Vec::new())); }; if !s.ends_with(')') { return Err(format!("malformed atom '{}'", s)); @@ -326,7 +287,7 @@ fn split_atom(s: &str) -> Result<(&str, Vec<&str>), String> { Ok((functor, args)) } -/// Map an atom to a triple *pattern* (rule context: uppercase args are variables). +/// Map an atom to a triple *pattern* (rule context: uppercase args are variables) fn atom_to_pattern(s: &str, dict: &SharedTerms, _rule_ctx: bool) -> Result { let (functor, args) = split_atom(s)?; if functor.is_empty() { @@ -356,7 +317,7 @@ fn atom_to_pattern(s: &str, dict: &SharedTerms, _rule_ctx: bool) -> Result Result { let (functor, args) = split_atom(s)?; if functor.is_empty() { @@ -393,7 +354,7 @@ fn atom_to_ground_triple(s: &str, dict: &SharedTerms) -> Result }) } -/// A rule-context term: uppercase first letter => variable, else constant. +/// A rule-context term: uppercase first letter => variable, else constant fn term_of(tok: &str, dict: &SharedTerms) -> Result { let tok = tok.trim(); let Some(first) = tok.chars().next() else { @@ -410,11 +371,7 @@ fn encode(dict: &SharedTerms, s: &str) -> u32 { dict.encode(s) } -// ──────────────────────────────────────────────────────────────── -// Low-level string helpers -// ──────────────────────────────────────────────────────────────── - -/// Strip comments (`#`/`//`) and all whitespace, mirroring MeTeoR's `replace(" ","")`. +/// Strip comments (`#`/`//`) and all whitespace, mirroring MeTeoR's `replace(" ","")` fn strip_line(raw: &str) -> String { let mut s = raw; if let Some(i) = s.find('#') { @@ -437,8 +394,7 @@ fn parse_int(s: &str) -> Result { }) } -/// Read a bracketed group at the start of `s`. Returns -/// (open_char, inner, close_char, tail_after_close). +/// Read a bracketed group at the start of `s` fn read_bracket(s: &str) -> Result<(char, &str, char, &str), String> { let bytes = s.as_bytes(); let open = bytes[0] as char; @@ -464,8 +420,7 @@ fn read_bracket(s: &str) -> Result<(char, &str, char, &str), String> { Err(format!("unbalanced brackets in '{}'", s)) } -/// Read a *closed integer* operator interval `[a,b]` (or `[a]`) at the start of `s`. -/// Returns the parsed `Interval` and the remaining tail. +/// Read a *closed integer* operator interval `[a,b]` (or `[a]`) at the start of `s` fn read_operator_interval(s: &str) -> Result<(Interval, &str), String> { let (open, inner, close, tail) = read_bracket(s)?; if open != '[' || close != ']' { @@ -486,7 +441,7 @@ fn read_operator_interval(s: &str) -> Result<(Interval, &str), String> { Ok((Interval { start, end }, tail)) } -/// Find keyword `kw` in `s` at bracket-depth 0. Returns byte index of match start. +/// Find keyword `kw` in `s` at bracket-depth 0 fn find_kw_depth0(s: &str, kw: &str) -> Option { let mut depth = 0i32; let bytes = s.as_bytes(); @@ -505,7 +460,7 @@ fn find_kw_depth0(s: &str, kw: &str) -> Option { None } -/// Split `s` on commas at bracket-depth 0 (MeTeoR `parse_body`). +/// Split `s` on commas at bracket-depth 0 (MeTeoR `parse_body`) fn split_top_commas(s: &str) -> Vec { let mut out = Vec::new(); let mut depth = 0i32; diff --git a/datalogmtl/src/rdf_parser.rs b/datalogmtl/src/rdf_parser.rs index a77edf99..74063116 100644 --- a/datalogmtl/src/rdf_parser.rs +++ b/datalogmtl/src/rdf_parser.rs @@ -8,68 +8,8 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Parser for **RDF triple-pattern** DatalogMTL syntax — the counterpart to -//! [`crate::parser`], which reads MeTeoR's `Pred(args)` syntax. -//! -//! Use this one whenever rules must line up with real RDF data: predicates and -//! constants are IRIs interned into the caller's [`Dictionary`], so the ids match -//! facts produced elsewhere. (The MeTeoR parser interns the bare functor, so -//! `inZone(D,Z)` yields a different id than `` and silently -//! matches nothing.) -//! -//! # Rules -//! -//! ```text -//! PREFIX dront: -//! -//! [sustainedGeofenceViolation] -//! (?d, dront:violatedZone, ?z) :- -//! Box[0,30](?d, dront:inZone, ?z), -//! (?z, dront:status, dront:Restricted). -//! ``` -//! -//! Rules are terminated by a `.` at depth 0 and may span lines. An optional -//! `[label]` before the head becomes the rule id (otherwise the rule index is -//! used). Operators: `Diamond`/`Diamondminus`, `Box`/`Boxminus`, `Prev`, and -//! `Since[a,b](phi, psi)`; in [`Mode::Static`] also `Diamondplus`, `Boxplus` and -//! `Until[a,b](phi, psi)`. Operators stack: `Box[0,5]Diamond[0,2](?x, :p, ?y)`. -//! -//! # Stream shapes -//! -//! ```text -//! STREAM -//! PATTERN (?obs, rdf:type, sosa:Observation) -//! (?obs, sosa:madeBySensor, ?drone) -//! KEY ?drone -//! STALENESS 10 -//! EXPIRY (?drone, utm:channelStatus, utm:expired) -//! . -//! ``` -//! -//! Keywords are case-sensitive and blocks are separated by a depth-0 `.`. -//! `STREAM` and `PATTERN` are required; `KEY` defaults to empty and `STALENESS` -//! to `0`. The unit of `STALENESS` is whatever unit the caller's timestamps use. -//! -//! `EXPIRY` declares facts transmitted on behalf of a channel that has gone -//! stale — silence is not itself an event, so without it a rule has nothing to -//! observe when a source stops reporting. Only `KEY` variables may appear there, -//! since the key binding is all that is still known about an expired channel. -//! See [`crate::stream::ShapeIngester::expiry_facts`]. -//! -//! # Terms -//! -//! | Form | Meaning | -//! |---|---| -//! | `?x` | variable | -//! | `` | IRI constant (brackets stripped before interning) | -//! | `pfx:local` | IRI constant, expanded against a `PREFIX` declaration | -//! | `:name` | with no empty `PREFIX` declared, interned verbatim as `":name"` | -//! | `"lit"` | literal constant, interned with its quotes | -//! -//! `PREFIX` declarations are per text blob — rules and stream shapes are parsed -//! independently, so each must declare the prefixes it uses. Anything inside -//! `<…>` or `"…"` is opaque to the scanner, so `#` in an IRI fragment is not -//! mistaken for a comment and `.` in a host name does not terminate a block. +//! Parser for **RDF triple-pattern** DatalogMTL syntax — the counterpart to [`crate::parser`], +//! which reads MeTeoR's `Pred(args)` syntax use std::collections::HashMap; @@ -81,12 +21,10 @@ use crate::stream::{StalenessPolicy, StreamShape}; use crate::syntax::{DatalogMTLRule, Interval, Mode, TemporalAtom}; use shared::term_encoder::SharedTerms; -/// Prefix label (without the trailing `:`) → IRI namespace. +/// Prefix label (without the trailing `:`) → IRI namespace type PrefixMap = HashMap; -// ── Public API ──────────────────────────────────────────────────────────────── - -/// Parse a rule program. `dict` receives every IRI/literal encountered. +/// Parse a rule program pub fn parse_rules( text: &str, dict: &mut Dictionary, @@ -103,8 +41,6 @@ pub fn parse_rules( continue; } - // Optional `[label]` before the head becomes the rule id. A head always - // starts with `(` or a term, so a leading `[` is unambiguous. let (label, rule_str) = split_label(rule_str)?; let rule_str = rule_str.trim(); @@ -131,7 +67,7 @@ pub fn parse_rules( Ok(rules) } -/// Parse `STREAM`/`PATTERN`/`KEY`/`STALENESS` blocks into stream shapes. +/// Parse `STREAM`/`PATTERN`/`KEY`/`STALENESS` blocks into stream shapes pub fn parse_stream_shapes(text: &str, dict: &mut Dictionary) -> Result, String> { let (prefixes, cleaned) = extract_prefixes(text)?; @@ -144,7 +80,6 @@ pub fn parse_stream_shapes(text: &str, dict: &mut Dictionary) -> Result = kws .iter() @@ -152,7 +87,6 @@ pub fn parse_stream_shapes(text: &str, dict: &mut Dictionary) -> Result = HashMap::new(); for i in 0..kw_positions.len() { let (kw, kw_pos) = kw_positions[i]; @@ -165,7 +99,6 @@ pub fn parse_stream_shapes(text: &str, dict: &mut Dictionary) -> Result Result Result = key_text .split_whitespace() @@ -196,9 +127,6 @@ pub fn parse_stream_shapes(text: &str, dict: &mut Dictionary) -> Result Result Result . -/// ``` -/// -/// Terms are whitespace-separated and the statement is `.`-terminated, so this -/// accepts N-Triples as produced by any RDF tool. `PREFIX` lines and the -/// resulting prefixed names are also accepted as a convenience, though using -/// them means the text is no longer strictly N-Triples. -/// -/// Variables are rejected — a fact has nothing to bind them from. +/// Parse ground facts written as **N-Triples** pub fn parse_facts(text: &str, dict: &mut Dictionary) -> Result, String> { let (prefixes, cleaned) = extract_prefixes(text)?; let mut facts = Vec::new(); @@ -277,11 +193,7 @@ pub fn parse_facts(text: &str, dict: &mut Dictionary) -> Result, Str Ok(facts) } -/// Split one N-Triples statement into its terms. -/// -/// Not a plain whitespace split: `<…>` and `"…"` are read as single units, so an -/// IRI or a literal containing spaces stays intact. Anything trailing a closing -/// quote (`@en`, `^^<…>`) is kept with the literal. +/// Split one N-Triples statement into its terms fn split_ntriples_terms(stmt: &str) -> Vec { let mut terms = Vec::new(); let chars: Vec = stmt.chars().collect(); @@ -298,19 +210,18 @@ fn split_ntriples_terms(stmt: &str) -> Vec { while i < chars.len() && chars[i] != '>' { i += 1; } - i = (i + 1).min(chars.len()); // include '>' + i = (i + 1).min(chars.len()); } '"' => { i += 1; while i < chars.len() { match chars[i] { - '\\' => i += 2, // escaped char, including \" + '\\' => i += 2, '"' => break, _ => i += 1, } } - i = (i + 1).min(chars.len()); // include closing quote - // Keep a language tag or datatype suffix with the literal. + i = (i + 1).min(chars.len()); while i < chars.len() && !chars[i].is_whitespace() { if chars[i] == '<' { while i < chars.len() && chars[i] != '>' { @@ -345,16 +256,13 @@ fn ground_term(term: &Term, index: usize, position: &str) -> Result } } -/// [`parse_facts`] for callers holding a shared dictionary. -/// -/// Takes the write lock for the whole parse — never call it while already -/// holding a lock on the same dictionary. +/// [`parse_facts`] for callers holding a shared dictionary pub fn parse_facts_shared(text: &str, dict: &SharedTerms) -> Result, String> { dict.try_with_terms(|guard| parse_facts(text, guard)) .map_err(|_| "dictionary lock poisoned".to_string())? } -/// Parse a bare `start,end` interval body (the text between `[` and `]`). +/// Parse a bare `start,end` interval body (the text between `[` and `]`) pub fn parse_interval(s: &str) -> Result { let parts: Vec<&str> = s.splitn(2, ',').collect(); if parts.len() != 2 { @@ -371,10 +279,7 @@ pub fn parse_interval(s: &str) -> Result { Ok(Interval { start, end }) } -/// [`parse_rules`] for callers holding a shared dictionary. -/// -/// Takes the write lock for the whole parse — never call it while already -/// holding a lock on the same dictionary. +/// [`parse_rules`] for callers holding a shared dictionary pub fn parse_rules_shared( text: &str, dict: &SharedTerms, @@ -384,10 +289,7 @@ pub fn parse_rules_shared( .map_err(|_| "dictionary lock poisoned".to_string())? } -/// [`parse_stream_shapes`] for callers holding a shared dictionary. -/// -/// Takes the write lock for the whole parse — never call it while already -/// holding a lock on the same dictionary. +/// [`parse_stream_shapes`] for callers holding a shared dictionary pub fn parse_stream_shapes_shared( text: &str, dict: &SharedTerms, @@ -396,10 +298,7 @@ pub fn parse_stream_shapes_shared( .map_err(|_| "dictionary lock poisoned".to_string())? } -// ── Comment stripping and PREFIX extraction ─────────────────────────────────── - -/// Strip a `#` line comment. `#` inside `<…>` or `"…"` is content, not a -/// comment — without this, `` loses its fragment. +/// Strip a `#` line comment fn strip_comment(line: &str) -> &str { let bytes = line.as_bytes(); let mut in_iri = false; @@ -416,10 +315,7 @@ fn strip_comment(line: &str) -> &str { line } -/// Pull `PREFIX name: ` declarations off the top of a blob, returning the -/// map and the remaining text joined into one line for depth-0 splitting. -/// -/// A declaration must sit on its own line; everything else is passed through. +/// Splits the `PREFIX name: ` declarations off the top of a blob fn extract_prefixes(text: &str) -> Result<(PrefixMap, String), String> { let mut prefixes = PrefixMap::new(); let mut body = Vec::new(); @@ -442,7 +338,7 @@ fn extract_prefixes(text: &str) -> Result<(PrefixMap, String), String> { Ok((prefixes, body.join(" "))) } -/// `rest` is everything after the `PREFIX` keyword: `name: `. +/// `rest` is everything after the `PREFIX` keyword: `name: ` fn parse_prefix_decl(rest: &str) -> Result<(String, String), String> { let rest = rest.trim(); let colon = rest @@ -459,7 +355,7 @@ fn parse_prefix_decl(rest: &str) -> Result<(String, String), String> { Ok((name, iri_part[1..iri_part.len() - 1].to_string())) } -/// If `s` starts with `word` as a whole word, return the remainder. +/// If `s` starts with `word` as a whole word, return the remainder fn strip_keyword<'a>(s: &'a str, word: &str) -> Option<&'a str> { let rest = s.strip_prefix(word)?; match rest.chars().next() { @@ -469,7 +365,7 @@ fn strip_keyword<'a>(s: &'a str, word: &str) -> Option<&'a str> { } } -/// Split a leading `[label]` off a rule, returning `(label, remainder)`. +/// Split a leading `[label]` off a rule, returning `(label, remainder)` fn split_label(rule_str: &str) -> Result<(Option, &str), String> { if !rule_str.starts_with('[') { return Ok((None, rule_str)); @@ -484,12 +380,7 @@ fn split_label(rule_str: &str) -> Result<(Option, &str), String> { Ok((Some(label), &rule_str[close + 1..])) } -// ── Scanning helpers (aware of `<…>` and `"…"`) ─────────────────────────────── - -/// Split on `sep` at paren/bracket depth 0, treating `<…>` and `"…"` as opaque. -/// -/// The opacity matters: a dotted host name in `` must -/// not terminate a block when splitting on `.`. +/// Split on `sep` at paren/bracket depth 0, treating `<…>` and `"…"` as opaque fn split_at_depth0(s: &str, sep: char) -> Vec { let mut parts = Vec::new(); let mut current = String::new(); @@ -544,7 +435,7 @@ fn split_at_depth0(s: &str, sep: char) -> Vec { parts } -/// Find the byte offset of `:-` at depth 0, outside `<…>` and `"…"`. +/// Find the byte offset of `:-` at depth 0, outside `<…>` and `"…"` fn find_rule_sep(s: &str) -> Option { let bytes = s.as_bytes(); let mut depth: i32 = 0; @@ -583,7 +474,7 @@ fn find_rule_sep(s: &str) -> Option { None } -/// Find `word` as a whole word, ignoring occurrences inside `<…>` or `"…"`. +/// Find `word` as a whole word, ignoring occurrences inside `<…>` or `"…"` fn find_whole_word(text: &str, word: &str) -> Option { let wlen = word.len(); let tbytes = text.as_bytes(); @@ -636,9 +527,7 @@ fn find_whole_word(text: &str, word: &str) -> Option { None } -// ── Terms and atoms ─────────────────────────────────────────────────────────── - -/// Resolve an IRI written as ``, `pfx:local`, or a bare token. +/// Resolve an IRI written as ``, `pfx:local`, or a bare token fn expand_iri_text(raw: &str, prefixes: &PrefixMap) -> String { if raw.starts_with('<') && raw.ends_with('>') && raw.len() >= 2 { return raw[1..raw.len() - 1].to_string(); @@ -658,10 +547,8 @@ fn parse_term(s: &str, dict: &mut Dictionary, prefixes: &PrefixMap) -> Result') && s.len() >= 2 { Ok(Term::Constant(dict.encode(&s[1..s.len() - 1]))) } else if s.starts_with('"') { - // literal kept with quotes Ok(Term::Constant(dict.encode(s))) } else if s.starts_with("_:") { - // N-Triples blank node, interned verbatim — `_` is not a prefix. Ok(Term::Constant(dict.encode(s))) } else if let Some(colon) = s.find(':') { let prefix = &s[..colon]; @@ -671,7 +558,6 @@ fn parse_term(s: &str, dict: &mut Dictionary, prefixes: &PrefixMap) -> Result Ok(Term::Constant(dict.encode(s))), None => Err(format!( "Unknown prefix '{}:' in term '{}' — declare it with `PREFIX {}: <...>`", @@ -683,7 +569,7 @@ fn parse_term(s: &str, dict: &mut Dictionary, prefixes: &PrefixMap) -> Result bool { let p = part.trim(); p.starts_with('(') @@ -710,8 +591,7 @@ fn looks_like_atom(part: &str) -> bool { .any(|k| p.strip_prefix(k).is_some_and(|rest| rest.starts_with('['))) } -/// Parse every balanced `(…)` group in a section into triple patterns. -/// Shared by the `PATTERN` and `EXPIRY` sections of a shape block. +/// Parse every balanced `(…)` group in a section into triple patterns fn parse_pattern_group( text: &str, dict: &mut Dictionary, @@ -777,8 +657,6 @@ fn parse_temporal_atom( if s.starts_with('(') { if s.ends_with(')') { let parts = split_at_depth0(&s[1..s.len() - 1], ','); - // `((a,b,c), (d,e,f))` is a conjunction under an operator; `(a,b,c)` - // is a single pattern, whose depth-0 parts are bare terms. if parts.len() >= 2 && parts.iter().all(|p| looks_like_atom(p)) { let mut atoms = Vec::new(); for part in &parts { @@ -786,15 +664,12 @@ fn parse_temporal_atom( } return Ok(TemporalAtom::Conj(atoms)); } - // Redundant grouping parens around one atom, as in - // `Diamond[0,10](Box[1,11](...))`. Unwrap and recurse. if parts.len() == 1 && looks_like_atom(&parts[0]) { return parse_temporal_atom(parts[0].trim(), dict, prefixes, mode); } } return Ok(TemporalAtom::Base(parse_triple_pattern(s, dict, prefixes)?)); } - // Past operators (`Diamond`≡`Diamondminus`, `Box`≡`Boxminus`). for (kw, kind) in [ ("Diamondminus[", "Diamond"), ("Diamond[", "Diamond"), @@ -809,7 +684,6 @@ fn parse_temporal_atom( if let Some(rest) = s.strip_prefix("Since[") { return parse_binary_atom(rest, dict, prefixes, mode, "Since"); } - // Future operators — static data only. for (kw, kind) in [("Diamondplus[", "Diamondplus"), ("Boxplus[", "Boxplus")] { if let Some(rest) = s.strip_prefix(kw) { require_static(mode, kind)?; @@ -857,7 +731,7 @@ fn parse_interval_atom( } } -/// Binary operator `Kind[a,b](phi, psi)` — `Since` (past) or `Until` (future). +/// Binary operator `Kind[a,b](phi, psi)` — `Since` (past) or `Until` (future) fn parse_binary_atom( rest: &str, dict: &mut Dictionary, diff --git a/datalogmtl/src/store.rs b/datalogmtl/src/store.rs index c7bc2601..49ef6ece 100644 --- a/datalogmtl/src/store.rs +++ b/datalogmtl/src/store.rs @@ -8,22 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Where temporal facts live, and the trait the evaluator reads them through. -//! -//! Two implementations of [`TemporalStore`], differing in representation rather -//! than in what they answer: -//! -//! - [`TemporalSnapshotStore`] keeps one index per timestamp, so memory grows with -//! the number of distinct timestamps held. -//! - [`IntervalFactStore`] keeps a validity-interval list per distinct fact and -//! extends the last interval when the same fact arrives at the next millisecond, -//! so memory grows with the number of contiguous segments. On a sampled stream, -//! where a reading repeats until it changes, that is far fewer. -//! -//! Both are bounded: facts older than the `horizon` are dropped by -//! [`evict`](TemporalStore::evict), which the evaluator calls each tick. A query -//! about an evicted time does not fail, it simply finds nothing, so the horizon -//! has to exceed the furthest any rule reaches back. +//! Where temporal facts live, and the trait the evaluator reads them through use shared::dataset_index::DatasetIndex; use shared::pattern::matches_rule_pattern; @@ -31,48 +16,29 @@ use shared::terms::{Bindings, TriplePattern}; use shared::triple::Triple; use std::collections::{BTreeMap, HashMap, HashSet}; -/// The stable store interface. Both Phase 1 and Phase 2 implement this. -/// Define as a trait so the evaluator is generic over the store. +/// The stable store interface pub trait TemporalStore { - /// Records that `triple` holds at `t`. Inserting the same fact twice at the - /// same time is not an error. + /// Records that `triple` holds at `t` fn insert(&mut self, triple: &Triple, t: u64); - /// Bindings for `pattern` from the facts holding at exactly `t`. - /// - /// A time with no facts and a time already evicted are indistinguishable: - /// both answer with nothing. + /// Bindings for `pattern` from the facts holding at exactly `t` fn query_at(&self, pattern: &TriplePattern, t: u64) -> Bindings; - /// Timestamps in `lo..=hi` that hold at least one fact, ascending. The - /// bounds are inclusive, and the result skips times where nothing happened, - /// which is what lets an existential operator scan only occupied points. + /// Timestamps in `lo..=hi` that hold at least one fact, ascending fn timestamps_in(&self, lo: u64, hi: u64) -> Vec; - /// Discards everything strictly before `cutoff`. Irreversible: a later query - /// about a dropped time simply finds nothing. + /// Discards everything strictly before `cutoff` fn evict(&mut self, cutoff: u64); - /// Distinct timestamps currently held. Diagnostic only. + /// Distinct timestamps currently held fn snapshot_count(&self) -> usize; - /// Facts currently held, summed over all timestamps. Diagnostic only, and - /// not necessarily cheap. + /// Facts currently held, summed over all timestamps fn total_triple_count(&self) -> usize; } -// ──────────────────────────────────────────────────────────────── -// Phase 1: TemporalSnapshotStore -// ──────────────────────────────────────────────────────────────── - -/// Phase 1: one DatasetIndex per timestamp. O(w * |facts|) memory. +/// Phase 1: one DatasetIndex per timestamp pub struct TemporalSnapshotStore { snapshots: BTreeMap, - /// How far back to keep facts. Advisory: this store does not evict on its - /// own, so the evaluator decides the cutoff and calls - /// [`evict`](TemporalStore::evict). + /// How far back to keep facts pub horizon: u64, } -// Summarised rather than derived: one index per timestamp, so the derived form would -// print every fact at every time point. Counting snapshots is O(1); counting the -// facts in them is not, and a `Debug` impl should not be the expensive call in a log -// line. impl std::fmt::Debug for TemporalSnapshotStore { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("TemporalSnapshotStore") @@ -83,7 +49,7 @@ impl std::fmt::Debug for TemporalSnapshotStore { } impl TemporalSnapshotStore { - /// An empty store keeping facts for `horizon` time units. + /// An empty store keeping facts for `horizon` time units pub fn new(horizon: u64) -> Self { Self { snapshots: BTreeMap::new(), @@ -101,8 +67,6 @@ impl TemporalStore for TemporalSnapshotStore { } fn query_at(&self, pattern: &TriplePattern, t: u64) -> Bindings { - // Use the index to fetch only triples whose constant positions match the - // pattern, instead of scanning every fact at t. let facts = self .snapshots .get(&t) @@ -138,48 +102,37 @@ impl TemporalStore for TemporalSnapshotStore { } } -// ──────────────────────────────────────────────────────────────── -// Phase 2: IntervalFactStore -// ──────────────────────────────────────────────────────────────── - -/// A closed validity interval [start, end] in absolute milliseconds. +/// A closed validity interval [start, end] in absolute milliseconds #[derive(Debug, Clone, PartialEq, Eq)] pub struct ValidityInterval { - /// First time the fact holds, inclusive. + /// First time the fact holds, inclusive pub start: u64, - /// Last time it holds, inclusive. A single-instant fact has `end == start`. + /// Last time it holds, inclusive pub end: u64, } impl ValidityInterval { - /// Whether the fact holds at `t`. Both bounds count. + /// Whether the fact holds at `t` pub fn contains(&self, t: u64) -> bool { t >= self.start && t <= self.end } - /// Whether the fact holds anywhere in the closed range `lo..=hi`. Touching - /// at a single endpoint counts as overlapping. + /// Whether the fact holds anywhere in the closed range `lo..=hi` pub fn overlaps(&self, lo: u64, hi: u64) -> bool { self.start <= hi && self.end >= lo } } -/// Phase 2: interval-valued fact store. O(k * |distinct facts|) memory, -/// where k = number of contiguous interval segments (k << w on persistent streams). +/// Phase 2: interval-valued fact store pub struct IntervalFactStore { - /// For each distinct triple, a sorted list of non-overlapping validity intervals. facts: HashMap>, - /// Current tick's index, rebuilt on each advance() for Base atom joins. current_index: DatasetIndex, current_tick: u64, /// How far back to keep facts, with the same advisory meaning as on - /// [`TemporalSnapshotStore::horizon`]. + /// [`TemporalSnapshotStore::horizon`] pub horizon: u64, } -// Summarised rather than derived, for the same reason as -// [`TemporalSnapshotStore`]. The segment count is the interesting number here, since -// it is what this representation exists to keep small. impl std::fmt::Debug for IntervalFactStore { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { let segments: usize = self.facts.values().map(Vec::len).sum(); @@ -193,7 +146,7 @@ impl std::fmt::Debug for IntervalFactStore { } impl IntervalFactStore { - /// An empty store keeping facts for `horizon` time units. + /// An empty store keeping facts for `horizon` time units pub fn new(horizon: u64) -> Self { Self { facts: HashMap::new(), @@ -203,8 +156,7 @@ impl IntervalFactStore { } } - /// Insert triple at time t. Extends the last interval by 1ms if contiguous - /// (common case for step function streams), otherwise appends a new interval. + /// Insert triple at time t fn insert_interval(&mut self, triple: Triple, t: u64) { let intervals = self.facts.entry(triple).or_insert_with(Vec::new); if let Some(last) = intervals.last_mut() { @@ -225,7 +177,6 @@ impl TemporalStore for IntervalFactStore { } fn query_at(&self, pattern: &TriplePattern, t: u64) -> Bindings { - // Collect all triples valid at t, then match pattern against them. let candidates: Vec = self .facts .iter() @@ -243,14 +194,10 @@ impl TemporalStore for IntervalFactStore { } fn timestamps_in(&self, lo: u64, hi: u64) -> Vec { - // Collect all distinct timestamps in [lo, hi] that have at least one active fact. - // For Phase 2 this is derived from interval endpoints, not stored directly. - // Collect all interval boundaries within [lo, hi] and the endpoints themselves. let mut ts: HashSet = HashSet::new(); for intervals in self.facts.values() { for iv in intervals { if iv.overlaps(lo, hi) { - // Add boundary points clamped to [lo, hi] ts.insert(iv.start.max(lo)); ts.insert(iv.end.min(hi)); } @@ -263,7 +210,6 @@ impl TemporalStore for IntervalFactStore { fn evict(&mut self, cutoff: u64) { for intervals in self.facts.values_mut() { - // Clip interval starts to cutoff, drop fully expired intervals. intervals.retain_mut(|iv| { if iv.end < cutoff { return false; @@ -275,12 +221,9 @@ impl TemporalStore for IntervalFactStore { }); } self.facts.retain(|_, ivs| !ivs.is_empty()); - // Also clear current_index entries — we don't need to evict it separately - // since it's rebuilt on each tick. } fn snapshot_count(&self) -> usize { - // For Phase 2, "snapshot count" is the number of distinct interval segments. self.facts.values().map(|ivs| ivs.len()).sum() } diff --git a/datalogmtl/src/stream.rs b/datalogmtl/src/stream.rs index b48da2ba..8dbc1e49 100644 --- a/datalogmtl/src/stream.rs +++ b/datalogmtl/src/stream.rs @@ -8,99 +8,74 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Turning RDF events into temporal facts: stream shapes, channels and staleness. -//! -//! An event is a small graph of triples arriving at a timestamp. A -//! [`StreamShape`] says which events belong to a stream, which variables identify -//! one logical channel — a particular sensor, say — and how long a reading from -//! that channel stays valid. -//! -//! # Readings are step functions, and silence is an event -//! -//! A channel's latest reading holds until the next one arrives or until -//! `max_gap_ms` has passed, so [`ShapeIngester`] produces intervals of validity -//! rather than instants. When a channel does go stale, -//! [`StreamShape::on_expiry`] facts are emitted on its behalf, because a rule -//! cannot observe the absence of an event: without them, a source that has stopped -//! reporting looks exactly like one reporting normally. Only -//! [`ChannelKey`] variables can appear in those patterns, since the key binding is -//! all that is still known about a channel whose reading has expired. +//! Turning RDF events into temporal facts: stream shapes, channels and staleness use shared::pattern::matches_rule_pattern; use shared::terms::TriplePattern; use shared::triple::Triple; use std::collections::HashMap; -/// IRI identifying a named stream. +/// IRI identifying a named stream pub type StreamIri = String; -/// A BGP event pattern: one or more triple patterns that must all match -/// the triples of one incoming RDF event graph. +/// A BGP event pattern: one or more triple patterns that must all match the triples of one incoming +/// RDF event graph pub type EventPattern = Vec; -/// The composite primary key for a stream channel. -/// A list of variable names whose bindings together identify one logical channel. +/// The composite primary key for a stream channel pub type ChannelKey = Vec; -/// A key binding: the actual values for a specific channel. +/// A key binding: the actual values for a specific channel pub type KeyBinding = HashMap; -/// Staleness policy for a stream shape. +/// Staleness policy for a stream shape #[derive(Debug, Clone)] pub struct StalenessPolicy { - /// If no new event arrives within max_gap_ms, the channel's reading expires. + /// If no new event arrives within max_gap_ms, the channel's reading expires pub max_gap_ms: u64, } -/// A stream shape declaration. +/// A stream shape declaration #[derive(Debug, Clone)] pub struct StreamShape { - /// Stream this shape applies to. An event from another stream is ignored. + /// Stream this shape applies to pub stream_iri: StreamIri, - /// Patterns that must all match within one event for it to count. + /// Patterns that must all match within one event for it to count pub event_pattern: EventPattern, - /// Variables whose bindings identify a channel. Two events agreeing on these - /// are successive readings from the same source; the later replaces the - /// earlier. + /// Variables whose bindings identify a channel pub channel_key: ChannelKey, - /// How long a reading survives without a successor. + /// How long a reading survives without a successor pub staleness: StalenessPolicy, /// Facts transmitted on behalf of a channel that has gone stale, produced by - /// [`ShapeIngester::expiry_facts`]. Silence is not itself an event, so - /// without this a downstream rule has nothing to observe when a source stops - /// reporting. - /// - /// Only [`ChannelKey`] variables may appear here — the key binding is the - /// only thing still known about a channel once its reading has expired. + /// [`ShapeIngester::expiry_facts`] pub on_expiry: Vec, } -/// The active state of one logical channel (one distinct key binding). +/// The active state of one logical channel (one distinct key binding) #[derive(Debug, Clone)] pub struct ChannelState { - /// The triples belonging to the most recent event on this channel. + /// The triples belonging to the most recent event on this channel pub active_triples: Vec, - /// When this reading became valid (absolute ms). + /// When this reading became valid (absolute ms) pub validity_start: u64, - /// When this reading expires (absolute ms = last_event_time + max_gap_ms). + /// When this reading expires (absolute ms = last_event_time + max_gap_ms) pub expiry_time: u64, - /// Timestamp of the most recent event on this channel. + /// Timestamp of the most recent event on this channel pub last_event_time: u64, } -/// An incoming RDF event: a small graph of triples arriving together at time t. +/// An incoming RDF event: a small graph of triples arriving together at time t #[derive(Debug, Clone)] pub struct RdfEvent { - /// Stream the event arrived on. + /// Stream the event arrived on pub stream_iri: StreamIri, - /// Arrival time, absolute, in the unit the rules are written in. + /// Arrival time, absolute, in the unit the rules are written in pub timestamp: u64, - /// The event's graph. Matched as a whole: a shape's patterns must all find a - /// triple here, so a partial event matches nothing. + /// The event's graph pub triples: Vec, } -/// Stable, hashable representation of a channel key (sorted pairs). +/// Stable, hashable representation of a channel key (sorted pairs) type ChannelId = (usize, Vec<(String, u32)>); fn key_binding_to_id(shape_idx: usize, binding: &KeyBinding) -> ChannelId { @@ -109,16 +84,12 @@ fn key_binding_to_id(shape_idx: usize, binding: &KeyBinding) -> ChannelId { (shape_idx, pairs) } -/// The shape ingester. Maintains channel state per shape and emits -/// step-function-derived (triple, validity_interval) pairs to the evaluator. +/// The shape ingester pub struct ShapeIngester { shapes: Vec, - /// Per (shape_index, sorted_key_pairs), the current channel state. channels: HashMap, } -// Summarised rather than derived: each live channel holds the triples of its most -// recent event, so the derived form grows with the stream. impl std::fmt::Debug for ShapeIngester { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("ShapeIngester") @@ -129,13 +100,7 @@ impl std::fmt::Debug for ShapeIngester { } impl ShapeIngester { - // Took an `Arc>` until step 3. It was stored and never read: - // events arrive already encoded, so ingestion does no interning. Six call sites - // were threading a dictionary here for nothing. - /// An ingester for `shapes`, with no channels yet. - /// - /// Shape order is the index used internally to identify a channel, so it is - /// fixed for the ingester's lifetime. + /// An ingester for `shapes`, with no channels yet pub fn new(shapes: Vec) -> Self { Self { shapes, @@ -143,8 +108,7 @@ impl ShapeIngester { } } - /// Process an incoming event. Returns triples to insert into the store at - /// the event's timestamp (newly active triples for matching channels). + /// Process an incoming event pub fn process_event(&mut self, event: &RdfEvent) -> Vec<(Triple, u64)> { let mut to_insert = Vec::new(); @@ -185,7 +149,7 @@ impl ShapeIngester { to_insert } - /// Check for expired channels at time t. + /// Check for expired channels at time t pub fn expired_channels(&self, t: u64) -> Vec { self.channels .iter() @@ -194,16 +158,7 @@ impl ShapeIngester { .collect() } - /// Facts to transmit at `t` for every channel whose reading has expired. - /// - /// This is a *standing* condition, not an edge: as long as the channel stays - /// stale it keeps producing, which is what universal operators need — a - /// `Box[0,n]` rule requires the fact at every integer point in its window, so - /// a one-shot expiry edge could never satisfy one. - /// - /// Call it after [`Self::process_event`] for the tick, so a channel that just - /// reported is not also reported stale. Channels evicted by - /// [`Self::evict_expired`] stop producing. + /// Facts to transmit at `t` for every channel whose reading has expired pub fn expiry_facts(&self, t: u64) -> Vec<(Triple, u64)> { let mut out = Vec::new(); for ((shape_idx, key), state) in &self.channels { @@ -238,19 +193,18 @@ impl ShapeIngester { out } - /// Remove expired channels at time t. + /// Remove expired channels at time t pub fn evict_expired(&mut self, t: u64) { self.channels.retain(|_, state| state.expiry_time >= t); } - /// Returns all currently active channel states. + /// Returns all currently active channel states pub fn active_channels(&self) -> &HashMap { &self.channels } } -/// Attempt to match an EventPattern against the triples of one incoming event. -/// Returns Some(bindings) if all patterns match, None if any pattern fails. +/// Attempt to match an EventPattern against the triples of one incoming event fn match_event_pattern( pattern: &EventPattern, event_triples: &[Triple], diff --git a/datalogmtl/src/syntax.rs b/datalogmtl/src/syntax.rs index ccaeb635..af27751a 100644 --- a/datalogmtl/src/syntax.rs +++ b/datalogmtl/src/syntax.rs @@ -8,42 +8,26 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The rule and operator vocabulary: what a DatalogMTL program can say. -//! -//! Every [`Interval`] here is a *relative* offset and not an absolute time, and -//! which direction it runs in is the operator's business, not the interval's. The -//! past operators ([`TemporalAtom::Box_`], [`Diamond`](TemporalAtom::Diamond), -//! [`Prev`](TemporalAtom::Prev), [`Since`](TemporalAtom::Since)) read it -//! backwards from the point being evaluated; the future operators read it forwards -//! and are only legal in [`Mode::Static`], because looking forward on a stream -//! means looking at data that has not arrived. -//! -//! The unit is whatever the data uses. That sounds free and is not: see -//! [`evaluator`](crate::evaluator) for what the tick engine does with it. +//! The rule and operator vocabulary: what a DatalogMTL program can say use shared::terms::TriplePattern; -/// Interval [start, end] in milliseconds, representing a range into the past. -/// start=0, end=0 means exactly now. -/// start=1000, end=5000 means "between 1 and 5 seconds ago." +/// Interval [start, end] in milliseconds, representing a range into the past #[derive(Debug, Clone, PartialEq, Eq, Hash)] pub struct Interval { - /// Inclusive bound nearer the evaluation point. - pub start: u64, // inclusive lower bound, ms into past - /// Inclusive bound further from it. Must be `>= start`; [`validate`] rejects - /// a rule where it is not, rather than silently matching nothing. - /// - /// [`validate`]: crate::validate - pub end: u64, // inclusive upper bound, ms into past + /// Inclusive bound nearer the evaluation point + pub start: u64, + /// Inclusive bound further from it + pub end: u64, } impl Interval { - /// Returns true if offset_ms falls within [start, end]. + /// Returns true if offset_ms falls within [start, end] pub fn contains_offset(&self, offset_ms: u64) -> bool { offset_ms >= self.start && offset_ms <= self.end } - /// Given current time t, return the absolute time range [t-end, t-start]. + /// Given current time t, return the absolute time range [t-end, t-start] pub fn absolute_range(&self, t: u64) -> (u64, u64) { let lo = t.saturating_sub(self.end); let hi = t.saturating_sub(self.start); @@ -51,101 +35,85 @@ impl Interval { } } -/// A temporal atom: a triple pattern optionally wrapped in a past metric operator. -/// All operators are past-only (forward-propagating fragment, suitable for streaming). +/// A temporal atom: a triple pattern optionally wrapped in a past metric operator #[derive(Debug, Clone)] pub enum TemporalAtom { - /// Base(?s, ?p, ?o): plain triple pattern at current time t. + /// Base(?s, ?p, ?o): plain triple pattern at current time t Base(TriplePattern), - /// `Diamond[a,b]` phi: phi holds at SOME t' in [t-b, t-a]. + /// `Diamond[a,b]` phi: phi holds at SOME t' in [t-b, t-a] Diamond { - /// How far back to look. + /// How far back to look interval: Interval, - /// What to look for. + /// What to look for inner: Box, }, - /// `Box[a,b]` phi: phi holds at EVERY integer point t' in [t-b, t-a] - /// (dense semantics — an integer point with no supporting fact fails Box). + /// `Box[a,b]` phi: phi holds at EVERY integer point t' in [t-b, t-a] (dense semantics — an + /// integer point with no supporting fact fails Box) Box_ { - /// Window that must be satisfied throughout. + /// Window that must be satisfied throughout interval: Interval, - /// What must hold at every point of it. + /// What must hold at every point of it inner: Box, }, - /// `Prev[a,b]` phi: phi holds at the MOST RECENT t' in [t-b, t-a]. + /// `Prev[a,b]` phi: phi holds at the MOST RECENT t' in [t-b, t-a] Prev { - /// Window to search backwards through. + /// Window to search backwards through interval: Interval, - /// What to find the most recent occurrence of. + /// What to find the most recent occurrence of inner: Box, }, - /// phi `Since[a,b]` psi: - /// EXISTS t' in [t-b, t-a]: psi holds at t' - /// AND FORALL t'' in (t', t]: phi holds at t'' + /// phi `Since[a,b]` psi: EXISTS t' in [t-b, t-a]: psi holds at t' AND FORALL t'' in (t', t]: + /// phi holds at t'' Since { - /// Window in which the trigger must have occurred. + /// Window in which the trigger must have occurred interval: Interval, - /// Continuation condition, required at every point since the trigger. - /// Universal, so it binds no variables — see [`validate`](crate::validate). - phi: Box, // continuation condition (holds after reset) - /// Trigger condition, whose occurrence starts the interval. - psi: Box, // reset/trigger condition + /// Continuation condition, required at every point since the trigger + phi: Box, + /// Trigger condition, whose occurrence starts the interval + psi: Box, }, - // ── Future operators (static data only — see `Mode`) ── - /// `DiamondPlus[a,b]` phi: phi holds at SOME t' in [t+a, t+b]. + /// `DiamondPlus[a,b]` phi: phi holds at SOME t' in [t+a, t+b] DiamondPlus { - /// How far forward to look. + /// How far forward to look interval: Interval, - /// What to look for. + /// What to look for inner: Box, }, - /// `BoxPlus[a,b]` phi: phi holds at EVERY integer point t' in [t+a, t+b]. + /// `BoxPlus[a,b]` phi: phi holds at EVERY integer point t' in [t+a, t+b] BoxPlus { - /// Forward window that must be satisfied throughout. + /// Forward window that must be satisfied throughout interval: Interval, - /// What must hold at every point of it. + /// What must hold at every point of it inner: Box, }, - /// phi `Until[a,b]` psi: - /// EXISTS t' in [t+a, t+b]: psi holds at t' - /// AND FORALL t'' in [t, t'): phi holds at t'' + /// phi `Until[a,b]` psi: EXISTS t' in [t+a, t+b]: psi holds at t' AND FORALL t'' in [t, t'): + /// phi holds at t'' Until { - /// Window in which the trigger must occur. + /// Window in which the trigger must occur interval: Interval, - /// Continuation condition, required at every point until the trigger. - phi: Box, // continuation condition (holds until reset) - /// Trigger condition, whose occurrence ends the interval. - psi: Box, // reset/trigger condition + /// Continuation condition, required at every point until the trigger + phi: Box, + /// Trigger condition, whose occurrence ends the interval + psi: Box, }, - /// Conjunction of atoms, all evaluated at the SAME time point. - /// - /// This is what lets an operator scope more than one triple pattern, e.g. - /// `Box[0,10]((?d, :inZone, ?z), (?d, :altitude, ?a))` — "?d was in ?z at - /// altitude ?a throughout the window". That is strictly more expressive than - /// hoisting the second atom out: `Box[0,10]A, B` only requires `B` at the - /// current time, whereas `Box[0,10](A, B)` requires it at every point of the - /// window. (The two coincide when `B` is time-invariant.) - /// - /// Variables are shared across the conjuncts, so later atoms can be - /// constrained by bindings from earlier ones. + /// Conjunction of atoms, all evaluated at the SAME time point Conj(Vec), } -/// Evaluation mode. Future operators look into not-yet-arrived time, so they are -/// only valid on complete (static) data, never on a stream. +/// Evaluation mode #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum Mode { - /// Incremental data; only past/present operators allowed. + /// Incremental data; only past/present operators allowed Streaming, - /// Whole timeline known; past and future operators allowed. + /// Whole timeline known; past and future operators allowed Static, } @@ -155,17 +123,14 @@ impl Default for Mode { } } -/// A DatalogMTL^RDF rule. Head is always a plain triple pattern. -/// Body is a conjunction of TemporalAtoms evaluated left to right. +/// A DatalogMTL^RDF rule #[derive(Debug, Clone)] pub struct DatalogMTLRule { - /// Identifier used in validation errors and metrics. Not checked for - /// uniqueness, so two rules may share one. + /// Identifier used in validation errors and metrics pub id: String, - /// Conclusion, derived at the time point being evaluated. Always plain: a - /// temporal operator in head position is not part of this fragment. + /// Conclusion, derived at the time point being evaluated pub head: TriplePattern, - /// Premises, evaluated left to right with bindings threaded through, so a - /// later atom sees what the earlier ones bound. + /// Premises, evaluated left to right with bindings threaded through, so a later atom sees what + /// the earlier ones bound pub body: Vec, } diff --git a/datalogmtl/src/tests/automata.rs b/datalogmtl/src/tests/automata.rs index 5ae02891..167a95e8 100644 --- a/datalogmtl/src/tests/automata.rs +++ b/datalogmtl/src/tests/automata.rs @@ -21,7 +21,7 @@ fn op(s: u64, e: u64) -> Interval { Interval { start: s, end: e } } -/// Diamond dilates: [2,17] under Diamondminus[0,2] -> [2,19]. +/// Diamond dilates: [2,17] under Diamondminus[0,2] -> [2,19] #[test] fn test_diamond_transducer() { assert_eq!( @@ -30,7 +30,7 @@ fn test_diamond_transducer() { ); } -/// Box erodes: [2,19] under Boxminus[0,5] -> [7,19]; a too-short interval drops. +/// Box erodes: [2,19] under Boxminus[0,5] -> [7,19]; a too-short interval drops #[test] fn test_box_transducer() { assert_eq!(transducer::box_(&[iv(2, 19)], &op(0, 5)), vec![iv(7, 19)]); @@ -40,15 +40,13 @@ fn test_box_transducer() { ); } -/// Future ops: Diamondplus shifts earlier; Boxplus erodes from the right. +/// Future ops: Diamondplus shifts earlier; Boxplus erodes from the right #[test] fn test_future_transducers() { - // Diamondplus[0,1]: [5,10] -> [5-1, 10-0] = [4,10]. assert_eq!( transducer::diamond_plus(&[iv(5, 10)], &op(0, 1)), vec![iv(4, 10)] ); - // Boxplus[0,2]: [5,10] -> [5-0, 10-2] = [5,8]; a too-short interval drops. assert_eq!( transducer::box_plus(&[iv(5, 10)], &op(0, 2)), vec![iv(5, 8)] @@ -57,14 +55,13 @@ fn test_future_transducers() { transducer::box_plus(&[iv(5, 6)], &op(0, 2)), Vec::::new() ); - // A(a)@[0,20] Until[1,2] B(a)@[5,5] -> [3,4]. assert_eq!( transducer::until(&[iv(0, 20)], &[iv(5, 5)], &op(1, 2)), vec![iv(3, 4)] ); } -/// End-to-end future op in the interval evaluator (static). +/// End-to-end future op in the interval evaluator (static) #[test] fn test_future_materialize() { let dict = make_dict(); @@ -97,22 +94,19 @@ fn test_future_materialize() { assert_eq!(db.facts.get(&b_a), Some(&vec![iv(4, 10)])); } -/// Real-line coalescing: touching closed intervals merge; integer-adjacent -/// (real-separated) ones do NOT — the ℤ-vs-ℝ fix. +/// Real-line coalescing: touching closed intervals merge; integer-adjacent (real-separated) ones do +/// NOT — the ℤ-vs-ℝ fix #[test] fn test_coalesce_real_line() { - assert_eq!(coalesce(vec![iv(1, 5), iv(5, 8)]), vec![iv(1, 8)]); // touch - assert_eq!(coalesce(vec![iv(1, 5), iv(6, 8)]), vec![iv(1, 5), iv(6, 8)]); // gap (1,6) + assert_eq!(coalesce(vec![iv(1, 5), iv(5, 8)]), vec![iv(1, 8)]); + assert_eq!(coalesce(vec![iv(1, 5), iv(6, 8)]), vec![iv(1, 5), iv(6, 8)]); assert_eq!( coalesce(vec![iv(2, 19), iv(20, 22)]), vec![iv(2, 19), iv(20, 22)] ); } -/// End-to-end: the g28 chained-operator case that the tick engine got wrong. -/// RAC(X) :- Diamondminus[0,2] GraduateStudent(X) -/// RA(X) :- Boxminus[0,5] RAC(X) -/// GS(g)@[2,17], GS(g)@[20,20] => RAC(g)=[2,19]∪[20,22], RA(g)=[7,19] (MeTeoR). +/// End-to-end: the g28 chained-operator case that the tick engine got wrong #[test] fn test_chained_operators_zvsr() { let dict = make_dict(); @@ -178,7 +172,7 @@ fn test_chained_operators_zvsr() { ); } -/// A binary temporal join: Near(X,Y) :- Loc(X,L), Diamondminus[1,5] Loc(Y,L). +/// A binary temporal join: Near(X,Y) :- Loc(X,L), Diamondminus[1,5] Loc(Y,L) #[test] fn test_binary_temporal_join() { let dict = make_dict(); @@ -199,8 +193,6 @@ fn test_binary_temporal_join() { }, ], }; - // a at r1 over [3,3]; b at r1 over [0,0]. Near(a,b) holds where Loc(a,r1) AND - // Diamond[1,5]Loc(b,r1): b's [0,0] dilates to [1,5]; intersect [3,3] -> [3,3]. let facts = vec![ ( Triple { @@ -228,10 +220,8 @@ fn test_binary_temporal_join() { assert_eq!(db.facts.get(&near_ab), Some(&vec![iv(3, 3)])); } -/// ω: an eventually-always recursion saturates to `[l, +∞)` and answers -/// far-future entailment without materializing to that time. -/// Alarm(X) :- Trigger(X) -/// Alarm(X) :- Diamondminus[0,1] Alarm(X) (overlapping copies -> continuous) +/// ω: an eventually-always recursion saturates to `[l, +∞)` and answers far-future entailment +/// without materializing to that time #[test] fn test_omega_eventually_always() { let dict = make_dict(); @@ -275,17 +265,15 @@ fn test_omega_eventually_always() { Some(&vec![TInterval::from_to_inf(5, false)]), "Alarm(a) must saturate to [5, +∞)", ); - // Far-future entailment, answered from the lasso representation. assert!(entails(&model, &alarm_a, iv(1_000_000, 1_000_000))); - assert!(entails(&model, &alarm_a, iv(5, 1_000_000))); // whole span (continuous) + assert!(entails(&model, &alarm_a, iv(5, 1_000_000))); assert!(entails(&model, &alarm_a, iv(5, 5))); assert!(!entails(&model, &alarm_a, iv(4, 4))); assert_eq!(model.db.facts[&alarm_a][0].end, POS_INF); } -/// ω: a genuinely periodic (gappy) program is *extracted* (prefix + period) and -/// answers far-future entailment by extrapolation. -/// P(X) :- Seed(X); P(X) :- Diamondminus[2,2] P(X) (holds at even ticks, forever) +/// ω: a genuinely periodic (gappy) program is *extracted* (prefix + period) and answers far-future +/// entailment by extrapolation #[test] fn test_omega_periodic_extracted() { let dict = make_dict(); @@ -324,7 +312,6 @@ fn test_omega_periodic_extracted() { predicate: rdf_type, object: p, }; - // Holds at even ticks forever, not odd ones, and never over a 2-wide span. assert!(entails(&model, &p_a, iv(1000, 1000))); assert!(entails(&model, &p_a, iv(1_000_000, 1_000_000))); assert!(!entails(&model, &p_a, iv(1001, 1001))); @@ -335,8 +322,7 @@ fn test_omega_periodic_extracted() { ); } -/// ω + future: backward recursion gives a LEFT (past) period. -/// P(X) :- Seed(X); P(X) :- Diamondplus[2,2] P(X) (holds at even ticks toward −∞) +/// ω + future: backward recursion gives a LEFT (past) period #[test] fn test_omega_left_periodic() { let dict = make_dict(); @@ -375,17 +361,16 @@ fn test_omega_left_periodic() { predicate: rdf_type, object: p, }; - assert!(entails(&model, &p_a, iv(-1000, -1000))); // even → holds forever into the past + assert!(entails(&model, &p_a, iv(-1000, -1000))); assert!(entails(&model, &p_a, iv(-1_000_000, -1_000_000))); - assert!(!entails(&model, &p_a, iv(-1001, -1001))); // odd → never + assert!(!entails(&model, &p_a, iv(-1001, -1001))); assert!( !entails(&model, &p_a, iv(-1000, -999)), "no continuous coverage across the gap" ); } -/// ω + future: overlapping backward recursion saturates to `(−∞, e]`. -/// Q(X) :- Seed(X); Q(X) :- Diamondplus[0,1] Q(X) +/// ω + future: overlapping backward recursion saturates to `(−∞, e]` #[test] fn test_omega_past_always() { let dict = make_dict(); @@ -427,12 +412,12 @@ fn test_omega_past_always() { Some(&vec![TInterval::from_neg_inf(0, false)]), "Q must saturate to (−∞, 0]" ); - assert!(entails(&model, &q_a, iv(-1_000_000, 0))); // whole past span + assert!(entails(&model, &q_a, iv(-1_000_000, 0))); assert!(entails(&model, &q_a, iv(-1_000_000, -1_000_000))); - assert!(!entails(&model, &q_a, iv(1, 1))); // nothing in the future + assert!(!entails(&model, &q_a, iv(1, 1))); } -/// ω over a finite program agrees with the finite evaluator (generalization check). +/// ω over a finite program agrees with the finite evaluator (generalization check) #[test] fn test_omega_finite_agrees() { let dict = make_dict(); diff --git a/datalogmtl/src/tests/base.rs b/datalogmtl/src/tests/base.rs index 0be118ed..21b6f6fd 100644 --- a/datalogmtl/src/tests/base.rs +++ b/datalogmtl/src/tests/base.rs @@ -13,8 +13,7 @@ use crate::evaluator::DatalogMTLEvaluator; use crate::store::{TemporalSnapshotStore, TemporalStore}; use crate::syntax::{DatalogMTLRule, TemporalAtom}; -/// Test 1: Base atom join — identity rule with variable predicate. -/// Rule: (?s ?p ?o) :- (?s ?p ?o) +/// Test 1: Base atom join — identity rule with variable predicate #[test] fn test_base_identity_variable_predicate() { let dict = make_dict(); @@ -39,9 +38,6 @@ fn test_base_identity_variable_predicate() { let triples = vec![triple(a, p1, v1), triple(a, p2, v2), triple(b, p3, v3)]; let (derived, _) = eval.advance(0, triples); - // All 3 triples must be present (identity rule re-derives them). - // Derived may be empty since input triples already in store and identity. - // What matters: all triples queryable in the store. let store_triples: Vec<_> = eval .store .query_at(&(v("s"), v("p"), v("o")), 0) @@ -61,9 +57,6 @@ fn test_base_identity_variable_predicate() { "All 3 input triples should be in the store" ); - // Identity rule: derived set should contain the same 3 facts - // (or 0 if we treat input==derived as not new — depends on implementation). - // Assert at least the store contains all 3. let expected = [triple(a, p1, v1), triple(a, p2, v2), triple(b, p3, v3)]; for t in &expected { assert!( @@ -75,8 +68,7 @@ fn test_base_identity_variable_predicate() { let _ = derived; } -/// Test 2: Multi-atom Base join — transitive step. -/// Rule: (?x :connected ?z) :- (?x :knows ?y), (?y :knows ?z) +/// Test 2: Multi-atom Base join — transitive step #[test] fn test_base_multi_atom_join() { let dict = make_dict(); @@ -107,7 +99,6 @@ fn test_base_multi_atom_join() { derived ); - // (b :connected a) should NOT be derived let not_expected = triple(b, connected, a); assert!( !derived.contains(¬_expected), diff --git a/datalogmtl/src/tests/box_.rs b/datalogmtl/src/tests/box_.rs index 4094d247..7cf82a35 100644 --- a/datalogmtl/src/tests/box_.rs +++ b/datalogmtl/src/tests/box_.rs @@ -13,9 +13,7 @@ use crate::evaluator::DatalogMTLEvaluator; use crate::store::TemporalSnapshotStore; use crate::syntax::{DatalogMTLRule, Interval, TemporalAtom}; -/// Test 4a: Box positive — inner holds at every integer point in the window. -/// Rule: (?x :stableReading ?v) :- Box[0, 5] (?x :sensor ?v) -/// Dense semantics: the sensor must fire at every integer tick 0..=5. +/// Test 4a: Box positive — inner holds at every integer point in the window #[test] fn test_box_positive() { let dict = make_dict(); @@ -36,7 +34,6 @@ fn test_box_positive() { let store = TemporalSnapshotStore::new(100); let mut eval = DatalogMTLEvaluator::new(vec![rule], store, dict.clone()).unwrap(); - // Feed (S :sensor :v42) at every integer tick from 0 to 5. let mut last_derived = Vec::new(); for t in 0..=5u64 { let (d, _) = eval.advance(t, vec![triple(sensor_s, sensor_p, val42)]); @@ -51,8 +48,7 @@ fn test_box_positive() { ); } -/// Test 4b: Box negative — a different value at one integer point breaks it. -/// Dense semantics: any point where the exact fact is absent fails Box. +/// Test 4b: Box negative — a different value at one integer point breaks it #[test] fn test_box_value_inconsistency() { let dict = make_dict(); @@ -74,13 +70,11 @@ fn test_box_value_inconsistency() { let store = TemporalSnapshotStore::new(100); let mut eval = DatalogMTLEvaluator::new(vec![rule], store, dict.clone()).unwrap(); - // Feed val42 at every tick 0..=5, but a DIFFERENT value at t=3. for t in 0..=5u64 { let val = if t == 3 { val99 } else { val42 }; eval.advance(t, vec![triple(sensor_s, sensor_p, val)]); } - // At t=5, Box[0,5](?x :sensor :v42) fails because :v42 is absent at t=3. let (derived, _) = eval.advance(5, vec![]); let expected42 = triple(sensor_s, stable, val42); @@ -93,8 +87,7 @@ fn test_box_value_inconsistency() { ); } -/// Test 4c: Box fails when window is empty (no timestamps in range). -/// Box[1000, 5000] at t=0: t < interval.start (0 < 1000) → early exit, no derivation. +/// Test 4c: Box fails when window is empty (no timestamps in range) #[test] fn test_box_vacuous() { let dict = make_dict(); @@ -103,8 +96,6 @@ fn test_box_vacuous() { let stable = enc(&dict, ":stableReading"); let val42 = enc(&dict, ":v42"); - // Box[1000, 5000]: at t=0, t < interval.start → early exit. - // Even if timestamps_in were called, an empty window must yield [] (no vacuous truth). let rule = DatalogMTLRule { id: "stableReading".into(), head: (v("x"), c(stable), v("val")), @@ -120,7 +111,6 @@ fn test_box_vacuous() { let store = TemporalSnapshotStore::new(10_000); let mut eval = DatalogMTLEvaluator::new(vec![rule], store, dict.clone()).unwrap(); - // At t=0, t < 1000 (interval.start) → guard fires, no derivation expected. let (derived, metrics) = eval.advance(0, vec![triple(sensor_s, sensor_p, val42)]); assert!(metrics.fixpoint_iterations >= 1); let expected = triple(sensor_s, stable, val42); diff --git a/datalogmtl/src/tests/conj.rs b/datalogmtl/src/tests/conj.rs index 49312b0b..5db667c8 100644 --- a/datalogmtl/src/tests/conj.rs +++ b/datalogmtl/src/tests/conj.rs @@ -8,7 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Conjunction scoped under a temporal operator (`TemporalAtom::Conj`). +//! Conjunction scoped under a temporal operator (`TemporalAtom::Conj`) use crate::evaluator::DatalogMTLEvaluator; use crate::rdf_parser::parse_rules; @@ -19,8 +19,8 @@ use shared::dictionary::Dictionary; use super::{enc, make_dict, triple}; use shared::term_encoder::SharedTerms; -/// The zone-transition rule: in one restricted zone for 10 consecutive ticks, -/// having been in a *differently* classified zone 10–20 ticks before that. +/// The zone-transition rule: 10 ticks in a restricted zone after 10–20 in a differently classified +/// one const TRANSITION_RULE: &str = r#" PREFIX d: @@ -57,9 +57,8 @@ fn fixture() -> Fixture { } } -/// Drive the rule over a tick timeline: zoneA (Restricted1) for ticks 0..=12, -/// then zoneB (Restricted2) for ticks 13..=25. Returns the ticks at which the -/// head was derived. +/// Drive the rule over a tick timeline: zoneA (Restricted1) for ticks 0..=12, then zoneB +/// (Restricted2) for ticks 13..=25 fn run(rule_text: &str, f: &Fixture) -> Vec { let rules = f .dict @@ -71,7 +70,6 @@ fn run(rule_text: &str, f: &Fixture) -> Vec { let mut fired = Vec::new(); for t in 0..=25u64 { - // Zone classification is background data, asserted at every tick. let mut facts = vec![ triple(f.zone_a, f.status, f.r1), triple(f.zone_b, f.status, f.r2), @@ -93,8 +91,8 @@ fn run(rule_text: &str, f: &Fixture) -> Vec { fired } -/// The rule fires only once both halves are satisfied: 10 full ticks in zoneB -/// (so t >= 23) with zoneA seen 10–20 ticks earlier. +/// The rule fires only once both halves are satisfied: 10 full ticks in zoneB (so t >= 23) with +/// zoneA seen 10–20 ticks earlier #[test] fn test_zone_transition_rule() { let f = fixture(); @@ -106,18 +104,12 @@ fn test_zone_transition_rule() { 23, "needs ticks 13..=23 in zoneB, and zoneA within [t-20, t-10]" ); - // By t=33 zoneA would fall out of the Diamond window, but the run stops at 25. assert!(fired.iter().all(|t| *t >= 23)); } -/// The point of scoping a conjunction under the operator: `Box[0,10](A, B)` -/// requires B at every point of the window, whereas hoisting B out only requires -/// it at the current time. Here B is `(?z, status, Restricted2)` — a fact that -/// only starts holding at tick 13 — so the two forms disagree. +/// `Box[0,10](A, B)` requires B at every point of the window, not only at the current time #[test] fn test_conjunction_under_box_is_stronger_than_hoisting() { - // Classification for zoneB is withheld until tick 20, well after the drone - // enters it at tick 13. let f = fixture(); let rules = f .dict @@ -155,13 +147,11 @@ fn test_conjunction_under_box_is_stronger_than_hoisting() { } } - // Hoisted: needs inZone over [t-5,t] and the class at t only -> fires at 20. assert_eq!( hoisted_first, Some(20), "hoisted form only needs the class now" ); - // Scoped: needs the class at every point of [t-5,t] too -> fires 5 ticks later. assert_eq!( scoped_first, Some(25), @@ -169,8 +159,7 @@ fn test_conjunction_under_box_is_stronger_than_hoisting() { ); } -/// Nested operators read additively, and `compute_w_max` must say so — it drives -/// store eviction, and under-reporting it silently starves the rule of history. +/// Nested operators read additively in `compute_w_max` #[test] fn test_w_max_is_additive_for_nested_operators() { use crate::evaluator::compute_w_max; diff --git a/datalogmtl/src/tests/diamond.rs b/datalogmtl/src/tests/diamond.rs index ec6e013d..d58fa138 100644 --- a/datalogmtl/src/tests/diamond.rs +++ b/datalogmtl/src/tests/diamond.rs @@ -13,8 +13,7 @@ use crate::evaluator::DatalogMTLEvaluator; use crate::store::TemporalSnapshotStore; use crate::syntax::{DatalogMTLRule, Interval, TemporalAtom}; -/// Test 3: Diamond operator. -/// Rule: (?x :wasNear ?y) :- (?x :location ?loc), Diamond[1000, 5000] (?y :location ?loc) +/// Test 3: Diamond operator #[test] fn test_diamond_basic() { let dict = make_dict(); @@ -24,7 +23,6 @@ fn test_diamond_basic() { let was_near = enc(&dict, ":wasNear"); let room1 = enc(&dict, ":room1"); - // Rule: (?x :wasNear ?y) :- (?x :location ?loc), Diamond[1000,5000] (?y :location ?loc) let rule = DatalogMTLRule { id: "wasNear".into(), head: (v("x"), c(was_near), v("y")), @@ -43,10 +41,8 @@ fn test_diamond_basic() { let store = TemporalSnapshotStore::new(10_000); let mut eval = DatalogMTLEvaluator::new(vec![rule], store, dict.clone()).unwrap(); - // t=0: B is in room1 eval.advance(0, vec![triple(b, location, room1)]); - // t=3000: A is in room1 — B was in room1 3000ms ago, within [1000, 5000] let (derived, _) = eval.advance(3000, vec![triple(a, location, room1)]); let expected = triple(a, was_near, b); @@ -56,7 +52,6 @@ fn test_diamond_basic() { derived ); - // t=6000: A still in room1, but B at t=0 is 6000ms ago — outside [1000, 5000] let (derived2, _) = eval.advance(6000, vec![triple(a, location, room1)]); assert!( !derived2.contains(&expected), diff --git a/datalogmtl/src/tests/integration.rs b/datalogmtl/src/tests/integration.rs index f4933a15..e6edf954 100644 --- a/datalogmtl/src/tests/integration.rs +++ b/datalogmtl/src/tests/integration.rs @@ -32,7 +32,7 @@ fn make_diamond_rules(location: u32, was_near: u32) -> Vec { }] } -/// Test 7: Phase 2 parity — derived triples must match Phase 1 at every tick. +/// Test 7: Phase 2 parity — derived triples must match Phase 1 at every tick #[test] fn test_phase2_parity() { let dict1 = make_dict(); @@ -43,7 +43,6 @@ fn test_phase2_parity() { let location = dict1.encode(":location"); let was_near = dict1.encode(":wasNear"); let room1 = dict1.encode(":room1"); - // Mirror in dict2 let _ = dict2.encode(":a"); let _ = dict2.encode(":b"); let _ = dict2.encode(":location"); @@ -79,14 +78,13 @@ fn test_phase2_parity() { ); } - // Phase 2 total_triple_count <= Phase 1 total_triple_count assert!( eval2.store.total_triple_count() <= eval1.store.total_triple_count(), "Phase 2 should use <= triples vs Phase 1" ); } -/// Test 10: Eviction — after t=10000, no snapshot for t=0 (w_max=5000). +/// Test 10: Eviction — after t=10000, no snapshot for t=0 (w_max=5000) #[test] fn test_eviction() { let dict = make_dict(); @@ -95,7 +93,6 @@ fn test_eviction() { let val42 = enc(&dict, ":v42"); let stable = enc(&dict, ":stableReading"); - // Rule with max interval 5000ms. let rule = DatalogMTLRule { id: "stable".into(), head: (v("x"), c(stable), v("v")), @@ -115,8 +112,6 @@ fn test_eviction() { eval.advance(t, vec![triple(sensor_s, sensor_p, val42)]); } - // After t=10000, cutoff = 10000 - 5000 = 5000. - // Phase 1: no snapshot at t=0 (evicted). let ts_at_0 = eval.store.timestamps_in(0, 0); assert!( ts_at_0.is_empty(), @@ -124,7 +119,7 @@ fn test_eviction() { ); } -/// Test 10b: Phase 2 eviction. +/// Test 10b: Phase 2 eviction #[test] fn test_eviction_phase2() { let dict = make_dict(); @@ -152,7 +147,6 @@ fn test_eviction_phase2() { eval.advance(t, vec![triple(sensor_s, sensor_p, val42)]); } - // After t=10000, cutoff = 5000. No interval should start before t=5000. let ts_before_cutoff = eval.store.timestamps_in(0, 4999); assert!( ts_before_cutoff.is_empty(), diff --git a/datalogmtl/src/tests/parser.rs b/datalogmtl/src/tests/parser.rs index 4a32f6f6..dfd4d6c9 100644 --- a/datalogmtl/src/tests/parser.rs +++ b/datalogmtl/src/tests/parser.rs @@ -17,7 +17,7 @@ fn dict() -> SharedTerms { SharedTerms::new() } -/// Parse in streaming mode (past-only) — the default for these tests. +/// Parse in streaming mode (past-only) — the default for these tests fn parse_program(text: &str, d: &SharedTerms) -> Result, String> { parse_program_raw(text, d, Mode::Streaming) } @@ -26,7 +26,7 @@ fn id(d: &SharedTerms, s: &str) -> u32 { d.encode(s) } -/// Unary body atom maps to (?x rdf:type A) and Diamondminus -> Diamond. +/// Unary body atom maps to (?x rdf:type A) and Diamondminus -> Diamond #[test] fn test_unary_diamond_mapping() { let d = dict(); @@ -34,7 +34,6 @@ fn test_unary_diamond_mapping() { assert_eq!(rules.len(), 1); let rule = &rules[0]; - // Head: (X, rdf:type, B) assert_eq!(rule.head.0, Term::Variable("X".into())); assert_eq!(rule.head.1, Term::Constant(id(&d, RDF_TYPE))); assert_eq!(rule.head.2, Term::Constant(id(&d, "B"))); @@ -55,7 +54,7 @@ fn test_unary_diamond_mapping() { } } -/// Binary atom maps to (?x C ?y). +/// Binary atom maps to (?x C ?y) #[test] fn test_binary_mapping() { let d = dict(); @@ -72,7 +71,7 @@ fn test_binary_mapping() { } } -/// `L Since[a,b] R` -> Since { phi = L, psi = R }. +/// `L Since[a,b] R` -> Since { phi = L, psi = R } #[test] fn test_since_mapping() { let d = dict(); @@ -87,7 +86,7 @@ fn test_since_mapping() { } } -/// Stacked operators nest outermost-first. +/// Stacked operators nest outermost-first #[test] fn test_stacked_operators() { let d = dict(); @@ -101,23 +100,21 @@ fn test_stacked_operators() { } } -/// Facts parse into grounded triples with closed integer intervals. +/// Facts parse into grounded triples with closed integer intervals #[test] fn test_fact_parsing() { let d = dict(); let facts = parse_data("A(a)@[0,10]\nC(a,b)@3", &d).unwrap(); assert_eq!(facts.len(), 2); - // A(a)@[0,10] -> (a, rdf:type, A) over [0,10] assert_eq!(facts[0].triple.subject, id(&d, "a")); assert_eq!(facts[0].triple.predicate, id(&d, RDF_TYPE)); assert_eq!(facts[0].triple.object, id(&d, "A")); assert_eq!((facts[0].start, facts[0].end), (0, 10)); - // C(a,b)@3 -> (a, C, b) at [3,3] assert_eq!(facts[1].triple.predicate, id(&d, "C")); assert_eq!((facts[1].start, facts[1].end), (3, 3)); } -/// Out-of-fragment inputs are rejected with a descriptive error. +/// Out-of-fragment inputs are rejected with a descriptive error #[test] fn test_fragment_rejections() { let d = dict(); @@ -151,11 +148,10 @@ fn test_fragment_rejections() { ); } -/// Future operators parse only in Static mode, into the right variants. +/// Future operators parse only in Static mode, into the right variants #[test] fn test_future_operators_static() { let d = dict(); - // Streaming rejects; static accepts. assert!(parse_program_raw("B(X):-Boxplus[1,2]A(X)", &d, Mode::Streaming).is_err()); assert!(parse_program_raw("B(X):-Boxplus[1,2]A(X)", &d, Mode::Static).is_ok()); diff --git a/datalogmtl/src/tests/rdf_parser.rs b/datalogmtl/src/tests/rdf_parser.rs index 92484ce9..89f2b2db 100644 --- a/datalogmtl/src/tests/rdf_parser.rs +++ b/datalogmtl/src/tests/rdf_parser.rs @@ -8,7 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Tests for the RDF triple-pattern parser (`crate::rdf_parser`). +//! Tests for the RDF triple-pattern parser (`crate::rdf_parser`) use shared::dictionary::Dictionary; use shared::terms::Term; @@ -23,10 +23,7 @@ fn cst(t: &Term) -> u32 { } } -// ── The two IRI bugs the lift had to fix ────────────────────────────────────── - -/// `#` inside `<…>` is part of the IRI, not the start of a line comment. -/// Without this, every `rdf:type` pattern lost its fragment. +/// `#` inside `<…>` is part of the IRI, not the start of a line comment #[test] fn test_comment_strip_preserves_iri_fragment() { let mut dict = Dictionary::new(); @@ -44,7 +41,7 @@ fn test_comment_strip_preserves_iri_fragment() { ); } -/// A real `#` comment after an IRI still terminates the line. +/// A real `#` comment after an IRI still terminates the line #[test] fn test_comment_after_iri_still_strips() { let mut dict = Dictionary::new(); @@ -57,7 +54,7 @@ fn test_comment_after_iri_still_strips() { assert_eq!(rules.len(), 1, "commented-out rule must not be parsed"); } -/// Dots in a host name must not terminate a shape block. +/// Dots in a host name must not terminate a shape block #[test] fn test_stream_iri_with_dots() { let mut dict = Dictionary::new(); @@ -77,9 +74,7 @@ fn test_stream_iri_with_dots() { assert_eq!(shapes[0].staleness.max_gap_ms, 10); } -// ── Ground facts ────────────────────────────────────────────────────────────── - -/// Plain N-Triples, as any RDF tool would emit it. +/// Plain N-Triples, as any RDF tool would emit it #[test] fn test_parse_facts_ntriples() { let mut dict = Dictionary::new(); @@ -101,7 +96,7 @@ fn test_parse_facts_ntriples() { ); } -/// Dots inside IRIs must not terminate the statement. +/// Dots inside IRIs must not terminate the statement #[test] fn test_parse_facts_dotted_iris() { let mut dict = Dictionary::new(); @@ -117,7 +112,7 @@ fn test_parse_facts_dotted_iris() { ); } -/// A literal object may contain spaces and a trailing language tag. +/// A literal object may contain spaces and a trailing language tag #[test] fn test_parse_facts_literal_with_spaces() { let mut dict = Dictionary::new(); @@ -133,7 +128,7 @@ fn test_parse_facts_literal_with_spaces() { ); } -/// PREFIX remains available as a convenience, though it is not strict N-Triples. +/// PREFIX remains available as a convenience, though it is not strict N-Triples #[test] fn test_parse_facts_prefixed_convenience() { let mut dict = Dictionary::new(); @@ -186,8 +181,6 @@ fn test_parse_facts_empty_is_ok() { .is_empty()); } -// ── EXPIRY ──────────────────────────────────────────────────────────────────── - #[test] fn test_expiry_patterns_parse() { let mut dict = Dictionary::new(); @@ -212,8 +205,8 @@ fn test_expiry_patterns_parse() { ); } -/// A shape with no EXPIRY section is still valid — it just transmits nothing -/// when a channel goes quiet, which is the old behaviour. +/// A shape with no EXPIRY section is still valid — it just transmits nothing when a channel goes +/// quiet, which is the old behaviour #[test] fn test_expiry_is_optional() { let mut dict = Dictionary::new(); @@ -225,8 +218,8 @@ fn test_expiry_is_optional() { assert!(shapes[0].on_expiry.is_empty()); } -/// Only the channel key survives expiry, so a non-key variable can never be -/// instantiated — reject it at parse time rather than emitting nothing. +/// Only the channel key survives expiry, so a non-key variable can never be instantiated — reject +/// it at parse time rather than emitting nothing #[test] fn test_expiry_rejects_non_key_variable() { let mut dict = Dictionary::new(); @@ -248,8 +241,6 @@ fn test_expiry_rejects_non_key_variable() { assert!(err.contains("KEY"), "error should mention KEY: {}", err); } -// ── PREFIX handling ─────────────────────────────────────────────────────────── - #[test] fn test_prefix_expansion_matches_full_iri() { let mut dict = Dictionary::new(); @@ -276,8 +267,8 @@ fn test_prefix_expansion_matches_full_iri() { ); } -/// Back-compat: `:name` with no empty prefix declared is interned verbatim, -/// exactly as the playground and http-server have always relied on. +/// Back-compat: `:name` with no empty prefix declared is interned verbatim, exactly as the +/// playground and http-server have always relied on #[test] fn test_bare_colon_name_interned_verbatim() { let mut dict = Dictionary::new(); @@ -321,8 +312,6 @@ fn test_unknown_prefix_is_an_error() { ); } -// ── Rule labels ─────────────────────────────────────────────────────────────── - #[test] fn test_rule_label_becomes_id_else_index() { let mut dict = Dictionary::new(); @@ -338,8 +327,6 @@ fn test_rule_label_becomes_id_else_index() { assert_eq!(rules[1].id, "1", "unlabelled rules keep the index id"); } -// ── Operators ───────────────────────────────────────────────────────────────── - #[test] fn test_since_binary_atom() { let mut dict = Dictionary::new(); @@ -397,13 +384,6 @@ fn test_stacked_operators() { } } -// ── The drone demo's shipped configuration ──────────────────────────────────── -// -// Mirrors DEFAULT_RULES / DEFAULT_SHAPES in -// kolibrie/examples/real_scenario/drone_traffic_safety_demo.rs. The demo also -// parses its own text at startup and panics on failure, so drift shows up -// immediately when running it; this test guards the parser features it needs. - const DEMO_RULES: &str = r#" PREFIX dront: PREFIX utm: @@ -507,8 +487,6 @@ fn test_demo_defaults_parse() { ] ); - // Background facts must be valid N-Triples and dual-classify every zone: - // the generic class feeds the geofence rules, the numbered one zoneTour. let facts = parse_facts(DEMO_STATIC, &mut dict).expect("demo background facts must parse"); assert_eq!(facts.len(), 6, "three zones x (generic + numbered) class"); @@ -519,17 +497,14 @@ fn test_demo_defaults_parse() { assert_eq!(shapes[0].event_pattern.len(), 8); assert_eq!(shapes[0].channel_key, vec!["drone".to_string()]); assert_eq!(shapes[0].staleness.max_gap_ms, 10); - // controlLinkLoss has nothing to observe without this. assert_eq!(shapes[0].on_expiry.len(), 1); } -/// The demo's rules and shape must agree with `init_vocab`'s full IRIs — a -/// mismatch would parse and validate, then silently match nothing at runtime. +/// The demo's rules and shape use the same full IRIs as `init_vocab` #[test] fn test_demo_defaults_match_vocab_iris() { let mut dict = Dictionary::new(); - // Intern the vocab IRIs first, the way init_vocab does. let rdf_type = dict.encode("http://www.w3.org/1999/02/22-rdf-syntax-ns#type"); let in_zone = dict.encode("http://example.org/dront/inZone"); let violated = dict.encode("http://utm.example.org/violatedZone"); @@ -546,7 +521,6 @@ fn test_demo_defaults_match_vocab_iris() { }, other => panic!("expected Box, got {:?}", other), } - // `` is the demo's quoting device for the bare string "false". match &rules[2].body[0] { TemporalAtom::Base(p) => assert_eq!(cst(&p.2), xsd_false), other => panic!("expected base atom, got {:?}", other), @@ -554,10 +528,8 @@ fn test_demo_defaults_match_vocab_iris() { assert_eq!(cst(&shapes[0].event_pattern[0].1), rdf_type); } -// ── Conjunction under an operator ───────────────────────────────────────────── - -/// `Op[a,b]((p1), (p2))` scopes the operator over BOTH patterns, which is -/// strictly stronger than hoisting the second one out of the operator. +/// `Op[a,b]((p1), (p2))` scopes the operator over BOTH patterns, which is strictly stronger than +/// hoisting the second one out of the operator #[test] fn test_operator_over_conjunction() { let mut dict = Dictionary::new(); @@ -581,8 +553,8 @@ fn test_operator_over_conjunction() { } } -/// A single parenthesised pattern must still parse as one Base atom, not a -/// one-element conjunction — the two forms must stay distinguishable. +/// A single parenthesised pattern must still parse as one Base atom, not a one-element conjunction +/// — the two forms must stay distinguishable #[test] fn test_single_pattern_is_not_a_conjunction() { let mut dict = Dictionary::new(); @@ -601,7 +573,7 @@ fn test_single_pattern_is_not_a_conjunction() { } } -/// Conjuncts may themselves be operator atoms, not just plain patterns. +/// Conjuncts may themselves be operator atoms, not just plain patterns #[test] fn test_conjunction_of_nested_operators() { let mut dict = Dictionary::new(); @@ -624,8 +596,7 @@ fn test_conjunction_of_nested_operators() { } } -/// Redundant grouping parens around a single operator atom must be transparent: -/// `Diamond[0,10](Box[1,11](...))` is the same as writing the operators adjacent. +/// Redundant parentheses around a single operator atom are transparent #[test] fn test_redundant_grouping_parens() { let mut dict = Dictionary::new(); @@ -648,7 +619,7 @@ fn test_redundant_grouping_parens() { } } -/// A bracketed literal must not be mistaken for an operator keyword. +/// A bracketed literal must not be mistaken for an operator keyword #[test] fn test_literal_with_brackets_is_not_an_operator() { let mut dict = Dictionary::new(); diff --git a/datalogmtl/src/tests/recursive.rs b/datalogmtl/src/tests/recursive.rs index dcf8660a..51a99f43 100644 --- a/datalogmtl/src/tests/recursive.rs +++ b/datalogmtl/src/tests/recursive.rs @@ -13,13 +13,7 @@ use crate::evaluator::DatalogMTLEvaluator; use crate::store::{TemporalSnapshotStore, TemporalStore}; use crate::syntax::{DatalogMTLRule, TemporalAtom}; -/// Test 6: Recursive rules — transitive ancestor. -/// Rules: -/// (?x :ancestor ?z) :- (?x :parent ?z) -/// (?x :ancestor ?z) :- (?x :parent ?y), (?y :ancestor ?z) -/// -/// ABox at t=0: (a :parent b), (b :parent c), (c :parent d) -/// Expected: 6 ancestor triples: a->b, a->c, a->d, b->c, b->d, c->d +/// Test 6: Recursive rules — transitive ancestor #[test] fn test_recursive_ancestor() { let dict = make_dict(); @@ -57,7 +51,6 @@ fn test_recursive_ancestor() { ], ); - // Expected 6 ancestor triples let expected = vec![ triple(a, ancestor, b), triple(a, ancestor, cc), diff --git a/datalogmtl/src/tests/since.rs b/datalogmtl/src/tests/since.rs index bb50e4ab..86da5810 100644 --- a/datalogmtl/src/tests/since.rs +++ b/datalogmtl/src/tests/since.rs @@ -13,9 +13,7 @@ use crate::evaluator::DatalogMTLEvaluator; use crate::store::{TemporalSnapshotStore, TemporalStore}; use crate::syntax::{DatalogMTLRule, Interval, TemporalAtom}; -/// Test 5: Since operator. -/// Rule: (?x :continuouslyActive) :- -/// Since[0, 60000] ((?x :active :true), (?x :started :true)) +/// Test 5: Since operator #[test] fn test_since_basic() { let dict = make_dict(); @@ -25,8 +23,6 @@ fn test_since_basic() { let cont_active = enc(&dict, ":continuouslyActive"); let true_val = enc(&dict, ":true"); - // Rule: (?x :continuouslyActive) :- - // Since[0, 60000] phi=(?x :active :true), psi=(?x :started :true) let rule = DatalogMTLRule { id: "continuouslyActive".into(), head: (v("x"), c(cont_active), c(true_val)), @@ -43,17 +39,14 @@ fn test_since_basic() { let store = TemporalSnapshotStore::new(1_000); let mut eval = DatalogMTLEvaluator::new(vec![rule], store, dict.clone()).unwrap(); - // t=0: W started (the reset/trigger psi). eval.advance(0, vec![triple(w, started, true_val)]); - // t=1..=6: W active at every integer tick (dense continuation phi). let mut last_metrics = None; for t in 1..=6u64 { let (_, m) = eval.advance(t, vec![triple(w, active, true_val)]); last_metrics = Some(m); } - // At t=6, :started held at 0 and :active held continuously on (0,6]. let (derived6, metrics6) = eval.advance(6, vec![triple(w, active, true_val)]); let expected = triple(w, cont_active, true_val); @@ -79,7 +72,6 @@ fn test_since_basic() { "since_scan_depth should be > 0" ); - // Stop feeding :active. Advance to t=8 with no new triples. let (derived8, _) = eval.advance(8, vec![]); let all8: Vec<_> = eval .store @@ -94,7 +86,6 @@ fn test_since_basic() { }) .collect(); - // Without :active at t=7,8, phi fails in (0, 8], so Since fails. assert!( !all8.contains(&expected) && !derived8.contains(&expected), "(W :continuouslyActive) should NOT hold at t=8 without :active" diff --git a/datalogmtl/src/tests/stream.rs b/datalogmtl/src/tests/stream.rs index 25741bfe..8c1b9726 100644 --- a/datalogmtl/src/tests/stream.rs +++ b/datalogmtl/src/tests/stream.rs @@ -11,7 +11,7 @@ use super::{enc, make_dict, triple}; use crate::stream::{RdfEvent, ShapeIngester, StalenessPolicy, StreamShape}; -/// Test 8: Stream shape — single sensor channel. +/// Test 8: Stream shape — single sensor channel #[test] fn test_stream_single_sensor() { let dict = make_dict(); @@ -44,7 +44,6 @@ fn test_stream_single_sensor() { let mut ingester = ShapeIngester::new(vec![shape]); - // Events at t=0, 1000, 2000 — same room, different obs IRIs, same val. for (t, obs) in [(0u64, obs1), (1000, obs2), (2000, obs3)] { let event = RdfEvent { stream_iri: "http://example.org/tempStream".into(), @@ -64,13 +63,11 @@ fn test_stream_single_sensor() { } } - // Channel {room: room42} should be active after t=2000. let active = ingester.active_channels(); assert_eq!(active.len(), 1, "Should have exactly 1 active channel"); let channel_state = active.values().next().unwrap(); assert_eq!(channel_state.last_event_time, 2000); - // At t=5000 (gap > 2000ms after t=2000), channel should be expired. let expired = ingester.expired_channels(5000); assert_eq!(expired.len(), 1, "Channel should be expired at t=5000"); @@ -82,7 +79,7 @@ fn test_stream_single_sensor() { ); } -/// Test 9: Stream shape — composite key, two sensors in same room. +/// Test 9: Stream shape — composite key, two sensors in same room #[test] fn test_stream_composite_key() { let dict = make_dict(); @@ -124,7 +121,6 @@ fn test_stream_composite_key() { let mut ingester = ShapeIngester::new(vec![shape]); - // t=0: sensorA in room42, val=22 ingester.process_event(&RdfEvent { stream_iri: "http://example.org/tempStream".into(), timestamp: 0, @@ -135,7 +131,6 @@ fn test_stream_composite_key() { ], }); - // t=0: sensorB in room42, val=23 ingester.process_event(&RdfEvent { stream_iri: "http://example.org/tempStream".into(), timestamp: 0, @@ -146,14 +141,12 @@ fn test_stream_composite_key() { ], }); - // Two distinct channels should exist. assert_eq!( ingester.active_channels().len(), 2, "Should have 2 distinct channels for sensorA and sensorB" ); - // t=1000: sensorA update ingester.process_event(&RdfEvent { stream_iri: "http://example.org/tempStream".into(), timestamp: 1000, @@ -164,14 +157,12 @@ fn test_stream_composite_key() { ], }); - // Still 2 channels. assert_eq!( ingester.active_channels().len(), 2, "sensorA update should not affect sensorB channel count" ); - // Find sensorB channel — its last_event_time should still be 0. let channels = ingester.active_channels(); let sensor_b_state = channels .iter() @@ -187,12 +178,7 @@ fn test_stream_composite_key() { ); } -/// A channel that goes quiet transmits its shape's EXPIRY facts. -/// -/// Silence is not an event, so without this a rule has nothing to observe when a -/// source stops reporting. The facts must REPEAT for as long as the channel -/// stays stale: a `Box[0,n]` rule needs the fact at every integer point in its -/// window, so a one-shot expiry edge could never satisfy one. +/// A channel that goes quiet transmits its shape's EXPIRY facts #[test] fn test_expiry_facts_repeat_while_stale() { use shared::terms::Term; @@ -227,7 +213,6 @@ fn test_expiry_facts_repeat_while_stale() { triples: vec![triple(obs, made_by, sensor)], }); - // Still fresh: expiry_time is 0 + 10, so nothing until t > 10. assert!( ingester.expiry_facts(5).is_empty(), "fresh channel must not expire" @@ -237,7 +222,6 @@ fn test_expiry_facts_repeat_while_stale() { "boundary is not yet stale" ); - // Stale, and it keeps reporting on every later tick. for t in [11u64, 12, 50] { let facts = ingester.expiry_facts(t); assert_eq!(facts.len(), 1, "stale channel should transmit at t={}", t); @@ -245,7 +229,6 @@ fn test_expiry_facts_repeat_while_stale() { assert_eq!(facts[0].1, t, "fact is stamped at the querying tick"); } - // A fresh event silences it again. ingester.process_event(&RdfEvent { stream_iri: "http://example.org/s".into(), timestamp: 50, diff --git a/datalogmtl/src/validate.rs b/datalogmtl/src/validate.rs index 193a5e77..50e0f131 100644 --- a/datalogmtl/src/validate.rs +++ b/datalogmtl/src/validate.rs @@ -8,28 +8,13 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! What a program must satisfy before it can be evaluated. -//! -//! Two checks, both of which reject a rule rather than let it quietly derive -//! nothing: every interval must have `start <= end`, and every head variable must -//! be bound somewhere in the body that can actually witness a value. -//! -//! # Why `Box` does not bind -//! -//! Only [`Base`](crate::syntax::TemporalAtom::Base) and -//! [`Diamond`](crate::syntax::TemporalAtom::Diamond) atoms make a variable safe. -//! The universal operators have no witness to take a value from — `Box` asserts -//! something about every point in a window rather than finding a point — and the -//! `phi` side of `Since` is universal for the same reason. A rule whose head -//! variable occurs only there could never bind it, so it is an error at -//! construction instead of an empty result at every tick. +//! What a program must satisfy before it can be evaluated use crate::syntax::{DatalogMTLRule, TemporalAtom}; use shared::terms::Term; use std::collections::HashSet; -/// Validate a set of DatalogMTL^RDF rules. -/// Returns Err with a descriptive message on the first violation found. +/// Validate a set of DatalogMTL^RDF rules pub fn validate_rules(rules: &[DatalogMTLRule]) -> Result<(), String> { for rule in rules { validate_variable_safety(rule)?; @@ -38,8 +23,7 @@ pub fn validate_rules(rules: &[DatalogMTLRule]) -> Result<(), String> { Ok(()) } -/// Head variables must appear in at least one Base or Diamond body atom. -/// Variables inside Box or the phi of Since are NOT safe. +/// Head variables must appear in at least one Base or Diamond body atom fn validate_variable_safety(rule: &DatalogMTLRule) -> Result<(), String> { let mut safe: HashSet = HashSet::new(); for atom in &rule.body { @@ -70,19 +54,13 @@ fn collect_safe_vars(atom: &TemporalAtom, safe: &mut HashSet) { } TemporalAtom::Diamond { inner, .. } => collect_safe_vars(inner, safe), TemporalAtom::Prev { inner, .. } => collect_safe_vars(inner, safe), - // psi is existential in Since, so its variables are safe. TemporalAtom::Since { psi, .. } => collect_safe_vars(psi, safe), - // Box: variables in the inner pattern are safe because the evaluator - // collects consistent bindings that hold at every timestamp. TemporalAtom::Box_ { inner, .. } => collect_safe_vars(inner, safe), - // Every conjunct must hold, so each one binds. TemporalAtom::Conj(atoms) => { for a in atoms { collect_safe_vars(a, safe); } } - // Future operators mirror their past duals: Diamond/Box inner vars are - // safe; Until's psi (existential trigger) is safe like Since's. TemporalAtom::DiamondPlus { inner, .. } => collect_safe_vars(inner, safe), TemporalAtom::BoxPlus { inner, .. } => collect_safe_vars(inner, safe), TemporalAtom::Until { psi, .. } => collect_safe_vars(psi, safe), diff --git a/docs/api.md b/docs/api.md deleted file mode 100644 index 2677a400..00000000 --- a/docs/api.md +++ /dev/null @@ -1,1660 +0,0 @@ -# Public API surface - -Generated by `scripts/api_surface.py`. Do not edit: run the script. - -This is an index, not a reference. `cargo doc --workspace --open` is the -authority on what a consumer can name, with the types, the bounds and the -prose; this exists so that a change to the surface is visible in a diff, and -so no hand-written list can quietly disagree with the code again. - -It is read lexically from the source, so a `pub` item inside a private module -appears here although nothing outside can reach it, and a re-export is listed -where it is written rather than where it is reachable. - -## `kolibrie-api` - -The facade: one dependency that re-exports the rest. - -### `kolibrie-api/src/lib.rs` - -```rust -pub use kolibrie::execute_query::{ execute, execute_sparql_query, execute_sparql_update, Solution, Solutions, UpdateSummary, } -pub use kolibrie::parse_error::{Expected, ParseError, ParseResult} -pub use kolibrie::query_error::QueryError -pub use kolibrie::sparql_database::SparqlDatabase -pub use kolibrie::query_builder::QueryBuilder -pub use shared::dictionary::Dictionary -pub use shared::rule::Rule -pub use shared::terms::{Binding, Bindings, Term, TriplePattern} -pub use shared::triple::Triple -pub use datalog::reasoning::Reasoner -pub use prelude::* -pub use kolibrie -pub use shared -pub use datalog -pub use datalogmtl -``` - -_15 public items across 1 file._ - -## `kolibrie` - -The engine: store, parser, query execution, RSP. - -### `kolibrie/src/execute_query.rs` - -```rust -pub use crate::query_error::QueryError -pub struct Solutions -pub fn variables(&self) -> &[String] -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -pub fn has_variable(&self, variable: &str) -> bool -pub fn into_rows(self) -> Vec> -pub fn iter(&self) -> impl Iterator> -pub struct Solution<'a> -pub fn get(&self, variable: &str) -> Option<&'a str> -pub fn values(&self) -> impl Iterator> + 'a -pub fn execute(sparql: &str, database: &mut SparqlDatabase) -> Result -pub fn validate_query_policy(sparql: &str, database: &SparqlDatabase) -> Result<(), QueryError> -pub fn execute_sparql_with_ml_context( sparql: &str, database: &mut SparqlDatabase, context: &crate::ml_policy::MlExecutionContext, ) -> Result>, QueryError> -pub struct UpdateSummary -pub fn execute_query_rayon_parallel2_volcano( sparql: &str, database: &mut SparqlDatabase, ) -> Vec> -pub fn execute_sparql_query( sparql: &str, database: &mut SparqlDatabase, ) -> Result>, QueryError> -pub fn execute_sparql_update( sparql: &str, database: &mut SparqlDatabase, ) -> Result -``` - -### `kolibrie/src/ml/execute.rs` - -```rust -pub struct MLPredictTiming -pub fn print_breakdown(&self) -pub fn execute_ml_prediction_from_clause( ml_predict: &MLPredictClause, database: &SparqlDatabase, model: &str, extract_data: F, predict: G, ) -> Result<(Vec, MLPredictTiming), Box> -pub fn execute_ml_prediction_with_handler( ml_predict: &MLPredictClause, database: &SparqlDatabase, ml_handler: &MLHandler, best_model_name: &str, extract_data: F, predict: G, ) -> Result<(Vec, MLPredictTiming), Box> -pub fn setup_ml_handler(model: &str) -> Result<(MLHandler, String), Box> -``` - -### `kolibrie/src/ml/execute_train.rs` - -```rust -pub struct OwnedNeuralChoice -pub enum OwnedNeuralGroupType -pub struct OwnedNeuralCallSpec -pub struct OwnedNeuralTrainingClause -pub struct NeuralTrainingTimings -pub fn execute_ml_training_owned( clause: &OwnedNeuralTrainingClause, base_reasoner: &Reasoner, db: &mut SparqlDatabase, ) -> TrainResult -pub fn execute_ml_training_owned_seeded( clause: &OwnedNeuralTrainingClause, base_reasoner: &Reasoner, db: &mut SparqlDatabase, seed: u64, ) -> TrainResult -pub fn execute_ml_training_owned_seeded_timed( clause: &OwnedNeuralTrainingClause, base_reasoner: &Reasoner, db: &mut SparqlDatabase, seed: u64, ) -> TrainResult<(MlpNeuralPredicate, NeuralTrainingTimings)> -pub fn execute_supervised_training( clause: &OwnedNeuralTrainingClause, hidden: &[usize], db: &mut SparqlDatabase, seed: Option, ) -> TrainResult<(MlpNeuralPredicate, NeuralTrainingTimings)> -pub fn build_ground_reasoner_from_db(db: &SparqlDatabase, extra_rule: Option) -> Reasoner -``` - -### `kolibrie/src/ml/neural_relations.rs` - -```rust -pub use crate::neural_registry::register_neural_declarations -pub const DISTRIBUTION_REQUIRES_PROGRAM: &str = "ML.PREDICT ... OUTPUT ?v DISTRIBUTION runs only through kolibrie::program -pub fn lower_train_decl_to_owned( database: &SparqlDatabase, train_decl: &TrainNeuralRelationDecl, ) -> Result -pub fn execute_train_decl( database: &mut SparqlDatabase, train_decl: &TrainNeuralRelationDecl, ) -> NeuralResult<()> -pub fn execute_top_level_ml_predict( database: &mut SparqlDatabase, ml_predict: &MLPredictClause<'_>, prefixes: &HashMap, ) -> NeuralResult<()> -pub fn execute_neural_program( database: &mut SparqlDatabase, program: &str, ) -> Result<(), crate::execute_query::QueryError> -pub fn materialize_neural_relation( database: &mut SparqlDatabase, predicate: &str, ) -> NeuralResult<()> -pub fn materialize_neural_relations_for_patterns( database: &mut SparqlDatabase, patterns: &[(&str, &str, &str)], prefixes: &HashMap, ) -> Result<(), String> -pub use crate::ml::syntax::lower_ml_predict_alias -pub struct NeuralRuntime -``` - -### `kolibrie/src/ml/neural_relations_disabled.rs` - -```rust -pub use crate::ml::syntax::lower_ml_predict_alias -pub use crate::neural_registry::register_neural_declarations -pub fn execute_train_decl( _db: &mut SparqlDatabase, _train: &TrainNeuralRelationDecl, ) -> Result<(), Box> -pub fn materialize_neural_relations_for_patterns( db: &mut SparqlDatabase, _patterns: &[(&str, &str, &str)], _prefixes: &HashMap, ) -> Result<(), String> -pub fn execute_neural_program(_db: &mut SparqlDatabase, _program: &str) -> Result<(), String> -pub struct NeuralRuntime -``` - -### `kolibrie/src/ml/runtime.rs` - -```rust -pub trait MlRuntime: Send + Sync -``` - -### `kolibrie/src/ml/syntax.rs` - -```rust -pub fn lower_ml_predict_alias( ml_predict: &MLPredictClause<'_>, ) -> Result -``` - -### `kolibrie/src/ml_policy.rs` - -```rust -pub enum MlError -pub struct MlExecutionContext -pub struct ApprovedModel -pub enum Backend -pub fn validate_model_name(name: &str) -> Result<(), MlError> -pub struct PredictInputs<'a> -pub fn new(entries: Vec, PredictInputs<'a>), MlError>>) -> Self -pub fn validate_artifact_filename(name: &str) -> Result<(), MlError> -pub fn verified_bytes(path: &Path, expected: &str) -> Result, MlError> -pub fn verified_bytes(_path: &Path, _expected: &str) -> Result, MlError> -pub fn predict(&self, name: &str, input: &[Vec]) -> Result, MlError> -pub fn predict(&self, _name: &str, _input: &[Vec]) -> Result, MlError> -pub fn disabled() -> Self -pub fn from_environment() -> Result -pub fn from_registry(path: &Path) -> Result -pub fn trusted_local(output: &Path) -> Result -pub fn require_local(&self) -> Result<(), MlError> -pub fn approved_model(&self, name: &str) -> Result<&ApprovedModel, MlError> -pub fn local_artifact(&self, filename: &str) -> Result -pub fn save_local_artifact(&self, filename: &str, bytes: &[u8]) -> Result<(), MlError> -pub fn validate_request( &self, query: &CombinedQuery<'_>, predicts: &PredictInputs<'_>, has_registered_relations: bool, ) -> Result<(), MlError> -``` - -### `kolibrie/src/neural_registry.rs` - -```rust -pub fn default_model_artifact_path(model_name: &str) -> String -pub fn register_neural_declarations( database: &mut SparqlDatabase, prefixes: &HashMap, model_decls: &[ModelDecl], neural_relation_decls: &[NeuralRelationDecl], train_neural_relation_decls: &[TrainNeuralRelationDecl], ) -pub fn register_neural_declarations_checked( database: &mut SparqlDatabase, prefixes: &HashMap, model_decls: &[ModelDecl], neural_relation_decls: &[NeuralRelationDecl], train_neural_relation_decls: &[TrainNeuralRelationDecl], ) -> Result<(), String> -``` - -### `kolibrie/src/parse_error.rs` - -```rust -pub enum Expected -pub struct ParseError<'a> -pub fn offending(&self) -> &'a str -pub fn is_incomplete(&self) -> bool -pub fn expected(&self) -> Expected -pub fn offset_in(&self, input: &str) -> usize -pub type ParseResult<'a, T> = Result<(&'a str, T), ParseError<'a>> -``` - -### `kolibrie/src/parser.rs` - -```rust -pub fn convert_combined_rule<'a>( cr: CombinedRule<'a>, dict: &mut Dictionary, prefixes: &HashMap, ) -> Rule -pub fn parse_sparql_query(input: &str) -> ParseResult<'_, SelectQuery<'_>> -pub fn parse_standalone_rule<'a>( input: &'a str, ) -> ParseResult<'a, (CombinedRule<'a>, HashMap)> -pub fn parse_combined_query(input: &str) -> ParseResult<'_, CombinedQuery<'_>> -pub fn parse_combined_query_with_options( input: &str, allow_data_aliases: bool, ) -> ParseResult<'_, CombinedQuery<'_>> -pub fn parse_quoted_triple(input: &str) -> ParseResult<'_, &str> -pub fn parse_triple_block(input: &str) -> ParseResult<'_, Vec<(&str, &str, &str)>> -pub fn parse_filter(input: &str) -> ParseResult<'_, FilterExpression<'_>> -pub fn parse_where(input: &str) -> ParseResult<'_, WhereClause<'_>> -pub fn parse_prefix(input: &str) -> ParseResult<'_, (&str, &str)> -pub fn parse_comparison(input: &str) -> ParseResult<'_, FilterExpression<'_>> -pub fn parse_group_by(input: &str) -> ParseResult<'_, Vec<&str>> -pub fn parse_order_by(input: &str) -> ParseResult<'_, Vec>> -pub fn parse_limit(input: &str) -> ParseResult<'_, usize> -pub fn parse_from_named_window(input: &str) -> ParseResult<'_, WindowClause<'_>> -pub fn parse_stream_type(input: &str) -> ParseResult<'_, StreamType<'_>> -pub fn parse_window_spec(input: &str) -> ParseResult<'_, WindowSpec<'_>> -``` - -### `kolibrie/src/prelude.rs` - -```rust -pub use crate::sparql_database::SparqlDatabase -pub use crate::parser::parse_combined_query -pub use crate::execute_query::execute -pub use crate::execute_query::QueryError -pub use crate::query_builder::Queryable -``` - -### `kolibrie/src/program.rs` - -```rust -pub const DISTRIBUTION_TOLERANCE: f64 = 1e-7 -pub const PROB_VALUE_IRI: &str = "http://www.w3.org/ns/prob#value" -pub enum ProgramError -pub enum ResourceError -pub struct ProgramLimits -pub struct ProgramOptions -pub enum RuleMode -pub struct CompiledRule -pub struct CompiledProgram -pub fn rules(&self) -> &[CompiledRule] -pub fn mode(&self) -> Option -pub fn prefixes(&self) -> &HashMap -pub fn has_prediction(&self) -> bool -pub fn has_training(&self) -> bool -pub fn datalog_rules(&self, dictionary: &mut Dictionary) -> Vec -pub fn compile_program( text: &str, policy: &MlExecutionContext, ) -> Result -pub struct NeuralSnapshotMetadata -pub struct PredictionSnapshot -pub fn new() -> Self -pub fn is_empty(&self) -> bool -pub fn choice_groups(&self) -> usize -pub fn independent_facts(&self) -> usize -pub fn labels(&self, relation: &str) -> Option<&[String]> -pub fn distribution(&self, relation: &str, anchor: &str) -> Option<&[f64]> -pub fn anchors(&self, relation: &str) -> Vec<&str> -pub fn neural_metadata(&self) -> &[NeuralSnapshotMetadata] -pub fn declare_exclusive_relation( &mut self, relation: &str, labels: &[String], ) -> Result<(), ProgramError> -pub fn add_distribution( &mut self, relation: &str, anchor: &str, probabilities: &[f64], features: Option<&[f64]>, ) -> Result<(), ProgramError> -pub fn add_independent( &mut self, subject: &str, predicate: &str, object: &str, probability: f64, ) -> Result<(), ProgramError> -pub fn merge(&mut self, other: &PredictionSnapshot) -> Result<(), ProgramError> -pub struct Coverage -pub struct TrainingReport -pub struct ProgramTimings -pub struct ProgramResult -pub struct PreparedAnswers -pub fn recover(&self) -> Vec -pub fn dataset(&self) -> &SparqlDatabase -pub fn snapshot(&self) -> &PredictionSnapshot -pub fn coverage(&self) -> &Coverage -pub fn training(&self) -> &[TrainingReport] -pub fn timings(&self) -> &ProgramTimings -pub fn mode(&self) -> Option -pub fn derived_facts(&self) -> usize -pub fn hypothesis_count(&self) -> usize -pub fn fact_probability(&self, subject: &str, predicate: &str, object: &str) -> Option -pub fn answer_probability( &self, subject: &str, predicate: &str, object: &str, ) -> Result -pub fn answer_set(&self, predicate: &str) -> Vec<(String, String, f64)> -pub fn prepare_answers( &self, answers: &[(String, String, String)], ) -> Result -pub struct PublishedProgramResult -pub fn new() -> Self -pub fn current(&self) -> Option<&ProgramResult> -pub fn current_mut(&mut self) -> Option<&mut ProgramResult> -pub fn publish( &mut self, outcome: Result, ) -> Result<&mut ProgramResult, ProgramError> -pub fn query_program_result( result: &mut ProgramResult, select: &str, ) -> Result>, ProgramError> -pub fn parse_probability_literal(term: &str) -> Option -pub fn execute_program( asserted_input: &mut SparqlDatabase, program: &CompiledProgram, options: &ProgramOptions, ) -> Result -``` - -### `kolibrie/src/query_builder.rs` - -```rust -pub struct QueryBuilder<'a> -pub enum TripleFilter -pub enum JoinCondition -pub enum SortDirection -pub trait Queryable -pub fn new(db: &'a SparqlDatabase) -> Self -pub fn with_subject(mut self, subject: &str) -> Self -pub fn with_subject_like(mut self, pattern: &str) -> Self -pub fn with_subject_starting(mut self, prefix: &str) -> Self -pub fn with_subject_ending(mut self, suffix: &str) -> Self -pub fn with_predicate(mut self, predicate: &str) -> Self -pub fn with_predicate_like(mut self, pattern: &str) -> Self -pub fn with_predicate_starting(mut self, prefix: &str) -> Self -pub fn with_predicate_ending(mut self, suffix: &str) -> Self -pub fn with_object(mut self, object: &str) -> Self -pub fn with_object_like(mut self, pattern: &str) -> Self -pub fn with_object_starting(mut self, prefix: &str) -> Self -pub fn with_object_ending(mut self, suffix: &str) -> Self -pub fn filter(mut self, predicate: F) -> Self -pub fn join(mut self, other: &'a SparqlDatabase) -> Self -pub fn join_on_subject(mut self) -> Self -pub fn join_on_predicate(mut self) -> Self -pub fn join_on_object(mut self) -> Self -pub fn join_with(mut self, condition: F) -> Self -pub fn distinct(mut self) -> Self -pub fn order_by(mut self, key: F) -> Self -pub fn desc(mut self) -> Self -pub fn asc(mut self) -> Self -pub fn limit(mut self, n: usize) -> Self -pub fn offset(mut self, n: usize) -> Self -pub fn get_triples(self) -> BTreeSet -pub fn get_ordered_triples(self) -> Vec -pub fn get_ordered_decoded_triples(self) -> Vec<(String, String, String)> -pub fn get_decoded_triples(self) -> Vec<(String, String, String)> -pub fn get_subjects(self) -> Vec -pub fn get_predicates(self) -> Vec -pub fn get_objects(self) -> Vec -pub fn count(self) -> usize -pub fn group_by(self, key_fn: F) -> BTreeMap> -pub fn window(mut self, width: usize, slide: usize) -> Self -pub fn with_report_strategy(mut self, strategy: ReportStrategy) -> Self -pub fn with_tick_strategy(mut self, tick: Tick) -> Self -pub fn with_stream_operator(mut self, operator: StreamOperator) -> Self -pub fn as_stream(mut self) -> Result -pub fn add_stream_triple( &mut self, subject: &str, predicate: &str, object: &str, timestamp: usize, ) -> Result<(), String> -pub fn get_stream_results(&mut self) -> Vec> -pub fn get_all_stream_results(&self) -> Vec> -pub fn clear_stream_results(&mut self) -pub fn stop_stream(&mut self) -pub fn is_streaming(&self) -> bool -``` - -### `kolibrie/src/query_error.rs` - -```rust -pub enum QueryError -``` - -### `kolibrie/src/rsp/r2r.rs` - -```rust -pub trait AsAnyMut -pub trait R2ROperator: Send + AsAnyMut -``` - -### `kolibrie/src/rsp/r2s.rs` - -```rust -pub enum StreamOperator -pub struct Relation2StreamOperator -pub fn new(stream_operator: StreamOperator, _start_time: usize) -> Relation2StreamOperator -pub fn eval(&mut self, new_response: Vec, _ts: usize) -> Vec -``` - -### `kolibrie/src/rsp/s2r.rs` - -```rust -pub enum ReportStrategy -pub enum Tick -pub struct Report -pub fn new() -> Report -pub fn add(&mut self, strategy: ReportStrategy) -pub fn report(&mut self, window: &Window, content: &ContentContainer, ts: usize) -> bool -pub struct Window -pub struct ProbabilisticOccurrence -pub struct ContentContainer -pub fn len(&self) -> usize -pub fn get_last_timestamp_changed(&self) -> usize -pub fn iter(&self) -> Keys<'_, I, usize> -pub fn iter_with_timestamps(&self) -> impl Iterator -pub fn probabilistic_occurrences(&self) -> &[ProbabilisticOccurrence] -pub fn is_deterministic(&self, item: &I) -> bool -pub fn into_iter(mut self) -> IntoKeys -pub struct CSPARQLWindow -pub fn new( width: usize, slide: usize, report: Report, tick: Tick, uri: String, ) -> CSPARQLWindow -pub fn add_to_window(&mut self, event_item: I, ts: usize) -pub fn add_probabilistic_to_window(&mut self, occurrence: ProbabilisticOccurrence) -pub fn register(&mut self) -> Receiver> -pub fn register_callback( &mut self, function: Box) -> () + Send + 'static>, ) -pub fn flush(&mut self) -pub fn stop(&mut self) -pub type WindowTriple = shared::rdf_term::TermTriple -``` - -### `kolibrie/src/rsp/simple_r2r.rs` - -```rust -pub enum QueryExecutionMode -pub struct SimpleR2R -pub fn new() -> Self -pub fn with_execution_mode(execution_mode: QueryExecutionMode) -> Self -pub fn add_reasoning_rules(&mut self, rules: Vec) -pub fn set_hybrid_config(&mut self, config: HybridConfig) -> Result<(), HybridError> -pub fn set_live_seed_snapshot(&mut self, snapshot: SeedSnapshot) -pub fn last_hybrid_results(&self) -> &HashMap -pub fn last_hybrid_error(&self) -> Option<&str> -pub fn hybrid_enabled(&self) -> bool -``` - -### `kolibrie/src/rsp/window_runner.rs` - -```rust -pub use crate::rsp::s2r::WindowTriple -pub struct WindowSpec -pub struct WindowRunner -pub fn new(spec: WindowSpec, uri: String) -> Self -pub fn start_receiver(&mut self) -pub fn push(&mut self, item: I, ts: usize) -pub fn drain(&mut self) -> Vec> -pub fn add_to_window(&mut self, item: I, ts: usize) -pub fn add_probabilistic_to_window(&mut self, occurrence: ProbabilisticOccurrence) -pub fn register(&mut self) -> Receiver> -pub fn register_callback( &mut self, f: Box) -> () + Send + 'static>, ) -pub fn flush(&mut self) -pub fn stop(&mut self) -``` - -### `kolibrie/src/rsp_engine/builder.rs` - -```rust -pub struct RSPQueryConfig<'a> -pub struct RSPBuilder<'a, I, O> -pub fn new() -> RSPBuilder<'a, I, O> -pub fn set_sync_policy(mut self, policy: SyncPolicy) -> RSPBuilder<'a, I, O> -pub fn add_reasoning_rules(mut self, rules: Vec) -> RSPBuilder<'a, I, O> -pub fn add_sparql_rules(mut self, rules: Vec) -> RSPBuilder<'a, I, O> -pub fn add_cross_window_rules(mut self, n3_rules: &'a str) -> RSPBuilder<'a, I, O> -pub fn set_cross_window_reasoning_mode( mut self, mode: CrossWindowReasoningMode, ) -> RSPBuilder<'a, I, O> -pub fn add_rsp_ql_query(mut self, query: &'a str) -> RSPBuilder<'a, I, O> -pub fn add_triples(mut self, triples: &'a str) -> RSPBuilder<'a, I, O> -pub fn add_rules(mut self, rules: &'a str) -> RSPBuilder<'a, I, O> -pub fn add_consumer(mut self, consumer: ResultConsumer) -> RSPBuilder<'a, I, O> -pub fn add_r2r( mut self, r2r: Box, O>>, ) -> RSPBuilder<'a, I, O> -pub fn set_operation_mode(mut self, operation_mode: OperationMode) -> RSPBuilder<'a, I, O> -pub fn set_query_execution_mode(mut self, mode: QueryExecutionMode) -> RSPBuilder<'a, I, O> -pub fn build(mut self) -> Result, String> -``` - -### `kolibrie/src/rsp_engine/mod.rs` - -```rust -pub use builder::{RSPBuilder, RSPQueryConfig} -pub use crate::rsp::simple_r2r::QueryExecutionMode -pub use crate::rsp::simple_r2r::SimpleR2R -pub enum OperationMode -pub enum CrossWindowReasoningMode -pub struct RSPWindow -pub struct RSPQueryPlan -pub struct WindowResult -pub struct ResultConsumer -pub struct RSPEngine -pub fn new( query_config: RSPQueryConfig, triples: &str, syntax: String, rules: &str, result_consumer: ResultConsumer, r2r: Box, O>>, operation_mode: OperationMode, query_execution_mode: QueryExecutionMode, rsp_query_plan: RSPQueryPlan, sync_policy: SyncPolicy, reasoning_rules: Vec -pub fn add_to_stream(&mut self, stream_iri: &str, event_item: I, ts: usize) -pub fn process_single_thread_window_results(&mut self) -pub fn add(&mut self, event_item: I, ts: usize) -pub fn stop(&mut self) -pub fn parse_data(&mut self, data: &str) -> Vec -pub fn add_static_ntriples(&mut self, data: &str) -pub fn get_window_info(&self) -> Vec<&RSPWindow> -pub fn get_query_plan(&self) -> &RSPQueryPlan -pub fn get_cross_window_context(&self) -> Option<&WindowContext> -pub fn stream_iris(&self) -> Vec -pub fn add_probabilistic_to_stream( &mut self, stream_iri: &str, triple: Triple, probability: f64, timestamp: usize, ) -> Result -pub fn latest_hybrid_results( &self, window_iri: &str, ) -> Option> -``` - -### `kolibrie/src/rule_execution.rs` - -```rust -pub fn process_rule_definition( rule_input: &str, database: &mut SparqlDatabase, ) -> Result<(Rule, Vec), QueryError> -pub fn process_retrieve_clause( retrieve_clause: &RetrieveClause, database: &mut SparqlDatabase, ) -> Result, String> -``` - -### `kolibrie/src/sparql_database.rs` - -```rust -pub enum PlanningStatsPolicy -pub const DEFAULT_BOUNDED: Self = PlanningStatsPolicy::Bounded { minimum_batch: 100, refresh_fraction: 0.5, } -pub struct PlanningStatsSnapshot -pub type DictionaryRef<'a> = shared::term_encoder::TermsRef<'a> -pub type DictionaryMut<'a> = shared::term_encoder::TermsMut<'a> -pub struct QuotedTriplesRef<'a>(RwLockReadGuard<'a, QuotedTripleStore>) -pub struct QuotedTriplesMut<'a>(RwLockWriteGuard<'a, QuotedTripleStore>) -pub struct SparqlDatabase -pub fn with_ml_context(context: crate::ml_policy::MlExecutionContext) -> Self -pub fn new() -> Self -pub fn encode_term_star(&self, term: &str) -> u32 -pub fn decode_any(&self, id: u32) -> Option -pub fn split_quoted_triple_content(content: &str) -> (String, String, String) -pub fn set_prefixes(&mut self, prefixes: HashMap) -pub fn get_or_build_stats(&mut self) -> Arc -pub fn get_or_build_planning_stats(&mut self) -> Arc -pub fn gather_stats(&self) -> DatabaseStats -pub fn invalidate_stats_cache(&mut self) -pub fn replace_dataset_index(&mut self, dataset_index: DatasetIndex) -pub fn stats_rebuild_metrics(&self) -> (u64, std::time::Duration) -pub fn add_triple(&mut self, triple: Triple) -pub fn delete_triple(&mut self, triple: &Triple) -> bool -pub fn add_quad(&mut self, quad: Quad) -> bool -pub fn delete_quad(&mut self, quad: &Quad) -> bool -pub fn add_quad_parts( &mut self, subject: &str, predicate: &str, object: &str, graph: &str, ) -> bool -pub fn query_default_triples( &self, s: Option, p: Option, o: Option, ) -> Vec -pub fn query_graph_quads( &self, graph: GraphId, s: Option, p: Option, o: Option, ) -> Vec -pub fn add_triple_parts(&mut self, subject: &str, predicate: &str, object: &str) -pub fn add_tagged_triple( &mut self, subject: &str, predicate: &str, object: &str, probability: f64, ) -pub fn delete_triple_parts(&mut self, subject: &str, predicate: &str, object: &str) -> bool -pub fn generate_rdf_xml(&mut self) -> String -pub fn generate_ntriples(&self) -> String -pub fn generate_nquads_skolemised(&self, base: &str) -> String -pub fn generate_ntriples_skolemised(&self, base: &str) -> String -pub fn generate_nquads(&self) -> String -pub fn generate_turtle(&self) -> String -pub fn parse_rdf(&mut self, rdf_xml: &str) -pub fn parse_rdf_from_file(&mut self, filename: &str) -pub fn parse_turtle(&mut self, turtle_data: &str) -pub fn parse_n3(&mut self, n3_data: &str) -pub fn parse_ntriples_and_add(&mut self, ntriples_data: &str) -pub fn parse_ntriples(&mut self, ntriples_data: &str) -> Vec> -pub fn parse_ntriples_in_scope( &mut self, ntriples_data: &str, document: u64, ) -> Vec> -pub fn encode_triples(&mut self, non_encoded_triples: Vec>) -> Vec -pub fn parse_and_encode_ntriples(&mut self, ntriples_data: &str) -> Vec -pub fn parse_and_encode_ntriples_in_scope( &mut self, ntriples_data: &str, document: u64, ) -> Vec -pub fn parse_nquads_and_add(&mut self, nquads_data: &str) -pub fn register_prefixes_from_query(&mut self, query: &str) -pub fn share_prefixes_with(&self, prefixes: &mut HashMap) -pub fn resolve_query_term(&self, term: &str, prefixes: &HashMap) -> String -pub fn union(&mut self, other: &SparqlDatabase) -> Self -pub fn handle_query(&mut self, query: &str) -> String -pub fn debug_print_triples(&self) -pub fn register_udf(&mut self, name: &str, f: F) -pub fn default_triple_count(&self) -> usize -pub fn quad_count(&self) -> usize -pub fn all_quads(&self) -> Vec -pub fn graphs(&self) -> Vec -pub fn named_graphs(&self) -> Vec -pub fn create_graph(&mut self, graph: GraphId) -> bool -pub fn insert_triple(&mut self, triple: &Triple) -> bool -pub fn clear_dataset(&mut self) -pub fn dictionary(&self) -> DictionaryRef<'_> -pub fn dictionary_mut(&self) -> DictionaryMut<'_> -pub fn shares_dictionary_with(&self, other: &SparqlDatabase) -> bool -pub fn terms(&self) -> SharedTerms -pub fn adopt_terms(&mut self, terms: SharedTerms) -pub fn ml_context(&self) -> &crate::ml_policy::MlExecutionContext -pub fn set_ml_context(&mut self, context: crate::ml_policy::MlExecutionContext) -pub fn planning_stats_policy(&self) -> PlanningStatsPolicy -pub fn set_planning_stats_policy(&mut self, policy: PlanningStatsPolicy) -pub fn stats_rebuild_count(&self) -> u64 -pub fn prefixes(&self) -> &HashMap -pub fn add_prefix(&mut self, prefix: impl Into, uri: impl Into) -pub fn extend_prefixes(&mut self, bindings: impl IntoIterator) -pub fn model_decls(&self) -> &HashMap -pub fn neural_relation_decls(&self) -> &HashMap -pub fn neural_model_artifacts(&self) -> &HashMap -pub fn neural_materialized_triples(&self) -> &HashMap> -pub fn take_neural_materialized(&mut self, predicate: &str) -> Option> -pub fn probability_seeds(&self) -> &HashMap -pub fn add_rule_mapping(&mut self, name: impl Into, expanded: impl Into) -pub fn quoted_triples(&self) -> QuotedTriplesRef<'_> -pub fn quoted_triples_mut(&self) -> QuotedTriplesMut<'_> -pub fn shares_quoted_triples_with(&self, other: &SparqlDatabase) -> bool -pub fn training_seed(&self) -> Option -pub fn set_training_seed(&mut self, seed: Option) -pub fn build_all_indexes(&mut self) -pub fn triple_to_string(&self, triple: &Triple, dict: &Dictionary) -> String -``` - -### `kolibrie/src/sparql_protocol.rs` - -```rust -pub fn answer(database: &mut SparqlDatabase, request: &str) -> String -pub fn update_result(database: &mut SparqlDatabase, update: &str) -> (&'static str, String) -``` - -### `kolibrie/src/stats/database_stats.rs` - -```rust -pub struct TermHasher(u64) -pub type TermBuildHasher = BuildHasherDefault -pub type TermMap = HashMap -pub type TermSet = HashSet -pub type GraphMap = HashMap -pub struct DatabaseStats -pub fn new() -> Self -pub fn gather_stats_fast(index: &DatasetIndex, quoted_triple_count: u64) -> Self -pub fn get_predicate_distinct_subjects(&self, predicate: u32) -> u64 -pub fn get_predicate_distinct_objects(&self, predicate: u32) -> u64 -pub fn distinct_predicates(&self) -> u64 -pub fn get_predicate_cardinality(&self, predicate: u32) -> u64 -pub fn get_subject_cardinality(&self, subject: u32) -> u64 -pub fn get_object_cardinality(&self, object: u32) -> u64 -pub fn get_graph_cardinality(&self, graph: GraphId) -> u64 -pub fn update_stats(&mut self, subject: u32, predicate: u32, object: u32) -pub fn remove_stats(&mut self, subject: u32, predicate: u32, object: u32) -``` - -### `kolibrie/src/stats/mod.rs` - -```rust -pub use database_stats::{DatabaseStats, GraphMap, TermBuildHasher, TermHasher, TermMap, TermSet} -``` - -### `kolibrie/src/streamertail_optimizer/cost/estimator.rs` - -```rust -pub fn normalize_variable(variable: &str) -> &str -pub fn collect_pattern_variables(pattern: &QuadPattern, out: &mut HashSet) -pub struct CostConstants -pub const COST_PER_ROW_SCAN: u64 = 100 -pub const COST_PER_ROW_INDEX_SCAN: u64 = 1 -pub const COST_PER_SCAN_PROBE: u64 = 1 -pub const UNMEASURED_GRAPH_CARDINALITY: u64 = 1 -pub const COST_PER_FILTER: u64 = 1 -pub const COST_PER_ROW_JOIN: u64 = 2 -pub const COST_PER_ROW_NESTED_LOOP: u64 = 10 -pub const COST_PER_PROJECTION: u64 = 1 -pub const COST_PER_PROBE: u64 = 2 -pub const TUPLE_COST: u64 = 1 -pub struct CostEstimator<'a> -pub fn new(stats: &'a DatabaseStats) -> Self -pub fn with_dataset(stats: &'a DatabaseStats, dataset: &'a DatasetView) -> Self -pub fn estimate_cost(&self, plan: &PhysicalOperator) -> u64 -pub fn estimate_cardinality(&self, pattern: &TriplePattern) -> u64 -pub fn estimate_bound_scan_cardinality( &self, pattern: &QuadPattern, bound: &HashSet, ) -> u64 -pub fn estimate_quad_cardinality(&self, pattern: &QuadPattern) -> u64 -pub fn visible_named_graph_count(&self) -> u64 -pub fn fixed_graph_is_visible(&self, graph: u32) -> bool -pub fn estimate_selectivity(&self, condition: &Condition) -> f64 -pub fn collect_scan_patterns(plan: &PhysicalOperator, out: &mut Vec) -pub fn estimate_join_cardinality_from_patterns( &self, left_cardinality: u64, right_cardinality: u64, left_patterns: &[QuadPattern], right_patterns: &[QuadPattern], ) -> u64 -pub fn estimate_output_cardinality(&self, plan: &PhysicalOperator) -> u64 -``` - -### `kolibrie/src/streamertail_optimizer/cost/mod.rs` - -```rust -pub use estimator::{collect_pattern_variables, normalize_variable, CostConstants, CostEstimator} -``` - -### `kolibrie/src/streamertail_optimizer/execution/engine.rs` - -```rust -pub struct DatasetView -pub fn from_database(database: &SparqlDatabase) -> Self -pub fn new( default_graphs: impl IntoIterator, named_graphs: impl IntoIterator, ) -> Self -pub fn empty_default(named_graphs: impl IntoIterator) -> Self -pub fn is_named_visible(&self, graph: GraphId) -> bool -pub struct ExecutionContext -pub fn new(dataset: DatasetView) -> Self -pub struct ExecutionEngine -pub fn execute( operator: &PhysicalOperator, database: &mut SparqlDatabase, ) -> Vec> -pub fn execute_with_dataset( operator: &PhysicalOperator, database: &mut SparqlDatabase, dataset: &DatasetView, ) -> Vec> -pub fn execute_with_ids( operator: &PhysicalOperator, database: &mut SparqlDatabase, ) -> Bindings -pub fn execute_with_ids_and_dataset( operator: &PhysicalOperator, database: &mut SparqlDatabase, dataset: &DatasetView, ) -> Bindings -pub fn execute_with_ids_and_context( operator: &PhysicalOperator, database: &SparqlDatabase, context: &ExecutionContext, ) -> Bindings -pub fn execute_with_ids_and_input( operator: &PhysicalOperator, database: &SparqlDatabase, context: &ExecutionContext, incoming: Bindings, ) -> Bindings -``` - -### `kolibrie/src/streamertail_optimizer/execution/exec_stats.rs` - -```rust -pub static SCAN_PROBES: AtomicU64 = AtomicU64::new(0) -pub static QUADS_EXAMINED: AtomicU64 = AtomicU64::new(0) -pub static ROWS_EMITTED: AtomicU64 = AtomicU64::new(0) -pub struct Snapshot -pub fn reset() -pub fn snapshot() -> Snapshot -pub use counters::{reset, snapshot, Snapshot} -``` - -### `kolibrie/src/streamertail_optimizer/execution/mod.rs` - -```rust -pub use engine::{DatasetView, ExecutionContext, ExecutionEngine} -``` - -### `kolibrie/src/streamertail_optimizer/mod.rs` - -```rust -pub use crate::stats::{DatabaseStats, GraphMap, TermMap, TermSet} -pub use cost::{CostConstants, CostEstimator} -pub use execution::{DatasetView, ExecutionContext, ExecutionEngine} -pub use operators::{LogicalOperator, PhysicalOperator} -pub use optimizer::Streamertail -pub use types::{ Condition, ConditionArithmetic, ConditionExpression, IdResult, SubqueryProjection, SubquerySpec, } -pub use utils::{ build_logical_plan, build_logical_plan_from_group, build_logical_plan_from_subquery, compile_graph_term, compile_term, compile_triple, extract_pattern, pattern_contains_variable, } -pub use utils::estimate_operator_selectivity -``` - -### `kolibrie/src/streamertail_optimizer/operators/logical.rs` - -```rust -pub enum LogicalOperator -pub fn requires_cost_based_join_ordering(&self) -> bool -pub fn unit() -> Self -pub fn scan(pattern: TriplePattern) -> Self -pub fn quad_scan(pattern: QuadPattern) -> Self -pub fn union(branches: Vec) -> Self -pub fn graph(input: LogicalOperator, graph: GraphTerm) -> Self -pub fn selection(predicate: LogicalOperator, condition: Condition) -> Self -pub fn projection(predicate: LogicalOperator, variables: Vec) -> Self -pub fn join(left: LogicalOperator, right: LogicalOperator) -> Self -pub fn buffer(content: Bindings, origin: String) -> Self -pub fn subquery(inner: LogicalOperator, spec: SubquerySpec) -> Self -pub fn bind( input: LogicalOperator, function_name: String, arguments: Vec, output_variable: String, ) -> Self -pub fn values(variables: Vec, values: Vec>>) -> Self -pub fn ml_predict( input: LogicalOperator, model_name: String, input_variables: Vec, output_variable: String, ) -> Self -``` - -### `kolibrie/src/streamertail_optimizer/operators/mod.rs` - -```rust -pub use logical::LogicalOperator -pub use physical::PhysicalOperator -``` - -### `kolibrie/src/streamertail_optimizer/operators/physical.rs` - -```rust -pub enum PhysicalOperator -pub fn unit() -> Self -pub fn table_scan(pattern: TriplePattern) -> Self -pub fn quad_table_scan(pattern: QuadPattern) -> Self -pub fn index_scan(pattern: TriplePattern) -> Self -pub fn quad_index_scan(pattern: QuadPattern) -> Self -pub fn union(branches: Vec) -> Self -pub fn graph(input: PhysicalOperator, graph: GraphTerm) -> Self -pub fn filter(input: PhysicalOperator, condition: Condition) -> Self -pub fn bind_join(left: PhysicalOperator, right: PhysicalOperator) -> Self -pub fn hash_join(left: PhysicalOperator, right: PhysicalOperator) -> Self -pub fn nested_loop_join(left: PhysicalOperator, right: PhysicalOperator) -> Self -pub fn projection(input: PhysicalOperator, variables: Vec) -> Self -pub fn buffer(content: Bindings, origin: String) -> Self -pub fn subquery(inner: PhysicalOperator, spec: SubquerySpec) -> Self -pub fn bind( input: PhysicalOperator, function_name: String, arguments: Vec, output_variable: String, ) -> Self -pub fn values(variables: Vec, values: Vec>>) -> Self -pub fn ml_predict( input: PhysicalOperator, model_name: String, model_path: String, input_variables: Vec, output_variable: String, ) -> Self -pub fn execute( &self, database: &mut crate::sparql_database::SparqlDatabase, ) -> Vec> -pub fn execute_with_ids( &self, database: &mut crate::sparql_database::SparqlDatabase, ) -> Bindings -pub fn output_variables(&self, input: &[String]) -> Vec -``` - -### `kolibrie/src/streamertail_optimizer/optimizer.rs` - -```rust -pub struct Streamertail -pub fn new(database: &SparqlDatabase) -> Self -pub fn with_cached_stats(stats: Arc) -> Self -pub fn with_cached_stats_and_dataset(stats: Arc, dataset: DatasetView) -> Self -pub fn find_best_plan(&mut self, logical_plan: &LogicalOperator) -> PhysicalOperator -pub fn execute_plan( &self, plan: &PhysicalOperator, database: &mut SparqlDatabase, ) -> Vec> -pub fn execute_plan_with_dataset( &self, plan: &PhysicalOperator, database: &mut SparqlDatabase, dataset: &DatasetView, ) -> Vec> -pub fn optimize_and_execute( &mut self, logical_plan: &LogicalOperator, database: &mut SparqlDatabase, ) -> Vec> -pub fn update_stats(&mut self, database: &SparqlDatabase) -pub fn set_selected_variables(&mut self, variables: Vec) -pub fn get_stats(&self) -> &DatabaseStats -``` - -### `kolibrie/src/streamertail_optimizer/types.rs` - -```rust -pub enum ConditionArithmetic -pub enum ConditionExpression -pub struct Condition -pub struct IdResult -pub struct SubqueryProjection -pub struct SubquerySpec -pub fn new(variable: String, operator: String, value: String) -> Self -pub fn from_filter(filter: FilterExpression<'_>) -> Self -pub fn from_filter_with_resolver(filter: &FilterExpression<'_>, resolve_constant: F) -> Self -pub fn evaluate(&self, result: &HashMap) -> bool -pub fn evaluate_with_ids( &self, row: &[u32], schema: &[String], dictionary: &Dictionary, ) -> bool -pub fn new() -> Self -pub fn with_bindings(bindings: HashMap) -> Self -pub fn insert(&mut self, variable: String, id: u32) -pub fn get(&self, variable: &str) -> Option<&u32> -pub fn contains(&self, variable: &str) -> bool -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -``` - -### `kolibrie/src/streamertail_optimizer/utils.rs` - -```rust -pub fn extract_pattern(op: &PhysicalOperator) -> Option<&QuadPattern> -pub fn pattern_contains_variable(pattern: &TriplePattern, var: &str) -> bool -pub fn estimate_operator_selectivity(op: &LogicalOperator, _database: &SparqlDatabase) -> u64 -pub fn build_logical_plan( variables: Vec<(&str, &str)>, patterns: Vec<(&str, &str, &str)>, filters: Vec, prefixes: &HashMap, database: &mut SparqlDatabase, binds: &[(&str, Vec<&str>, &str)], values_clause: Option<&ValuesClause>, ) -> LogicalOperator -pub fn compile_term( term_str: &str, prefixes: &HashMap, database: &mut SparqlDatabase, ) -> Term -pub fn compile_triple( pattern: (&str, &str, &str), prefixes: &HashMap, database: &mut SparqlDatabase, ) -> TriplePattern -pub fn compile_graph_term( graph: &str, prefixes: &HashMap, database: &mut SparqlDatabase, ) -> Result -pub fn build_logical_plan_from_group( pattern: &GroupGraphPattern<'_>, prefixes: &HashMap, database: &mut SparqlDatabase, ) -> Result -pub fn build_logical_plan_from_subquery( subquery: &SubQuery, prefixes: &HashMap, database: &mut SparqlDatabase, ) -> Result -``` - -### `kolibrie/src/utils.rs` - -```rust -pub struct ClonableFn(Arc) -> String + Send + Sync>) -pub fn new(f: F) -> Self -pub fn call(&self, args: Vec<&str>) -> String -pub fn current_timestamp() -> u64 -pub fn get_num_cpus() -> usize -``` - -_553 public items across 40 files._ - -## `shared` - -Types every crate shares: terms, triples, rules, dictionary. - -### `shared/src/dataset_index.rs` - -```rust -pub enum GraphId -pub struct Quad -pub fn triple(&self) -> Triple -pub enum GraphTerm -pub struct QuadPattern -pub struct DatasetIndex -pub fn new() -> Self -pub fn generation(&self) -> u64 -pub fn resets(&self) -> u64 -pub fn insert_triple(&mut self, triple: &Triple) -> bool -pub fn delete_triple(&mut self, triple: &Triple) -> bool -pub fn insert(&mut self, triple: &Triple) -> bool -pub fn delete(&mut self, triple: &Triple) -> bool -pub fn query(&self, s: Option, p: Option, o: Option) -> Vec -pub fn insert_quad(&mut self, quad: &Quad) -> bool -pub fn delete_quad(&mut self, quad: &Quad) -> bool -pub fn contains_quad(&self, quad: &Quad) -> bool -pub fn query_default(&self, s: Option, p: Option, o: Option) -> Vec -pub fn query_merged_graphs( &self, source_graphs: &[GraphId], s: Option, p: Option, o: Option, ) -> Vec -pub fn query_graph( &self, graph: GraphId, s: Option, p: Option, o: Option, ) -> Vec -pub fn query_named_graphs( &self, s: Option, p: Option, o: Option, visible_graphs: Option<&HashSet>, ) -> Vec -pub fn query_quads( &self, s: Option, p: Option, o: Option, graph: Option, ) -> Vec -pub fn get_matching_triples(&self, pattern: &TriplePattern) -> Vec -pub fn graph_exists(&self, graph: GraphId) -> bool -pub fn named_graphs(&self) -> Vec -pub fn graphs(&self) -> Vec -pub fn create_graph(&mut self, graph: GraphId) -> bool -pub fn drop_graph(&mut self, graph: GraphId) -> bool -pub fn all_quads(&self) -> Vec -pub fn graphs_for_triple(&self, triple: &Triple) -> Vec -pub fn clear_graph(&mut self, graph: GraphId) -pub fn clear(&mut self) -pub fn for_each_quad(&self, mut visit: impl FnMut(GraphId, u32, u32, u32)) -pub fn for_each_quad_in_graph(&self, graph: GraphId, mut visit: impl FnMut(u32, u32, u32)) -pub fn count_graph(&self, graph: GraphId) -> usize -pub fn count_quads(&self) -> usize -pub fn len_graph(&self, graph: GraphId) -> usize -pub fn len_default(&self) -> usize -``` - -### `shared/src/dictionary.rs` - -```rust -pub struct Dictionary -pub fn new() -> Self -pub fn encode(&mut self, value: &str) -> u32 -pub fn decode(&self, id: u32) -> Option<&str> -pub fn decode_triple(&self, triple: &Triple) -> String -pub fn decode_term(&self, id: u32, qt_store: &QuotedTripleStore) -> Option -pub fn decode_triple_star(&self, triple: &Triple, qt_store: &QuotedTripleStore) -> String -pub fn merge(&mut self, other: &Dictionary) -``` - -### `shared/src/diff_sdd.rs` - -```rust -pub fn wmc_gradient(manager: &mut SddManager, id: SddId) -> HashMap -``` - -### `shared/src/hybrid.rs` - -```rust -pub trait HybridClock: Send + Sync -pub struct SystemHybridClock -pub struct SeedId(u32) -pub fn get(self) -> u32 -pub struct EventKey -pub enum SeedKind -pub struct SeedRecord -pub enum HybridError -pub struct SeedRegistry -pub fn new() -> Self -pub fn next_event_key(&mut self, stream_iri: &str, event_time: usize) -> EventKey -pub fn register_occurrence( &mut self, event: EventKey, triple: Triple, probability: f64, ) -> Result -pub fn register_static( &mut self, triple: Triple, probability: f64, ) -> Result -pub fn register_exclusive( &mut self, group_id: u32, triple: Triple, probability: f64, ) -> Result -pub fn snapshot_all(&self) -> SeedSnapshot -pub fn snapshot_for_ids(&self, ids: I) -> Result -pub fn normalize_stream_iri(value: &str) -> String -pub struct SeedSnapshot -pub fn from_probability_seeds(seeds: &HashMap) -> Result -pub fn from_seed_specs(specs: &[SeedSpec]) -> Result -pub fn record(&self, id: SeedId) -> Option<&SeedRecord> -pub fn ids_for_triple(&self, triple: &Triple) -> &[SeedId] -pub fn records(&self) -> impl Iterator -pub fn triples(&self) -> impl Iterator -pub fn group(&self, group: u32) -> Option<&[SeedId]> -pub struct LineageId(u32) -pub const FALSE: Self = Self(0) -pub const TRUE: Self = Self(1) -pub fn get(self) -> u32 -pub enum LineageNode -pub struct LineageMetadata -pub struct LineageStore -pub fn new() -> Self -pub fn node(&self, id: LineageId) -> &LineageNode -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -pub fn literal(&mut self, seed: SeedId) -> LineageId -pub fn not(&mut self, id: LineageId) -> LineageId -pub fn and>(&mut self, items: I) -> LineageId -pub fn or>(&mut self, items: I) -> LineageId -pub fn metadata(&self, root: LineageId, seeds: &SeedSnapshot) -> LineageMetadata -pub struct LineageProvenance -pub fn new(seeds: Arc) -> Self -pub fn store(&self) -> &Arc> -pub fn seeds(&self) -> &Arc -pub fn literal(&self, seed: SeedId) -> LineageId -pub struct HybridConfig -pub enum ThresholdPolicyKind -pub fn as_str(self) -> &'static str -pub struct ProbabilityInterval -pub fn new(lower: f64, upper: f64) -> Result -pub fn width(self) -> f64 -pub fn contains(self, probability: f64) -> bool -pub fn validate(&self) -> Result<(), HybridError> -pub enum AlertDecision -pub enum HybridReason -pub fn as_str(&self) -> &'static str -pub struct HybridMetrics -pub fn total_latency(&self) -> Duration -pub enum HybridProbabilityResult -pub fn status(&self) -> &'static str -pub fn decision(&self) -> AlertDecision -pub fn reason(&self) -> &HybridReason -pub fn metrics(&self) -> &HybridMetrics -pub fn interval(&self) -> Option -pub struct TopKEvaluation -pub fn evaluate_topk( store: &LineageStore, seeds: &SeedSnapshot, root: LineageId, k: usize, budget: Duration, node_budget: usize, ) -> Result -pub struct CompiledSdd -pub fn compile_lineage_to_sdd( store: &LineageStore, seeds: &SeedSnapshot, root: LineageId, budget: Duration, node_budget: usize, ) -> Result -pub fn compile_lineage_to_sdd_with_clock( store: &LineageStore, seeds: &SeedSnapshot, root: LineageId, budget: Duration, node_budget: usize, clock: &dyn HybridClock, ) -> Result -pub fn evaluate_hybrid( store: &Arc>, seeds: &Arc, root: LineageId, config: &HybridConfig, ) -> HybridProbabilityResult -pub fn evaluate_hybrid_with_clock( store: &Arc>, seeds: &Arc, root: LineageId, config: &HybridConfig, clock: &dyn HybridClock, ) -> HybridProbabilityResult -pub fn encode_hybrid_results_as_rdf_star( results: &HashMap, dict: &mut Dictionary, quoted: &mut QuotedTripleStore, ) -> Vec -``` - -### `shared/src/join_algorithm.rs` - -```rust -pub fn perform_hash_join_for_rules( premise: &TriplePattern, triples: &[Triple], dict: &Dictionary, final_results: Vec>, ) -> Vec> -``` - -### `shared/src/pattern.rs` - -```rust -pub fn matches_rule_pattern( pattern: &TriplePattern, fact: &Triple, variable_bindings: &mut HashMap, ) -> bool -pub fn construct_triple( conclusion: &TriplePattern, vars: &HashMap, _dict: &mut Dictionary, ) -> Triple -``` - -### `shared/src/provenance.rs` - -```rust -pub trait Provenance: Clone + 'static -pub struct MinMaxProbability -pub struct AddMultProbability -pub struct BooleanProvenance -pub type Proof = BTreeSet -pub type TopKTag = Vec -pub struct TopKProofs -pub fn new(k: usize) -> Self -pub type WmcLiteral = (u32, bool) -pub type WmcClause = BTreeSet -pub type WmcFormula = BTreeSet -pub struct DnfWmcProvenance -pub fn new() -> Self -pub type WmcProvenance = DnfWmcProvenance -pub struct ExpirationProvenance -``` - -### `shared/src/quad_source.rs` - -```rust -pub trait QuadSource -``` - -### `shared/src/query.rs` - -```rust -pub enum FilterExpression<'a> -pub enum ArithmeticExpression<'a> -pub fn evaluate Option>(&self, resolve: &F) -> Result -pub enum Value -pub struct ValuesClause<'a> -pub type LexicalTriplePattern<'a> = (&'a str, &'a str, &'a str) -pub struct LexicalQuadPattern<'a> -pub type BindClause<'a> = (&'a str, Vec<&'a str>, &'a str) -pub struct WhereClause<'a> -pub struct InsertClause<'a> -pub struct DeleteClause<'a> -pub enum GroupGraphPattern<'a> -pub struct SubQuery<'a> -pub struct RuleHead<'a> -pub struct MLPredictClause<'a> -pub enum LossFn -pub enum OptimizerKind -pub enum ModelArch -pub enum NeuralOutputKind -pub struct ModelDecl -pub struct NeuralRelationDecl -pub enum TrainingDataSource -pub struct TrainNeuralRelationDecl -pub struct WindowClause<'a> -pub struct WindowSpec<'a> -pub enum WindowType -pub enum Fallback -pub enum SyncPolicy -pub enum StreamType<'a> -pub struct RegisterClause<'a> -pub struct RSPQLSelectQuery<'a> -pub struct WindowBlock<'a> -pub struct ProbAnnotation<'a> -pub struct CombinedRule<'a> -pub enum RetrieveMode -pub enum StreamState -pub struct RetrieveClause<'a> -pub enum SortDirection -pub struct OrderCondition<'a> -pub struct SelectQuery<'a> -pub enum UpdateOperation<'a> -pub enum SparqlOperation<'a> -pub struct CombinedQuery<'a> -pub fn single_rule(&self) -> Result>, String> -``` - -### `shared/src/quoted_triple_store.rs` - -```rust -pub const QUOTED_TRIPLE_ID_BIT: u32 = 0x8000_0000 -pub fn is_quoted_triple_id(id: u32) -> bool -pub struct QuotedTripleStore -pub fn new() -> Self -pub fn encode(&mut self, subject: u32, predicate: u32, object: u32) -> u32 -pub fn decode(&self, id: u32) -> Option<(u32, u32, u32)> -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -pub fn merge(&mut self, other: &QuotedTripleStore) -``` - -### `shared/src/rdf_term.rs` - -```rust -pub enum TermKind -pub struct Literal -pub const XSD_STRING: &str = "http://www.w3.org/2001/XMLSchema#string" -pub fn escape_lexical(lexical: &str) -> String -pub fn spell_literal(lexical: &str, datatype: Option<&str>, language: Option<&str>) -> String -pub fn kind_of(spelling: &str) -> TermKind -pub fn canonicalise_literal(text: &str) -> Option -pub struct TermTriple -pub fn new( subject: impl Into, predicate: impl Into, object: impl Into, ) -> Self -pub fn to_ntriples(&self) -> String -pub fn terms(&self) -> [&str -pub const SKOLEM_PATH: &str = "/.well-known/genid/" -pub fn skolem_iri(label: &str, base: &str) -> String -pub fn skolemise<'a>(spelling: &'a str, base: &str) -> std::borrow::Cow<'a, str> -pub fn deskolemise(spelling: &str) -> Option -pub fn to_ntriples_term(spelling: &str) -> String -pub fn spell_blank_node(label: &str, document: u64) -> String -pub fn next_document_scope() -> u64 -pub fn scope_blank_node<'a>(term: &'a str, document: u64) -> std::borrow::Cow<'a, str> -pub fn numeric_value(spelling: &str) -> Option -pub fn lexical_value(spelling: &str) -> &str -pub fn read_literal(spelling: &str) -> Option -``` - -### `shared/src/rule.rs` - -```rust -pub struct FilterCondition -pub struct Rule -pub fn pattern_variables(pat: &TriplePattern) -> impl Iterator -pub fn check_rule_safety(rule: &Rule) -> Result<(), String> -``` - -### `shared/src/rule_index.rs` - -```rust -pub type RuleId = usize -pub const WILDCARD: u32 = u32::MAX -pub struct RuleIndex -pub fn new() -> Self -pub fn clear(&mut self) -pub fn insert_premise_pattern(&mut self, pattern: &TriplePattern, rule_id: RuleId) -pub fn query_candidate_rules( &self, s: Option, p: Option, o: Option, ) -> HashSet -``` - -### `shared/src/sdd.rs` - -```rust -pub struct SddId(u32) -pub const FALSE: SddId = SddId(0) -pub const TRUE: SddId = SddId(1) -pub enum BoolOp -pub enum SddBudgetError -pub struct SddOperationBudget<'a> -pub fn new(max_nodes: usize, deadline_available: &'a mut dyn FnMut() -> bool) -> Self -pub enum VarKind -pub struct SddManager -pub fn new() -> Self -pub fn ensure_variable(&mut self, var: u32, prob: f64) -pub fn ensure_variable_weights(&mut self, var: u32, pos: f64, neg: f64, kind: VarKind) -pub fn exactly_one(&mut self, vars: &[u32]) -> SddId -pub fn pos_weight(&self) -> &[f64] -pub fn neg_weight(&self) -> &[f64] -pub fn set_pos_weight(&mut self, var: u32, w: f64) -pub fn set_neg_weight(&mut self, var: u32, w: f64) -pub fn variable_ids(&self) -> impl Iterator + '_ -pub fn var_kind(&self, var: u32) -> VarKind -pub fn literal(&mut self, var: u32, polarity: bool) -> SddId -pub fn apply(&mut self, a: SddId, b: SddId, op: BoolOp) -> SddId -pub fn negate(&mut self, id: SddId) -> SddId -pub fn wmc(&self, id: SddId) -> f64 -pub fn enumerate_models(&self, id: SddId) -> Vec> -pub fn node_count(&self) -> usize -pub fn try_literal( &mut self, var: u32, polarity: bool, budget: &mut SddOperationBudget<'_>, ) -> Result -pub fn try_apply( &mut self, a: SddId, b: SddId, op: BoolOp, budget: &mut SddOperationBudget<'_>, ) -> Result -pub fn try_negate( &mut self, id: SddId, budget: &mut SddOperationBudget<'_>, ) -> Result -pub fn try_exactly_one( &mut self, vars: &[u32], budget: &mut SddOperationBudget<'_>, ) -> Result -pub struct SddProvenance -pub struct SddLimits -pub fn new() -> Self -pub fn with_limits(limits: SddLimits) -> Self -pub fn manager(&self) -> &Arc> -pub fn budget_error(&self) -> Option -pub fn try_with_budget( &self, operation: impl FnOnce( &mut SddManager, &mut SddOperationBudget<'_>, ) -> Result, ) -> Result -``` - -### `shared/src/seed_spec.rs` - -```rust -pub enum SeedSpec -pub struct ExclusiveChoice -``` - -### `shared/src/tag_store.rs` - -```rust -pub struct TagStore -pub fn new(provenance: P) -> Self -pub fn provenance(&self) -> &P -pub fn get_tag(&self, triple: &Triple) -> P::Tag -pub fn set_tag(&mut self, triple: &Triple, tag: P::Tag) -pub fn update_disjunction(&mut self, triple: &Triple, new_tag: &P::Tag) -> bool -pub fn has_explicit_tag(&self, triple: &Triple) -> bool -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -pub fn iter(&self) -> impl Iterator -pub fn encode_as_rdf_star( &self, dict: &mut Dictionary, qt_store: &mut QuotedTripleStore, ) -> Vec -pub fn encode_as_rdf_star_with_explanation( &self, dict: &mut Dictionary, qt_store: &mut QuotedTripleStore, ) -> Vec -pub fn encode_as_rdf_star_with_explanation( &self, dict: &mut Dictionary, qt_store: &mut QuotedTripleStore, ) -> Vec -``` - -### `shared/src/term_encoder.rs` - -```rust -pub trait TermEncoder -pub struct LocalTerms(RefCell) -pub fn new() -> Self -pub fn from_dictionary(dictionary: Dictionary) -> Self -pub fn into_dictionary(self) -> Dictionary -pub struct SharedTerms(Arc>) -pub fn new() -> Self -pub fn from_dictionary(dictionary: Dictionary) -> Self -pub fn shares_space_with(&self, other: &SharedTerms) -> bool -pub fn encode(&self, text: &str) -> u32 -pub fn encode_triple(&self, subject: &str, predicate: &str, object: &str) -> (u32, u32, u32) -pub fn decode(&self, id: u32) -> Option -pub fn read(&self) -> TermsRef<'_> -pub fn write(&self) -> TermsMut<'_> -pub struct TermsRef<'a>(RwLockReadGuard<'a, Dictionary>) -pub struct TermsMut<'a>(RwLockWriteGuard<'a, Dictionary>) -pub struct PoisonedTerms -pub struct TermView(Dictionary) -pub fn decode(&self, id: u32) -> Option<&str> -pub fn decode_term( &self, id: u32, qt_store: &crate::quoted_triple_store::QuotedTripleStore, ) -> Option -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -pub fn with_terms_read(&self, visit: impl FnOnce(&Dictionary) -> R) -> R -pub fn try_with_terms( &self, visit: impl FnOnce(&mut Dictionary) -> R, ) -> Result -pub fn view(&self) -> TermView -``` - -### `shared/src/terms.rs` - -```rust -pub enum Term -pub type TriplePattern = (Term, Term, Term) -pub type Binding = HashMap -pub const UNBOUND: u32 = u32::MAX -pub struct Bindings -pub struct BindingRef<'a> -pub fn get(&self, variable: &str) -> Option -pub fn contains_key(&self, variable: &str) -> bool -pub fn keys(&self) -> impl Iterator + 'a -pub fn values(&self) -> impl Iterator + 'a -pub fn iter(&self) -> impl Iterator + 'a -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -pub fn to_binding(&self) -> Binding -pub fn cells(&self) -> &'a [u32] -pub fn empty() -> Self -pub fn unit() -> Self -pub fn single(binding: Binding) -> Self -pub fn with_schema(schema: Vec) -> Self -pub fn with_capacity(rows: usize) -> Self -pub fn schema(&self) -> &[String] -pub fn column(&self, variable: &str) -> Option -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -pub fn first(&self) -> Option> -pub fn row(&self, index: usize) -> Option> -pub fn iter(&self) -> impl Iterator> + '_ -pub fn push(&mut self, binding: Binding) -pub fn push_cells(&mut self, row: Vec) -pub fn append(&mut self, other: Self) -pub fn extend(&mut self, other: impl IntoIterator) -pub fn retain(&mut self, mut keep: impl FnMut(BindingRef<'_>) -> bool) -pub fn truncate(&mut self, rows: usize) -pub fn clear(&mut self) -pub fn dedup(&mut self) -pub fn sort_by( &mut self, mut compare: impl FnMut(BindingRef<'_>, BindingRef<'_>) -> std::cmp::Ordering, ) -pub fn bind_column(&mut self, variable: &str, values: impl IntoIterator) -pub fn cells(&self) -> &[Vec] -pub fn cells_mut(&mut self) -> &mut [Vec] -pub fn into_parts(self) -> (Vec, Vec>) -pub fn from_parts(schema: Vec, cells: Vec>) -> Self -pub fn par_iter(&self) -> impl rayon::iter::IndexedParallelIterator> -pub fn to_maps(&self) -> Vec -pub enum UnresolvedTerm -pub type UnresolvedTriple = (UnresolvedTerm, UnresolvedTerm, UnresolvedTerm) -pub fn is_var(&self) -> bool -pub fn is_quoted_triple(&self) -> bool -pub struct TextBindings -pub fn new() -> Self -pub fn with_schema(schema: Vec) -> Self -pub fn schema(&self) -> &[String] -pub fn len(&self) -> usize -pub fn is_empty(&self) -> bool -pub fn column(&self, variable: &str) -> Option -pub fn get(&self, row: usize, variable: &str) -> Option<&str> -pub fn rows(&self) -> &[Vec>] -pub fn pairs(&self, row: usize) -> impl Iterator + '_ -pub fn push_pairs(&mut self, pairs: I) -pub fn join(&self, other: &TextBindings) -> TextBindings -pub fn extend(&mut self, other: TextBindings) -pub fn from_maps(maps: &[HashMap]) -> Self -``` - -### `shared/src/triple.rs` - -```rust -pub struct Triple -pub fn to_pattern(&self) -> TriplePattern -``` - -_364 public items across 19 files._ - -## `datalog` - -Datalog reasoning: forward and backward chaining, N3 rules. - -### `datalog/src/cross_window_sds.rs` - -```rust -pub fn annotate_predicate(window_iri: &str, local_name: &str) -> String -pub fn strip_window_prefix<'a, 'b>( annotated: &'b str, known_iris: &'a [String], ) -> Option<(&'a str, &'b str)> -pub struct WindowedTriple -pub struct WindowData -pub struct Sds -pub fn new() -> Self -pub fn all_component_iris(sds: &Sds) -> Vec -pub fn translate_sds_to_datalog( sds: &Sds, dict: &SharedTerms, current_time: u64, ) -> Vec<(Triple, u64)> -pub fn translate_datalog_back( facts: &[Triple], dict: &SharedTerms, sds: &Sds, ) -> HashMap> -pub fn sds_with_expiry_to_external( internal: &HashMap>, dict: &SharedTerms, component_iris: &[String], ) -> HashMap> -``` - -### `datalog/src/parser_n3_logic.rs` - -```rust -pub struct WindowContext -pub fn parse_n3_rule<'a>( input: &'a str, graph: &mut Reasoner, ) -> IResult<&'a str, (Vec<(&'a str, &'a str)>, Rule)> -pub fn parse_n3_document<'a>( input: &'a str, graph: &mut Reasoner, ) -> IResult<&'a str, (HashMap, Vec)> -pub fn parse_n3_rules_for_sds( input: &str, graph: &mut Reasoner, window_widths: HashMap, ) -> Result<(Vec, WindowContext), String> -``` - -### `datalog/src/reasoning/backward_chaining.rs` - -```rust -pub fn resolve_term<'a>(term: &'a Term, bindings: &'a HashMap) -> Term -pub fn backward_chaining(&self, query: &TriplePattern) -> Vec> -``` - -### `datalog/src/reasoning/helpers.rs` - -```rust -pub fn get_readable_binding( bindings: &HashMap, dictionary: &Dictionary, ) -> HashMap -``` - -### `datalog/src/reasoning/materialisation/cross_window_incremental.rs` - -```rust -pub type SdsWithExpiry = HashMap> -pub fn incremental_sds_plus( rules: &[Rule], sds_current: &Sds, sds_plus_old: &SdsWithExpiry, dict: &SharedTerms, current_time: u64, ) -> SdsWithExpiry -``` - -### `datalog/src/reasoning/materialisation/cross_window_naive.rs` - -```rust -pub fn naive_sds_plus( rules: &[Rule], sds: &Sds, dict: &SharedTerms, current_time: u64, ) -> HashMap> -``` - -### `datalog/src/reasoning/materialisation/hybrid_materialisation.rs` - -```rust -pub struct LineageMaterialization -pub fn lineage(&self, triple: &Triple) -> LineageId -pub fn evaluate(&self, triple: &Triple, config: &HybridConfig) -> HybridProbabilityResult -pub fn compile_exact( &self, triple: &Triple, config: &HybridConfig, ) -> Result -pub fn validate_hybrid_rules(rules: &[Rule]) -> Result<(), HybridError> -pub fn materialize_lineage( reasoner: &mut Reasoner, base: Option<&dyn QuadSource>, snapshot: SeedSnapshot, ) -> Result -pub fn infer_new_facts_with_hybrid( &mut self, snapshot: SeedSnapshot, config: &HybridConfig, ) -> Result< ( Vec, HashMap, LineageMaterialization, ), HybridError, > -pub fn infer_new_facts_with_hybrid_over( &mut self, base: Option<&dyn QuadSource>, snapshot: SeedSnapshot, config: &HybridConfig, ) -> Result< ( Vec, HashMap, LineageMaterialization, ), HybridError -``` - -### `datalog/src/reasoning/materialisation/infer_generic.rs` - -```rust -pub type SolutionMapping = HashMap -pub trait InferenceStrategy -pub fn infer_with_strategy(&mut self, strat: S) -> Vec -pub fn infer_with_strategy_over( &mut self, base: Option<&dyn QuadSource>, mut strat: S, ) -> Vec -``` - -### `datalog/src/reasoning/materialisation/my_naive.rs` - -```rust -pub fn infer_new_facts_naive(&mut self) -> Vec -pub fn infer_new_facts(&mut self) -> Vec -``` - -### `datalog/src/reasoning/materialisation/provenance_infer_generic.rs` - -```rust -pub struct ProvenanceInferResult -pub trait ProvenanceInferenceStrategy -pub fn infer_with_provenance_strategy( &mut self, base: Option<&dyn QuadSource>, strat: S, tag_store: &mut TagStore

, ) -> Vec -pub fn infer_with_provenance_strategy_and_rules( &mut self, base: Option<&dyn QuadSource>, mut strat: S, tag_store: &mut TagStore

, rules: &[Rule], ) -> Vec -``` - -### `datalog/src/reasoning/materialisation/provenance_semi_naive.rs` - -```rust -pub fn infer_new_facts_with_provenance( &mut self, provenance: P, ) -> (Vec, TagStore

) -pub fn infer_new_facts_with_provenance_over( &mut self, base: Option<&dyn QuadSource>, provenance: P, ) -> (Vec, TagStore

) -pub fn semi_naive_with_initial_tags( reasoner: &mut Reasoner, base: Option<&dyn QuadSource>, provenance: P, mut initial_tags: TagStore

, ) -> (Vec, TagStore

) -pub fn semi_naive_with_initial_tags_and_delta( reasoner: &mut Reasoner, base: Option<&dyn QuadSource>, _provenance: P, mut initial_tags: TagStore

, initial_delta: Vec, ) -> (Vec, TagStore

) -``` - -### `datalog/src/reasoning/materialisation/sdd_seed_materialise.rs` - -```rust -pub fn infer_new_facts_with_sdd_seed_specs( reasoner: &mut Reasoner, seeds: Vec, ) -> (Vec, TagStore) -pub fn try_infer_new_facts_with_sdd_seed_specs( reasoner: &mut Reasoner, seeds: Vec, limits: SddLimits, ) -> Result<(Vec, TagStore), SddBudgetError> -``` - -### `datalog/src/reasoning/materialisation/semi_naive.rs` - -```rust -pub fn infer_new_facts_semi_naive(&mut self) -> Vec -pub fn infer_new_facts_semi_naive_over( &mut self, base: &dyn shared::quad_source::QuadSource, ) -> Vec -``` - -### `datalog/src/reasoning/materialisation/semi_naive_parallel.rs` - -```rust -pub fn infer_new_facts_semi_naive_parallel(&mut self) -> Vec -``` - -### `datalog/src/reasoning/materialisation.rs` - -```rust -pub fn replace_variables_with_bound_values( conclusion: &TriplePattern, vars: &HashMap, dict: &mut Dictionary, ) -> Triple -``` - -### `datalog/src/reasoning/rules.rs` - -```rust -pub use shared::pattern::{construct_triple, matches_rule_pattern} -pub fn join_rule(rule: &Rule, all_facts: &HashSet, delta: &HashSet) -> Bindings -pub fn join_remaining( rule: &Rule, changed_idx: usize, all_facts: &HashSet, binding: HashMap, ) -> Bindings -pub fn evaluate_filters( bindings: &HashMap, filters: &Vec, dict: &Dictionary, ) -> bool -pub fn join_premise_with_hash_join( premise: &TriplePattern, all_facts: &[Triple], current_bindings: Vec>, dict: &Dictionary, ) -> Vec> -pub fn add_rule(&mut self, rule: Rule) -pub fn try_add_rule(&mut self, rule: Rule) -> Result<(), String> -``` - -### `datalog/src/reasoning/to_dot.rs` - -```rust -pub trait ToDot -``` - -### `datalog/src/reasoning.rs` - -```rust -pub use rules::{construct_triple, join_remaining, join_rule, matches_rule_pattern} -pub struct Reasoner -pub fn convert_string_binding_to_u32( binding: &BTreeMap, dict: &Dictionary, ) -> HashMap -pub fn adopt_terms(&mut self, terms: SharedTerms) -pub fn terms(&self) -> SharedTerms -pub fn with_terms(&self, visit: &mut dyn FnMut(&mut Dictionary)) -pub fn encode_term(&self, text: &str) -> u32 -pub fn term_view(&self) -> TermView -pub fn encode_triple(&self, subject: &str, predicate: &str, object: &str) -> (u32, u32, u32) -pub fn encode_pattern( &self, subject: Option<&str>, predicate: Option<&str>, object: Option<&str>, ) -> (Option, Option, Option) -pub fn decode_term(&self, id: u32) -> Option -pub fn new() -> Self -pub fn add_tagged_triple( &mut self, subject: &str, predicate: &str, object: &str, probability: f64, ) -pub fn materialize_tags_as_rdf_star(&mut self, tag_store: &TagStore

) -pub fn add_abox_triple(&mut self, subject: &str, predicate: &str, object: &str) -pub fn insert_ground_triple(&mut self, triple: Triple) -pub fn query_abox( &mut self, subject: Option<&str>, predicate: Option<&str>, object: Option<&str>, ) -> Vec -``` - -_73 public items across 18 files._ - -## `datalogmtl` - -Metric temporal Datalog. - -### `datalogmtl/src/automata/eval.rs` - -```rust -pub fn materialize( rules: &[DatalogMTLRule], facts: Vec<(Triple, TInterval)>, max_iters: usize, ) -> Database -pub fn eval_rule(rule: &DatalogMTLRule, db: &Database) -> Vec<(Triple, Vec)> -``` - -### `datalogmtl/src/automata/interval.rs` - -```rust -pub const NEG_INF: i64 = i64::MIN -pub const POS_INF: i64 = i64::MAX -pub struct TInterval -pub fn closed(start: i64, end: i64) -> Self -pub fn from_to_inf(start: i64, start_open: bool) -> Self -pub fn from_neg_inf(end: i64, end_open: bool) -> Self -pub fn is_finite(&self) -> bool -pub fn contains_point(&self, t: i64) -> bool -pub fn shifted(&self, delta: i64) -> TInterval -pub fn is_valid(start: i64, end: i64, start_open: bool, end_open: bool) -> bool -pub fn integer_points_upto(&self, cap: u64) -> impl Iterator -pub fn add(v1: TInterval, v2: TInterval) -> TInterval -pub fn sub(v1: TInterval, v2: TInterval) -> TInterval -pub fn circle_add(v1: TInterval, v2: TInterval) -> Option -pub fn circle_sub(v1: TInterval, v2: TInterval) -> Option -pub fn intersection(v1: TInterval, v2: TInterval) -> Option -pub fn union(v1: TInterval, v2: TInterval) -> Option -pub fn inclusion(v1: TInterval, v2: TInterval) -> bool -pub fn coalesce(mut intervals: Vec) -> Vec -pub fn intersect_lists(a: &[TInterval], b: &[TInterval]) -> Vec -``` - -### `datalogmtl/src/automata/mod.rs` - -```rust -pub use eval::materialize -pub use interval::TInterval -pub use omega::{entails, materialize_omega, PeriodicModel} -pub use relation::Database -``` - -### `datalogmtl/src/automata/omega.rs` - -```rust -pub struct PeriodicModel -pub fn materialize_omega( rules: &[DatalogMTLRule], facts: Vec<(Triple, TInterval)>, ) -> Result -pub fn entails(model: &PeriodicModel, triple: &Triple, query: TInterval) -> bool -``` - -### `datalogmtl/src/automata/relation.rs` - -```rust -pub struct Database -pub fn new() -> Self -pub fn add_raw(&mut self, t: Triple, iv: TInterval) -pub fn coalesce_all(&mut self) -pub fn add_intervals(&mut self, t: Triple, new_ivs: &[TInterval]) -> bool -pub struct BindingRelation -pub fn eval_atom(atom: &TemporalAtom, db: &Database) -> BindingRelation -pub fn join_with( a: &BindingRelation, b: &BindingRelation, combine: impl Fn(&[TInterval], &[TInterval]) -> Vec, ) -> BindingRelation -pub fn conjoin(a: &BindingRelation, b: &BindingRelation) -> BindingRelation -``` - -### `datalogmtl/src/automata/transducer.rs` - -```rust -pub fn diamond(inner: &[TInterval], op: &OpInterval) -> Vec -pub fn box_(inner: &[TInterval], op: &OpInterval) -> Vec -pub fn since(phi: &[TInterval], psi: &[TInterval], op: &OpInterval) -> Vec -pub fn diamond_plus(inner: &[TInterval], op: &OpInterval) -> Vec -pub fn box_plus(inner: &[TInterval], op: &OpInterval) -> Vec -pub fn until(phi: &[TInterval], psi: &[TInterval], op: &OpInterval) -> Vec -``` - -### `datalogmtl/src/evaluator.rs` - -```rust -pub struct DatalogMTLEvaluator -pub fn new( rules: Vec, store: S, dictionary: SharedTerms, ) -> Result -pub fn advance(&mut self, t: u64, new_triples: Vec) -> (Vec, TickMetrics) -pub fn compute_w_max(rules: &[DatalogMTLRule]) -> u64 -``` - -### `datalogmtl/src/meteor_fmt.rs` - -```rust -pub fn atom_string(triple: &Triple, dict: &Dictionary) -> String -pub fn format_tick_sets(ticks: &HashMap>, dict: &Dictionary) -> Vec -``` - -### `datalogmtl/src/metrics.rs` - -```rust -pub struct TickMetrics -pub fn new(timestamp: u64) -> Self -pub fn csv_header() -> &'static str -pub fn to_csv_row(&self) -> String -pub fn write_metrics_csv(path: &str, metrics: &[TickMetrics]) -> std::io::Result<()> -``` - -### `datalogmtl/src/parser.rs` - -```rust -pub const RDF_TYPE: &str = "http://www.w3.org/1999/02/22-rdf-syntax-ns#type" -pub struct TemporalFact -pub fn parse_program( text: &str, dict: &SharedTerms, mode: Mode, ) -> Result, String> -pub fn parse_data(text: &str, dict: &SharedTerms) -> Result, String> -``` - -### `datalogmtl/src/rdf_parser.rs` - -```rust -pub fn parse_rules( text: &str, dict: &mut Dictionary, mode: Mode, ) -> Result, String> -pub fn parse_stream_shapes(text: &str, dict: &mut Dictionary) -> Result, String> -pub fn parse_facts(text: &str, dict: &mut Dictionary) -> Result, String> -pub fn parse_facts_shared(text: &str, dict: &SharedTerms) -> Result, String> -pub fn parse_interval(s: &str) -> Result -pub fn parse_rules_shared( text: &str, dict: &SharedTerms, mode: Mode, ) -> Result, String> -pub fn parse_stream_shapes_shared( text: &str, dict: &SharedTerms, ) -> Result, String> -``` - -### `datalogmtl/src/store.rs` - -```rust -pub trait TemporalStore -pub struct TemporalSnapshotStore -pub fn new(horizon: u64) -> Self -pub struct ValidityInterval -pub fn contains(&self, t: u64) -> bool -pub fn overlaps(&self, lo: u64, hi: u64) -> bool -pub struct IntervalFactStore -pub fn new(horizon: u64) -> Self -``` - -### `datalogmtl/src/stream.rs` - -```rust -pub type StreamIri = String -pub type EventPattern = Vec -pub type ChannelKey = Vec -pub type KeyBinding = HashMap -pub struct StalenessPolicy -pub struct StreamShape -pub struct ChannelState -pub struct RdfEvent -pub struct ShapeIngester -pub fn new(shapes: Vec) -> Self -pub fn process_event(&mut self, event: &RdfEvent) -> Vec<(Triple, u64)> -pub fn expired_channels(&self, t: u64) -> Vec -pub fn expiry_facts(&self, t: u64) -> Vec<(Triple, u64)> -pub fn evict_expired(&mut self, t: u64) -pub fn active_channels(&self) -> &HashMap -``` - -### `datalogmtl/src/syntax.rs` - -```rust -pub struct Interval -pub fn contains_offset(&self, offset_ms: u64) -> bool -pub fn absolute_range(&self, t: u64) -> (u64, u64) -pub enum TemporalAtom -pub enum Mode -pub struct DatalogMTLRule -``` - -### `datalogmtl/src/tests/mod.rs` - -```rust -pub fn make_dict() -> SharedTerms -pub fn enc(dict: &SharedTerms, s: &str) -> u32 -pub fn triple(s: u32, p: u32, o: u32) -> Triple -pub fn c(id: u32) -> Term -pub fn v(name: &str) -> Term -``` - -### `datalogmtl/src/validate.rs` - -```rust -pub fn validate_rules(rules: &[DatalogMTLRule]) -> Result<(), String> -``` - -_101 public items across 16 files._ - -## `ml` - -Machine learning integration, behind the `ml` feature. - -### `ml/src/candle_model.rs` - -```rust -pub enum OutputType -pub struct MlpNeuralPredicate -pub fn new(input_dim: usize, hidden: &[usize], output_type: OutputType) -> MlResult -pub fn new_seeded( input_dim: usize, hidden: &[usize], output_type: OutputType, seed: u64, ) -> MlResult -pub fn forward_with_grads(&self, rows: &[Vec]) -> MlResult<(Tensor, Vec>)> -pub fn predict(&self, rows: &[Vec]) -> MlResult>> -pub fn surrogate_backward( &self, tracked: &Tensor, wmc_grads: &[HashMap], var_to_col: &HashMap, ) -> MlResult<()> -pub fn zero_grads(&self) -pub fn optimizer_step( &self, optimizer: OptimizerKind, learning_rate: f64, example_count: usize, ) -pub fn save(&self, path: &str) -> MlResult<()> -pub fn to_bytes(&self) -> MlResult> -pub fn load( input_dim: usize, hidden: &[usize], output_type: OutputType, path: &str, ) -> MlResult -pub fn from_bytes( input_dim: usize, hidden: &[usize], output_type: OutputType, bytes: &[u8], ) -> MlResult -``` - -### `ml/src/lib.rs` - -```rust -pub use candle_model::{MlpNeuralPredicate, OutputType} -pub struct MLPredictionResult -pub struct PredictionTiming -pub struct ModelPerformanceMetrics -pub struct MLHandler -pub fn new() -> PyResult -pub fn load_model_with_schema( &mut self, model_name: &str, model_path: &str, ) -> PyResult -pub fn load_model( &mut self, model_name: &str, model_path: &str, module_name: Option<&str>, ) -> PyResult<()> -pub fn predict_approved( module_name: &str, module_path: &str, module_bytes: &[u8], artifact_bytes: &[u8], input: &[Vec], ) -> PyResult> -pub fn compare_models<'a>(&mut self, model_names: &[&'a str]) -> Option<&'a str> -pub fn predict( &self, model_name: &str, input_data: Vec>, ) -> PyResult -pub fn discover_and_load_models( &mut self, model_dir: &Path, model_module: &str, ) -> PyResult> -pub fn generate_ml_models(model_dir: &Path, model: &str) -> Result<(), Box> -``` - -_26 public items across 2 files._ diff --git a/docs/architecture.md b/docs/architecture.md deleted file mode 100644 index 7ac3b110..00000000 --- a/docs/architecture.md +++ /dev/null @@ -1,202 +0,0 @@ -# Architecture - -Generated from the code, not drawn by hand: every edge below is a `crate::` -reference that exists in the source. Doc-comment links are excluded, because a link -is not a dependency. `scripts/module_graph.py` reproduces all of it. - -## Workspace crates - -```mermaid -graph TD - shared[shared] - datalog[datalog] --> shared - datalogmtl[datalogmtl] --> shared - ml[ml] --> shared - kolibrie[kolibrie] --> datalog - kolibrie --> datalogmtl - kolibrie --> shared - kolibrie -.->|optional: ml feature| ml - cli[cli] --> kolibrie - server[kolibrie-http-server] --> kolibrie - server --> datalog - server --> datalogmtl - server --> shared - python[kolibrie-python] --> kolibrie - python --> datalog - python --> shared -``` - -`shared` is the vocabulary every crate agrees on: `Dictionary`, `Triple`, `Term`, -`Rule`, and the parsed-query AST. `datalog` and `datalogmtl` are reasoners over that -vocabulary rather than layers on the engine, which is why neither depends on -`kolibrie`. - -## Modules inside `kolibrie` - -28 modules, 88 dependency edges, arranged in 7 layers by -longest path. **The graph is acyclic** — no module participates in a dependency -cycle, verified with Tarjan's algorithm rather than asserted. - -The diagram shows the *transitive reduction*: 40 of the 88 edges, -with those implied by a longer path removed. Reachability is identical and the full -set is in the table below. Drawing all 88 is unreadable. - -```mermaid -graph TD - subgraph L0["layer 0"] - parser["parser
3649 lines"] - ml_policy["ml_policy
622 lines"] - stats["stats
586 lines"] - error_handler["error_handler
298 lines"] - term_order["term_order
212 lines"] - ml_feature_loader["ml_feature_loader
92 lines"] - utils["utils
68 lines"] - end - subgraph L1["layer 1"] - sparql_database["sparql_database
2709 lines"] - ml_syntax["ml_syntax
98 lines"] - query_error["query_error
83 lines"] - aggregate["aggregate
78 lines"] - end - subgraph L2["layer 2"] - streamertail_optimizer["streamertail
6442 lines"] - execute_ml["execute_ml
261 lines"] - neural_registry["neural_registry
189 lines"] - ml_runtime["ml_runtime
92 lines"] - end - subgraph L3["layer 3"] - rsp["rsp
1410 lines"] - execute_query["execute_query
1288 lines"] - ml_predict_candle["ml_predict_candle
271 lines"] - neural_relations_disabled["neural_disabled
103 lines"] - end - subgraph L4["layer 4"] - query_builder["query_builder
931 lines"] - execute_ml_train["execute_ml_train
918 lines"] - rule_execution["rule_execution
556 lines"] - ml_predict_runtime["ml_predict_runtime
532 lines"] - sparql_protocol["sparql_protocol
198 lines"] - end - subgraph L5["layer 5"] - rsp_engine["rsp_engine
1809 lines"] - neural_relations["neural_relations
873 lines"] - prelude["prelude
50 lines"] - end - subgraph L6["layer 6"] - program["program
2491 lines"] - end - aggregate --> term_order - execute_ml --> sparql_database - execute_ml_train --> execute_query - execute_query --> error_handler - execute_query --> ml_feature_loader - execute_query --> neural_registry - execute_query --> query_error - execute_query --> streamertail_optimizer - ml_predict_candle --> ml_feature_loader - ml_predict_candle --> neural_registry - ml_predict_runtime --> execute_query - ml_predict_runtime --> ml_predict_candle - ml_runtime --> query_error - ml_runtime --> sparql_database - ml_syntax --> parser - neural_registry --> sparql_database - neural_relations --> execute_ml_train - neural_relations --> ml_predict_runtime - neural_relations --> ml_runtime - neural_relations --> ml_syntax - neural_relations --> rule_execution - neural_relations_disabled --> ml_runtime - neural_relations_disabled --> ml_syntax - neural_relations_disabled --> neural_registry - prelude --> execute_query - prelude --> query_builder - program --> neural_relations - query_builder --> rsp - query_error --> ml_policy - rsp --> streamertail_optimizer - rsp_engine --> rule_execution - rule_execution --> execute_query - rule_execution --> rsp - sparql_database --> ml_policy - sparql_database --> stats - sparql_database --> utils - sparql_protocol --> execute_query - streamertail_optimizer --> aggregate - streamertail_optimizer --> parser - streamertail_optimizer --> sparql_database -``` - -Arrows point from a module to what it depends on, so they run downward and -layer 0 is the floor: nothing below it to depend on. - -### Every module and its direct dependencies - -The authoritative list: all 88 edges, not the reduced set. - -| layer | module | lines | depends on | -|---|---|---:|---| -| 0 | `parser` | 3649 | — nothing in the crate | -| 0 | `ml_policy` | 622 | — nothing in the crate | -| 0 | `stats` | 586 | — nothing in the crate | -| 0 | `error_handler` | 298 | — nothing in the crate | -| 0 | `term_order` | 212 | — nothing in the crate | -| 0 | `ml_feature_loader` | 92 | — nothing in the crate | -| 0 | `utils` | 68 | — nothing in the crate | -| 1 | `sparql_database` | 2709 | `ml_policy`, `stats`, `utils` | -| 1 | `ml_syntax` | 98 | `parser` | -| 1 | `query_error` | 83 | `ml_policy` | -| 1 | `aggregate` | 78 | `term_order` | -| 2 | `streamertail_optimizer` | 6442 | `aggregate`, `parser`, `sparql_database`, `stats`, `term_order` | -| 2 | `execute_ml` | 261 | `ml_policy`, `sparql_database` | -| 2 | `neural_registry` | 189 | `ml_policy`, `sparql_database` | -| 2 | `ml_runtime` | 92 | `query_error`, `sparql_database` | -| 3 | `rsp` | 1410 | `sparql_database`, `streamertail_optimizer` | -| 3 | `execute_query` | 1288 | `aggregate`, `error_handler`, `ml_feature_loader`, `ml_policy`, `neural_registry`, `parser`, `query_error`, `sparql_database`, `stats`, `streamertail_optimizer`, `term_order` | -| 3 | `ml_predict_candle` | 271 | `ml_feature_loader`, `neural_registry`, `sparql_database` | -| 3 | `neural_relations_disabled` | 103 | `ml_policy`, `ml_runtime`, `ml_syntax`, `neural_registry`, `query_error`, `sparql_database` | -| 4 | `query_builder` | 931 | `rsp`, `sparql_database` | -| 4 | `execute_ml_train` | 918 | `execute_query`, `ml_feature_loader`, `neural_registry`, `sparql_database` | -| 4 | `rule_execution` | 556 | `execute_query`, `neural_registry`, `parser`, `rsp`, `sparql_database` | -| 4 | `ml_predict_runtime` | 532 | `execute_query`, `ml_feature_loader`, `ml_predict_candle`, `sparql_database` | -| 4 | `sparql_protocol` | 198 | `execute_query`, `ml_policy`, `sparql_database` | -| 5 | `rsp_engine` | 1809 | `parser`, `rsp`, `rule_execution`, `sparql_database`, `streamertail_optimizer` | -| 5 | `neural_relations` | 873 | `execute_ml_train`, `execute_query`, `ml_feature_loader`, `ml_policy`, `ml_predict_candle`, `ml_predict_runtime`, `ml_runtime`, `ml_syntax`, `neural_registry`, `parser`, `query_error`, `rule_execution`, `sparql_database` | -| 5 | `prelude` | 50 | `execute_query`, `parser`, `query_builder`, `sparql_database` | -| 6 | `program` | 2491 | `error_handler`, `execute_ml_train`, `execute_query`, `ml_feature_loader`, `ml_policy`, `neural_registry`, `neural_relations`, `parser`, `sparql_database` | - -### What the layering says - -**`parser` depends on nothing in the crate.** It turns text into the AST in -`shared::query` and stops there. Running a parsed rule against a store is -`rule_execution`, four layers up; the two used to be one module, which is why a -parser also validated the ML policy and fired stream windows. - -**`sparql_database` sits at layer 1, not at the centre.** It holds the data and the -dictionary and depends only on the policy, the statistics and small helpers. -Everything that *does* something with a store — planning, evaluation, streaming, -training — sits above it, and it carries no method that reaches upward. - -**`ml_policy` is a leaf.** The authority boundary for every ML operation depends on -`shared` alone, so what may run is decided without reference to anything that runs it. - -**The ML runtime is reached through a trait.** `execute_query` and `rule_execution` -have to let models act — a `SELECT` over a neural relation must derive its facts -before matching — but they call `ml_runtime::MlRuntime`, implemented one layer up by -`neural_relations`. The crate root picks the implementation, being the only place that -names both. Without that indirection these five modules form a cycle. - -**`program` is the top.** The probabilistic and provenance-aware entry point composes -everything below it, and nothing depends on it. - -### Reproducing this - -```bash -scripts/module_graph.py # layers, edges, and the cycle check -scripts/module_graph.py --mermaid # the diagram above -``` - -Worth running when adding a module. The graph was 87% one strongly-connected -component before it was first measured, and every module that left the cycle was held -there by a handful of references — usually a type declared in the wrong place, or a -convenience method pointing the wrong way. diff --git a/kolibrie-api/src/lib.rs b/kolibrie-api/src/lib.rs index 7600af5b..adf9630a 100644 --- a/kolibrie-api/src/lib.rs +++ b/kolibrie-api/src/lib.rs @@ -8,102 +8,61 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The supported surface of Kolibrie: one crate for a consumer to depend on. -//! -//! # Why this exists -//! -//! The workspace is six crates, and using it meant depending on several and knowing -//! which held what. Measured across the consumers in this repository -- the CLI, the -//! HTTP server, the Python bindings and the examples -- they reach into five: -//! `kolibrie` 121 times, `shared` 41, `datalog` 15, `ml` 6 and `datalogmtl` 6. A -//! reader had to know that a query runs through `kolibrie` but a [`Term`] lives in -//! `shared`, which is a fact about how this repository is organised and not about -//! RDF. -//! -//! So this crate re-exports what those consumers actually use, and nothing else. It -//! holds no logic: every item here is defined in one of the crates below. +//! The supported surface of Kolibrie: one crate for a consumer to depend on //! //! ``` //! use kolibrie_api::prelude::*; //! -//! let mut db = SparqlDatabase::new(); +//! let mut db = KolibrieDB::new(); //! db.add_triple_parts("urn:alice", "urn:knows", "urn:bob"); //! let answers = execute("SELECT ?who WHERE { ?who }", &mut db) //! .expect("the query is valid"); //! assert_eq!(answers.len(), 1); //! ``` //! -//! Anything the prelude leaves out is still reachable, because the crates it fronts -//! are re-exported. Nothing is walled off, and a consumer never needs a second -//! dependency entry to get at a specialised type: -//! //! ``` //! use kolibrie_api::prelude::*; -//! // Not in the prelude, and still no second dependency entry. +//! // Not in the prelude, and still no second dependency entry //! use kolibrie_api::shared::provenance::Provenance; //! //! fn over_any_provenance() {} //! over_any_provenance::(); //! -//! let mut db = SparqlDatabase::new(); +//! let mut db = KolibrieDB::new(); //! db.add_triple_parts("urn:a", "urn:p", "urn:b"); //! let answers = execute("SELECT ?o WHERE { ?o }", &mut db).unwrap(); //! assert_eq!(answers.len(), 1); //! ``` -//! -//! # What it deliberately leaves out -//! -//! The engine's internals: the optimizer's operators, the execution engine, the -//! parser's combinators, the window and stream machinery. Those are reachable by -//! depending on [`kolibrie`] directly, which is the right thing to do for work on the -//! engine and the wrong thing for using it. Keeping them out is what lets them change -//! without breaking a consumer. -//! -//! # The crates underneath -//! -//! | crate | what it holds | -//! |---|---| -//! | [`kolibrie`] | the store, the parser, the query engine, RSP | -//! | [`shared`] | the vocabulary: terms, triples, rules, dictionaries | -//! | [`datalog`] | the Datalog reasoner | -//! | [`datalogmtl`] | metric temporal Datalog | -/// Everything a consumer normally needs, in one import. -/// -/// Re-exported rather than defined, so there is one definition of each item and this -/// crate cannot drift from the crates it fronts. +/// Everything a consumer normally needs, in one import pub mod prelude { - // Running a request, and what comes back. + pub use kolibrie::database::KolibrieDB; pub use kolibrie::execute_query::{ execute, execute_sparql_query, execute_sparql_update, Solution, Solutions, UpdateSummary, }; pub use kolibrie::parse_error::{Expected, ParseError, ParseResult}; pub use kolibrie::query_error::QueryError; - pub use kolibrie::sparql_database::SparqlDatabase; - // Building a request without writing SPARQL text. pub use kolibrie::query_builder::QueryBuilder; - // The vocabulary. A reader should not have to know these live in another crate. pub use shared::dictionary::Dictionary; pub use shared::rule::Rule; pub use shared::terms::{Binding, Bindings, Term, TriplePattern}; pub use shared::triple::Triple; - // Reasoning. pub use datalog::reasoning::Reasoner; } pub use prelude::*; -/// The engine crate, for work that needs more than the [`prelude`]. +/// The engine crate, for work that needs more than the [`prelude`] pub use kolibrie; -/// The shared vocabulary crate. +/// The shared vocabulary crate pub use shared; -/// The Datalog reasoner. +/// The Datalog reasoner pub use datalog; -/// Metric temporal Datalog. +/// Metric temporal Datalog pub use datalogmtl; diff --git a/kolibrie-http-server/src/main.rs b/kolibrie-http-server/src/main.rs index d7fc0e4c..a09b9134 100644 --- a/kolibrie-http-server/src/main.rs +++ b/kolibrie-http-server/src/main.rs @@ -8,28 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The Kolibrie HTTP server: a REST surface and the browser playground, on port 8080. -//! -//! A hand-rolled server over [`std::net::TcpListener`] with a thread -//! per connection — no framework. Three families of endpoint, each keyed by a -//! session the client creates first: -//! -//! | endpoint | purpose | -//! |---|---| -//! | `POST /query` | one-shot SPARQL over posted data | -//! | `POST /rsp-query`, `/rsp/register`, `/rsp/push`, `GET /rsp/events/` | RSP-QL streaming, results delivered as server-sent events | -//! | `POST /datalogmtl/register`, `/datalogmtl/push`, `/datalogmtl/static`, `GET /datalogmtl/events/` | DatalogMTL, streaming or whole-timeline | -//! -//! # Trust -//! -//! The ML context is resolved once at startup and the process **exits** if it is -//! invalid, rather than starting and refusing requests later. Over HTTP that -//! context never carries trusted-local authority: the server does not train, does -//! not write artifacts, and does not accept model declarations from a query. See -//! `kolibrie`'s `ml::policy` for what the modes mean. -//! -//! Binds `0.0.0.0`, so it is reachable from outside the host. There is no -//! authentication of any kind. +//! The Kolibrie HTTP server: a REST surface and the browser playground, on port 8080 use datalog::parser_n3_logic::parse_n3_rule; use datalog::reasoning::Reasoner; @@ -46,12 +25,12 @@ use datalogmtl::rdf_parser::{ use datalogmtl::store::IntervalFactStore; use datalogmtl::stream::{RdfEvent, ShapeIngester}; use datalogmtl::syntax::{DatalogMTLRule, Mode}; +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::QueryError; use kolibrie::rsp_engine::{ OperationMode, QueryExecutionMode, RSPBuilder, ResultConsumer, SimpleR2R, }; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; use serde::{Deserialize, Serialize}; use shared::dictionary::Dictionary; use shared::term_encoder::SharedTerms; @@ -65,18 +44,13 @@ use std::sync::{Arc, Mutex}; use std::thread; use std::time::Duration; -// ── Session state for persistent RSP engines ──────────────────────────────── - struct EngineSession { engine: kolibrie::rsp_engine::RSPEngine>, - /// Lazily set when the SSE client connects. sse_sender: Arc>>>, } type Sessions = Arc>>; -// ── DatalogMTL^RDF session state ───────────────────────────────────────────── - struct DmtlSession { evaluator: DatalogMTLEvaluator, shape_ingester: Option, @@ -97,8 +71,6 @@ struct HttpRequest { body: Vec, } -// ── Request/response types ─────────────────────────────────────────────────── - #[derive(Debug, Deserialize)] struct QueryRequest { #[serde(default)] @@ -107,9 +79,6 @@ struct QueryRequest { queries: Option>, #[serde(default)] rdf: Option, - // N3 logic rules in { pattern } => { conclusion } syntax with @prefix declarations. - // Parsed by parse_n3_rule (parser_n3_logic.rs) via the Reasoner — completely separate - // from the SPARQL RULE syntax handled by process_rule_definition (parser.rs). #[serde(default)] n3logic: Option, #[serde(default)] @@ -120,7 +89,6 @@ struct QueryRequest { format: String, } -// Default format is RDF/XML for backwards compatibility fn default_format() -> String { "rdfxml".to_string() } @@ -143,8 +111,6 @@ struct ErrorResponse { error: String, } -// ── RSP-QL stateless endpoint (legacy) ────────────────────────────────────── - #[derive(Debug, Deserialize)] struct StreamEvent { stream: String, @@ -170,8 +136,6 @@ struct RspQueryResponse { execution_time_ms: f64, } -// ── RSP-QL persistent session endpoints ───────────────────────────────────── - #[derive(Debug, Deserialize)] struct RspRegisterRequest { query: String, @@ -199,8 +163,6 @@ struct RspPushRequest { ntriples: String, } -// ── DatalogMTL^RDF request/response types ──────────────────────────────────── - #[derive(Debug, Deserialize)] struct DmtlRegisterRequest { rules: String, @@ -234,21 +196,14 @@ struct DmtlTickResult { metrics: serde_json::Value, } -// ── Static (batch) materialization over interval-valued data ───────────────── - #[derive(Debug, Deserialize)] struct DmtlStaticRequest { - /// Rules — RDF triple-pattern (`(?x,:p,?o):-...`) or MeTeoR (`B(X):-Boxminus[0,5]A(X)`). program: String, - /// Facts with validity intervals — `

@[l,r] .` (rdf) or `A(a)@[0,10]` (meteor). data: String, - /// "interval" (finite materialization) or "omega" (unbounded, periodic). #[serde(default)] strategy: Option, - /// Optional entailment queries (one fact per line), for omega. #[serde(default)] queries: Option, - /// Input syntax: "rdf" (default) or "meteor". #[serde(default)] syntax: Option, } @@ -261,17 +216,12 @@ struct DmtlEntailAnswer { #[derive(Debug, Serialize)] struct DmtlStaticResponse { - /// Derived (and base) facts as `Pred(args)@[l,r]` lines, sorted. facts: Vec, entailment: Vec, note: Option, } -// ── Helpers ────────────────────────────────────────────────────────────────── - -/// Format a decoded dictionary term as an N-Triples token: -/// - plain URIs → `` -/// - literals (start with `"`) and blank nodes (`_:`) → as-is +/// Format a decoded dictionary term as an N-Triples token fn term_to_ntriples_token(term: &str) -> String { if term.starts_with('"') || term.starts_with("_:") { term.to_string() @@ -280,8 +230,8 @@ fn term_to_ntriples_token(term: &str) -> String { } } -/// Export every triple in `db` as an N-Triples string. -fn db_to_ntriples(db: &SparqlDatabase) -> String { +/// Export every triple in `db` as an N-Triples string +fn db_to_ntriples(db: &KolibrieDB) -> String { let dict = db.dictionary(); let mut out = String::new(); let triples = db.query_default_triples(None, None, None); @@ -302,12 +252,11 @@ fn db_to_ntriples(db: &SparqlDatabase) -> String { out } -/// Convert collected RSP result rows into a table (first row = headers). +/// Convert collected RSP result rows into a table (first row = headers) fn results_to_table(results: &[Vec<(String, String)>]) -> Vec> { if results.is_empty() { return vec![]; } - // Collect all variable names while preserving first-seen order. let mut headers: Vec = Vec::new(); for row in results { for (key, _) in row { @@ -328,8 +277,6 @@ fn results_to_table(results: &[Vec<(String, String)>]) -> Vec> { table } -// ── Main & connection handling ─────────────────────────────────────────────── - fn has_n3_rule_text(text: &str) -> bool { text.lines() .map(str::trim_start) @@ -410,18 +357,7 @@ fn http_ml_context( .as_ref() } -/// The response for a failed query, mapping [`QueryError`] to this server's wire -/// codes. -/// -/// Goes through [`ml_error_response`] rather than matching the variants against a -/// second status table: three other callers still pass bare strings, so the table has -/// to stay there, and two copies of it would drift. -/// -/// The mapping is deliberately the one that was in place when these errors were -/// `String`s. Every non-ML failure — a syntax error included — reports as -/// `QUERY_EXECUTION_FAILED` with 400. For a parse failure that is misleading, and the -/// variants now make a `QUERY_PARSE_ERROR` possible, but adding one changes what -/// clients receive and is not part of this change. +/// The response for a failed query, mapping [`QueryError`] to this server's wire codes fn query_error_response(error: &QueryError) -> String { let code = match error { QueryError::Ml(policy) => policy.to_string(), @@ -475,8 +411,6 @@ fn main() { fn handle_client(mut stream: TcpStream, sessions: Sessions, dmtl_sessions: DmtlSessions) { match read_http_request(&mut stream) { Ok(request) => { - // SSE handler must keep the connection open, so it is handled here - // rather than returning a String from handle_request. if request.method == "GET" && request.path.starts_with("/rsp/events/") { let session_id = request.path["/rsp/events/".len()..].to_string(); rsp_events_sse(&session_id, stream, &sessions); @@ -838,8 +772,6 @@ fn handle_request( error_response(404, "Not Found") } -// ── RSP persistent session handlers ───────────────────────────────────────── - fn request_body(body: &[u8]) -> Option<&str> { match std::str::from_utf8(body) { Ok(body) => Some(body), @@ -859,8 +791,7 @@ fn rsp_register(body: &str, sessions: &Sessions) -> String { } }; - // Validate RSP policy before creating a session - let policy_db = SparqlDatabase::new(); + let policy_db = KolibrieDB::new(); for text in std::iter::once(&req.query).chain(req.sparql_rules.iter().flatten()) { if let Err(error) = kolibrie::execute_query::validate_query_policy(&strip_hash_comments(text), &policy_db) @@ -870,11 +801,9 @@ fn rsp_register(body: &str, sessions: &Sessions) -> String { } println!("RSP register: building engine for new session"); - // The SSE sender is lazily populated when the browser opens the SSE connection. let sse_sender: Arc>>> = Arc::new(Mutex::new(None)); let sse_sender_for_consumer = Arc::clone(&sse_sender); - // Consumer: serialize each result row as JSON and forward to the SSE channel. let result_consumer = ResultConsumer::> { function: Arc::new(move |row: Vec<(String, String)>| { let map: serde_json::Map = row @@ -921,7 +850,6 @@ fn rsp_register(body: &str, sessions: &Sessions) -> String { } }; - // Load static background data if provided. if let Some(static_rdf) = &req.static_rdf { if !static_rdf.trim().is_empty() { let cleaned_static_rdf; @@ -935,7 +863,7 @@ fn rsp_register(body: &str, sessions: &Sessions) -> String { let ntriples = match req.static_format.as_str() { "ntriples" => static_rdf_for_parse.to_string(), _ => { - let mut static_db = SparqlDatabase::new(); + let mut static_db = KolibrieDB::new(); match req.static_format.as_str() { "turtle" => static_db.parse_turtle(static_rdf_for_parse), _ => static_db.parse_rdf(static_rdf_for_parse), @@ -1014,11 +942,8 @@ fn rsp_push(body: &str, sessions: &Sessions) -> String { .add_to_stream(&req.stream, triple, req.timestamp); } - // Flush pending multi-window results session.engine.process_single_thread_window_results(); - // Signal end-of-firing to the SSE client so it can flush its display buffer - // immediately rather than waiting for the debounce timeout. if let Some(tx) = session.sse_sender.lock().unwrap().as_ref() { let _ = tx.send("__FIRING_END__".to_string()); } @@ -1026,10 +951,9 @@ fn rsp_push(body: &str, sessions: &Sessions) -> String { json_ok() } -/// SSE handler — writes the event-stream headers and then blocks, forwarding -/// results to the browser as they arrive via an in-process channel. +/// SSE handler — writes the event-stream headers and then blocks, forwarding results to the browser +/// as they arrive via an in-process channel fn rsp_events_sse(session_id: &str, mut stream: TcpStream, sessions: &Sessions) { - // Clone the Arc so we can release the sessions lock before blocking. let sse_sender_arc = { let lock = sessions.lock().unwrap(); match lock.get(session_id) { @@ -1045,7 +969,6 @@ fn rsp_events_sse(session_id: &str, mut stream: TcpStream, sessions: &Sessions) let (tx, rx) = mpsc::channel::(); sse_sender_arc.lock().unwrap().replace(tx); - // Write SSE headers — no Content-Length, connection stays open. if stream .write_all( b"HTTP/1.1 200 OK\r\n\ @@ -1062,10 +985,8 @@ fn rsp_events_sse(session_id: &str, mut stream: TcpStream, sessions: &Sessions) println!("RSP_CLIENT_CONNECTED"); - // Block-forward events until the client disconnects or the tx is dropped. for received in rx { let msg = if received == "__FIRING_END__" { - // Named event so the browser can flush its firing buffer immediately. "event: firing\ndata: {}\n\n".to_string() } else { format!("data: {}\n\n", received) @@ -1079,8 +1000,6 @@ fn rsp_events_sse(session_id: &str, mut stream: TcpStream, sessions: &Sessions) println!("RSP_CLIENT_DISCONNECTED"); } -// ── Existing SPARQL and legacy RSP-QL handlers ─────────────────────────────── - fn serve_playground() -> String { let html = include_str!("../../web/playground.html"); format!( @@ -1102,7 +1021,6 @@ fn execute_sparql_with_context(body: &str) -> String { } } -// Use only the host-selected ML policy fn execute_sparql_with_policy( body: &str, context: &kolibrie::ml_policy::MlExecutionContext, @@ -1115,7 +1033,6 @@ fn execute_sparql_with_policy( } }; - // Collect all queries let mut queries = Vec::new(); if let Some(single_query) = request.sparql { queries.push(single_query); @@ -1128,7 +1045,6 @@ fn execute_sparql_with_policy( return json_error_response("No queries provided"); } - // Collect all rules let mut rules = Vec::new(); if let Some(single_rule) = request.rule { rules.push(single_rule); @@ -1143,9 +1059,8 @@ fn execute_sparql_with_policy( rules.len() ); - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.set_ml_context(context.clone()); - // Validate the full request before changing database state for text in queries.iter().chain(rules.iter()) { if let Err(error) = kolibrie::execute_query::validate_query_policy(&strip_hash_comments(text), &database) @@ -1154,7 +1069,6 @@ fn execute_sparql_with_policy( } } - // Load RDF data once if let Some(rdf_data) = request.rdf { if !rdf_data.trim().is_empty() { let cleaned_rdf_data; @@ -1188,19 +1102,14 @@ fn execute_sparql_with_policy( } } - // Process N3 rules with the datalog reasoner if let Some(ref n3_rules_text) = request.n3logic { let n3_rules_text = strip_hash_comments(n3_rules_text); if has_n3_rule_text(&n3_rules_text) { println!("Processing N3 logic rules from N3 Logic sub-tab..."); - // One term space between the store and the reasoner, so a term either - // interns once or decodes consistently on both sides. let mut kg = Reasoner::new(); kg.adopt_terms(database.terms()); - // Decode all triples to owned strings using database.dictionary BEFORE - // creating kg's mutable borrow — avoids the RwLockReadGuard conflict. let decoded_triples: Vec<(String, String, String)> = { let dict_guard = kg.term_view(); database @@ -1221,7 +1130,6 @@ fn execute_sparql_with_policy( println!("Decoded triple count: {}", decoded_triples.len()); - // Now load into the Reasoner — no borrow conflict for (s, p, o) in &decoded_triples { kg.add_abox_triple(s, p, o); } @@ -1236,26 +1144,18 @@ fn execute_sparql_with_policy( rule.conclusion.len(), ); - // **IMPORTANT**: Register N3 prefixes with the database so SPARQL rules can use them for (prefix, uri) in prefixes { database.add_prefix(prefix.to_string(), uri.to_string()); } - // Register the rule and infer new facts kg.add_rule(rule); let inferred = kg.infer_new_facts_semi_naive(); println!("N3 rule inferred {} fact(s)", inferred.len()); - // Push inferred triples into the database triple store for triple in inferred { database.add_triple(triple); } - // No dictionary sync here on purpose. The store and the - // reasoner share one encoding, so terms interned while inferring - // were already visible to SPARQL; the assignment that used to sit - // here handed the store back the handle it already held. - if database.default_triple_count() > 0 { database.invalidate_stats_cache(); database.get_or_build_stats(); @@ -1270,7 +1170,6 @@ fn execute_sparql_with_policy( } } - // Process all SPARQL-syntax rules (RULE :Name :- CONSTRUCT { } WHERE { }) for (idx, rule_def) in rules.iter().enumerate() { let rule_def = strip_hash_comments(rule_def); if !rule_def.trim().is_empty() { @@ -1296,7 +1195,6 @@ fn execute_sparql_with_policy( } } - // Execute all queries let mut all_results = Vec::new(); for (idx, query) in queries.iter().enumerate() { @@ -1359,13 +1257,12 @@ fn execute_rsp_query(body: &str) -> String { if let Err(error) = kolibrie::execute_query::validate_query_policy( &strip_hash_comments(&request.query), - &SparqlDatabase::new(), + &KolibrieDB::new(), ) { return query_error_response(&error); } println!("RSP_REQUEST event_count={}", request.events.len()); - // Collect results via a shared container that the engine writes into. let result_container: Arc>>> = Arc::new(Mutex::new(Vec::new())); let rc_clone = Arc::clone(&result_container); @@ -1395,7 +1292,6 @@ fn execute_rsp_query(body: &str) -> String { } }; - // Load static background data from the SPARQL tab (if provided). if let Some(static_rdf) = &request.static_rdf { if !static_rdf.trim().is_empty() { let cleaned_static_rdf; @@ -1409,7 +1305,7 @@ fn execute_rsp_query(body: &str) -> String { let ntriples = match request.static_format.as_str() { "ntriples" => static_rdf_for_parse.to_string(), _ => { - let mut static_db = SparqlDatabase::new(); + let mut static_db = KolibrieDB::new(); match request.static_format.as_str() { "turtle" => static_db.parse_turtle(static_rdf_for_parse), _ => static_db.parse_rdf(static_rdf_for_parse), @@ -1427,7 +1323,6 @@ fn execute_rsp_query(body: &str) -> String { } } - // Sort events by timestamp, then push them to the engine. let mut events = request.events; events.sort_by_key(|e| e.timestamp); @@ -1443,7 +1338,6 @@ fn execute_rsp_query(body: &str) -> String { } } - // Flush all pending window results. engine.stop(); let execution_time = start_time.elapsed().as_secs_f64() * 1000.0; @@ -1484,8 +1378,6 @@ fn execute_rsp_query(body: &str) -> String { ) } -// ── Response helpers ───────────────────────────────────────────────────────── - #[cfg(test)] mod tests { use super::strip_hash_comments; @@ -1633,10 +1525,6 @@ mod tests { assert!(response.contains("7")); } - // Pins the wire format across the move from `String` errors to `QueryError`. - // Each case is the response the string-matching version produced for the same - // failure, so a change to `query_error_response` that clients would notice fails - // here rather than in a browser. #[test] fn query_error_responses_keep_their_wire_codes() { use kolibrie::execute_query::QueryError; @@ -1653,8 +1541,6 @@ mod tests { QueryError::Ml(MlError::InvalidConfiguration), "ML_INVALID_CONFIGURATION", ), - // Not in the status table, so it lands on the default arm, as it did - // when it arrived as the string "ML_EXECUTION_FAILED". ( QueryError::Ml(MlError::ExecutionFailed), "QUERY_EXECUTION_FAILED", @@ -1679,7 +1565,6 @@ mod tests { } } - // A parse failure carries the offending snippet. It must not reach the client. #[test] fn query_error_responses_do_not_leak_the_request() { use kolibrie::execute_query::QueryError; @@ -1730,7 +1615,7 @@ fn json_ok() -> String { ) } -/// A 200 response carrying an arbitrary JSON body. +/// A 200 response carrying an arbitrary JSON body fn json_response(json: &str) -> String { format!( "HTTP/1.1 200 OK\r\n\ @@ -1787,7 +1672,6 @@ fn error_response(code: u16, message: &str) -> String { message ) } -// ── DatalogMTL^RDF handlers ─────────────────────────────────────────────────── fn handle_dmtl_register(body: &str, dmtl_sessions: &DmtlSessions) -> String { let req: DmtlRegisterRequest = match serde_json::from_str(body) { @@ -1806,7 +1690,6 @@ fn handle_dmtl_register(body: &str, dmtl_sessions: &DmtlSessions) -> String { println!("DMTL register: {} rule(s) parsed", rules.len()); - // Parse stream shapes into the same dict before Arc-wrapping let (shapes, stream_iris) = if let Some(ref shape_text) = req.stream_shapes { if !shape_text.trim().is_empty() { match parse_stream_shapes(shape_text, &mut dict) { @@ -1907,8 +1790,6 @@ fn handle_dmtl_push(body: &str, dmtl_sessions: &DmtlSessions) -> String { timestamp: req.timestamp, triples: raw, }; - // Emit the shape's EXPIRY facts for channels that went quiet before - // evicting them, otherwise a stale source disappears silently. let expired: Vec = ingester .expiry_facts(req.timestamp) .into_iter() @@ -2015,11 +1896,7 @@ fn handle_dmtl_sse(session_id: &str, mut stream: TcpStream, dmtl_sessions: &Dmtl println!("DMTL SSE: client disconnected for session {}", session_id); } -// ── DatalogMTL N-Triples helpers ───────────────────────────────────────────── - -/// Decode a dictionary term to an N-Triples token. -/// Literals (start with `"`) and blank nodes (`_:`) are returned as-is; -/// everything else is wrapped in angle brackets. +/// Decode a dictionary term to an N-Triples token fn decode_dmtl_term(val: Option<&str>) -> String { match val { None => "?".to_string(), @@ -2028,14 +1905,11 @@ fn decode_dmtl_term(val: Option<&str>) -> String { } } -/// Tokenize N-Triples text and encode each term into the shared dictionary. +/// Tokenize N-Triples text and encode each term into the shared dictionary fn tokenize_and_encode_ntriples(ntriples: &str, dict_arc: &SharedTerms) -> Vec { let mut triples = Vec::new(); for line in ntriples.lines() { if let Some([s, p, o]) = tokenize_ntriples_line(line) { - // The shared space, not a view: these terms have to be interned where - // everything else will look them up. A view refused the call, which is - // what it is for. let (subject, predicate, object) = dict_arc.encode_triple(&s, &p, &o); triples.push(Triple { subject, @@ -2047,9 +1921,7 @@ fn tokenize_and_encode_ntriples(ntriples: &str, dict_arc: &SharedTerms) -> Vec Option<[String; 3]> { let line = line.trim(); if line.is_empty() || line.starts_with('#') { @@ -2060,7 +1932,6 @@ fn tokenize_ntriples_line(line: &str) -> Option<[String; 3]> { let mut tokens: Vec = Vec::new(); while tokens.len() < 3 && i < bytes.len() { - // Skip whitespace while i < bytes.len() && (bytes[i] == b' ' || bytes[i] == b'\t') { i += 1; } @@ -2078,7 +1949,7 @@ fn tokenize_ntriples_line(line: &str) -> Option<[String; 3]> { tokens.push(String::from_utf8_lossy(&bytes[start..i]).into_owned()); if i < bytes.len() { i += 1; - } // skip '>' + } } b'"' => { let start = i; @@ -2094,7 +1965,6 @@ fn tokenize_ntriples_line(line: &str) -> Option<[String; 3]> { } i += 1; } - // Consume optional @lang tag or ^^ if i < bytes.len() && bytes[i] == b'@' { while i < bytes.len() && bytes[i] != b' ' && bytes[i] != b'\t' { i += 1; @@ -2120,7 +1990,7 @@ fn tokenize_ntriples_line(line: &str) -> Option<[String; 3]> { } tokens.push(String::from_utf8_lossy(&bytes[start..i]).into_owned()); } - b'.' => break, // end-of-triple marker + b'.' => break, _ => { i += 1; } @@ -2134,23 +2004,13 @@ fn tokenize_ntriples_line(line: &str) -> Option<[String; 3]> { } } -// ── DatalogMTL rule text parser ─────────────────────────────────────────────── -// -// Rule format: -// (?x, :wasNear, ?y) :- (?x, :loc, ?l), Diamond[1000,5000](?y, :loc, ?l). -// -// Terms: ?var = Variable, = Constant (brackets stripped), -// :name = Constant (stored as ":name"), "lit" = Constant (stored with quotes). - -/// Static (batch) materialization over interval-valued data, using the -/// interval-native engine (MeTeoR-syntax rules + facts, past AND future operators). +/// Batch materialisation over interval-valued data with the interval-native engine fn handle_dmtl_static(body: &str) -> String { let req: DmtlStaticRequest = match serde_json::from_str(body) { Ok(r) => r, Err(e) => return json_error_response(&format!("Invalid JSON: {}", e)), }; let strategy = req.strategy.as_deref().unwrap_or("interval"); - // Syntax: "rdf" (triple patterns + N-Triples intervals) or "meteor" (Pred(args)@[l,r]). let rdf = req.syntax.as_deref().unwrap_or("rdf") != "meteor"; let dict: SharedTerms = SharedTerms::new(); @@ -2208,9 +2068,8 @@ type StaticInput = ( Vec<(Triple, TInterval)>, ); -/// Parse the static request in RDF-style: triple-pattern rules + `

@[l,r]` facts. +/// Parse the static request in RDF-style: triple-pattern rules + `

@[l,r]` facts fn parse_static_rdf(req: &DmtlStaticRequest, dict: &SharedTerms) -> Result { - // Parsing interns, so it needs the shared space rather than a view. let rules = dict .try_with_terms(|d| parse_dmtl_rules(&req.program, d, Mode::Static)) .map_err(|e| e.to_string())? @@ -2223,7 +2082,7 @@ fn parse_static_rdf(req: &DmtlStaticRequest, dict: &SharedTerms) -> Result Result { let rules = parse_program(&req.program, dict, Mode::Static) .map_err(|e| format!("Program parse error: {}", e))?; @@ -2242,7 +2101,7 @@ fn parse_static_meteor(req: &DmtlStaticRequest, dict: &SharedTerms) -> Result

@[l,r] .` (one per line). +/// Parse RDF interval facts: `

@[l,r] .` (one per line) fn parse_dmtl_interval_facts( text: &str, dict: &SharedTerms, @@ -2267,7 +2126,6 @@ fn parse_dmtl_interval_facts( let interval = parse_dmtl_interval(&iv_inner[..rb])?; let toks = tokenize_ntriples_line(&line[..at]) .ok_or_else(|| format!("data line {}: could not parse triple: '{}'", i + 1, line))?; - // Interning, so the shared space rather than a view. let (subject, predicate, object) = dict.encode_triple(&toks[0], &toks[1], &toks[2]); let triple = Triple { subject, @@ -2282,7 +2140,7 @@ fn parse_dmtl_interval_facts( Ok(out) } -/// Format a materialized fact as `@[l,r]` in the requested syntax. +/// Format a materialized fact as `@[l,r]` in the requested syntax fn fmt_fact(triple: &Triple, iv: &TInterval, dict: &Dictionary, rdf: bool) -> String { let atom = if rdf { fmt_rdf_triple(triple, dict) @@ -2292,7 +2150,7 @@ fn fmt_fact(triple: &Triple, iv: &TInterval, dict: &Dictionary, rdf: bool) -> St format!("{}@{}", atom, fmt_tinterval(iv)) } -/// Render a triple in RDF/N-Triples style: `

` (literals/blanks as-is). +/// Render a triple in RDF/N-Triples style: `

` (literals/blanks as-is) fn fmt_rdf_triple(triple: &Triple, dict: &Dictionary) -> String { let d = |id| dict.decode(id).unwrap_or("?"); format!( @@ -2303,7 +2161,7 @@ fn fmt_rdf_triple(triple: &Triple, dict: &Dictionary) -> String { ) } -/// Render a `TInterval` as `[l,r]` / `(l,r)` with `±inf` for unbounded tails. +/// Render a `TInterval` as `[l,r]` / `(l,r)` with `±inf` for unbounded tails fn fmt_tinterval(iv: &TInterval) -> String { let l = if iv.start == NEG_INF { "-inf".to_string() diff --git a/kolibrie/Cargo.toml b/kolibrie/Cargo.toml index 87816339..778c7604 100644 --- a/kolibrie/Cargo.toml +++ b/kolibrie/Cargo.toml @@ -101,8 +101,7 @@ name = "micro_dictionary" path = "benches/micro_dictionary.rs" harness = false -# Heavy tier: thread-contention sweep over the shared dictionary. Research rather -# than a refactor guard, so `perf_bench.sh routine` does not run it. +# Heavy tier: thread-contention sweep over the shared dictionary. [[bench]] name = "contended_dictionary" path = "benches/contended_dictionary.rs" diff --git a/kolibrie/benches/citybench_cross_window_compare.rs b/kolibrie/benches/citybench_cross_window_compare.rs index 132cfcb4..ef762f5e 100644 --- a/kolibrie/benches/citybench_cross_window_compare.rs +++ b/kolibrie/benches/citybench_cross_window_compare.rs @@ -110,8 +110,6 @@ fn make_citybench_sds(size: usize, update_ratio_percent: usize) -> Sds { } fn signature(result: &HashMap>, dict: &SharedTerms) -> BTreeSet { - // A copy: this decodes every triple it prints, and a scoped encoder will not - // hold its lock across the walk. let d = dict.view(); let mut out = BTreeSet::new(); @@ -175,7 +173,6 @@ fn bench_citybench_cross_window(c: &mut Criterion) { let rules = parse_rules(&dict); let next_sds = make_citybench_sds(size, ratio); - // Explicit warmup outside the measured loop. black_box(naive_sds_plus(&rules, &next_sds, &dict, CURRENT_TIME)); b.iter(|| { @@ -198,7 +195,6 @@ fn bench_citybench_cross_window(c: &mut Criterion) { let initial_sds = make_citybench_sds(size, 0); let next_sds = make_citybench_sds(size, ratio); - // Warmup/state preparation is intentionally outside the measured loop let previous = prepare_incremental_state(&rules, &initial_sds, &dict); black_box(incremental_sds_plus( &rules, diff --git a/kolibrie/benches/contended_dictionary.rs b/kolibrie/benches/contended_dictionary.rs index 27ae4d80..55b8c2ab 100644 --- a/kolibrie/benches/contended_dictionary.rs +++ b/kolibrie/benches/contended_dictionary.rs @@ -8,68 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Term interning under contention — what a shared dictionary costs across threads. -//! -//! `micro_dictionary` measures interning single-threaded, where every lock is -//! uncontended. That is the cheap case, and it produced a wrong conclusion once: -//! locking looked like 6 to 17%, which made an `encode(&self)` abstraction with -//! per-call interior locking seem affordable. -//! -//! It is not. Dividing the same 10,000 terms among `n` threads, against the -//! single-threaded reference: -//! -//! | threads | encode, own dict | encode, shared | decode, shared | -//! |---|---:|---:|---:| -//! | 2 | 1.4x faster | 1.6x slower | 3.3x slower | -//! | 4 | 2.0x faster | 2.2x slower | 5.4x slower | -//! | 8 | 1.7x faster | 3.9x slower | 12.3x slower | -//! -//! Per-call locking on a shared dictionary is slower than one thread at two threads. -//! Independent dictionaries scale about 2x, so the cost is the sharing rather than -//! the threading, and reads are worse than writes because a read acquisition still -//! does an atomic read-modify-write on one cache line and the acquisitions -//! ping-pong it between cores. -//! -//! `rsp_engine` spawns a thread per window plus a coordinator, all encoding into one -//! dictionary, so this is the access pattern the real system has. -//! -//! # What scoped access recovers -//! -//! `encode_scoped` uses [`TermEncoder::with_terms`](shared::term_encoder::TermEncoder), -//! which acquires once per chunk instead of once per term — the shape the trait -//! offers, measured beside the one it exists to avoid: -//! -//! | threads | per-call lock | scoped | own dict | -//! |---|---:|---:|---:| -//! | 2 | 299us | 256us | 161us | -//! | 4 | 643us | 283us | 95us | -//! | 8 | 697us | 330us | 108us | -//! -//! Scoped access roughly halves the contended cost at 4 and 8 threads, and stops -//! the degradation compounding: 1.4x to 1.8x slower than one thread across 2 to 8 -//! threads, against 1.6x to 3.8x for per-call. -//! -//! It does **not** make threading profitable. `with_terms` holds an exclusive lock -//! for the whole chunk, so the threads serialise and the result is single-threaded -//! throughput minus handoff. Parallel speedup on encoding would need a sharded -//! dictionary or a thread-local cache in front of a shared one; this only removes -//! the pathology. -//! -//! # These figures are directional -//! -//! Contended measurements depend on scheduling and move far more between runs than -//! the single-threaded ones: `encode_shared/4` measured 413us and 643us on two runs -//! of identical code, a 56% swing. The ordering has been stable across every run — -//! per-call worst, scoped in between, independent best, and all three worsening -//! past 4 threads — so read the shape and not the multipliers, and do not use these -//! as regression thresholds. -//! -//! # Why the `heavy` tier -//! -//! It answers a design question rather than guarding a refactor, which is what that -//! tier is for, and it has already answered it. It also costs about 45s per run. -//! Run it when the term-encoding design is revisited, or when something changes how -//! the dictionary is shared. +//! Term interning under contention — what a shared dictionary costs across threads extern crate criterion; @@ -86,23 +25,7 @@ fn iris() -> Vec { .collect() } -/// Encode and decode under real contention, which the group above cannot show. -/// -/// Everything above is single-threaded, so its `RwLock` is always uncontended — and -/// an uncontended lock is the cheap case. That matters because the reason the -/// dictionary is an `Arc>` at all is cross-thread sharing: `rsp_engine` -/// spawns a thread per window plus a coordinator, and they encode into one -/// dictionary. So the 6% the group above attributes to locking is a floor, not the -/// cost in the mode that forced the lock to exist. -/// -/// Each benchmark divides the same 10,000 terms among `n` threads, so total work is -/// constant and only the sharing changes. `independent` gives each thread its own -/// dictionary: it pays the same spawn and join cost but takes no shared lock, which -/// isolates contention from threading overhead. Read the contention cost as -/// `shared` minus `independent` at the same thread count. -/// -/// `encode` takes a write lock and therefore serialises completely; `decode` takes a -/// read lock and should not. Whether that asymmetry holds is the question. +/// Encode and decode under real contention, which the group above cannot show fn bench_contention(c: &mut Criterion) { let iris = iris(); let mut warm = Dictionary::new(); @@ -133,10 +56,6 @@ fn bench_contention(c: &mut Criterion) { }); group.bench_function(format!("encode_independent/{threads}"), |b| { - // Each thread gets its own *warm* dictionary, cloned once outside the - // timing loop. Starting them empty would compare cold encodes against - // the shared version's warm ones and overstate contention by the whole - // difference between the two, which is a factor of five. let mut owns: Vec = (0..threads).map(|_| warm.clone()).collect(); b.iter(|| { std::thread::scope(|scope| { @@ -168,9 +87,6 @@ fn bench_contention(c: &mut Criterion) { }); } - // The shape `TermEncoder::with_terms` actually offers: acquire once per thread, - // do the whole chunk inside. This is the claim the trait's design rests on, so - // it is measured next to the per-call variant it exists to avoid. for threads in [2usize, 4, 8] { let chunk = iris.len() / threads; group.bench_function(format!("encode_scoped/{threads}"), |b| { diff --git a/kolibrie/benches/cross_window_benchmark.rs b/kolibrie/benches/cross_window_benchmark.rs index c6ef845b..48556111 100644 --- a/kolibrie/benches/cross_window_benchmark.rs +++ b/kolibrie/benches/cross_window_benchmark.rs @@ -100,8 +100,6 @@ fn make_sds(n: usize, update_ratio_percent: usize) -> Sds { } fn signature(result: &HashMap>, dict: &SharedTerms) -> BTreeSet { - // A copy: this decodes every triple it prints, and a scoped encoder will not - // hold its lock across the walk. let d = dict.view(); let mut out = BTreeSet::new(); for (component, triples) in result { diff --git a/kolibrie/benches/family_tree_cross_window_compare.rs b/kolibrie/benches/family_tree_cross_window_compare.rs index 6c317301..fca9608a 100644 --- a/kolibrie/benches/family_tree_cross_window_compare.rs +++ b/kolibrie/benches/family_tree_cross_window_compare.rs @@ -41,9 +41,7 @@ const FAMILY_RULES: &str = r#" #[derive(Clone)] struct TwoStreamFamilyFacts { - // SDS stream 1: parentOf only parent_of: Vec, - // SDS stream 2: all non-parentOf asserted family facts other: Vec, } @@ -149,8 +147,6 @@ fn build_next_sds(facts: &TwoStreamFamilyFacts) -> Sds { } fn signature(result: &HashMap>, dict: &SharedTerms) -> BTreeSet { - // A copy: this decodes every triple it prints, and a scoped encoder will not - // hold its lock across the walk. let d = dict.view(); let mut out = BTreeSet::new(); diff --git a/kolibrie/benches/micro_dictionary.rs b/kolibrie/benches/micro_dictionary.rs index 5b296dff..6f1dbc27 100644 --- a/kolibrie/benches/micro_dictionary.rs +++ b/kolibrie/benches/micro_dictionary.rs @@ -8,96 +8,10 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Term interning — the hot path underneath every parse and every query. -//! -//! Guards the planned sealing of `Dictionary` (three public fields today) and the -//! `TermId` newtype. Both are changes to a type that every crate touches, and the -//! interning strategy itself may later change to an arena or to content-addressed -//! ids for distribution. This bench is what tells us what such a change costs. -//! -//! Benched from `kolibrie` rather than `shared` deliberately: see the note in -//! `micro_pattern.rs`. -//! -//! # The `TermEncoder` shapes -//! -//! The `micro_term_encoder` group answers the question `micro_scan` answered for -//! `QuadSource`: what does putting a trait in front of this cost? Two choices are -//! contested, and unlike the scan case they are not obviously free, because a warm -//! encode is tens of nanoseconds rather than microseconds. -//! -//! 1. `encode(&self)` with locking inside the implementation, versus -//! `encode(&mut self)` with the caller holding a guard. Only the first keeps -//! `Arc>` out of public signatures, which is the whole point of the -//! abstraction; the second preserves the batching that 125 call sites in this -//! workspace currently rely on, each holding one guard across 1 to 9 calls. -//! -//! 2. What `decode` returns. Under interior locking it *cannot* hand back -//! `Option<&str>`, because the borrow would outlive the guard, so it must -//! allocate a `String` per call. Today's `decode` is a few nanoseconds, so an -//! allocation is not a rounding error. -//! -//! The group separates the three costs — the lock, the vtable, and the allocation — -//! so a design can be argued from numbers rather than from taste. -//! -//! ## What it measured -//! -//! Over 10,000 warm terms, against direct access: -//! -//! Over 10,000 warm terms, from two independent four-run baselines. Quote only the -//! ratios between the low-noise rows: every shape that locks per call measures -//! stably, and every direct or guard-held one does not. -//! -//! | shape | median | noise | usable as an anchor | -//! |---|---:|---:|---| -//! | `decode` direct | 74us | 31% | no | -//! | `decode_batched_guard` | 82us | 11% | no | -//! | `decode_dyn_guard` | 77us | 8% | no | -//! | `decode_lock_per_call` | 83us | 1.0% | yes | -//! | `decode_dyn_visitor` | 109us | 1.0% | yes | -//! | `decode_dyn_interior_owned` | 278us | 0.5% | yes | -//! | `encode_warm` direct | 183us | 8% | no | -//! | `encode_lock_per_call` | 193us | 2.1% | yes | -//! | `encode_dyn_interior` | 200us | 1.2% | yes | -//! -//! Per-call locking dominates its own measurement and so swamps scheduling -//! variance; the tight loops are at the mercy of it. `decode` direct measured 4.2% -//! noise in one baseline and 31.2% in the next on identical code, which is why no -//! figure here is given relative to it. -//! -//! What the stable rows support: -//! -//! * **An owned `String` return costs 2.55x a visitor** (278us against 109us). -//! Interior locking forces the owned return, because a borrow cannot outlive -//! the guard it came from, so this is the cost of the obvious design and the -//! reason the visitor exists. -//! * **A visitor costs 31% over per-call locking that lends** (109us against -//! 83us) — the price of not handing out a guard. -//! * **A vtable costs about 4% on encode** (200us against 193us, both stable). -//! This is the only dispatch figure here worth anything; an earlier claim of -//! 22% on decode came from comparing two rows that both turned out to be noisy. -//! -//! What they do not support: any statement of overhead against direct access. The -//! direct rows are too unstable, so the absolute cost of the abstraction is not -//! measured here, only the differences between its candidate shapes. -//! +//! Term interning — the hot path underneath every parse and every query -//! ## Under contention -//! -//! Everything here is single-threaded, so every lock is uncontended — the cheap -//! case, and a misleading one. `benches/contended_dictionary.rs` measures the same -//! operations across threads and reaches the opposite conclusion: per-call locking -//! on a shared dictionary is slower than a single thread at two threads and 4 to 12 -//! times worse at eight. Read that before drawing anything from the locking rows -//! here. It sits in the `heavy` tier because it answers a design question rather -//! than guarding a refactor. -//! -//! **The two guard-held shapes could not be measured.** Both carry about 21% noise -//! across runs — the worst in the whole suite — and their medians move enough -//! between runs to reverse their order, so no claim about the cost of batching, or -//! about the cost of a vtable, can be drawn from them. A single run suggested the -//! vtable cost 22% on decode; four runs put the dyn variant *below* the concrete -//! one. Treat both rows as absent rather than as evidence, and do not use them as -//! regression guards until they are stabilised. +//! Everything here is single-threaded, so every lock is uncontended — the cheap case, and a +//! misleading one extern crate criterion; @@ -117,8 +31,6 @@ fn bench_encode(c: &mut Criterion) { let iris = iris(); let mut group = c.benchmark_group("micro_dictionary"); - // Cold: every term is new, so every call allocates and inserts. This is the - // parse-a-fresh-dataset path. group.bench_function("encode_cold", |b| { b.iter_batched( Dictionary::new, @@ -132,8 +44,6 @@ fn bench_encode(c: &mut Criterion) { ) }); - // Warm: every term already exists, so this is a pure lookup. This is the common - // case during query execution and the one most sensitive to a representation change. let mut warm = Dictionary::new(); for iri in &iris { warm.encode(iri); @@ -158,23 +68,15 @@ fn bench_encode(c: &mut Criterion) { group.finish(); } -// ── Stand-ins for the proposed TermEncoder shapes ─────────────────────────────── - -/// `&self`, locking inside. No guard reaches the caller, so `Arc>` stays -/// an implementation detail and P5 is satisfied. `decode` has to return an owned -/// `String`: a borrow into the dictionary cannot outlive the guard taken to get it. +/// `&self`, locking inside trait EncodeShared { fn encode(&self, value: &str) -> u32; fn decode(&self, id: u32) -> Option; - /// The way out of the allocation: lend the term inside the guard instead of - /// handing it back. Same shape as `QuadSource::for_each_quad`, and for the same - /// reason — one closure per call beats one allocation per call. + /// The way out of the allocation: lend the term inside the guard instead of handing it back fn with_decoded(&self, id: u32, visit: &mut dyn FnMut(Option<&str>)); } -/// `&mut self`, with the caller holding the guard. Batching survives and `decode` -/// can still lend a `&str`, but the handle has to reach the caller, so the thing -/// the abstraction exists to hide stays visible. +/// `&mut self`, with the caller holding the guard trait EncodeExclusive { fn encode(&mut self, value: &str) -> u32; fn decode(&self, id: u32) -> Option<&str>; @@ -201,8 +103,7 @@ impl EncodeExclusive for Dictionary { } } -/// Encoding and decoding under each shape, over the same 10,000 warm terms as the -/// group above, so a per-term cost can be read straight off the ratio. +/// Encoding and decoding under each shape over the same 10,000 warm terms fn bench_encoder_shapes(c: &mut Criterion) { let iris = iris(); let mut dict = Dictionary::new(); @@ -214,8 +115,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { let mut group = c.benchmark_group("micro_term_encoder"); - // ── encode ────────────────────────────────────────────────────────────────── - // One guard for the whole loop: what the 125 batching call sites do today. group.bench_function("encode_batched_guard", |b| { b.iter(|| { let mut guard = handle.write().unwrap(); @@ -225,7 +124,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { }) }); - // A lock per call, called concretely. Isolates the lock from the vtable. group.bench_function("encode_lock_per_call", |b| { b.iter(|| { for iri in &iris { @@ -234,8 +132,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { }) }); - // A lock per call, through `&dyn`. The difference from the line above is the - // vtable and nothing else. group.bench_function("encode_dyn_interior", |b| { let encoder: &dyn EncodeShared = &handle; b.iter(|| { @@ -245,7 +141,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { }) }); - // `&mut dyn` with the caller holding the guard: batching plus a vtable. group.bench_function("encode_dyn_guard", |b| { b.iter(|| { let mut guard = handle.write().unwrap(); @@ -256,8 +151,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { }) }); - // ── decode ────────────────────────────────────────────────────────────────── - // One read guard, lending `&str`: free of both the lock and the allocation. group.bench_function("decode_batched_guard", |b| { b.iter(|| { let guard = handle.read().unwrap(); @@ -267,7 +160,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { }) }); - // A read lock per call, still lending within the call. group.bench_function("decode_lock_per_call", |b| { b.iter(|| { for id in &ids { @@ -276,7 +168,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { }) }); - // What interior locking actually forces: a lock *and* a `String` per call. group.bench_function("decode_dyn_interior_owned", |b| { let encoder: &dyn EncodeShared = &handle; b.iter(|| { @@ -286,9 +177,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { }) }); - // The cheapest guard-based dyn decode: one read guard, lending `&str` through a - // vtable. The floor for any trait-based decode, and the shape that is only - // available if the guard reaches the caller. group.bench_function("decode_dyn_guard", |b| { b.iter(|| { let guard = handle.read().unwrap(); @@ -299,7 +187,6 @@ fn bench_encoder_shapes(c: &mut Criterion) { }) }); - // The visitor shape: interior locking without the allocation. group.bench_function("decode_dyn_visitor", |b| { let encoder: &dyn EncodeShared = &handle; b.iter(|| { diff --git a/kolibrie/benches/micro_field_access.rs b/kolibrie/benches/micro_field_access.rs index 6d7334c9..6c7cacdf 100644 --- a/kolibrie/benches/micro_field_access.rs +++ b/kolibrie/benches/micro_field_access.rs @@ -8,53 +8,22 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Reaching the term dictionary: what the sealed accessors cost. -//! -//! **The question this was written for is answered.** It originally compared a direct -//! `db.dictionary` field read against an `#[inline]` accessor and an -//! `#[inline(never)]` one, to find out whether sealing `SparqlDatabase`'s fields -//! would cost anything before converting twenty-odd fields and their call sites. The -//! three arms landed within 1.3% of each other — inside the noise floor even for the -//! outlined version — so sealing went ahead on measured rather than assumed grounds. -//! That comparison is no longer expressible here: `dictionary` is private, so a bench -//! outside the crate cannot read the field. The original is in the history at the -//! commit that sealed it. -//! -//! What it guards now is the two paths a caller actually has: -//! -//! * `dictionary()` — the guard accessor, which is what essentially all 96 -//! rewritten call sites use. -//! * `terms()` then `with_terms` — the way to share the encoding with another -//! engine, which costs an `Arc` clone before the lock. This arm used to measure -//! `dictionary_handle()`, which did the same two things and was removed when the -//! last `Arc>` left the public API; the baseline therefore has -//! `handle_then_lock` where this now reads `terms_then_scope`. -//! -//! Measured, the new arm is not slower: 5.09us against the removed arm's baselined -//! 8.53us. Do not read that as a 40% improvement. It also comes in slightly under -//! `guard_accessor` at 5.41us, despite doing an `Arc` clone and taking a *write* -//! lock where that arm takes a read lock — which is the sign that the clone and its -//! matching drop are being cancelled, because the `SharedTerms` provably does not -//! escape this loop. A call site that keeps the encoder will not see that. The arm -//! is worth having as a floor and not as a figure to quote. -//! -//! The outlined arm stays as the pessimistic bound: if the guard ever stops being -//! inlined, the gap between it and `guard_accessor` is where that shows up. +//! Reaching the term dictionary: what the sealed accessors cost extern crate criterion; use criterion::{black_box, criterion_group, criterion_main, Criterion}; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; use shared::term_encoder::TermEncoder; -/// Deliberately not inlinable, as the pessimistic bound on the guard accessor. +/// Deliberately not inlinable, as the pessimistic bound on the guard accessor #[inline(never)] -fn next_id_outlined(db: &SparqlDatabase) -> u32 { +fn next_id_outlined(db: &KolibrieDB) -> u32 { db.dictionary().next_id } fn bench_access(c: &mut Criterion) { - let database = SparqlDatabase::new(); + let database = KolibrieDB::new(); for i in 0..1_000 { database .dictionary_mut() diff --git a/kolibrie/benches/micro_join.rs b/kolibrie/benches/micro_join.rs index 698e41af..a902bccc 100644 --- a/kolibrie/benches/micro_join.rs +++ b/kolibrie/benches/micro_join.rs @@ -8,19 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Hash join over solution bindings, in the current representation and the proposed one. -//! -//! This bench exists to falsify a premise, not only to guard a change. The -//! architecture proposal replaces `MapRows = Vec>` with a -//! schema+tuple `Relation` (variable names resolved once into column positions, -//! rows as `Vec`), on the argument that the current shape re-hashes a `String` -//! key for every column of every row and allocates a `HashMap` per row. -//! -//! If schema+tuple is *not* faster here, a central premise of the shared join -//! library is wrong and we should know that before building on it. -//! -//! `Relation` below is a deliberately minimal stand-in — enough to measure the -//! representation, not a proposal for the real API. +//! Hash join over solution bindings, in the current representation and the proposed one extern crate criterion; @@ -30,15 +18,9 @@ use std::collections::HashMap; const LEFT_ROWS: usize = 20_000; const RIGHT_ROWS: usize = 20_000; -/// Distinct join-key values. Fewer keys means more matches per key, which is what -/// separates "hashing cost dominates" from "output size dominates". +/// Distinct join-key values const KEYS: u32 = 2_000; -// ── The shape that was replaced: Vec> ───────────────────── -// -// Named `MapRows` here because `MapRows` now means the schema-and-tuples type this -// benchmark was written to argue for. Keeping both lets the comparison still run. - type MapRows = Vec; fn make_bindings(rows: usize, vars: &[&str], key_var: &str) -> MapRows { @@ -60,8 +42,8 @@ fn make_bindings(rows: usize, vars: &[&str], key_var: &str) -> MapRows { .collect() } -/// Mirrors the shape of the engine's existing hash join: build a map keyed by the -/// shared variables, then probe and merge whole rows. +/// Mirrors the shape of the engine's existing hash join: build a map keyed by the shared variables, +/// then probe and merge whole rows fn hash_join_maps(left: &MapRows, right: &MapRows, shared: &[&str]) -> MapRows { let mut index: HashMap, Vec<&HashMap>> = HashMap::new(); for row in right { @@ -93,15 +75,7 @@ fn hash_join_maps(left: &MapRows, right: &MapRows, shared: &[&str]) -> MapRows { out } -// ── Control: a struct that genuinely only wraps the map shape ─────────────────── - -/// `MapRows` behind a struct with accessors, keeping one `HashMap` per row. -/// -/// This exists to answer "why would wrapping a HashMap in a struct cost anything?" -/// It should not, and this arm measures that it does not. `Relation` is a different -/// thing: it does not wrap the per-row map, it removes it, which is where its win -/// and its conversion cost both come from. Separating the two means neither gets -/// credited to the other. +/// `MapRows` behind a struct with accessors, keeping one `HashMap` per row struct WrappedMaps { rows: MapRows, } @@ -124,8 +98,8 @@ impl WrappedMaps { } } -/// The same join as `hash_join_maps`, reaching every row through the accessors -/// above rather than touching the field. Identical work, one indirection added. +/// The same join as `hash_join_maps`, reaching every row through the accessors above rather than +/// touching the field fn hash_join_wrapped(left: &WrappedMaps, right: &WrappedMaps, shared: &[&str]) -> WrappedMaps { let mut index: HashMap, Vec<&HashMap>> = HashMap::new(); for i in 0..right.len() { @@ -154,8 +128,6 @@ fn hash_join_wrapped(left: &WrappedMaps, right: &WrappedMaps, shared: &[&str]) - WrappedMaps::new(out) } -// ── Proposed representation: schema + tuples ──────────────────────────────────── - struct Relation { schema: Vec, rows: Vec>, @@ -188,8 +160,8 @@ fn make_relation(rows: usize, vars: &[&str], key_var: &str) -> Relation { Relation { schema, rows } } -/// The point: variable names are resolved to column indices **once**, before the -/// loop, so the inner loop touches only integers. +/// The point: variable names are resolved to column indices **once**, before the loop, so the inner +/// loop touches only integers fn hash_join_relation(left: &Relation, right: &Relation, shared: &[&str]) -> Relation { let lcols: Vec = shared.iter().filter_map(|v| left.column(v)).collect(); let rcols: Vec = shared.iter().filter_map(|v| right.column(v)).collect(); @@ -226,11 +198,7 @@ fn hash_join_relation(left: &Relation, right: &Relation, shared: &[&str]) -> Rel } } -// ── Conversion at the boundary ─────────────────────────────────────────────────── - -/// `Relation` to the map shape, which is what an engine adopting `Relation` -/// internally would have to do at every edge that still returns the old shape — -/// `MtlStore::query_at` in `datalogmtl`, and the four optimizer entry points. +/// Converts a `Relation` to the map shape that older entry points return fn relation_to_maps(r: &Relation) -> MapRows { r.rows .iter() @@ -246,15 +214,8 @@ fn relation_to_maps(r: &Relation) -> MapRows { .collect() } -/// The map shape to `Relation`, which is what the same engine would do at every -/// edge that still *supplies* the old shape. -/// -/// This mirrors `shared::terms::Bindings`'s own construction, including taking the -/// schema as the **union** of every row's variables rather than the first row's. -/// That costs a pass over all rows and a membership test per key, and it is not -/// optional: solution mappings are partial, so a variable may be bound only in a -/// later row. An earlier version of this helper read the first row alone, which -/// measured a conversion the real type cannot perform. +/// The map shape to `Relation`, which is what the same engine would do at every edge that still +/// *supplies* the old shape fn maps_to_relation(b: &MapRows) -> Relation { let mut schema: Vec = Vec::new(); for row in b { @@ -278,13 +239,8 @@ fn maps_to_relation(b: &MapRows) -> Relation { Relation { schema, rows } } -/// The same hash join over the real `shared::terms::Bindings`, through its public -/// API rather than the stand-in's bare fields. -/// -/// The stand-in has `schema` and `rows` public and is local to this file, so the -/// optimiser sees everything. The real type encapsulates both behind accessors in -/// another crate. This arm exists to check that the measured win survives the API, -/// rather than assuming it does. +/// The same hash join over the real `shared::terms::Bindings`, through its public API rather than +/// the stand-in's bare fields fn hash_join_real(left: &Bindings, right: &Bindings, shared: &[&str]) -> Bindings { let lk: Vec = shared.iter().filter_map(|v| left.column(v)).collect(); let rk: Vec = shared.iter().filter_map(|v| right.column(v)).collect(); @@ -321,8 +277,6 @@ fn hash_join_real(left: &Bindings, right: &Bindings, shared: &[&str]) -> Binding } fn make_real(rows: usize, vars: &[&str], key_var: &str) -> Bindings { - // No conversion step any more: the production type *is* the tuple shape, which - // is what this bench existed to argue for. make_bindings(rows, vars, key_var).into() } @@ -336,8 +290,6 @@ fn bench_join(c: &mut Criterion) { let lr = make_relation(LEFT_ROWS, &left_vars, "x"); let rr = make_relation(RIGHT_ROWS, &right_vars, "x"); - // Sanity: both shapes must produce the same number of rows, or the comparison - // is meaningless. A bench that silently measures different work is worse than none. let n_maps = hash_join_maps(&lb, &rb, &shared).len(); let n_rel = hash_join_relation(&lr, &rr, &shared).rows.len(); assert_eq!( @@ -371,8 +323,6 @@ fn bench_join(c: &mut Criterion) { ) }) }); - // What the incremental path would actually cost: join cheaply in `Relation` - // form, then hand the old shape back across an unchanged boundary. group.bench_function("relation_join_then_to_maps", |b| { b.iter(|| { let joined = hash_join_relation(black_box(&lr), black_box(&rr), &shared); @@ -380,7 +330,6 @@ fn bench_join(c: &mut Criterion) { }) }); - // The other direction: take the old shape in, convert once, then join. group.bench_function("maps_to_relation_then_join", |b| { b.iter(|| { let l = maps_to_relation(black_box(&lb)); @@ -389,7 +338,6 @@ fn bench_join(c: &mut Criterion) { }) }); - // The conversions alone, so the two above can be decomposed. let joined_rel = hash_join_relation(&lr, &rr, &shared); group.bench_function("convert_result_to_maps", |b| { b.iter(|| black_box(relation_to_maps(black_box(&joined_rel)).len())) @@ -398,7 +346,6 @@ fn bench_join(c: &mut Criterion) { b.iter(|| black_box(maps_to_relation(black_box(&lb)).rows.len())) }); - // The real type, through its public API, against the stand-in above. group.bench_function("schema_plus_tuples_real_type", |b| { b.iter(|| black_box(hash_join_real(black_box(&lreal), black_box(&rreal), &shared).len())) }); diff --git a/kolibrie/benches/micro_pattern.rs b/kolibrie/benches/micro_pattern.rs index eef83623..73ea6998 100644 --- a/kolibrie/benches/micro_pattern.rs +++ b/kolibrie/benches/micro_pattern.rs @@ -8,22 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Pattern matching and triple construction — the reasoner's inner loop. -//! -//! **Why this bench lives in `kolibrie` and not in `datalog`.** Benching from a crate -//! other than the one defining the function is the measurement that matters: a bench -//! inside the defining crate inlines freely and tells us nothing about what callers -//! actually pay. Cross-crate calls are inlined only through LTO, and `lto = "thin"` -//! reduces that loss without eliminating it. (`[profile.bench]` inherits `release`, -//! so thin LTO is in effect here.) -//! -//! Step 6 moved both functions to `shared::pattern`, and this file now imports from -//! there. Be clear about what that before/after comparison can and cannot show: this -//! call site was *already* cross-crate, `kolibrie -> datalog`, and is now -//! `kolibrie -> shared`, so it is close to unchanged by construction. The boundary the -//! move actually introduces is `datalog -> shared` inside the reasoner's own loops, -//! which this bench does not exercise — `family_tree_cross_window_compare` is the -//! probe for that, since it drives `cross_window_incremental` through the reasoner. +//! Pattern matching and triple construction — the reasoner's inner loop extern crate criterion; @@ -38,8 +23,7 @@ const FACTS: usize = 10_000; const SUBJECTS: u32 = 500; const PREDICATES: u32 = 10; -/// A fact table with a realistic mix: many subjects, few predicates, so a -/// predicate-bound pattern matches a meaningful fraction rather than all or nothing. +/// A fact table with many subjects and few predicates fn facts() -> Vec { (0..FACTS as u32) .map(|i| Triple { @@ -58,9 +42,6 @@ fn bench_matches(c: &mut Criterion) { let facts = facts(); let mut group = c.benchmark_group("micro_pattern/matches_rule_pattern"); - // Three selectivities. The all-variable case is the reasoner's common shape; - // the fully-bound case isolates the call overhead itself, which is what a lost - // inline shows up in most clearly. let patterns: Vec<(&str, TriplePattern)> = vec![ ("all_variables", (var("s"), var("p"), var("o"))), ( diff --git a/kolibrie/benches/micro_scan.rs b/kolibrie/benches/micro_scan.rs index 4e8a10fd..60eb28a2 100644 --- a/kolibrie/benches/micro_scan.rs +++ b/kolibrie/benches/micro_scan.rs @@ -8,22 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Index scanning — and the cost of putting a trait in front of it. -//! -//! This bench answers a design question rather than only guarding a change. The -//! architecture proposal replaces direct `DatasetIndex` access with a `QuadSource` -//! trait so the planner and the reasoners can compile without the engine. Two -//! choices in that trait are contested: -//! -//! 1. static dispatch (`&impl QuadSource`) vs dynamic (`&dyn QuadSource`) -//! 2. row-at-a-time (`Iterator`) vs batch (`Iterator>`) -//! -//! Dynamic dispatch and per-call allocation are both cheap in isolation and -//! expensive in a hot loop, so the honest way to settle it is to measure all four -//! shapes against the current direct call before committing. -//! -//! The traits below are throwaway stand-ins for the proposed API, defined here so -//! the measurement can happen without first building the real thing. +//! Index scanning — and the cost of putting a trait in front of it extern crate criterion; @@ -48,20 +33,18 @@ fn index() -> DatasetIndex { index } -// ── Stand-ins for the proposed QuadSource shapes ──────────────────────────────── - -/// Statically dispatched, returns an owned Vec — closest to today's `query_graph`. +/// Statically dispatched, returns an owned Vec — closest to today's `query_graph` trait ScanStatic { fn scan(&self, g: GraphId, s: Option, p: Option, o: Option) -> Vec; } -/// Dynamically dispatched. Object-safe, so it can be stored as `Box`. +/// Dynamically dispatched trait ScanDyn { fn scan(&self, g: GraphId, s: Option, p: Option, o: Option) -> Vec; } -/// Dynamically dispatched AND row-at-a-time, which is the shape that allocates a -/// boxed iterator per call. This is the combination most likely to cost real time. +/// Dynamically dispatched AND row-at-a-time, which is the shape that allocates a boxed iterator per +/// call trait ScanDynIter { fn scan<'a>( &'a self, @@ -105,8 +88,6 @@ fn bench_dispatch(c: &mut Criterion) { let dyn_ref: &dyn ScanDyn = &index; let dyn_iter_ref: &dyn ScanDynIter = &index; - // Three selectivities, because dispatch overhead is a fixed cost per call: it is - // invisible on a scan returning 160k quads and dominant on one returning 8. let cases: [(&str, Option, Option, Option); 3] = [ ("selective_subject", Some(42), None, None), ("medium_predicate", None, Some(SUBJECTS), None), diff --git a/kolibrie/benches/my_benchmark.rs b/kolibrie/benches/my_benchmark.rs index 2062e3de..551a6e4a 100644 --- a/kolibrie/benches/my_benchmark.rs +++ b/kolibrie/benches/my_benchmark.rs @@ -14,18 +14,16 @@ extern crate kolibrie; use criterion::*; use kolibrie::prelude::*; -// Simple query -fn setup_database() -> SparqlDatabase { - // Set current directory to the root of the project +fn setup_database() -> KolibrieDB { std::env::set_current_dir(std::path::Path::new(env!("CARGO_MANIFEST_DIR"))) .expect("Failed to set project root as current directory"); let file_path = "../datasets/synthetic_data_employee_100K.rdf"; - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_rdf_from_file(&file_path); db } -fn execute_sample_query(database: &mut SparqlDatabase) { +fn execute_sample_query(database: &mut KolibrieDB) { let sparql = r#" PREFIX ds: PREFIX foaf: @@ -39,7 +37,7 @@ fn execute_sample_query(database: &mut SparqlDatabase) { .into_rows(); } -fn execute_sample_query_volcano(database: &mut SparqlDatabase) { +fn execute_sample_query_volcano(database: &mut KolibrieDB) { let sparql = r#" PREFIX ds: PREFIX foaf: @@ -53,9 +51,7 @@ fn execute_sample_query_volcano(database: &mut SparqlDatabase) { .into_rows(); } -///////////////////////////////////////////////////////////////////////// -// Complex query -fn execute_sample_query_complex(database: &mut SparqlDatabase) { +fn execute_sample_query_complex(database: &mut KolibrieDB) { let sparql = r#" PREFIX foaf: PREFIX ds: @@ -74,7 +70,7 @@ fn execute_sample_query_complex(database: &mut SparqlDatabase) { .into_rows(); } -fn execute_sample_query_volcano_complex(database: &mut SparqlDatabase) { +fn execute_sample_query_volcano_complex(database: &mut KolibrieDB) { let sparql = r#" PREFIX foaf: PREFIX ds: @@ -96,7 +92,6 @@ fn execute_sample_query_volcano_complex(database: &mut SparqlDatabase) { fn my_benchmark(c: &mut Criterion) { let mut db = setup_database(); - // Benchmark for executing SPARQL query c.bench_function("execute_query_join parallel", |b| { b.iter(|| execute_sample_query(&mut db)) }); @@ -109,7 +104,6 @@ fn my_benchmark(c: &mut Criterion) { fn my_benchmark2(c: &mut Criterion) { let mut db = setup_database(); - // Benchmark for executing SPARQL query c.bench_function("COMPLEX QUERY: execute_query_join parallel", |b| { b.iter(|| execute_sample_query_complex(&mut db)) }); diff --git a/kolibrie/benches/optimizer_hotpath.rs b/kolibrie/benches/optimizer_hotpath.rs index 0a802150..587d1217 100644 --- a/kolibrie/benches/optimizer_hotpath.rs +++ b/kolibrie/benches/optimizer_hotpath.rs @@ -12,8 +12,8 @@ extern crate criterion; extern crate kolibrie; use criterion::*; +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_update}; -use kolibrie::sparql_database::SparqlDatabase; const SUBJECTS: usize = 20_000; const NAMED_GRAPHS: usize = 4; @@ -23,8 +23,8 @@ const REGIONS: usize = 10; const EX: &str = "http://example.org/"; /// Builds the fixture: salary and department in the default graph, region -fn setup_database() -> SparqlDatabase { - let mut database = SparqlDatabase::new(); +fn setup_database() -> KolibrieDB { + let mut database = KolibrieDB::new(); let mut default_graph = String::with_capacity(SUBJECTS * 160); for i in 0..SUBJECTS { @@ -63,7 +63,7 @@ const GRAPH_VARIABLE_SCAN: &str = r#" SELECT ?s ?r WHERE { GRAPH ?g { ?s ?r } }"#; -/// Scans a single named graph with no graph variable to bind. The seed row +/// Scans a single named graph with no graph variable to bind const GRAPH_FIXED_SCAN: &str = r#" SELECT ?s ?r WHERE { GRAPH { ?s ?r } }"#; @@ -95,11 +95,7 @@ WHERE { ?s ?d }"#; -/// A projection whose values repeat: 20,000 solutions over 50 distinct departments. -/// -/// The counterpart to `join_two_patterns`, whose columns are a distinct IRI and a -/// near-distinct literal per row. Grouping columns, rdf:type and graph names all have -/// this shape, and it is the case where storing each value once can pay. +/// A projection whose values repeat: 20,000 solutions over 50 distinct departments const LOW_CARDINALITY_PROJECTION: &str = r#" SELECT ?d WHERE { @@ -130,12 +126,6 @@ fn hot_paths(c: &mut Criterion) { }); } - // The same queries read through the borrowing API instead of `into_rows`. - // - // `into_rows` materialises an owned String per cell whatever the result holds - // internally, so it cannot show a difference between owning the values and - // referring to them. `iter` and `get` return `&str`, so these arms are the ones - // a representation change can move. for (name, sparql) in [ ("borrowed_read/join_two_patterns", JOIN_TWO_PATTERNS), ( diff --git a/kolibrie/examples/policy/automate_policy.rs b/kolibrie/examples/policy/automate_policy.rs index 2fd4e52b..937ef41f 100644 --- a/kolibrie/examples/policy/automate_policy.rs +++ b/kolibrie/examples/policy/automate_policy.rs @@ -20,9 +20,6 @@ fn main() -> Result<(), Box> { })), }; - // Replaces the old `set_sliding_window(10, 5)` + imperative `auto_policy_evaluation` - // with an RSP-QL query that declares a 10-tick window sliding every 5 ticks, firing - // on window close and streaming out via RSTREAM. let rsp_query = r#" PREFIX ex: @@ -50,18 +47,10 @@ fn main() -> Result<(), Box> { counter, counter, counter ); for triple in engine.parse_data(&ntriples) { - // `counter` is the stream's clock. The window is `[RANGE 10 STEP 5]` - // with no `TICK` clause, so it fires on these timestamps and not on - // elapsed wall time -- which is why this loop does not sleep. It used - // to sleep a second per iteration, twenty-one seconds in all, for no - // effect on the result beyond making the example the slowest thing in - // `scripts/verify.sh` after one other. engine.add_to_stream("policyStream", triple, counter); } } - // No sleep after stopping either: `stop` flushes every window and waits for the - // coordinator, so every result has been delivered by the time it returns. engine.stop(); Ok(()) } diff --git a/kolibrie/examples/rdf_terms/rdf_terms.rs b/kolibrie/examples/rdf_terms/rdf_terms.rs index 4e8c06f6..04222bf6 100644 --- a/kolibrie/examples/rdf_terms/rdf_terms.rs +++ b/kolibrie/examples/rdf_terms/rdf_terms.rs @@ -8,17 +8,9 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Building and reading RDF terms: IRIs, blank nodes and literals. -//! -//! Run with `cargo run --example rdf_terms`. -//! -//! The store keys every term on its text, so a term's spelling *is* its identity: -//! two terms spelled alike share one id and can never be told apart again. That makes -//! `shared::rdf_term` the place where spelling is decided, and this example is the -//! guided tour — what each kind of term looks like, which terms RDF says are the same, -//! and how to read a value back out of one. - -use kolibrie::sparql_database::SparqlDatabase; +//! Building and reading RDF terms: IRIs, blank nodes and literals + +use kolibrie::database::KolibrieDB; use shared::rdf_term::{ self, kind_of, lexical_value, numeric_value, read_literal, spell_blank_node, spell_literal, TermKind, XSD_STRING, @@ -41,41 +33,30 @@ fn heading(text: &str) { println!("\n── {text} {}", "─".repeat(rule)); } -/// An IRI is spelled as itself. +/// An IRI is spelled as itself fn iris() { heading("IRIs"); - // There is no constructor, and that is the point: an IRI is its own text, so one - // would be the identity function and would only suggest otherwise. Angle brackets - // are syntax belonging to a document, not part of the term. let iri = "http://example.org/alice"; println!(" {iri:?} is {:?}", kind_of(iri)); - // Being an IRI is decided by having a scheme, which is also what keeps IRIs from - // colliding with plain literals: `25` has no scheme, so it is a literal. for candidate in ["urn:a", "http://example.org/x", "25", "hello world"] { println!(" {candidate:?} is {:?}", kind_of(candidate)); } } -/// A literal is a lexical form, plus at most one of a datatype or a language tag. +/// A literal is a lexical form, plus at most one of a datatype or a language tag fn literals() { heading("Literals"); - // Plain: spelled bare, exactly the text it holds. let plain = spell_literal("25", None, None); - // Typed: quoted, carrying the datatype IRI. let typed = spell_literal("25", Some(XSD_INTEGER), None); - // Language-tagged: quoted, carrying the tag. let tagged = spell_literal("hello", None, Some("en")); println!(" plain {plain}"); println!(" xsd:integer {typed}"); println!(" @en {tagged}"); - // Bare where it can be, quoted where it must be. A well-formed IRI cannot begin - // with a quote, so nothing quoted can be mistaken for one -- which is what lets - // the common cases stay unquoted and unchanged. println!( " all three are literals: {}", [&plain, &typed, &tagged] @@ -83,8 +64,6 @@ fn literals() { .all(|term| kind_of(term) == TermKind::Literal) ); - // A lexical form containing a quote or a backslash is escaped, or the spelling - // would read back as a different term. let awkward = spell_literal("a\"b\\c", Some(XSD_INTEGER), None); println!(" needs escaping {awkward}"); println!( @@ -93,12 +72,10 @@ fn literals() { ); } -/// Which spellings denote one term, and which denote two. +/// Which spellings denote one term, and which denote two fn which_terms_are_the_same() { heading("Term identity"); - // RDF 1.1 §3.3: a plain literal *is* an `xsd:string` literal, so both get one - // spelling and therefore one id. let plain = spell_literal("25", None, None); let as_string = spell_literal("25", Some(XSD_STRING), None); println!( @@ -106,16 +83,12 @@ fn which_terms_are_the_same() { plain == as_string ); - // Differing only in datatype is still two terms. This is the distinction the - // readers used to lose: both became `25`, and the datatype was unrecoverable. let as_integer = spell_literal("25", Some(XSD_INTEGER), None); println!( " \"25\" and \"25\"^^xsd:integer same term? {}", plain == as_integer ); - // A language-tagged literal and a plain literal spelled the same way are two - // terms. Gluing the tag onto the text made them one. let tagged = spell_literal("hello", None, Some("en")); let looks_tagged = spell_literal("hello@en", None, None); println!( @@ -123,8 +96,6 @@ fn which_terms_are_the_same() { tagged == looks_tagged ); - // Term identity is not value equality: these denote one number and remain two - // terms, because their lexical forms differ. let one = spell_literal("1", Some(XSD_INTEGER), None); let also_one = spell_literal("01", Some(XSD_INTEGER), None); println!( @@ -134,20 +105,15 @@ fn which_terms_are_the_same() { ); } -/// Reading the value a term denotes, rather than the term. +/// Reading the value a term denotes, rather than the term fn reading_a_value_back() { heading("Reading a value"); - // A literal's number lives in its lexical form, not in its spelling, so anything - // comparing or summing terms has to look past the spelling. Parsing the spelling - // directly found nothing once the datatype was kept, which turned ORDER BY into - // string ordering and SUM into no answer at all. let typed = spell_literal("30", Some(XSD_INTEGER), None); println!(" {typed}"); println!(" numeric_value {:?}", numeric_value(&typed)); println!(" lexical_value {:?}", lexical_value(&typed)); - // Taken apart, a literal is its three pieces. let double = spell_literal("0.8", Some(XSD_DOUBLE), None); let literal = read_literal(&double).expect("a literal"); println!(" {double}"); @@ -156,15 +122,11 @@ fn reading_a_value_back() { literal.lexical, literal.datatype, literal.language ); - // An IRI denotes no number and is not a literal, so both answers say so rather - // than guessing. let iri = "http://example.org/25"; println!(" {iri}"); println!(" numeric_value {:?}", numeric_value(iri)); println!(" read_literal {:?}", read_literal(iri)); - // With N-Triples text already in hand -- from a file, a request body, a header -- - // this is what turns it into the term the readers would have produced. let from_text = rdf_term::canonicalise_literal("\"42\"^^"); println!( @@ -173,16 +135,13 @@ fn reading_a_value_back() { ); } -/// A blank node label belongs to one document and means nothing outside it. +/// A blank node label belongs to one document and means nothing outside it fn blank_nodes() { heading("Blank nodes"); - // A label identifies a node only within the document it was read from (RDF 1.1 - // §3.4), so a scope has to come from somewhere. One per document. let first_document = rdf_term::next_document_scope(); let second_document = rdf_term::next_document_scope(); - // Built from the bare label: the `_:` prefix and the scoping are both handled. let in_first = spell_blank_node("b", first_document); let in_second = spell_blank_node("b", second_document); @@ -195,26 +154,20 @@ fn blank_nodes() { kind_of(&in_second) ); - // Within one document the label does identify one node, which is what makes a - // blank node useful: it links statements together. println!( " `_:b` twice in one document is one node? {}", spell_blank_node("b", first_document) == spell_blank_node("b", first_document) ); - // Scoping replaces a previous scope rather than nesting in it, so a graph read - // back from its own serialisation does not collect a prefix per round trip. let round_tripped = rdf_term::scope_blank_node(&in_first, second_document); println!(" re-read in another document {round_tripped}"); } -/// The same terms, going into a store and coming back out. +/// The same terms, going into a store and coming back out fn terms_in_a_store() { heading("Terms in a store"); - // Every reader produces these spellings, so a term built by hand and a term read - // from a document are the same term and join against each other. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add(&format!( " \"30\"^^<{XSD_INTEGER}> .\n\ \"12\"^^<{XSD_INTEGER}> .\n\ @@ -245,16 +198,11 @@ fn terms_in_a_store() { println!(" {predicate:<10} {object}"); } - // The typed literal is a number to the engine, so it aggregates and orders as - // one, and the datatype is still there to be written back out. let summed = kolibrie::execute_query::execute( "SELECT SUM(?age) WHERE { ?s ?age }", &mut database, ) .expect("query should run") .into_rows(); - // 30 + 12. Read as numbers through their lexical forms: summing the spellings - // would have found nothing, which is what used to happen once the datatype was - // kept and the spelling stopped being the lexical form. println!("\n SUM over the two xsd:integer ages (30 + 12): {summed:?}"); } diff --git a/kolibrie/examples/real_dataset/real_dataset.rs b/kolibrie/examples/real_dataset/real_dataset.rs index dfcd6d5d..1d764e6c 100644 --- a/kolibrie/examples/real_dataset/real_dataset.rs +++ b/kolibrie/examples/real_dataset/real_dataset.rs @@ -24,18 +24,15 @@ fn filter_example() { file.read_to_string(&mut rdf_data) .expect("Unable to read file"); - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(&rdf_data); - // Use the full predicate ending to be more flexible with namespaces let high_salary_triples = database .query() .filter(|triple| { let dict = database.dictionary(); - // First check if this is a salary predicate if let Some(predicate) = dict.decode(triple.predicate) { if predicate.ends_with("annual_salary") { - // Then check if salary > 80000 if let Some(object) = dict.decode(triple.object) { if let Ok(salary) = object.parse::() { return salary > 80000.0; @@ -47,7 +44,6 @@ fn filter_example() { }) .get_triples(); - // Create a set of subject IDs with high salaries for lookup let mut high_salary_subjects = HashSet::new(); let mut subject_to_salary = HashMap::new(); @@ -60,12 +56,10 @@ fn filter_example() { } drop(dict); - // Find the names associated with the high-salary subjects let name_triples = database .query() .filter(|triple| { let dict = database.dictionary(); - // Check if this is a name predicate for a high-salary subject if let Some(predicate) = dict.decode(triple.predicate) { if predicate.ends_with("name") && high_salary_subjects.contains(&triple.subject) { return true; @@ -75,7 +69,6 @@ fn filter_example() { }) .get_triples(); - // Print name and salary println!("Employees with salary > 80000:"); let dict = database.dictionary(); for triple in name_triples.clone() { diff --git a/kolibrie/examples/real_scenario/drone_traffic_safety_demo.rs b/kolibrie/examples/real_scenario/drone_traffic_safety_demo.rs index 8434f12e..272e4f85 100644 --- a/kolibrie/examples/real_scenario/drone_traffic_safety_demo.rs +++ b/kolibrie/examples/real_scenario/drone_traffic_safety_demo.rs @@ -33,16 +33,9 @@ const TICK_MS: u64 = 1000; const TELEMETRY_STREAM: &str = "http://utm.example.org/telemetry"; const MAX_GAP_MS: u64 = 10_000; -// The evaluator uses DENSE integer semantics: Box/Since require the inner atom -// to hold at EVERY integer time point in their window. So the reasoner's time -// unit must be the sampling period — feeding it milliseconds while sampling -// once a second leaves 999 unsatisfied points between every pair of samples, -// and no universal operator can ever hold. Wall-clock ms stay in the UI only. -/// Fallback staleness when a config declares no stream shape. Live configs take -/// this from the shape's `STALENESS`; rule windows live in `DEFAULT_RULES`. +/// Fallback staleness when a config declares no stream shape const MAX_GAP_TICKS: u64 = MAX_GAP_MS / TICK_MS; -/// Nominal store retention. Actual eviction is driven by the evaluator's `w_max` -/// (the widest rule window), which is recomputed on every config swap. +/// Nominal store retention const STORE_HORIZON_TICKS: u64 = 620; #[derive(Debug, Clone, Copy, Serialize, Deserialize)] @@ -91,7 +84,6 @@ struct DroneView { link: String, current_zone: Option, off_plan: bool, - /// Consecutive ticks in `current_zone` (or in open air when that is null). dwell_ticks: u64, } @@ -112,11 +104,8 @@ struct TickView { rdf_events: Vec, derived: Vec, alerts: Vec, - /// Version of the rule/shape config that produced this tick. The UI watches - /// this to confirm a submitted swap has actually landed. config_version: u64, active_rules: Vec, - /// Set when a submitted config failed to apply; the old rules keep running. config_error: Option, metrics: MetricsView, } @@ -127,10 +116,7 @@ struct MetricsView { new_triples: usize, snapshots: usize, eval_time_us: u64, - /// Triples handed to the evaluator this tick — drops to near zero if the - /// stream shape stops matching the telemetry. stream_facts: usize, - /// Widest rule window; drives store eviction. Confirms a swap refreshed it. w_max_ticks: u64, } @@ -143,9 +129,6 @@ struct DroneRuntime { automated: bool, last_telemetry_ms: u64, previous_zones: HashSet<&'static str>, - /// Zone the dwell counter is running for, and how many consecutive ticks it - /// has been there. Counted in TICKS, not wall-clock ms, so it is exactly the - /// number a `Box[0,n]` window is waiting for. dwell_zone: Option<&'static str>, dwell_ticks: u64, } @@ -174,15 +157,10 @@ struct Vocab { dront_in_zone: u32, dront_entered_zone: u32, dront_on_flight_plan: u32, - // Zone classification (dront:status / dront:Restricted) is no longer listed - // here: it moved into DEFAULT_STATIC so it can be edited during a demo. utm_position: u32, utm_altitude: u32, utm_ais_status: u32, utm_active: u32, - // The rules' OUTPUT vocabulary (violatedZone / status / linkLost / - // offCourse) is not listed here: it now lives in DEFAULT_RULES, and the - // parser interns it into this same dictionary when the rules are loaded. xsd_false: u32, xsd_true: u32, drone_a: u32, @@ -195,7 +173,7 @@ struct Vocab { type EventLog = Arc>>; type Config = Arc; -/// The rule/shape text currently driving the engine. +/// The rule/shape text currently driving the engine #[derive(Debug, Clone, Serialize)] struct ConfigText { rules: String, @@ -204,7 +182,7 @@ struct ConfigText { version: u64, } -/// A parsed, validated configuration waiting to be picked up by the tick loop. +/// A parsed, validated configuration waiting to be picked up by the tick loop struct PendingConfig { rules: Vec, shapes: Vec, @@ -213,12 +191,7 @@ struct PendingConfig { reset_store: bool, } -/// Shared between the HTTP handlers and the tick loop. -/// -/// The handler parses and validates synchronously (so it can report precise -/// errors) and parks the result in `pending`; the tick loop drains it at the top -/// of the next tick. That keeps the swap off the request thread and means no -/// lock is ever held across `evaluator.advance`. +/// Shared between the HTTP handlers and the tick loop struct ConfigHandle { current: Mutex, pending: Mutex>, @@ -244,9 +217,6 @@ fn main() { let events = Arc::new(Mutex::new(Vec::new())); let state = Arc::new(Mutex::new(DemoState::new())); - // The dictionary lives out here so the HTTP thread can intern IRIs from - // user-typed rules into the SAME dictionary the engine matches against — - // Term::Constant ids are per-Dictionary and would otherwise match nothing. let dictionary = SharedTerms::new(); let vocab = init_vocab(&dictionary); let config: Config = Arc::new(ConfigHandle::new( @@ -321,9 +291,6 @@ fn start_demo_loop( config: Config, ) { thread::spawn(move || { - // Parse the shipped defaults through the same parser the editor uses, so - // a broken default is a loud startup failure rather than a silent - // never-fires. let shapes = rdf_parser::parse_stream_shapes_shared(DEFAULT_SHAPES, &dictionary) .expect("default stream shape must parse"); let rules = rdf_parser::parse_rules_shared(DEFAULT_RULES, &dictionary, Mode::Streaming) @@ -342,19 +309,10 @@ fn start_demo_loop( let mut config_error: Option = None; - // The reasoner's clock is a COUNTER, not the wall clock. Deriving `t` - // from elapsed time meant each iteration took `TICK_MS + work`, so the - // drift eventually swallowed an integer tick — and a hole is fatal under - // dense semantics: `Box[0,n]` needs the fact at EVERY integer point, so - // one skipped tick silently disables every universal operator whose - // window spans it. Sleeping to an absolute deadline keeps the tick - // sequence contiguous and still self-corrects against the wall clock. let started = state.lock().unwrap().started; let mut tick: u64 = 0; loop { - // Drain any pending swap before the tick, so we never hold a lock - // across `advance`. let pending = config.pending.lock().unwrap().take(); if let Some(p) = pending { match apply_config( @@ -367,7 +325,6 @@ fn start_demo_loop( &config, ) { Ok(()) => config_error = None, - // Keep running on the old rules rather than dropping the demo. Err(e) => config_error = Some(e), } } @@ -398,14 +355,11 @@ fn start_demo_loop( if target_ms > elapsed_ms { thread::sleep(Duration::from_millis(target_ms - elapsed_ms)); } - // If the work overran its budget we simply continue immediately: - // time is caught up by running sooner, never by skipping a tick. } }); } -/// Staleness of the first shape, in ticks. Falls back to the built-in default -/// when a config declares no shapes. +/// Staleness of the first shape, in ticks fn shape_staleness(shapes: &[StreamShape]) -> u64 { shapes .first() @@ -413,13 +367,7 @@ fn shape_staleness(shapes: &[StreamShape]) -> u64 { .unwrap_or(MAX_GAP_TICKS) } -/// Swap in a new rule set and stream shape. -/// -/// Both the ingester and the evaluator are rebuilt rather than mutated: -/// `ShapeIngester` has no way to replace its shapes, and `DatalogMTLEvaluator` -/// computes its private `w_max` (which drives store eviction) only in `new` — -/// assigning `.rules` directly would leave eviction pinned to the OLD window and -/// a wider new rule could never fire. +/// Swap in a new rule set and stream shape fn apply_config( p: PendingConfig, ingester: &mut ShapeIngester, @@ -429,12 +377,8 @@ fn apply_config( dictionary: &SharedTerms, config: &Config, ) -> Result<(), String> { - // Re-validate here: it is the only fallible step in `new`, so checking first - // means the store below is never stranded by a failing rebuild. validate_rules(&p.rules)?; - // Carry the fact history over unless asked to clear it, so windows do not - // have to refill from scratch after every edit. let store = if p.reset_store { IntervalFactStore::new(STORE_HORIZON_TICKS) } else { @@ -472,22 +416,16 @@ fn start_http_server( }); } -/// Largest request body accepted, so a bad client cannot exhaust memory. +/// Largest request body accepted, so a bad client cannot exhaust memory const MAX_BODY_BYTES: usize = 1024 * 1024; -/// Read one request, honouring `Content-Length`. -/// -/// A single fixed-size `read` is not enough here: a rules program easily exceeds -/// one TCP segment, and a truncated body would surface as a confusing JSON parse -/// error. GET requests carry no `Content-Length`, so this returns as soon as the -/// headers are complete and `/` and `/events` behave exactly as before. +/// Read one request, honouring `Content-Length` fn read_request(stream: &mut TcpStream) -> Option<(String, String, String)> { let _ = stream.set_read_timeout(Some(Duration::from_secs(10))); let mut raw: Vec = Vec::new(); let mut chunk = [0u8; 8192]; - // Headers first. let header_end = loop { if let Some(pos) = raw.windows(4).position(|w| w == b"\r\n\r\n") { break pos + 4; @@ -525,7 +463,6 @@ fn read_request(stream: &mut TcpStream) -> Option<(String, String, String)> { .unwrap_or(0) .min(MAX_BODY_BYTES); - // Then top up until the declared body has arrived. while raw.len() - header_end < content_length { match stream.read(&mut chunk) { Ok(0) | Err(_) => break, @@ -583,12 +520,7 @@ fn get_config(stream: TcpStream, config: &Config) { write_json(stream, 200, &payload.to_string()); } -/// Parse and validate a submitted config, then park it for the tick loop. -/// -/// All three failure modes are reported separately so the editor can say which -/// box is wrong. `validate_rules` matters here because the RDF parser — unlike -/// the MeTeoR one — does not run it, so an unsafe head variable would otherwise -/// only fail later, inside the tick loop, where the user cannot see it. +/// Parse and validate a submitted config, then park it for the tick loop fn post_config(stream: TcpStream, body: &str, dictionary: &SharedTerms, config: &Config) { let req: ConfigUpdate = match serde_json::from_str(body) { Ok(r) => r, @@ -620,8 +552,6 @@ fn post_config(stream: TcpStream, body: &str, dictionary: &SharedTerms, config: "version": version, "rule_ids": rule_ids, "w_max_ticks": w_max, - // Widening a window still needs history the store never kept, so tell - // the UI how long the new rules need before they can be trusted. "warmup_ticks": if req.reset_store { w_max } else { 0 }, }); @@ -759,19 +689,12 @@ fn build_tick( t: u64, config_error: Option, ) -> TickView { - // The stream shape's STALENESS is in ticks; the drone-B freshness checks - // below are in wall-clock ms, so convert once here. Editing STALENESS in the - // Rules tab therefore really does change when the link reads as expired. let max_gap_ms = staleness_ticks.saturating_mul(TICK_MS); - // `t` is the caller's contiguous tick counter — never derive it from elapsed - // time, or drift will skip integers and break dense operators. The animation - // and the UI clock use the nominal wall-clock position of that tick. let tick_ms = t * TICK_MS; state.drone_a.position = scripted_drone_a(tick_ms); state.drone_a.last_telemetry_ms = tick_ms; let zones = zones(); - // Re-asserted every tick; see DEFAULT_STATIC for why. let mut triples = static_facts.to_vec(); let mut rdf_events = Vec::new(); @@ -788,10 +711,6 @@ fn build_tick( rdf_events.extend(b_triples.1); } - // Facts for channels that have gone quiet. These come from the shape's - // EXPIRY declaration, so what a stale drone "says" is editable alongside - // everything else rather than hardcoded here. Must run after the telemetry - // above, or a drone that just reported would also be reported stale. let expiry = ingester.expiry_facts(t); if !expiry.is_empty() { rdf_events.extend(decode_triples( @@ -804,7 +723,6 @@ fn build_tick( state.drone_a.previous_zones = drone_a.previous_zones; state.drone_b.previous_zones = drone_b.previous_zones; - // One dwell step per tick, after positions are settled. tick_dwell(&mut state.drone_a, &zones); tick_dwell(&mut state.drone_b, &zones); @@ -931,19 +849,14 @@ fn telemetry_for_drone( (facts, lines) } -/// The zone containing this position, if any. `drone_view` and the dwell counter -/// must agree on "current zone", so both go through here. +/// The zone containing this position, if any fn zone_at(position: LatLng, zones: &[Zone]) -> Option<&Zone> { zones .iter() .find(|zone| haversine_m(position, zone.center) <= zone.radius_m) } -/// Advance the drone's dwell counter by one tick. -/// -/// Counts consecutive ticks in the same zone (or consecutively in open air), and -/// resets the moment that changes — so it reads as exactly the number a -/// `Box[0,n]` window is still waiting for. +/// Advance the drone's dwell counter by one tick fn tick_dwell(drone: &mut DroneRuntime, zones: &[Zone]) { let here = zone_at(drone.position, zones).map(|z| z.id); if here == drone.dwell_zone { @@ -970,8 +883,7 @@ fn drone_view(drone: &DroneRuntime, link: &str, zones: &[Zone]) -> DroneView { } } -/// Level and wording for the rules the demo ships with, keyed by rule id. -/// Custom rules fall back to a generic alert. +/// Level and wording for the rules the demo ships with, keyed by rule id const ALERT_TEMPLATES: &[(&str, &str, &str)] = &[ ( "sustainedGeofenceViolation", @@ -1000,16 +912,10 @@ const ALERT_TEMPLATES: &[(&str, &str, &str)] = &[ ), ]; -/// Maximum alerts surfaced per tick, so a runaway recursive rule cannot flood -/// the UI. +/// Maximum alerts surfaced per tick, so a runaway recursive rule cannot flood the UI const MAX_ALERTS: usize = 20; -/// Turn derived triples into alerts. -/// -/// `advance` returns triples with no rule provenance, so attribution is -/// reconstructed by finding the rule whose head unifies with each derived -/// triple. Every derived triple came from some rule's head; the only ambiguity -/// is two rules sharing a head shape, which is acceptable here. +/// Turn derived triples into alerts fn alerts_from_derived( derived: &[Triple], rules: &[DatalogMTLRule], @@ -1032,7 +938,6 @@ fn alerts_from_derived( Some((_, level, message)) => (level.to_string(), message.to_string()), None => ("warning".to_string(), dict.decode_triple(t)), }; - // Only report a zone when the rule's head actually carries one. let zone = matches!(rule.head.2, Term::Variable(_)).then(|| local(&dict, t.object)); alerts.push(AlertView { @@ -1106,14 +1011,7 @@ fn init_vocab(dictionary: &SharedTerms) -> Vocab { } } -/// Default rule program, in the RDF triple-pattern syntax of -/// `datalogmtl::rdf_parser`. This text is the source of truth: it is parsed at -/// startup and shipped to the Rules tab, so what you see is what is running. -/// -/// All windows are in TICKS (1 tick == TICK_MS), not milliseconds — the -/// evaluator's dense integer semantics require the time unit to be the sampling -/// period. IRIs must match `init_vocab` exactly or the rules match nothing; -/// `` is the quoting device for the bare string `vocab.xsd_false`. +/// Default rule program, in the RDF triple-pattern syntax of `datalogmtl::rdf_parser` const DEFAULT_RULES: &str = r#" PREFIX dront: PREFIX utm: @@ -1164,16 +1062,7 @@ PREFIX utm: ). "#; -/// Default stream shape. `STALENESS` is in ticks, like every other window here. -/// -/// `EXPIRY` is what a channel transmits once it has gone quiet for `STALENESS` -/// ticks — silence is not itself an event, so `controlLinkLoss` would have -/// nothing to observe without it. It repeats every tick while the channel stays -/// stale, which is what that rule's `Box[0,10]` needs. -/// -/// Note the PATTERN can only ever match LESS than the telemetry the demo emits — -/// `telemetry_for_drone` builds a fixed set of 8 triples, so adding a pattern -/// that nothing produces makes the whole event stop matching. +/// Default stream shape const DEFAULT_SHAPES: &str = r#" PREFIX rdf: PREFIX sosa: @@ -1195,17 +1084,7 @@ STREAM . "#; -/// Default background facts, as N-Triples: which zones are restricted. -/// -/// Both `sustainedGeofenceViolation` and `offCourseSinceRestrictedEntry` join -/// against `(?z, dront:status, dront:Restricted)`, so declassifying a zone here -/// disarms those rules over it — the quickest thing to change during a live -/// demo. Zone geometry stays in Rust; only the classification is reasoned over. -/// -/// These are re-asserted on every tick: the store is time-indexed and evicts -/// below `t - w_max`, so a fact inserted only once would age out and the joins -/// would silently stop matching. Coalescing makes that cheap (one interval per -/// fact, not one per tick). +/// Default background facts, as N-Triples: which zones are restricted const DEFAULT_STATIC: &str = r#" # Zone classification. Plain N-Triples — paste in anything an RDF tool emits. # diff --git a/kolibrie/examples/real_scenario/fraud_detection_system.rs b/kolibrie/examples/real_scenario/fraud_detection_system.rs index 4139e3c3..df31dfe1 100644 --- a/kolibrie/examples/real_scenario/fraud_detection_system.rs +++ b/kolibrie/examples/real_scenario/fraud_detection_system.rs @@ -1,44 +1,11 @@ -/* - Pipeline (per transaction, per step): - RSP-QL window -> Datalog Pass 1 (Symbolic -> ML) - -> GradientBoosting ML - -> Datalog Pass 2 (ML -> Symbolic) - -> Fusion -> Verdict - - RSP-QL: RANGE 300 STEP 60 (5-min sliding window, 1-min step). - - Pass-1 Datalog rules (raw features -> symbolic flags): - R1 velocity1h > 5 -> highVelocity - R2 amount > 1000 -> largeAmount - R3 merchantRisk > 70 -> highMerchantRisk - R4 isForeign > 0 ∧ risk > 70 -> foreignHighRisk - R5 amount > 1000 ∧ vel > 5 -> riskLevel:high - R1b windowVelocity > 3 -> highWindowActivity (RSP-derived) - - Pass-2 Datalog rules (ML score -> symbolic flags): - R6 mlFraudScore > 40 ∧ vel > 3 -> mlAssistedAlert - R7 recentFraudCount > 1 -> historicalPattern - - Fusion thresholds: - P > 0.80 -> FRAUD - P > 0.50 ∧ riskLevel = high -> FRAUD - riskLevel = high -> SUSPICIOUS - P > 0.60 ∨ any flag -> REVIEW - otherwise -> CLEAR - - Parser constraints: - • SELECT variables must be on a single line (space1 does not match '\n'). - • FILTER thresholds must be integers (parser stops at '.'). -*/ - use datalog::reasoning::Reasoner; +use kolibrie::database::KolibrieDB; use kolibrie::ml::execute::{ execute_ml_prediction_with_handler, setup_ml_handler, MLPredictTiming, }; use kolibrie::parser::parse_standalone_rule; use kolibrie::rsp_engine::{QueryExecutionMode, RSPBuilder, ResultConsumer, SimpleR2R}; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; use ml::MLHandler; use pyo3::prepare_freethreaded_python; use shared::rule::Rule; @@ -110,7 +77,6 @@ fn handle_sse_connection( if batch.is_empty() { idle_ms += 100; if idle_ms >= 15_000 { - // heartbeat every 15 s to keep connection alive if stream.write_all(b": heartbeat\n\n").is_err() { return; } @@ -130,13 +96,11 @@ fn handle_sse_connection( } "/" | "/index.html" => { let html_path = { - // 1. Co-located next to this source file let sibling = std::path::PathBuf::from(env!("CARGO_MANIFEST_DIR")) .join("examples/real_scenario/fraud_detection_dashboard.html"); if sibling.exists() { sibling } else { - // 2. Walk up for any other location let mut p = std::path::PathBuf::from(env!("CARGO_MANIFEST_DIR")); loop { let candidate = p.join("fraud_detection_dashboard.html"); @@ -251,21 +215,13 @@ fn start_sse_server( struct Transaction { tx_id: String, account_id: String, - /// USD amount: f64, - /// 0–23 hour_of_day: u8, - /// 0 (Mon) – 6 (Sun) day_of_week: u8, - /// 0–100; stored as integer to keep SPARQL FILTER thresholds parse-safe merchant_risk: u32, - /// Transactions on this account in the last hour velocity_1h: u32, - /// km from account home location distance_km: f64, - /// 1 = cross-border is_foreign: u8, - /// 1 = physical card present card_present: u8, } @@ -300,7 +256,7 @@ impl AccountHistory { } } - /// Increments on Fraud/Suspicious (cap 10), decrements otherwise (floor 0). + /// Increments on Fraud/Suspicious (cap 10), decrements otherwise (floor 0) fn update(&mut self, _score: f64, verdict: &Verdict) { match verdict { Verdict::Fraud | Verdict::Suspicious => { @@ -363,14 +319,12 @@ fn main() -> Result<(), Box> { let mut database = setup_knowledge_base(); - // Symbolic rules over raw transaction features let (rule_velocity, _) = process_rule_definition(&rule_suspicious_velocity(), &mut database)?; let (rule_amount, _) = process_rule_definition(&rule_suspicious_amount(), &mut database)?; let (rule_merch_risk, _) = process_rule_definition(&rule_high_merchant_risk(), &mut database)?; let (rule_pattern, _) = process_rule_definition(&rule_foreign_high_risk(), &mut database)?; let (rule_high_risk, _) = process_rule_definition(&rule_high_risk_chained(), &mut database)?; - // R1b reads ex:windowVelocity, which is written from the RSP snapshot let (rule_win_vel, _) = process_rule_definition(&rule_high_window_activity(), &mut database)?; let pass1_rules = vec![ @@ -382,13 +336,11 @@ fn main() -> Result<(), Box> { rule_win_vel, ]; - // Rules that read the ML score written back to RDF let (rule_ml_alert, _) = process_rule_definition(&rule_ml_assisted_alert(), &mut database)?; let (rule_hist_pat, _) = process_rule_definition(&rule_historical_pattern(), &mut database)?; let pass2_rules = vec![rule_ml_alert, rule_hist_pat]; - // Shared buffer: RSP window thread deposits bindings here let window_results: Arc>>> = Arc::new(Mutex::new(Vec::new())); let window_results_clone = window_results.clone(); @@ -486,10 +438,9 @@ fn main() -> Result<(), Box> { Ok(()) } -/// Writes Pass-1 symbolic flags and account history count as numeric (0/1) RDF triples -/// so the ML.PREDICT INPUT query can SELECT them as features. +/// Writes Pass-1 flags and account history counts as numeric triples for the ML.PREDICT input query fn write_numeric_flags_to_db( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, tx_uri: &str, flags_p1: &[String], hist_fraud_count: u32, @@ -522,10 +473,9 @@ fn write_numeric_flags_to_db( ); } -/// Extracts all 14 ML feature values for a single transaction from the RDF database. -/// Returns a single-row Vec> (dictionary-encoded values). +/// Extracts all 14 ML feature values for a single transaction from the RDF database fn extract_data_for_ml_fraud( - database: &SparqlDatabase, + database: &KolibrieDB, tx_uri: &str, ) -> Result>, Box> { let feature_predicates = [ @@ -575,7 +525,6 @@ fn extract_data_for_ml_fraud( } } - // Only return the row if we have all 14 features + the tx subject (15 total) if row.len() == 15 { Ok(vec![row]) } else { @@ -584,11 +533,9 @@ fn extract_data_for_ml_fraud( } /// Runs ML prediction for one transaction driven by the ML.PREDICT clause in -/// `rule_ml_fraud_predict()`. The rule is parsed to extract the model name and -/// input/output contract; `execute_ml_prediction_from_clause` handles model -/// discovery and loading automatically. +/// `rule_ml_fraud_predict()` fn run_ml_predict_from_clause( - database: &SparqlDatabase, + database: &KolibrieDB, tx_uri: &str, rule_str: &str, ml_handler: &MLHandler, @@ -602,9 +549,8 @@ fn run_ml_predict_from_clause( .as_ref() .ok_or("rule_ml_fraud_predict has no ML.PREDICT clause")?; - // extract closure: decode feature dict IDs → f64 values for this transaction let tx_uri_owned = tx_uri.to_string(); - let extract_fn = move |db: &SparqlDatabase| { + let extract_fn = move |db: &KolibrieDB| { let rows = extract_data_for_ml_fraud(db, &tx_uri_owned)?; if rows.is_empty() { return Ok(vec![]); @@ -625,8 +571,6 @@ fn run_ml_predict_from_clause( Ok(decoded) }; - // predict closure: build ordered feature vector from clause-declared variable names, - // then call the ML model; returns score + timing let predict_fn = |handler: &MLHandler, best_model: &str, data: &[HashMap], @@ -687,8 +631,8 @@ fn run_ml_predict_from_clause( Ok((score, t_r2p, t_p2r)) } -fn setup_knowledge_base() -> SparqlDatabase { - let mut db = SparqlDatabase::with_ml_context(local_ml::trusted_context()); +fn setup_knowledge_base() -> KolibrieDB { + let mut db = KolibrieDB::with_ml_context(local_ml::trusted_context()); db.add_prefix("ex", "http://fraud.example.org/"); db.add_prefix("rdf", "http://www.w3.org/1999/02/22-rdf-syntax-ns#"); @@ -827,10 +771,6 @@ WHERE { .into() } -// R6: amplifies a weak ML signal (score > 40/100) when velocity is also -// elevated, catching cases the raw rules alone would miss -// R7: flags any transaction from an account with a poor verdict history, -// injecting longitudinal account context into per-transaction decisions fn rule_ml_assisted_alert() -> String { r#"PREFIX ex: PREFIX rdf: @@ -920,9 +860,9 @@ ML.PREDICT(MODEL "fraud_predictor", fn run_simulation( engine: &mut kolibrie::rsp_engine::RSPEngine>, - database: &mut SparqlDatabase, - pass1_rules: &[Rule], // R1–R5, R1b: symbolic rules on raw features - pass2_rules: &[Rule], // R6–R7: rules that read ML output from RDF + database: &mut KolibrieDB, + pass1_rules: &[Rule], + pass2_rules: &[Rule], window_results: Arc>>>, events: EventLog, ml_handler: &MLHandler, @@ -975,16 +915,11 @@ fn run_simulation( step_txs.push((tx, is_injected_fraud, tx_uri, acct_uri)); } - // bindings into window_results (5 × 9 = 45 triples, ~200 ms) thread::sleep(Duration::from_millis(200)); - // drain window snapshot, compute per-account - // windowVelocity, then run the full reasoning pipeline let current_window: Vec> = { window_results.lock().unwrap().drain(..).collect() }; - // Count transactions per account visible in the current window - // Strip angle brackets from URIs that the RSP engine may include let mut rsp_vel_map: HashMap = HashMap::new(); for binding in ¤t_window { if let Some(acct_uri) = binding.get("account") { @@ -1032,13 +967,11 @@ fn run_simulation( } for (tx, is_injected_fraud, tx_uri, acct_uri) in &step_txs { - // windowVelocity: RSP-observed count, not the event-embedded velocity_1h let window_vel = rsp_vel_map .get(tx.account_id.as_str()) .copied() .unwrap_or(0); - // Write transaction features and RSP-derived window velocity to RDF database.add_triple_parts( &tx_uri, "http://www.w3.org/1999/02/22-rdf-syntax-ns#type", @@ -1086,7 +1019,6 @@ fn run_simulation( &tx.card_present.to_string(), ); - // Bridge triple: connects RSP stream layer to Datalog (consumed by R1b) database.add_triple_parts( &tx_uri, "http://fraud.example.org/windowVelocity", @@ -1098,14 +1030,10 @@ fn run_simulation( .or_insert_with(AccountHistory::new); let hist_fraud_count = history.recent_fraud_count; - // Pass 1: symbolic rules on raw features let flags_p1 = run_reasoning(database, pass1_rules, &tx_uri); - // Write Pass-1 flags and account history as numeric RDF triples - // so ML.PREDICT INPUT can SELECT them as features write_numeric_flags_to_db(database, &tx_uri, &flags_p1, hist_fraud_count); - // ML.PREDICT — driven by rule_ml_fraud_predict() clause let (fraud_score, t_r2p, t_p2r) = run_ml_predict_from_clause( database, &tx_uri, @@ -1116,7 +1044,6 @@ fn run_simulation( println!(" [Timing] Rust -> Python overhead: {:?}", t_r2p); println!(" [Timing] Python -> Rust overhead: {:?}", t_p2r); - // Write ML score back to RDF for Pass-2 Datalog rules (R6, R7) let ml_score_int = (fraud_score * 100.0).round() as u32; database.add_triple_parts( &tx_uri, @@ -1213,7 +1140,7 @@ fn run_simulation( } } -fn run_reasoning(database: &mut SparqlDatabase, rules: &[Rule], tx_uri: &str) -> Vec { +fn run_reasoning(database: &mut KolibrieDB, rules: &[Rule], tx_uri: &str) -> Vec { let decoded_triples: Vec<(String, String, String)> = { let dict = database.dictionary(); database @@ -1375,7 +1302,7 @@ fn try_open_firewall(port: u16) { } fn compile_fraud_rules( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result<(Vec, Vec), Box> { let (rule_velocity, _) = process_rule_definition(&rule_suspicious_velocity(), database)?; let (rule_amount, _) = process_rule_definition(&rule_suspicious_amount(), database)?; @@ -1437,7 +1364,6 @@ fn process_demo_transaction( "FRAUD scenario", ), DemoTransferScenario::Suspicious => { - // Generate a domestic high-amount/high-velocity profile; the verdict still comes from rules + ML. let seed = count as u64 + 1; ( Transaction { @@ -1733,11 +1659,6 @@ a.back{{display:block;text-align:center;margin-top:22px;font-size:14px;color:#1a ) } -// -// Pseudo-random number generator (splitmix64 finalisation mix) -// -// Produces a well-distributed f64 in [0.0, 1.0) from two u64 seeds. -// Different seed constants per feature slot ensure independence. fn prng(a: u64, b: u64) -> f64 { let mut x = a .wrapping_mul(0x9e3779b97f4a7c15) @@ -1750,20 +1671,6 @@ fn prng(a: u64, b: u64) -> f64 { (x >> 11) as f64 / (1u64 << 53) as f64 } -// -// Stochastic transaction synthesis -// -// Scenario distribution (seed-determined, no hardcoded patterns): -// [0.00, 0.25) -> FRAUD late-night, foreign, CNP, high-risk merchant, -// large amount, velocity burst -> ML ≈ 1.0 -// [0.25, 0.45) -> SUSPICIOUS large amount + high velocity (R5 -> risk:high), -// but domestic / daytime / card-present -> ML ≈ 0.0 -// [0.45, 0.65) -> REVIEW one flag fires (highVelocity or highMerchantRisk), -// amount < 1000 so R5 cannot chain -> no risk:high -// [0.65, 1.00) -> CLEAR all features below every rule threshold -> ML ≈ 0.0 -// -// is_injected_fraud is true only for the FRAUD scenario, keeping the console -// marker "← injected fraud" unambiguous. fn synthesise_transaction( time_step: u64, account_idx: usize, @@ -1774,13 +1681,12 @@ fn synthesise_transaction( let scenario = prng(time_step.wrapping_mul(31), account_idx as u64 + 1); let (tx, is_fraud) = if scenario < 0.25 { - // FRAUD: all signals active *vel_entry = (*vel_entry + 4).min(15); let vel = *vel_entry; let amount = 1_200.0 + prng(time_step, account_idx as u64 + 100) * 8_000.0; - let hour = (prng(time_step, account_idx as u64 + 101) * 4.0) as u8; // 0–3 + let hour = (prng(time_step, account_idx as u64 + 101) * 4.0) as u8; let dow = (prng(time_step, account_idx as u64 + 102) * 7.0) as u8; - let merch_risk = 75 + (prng(time_step, account_idx as u64 + 103) * 20.0) as u32; // 75–95 + let merch_risk = 75 + (prng(time_step, account_idx as u64 + 103) * 20.0) as u32; let distance_km = 1_000.0 + prng(time_step, account_idx as u64 + 104) * 9_000.0; let tx = Transaction { tx_id: format!("TX-{}-{}-FRAUD", account, time_step), @@ -1796,14 +1702,12 @@ fn synthesise_transaction( }; (tx, true) } else if scenario < 0.45 { - // SUSPICIOUS: R5 fires (amount > 1000 ∧ vel > 5 -> risk:high), - // but domestic context keeps ML ≈ 0.0 -> fuse: risk:high ∧ P ≤ 0.50. - let vel = 6 + (prng(time_step, account_idx as u64 + 200) * 5.0) as u32; // 6–10 + let vel = 6 + (prng(time_step, account_idx as u64 + 200) * 5.0) as u32; *vel_entry = vel; let amount = 1_050.0 + prng(time_step, account_idx as u64 + 201) * 2_000.0; - let hour = 9 + (prng(time_step, account_idx as u64 + 202) * 9.0) as u8; // 9–17 - let dow = (prng(time_step, account_idx as u64 + 203) * 5.0) as u8; // Mon–Fri - let merch_risk = 15 + (prng(time_step, account_idx as u64 + 204) * 45.0) as u32; // 15–60 + let hour = 9 + (prng(time_step, account_idx as u64 + 202) * 9.0) as u8; + let dow = (prng(time_step, account_idx as u64 + 203) * 5.0) as u8; + let merch_risk = 15 + (prng(time_step, account_idx as u64 + 204) * 45.0) as u32; let distance_km = 2.0 + prng(time_step, account_idx as u64 + 205) * 30.0; let tx = Transaction { tx_id: format!("TX-{}-{}-SUSP", account, time_step), @@ -1819,20 +1723,18 @@ fn synthesise_transaction( }; (tx, false) } else if scenario < 0.65 { - // REVIEW: one flag fires but amount < 1000 prevents R5 from chaining - // Sub-variant a: highMerchantRisk only or Sub-variant b: highVelocity only let sub = prng(time_step, account_idx as u64 + 300); let (merch_risk, vel) = if sub < 0.5 { *vel_entry = (*vel_entry).saturating_sub(1).min(4); - let mr = 71 + (prng(time_step, account_idx as u64 + 301) * 24.0) as u32; // 71–95 + let mr = 71 + (prng(time_step, account_idx as u64 + 301) * 24.0) as u32; (mr, *vel_entry) } else { - let v = 6 + (prng(time_step, account_idx as u64 + 302) * 5.0) as u32; // 6–10 + let v = 6 + (prng(time_step, account_idx as u64 + 302) * 5.0) as u32; *vel_entry = v; - let mr = 10 + (prng(time_step, account_idx as u64 + 303) * 45.0) as u32; // 10–55 + let mr = 10 + (prng(time_step, account_idx as u64 + 303) * 45.0) as u32; (mr, v) }; - let amount = 20.0 + prng(time_step, account_idx as u64 + 304) * 920.0; // < 1000 + let amount = 20.0 + prng(time_step, account_idx as u64 + 304) * 920.0; let hour = 8 + (prng(time_step, account_idx as u64 + 305) * 12.0) as u8; let dow = (prng(time_step, account_idx as u64 + 306) * 7.0) as u8; let distance_km = 1.0 + prng(time_step, account_idx as u64 + 307) * 40.0; @@ -1850,10 +1752,9 @@ fn synthesise_transaction( }; (tx, false) } else { - // CLEAR: reset velocity to baseline so accumulated FRAUD velocity doesn't pollute this scenario *vel_entry = 1; let vel = 1u32; - let amount = 10.0 + prng(time_step, account_idx as u64 + 400) * 600.0; // < 1000 + let amount = 10.0 + prng(time_step, account_idx as u64 + 400) * 600.0; let hour = 8 + (prng(time_step, account_idx as u64 + 401) * 13.0) as u8; let dow = (prng(time_step, account_idx as u64 + 402) * 7.0) as u8; let merch_risk = 5 + (prng(time_step, account_idx as u64 + 403) * 50.0) as u32; diff --git a/kolibrie/examples/real_scenario/mqtt_example.rs b/kolibrie/examples/real_scenario/mqtt_example.rs index 88deb283..102b2d0f 100644 --- a/kolibrie/examples/real_scenario/mqtt_example.rs +++ b/kolibrie/examples/real_scenario/mqtt_example.rs @@ -10,10 +10,10 @@ use chrono::Utc; use datalog::reasoning::Reasoner; +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; use kolibrie::parser::convert_combined_rule; use kolibrie::prelude::*; -use kolibrie::sparql_database::SparqlDatabase; use rumqttc::{Client, MqttOptions, QoS}; use serde::{Deserialize, Serialize}; use shared::terms::Term; @@ -23,7 +23,6 @@ use std::thread; use std::time::Instant; use std::time::{Duration, SystemTime}; -// Define the MQTT message structures to match Python output #[derive(Debug, Deserialize, Serialize, Clone)] struct SensorData { id: String, @@ -50,7 +49,6 @@ struct CameraData { detections: Vec, } -// This structure will store sensor data as it arrives from MQTT struct MqttSensorState { room_data: HashMap, sensors: HashMap, @@ -72,13 +70,10 @@ impl MqttSensorState { } } - // Helper method to update room-level aggregated data fn update_aggregated_data(&mut self) { - // Initialize with default values - self.room_data.insert("noiseLevel".to_string(), 30.0); // Default noise level - self.room_data.insert("lightLevel".to_string(), 70.0); // Default light level + self.room_data.insert("noiseLevel".to_string(), 30.0); + self.room_data.insert("lightLevel".to_string(), 70.0); - // Calculate average noise level from noise sensors let mut total_noise = 0.0; let mut noise_count = 0; @@ -96,7 +91,6 @@ impl MqttSensorState { ); } - // Calculate light level as an aggregate of PIR sensors (as an approximation) let mut total_light = 0.0; let mut light_count = 0; @@ -115,7 +109,6 @@ impl MqttSensorState { } } - // Helper method to find the highest confidence object type from camera detections fn get_highest_confidence_object_type(&self) -> Option<(String, f64)> { let mut highest_conf_type = None; let mut highest_conf = 0.0; @@ -133,7 +126,6 @@ impl MqttSensorState { } } -// Function to start MQTT subscription in a background thread fn start_mqtt_collection(sensor_state: Arc>) -> thread::JoinHandle<()> { thread::spawn(move || { let mut mqttoptions = MqttOptions::new("rust_kolibrie_client", "localhost", 1883); @@ -142,7 +134,6 @@ fn start_mqtt_collection(sensor_state: Arc>) -> thread::J let (mut client, mut connection) = Client::new(mqttoptions, 10); - // Subscribe to the topics that our Python script is publishing to client.subscribe("sensors/pir/#", QoS::AtLeastOnce).unwrap(); client .subscribe("sensors/noise/#", QoS::AtLeastOnce) @@ -158,7 +149,6 @@ fn start_mqtt_collection(sensor_state: Arc>) -> thread::J if let Ok(rumqttc::Event::Incoming(rumqttc::Packet::Publish(msg))) = notification { let topic = msg.topic.clone(); if let Ok(payload) = String::from_utf8(msg.payload.to_vec()) { - // Parse the topic to determine message type let topic_parts: Vec<&str> = topic.split('/').collect(); if topic_parts.len() >= 2 { @@ -193,24 +183,20 @@ fn start_mqtt_collection(sensor_state: Arc>) -> thread::J } } - // Update room-level aggregated data sensor_state.update_aggregated_data(); } } } } - // Small sleep to prevent CPU spinning thread::sleep(Duration::from_millis(10)); } }) } -// Convert MQTT data to RDF triples fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { let mut rdf_parts = Vec::new(); - // Add XML header rdf_parts.push( r#" String { .to_string(), ); - // Add room definition let current_datetime = Utc::now().format("%Y-%m-%d %H:%M:%S").to_string(); let room_light_level = sensor_state .room_data @@ -251,7 +236,6 @@ fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { room_light_level, room_noise_level, current_datetime )); - // Define a mapping from MQTT sensor IDs to grid positions and descriptions let sensor_positions: HashMap<&str, (u32, u32, &str)> = [ ("sensor_nw_pir", (0, 0, "MotionSensor1")), ("sensor_ne_pir", (150, 0, "MotionSensor2")), @@ -266,7 +250,6 @@ fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { .cloned() .collect(); - // Add motion and noise sensors for (id, sensor) in &sensor_state.sensors { if let Some(&(grid_x, grid_y, description)) = sensor_positions.get(id.as_str()) { match sensor.sensor_type.as_str() { @@ -333,20 +316,16 @@ fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { } } - // Get the highest confidence object type from detections (if any) let highest_conf_obj = sensor_state.get_highest_confidence_object_type(); println!("Highest confidence object: {:?}", highest_conf_obj); - // Add camera data for (id, camera) in &sensor_state.cameras { if let Some(&(grid_x, grid_y, description)) = sensor_positions.get(id.as_str()) { - // Check if any person was detected by this camera let person_detected = camera .detections .iter() .any(|d| d.detection_type == "person"); - // If it's the static camera if id == "static_cam" { rdf_parts.push(format!( r#" @@ -363,10 +342,7 @@ fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { "#, id, description, id, grid_x, grid_y, person_detected, current_datetime )); - } - // If it's the rotating camera - else if id == "rotating_cam1" { - // Calculate a deterministic rotation angle based on the timestamp + } else if id == "rotating_cam1" { let angle = (camera.timestamp % 360) as u32; rdf_parts.push(format!( @@ -388,9 +364,7 @@ fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { )); } - // Process all detections to create detection events for (i, detection) in camera.detections.iter().enumerate() { - // Calculate a time for the detection based on the camera timestamp let detection_time = { let timestamp_seconds = camera.timestamp / 1000; let hour = (timestamp_seconds / 3600) % 24; @@ -398,7 +372,6 @@ fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { format!("{:02}:{:02}", hour, minute) }; - // Use the detection's object type directly rdf_parts.push(format!( r#" @@ -422,17 +395,15 @@ fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { } } - // Define time window definitions for different categories let object_types = ["car", "bus", "truck", "person"]; for object_type in object_types.iter() { - // Different time slots for different object types let (start_time1, end_time1, start_time2, end_time2) = match *object_type { "car" => ("08:00", "12:00", "14:00", "18:00"), "bus" => ("07:00", "10:00", "15:00", "20:00"), "truck" => ("10:00", "14:00", "16:00", "19:00"), - "person" => ("07:00", "22:00", "00:00", "00:00"), // Person allowed most of the day - _ => ("09:00", "17:00", "00:00", "00:00"), // Default for other types + "person" => ("07:00", "22:00", "00:00", "00:00"), + _ => ("09:00", "17:00", "00:00", "00:00"), }; rdf_parts.push(format!( @@ -454,38 +425,29 @@ fn mqtt_data_to_rdf(sensor_state: &MqttSensorState) -> String { )); } - // Close the RDF rdf_parts.push("".to_string()); - // Join all parts and return rdf_parts.join("\n") } fn main() { println!("Starting continuous MQTT data monitoring..."); - // Create a shared state for our MQTT sensor data let sensor_state = Arc::new(Mutex::new(MqttSensorState::new())); - // Start MQTT collection in background let _mqtt_thread = start_mqtt_collection(sensor_state.clone()); - // Wait a bit for initial data to arrive println!("Waiting for initial data..."); - thread::sleep(Duration::from_secs(10)); // Increased to 10 seconds to collect more initial data + thread::sleep(Duration::from_secs(10)); - // Create database and knowledge graph let mut database; let mut kg; - // Main processing loop let mut last_update_time = Instant::now(); - let update_interval = Duration::from_secs(10); // Process updates every 10 seconds + let update_interval = Duration::from_secs(10); loop { - // Check if it's time to process updates if last_update_time.elapsed() >= update_interval { - // Check if we have new data to process let should_process; { @@ -500,23 +462,18 @@ fn main() { if should_process { println!("\n=== Processing updated sensor data ==="); - // Convert the collected MQTT data to RDF/XML let rdf_xml_data = mqtt_data_to_rdf(&sensor_state.lock().unwrap()); println!("Generated RDF/XML data from MQTT messages"); - // Create fresh database and knowledge graph for this update - database = SparqlDatabase::new(); + database = KolibrieDB::new(); kg = Reasoner::new(); - // Parse the RDF data into the database database.parse_rdf(&rdf_xml_data); println!( "Updated RDF triples: {} triples", database.default_triple_count() ); - // Load data into knowledge graph - FIXED: Proper lock handling - // Collect triples first to avoid holding lock during iteration let triples_to_add = database.query_default_triples(None, None, None); for triple in triples_to_add { @@ -524,7 +481,7 @@ fn main() { let subject = dict.decode(triple.subject).map(|s| s.to_string()); let predicate = dict.decode(triple.predicate).map(|p| p.to_string()); let object = dict.decode(triple.object).map(|o| o.to_string()); - drop(dict); // Release lock before calling add_abox_triple + drop(dict); if let (Some(s), Some(p), Some(o)) = (subject, predicate, object) { kg.add_abox_triple(&s, &p, &o); @@ -536,7 +493,6 @@ fn main() { database.default_triple_count() ); - // Rule 1: If it's quiet (noise level < 30), use noise for detection let rule1 = r#"PREFIX ex: RULE :UseNoiseSensor :- WHERE { @@ -552,7 +508,6 @@ WHERE { :UseNoiseSensor(?room) }"#; - // Rule 2: Universally define motion sensor for all rooms (will be superseded by noise-based if quiet) let rule2 = r#"PREFIX ex: RULE :DefaultMotionSensor :- WHERE { @@ -567,7 +522,6 @@ WHERE { :DefaultMotionSensor(?room) }"#; - // Rule 3a: If it's not dark (light level > 50), use cameras for detection let rule3a = r#"PREFIX ex: RULE :UseCameraDetection :- WHERE { @@ -583,7 +537,6 @@ WHERE { :UseCameraDetection(?room) }"#; - // Rule 3b: If it's not dark (light level > 50), use cameras for identification let rule3b = r#"PREFIX ex: RULE :UseCameraIdentification :- WHERE { @@ -599,7 +552,6 @@ WHERE { :UseCameraIdentification(?room) }"#; - // Mark all detection events as unauthorized unless they are within their allowed time windows let rule_mark_all_unauthorized = r#"PREFIX ex: RULE :MarkAllEventsUnauthorized :- WHERE { @@ -614,7 +566,6 @@ WHERE { :MarkAllEventsUnauthorized(?event) }"#; - // Execute rules let rules = [rule1, rule2, rule3a, rule3b, rule_mark_all_unauthorized]; println!("Processing {} rules:", rules.len()); @@ -629,25 +580,23 @@ WHERE { { println!("Rule #{} - Successfully parsed", idx + 1); - // FIXED: Acquire write lock for conversion let mut dict = database.dictionary_mut(); let dynamic_rule = convert_combined_rule( rule_def, &mut dict, &combined_query.prefixes, ); - drop(dict); // Release lock + drop(dict); println!("Rule #{} - Adding to knowledge graph", idx + 1); kg.add_rule(dynamic_rule.clone()); - // FIXED: Handle mapping the rule name with proper lock if let Some(first_conclusion) = &dynamic_rule.conclusion.first() { if let Term::Constant(code) = first_conclusion.1 { let dict = database.dictionary(); if let Some(expanded) = dict.decode(code) { let expanded = expanded.to_string(); - drop(dict); // Release lock + drop(dict); if let Some(idx) = expanded.rfind('#') { let local = &expanded[idx + 1..]; @@ -672,12 +621,10 @@ WHERE { } } - // Execute inference println!("Running inference engine..."); let inferred_facts = kg.infer_new_facts_semi_naive(); println!("Inferred {} new fact(s):", inferred_facts.len()); - // FIXED: Proper lock handling for decoding inferred facts let dict = database.dictionary(); for triple in inferred_facts.iter() { let s = dict.decode(triple.subject).unwrap_or_default(); @@ -685,14 +632,12 @@ WHERE { let o = dict.decode(triple.object).unwrap_or_default(); println!(" Inferred: {} {} {}", s, p, o); } - drop(dict); // Release lock before modifying database + drop(dict); - // Add inferred facts to database for triple in inferred_facts.iter() { database.add_triple(triple.clone()); } - // Query for sensors by grid position let query_grid_sensors = r#"PREFIX ex: SELECT ?sensor ?type ?x ?y WHERE { @@ -709,7 +654,6 @@ WHERE { println!("{:?}", row); } - // Query for detection strategies let query_strategies = r#"PREFIX ex: SELECT ?room ?strategy WHERE { @@ -724,7 +668,6 @@ WHERE { println!("{:?}", row); } - // Query for fallback strategies let query_fallbacks = r#"PREFIX ex: SELECT ?room ?strategy WHERE { @@ -739,7 +682,6 @@ WHERE { println!("{:?}", row); } - // Check for active motion detection let query_motion = r#"PREFIX ex: SELECT ?sensor ?detected WHERE { @@ -756,7 +698,6 @@ WHERE { println!("{:?}", row); } - // Check for detected objects by type let query_detections = r#"PREFIX ex: SELECT ?event ?category ?confidence ?time WHERE { @@ -773,7 +714,6 @@ WHERE { println!("{:?}", row); } - // Check for unauthorized events let query_unauthorized = r#"PREFIX ex: SELECT ?event ?category ?time WHERE { @@ -796,7 +736,6 @@ WHERE { last_update_time = Instant::now(); } - // Sleep a bit to prevent busy-waiting thread::sleep(Duration::from_millis(100)); } } diff --git a/kolibrie/examples/real_scenario/mqtt_real_scenario.rs b/kolibrie/examples/real_scenario/mqtt_real_scenario.rs index 0551ad7f..4c3ab070 100644 --- a/kolibrie/examples/real_scenario/mqtt_real_scenario.rs +++ b/kolibrie/examples/real_scenario/mqtt_real_scenario.rs @@ -10,10 +10,10 @@ use chrono::{Local, Timelike}; use datalog::reasoning::Reasoner; +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; use kolibrie::parser::convert_combined_rule; use kolibrie::prelude::*; -use kolibrie::sparql_database::SparqlDatabase; use rumqttc::{Client, MqttOptions, QoS, RecvTimeoutError}; use serde::{Deserialize, Serialize}; use shared::terms::Term; @@ -23,7 +23,6 @@ use std::thread; use std::time::Instant; use std::time::{Duration, SystemTime}; -// Add PIR sensor data structure #[derive(Debug, Deserialize, Serialize, Clone)] struct PirSensorData { id: String, @@ -33,7 +32,6 @@ struct PirSensorData { intensity: u32, } -// Define the MQTT message structure for object detection #[derive(Debug, Deserialize, Serialize, Clone)] struct Detection { #[serde(rename = "type")] @@ -53,14 +51,12 @@ struct CameraData { triggered_by: Vec, } -// New struct for schedule data #[derive(Debug, Deserialize, Serialize, Clone)] struct ScheduleData { hour: u32, time: String, } -// Store the detection data and state struct SecurityState { cameras: HashMap, schedule: Option, @@ -69,7 +65,6 @@ struct SecurityState { last_alarm_sent: SystemTime, new_data_available: bool, current_alarm_state: bool, - // Throttling parameters pir_sensors: HashMap, alarm_cooldown: Duration, pir_intensity_threshold: u32, @@ -85,14 +80,12 @@ impl SecurityState { last_alarm_sent: SystemTime::now(), new_data_available: false, current_alarm_state: false, - // Set throttling cooldown to 5 seconds pir_sensors: HashMap::new(), alarm_cooldown: Duration::from_secs(5), pir_intensity_threshold: 1, } } - // Check if any detection matches specified vehicle types fn has_vehicle_detection(&self) -> bool { for (_, camera) in &self.cameras { for detection in &camera.detections { @@ -109,7 +102,6 @@ impl SecurityState { false } - // Check if any motion is detected (person object) fn has_motion_detection(&self) -> bool { for (_, camera) in &self.cameras { for detection in &camera.detections { @@ -131,28 +123,24 @@ impl SecurityState { false } - // Check if enough time has passed to send a new alarm fn can_send_alarm(&self) -> bool { match self.last_alarm_sent.elapsed() { Ok(elapsed) => elapsed >= self.alarm_cooldown, - Err(_) => true, // If there's an error getting elapsed time, allow sending + Err(_) => true, } } - // Generate an alarm message with specific field order fn generate_alarm_message( &self, reason: &str, status: &str, detection_types: &[&str], ) -> String { - // Find the relevant detections to include in the alarm let mut detections_json = Vec::new(); let mut camera_ids = Vec::new(); for (camera_id, camera) in &self.cameras { for detection in &camera.detections { - // Only include detections that match the specified types let obj_type = detection.detection_type.to_lowercase(); let type_matches = detection_types.is_empty() || detection_types @@ -160,7 +148,6 @@ impl SecurityState { .any(|t| obj_type.contains(&t.to_lowercase())); if type_matches { - // Create detection JSON with specific field order let mut detection_obj = serde_json::Map::new(); detection_obj.insert( "type".to_string(), @@ -181,7 +168,6 @@ impl SecurityState { } } - // Create the alarm message with fields in the specific order let mut alarm_obj = serde_json::Map::new(); alarm_obj.insert( "timestamp".to_string(), @@ -192,18 +178,16 @@ impl SecurityState { alarm_obj.insert("detections".to_string(), serde_json::json!(detections_json)); alarm_obj.insert("camera_ids".to_string(), serde_json::json!(camera_ids)); - // Convert to JSON string with the ordered fields serde_json::Value::Object(alarm_obj).to_string() } } -// Start MQTT subscription in a background thread, subscribe to PIR topics fn start_mqtt_collection( security_state: Arc>, mqtt_client: Arc>>, ) -> thread::JoinHandle<()> { thread::spawn(move || { - let mqtt_broker = "YOUR_IP_ADDRESS"; // Use the specified broker + let mqtt_broker = "YOUR_IP_ADDRESS"; let mqtt_port = 1883; let client_id = "rust_security_client"; @@ -220,23 +204,18 @@ fn start_mqtt_collection( let dummy_update_interval = Duration::from_secs(10); loop { - // Create a new client connection without using catch_unwind let client_connection_result = Client::new(mqttoptions.clone(), 10); - // Normal Client::new returns a tuple, not a Result, so we're just proceeding with it let (mut client, mut connection) = client_connection_result; - // Store client reference to allow publishing from main thread *mqtt_client.lock().unwrap() = Some(client.clone()); - // Subscribe to both camera detection topics, schedule topic, and PIR sensor topics let subscription_result_cam0 = client.subscribe("camera/detections/0", QoS::AtLeastOnce); let subscription_result_cam1 = client.subscribe("camera/detections/1", QoS::AtLeastOnce); let subscription_result_schedule = client.subscribe("schedule", QoS::AtLeastOnce); - // Subscribe to PIR sensor topics let subscription_result_pir_nw = client.subscribe("sensors/pir/sensor_nw_pir", QoS::AtLeastOnce); let subscription_result_pir_sw = @@ -257,7 +236,6 @@ fn start_mqtt_collection( if subscriptions_ok { println!("Connected to MQTT broker and subscribed to required topics"); - // Process incoming messages let mut connection_active = true; while connection_active { match connection.recv_timeout(Duration::from_millis(100)) { @@ -282,7 +260,6 @@ fn start_mqtt_collection( let mut state = security_state.lock().unwrap(); - // Use the camera ID from the data or topic if empty let camera_id = if camera_data.id.is_empty() { if topic == "camera/detections/0" { "camera_0".to_string() @@ -293,7 +270,6 @@ fn start_mqtt_collection( camera_data.id.clone() }; - // Track which camera the data came from let mut camera_data = camera_data.clone(); camera_data.id = camera_id.clone(); @@ -323,7 +299,6 @@ fn start_mqtt_collection( ); } } else if topic.starts_with("sensors/pir/") { - // Handle PIR sensor data if let Ok(pir_data) = serde_json::from_str::(&payload) { @@ -344,9 +319,7 @@ fn start_mqtt_collection( } } Err(e) => { - // Only show timeout errors once per minute to reduce log spam if let RecvTimeoutError::Timeout = e { - // Timeout is normal, just continue continue; } else { println!("MQTT connection error: {:?}", e); @@ -355,7 +328,6 @@ fn start_mqtt_collection( } } - // Small sleep to prevent CPU spinning thread::sleep(Duration::from_millis(10)); } } else { @@ -373,11 +345,9 @@ fn start_mqtt_collection( println!(" - sensors/pir/sensor_nw_pir: {:?}", e); } - // During broker connectivity issues, insert dummy data for testing if last_dummy_update.elapsed() > dummy_update_interval { println!("Adding test data for development (broker unavailable)"); - // Use the correct format for the dummy data let dummy_camera_data = CameraData { id: "static_cam".to_string(), timestamp: chrono::Utc::now().timestamp_millis() as u64, @@ -398,12 +368,11 @@ fn start_mqtt_collection( triggered_by: vec![], }; - // Add dummy PIR sensor data let dummy_pir_data = PirSensorData { id: "sensor_nw_pir".to_string(), t: chrono::Utc::now().timestamp_millis() as u64, sensor_type: "PIR".to_string(), - intensity: 2, // Example intensity value + intensity: 2, }; let dummy_schedule_data = ScheduleData { @@ -426,14 +395,12 @@ fn start_mqtt_collection( } } - // Wait before reconnection attempt println!("MQTT connection lost or failed. Reconnecting in 5 seconds..."); thread::sleep(Duration::from_secs(5)); } }) } -// Send an MQTT message with the given topic and payload fn send_mqtt_message(mqtt_client: &Arc>>, topic: &str, payload: &str) -> bool { if let Some(client) = &mut *mqtt_client.lock().unwrap() { match client.publish(topic, QoS::AtLeastOnce, false, payload.as_bytes()) { @@ -452,11 +419,9 @@ fn send_mqtt_message(mqtt_client: &Arc>>, topic: &str, payl } } -// Convert detection data to RDF triples fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> String { let mut rdf_parts = Vec::new(); - // Add XML header rdf_parts.push( r#" ) -> .to_string(), ); - // Get current time or use the test time if provided let (current_time_str, hour, _minute, date_str) = if let Some(test_time) = test_time { - // Parse the test time in format HH:MM let parts: Vec<&str> = test_time.split(':').collect(); if parts.len() == 2 { let hour: u32 = parts[0].parse().unwrap_or(0); @@ -478,7 +441,6 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> Local::now().format("%Y-%m-%d").to_string(), ) } else { - // Fallback to current time if test_time is invalid let now = Local::now(); ( format!("{:02}:{:02}", now.hour(), now.minute()), @@ -488,9 +450,8 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> ) } } else if let Some(schedule) = &security_state.schedule { - // Use schedule time if available let hour = schedule.hour; - let minute = 0; // Schedule doesn't provide minutes, so default to 0 + let minute = 0; ( format!("{:02}:{:02}", hour, minute), hour, @@ -498,7 +459,6 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> Local::now().format("%Y-%m-%d").to_string(), ) } else { - // Use current local time let now = Local::now(); ( format!("{:02}:{:02}", now.hour(), now.minute()), @@ -508,7 +468,6 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> ) }; - // Add environment definition with custom time rdf_parts.push(format!( r#" @@ -521,7 +480,6 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> current_time_str, hour, date_str )); - // Add schedule information if available if let Some(schedule) = &security_state.schedule { rdf_parts.push(format!( r#" @@ -534,7 +492,6 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> )); } - // Add security rule time windows rdf_parts.push( r#" @@ -562,9 +519,7 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> .to_string(), ); - // Process all camera data and detections for (camera_id, camera_data) in &security_state.cameras { - // Add camera definition rdf_parts.push(format!( r#" @@ -576,11 +531,9 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> camera_id, camera_data.status, camera_data.timestamp )); - // Add each detection as a separate event for (i, detection) in camera_data.detections.iter().enumerate() { let detection_id = format!("Detection_{}_{}", camera_id, i); - // Convert timestamp to time format for rule comparison let detection_time = { let timestamp_seconds = camera_data.timestamp / 1000; let hour = (timestamp_seconds / 3600) % 24; @@ -608,14 +561,11 @@ fn detections_to_rdf(security_state: &SecurityState, test_time: Option<&str>) -> } } - // Close the RDF rdf_parts.push("".to_string()); - // Join all parts and return rdf_parts.join("\n") } -// Check if a time is within a specified range (handles overnight ranges) fn time_in_range(time: &str, start: &str, end: &str) -> bool { let time_parts: Vec<&str> = time.split(':').collect(); let start_parts: Vec<&str> = start.split(':').collect(); @@ -637,12 +587,9 @@ fn time_in_range(time: &str, start: &str, end: &str) -> bool { let end_minute: u32 = end_parts[1].parse().unwrap_or(0); let end_minutes = end_hour * 60 + end_minute; - // Handle overnight ranges (end time less than start time) if end_minutes < start_minutes { - // Time is in range if it's after start OR before end time_minutes >= start_minutes || time_minutes <= end_minutes } else { - // Normal range check time_minutes >= start_minutes && time_minutes <= end_minutes } } @@ -650,32 +597,24 @@ fn time_in_range(time: &str, start: &str, end: &str) -> bool { fn main() { println!("Starting security monitoring system with MQTT object detection..."); - // Create a shared state for our security data let security_state = Arc::new(Mutex::new(SecurityState::new())); - // Create a shared MQTT client for sending messages let mqtt_client = Arc::new(Mutex::new(None::)); - // Set your test time here (in 24-hour format) - let test_time: Option = None; // Default to using schedule time instead of test time + let test_time: Option = None; - // Start MQTT collection in background let _mqtt_thread = start_mqtt_collection(security_state.clone(), mqtt_client.clone()); println!("Waiting for detection data..."); - // Create database and knowledge graph let mut database; let mut kg; - // Main processing loop let mut last_update_time = Instant::now(); - let update_interval = Duration::from_secs(2); // Process updates every 2 seconds + let update_interval = Duration::from_secs(2); loop { - // Check if it's time to process updates if last_update_time.elapsed() >= update_interval { - // Check if we have new data to process let should_process; { @@ -690,7 +629,6 @@ fn main() { if should_process { println!("\n=== Processing updated detection data ==="); - // Get current time from schedule or system let current_time_str; let is_morning; let is_night_time; @@ -700,7 +638,6 @@ fn main() { { let state = security_state.lock().unwrap(); if let Some(schedule) = &state.schedule { - // Use time from schedule current_time_str = format!("{:02}:00", schedule.hour); time_of_day = schedule.time.clone(); println!( @@ -708,12 +645,10 @@ fn main() { current_time_str, schedule.time ); } else if let Some(ref time) = test_time { - // Use test time current_time_str = time.to_string(); time_of_day = "unknown".to_string(); println!("Using test time: {}", current_time_str); } else { - // Use current system time let now = Local::now(); current_time_str = format!("{:02}:{:02}", now.hour(), now.minute()); time_of_day = if now.hour() >= 5 && now.hour() < 10 { @@ -728,29 +663,23 @@ fn main() { println!("Using current system time: {}", current_time_str); } - // Determine the time periods is_morning = time_in_range(¤t_time_str, "05:00", "10:00"); is_afternoon = time_in_range(¤t_time_str, "10:00", "16:00"); is_evening = time_in_range(¤t_time_str, "16:00", "22:00"); is_night_time = time_in_range(¤t_time_str, "22:00", "05:00"); } - // Convert the collected detection data to RDF/XML - pass the schedule time indirectly let rdf_xml_data = detections_to_rdf(&security_state.lock().unwrap(), None); - // Create fresh database and knowledge graph for this update - database = SparqlDatabase::new(); + database = KolibrieDB::new(); kg = Reasoner::new(); - // Parse the RDF data into the database database.parse_rdf(&rdf_xml_data); println!( "Database loaded with {} triples", database.default_triple_count() ); - // FIXED: Load data into knowledge graph with proper lock handling - // Collect triples first to avoid holding lock let triples_to_add = database.query_default_triples(None, None, None); for triple in triples_to_add { @@ -758,7 +687,7 @@ fn main() { let subject = dict.decode(triple.subject).map(|s| s.to_string()); let predicate = dict.decode(triple.predicate).map(|p| p.to_string()); let object = dict.decode(triple.object).map(|o| o.to_string()); - drop(dict); // Release lock before calling add_abox_triple + drop(dict); if let (Some(s), Some(p), Some(o)) = (subject, predicate, object) { kg.add_abox_triple(&s, &p, &o); @@ -767,7 +696,6 @@ fn main() { println!("KnowledgeGraph loaded"); - // Define rule templates let rule_night_motion_template = r#"PREFIX ex: RULE :UnauthorizedMotion :- WHERE { @@ -824,36 +752,28 @@ WHERE { :AuthorizedDaytime(?detection) }"#; - // Only apply rules based on the current time to avoid conflicts let mut active_rules = Vec::new(); - // Check which time-based rules should be active if is_night_time { - // Night time - apply night motion restriction println!( "Time ({}): Night hours - applying night motion rules", current_time_str ); active_rules.push(rule_night_motion_template); } else if is_morning || is_afternoon { - // Daytime - apply daytime allowed rule println!( "Time ({}): Daytime hours - applying daytime allowed rules", current_time_str ); active_rules.push(rule_daytime_allowed_template); - // Only apply vehicle restrictions from 16:00 to 10:00, but NOT during morning hours (after 5am) if is_morning { - // Morning hours (5am-10am): Special handling let state = security_state.lock().unwrap(); if let Some(schedule) = &state.schedule { - // If the schedule explicitly says "morning", do NOT apply vehicle restrictions if schedule.time == "morning" { println!( "Morning schedule detected: Vehicle restrictions DISABLED" ); - // Do not add vehicle restriction rule } else { println!( "Time ({}): Early morning hours - applying vehicle rules", @@ -862,7 +782,6 @@ WHERE { active_rules.push(rule_vehicle_restriction_template); } } else { - // No schedule info available, use standard logic println!( "Time ({}): Early morning hours - applying vehicle rules", current_time_str @@ -871,7 +790,6 @@ WHERE { } } } else if is_evening { - // Evening hours (16:00-22:00) - apply vehicle restrictions println!( "Time ({}): Evening hours - applying vehicle rules", current_time_str @@ -879,7 +797,6 @@ WHERE { active_rules.push(rule_vehicle_restriction_template); } - // Process only the active rules println!("Processing {} active security rules...", active_rules.len()); for (idx, rule) in active_rules.iter().enumerate() { match parse_combined_query(rule) { @@ -889,26 +806,24 @@ WHERE { .expect("expected at most one RULE block") .cloned() { - // FIXED: Acquire write lock for conversion let mut dict = database.dictionary_mut(); let dynamic_rule = convert_combined_rule( rule.clone(), &mut dict, &combined_query.prefixes, ); - drop(dict); // Release lock + drop(dict); println!("Dynamic rule #{}: {:#?}", idx + 1, dynamic_rule); kg.add_rule(dynamic_rule.clone()); println!("Rule #{} added to KnowledgeGraph.", idx + 1); - // FIXED: Handle mapping the rule name with proper lock if let Some(first_conclusion) = dynamic_rule.conclusion.first() { if let Term::Constant(code) = first_conclusion.1 { let dict = database.dictionary(); if let Some(expanded) = dict.decode(code) { let expanded = expanded.to_string(); - drop(dict); // Release lock + drop(dict); if let Some(idx) = expanded.rfind('#') { let local = &expanded[idx + 1..]; @@ -928,12 +843,10 @@ WHERE { } } - // Execute inference println!("Running inference engine..."); let inferred_facts = kg.infer_new_facts_semi_naive(); println!("Inferred {} new fact(s)", inferred_facts.len()); - // FIXED: Proper lock handling for decoding inferred facts let dict = database.dictionary(); for triple in inferred_facts.iter() { let s = dict.decode(triple.subject).unwrap_or_default(); @@ -941,23 +854,20 @@ WHERE { let o = dict.decode(triple.object).unwrap_or_default(); println!(" Inferred: {} {} {}", s, p, o); } - drop(dict); // Release lock before modifying database + drop(dict); - // Add inferred facts to database for triple in inferred_facts.iter() { database.add_triple(triple.clone()); } - // Direct time-based checks for detections let mut alarm_reason = String::new(); let mut alarm_needed = false; - let mut status = "0"; // Default to authorized + let mut status = "0"; let mut detection_types: Vec<&str> = Vec::new(); { let mut state = security_state.lock().unwrap(); - // Use PIR for morning, afternoon, and night (but NOT evening) let use_pir_sensors = is_morning || is_afternoon || is_night_time @@ -969,26 +879,24 @@ WHERE { println!("Using PIR sensors: {}", use_pir_sensors); if use_pir_sensors { - // Get PIR detection results if state.has_pir_motion() { - // Create appropriate message based on time of day if is_night_time || time_of_day == "night" { alarm_reason = "PIR motion detected during night hours (10pm-5am)".to_string(); - status = "1"; // Unauthorized during night + status = "1"; } else if is_morning || time_of_day == "morning" { alarm_reason = "PIR motion detected during morning hours (5am-10am)" .to_string(); - status = "0"; // Authorized during morning + status = "0"; } else if is_afternoon || time_of_day == "afternoon" { alarm_reason = "PIR motion detected during afternoon hours (10am-4pm)" .to_string(); - status = "0"; // Authorized during afternoon + status = "0"; } else { alarm_reason = "PIR motion detected".to_string(); - status = "0"; // Default to authorized + status = "0"; } alarm_needed = true; @@ -996,102 +904,83 @@ WHERE { println!("PIR motion detected with reason: {}", alarm_reason); } - } - // Use object detection only for EVENING - else if is_evening || time_of_day == "evening" { + } else if is_evening || time_of_day == "evening" { println!("Using camera object detection for evening hours"); - // Check for night time motion detection (10pm to 5am) if is_night_time && state.has_motion_detection() { alarm_reason = "Motion detected during restricted hours (10pm-5am)".to_string(); alarm_needed = true; - status = "1"; // Unauthorized + status = "1"; detection_types.push("person"); detection_types.push("teddy bear"); - } - // Check for vehicle detection between 4pm and 5am - else if time_in_range(¤t_time_str, "16:00", "05:00") + } else if time_in_range(¤t_time_str, "16:00", "05:00") && state.has_vehicle_detection() { alarm_reason = "Vehicle detected during restricted hours (4pm-5am)".to_string(); alarm_needed = true; - status = "1"; // Unauthorized + status = "1"; detection_types.extend(&["car", "bus", "truck", "train"]); - } - // Special handling for morning hours (5am-10am) - else if is_morning { + } else if is_morning { if let Some(schedule) = &state.schedule { - // If it's explicitly "morning" according to schedule, allow vehicles if schedule.time == "morning" { if state.has_motion_detection() || state.has_vehicle_detection() { alarm_reason = "Detection during morning hours (5am-10am)".to_string(); alarm_needed = true; - status = "0"; // Authorized - detection_types = Vec::new(); // Include all detections + status = "0"; + detection_types = Vec::new(); } - } - // Otherwise apply vehicle restrictions - else if state.has_vehicle_detection() { + } else if state.has_vehicle_detection() { alarm_reason = "Vehicle detected during early morning restricted hours (5am-10am)".to_string(); alarm_needed = true; - status = "1"; // Unauthorized + status = "1"; detection_types.extend(&["car", "bus", "truck", "train"]); } else if state.has_motion_detection() { alarm_reason = "Motion detected during allowed morning hours".to_string(); alarm_needed = true; - status = "0"; // Authorized + status = "0"; detection_types.push("person"); detection_types.push("teddy bear"); } - } - // No schedule available - use standard rules - else if state.has_vehicle_detection() { + } else if state.has_vehicle_detection() { alarm_reason = "Vehicle detected during early morning restricted hours (5am-10am)".to_string(); alarm_needed = true; - status = "1"; // Unauthorized + status = "1"; detection_types.extend(&["car", "bus", "truck", "train"]); } else if state.has_motion_detection() { alarm_reason = "Motion detected during allowed morning hours".to_string(); alarm_needed = true; - status = "0"; // Authorized + status = "0"; detection_types.push("person"); detection_types.push("teddy bear"); } - } - // Daytime allowed detections (10am to 4pm) - else if is_afternoon + } else if is_afternoon && (state.has_motion_detection() || state.has_vehicle_detection()) { alarm_reason = "Detection during allowed daytime hours (10am-4pm)".to_string(); alarm_needed = true; - status = "0"; // Authorized - detection_types = Vec::new(); // Include all detections - } - // Evening detection (16:00-22:00) - handle as original code - else if is_evening { - // Check for person detection + status = "0"; + detection_types = Vec::new(); + } else if is_evening { if state.has_motion_detection() { alarm_reason = "Person detected during evening hours (4pm-10pm)".to_string(); alarm_needed = true; - status = "0"; // Authorized + status = "0"; detection_types.push("person"); detection_types.push("teddy bear"); - } - // Check for vehicle detection (restricted) - else if state.has_vehicle_detection() { + } else if state.has_vehicle_detection() { alarm_reason = "Vehicle detected during restricted evening hours (4pm-10pm)" .to_string(); alarm_needed = true; - status = "1"; // Unauthorized + status = "1"; detection_types.extend(&["car", "bus", "truck", "train"]); } } @@ -1099,10 +988,8 @@ WHERE { println!("No active detection sources available - no PIR or camera detection active"); } - // Update alarm state - state.current_alarm_state = alarm_needed && status == "1"; // Only set alarm state for unauthorized + state.current_alarm_state = alarm_needed && status == "1"; - // Check if we can send an alarm message (throttling) if alarm_needed && state.can_send_alarm() { if status == "1" { println!("\n🚨🚨 ALARM TRIGGERED 🚨🚨\n"); @@ -1110,23 +997,18 @@ WHERE { println!("\n✅ AUTHORIZED DETECTION ✅\n"); } - // Generate the alarm message let alarm_message = state.generate_alarm_message(&alarm_reason, status, &detection_types); println!("Sending message to alarm topic: {}", alarm_message); - // Send the message via MQTT to the alarm topic if send_mqtt_message(&mqtt_client, "alarm", &alarm_message) { - // Update the last alarm sent time for throttling state.last_alarm_sent = SystemTime::now(); } } else if alarm_needed { - // Message is needed but we're in the cooldown period println!("Detection condition detected, but in cooldown period. Not sending another message."); } } - // Check authorized detections from SPARQL (keep this part) let query_authorized = r#"PREFIX ex: SELECT ?detection ?type WHERE { @@ -1141,7 +1023,6 @@ WHERE { { println!("\n==> AUTHORIZED DETECTIONS FROM SPARQL:"); - // Get the detection types from the results let types: Vec = authorized_results .iter() .filter_map(|row| { @@ -1153,7 +1034,6 @@ WHERE { }) .collect(); - // Just log println!("Detection during allowed hours (from SPARQL): {:?}", types); for row in authorized_results { @@ -1167,7 +1047,6 @@ WHERE { last_update_time = Instant::now(); } - // Sleep a bit to prevent busy-waiting thread::sleep(Duration::from_millis(100)); } } diff --git a/kolibrie/examples/real_scenario/real_scenario.rs b/kolibrie/examples/real_scenario/real_scenario.rs index 65572361..54ed312a 100644 --- a/kolibrie/examples/real_scenario/real_scenario.rs +++ b/kolibrie/examples/real_scenario/real_scenario.rs @@ -10,27 +10,23 @@ use chrono::Utc; use datalog::reasoning::Reasoner; +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; use kolibrie::parser::convert_combined_rule; use kolibrie::prelude::*; -use kolibrie::sparql_database::SparqlDatabase; use rand::Rng; use shared::terms::Term; /// Generates RDF/XML data with random sensor values but fixed grid positions fn generate_rdf_xml() -> String { - // Create random number generator let mut rng = rand::rng(); - // Get current timestamp let now = Utc::now(); let current_datetime = now.format("%Y-%m-%d %H:%M:%S").to_string(); - // Random light and noise levels for room let room_light_level = rng.random_range(60..95); let room_noise_level = rng.random_range(20..35); - // Generate random motion detection status and other sensor attributes let camera1_motion = rng.random_bool(0.7); let camera2_motion = rng.random_bool(0.4); let motion1_detection = rng.random_bool(0.8); @@ -39,11 +35,9 @@ fn generate_rdf_xml() -> String { let noise1_detection = rng.random_bool(0.3); let noise2_detection = rng.random_bool(0.4); - // Random noise levels for noise sensors let noise1_level = rng.random_range(5..20); let noise2_level = rng.random_range(8..25); - // Random camera attributes let camera1_coverage = match rng.random_range(0..3) { 0 => "Narrow", 1 => "Medium", @@ -53,15 +47,12 @@ fn generate_rdf_xml() -> String { let camera2_angle = rng.random_range(0..360); - // Generate random time for an event let random_hour = rng.random_range(0..24); let random_minute = rng.random_range(0..60); let random_event_time = format!("{:02}:{:02}", random_hour, random_minute); - // System user identifier (anonymous) let system_user = "system"; - // Generate random time slots for allowed access let cat_a_slot1_start = format!("{:02}:00", rng.random_range(7..10)); let cat_a_slot1_end = format!("{:02}:00", rng.random_range(11..14)); let cat_a_slot2_start = format!("{:02}:00", rng.random_range(13..16)); @@ -72,11 +63,9 @@ fn generate_rdf_xml() -> String { let cat_b_slot2_start = format!("{:02}:00", rng.random_range(14..17)); let cat_b_slot2_end = format!("{:02}:00", rng.random_range(18..21)); - // Shared time slot let shared_slot_start = "10:00"; let shared_slot_end = "10:30"; - // Build the RDF/XML content format!( r#" @@ -230,20 +219,16 @@ fn generate_rdf_xml() -> String { "#, - // Room attributes room_light_level, room_noise_level, current_datetime, system_user, - // Camera1 attributes camera1_motion, camera1_coverage, current_datetime, - // Camera2 attributes camera2_motion, camera2_angle, current_datetime, - // MotionSensor1 attributes motion1_detection, if rng.random_bool(0.5) { "High" @@ -251,7 +236,6 @@ fn generate_rdf_xml() -> String { "Medium" }, current_datetime, - // MotionSensor2 attributes motion2_detection, if rng.random_bool(0.5) { "High" @@ -259,7 +243,6 @@ fn generate_rdf_xml() -> String { "Medium" }, current_datetime, - // MotionSensor3 attributes motion3_detection, if rng.random_bool(0.5) { "High" @@ -267,59 +250,49 @@ fn generate_rdf_xml() -> String { "Medium" }, current_datetime, - // NoiseSensor1 attributes noise1_detection, noise1_level, current_datetime, - // NoiseSensor2 attributes noise2_detection, noise2_level, current_datetime, - // CategoryA time slots cat_a_slot1_start, cat_a_slot1_end, cat_a_slot2_start, cat_a_slot2_end, shared_slot_start, shared_slot_end, - // CategoryB time slots cat_b_slot1_start, cat_b_slot1_end, cat_b_slot2_start, cat_b_slot2_end, shared_slot_start, shared_slot_end, - // Detection event time and timestamp random_event_time, current_datetime ) } fn main() { - // Generate RDF/XML data let rdf_xml_data = generate_rdf_xml(); - // Create and populate the database - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(&rdf_xml_data); println!( "Database RDF triples: {:#?}", database.query_default_triples(None, None, None) ); - // FIXED: Load data into knowledge graph with proper lock handling let mut kg = Reasoner::new(); - // Collect triples first to avoid holding lock during iteration let triples_to_add = database.query_default_triples(None, None, None); for triple in triples_to_add { - // Acquire read lock, decode to owned strings, release lock let dict = database.dictionary(); let subject = dict.decode(triple.subject).map(|s| s.to_string()); let predicate = dict.decode(triple.predicate).map(|p| p.to_string()); let object = dict.decode(triple.object).map(|o| o.to_string()); - drop(dict); // Release lock before calling add_abox_triple + drop(dict); if let (Some(s), Some(p), Some(o)) = (subject, predicate, object) { kg.add_abox_triple(&s, &p, &o); @@ -327,7 +300,6 @@ fn main() { } println!("KnowledgeGraph ABox loaded."); - // Rule 1: If it's quiet (noise level < 30), use noise for detection let rule1 = r#"PREFIX ex: RULE :UseNoiseSensor :- CONSTRUCT { @@ -338,7 +310,6 @@ WHERE { FILTER (?level < 30) }"#; - // Rule 2: Universally define motion sensor for all rooms (will be superseded by noise-based if quiet) let rule2 = r#"PREFIX ex: RULE :DefaultMotionSensor :- CONSTRUCT { @@ -348,7 +319,6 @@ WHERE { ?room ex:noiseLevel ?level . }"#; - // Rule 3a: If it's not dark (light level > 50), use cameras for detection let rule3a = r#"PREFIX ex: RULE :UseCameraDetection :- CONSTRUCT { @@ -359,7 +329,6 @@ WHERE { FILTER (?level > 50) }"#; - // Rule 3b: If it's not dark (light level > 50), use cameras for identification let rule3b = r#"PREFIX ex: RULE :UseCameraIdentification :- CONSTRUCT { @@ -370,7 +339,6 @@ WHERE { FILTER (?level > 50) }"#; - // Mark all detection events as unauthorized: let rule_mark_all_unauthorized = r#"PREFIX ex: RULE :MarkAllEventsUnauthorized :- CONSTRUCT { @@ -380,7 +348,6 @@ WHERE { ?event ex:detectedCategory ?person . }"#; - // Override to authorized if event is within the allowed time slot: let rule_mark_authorized_if_within_schedule = r#"PREFIX ex: RULE :MarkAllEventsUnauthorized :- CONSTRUCT { @@ -390,7 +357,6 @@ WHERE { ?event ex:detectedCategory ?person . }"#; - // Execute rules let rules = [ rule1, rule2, @@ -409,21 +375,19 @@ WHERE { .expect("expected at most one RULE block") .cloned() { - // FIXED: Acquire write lock for conversion let mut dict = database.dictionary_mut(); let dynamic_rule = convert_combined_rule(rule_def, &mut dict, &combined_query.prefixes); - drop(dict); // Release lock + drop(dict); println!("Dynamic rule: {:#?}", dynamic_rule); kg.add_rule(dynamic_rule.clone()); - // FIXED: Handle mapping the rule name with proper lock if let Some(first_conclusion) = &dynamic_rule.conclusion.first() { if let Term::Constant(code) = first_conclusion.1 { let dict = database.dictionary(); if let Some(expanded) = dict.decode(code) { let expanded = expanded.to_string(); - drop(dict); // Release lock before inserting + drop(dict); if let Some(idx) = expanded.rfind('#') { let local = &expanded[idx + 1..]; @@ -435,11 +399,9 @@ WHERE { } } - // Execute inference let inferred_facts = kg.infer_new_facts_semi_naive(); println!("Inferred {} new fact(s):", inferred_facts.len()); - // FIXED: Print inferred facts with proper lock handling { let dict = database.dictionary(); for triple in inferred_facts.iter() { @@ -448,14 +410,12 @@ WHERE { let o = dict.decode(triple.object).unwrap_or_default(); println!("{} {} {}", s, p, o); } - } // Lock released here + } - // Add inferred facts to database for triple in inferred_facts.iter() { database.add_triple(triple.clone()); } - // Query for sensors by grid position let query_grid_sensors = r#"PREFIX ex: SELECT ?sensor ?type ?x ?y WHERE { @@ -472,7 +432,6 @@ WHERE { println!("{:?}", row); } - // Original unauthorized query let query_unauthorized = r#"PREFIX ex: SELECT ?event ?time WHERE { diff --git a/kolibrie/examples/replay_profile/replay_profile.rs b/kolibrie/examples/replay_profile/replay_profile.rs index c63d897f..984f954a 100644 --- a/kolibrie/examples/replay_profile/replay_profile.rs +++ b/kolibrie/examples/replay_profile/replay_profile.rs @@ -8,10 +8,8 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -// Usage: cargo run --release --example replay_profile -- [members] [evals] - +use kolibrie::database::{KolibrieDB, PlanningStatsPolicy}; use kolibrie::execute_query::execute; -use kolibrie::sparql_database::{PlanningStatsPolicy, SparqlDatabase}; use std::time::{Duration, Instant}; const BASE: &str = "http://www.semanticweb.org/ontologies/2015/trainbenchmark#"; @@ -35,7 +33,7 @@ SELECT ?switch ?segment WHERE { } "#; -/// The trainbenchmark RouteSensor shape, without its unsupported NOT EXISTS +/// The trainbenchmark RouteSensor shape without its NOT EXISTS clause const SEVEN_PATTERN_JOIN: &str = r#" PREFIX base: PREFIX rdf: @@ -51,7 +49,7 @@ SELECT ?route ?sensor ?swP ?sw WHERE { "#; /// Inserts one stream member -fn insert_member(database: &mut SparqlDatabase, member: usize) { +fn insert_member(database: &mut KolibrieDB, member: usize) { let switch = format!("{BASE}sw_{member}"); let segment = format!("{BASE}seg_{member}"); let sensor = format!("{BASE}sensor_{member}"); @@ -84,7 +82,7 @@ struct Replay { /// Inserts every member, evaluating the query once every `every` members fn replay(members: usize, every: usize, policy: PlanningStatsPolicy, query: &str) -> Replay { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.set_planning_stats_policy(policy); let started = Instant::now(); @@ -130,8 +128,7 @@ fn main() { let members = argument(1, 10_000); let evals = argument(2, 20); - // Ingest alone - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let started = Instant::now(); for member in 0..members { insert_member(&mut database, member); @@ -145,7 +142,6 @@ fn main() { ingest / quads.max(1) as u32 ); - // Cost of one statistics rebuild database.invalidate_stats_cache(); let started = Instant::now(); let _ = database.get_or_build_stats(); @@ -162,7 +158,6 @@ fn main() { } println!("stats rebuild (10x mean): {:?}", started.elapsed() / 10); - // Query cost with warm statistics let _ = execute(JOIN_OF_TWO, &mut database) .expect("query should run") .into_rows(); @@ -197,7 +192,6 @@ fn main() { started.elapsed() / 5 ); - // Writes and queries interleaved let every = 100; let count = members / every; println!("\nreplay shape: {count} evals interleaved with {members} member inserts"); diff --git a/kolibrie/examples/simple_volcano.rs b/kolibrie/examples/simple_volcano.rs index 5e321d69..0af57bf8 100644 --- a/kolibrie/examples/simple_volcano.rs +++ b/kolibrie/examples/simple_volcano.rs @@ -8,7 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; use kolibrie::streamertail_optimizer::operators::logical::LogicalOperator; use kolibrie::streamertail_optimizer::optimizer::Streamertail; use kolibrie::streamertail_optimizer::types::Condition; @@ -19,31 +19,24 @@ use std::time::Instant; fn main() { println!("=== Simple Volcano Optimizer Example ===\n"); - // Create a new database - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // Add some sample data add_sample_data(&mut database); - // Example 1: Simple scan query simple_scan_example(&mut database); - // Example 2: Join query join_example(&mut database); - // Example 3: Filter query filter_example(&mut database); println!("=== Example completed successfully! ==="); } -fn add_sample_data(database: &mut SparqlDatabase) { +fn add_sample_data(database: &mut KolibrieDB) { println!("Adding sample data..."); - // Acquire write lock on dictionary for all encoding operations let mut dict = database.dictionary_mut(); - // Add some triples about people let alice_id = dict.encode("http://example.org/alice"); let bob_id = dict.encode("http://example.org/bob"); let charlie_id = dict.encode("http://example.org/charlie"); @@ -60,10 +53,8 @@ fn add_sample_data(database: &mut SparqlDatabase) { let age_35 = dict.encode("35"); let company_id = dict.encode("http://example.org/company"); - // Release lock early drop(dict); - // Add triples database.add_triple(Triple { subject: alice_id, predicate: name_id, @@ -118,21 +109,19 @@ fn add_sample_data(database: &mut SparqlDatabase) { ); } -fn simple_scan_example(database: &mut SparqlDatabase) { +fn simple_scan_example(database: &mut KolibrieDB) { println!("=== Example 1: Simple Scan ==="); let mut dict = database.dictionary_mut(); let name_id = dict.encode("http://example.org/name"); drop(dict); - // Create a logical plan: scan for all names let logical_plan = LogicalOperator::scan(( Term::Variable("person".to_string()), Term::Constant(name_id), Term::Variable("name".to_string()), )); - // Create optimizer and find best plan let start = Instant::now(); let mut optimizer = Streamertail::new(database); let physical_plan = optimizer.find_best_plan(&logical_plan); @@ -141,7 +130,6 @@ fn simple_scan_example(database: &mut SparqlDatabase) { println!("Optimization completed in {:?}", optimization_time); println!("Physical plan: {:?}\n", physical_plan); - // Execute the plan let start = Instant::now(); let results = physical_plan.execute(database); let execution_time = start.elapsed(); @@ -154,7 +142,7 @@ fn simple_scan_example(database: &mut SparqlDatabase) { println!(); } -fn join_example(database: &mut SparqlDatabase) { +fn join_example(database: &mut KolibrieDB) { println!("=== Example 2: Join Query ==="); let mut dict = database.dictionary_mut(); @@ -162,7 +150,6 @@ fn join_example(database: &mut SparqlDatabase) { let age_id = dict.encode("http://example.org/age"); drop(dict); - // Create a logical plan: join names with ages let name_scan = LogicalOperator::scan(( Term::Variable("person".to_string()), Term::Constant(name_id), @@ -177,7 +164,6 @@ fn join_example(database: &mut SparqlDatabase) { let logical_plan = LogicalOperator::join(name_scan, age_scan); - // Create optimizer and find best plan let start = Instant::now(); let mut optimizer = Streamertail::new(database); let physical_plan = optimizer.find_best_plan(&logical_plan); @@ -186,7 +172,6 @@ fn join_example(database: &mut SparqlDatabase) { println!("Optimization completed in {:?}", optimization_time); println!("Physical plan: {:?}\n", physical_plan); - // Execute the plan let start = Instant::now(); let results = physical_plan.execute(database); let execution_time = start.elapsed(); @@ -199,14 +184,13 @@ fn join_example(database: &mut SparqlDatabase) { println!(); } -fn filter_example(database: &mut SparqlDatabase) { +fn filter_example(database: &mut KolibrieDB) { println!("=== Example 3: Filter Query ==="); let mut dict = database.dictionary_mut(); let name_id = dict.encode("http://example.org/name"); drop(dict); - // Create a logical plan: scan for all names and filter for "Alice" let scan = LogicalOperator::scan(( Term::Variable("person".to_string()), Term::Constant(name_id), @@ -216,7 +200,6 @@ fn filter_example(database: &mut SparqlDatabase) { let condition = Condition::new("name".to_string(), "=".to_string(), "Alice".to_string()); let logical_plan = LogicalOperator::selection(scan, condition); - // Create optimizer and find best plan let start = Instant::now(); let mut optimizer = Streamertail::new(database); let physical_plan = optimizer.find_best_plan(&logical_plan); @@ -225,7 +208,6 @@ fn filter_example(database: &mut SparqlDatabase) { println!("Optimization completed in {:?}", optimization_time); println!("Physical plan: {:?}\n", physical_plan); - // Execute the plan let start = Instant::now(); let results = physical_plan.execute(database); let execution_time = start.elapsed(); diff --git a/kolibrie/examples/sparql_syntax/advanced_join/advanced_join.rs b/kolibrie/examples/sparql_syntax/advanced_join/advanced_join.rs index e4cc71af..9135e65d 100644 --- a/kolibrie/examples/sparql_syntax/advanced_join/advanced_join.rs +++ b/kolibrie/examples/sparql_syntax/advanced_join/advanced_join.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// A little bit advanced join fn advanced_join() { let rdf_data = r#" "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ex: SELECT ?person ?location ?city ?zipcode WHERE {?person ex:worksAt ?location . ?location ex:located ?city . ?location ex:zipcode ?zipcode}"#; @@ -41,7 +40,6 @@ fn advanced_join() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [person, location, city, zip] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/advanced_sparql/advanced.rs b/kolibrie/examples/sparql_syntax/advanced_sparql/advanced.rs index 1e863b08..759986f8 100644 --- a/kolibrie/examples/sparql_syntax/advanced_sparql/advanced.rs +++ b/kolibrie/examples/sparql_syntax/advanced_sparql/advanced.rs @@ -65,7 +65,7 @@ fn query() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ex: SELECT ?person ?location ?city WHERE {?person ex:worksAt ?location . ?location ex:located ?city}"#; diff --git a/kolibrie/examples/sparql_syntax/aggregate_function/avg.rs b/kolibrie/examples/sparql_syntax/aggregate_function/avg.rs index 9c053708..926f6881 100644 --- a/kolibrie/examples/sparql_syntax/aggregate_function/avg.rs +++ b/kolibrie/examples/sparql_syntax/aggregate_function/avg.rs @@ -48,7 +48,7 @@ fn avg() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#" diff --git a/kolibrie/examples/sparql_syntax/aggregate_function/min.rs b/kolibrie/examples/sparql_syntax/aggregate_function/min.rs index 9080111c..6828058a 100644 --- a/kolibrie/examples/sparql_syntax/aggregate_function/min.rs +++ b/kolibrie/examples/sparql_syntax/aggregate_function/min.rs @@ -48,7 +48,7 @@ fn min_aggregate() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ds: SELECT MIN(?salary) AS ?minimum_salary WHERE {?employee ds:annual_salary ?salary} GROUP BY ?minimum_salary"#; @@ -57,7 +57,6 @@ fn min_aggregate() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [salary] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/aggregate_function/sum.rs b/kolibrie/examples/sparql_syntax/aggregate_function/sum.rs index ec67b76c..89874b0f 100644 --- a/kolibrie/examples/sparql_syntax/aggregate_function/sum.rs +++ b/kolibrie/examples/sparql_syntax/aggregate_function/sum.rs @@ -48,7 +48,7 @@ fn sum_aggregate() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX foaf: PREFIX ds: SELECT ?title SUM(?salary) AS ?total_salary WHERE {?employee foaf:title ?title . ?employee ds:annual_salary ?salary} GROUP BY ?title"#; @@ -57,7 +57,6 @@ fn sum_aggregate() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [title, salary] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/combination/combination.rs b/kolibrie/examples/sparql_syntax/combination/combination.rs index e3f9d2b8..b69de0f5 100644 --- a/kolibrie/examples/sparql_syntax/combination/combination.rs +++ b/kolibrie/examples/sparql_syntax/combination/combination.rs @@ -8,9 +8,9 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::prelude::*; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; fn main() { let rdf_xml_data = r#" @@ -44,14 +44,13 @@ fn main() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_xml_data); println!( "Database RDF triples: {:#?}", database.query_default_triples(None, None, None) ); - // Process the rule definition separately let rule_definition = r#"PREFIX ex: RULE :OverheatingAlert :- CONSTRUCT { @@ -84,7 +83,6 @@ WHERE { println!("Failed to process rule: {:?}", rule_result.err()); } - // Execute the separate SELECT query let select_query = r#"PREFIX ex: SELECT ?room WHERE { diff --git a/kolibrie/examples/sparql_syntax/combination/combination_logic_extend.rs b/kolibrie/examples/sparql_syntax/combination/combination_logic_extend.rs index 21833e99..3bbde31b 100644 --- a/kolibrie/examples/sparql_syntax/combination/combination_logic_extend.rs +++ b/kolibrie/examples/sparql_syntax/combination/combination_logic_extend.rs @@ -8,9 +8,9 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::prelude::*; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; fn main() { std::env::set_var("RUST_BACKTRACE", "1"); @@ -45,14 +45,13 @@ fn main() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_xml_data); println!( "Database RDF triples: {:#?}", database.query_default_triples(None, None, None) ); - // Define the rule separately let rule_definition = r#"PREFIX ex: RULE :OverheatingAlert :- CONSTRUCT { @@ -64,7 +63,6 @@ RULE :OverheatingAlert :- FILTER (?temp > 80) }"#; - // Process the rule definition let rule_result = process_rule_definition(rule_definition, &mut database); if let Ok((rule, inferred_facts)) = rule_result { @@ -78,17 +76,15 @@ RULE :OverheatingAlert :- } } else { println!("Failed to process rule definition: {:?}", rule_result.err()); - return; // Exit if rule processing failed + return; } - // Define the SELECT query separately let select_query = r#"PREFIX ex: SELECT ?room ?temp WHERE { ?room ex:overheatingAlert true . }"#; - // Execute the SELECT query that uses the rule let query_results = execute(select_query, &mut database) .expect("query should run") .into_rows(); diff --git a/kolibrie/examples/sparql_syntax/combination/combination_ml.rs b/kolibrie/examples/sparql_syntax/combination/combination_ml.rs index 6842c5ab..50869291 100644 --- a/kolibrie/examples/sparql_syntax/combination/combination_ml.rs +++ b/kolibrie/examples/sparql_syntax/combination/combination_ml.rs @@ -7,10 +7,10 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; use kolibrie::parser::parse_standalone_rule; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; use kolibrie::streamertail_optimizer::operators::LogicalOperator; use kolibrie::streamertail_optimizer::optimizer::Streamertail; use pyo3::prepare_freethreaded_python; @@ -62,9 +62,8 @@ fn main() -> Result<(), Box> { "#; - let mut database = SparqlDatabase::with_ml_context( - kolibrie::ml_policy::MlExecutionContext::from_environment()?, - ); + let mut database = + KolibrieDB::with_ml_context(kolibrie::ml_policy::MlExecutionContext::from_environment()?); database.parse_rdf(rdf_xml_data); database.get_or_build_stats(); println!("Database RDF triples loaded."); @@ -191,7 +190,7 @@ WHERE { fn build_ml_predict_logical_plan( ml_predict: &shared::query::MLPredictClause, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result> { let room_data = extract_data_for_ml(database)?; @@ -216,7 +215,7 @@ fn build_ml_predict_logical_plan( } fn extract_data_for_ml( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result>, Box> { let mut results = Vec::new(); @@ -317,7 +316,7 @@ fn process_ml_results(ml_results: Vec>) -> Vec Result, Box> { let dict = database.dictionary(); let default_triples = database.query_default_triples(None, None, None); @@ -40,12 +39,10 @@ fn extract_traffic_data_from_database( let traffic_data: Vec = default_triples .iter() .filter(|triple| { - // Use dict instead of database.dictionary dict.decode(triple.predicate) .map_or(false, |pred| pred.ends_with("avgVehicleSpeed")) }) .map(|triple| { - // All decode calls use dict let road_id = dict .decode(triple.subject) .unwrap_or_default() @@ -81,7 +78,6 @@ fn extract_traffic_data_from_database( }) .collect(); - // Release lock before printing drop(dict); println!("Found {} traffic data entries", traffic_data.len()); @@ -99,7 +95,6 @@ fn extract_traffic_data_from_database( Ok(traffic_data) } -// Function that handles congestion prediction logic fn predict_congestion( ml_handler: &MLHandler, best_model: &str, @@ -115,7 +110,6 @@ fn predict_congestion( println!("\n[Rust] Building feature vectors..."); let feature_build_start = std::time::Instant::now(); - // Build feature vectors based on selected variables let features: Vec> = traffic_data .iter() .map(|data| { @@ -142,17 +136,15 @@ fn predict_congestion( ); println!("Using features for congestion prediction: {:?}", features); - // Call ML prediction (this now includes detailed timing) let prediction_results = ml_handler.predict(best_model, features)?; - // Extract timing information from the ML prediction result let timing = kolibrie::ml::execute::MLPredictTiming { - total_time: 0.0, // Will be updated by execute_ml_prediction_from_clause - rust_to_python_time: 0.0, // Will be updated by execute_ml_prediction_from_clause + total_time: 0.0, + rust_to_python_time: 0.0, python_preprocessing_time: prediction_results.timing.preprocessing_time, actual_prediction_time: prediction_results.timing.actual_prediction_time, python_postprocessing_time: prediction_results.timing.postprocessing_time, - python_to_rust_time: 0.0, // Will be updated by execute_ml_prediction_from_clause + python_to_rust_time: 0.0, }; println!("\nML Model Performance:"); @@ -169,7 +161,6 @@ fn predict_congestion( prediction_results.performance_metrics.cpu_usage_percent ); - // Create congestion predictions with severity levels let predictions: Vec = traffic_data .iter() .zip(prediction_results.predictions.iter()) @@ -202,17 +193,16 @@ fn predict_congestion( Ok((predictions, timing)) } -// Struct to manage dynamic rule configurations struct DynamicRuleManager { current_rules: HashMap, - database: SparqlDatabase, + database: KolibrieDB, } impl DynamicRuleManager { fn new() -> Self { Self { current_rules: HashMap::new(), - database: SparqlDatabase::with_ml_context(local_ml::trusted_context()), + database: KolibrieDB::with_ml_context(local_ml::trusted_context()), } } @@ -221,7 +211,6 @@ impl DynamicRuleManager { println!("Initial RDF data loaded."); } - // Update rule with new definition fn update_rule( &mut self, rule_name: &str, @@ -281,7 +270,6 @@ impl DynamicRuleManager { } } } else { - // Non-ML rule processing remains unchanged match process_rule_definition(rule_definition, &mut self.database) { Ok((rule, inferred_facts)) => { println!("Rule '{}' processed successfully.", rule_name); @@ -323,7 +311,6 @@ impl DynamicRuleManager { Ok(()) } - // Manual implementation of emergency priority rule fn manually_apply_emergency_priority_rule(&mut self) { println!(" Manually applying emergency priority rule..."); @@ -336,14 +323,12 @@ impl DynamicRuleManager { road_uri, count ); - // Create priority level triple self.add_triple( &road_uri, "http://example.org/traffic#priorityLevel", "HIGH", ); - // Create clearance required triple self.add_triple( &road_uri, "http://example.org/traffic#clearanceRequired", @@ -353,7 +338,6 @@ impl DynamicRuleManager { } } - // Manual implementation of weather congestion rule fn manually_apply_weather_congestion_rule(&mut self) { println!(" Manually applying weather congestion rule..."); @@ -367,14 +351,12 @@ impl DynamicRuleManager { road_uri, congestion_level ); - // Create weather impact triple self.add_triple( &road_uri, "http://example.org/traffic#weatherImpact", "HIGH", ); - // Create adjusted congestion level triple self.add_triple( &road_uri, "http://example.org/traffic#adjustedCongestionLevel", @@ -384,7 +366,6 @@ impl DynamicRuleManager { } } - // Helper functions for manual rule application fn find_roads_with_emergency_vehicles(&self) -> Vec<(String, i32)> { let mut results = Vec::new(); let dict = self.database.dictionary(); @@ -458,7 +439,6 @@ impl DynamicRuleManager { ) { let road_subject = format!("http://example.org/traffic#{}", prediction.road_id); - // Encode with write lock let road_subject_id = { let mut dict = self.database.dictionary_mut(); dict.encode(&road_subject) @@ -480,7 +460,6 @@ impl DynamicRuleManager { .database .resolve_query_term(predicate_str, &self.database.prefixes().clone()); - // Acquire write lock for encoding let mut dict = self.database.dictionary_mut(); let predicate_id = dict.encode(&expanded_predicate); @@ -495,7 +474,7 @@ impl DynamicRuleManager { dict.encode(object_str) }; - drop(dict); // Release write lock + drop(dict); let enhanced_triple = Triple { subject: road_subject_id, @@ -506,7 +485,6 @@ impl DynamicRuleManager { if self.database.insert_triple(&enhanced_triple) { facts.push(enhanced_triple.clone()); - // Acquire read lock for printing let dict = self.database.dictionary(); println!( " Created triple: {} {} {}", @@ -519,20 +497,17 @@ impl DynamicRuleManager { } } - // Pattern match on FilterExpression enum fn prediction_matches_where_clause( &self, prediction: &CongestionPrediction, rule: &CombinedRule, ) -> bool { - // Get the road's data from the database let road_subject = format!("http://example.org/traffic#{}", prediction.road_id); let road_subject_id = { let mut dict = self.database.dictionary_mut(); dict.encode(&road_subject) }; - // Get road data let avg_speed = self .get_road_data(road_subject_id, "avgVehicleSpeed") .unwrap_or(0.0); @@ -545,11 +520,9 @@ impl DynamicRuleManager { prediction.road_id, avg_speed, vehicle_count ); - // Handle different rule types with specific filters let rule_predicate = rule.head.predicate; if rule_predicate.contains("CongestionWithSeverity") { - // Rule: FILTER (?speed < 30) let matches = avg_speed < 30.0; println!( " CongestionWithSeverity filter: speed {:.1} < 30? {}", @@ -559,7 +532,6 @@ impl DynamicRuleManager { } if rule_predicate.contains("IncidentResponse") { - // Rule: FILTER (?speed < 20 && ?count > 100) let speed_matches = avg_speed < 20.0; let count_matches = vehicle_count > 100.0; let overall_matches = speed_matches && count_matches; @@ -572,7 +544,6 @@ impl DynamicRuleManager { return overall_matches; } - // Parse filters properly (for generic cases) let (_, filters, _, _, _) = &rule.body; for filter in filters { @@ -621,7 +592,6 @@ impl DynamicRuleManager { true } - // Helper method to get road data fn get_road_data(&self, road_subject_id: u32, data_type: &str) -> Option { let dict = self.database.dictionary(); @@ -641,7 +611,6 @@ impl DynamicRuleManager { fn add_prediction_to_database(&mut self, prediction: &CongestionPrediction) { let subject = format!("http://example.org/traffic#{}", prediction.road_id); - // Add congestion level let congestion_predicate = "http://example.org/traffic#congestionLevel"; self.add_triple( &subject, @@ -649,11 +618,9 @@ impl DynamicRuleManager { &prediction.congestion_level.to_string(), ); - // Add severity let severity_predicate = "http://example.org/traffic#congestionSeverity"; self.add_triple(&subject, severity_predicate, &prediction.severity); - // Add confidence let confidence_predicate = "http://example.org/traffic#predictionConfidence"; self.add_triple( &subject, @@ -696,10 +663,8 @@ impl DynamicRuleManager { fn main() -> Result<(), Box> { prepare_freethreaded_python(); - // Initialize dynamic rule manager let mut rule_manager = DynamicRuleManager::new(); - // Load initial traffic data let traffic_data = r#" Result<(), Box> { println!("Dynamic Traffic Monitoring System Started"); println!("========================================================="); - // SCENARIO 1: Initial parameterless rule for basic congestion detection println!("\nSCENARIO 1: Basic Congestion Detection"); let basic_rule = r#"PREFIX ex: RULE :DetectCongestion :- @@ -751,7 +715,6 @@ RULE :DetectCongestion :- rule_manager.update_rule("basic_congestion", basic_rule)?; - // Query current congestion levels rule_manager.query( r#"PREFIX ex: SELECT ?road ?level WHERE { @@ -760,9 +723,8 @@ RULE :DetectCongestion :- "Current congestion levels", )?; - // SCENARIO 2: Update rule on the fly - Add severity classification println!("\nSCENARIO 2: Enhanced Rule with Severity (Dynamic Update)"); - std::thread::sleep(std::time::Duration::from_secs(2)); // Simulate time passing + std::thread::sleep(std::time::Duration::from_secs(2)); let enhanced_rule = r#"PREFIX ex: RULE :DetectCongestionWithSeverity :- @@ -789,7 +751,6 @@ RULE :DetectCongestionWithSeverity :- rule_manager.update_rule("enhanced_congestion", enhanced_rule)?; - // Query enhanced results rule_manager.query( r#"PREFIX ex: SELECT ?road ?level WHERE { @@ -814,10 +775,8 @@ RULE :DetectCongestionWithSeverity :- "Traffic alerts", )?; - // SCENARIO 3: Add new data and update rule logic again println!("\nSCENARIO 3: Adding Emergency Vehicles Data and Priority Rules"); - // Add new emergency vehicle data let emergency_data = r#" RULE :EmergencyPriority :- CONSTRUCT { @@ -853,7 +811,6 @@ RULE :EmergencyPriority :- rule_manager.update_rule("emergency_priority", priority_rule)?; - // Weather-aware congestion rule let weather_rule = r#"PREFIX ex: RULE :WeatherAwareCongestion :- CONSTRUCT { @@ -868,7 +825,6 @@ RULE :WeatherAwareCongestion :- rule_manager.update_rule("weather_congestion", weather_rule)?; - // Query comprehensive traffic status with separate queries rule_manager.query( r#"PREFIX ex: SELECT ?road ?congestion WHERE { @@ -901,10 +857,8 @@ RULE :WeatherAwareCongestion :- "Weather conditions", )?; - // SCENARIO 4: Real-time rule modification based on conditions println!("\nSCENARIO 4: Real-time Adaptive Rules"); - // Simulate a traffic incident and modify rules accordingly println!("Traffic incident detected on HighwayA1!"); let incident_rule = r#"PREFIX ex: @@ -933,7 +887,6 @@ RULE :IncidentResponse :- rule_manager.update_rule("incident_response", incident_rule)?; - // Final comprehensive queries rule_manager.query( r#"PREFIX ex: SELECT ?road ?congestion WHERE { @@ -966,7 +919,6 @@ RULE :IncidentResponse :- "Recommended actions", )?; - // Show all active rules rule_manager.list_current_rules(); println!("\nDynamic rule management demonstration completed!"); diff --git a/kolibrie/examples/sparql_syntax/combination/combination_ml_savings.rs b/kolibrie/examples/sparql_syntax/combination/combination_ml_savings.rs index 9cda4414..cbbe30e5 100644 --- a/kolibrie/examples/sparql_syntax/combination/combination_ml_savings.rs +++ b/kolibrie/examples/sparql_syntax/combination/combination_ml_savings.rs @@ -7,11 +7,11 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; use kolibrie::ml::execute::execute_ml_prediction_from_clause; use kolibrie::parser::parse_standalone_rule; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; use ml::MLHandler; use pyo3::prepare_freethreaded_python; use serde::{Deserialize, Serialize}; @@ -36,15 +36,12 @@ struct SavingsPrediction { timestamp: SystemTime, } -// Function to extract financial data from the database fn extract_financial_data_from_database( - database: &SparqlDatabase, + database: &KolibrieDB, ) -> Result, Box> { - // Acquire read lock once at the start let dict = database.dictionary(); let default_triples = database.query_default_triples(None, None, None); - // Extract data from database let financial_data: Vec = default_triples .iter() .filter(|triple| { @@ -66,7 +63,6 @@ fn extract_financial_data_from_database( .parse() .unwrap_or(0.0); - // Find spending and savings_rate let spending = default_triples .iter() .find(|t| { @@ -89,7 +85,7 @@ fn extract_financial_data_from_database( }) .and_then(|t| dict.decode(t.object)) .and_then(|v| v.parse().ok()) - .unwrap_or(0.10); // Default to 10% savings rate if not specified + .unwrap_or(0.10); FinancialData { user_id, @@ -101,7 +97,6 @@ fn extract_financial_data_from_database( }) .collect(); - // Release the lock before printing drop(dict); println!("Found {} financial data entries", financial_data.len()); @@ -115,7 +110,6 @@ fn extract_financial_data_from_database( ); } - // Ensure we have data before proceeding if financial_data.is_empty() { return Err("No input data found for ML prediction".into()); } @@ -123,7 +117,6 @@ fn extract_financial_data_from_database( Ok(financial_data) } -// Function that handles savings prediction logic fn predict_savings( ml_handler: &MLHandler, best_model: &str, @@ -136,21 +129,18 @@ fn predict_savings( ), Box, > { - // Dynamically build feature vectors based on selected variables let features: Vec> = financial_data .iter() .map(|data| { let mut feature_vector = Vec::new(); - // Only include features that were specified in the SELECT clause for feature_name in feature_names { match feature_name.as_str() { "income" => feature_vector.push(data.income), "spending" => feature_vector.push(data.spending), "savings_rate" => feature_vector.push(data.savings_rate), "disposable_income" => feature_vector.push(data.income - data.spending), - // Add other features as needed - _ => {} // Skip unknown features + _ => {} } } @@ -158,27 +148,23 @@ fn predict_savings( }) .collect(); - // Only proceed if we have features to process if features.is_empty() || features[0].is_empty() { return Err("No valid features found for prediction based on SELECT variables".into()); } println!("Using features for prediction: {:?}", features); - // Use the selected model let prediction_results = ml_handler.predict(best_model, features)?; - // Create timing structure let timing = kolibrie::ml::execute::MLPredictTiming { - total_time: 0.0, // Will be updated by execute_ml_prediction_from_clause - rust_to_python_time: 0.0, // Will be updated by execute_ml_prediction_from_clause + total_time: 0.0, + rust_to_python_time: 0.0, python_preprocessing_time: prediction_results.timing.preprocessing_time, actual_prediction_time: prediction_results.timing.actual_prediction_time, python_postprocessing_time: prediction_results.timing.postprocessing_time, - python_to_rust_time: 0.0, // Will be updated by execute_ml_prediction_from_clause + python_to_rust_time: 0.0, }; - // Print performance of the selected model during this prediction println!("\nPerformance during prediction:"); println!( " Prediction Time: {:.4} seconds", @@ -193,7 +179,6 @@ fn predict_savings( prediction_results.performance_metrics.cpu_usage_percent ); - // Create prediction objects let predictions: Vec = financial_data .iter() .zip(prediction_results.predictions.iter()) @@ -212,7 +197,6 @@ fn predict_savings( }) .collect(); - // Print out predictions with explanations for (i, prediction) in predictions.iter().enumerate() { let data = &financial_data[i]; let disposable_income = data.income - data.spending; @@ -228,7 +212,6 @@ fn predict_savings( ); println!(" Confidence: {:.2}%", prediction.confidence * 100.0); - // Provide some basic financial advice based on the prediction if prediction.predicted_savings < 0.0 { println!(" Warning: Negative savings predicted! Consider reducing expenses."); } else if prediction.predicted_savings < (data.income * 0.1) { @@ -282,11 +265,10 @@ fn main() -> Result<(), Box> { "#; - let mut database = SparqlDatabase::with_ml_context(local_ml::trusted_context()); + let mut database = KolibrieDB::with_ml_context(local_ml::trusted_context()); database.parse_rdf(rdf_xml_data); println!("Database RDF triples loaded."); - // Define the rule separately with CONSTRUCT and WHERE clauses let rule_definition = r#"PREFIX finance: PREFIX ex: @@ -311,12 +293,10 @@ RULE :SavingsAlert :- OUTPUT ?predicted_savings )"#; - // Use process_rule_definition to process the rule match process_rule_definition(rule_definition, &mut database) { Ok((rule, inferred_facts)) => { println!("Rule processed successfully."); - // Print rule details println!("Rule details:"); println!(" Premise patterns: {:?}", rule.premise); println!(" Filters: {:?}", rule.filters); @@ -324,14 +304,12 @@ RULE :SavingsAlert :- println!("Inferred {} new fact(s):", inferred_facts.len()); - // Acquire read lock for printing triples let dict = database.dictionary(); for triple in inferred_facts.iter() { println!(" {}", database.triple_to_string(triple, &*dict)); } drop(dict); - // Parse the rule to get ML.PREDICT clause if let Ok((_rest, (parsed_rule, _))) = parse_standalone_rule(rule_definition) { if let Some(ml_predict) = &parsed_rule.ml_predict { println!("Using enhanced ML.PREDICT execution with model comparison..."); @@ -353,10 +331,8 @@ RULE :SavingsAlert :- prediction.confidence ); - // Acquire write lock for encoding and adding triples let mut dict = database.dictionary_mut(); - // Add predictions to database let subject = format!("http://example.org#{}", prediction.user_id); let predicate = "http://example.org/finance#predictedSavings"; let subject_id = dict.encode(&subject); @@ -364,7 +340,7 @@ RULE :SavingsAlert :- let object_id = dict.encode(&prediction.predicted_savings.to_string()); - drop(dict); // Release lock before inserting + drop(dict); database.add_triple(Triple { subject: subject_id, @@ -372,10 +348,8 @@ RULE :SavingsAlert :- object: object_id, }); - // Re-acquire lock for confidence score let mut dict = database.dictionary_mut(); - // Also add confidence score to the database let confidence_predicate = "http://example.org/finance#predictionConfidence"; let confidence_predicate_id = dict.encode(confidence_predicate); @@ -390,10 +364,8 @@ RULE :SavingsAlert :- object: confidence_object_id, }); - // Re-acquire lock for timestamp let mut dict = database.dictionary_mut(); - // Add timestamp to the database let timestamp_predicate = "http://example.org/finance#predictionTimestamp"; let timestamp_str = format!( @@ -413,13 +385,11 @@ RULE :SavingsAlert :- }); } - // Acquire lock for metadata triple let mut dict = database.dictionary_mut(); - // Add an execution metadata triple with current timestamp let metadata_subject = "http://example.org#predictionExecution"; let timestamp_predicate = "http://example.org/metadata#executionTime"; - let timestamp_value = "2025-05-30 14:49:43"; // Current UTC time + let timestamp_value = "2025-05-30 14:49:43"; let metadata_subject_id = dict.encode(metadata_subject); let timestamp_predicate_id = dict.encode(timestamp_predicate); @@ -446,7 +416,6 @@ RULE :SavingsAlert :- } } - // Define the SELECT query to get users with savings alerts let select_query = r#"PREFIX finance: PREFIX ex: @@ -455,7 +424,6 @@ WHERE { ?user ex:savingsAlert ?alert . }"#; - // Execute the SELECT query to get results let query_results = execute(select_query, &mut database) .expect("query should run") .into_rows(); @@ -464,7 +432,6 @@ WHERE { query_results ); - // Execute a query to show predictions for comparison let predictions_query = r#"PREFIX finance: SELECT ?user ?predicted_savings ?confidence WHERE { @@ -477,7 +444,6 @@ WHERE { .into_rows(); println!("ML Predictions in database: {:?}", predictions_results); - // Execute a query to show all user financial data for comparison let all_users_query = r#"PREFIX finance: SELECT ?user ?income ?spending ?savings_rate WHERE { diff --git a/kolibrie/examples/sparql_syntax/combination/combination_stream.rs b/kolibrie/examples/sparql_syntax/combination/combination_stream.rs index c85a601e..c28964df 100644 --- a/kolibrie/examples/sparql_syntax/combination/combination_stream.rs +++ b/kolibrie/examples/sparql_syntax/combination/combination_stream.rs @@ -8,13 +8,12 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::parser::{parse_from_named_window, parse_stream_type, parse_window_spec}; use kolibrie::prelude::*; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; fn main() { - // Stream data simulating temperature readings over time let rdf_xml_data = r#" "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_xml_data); println!( "Database RDF triples: {:#?}", @@ -57,7 +56,6 @@ fn main() { ); println!("Total triples loaded: {}", database.default_triple_count()); - // Basic RSP rule with RSTREAM and sliding window println!("\n=== Basic RSP Windowing Rule ==="); let rsp_rule_basic = r#"PREFIX ex: PREFIX stream: @@ -76,7 +74,6 @@ WHERE { }"#; println!("Processing RSP rule with RSTREAM and sliding window..."); - // DEBUG: Track database state before windowing let triples_before_rsp = database.default_triple_count(); println!( "DEBUG: Database has {} triples before RSP processing", @@ -85,7 +82,6 @@ WHERE { let rule_result = process_rule_definition(rsp_rule_basic, &mut database); - // DEBUG: Track database state after windowing let triples_after_rsp = database.default_triple_count(); println!( "DEBUG: Database has {} triples after RSP processing (+{})", @@ -97,13 +93,11 @@ WHERE { Ok((rule, inferred_facts)) => { println!("RSP Rule processed successfully!"); - // DEBUG: Check if windowing was actually applied println!("DEBUG: RSP Windowing Analysis:"); println!(" - Inferred {} facts", inferred_facts.len()); println!(" - Rule premise patterns: {}", rule.premise.len()); println!(" - Rule filter conditions: {}", rule.filters.len()); - // DEBUG: Analyze inferred facts for windowing evidence for (i, triple) in inferred_facts.iter().enumerate() { let dict = database.dictionary(); let s = dict.decode(triple.subject).unwrap_or("unknown"); @@ -126,7 +120,6 @@ WHERE { Err(e) => println!("Failed to process RSP rule: {}", e), } - // ISTREAM with tumbling window println!("\n=== ISTREAM with Tumbling Window ==="); let rsp_rule_istream = r#"PREFIX ex: PREFIX stream: @@ -144,7 +137,6 @@ WHERE { }"#; println!("Processing ISTREAM rule with tumbling window..."); - // DEBUG: Track ISTREAM behavior let triples_before_istream = database.default_triple_count(); println!( "DEBUG: Database has {} triples before ISTREAM processing", @@ -164,7 +156,6 @@ WHERE { Ok((rule, inferred_facts)) => { println!("ISTREAM Rule processed successfully!"); - // DEBUG: Compare ISTREAM vs RSTREAM behavior println!("DEBUG: ISTREAM vs RSTREAM Comparison:"); println!( " - RSTREAM added: {} triples", @@ -195,7 +186,6 @@ WHERE { Err(e) => println!("Failed to process ISTREAM rule: {}", e), } - // DSTREAM with range window and ML.PREDICT println!("\n=== DSTREAM with Range Window and ML.PREDICT ==="); let rsp_rule_ml = r#"PREFIX ex: PREFIX stream: @@ -226,7 +216,6 @@ ML.PREDICT( )"#; println!("Processing DSTREAM rule with range window and ML.PREDICT..."); - // DEBUG: Track DSTREAM behavior let triples_before_dstream = database.default_triple_count(); println!( "DEBUG: Database has {} triples before DSTREAM processing", @@ -246,7 +235,6 @@ ML.PREDICT( Ok((rule, inferred_facts)) => { println!("DSTREAM + ML.PREDICT Rule processed successfully!"); - // DEBUG: Three-way comparison println!("DEBUG: Three-way Stream Operator Comparison:"); println!( " - RSTREAM added: {} triples", @@ -283,10 +271,8 @@ ML.PREDICT( Err(e) => println!("Failed to process DSTREAM + ML rule: {}", e), } - // Test parsing individual components println!("\n=== Testing Individual Parser Components ==="); - // Test stream type parsing let stream_tests = vec!["RSTREAM", "ISTREAM", "DSTREAM"]; for stream in stream_tests { match parse_stream_type(stream) { @@ -298,7 +284,6 @@ ML.PREDICT( } } - // Test window specification parsing let window_spec = "[SLIDING 10 SLIDE 2 REPORT ON_WINDOW_CLOSE TICK TIME_DRIVEN]"; match parse_window_spec(window_spec) { Ok((_, spec)) => { @@ -309,7 +294,6 @@ ML.PREDICT( println!(" Report: {:?}", spec.report_strategy); println!(" Tick: {:?}", spec.tick); - // DEBUG: Verify window parameters println!("DEBUG: Window parameters validation:"); println!(" - SLIDING window with width {} detected", spec.width); if let Some(slide) = spec.slide { @@ -330,7 +314,6 @@ ML.PREDICT( Err(e) => println!("Failed to parse window spec: {:?}", e), } - // Test FROM NAMED WINDOW parsing let window_clause = "FROM NAMED WINDOW ON [TUMBLING 5]"; match parse_from_named_window(window_clause) { Ok((_, clause)) => { @@ -339,7 +322,6 @@ ML.PREDICT( println!(" Stream IRI: {}", clause.stream_iri); println!(" Window Type: {:?}", clause.window_spec.window_type); - // DEBUG: Verify windowing clause parsing println!("DEBUG: Windowing clause validation:"); println!(" - Window IRI: {}", clause.window_iri); println!(" - Stream IRI: {}", clause.stream_iri); @@ -348,10 +330,8 @@ ML.PREDICT( Err(e) => println!("Failed to parse FROM NAMED WINDOW: {:?}", e), } - // Query the results println!("\n=== Querying RSP Results ==="); - // Query for high temperature alerts let alert_query = r#"PREFIX ex: SELECT ?room ?alert WHERE { @@ -364,7 +344,6 @@ WHERE { .into_rows(); println!("Alert query results: {:?}", query_results); - // DEBUG: Validate windowing results println!("DEBUG: Alert query validation:"); println!(" - Found {} alert results", query_results.len()); if query_results.len() > 0 { @@ -376,7 +355,6 @@ WHERE { println!(" NO ALERTS: No hasAlert predicates found - check windowing logic"); } - // Query for new high readings let new_reading_query = r#"PREFIX ex: SELECT ?room ?temp WHERE { @@ -389,7 +367,6 @@ WHERE { .into_rows(); println!("New reading query results: {:?}", reading_results); - // DEBUG: Validate ISTREAM results println!("DEBUG: ISTREAM reading validation:"); println!(" - Found {} reading results", reading_results.len()); if reading_results.len() > 0 { @@ -398,7 +375,6 @@ WHERE { println!(" NO READINGS: No newHighReading predicates found"); } - // Query for predicted levels let prediction_query = r#"PREFIX ex: SELECT ?room ?level WHERE { @@ -411,7 +387,6 @@ WHERE { .into_rows(); println!("Prediction query results: {:?}", prediction_results); - // DEBUG: Validate DSTREAM results println!("DEBUG: DSTREAM prediction validation:"); println!(" - Found {} prediction results", prediction_results.len()); if prediction_results.len() > 0 { @@ -420,7 +395,6 @@ WHERE { println!(" NO PREDICTIONS: No predictedLevel predicates found"); } - // DEBUG: Final windowing assessment println!("\nDEBUG: Final Windowing Assessment:"); let total_windowing_results = query_results.len() + reading_results.len() + prediction_results.len(); @@ -442,7 +416,6 @@ WHERE { ); println!("Parser successfully handled all RSP windowing syntax!"); - // DEBUG: Final database state println!("DEBUG: Final database state:"); println!( " - Started with: {} triples", @@ -455,5 +428,5 @@ WHERE { println!( " - Total added: {} triples", database.default_triple_count() - 15 - ); // 15 original triples + ); } diff --git a/kolibrie/examples/sparql_syntax/combination/combined_syntax_example.rs b/kolibrie/examples/sparql_syntax/combination/combined_syntax_example.rs index 2a33c62c..2fa204f8 100644 --- a/kolibrie/examples/sparql_syntax/combination/combined_syntax_example.rs +++ b/kolibrie/examples/sparql_syntax/combination/combined_syntax_example.rs @@ -8,14 +8,14 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::prelude::*; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; fn main() { println!("=== Combined SPARQL / RDF-star / RULE / Provenance RULE Example ===\n"); - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); println!("[Stage 1] Loading base RDF facts (standard triples)..."); database.add_triple_parts( @@ -249,7 +249,7 @@ SELECT ?sensor WHERE { println!(" — Stage 6 (provenance-based, uses Stage 4 output as premise)"); } -/// Shorten a full URI for display: keeps only the local name after the last '/' or '#'. +/// Shorten a full URI for display: keeps only the local name after the last '/' or '#' fn shorten(uri: &str) -> &str { if let Some(pos) = uri.rfind('/') { &uri[pos + 1..] diff --git a/kolibrie/examples/sparql_syntax/combination/continious_stream.rs b/kolibrie/examples/sparql_syntax/combination/continious_stream.rs index 0f77e1c4..2b7e35c8 100644 --- a/kolibrie/examples/sparql_syntax/combination/continious_stream.rs +++ b/kolibrie/examples/sparql_syntax/combination/continious_stream.rs @@ -1,6 +1,6 @@ +use kolibrie::database::KolibrieDB; use kolibrie::prelude::*; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; use rand::Rng; use std::thread; use std::time::{Duration, SystemTime, UNIX_EPOCH}; @@ -8,19 +8,10 @@ use std::time::{Duration, SystemTime, UNIX_EPOCH}; fn main() { println!("=== Continuous Real-Time Stream Testing with Windowing ===\n"); - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let mut stream_counter = 0; let mut total_alerts = 0; - // Eight batches, paced so the stream is visibly continuous without the example - // becoming the slowest thing in `scripts/verify.sh`. It used to run for - // twenty-four seconds at three seconds a batch; the same eight batches at half a - // second take four. - // - // Nothing here is driven by a wall clock. The `TICK TIME_DRIVEN` in the rule - // text below is parsed and the rule runs, but this example builds no RSPEngine - // and streams into no window -- so the pacing is narrative, and shortening it - // changes the output not at all. let stream_duration = Duration::from_secs(4); let batch_interval = Duration::from_millis(500); let start_time = SystemTime::now(); @@ -44,10 +35,8 @@ fn main() { elapsed.as_secs() ); - // Create a new batch of sensor readings with realistic temperature variations let sensor_data = generate_dynamic_sensor_batch(stream_counter, current_timestamp); - // Convert the sensor data to RDF format and load it into our database let rdf_xml = create_sensor_rdf_batch(sensor_data.clone(), stream_counter); database.parse_rdf(&rdf_xml); @@ -57,7 +46,6 @@ fn main() { database.default_triple_count() ); - // Display the current batch of sensor readings with their status for (room, temp, timestamp) in &sensor_data { let status = if *temp > 100 { "CRITICAL" @@ -74,11 +62,9 @@ fn main() { ); } - // Process the data through our windowing rules to detect anomalies and patterns let batch_alerts = apply_simplified_windowing_rules(&mut database, stream_counter); total_alerts += batch_alerts; - // Show current streaming statistics println!("Current Stream Statistics:"); println!(" - Batches processed so far: {}", stream_counter); println!( @@ -91,11 +77,9 @@ fn main() { (stream_duration.as_secs() as i64 - elapsed.as_secs() as i64).max(0) ); - // Wait for the next batch of data to arrive thread::sleep(batch_interval); } - // Show final results after all streaming is complete println!("=== STREAMING SIMULATION COMPLETE ==="); println!("Final Statistics Summary:"); println!(" - Total batches processed: {}", stream_counter); @@ -111,7 +95,6 @@ fn main() { ); } - // Run a comprehensive analysis of all the collected data println!("\nRunning Final Data Analysis:"); run_final_analysis(&mut database); } @@ -121,25 +104,20 @@ fn generate_dynamic_sensor_batch(batch_num: u32, base_timestamp: u64) -> Vec<(St let rooms = vec!["Room101", "Room102", "Room103", "Room104", "Room105"]; let mut batch_data = Vec::new(); - // Each batch contains 2-3 sensor readings to simulate realistic data flow let reading_count = rng.random_range(2..=3); for i in 0..reading_count { let room = rooms[rng.random_range(0..rooms.len())].to_string(); - // Generate realistic temperature variations based on room characteristics - // Each room has different baseline temperatures to simulate real environments let base_temp = match room.as_str() { - "Room101" => 85, // Equipment room - tends to run hot - "Room102" => 72, // Office space - normal temperature - "Room103" => 95, // Server room - very hot due to equipment - "Room104" => 68, // Well-ventilated room - cooler - "Room105" => 78, // Meeting room - moderate temperature + "Room101" => 85, + "Room102" => 72, + "Room103" => 95, + "Room104" => 68, + "Room105" => 78, _ => 75, }; - // Add random variation to simulate real sensor fluctuations - // Later batches get additional heat boost to simulate system stress let variation = rng.random_range(-10..=25); let heat_boost = if batch_num > 5 { rng.random_range(0..=15) @@ -148,7 +126,6 @@ fn generate_dynamic_sensor_batch(batch_num: u32, base_timestamp: u64) -> Vec<(St }; let temperature = (base_temp + variation + heat_boost).max(10).min(140); - // Stagger timestamps within the batch to simulate readings at different times let timestamp = base_timestamp + (i as u64 * 5); batch_data.push((room, temperature, timestamp)); @@ -158,7 +135,6 @@ fn generate_dynamic_sensor_batch(batch_num: u32, base_timestamp: u64) -> Vec<(St } fn create_sensor_rdf_batch(sensor_data: Vec<(String, i32, u64)>, batch_num: u32) -> String { - // Create the RDF/XML header with necessary namespace declarations let mut rdf = String::from( r#" , batch_num: u32) xmlns:stream="http://example.org/stream#">"#, ); - // Convert each sensor reading into RDF format - // Each reading gets a unique URI and contains room, temperature, timestamp, and batch info for (i, (room, temp, timestamp)) in sensor_data.iter().enumerate() { let reading_id = format!("http://example.org/stream#reading{}_{}", batch_num, i + 1); rdf.push_str(&format!( @@ -186,13 +160,9 @@ fn create_sensor_rdf_batch(sensor_data: Vec<(String, i32, u64)>, batch_num: u32) rdf } -fn apply_simplified_windowing_rules(database: &mut SparqlDatabase, _batch_num: u32) -> u32 { +fn apply_simplified_windowing_rules(database: &mut KolibrieDB, _batch_num: u32) -> u32 { let mut alert_count = 0; - // Apply different windowing rules to detect various types of anomalies - // Each rule uses a different stream processing approach (RSTREAM, ISTREAM, DSTREAM) - - // Rule 1: RSTREAM - Detects high temperature readings using sliding window println!("Applying RSTREAM rule for high temperature detection..."); let high_temp_rule = r#"PREFIX ex: PREFIX stream: @@ -219,7 +189,6 @@ WHERE { ); alert_count += inferred_facts.len() as u32; - // Show what alerts were generated for (i, triple) in inferred_facts.iter().enumerate() { let dict = database.dictionary(); let s = dict.decode(triple.subject).unwrap_or("unknown"); @@ -232,7 +201,6 @@ WHERE { Err(e) => println!("RSTREAM rule processing failed: {}", e), } - // Rule 2: ISTREAM - Detects new moderate temperature readings using tumbling window println!("Applying ISTREAM rule for moderate temperature detection..."); let moderate_rule = r#"PREFIX ex: PREFIX stream: @@ -258,7 +226,6 @@ WHERE { ); alert_count += inferred_facts.len() as u32; - // Show what new readings were detected for (i, triple) in inferred_facts.iter().enumerate() { let dict = database.dictionary(); let s = dict.decode(triple.subject).unwrap_or("unknown"); @@ -271,7 +238,6 @@ WHERE { Err(e) => println!("ISTREAM rule processing failed: {}", e), } - // Rule 3: DSTREAM - Detects extreme temperature readings using range-based window println!("Applying DSTREAM rule for extreme temperature detection..."); let extreme_rule = r#"PREFIX ex: PREFIX stream: @@ -298,7 +264,6 @@ WHERE { ); alert_count += inferred_facts.len() as u32; - // Show what extreme conditions were found for (i, triple) in inferred_facts.iter().enumerate() { let dict = database.dictionary(); let s = dict.decode(triple.subject).unwrap_or("unknown"); @@ -311,10 +276,8 @@ WHERE { Err(e) => println!("DSTREAM rule processing failed: {}", e), } - // Query the database to see cumulative results from all processed batches println!("Checking cumulative results in database..."); - // Count all high temperature alerts that have been generated let all_alerts_query = r#"PREFIX ex: SELECT ?room ?alert WHERE { @@ -331,7 +294,6 @@ WHERE { ); } - // Count all high temperature readings that have been flagged let all_readings_query = r#"PREFIX ex: SELECT ?room ?temp WHERE { @@ -348,7 +310,6 @@ WHERE { ); } - // Count all extreme temperature conditions let extreme_query = r#"PREFIX ex: SELECT ?room ?temp WHERE { @@ -365,7 +326,6 @@ WHERE { ); } - // Show total raw sensor data count for reference let batch_query = r#"PREFIX ex: SELECT ?reading ?room ?temp WHERE { @@ -384,10 +344,9 @@ WHERE { alert_count } -fn run_final_analysis(database: &mut SparqlDatabase) { +fn run_final_analysis(database: &mut KolibrieDB) { println!("Performing comprehensive analysis of all windowing results..."); - // Run multiple queries to analyze different aspects of the collected data let queries = vec![ ( "High Temperature Alerts", @@ -424,7 +383,6 @@ WHERE { .into_rows(); println!("{}: {} results found", name, results.len()); - // Show a few sample results to give insight into the data for (i, result) in results.iter().take(3).enumerate() { println!(" Sample {}. {:?}", i + 1, result); } @@ -434,7 +392,6 @@ WHERE { println!(); } - // Calculate and display final performance statistics let total_sensor_data = execute( r#"PREFIX ex: SELECT ?reading WHERE { ?reading ex:room ?room . }"#, @@ -473,7 +430,6 @@ SELECT ?room WHERE { ?room ex:hasAlert ?alert . }"#, } fn get_current_timestamp() -> u64 { - // Get the current Unix timestamp for labeling sensor readings SystemTime::now() .duration_since(UNIX_EPOCH) .unwrap() diff --git a/kolibrie/examples/sparql_syntax/combination/full_combination.rs b/kolibrie/examples/sparql_syntax/combination/full_combination.rs index d2ecf2b5..50f08c03 100644 --- a/kolibrie/examples/sparql_syntax/combination/full_combination.rs +++ b/kolibrie/examples/sparql_syntax/combination/full_combination.rs @@ -1,6 +1,6 @@ +use kolibrie::database::KolibrieDB; use kolibrie::rsp_engine::{QueryExecutionMode, RSPBuilder, ResultConsumer, SimpleR2R}; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; use ml::MLHandler; use datalog::reasoning::Reasoner; @@ -12,17 +12,13 @@ use std::thread; use std::time::Duration; fn main() -> Result<(), Box> { - // Setup ML Model let mut ml_handler = setup_ml_model()?; - // Setup database with initial ontology let mut database = setup_knowledge_base(); - // Define and load reasoning rule let rule_query = define_comfort_rule(); let (rule, _inferred) = process_rule_definition(&rule_query, &mut database)?; - // Setup RSP Engine with reasoning let result_container = Arc::new(Mutex::new(Vec::new())); let result_container_clone = result_container.clone(); @@ -31,7 +27,6 @@ fn main() -> Result<(), Box> { let mut results = result_container_clone.lock().unwrap(); results.push(bindings.clone()); - // Print real-time alerts let binding_map: HashMap<_, _> = bindings.iter().cloned().collect(); if let (Some(room), Some(temp)) = (binding_map.get("room"), binding_map.get("temp")) { println!("Stream Alert: Room {} at {}", room, temp); @@ -64,7 +59,6 @@ fn main() -> Result<(), Box> { ))) .build()?; - // Run the combined workflow println!( "Time | Room | Temp | Humidity | Occupancy | ML Predicted | Comfort Level | Action" ); @@ -74,7 +68,6 @@ fn main() -> Result<(), Box> { run_combined_workflow(&mut engine, &mut ml_handler, &mut database, &rule)?; - // Stop the engine engine.stop(); thread::sleep(Duration::from_secs(1)); @@ -97,7 +90,6 @@ fn setup_ml_model() -> Result> { std::fs::create_dir_all(&model_dir)?; - // Check if models exist let models_exist = std::fs::read_dir(&model_dir)? .filter_map(Result::ok) .filter(|entry| { @@ -126,17 +118,15 @@ fn setup_ml_model() -> Result> { Ok(ml_handler) } -fn setup_knowledge_base() -> SparqlDatabase { - let mut database = SparqlDatabase::with_ml_context(local_ml::trusted_context()); +fn setup_knowledge_base() -> KolibrieDB { + let mut database = KolibrieDB::with_ml_context(local_ml::trusted_context()); - // Register prefixes database.add_prefix("ex".to_string(), "http://example.org/".to_string()); database.add_prefix( "rdf".to_string(), "http://www.w3.org/1999/02/22-rdf-syntax-ns#".to_string(), ); - // Add initial ontology - room definitions database.add_triple_parts( "http://example.org/Office1", "http://www.w3.org/1999/02/22-rdf-syntax-ns#type", @@ -170,7 +160,7 @@ WHERE { fn run_combined_workflow( engine: &mut kolibrie::rsp_engine::RSPEngine>, ml_handler: &mut MLHandler, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, comfort_rule: &Rule, ) -> Result<(), Box> { let rooms = vec!["Office1", "Office2"]; @@ -183,16 +173,13 @@ fn run_combined_workflow( let humidity = 50.0 + (time as f64 * 2.0); let occupancy = 5 + time + room_idx; - // ML Prediction let input_data = vec![vec![temp, humidity, occupancy as f64]]; let ml_result = ml_handler.predict(&best_model, input_data)?; let predicted_temp = ml_result.predictions[0]; - // Create sensor and room URIs let sensor_uri = format!("http://example.org/Sensor_{}", room); let room_uri = format!("http://example.org/{}", room); - // Add triples database.add_triple_parts(&sensor_uri, "http://example.org/hasRoom", &room_uri); database.add_triple_parts( &sensor_uri, @@ -223,10 +210,8 @@ fn run_combined_workflow( engine.add_to_stream("sensorStream", triple.clone(), time); } - // Create a NEW reasoner with its own dictionary let mut reasoner = Reasoner::new(); - // Decode all triples FIRST with proper scoping let decoded_triples: Vec<(String, String, String)> = { let dict = database.dictionary(); database @@ -241,18 +226,14 @@ fn run_combined_workflow( .collect() }; - // Now add to reasoner (reasoner has its own dictionary) for (s, p, o) in decoded_triples { reasoner.add_abox_triple(&s, &p, &o); } - // Add the rule reasoner.add_rule(comfort_rule.clone()); - // Perform inference let inferred_facts = reasoner.infer_new_facts_semi_naive(); - // Add inferred facts back with proper encoding and error handling for fact in &inferred_facts { let decoded = match ( reasoner.decode_term(fact.subject), @@ -268,10 +249,8 @@ fn run_combined_workflow( } } - // Query for the inferred comfort level let comfort_level = query_comfort_level(database, &sensor_uri); - // Determine action let action = if temp > 25.0 || predicted_temp > 26.0 { "ACTIVATE COOLING" } else if predicted_temp > 24.0 { @@ -292,9 +271,7 @@ fn run_combined_workflow( Ok(()) } -// Helper function to query the inferred comfort level -fn query_comfort_level(database: &SparqlDatabase, sensor_uri: &str) -> String { - // Proper lock scoping +fn query_comfort_level(database: &KolibrieDB, sensor_uri: &str) -> String { let (comfort_pred_id, sensor_id) = { let dict = database.dictionary(); let comfort = dict diff --git a/kolibrie/examples/sparql_syntax/combination/multiple_conclusions.rs b/kolibrie/examples/sparql_syntax/combination/multiple_conclusions.rs index d53bb8ba..cf1ef41d 100644 --- a/kolibrie/examples/sparql_syntax/combination/multiple_conclusions.rs +++ b/kolibrie/examples/sparql_syntax/combination/multiple_conclusions.rs @@ -8,9 +8,9 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::prelude::*; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; fn main() { std::env::set_var("RUST_BACKTRACE", "1"); @@ -46,11 +46,10 @@ fn main() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_xml_data); println!("Database RDF triples loaded."); - // Define the rule separately let rule_definition = r#"PREFIX ex: PREFIX alert: RULE :OverheatingAlert :- @@ -66,12 +65,10 @@ RULE :OverheatingAlert :- FILTER (?temp > 80) }"#; - // Process the rule definition match process_rule_definition(rule_definition, &mut database) { Ok((rule, inferred_facts)) => { println!("Rule processed successfully."); - // Print rule details println!("Rule details:"); println!(" Premise patterns: {:?}", rule.premise); println!(" Filters: {:?}", rule.filters); @@ -79,21 +76,17 @@ RULE :OverheatingAlert :- println!("Inferred {} new fact(s):", inferred_facts.len()); - // Acquire read lock on dictionary let dict = database.dictionary(); for triple in inferred_facts.iter() { - // Dereference the RwLockReadGuard to get &Dictionary println!(" {}", database.triple_to_string(triple, &*dict)); } - // Lock is automatically released when dict goes out of scope } Err(error) => { eprintln!("Error processing rule definition: {}", error); - return; // Exit if rule processing failed + return; } } - // Define the SELECT query separately let select_query = r#"PREFIX ex: PREFIX alert: SELECT ?room ?temp ?status ?timestamp ?action @@ -106,11 +99,9 @@ WHERE { ?reading ex:temperature ?temp . }"#; - // Execute the SELECT query let query_results = execute(select_query, &mut database) .expect("query should run") .into_rows(); - // Format and display the results nicely println!("\nQuery results: {:?}", query_results); } diff --git a/kolibrie/examples/sparql_syntax/concat/concat.rs b/kolibrie/examples/sparql_syntax/concat/concat.rs index 34295e9a..17a363d5 100644 --- a/kolibrie/examples/sparql_syntax/concat/concat.rs +++ b/kolibrie/examples/sparql_syntax/concat/concat.rs @@ -23,7 +23,7 @@ fn concat() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#" diff --git a/kolibrie/examples/sparql_syntax/filter/filter_char.rs b/kolibrie/examples/sparql_syntax/filter/filter_char.rs index e82a4db6..980c9785 100644 --- a/kolibrie/examples/sparql_syntax/filter/filter_char.rs +++ b/kolibrie/examples/sparql_syntax/filter/filter_char.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Adding FILTER fn filter_char() { let rdf_data = r##" @@ -31,7 +30,7 @@ fn filter_char() { "##; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#" @@ -47,7 +46,6 @@ fn filter_char() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [title, author] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/filter/filter_complex.rs b/kolibrie/examples/sparql_syntax/filter/filter_complex.rs index bbd21a87..19e6c637 100644 --- a/kolibrie/examples/sparql_syntax/filter/filter_complex.rs +++ b/kolibrie/examples/sparql_syntax/filter/filter_complex.rs @@ -67,10 +67,9 @@ fn main() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); - // Using OR (||) operator to filter by event type let sparql = r#" PREFIX ex: diff --git a/kolibrie/examples/sparql_syntax/filter/filter_int.rs b/kolibrie/examples/sparql_syntax/filter/filter_int.rs index ac0fb7e6..dd0457a1 100644 --- a/kolibrie/examples/sparql_syntax/filter/filter_int.rs +++ b/kolibrie/examples/sparql_syntax/filter/filter_int.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Adding FILTER fn filter_query() { let rdf_data = r#" @@ -49,7 +48,7 @@ fn filter_query() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ds: SELECT ?employee ?salary WHERE {?employee ds:annual_salary ?salary FILTER(?salary > 75000)}"#; @@ -58,7 +57,6 @@ fn filter_query() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [employee, salary] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/filter/filter_int_arithmetic.rs b/kolibrie/examples/sparql_syntax/filter/filter_int_arithmetic.rs index cd6f7822..2a7f02f1 100644 --- a/kolibrie/examples/sparql_syntax/filter/filter_int_arithmetic.rs +++ b/kolibrie/examples/sparql_syntax/filter/filter_int_arithmetic.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Adding FILTER fn filter_query() { let rdf_data = r#" @@ -49,7 +48,7 @@ fn filter_query() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ds: @@ -63,7 +62,6 @@ fn filter_query() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [employee, salary] = &result[..] { @@ -84,7 +82,6 @@ fn filter_query() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results2 { if let [employee, salary] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/filter/filter_limit.rs b/kolibrie/examples/sparql_syntax/filter/filter_limit.rs index a321eecf..eae59f0c 100644 --- a/kolibrie/examples/sparql_syntax/filter/filter_limit.rs +++ b/kolibrie/examples/sparql_syntax/filter/filter_limit.rs @@ -67,7 +67,7 @@ fn main() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql_with_limit = r#" diff --git a/kolibrie/examples/sparql_syntax/filter/filter_with_orderby.rs b/kolibrie/examples/sparql_syntax/filter/filter_with_orderby.rs index a36fbd76..b1b7b1e5 100644 --- a/kolibrie/examples/sparql_syntax/filter/filter_with_orderby.rs +++ b/kolibrie/examples/sparql_syntax/filter/filter_with_orderby.rs @@ -67,7 +67,7 @@ fn main() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ex: diff --git a/kolibrie/examples/sparql_syntax/filter/simple_filter.rs b/kolibrie/examples/sparql_syntax/filter/simple_filter.rs index 41942d22..ee30661f 100644 --- a/kolibrie/examples/sparql_syntax/filter/simple_filter.rs +++ b/kolibrie/examples/sparql_syntax/filter/simple_filter.rs @@ -11,7 +11,6 @@ use kolibrie::prelude::*; fn select() { - // Define the RDF/XML string representing the inserted triples let rdf_xml = r#" @@ -26,14 +25,11 @@ fn select() { "#; - // Initialize a sample database (assuming SparqlDatabase and Triple are implemented) - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_xml); - // Define an INSERT SPARQL query let sparql_query = r#"PREFIX ex: SELECT ?person WHERE {?person ex:hasOccupation "Engineer"}"#; - // Execute the query on the database let results = execute(sparql_query, &mut database) .expect("query should run") .into_rows(); @@ -49,7 +45,6 @@ fn select() { } drop(dict); - // Output the results (if any) for result in results { if let [person] = &result[..] { println!("{}", person); diff --git a/kolibrie/examples/sparql_syntax/from_file/multi_thread.rs b/kolibrie/examples/sparql_syntax/from_file/multi_thread.rs index ee33a6f5..c88b58ea 100644 --- a/kolibrie/examples/sparql_syntax/from_file/multi_thread.rs +++ b/kolibrie/examples/sparql_syntax/from_file/multi_thread.rs @@ -8,13 +8,12 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::prelude::*; -use kolibrie::sparql_database::SparqlDatabase; fn simple_select_synth_data(file_path: &str) { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // Call `parser_rdf_from_file` function for multi-threading processing database.parse_rdf_from_file(file_path); let sparql = r#" @@ -33,7 +32,6 @@ fn simple_select_synth_data(file_path: &str) { println!("Results:"); for result in results { if let [employee, workplace_homepage, salary] = &result[..] { - // Process your results here println!( "?employee = {} ?workplaceHomepage = {} ?salary = {}", employee, workplace_homepage, salary @@ -43,7 +41,6 @@ fn simple_select_synth_data(file_path: &str) { } fn main() { - // Set current directory to the root of the project std::env::set_current_dir(std::path::Path::new(env!("CARGO_MANIFEST_DIR"))) .expect("Failed to set project root as current directory"); let file_path = "../datasets/synthetic_data_employee_100K.rdf"; diff --git a/kolibrie/examples/sparql_syntax/from_file/one_thread.rs b/kolibrie/examples/sparql_syntax/from_file/one_thread.rs index a7714495..cf4b55d8 100644 --- a/kolibrie/examples/sparql_syntax/from_file/one_thread.rs +++ b/kolibrie/examples/sparql_syntax/from_file/one_thread.rs @@ -8,12 +8,12 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::prelude::*; -use kolibrie::sparql_database::SparqlDatabase; fn simple_select_synth_data(file_path: &str) { let file = std::fs::read_to_string(file_path).expect("Error of finding file"); - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(&file); @@ -33,7 +33,6 @@ fn simple_select_synth_data(file_path: &str) { println!("Results:"); for result in results { if let [employee, workplace_homepage, salary] = &result[..] { - // Process your results here println!( "?employee = {} ?workplaceHomepage = {} ?salary = {}", employee, workplace_homepage, salary @@ -43,7 +42,6 @@ fn simple_select_synth_data(file_path: &str) { } fn main() { - // Set current directory to the root of the project std::env::set_current_dir(std::path::Path::new(env!("CARGO_MANIFEST_DIR"))) .expect("Failed to set project root as current directory"); let file_path = "../datasets/synthetic_data_employee_100K.rdf"; diff --git a/kolibrie/examples/sparql_syntax/insert/insert.rs b/kolibrie/examples/sparql_syntax/insert/insert.rs index d788e1f5..ffa64ebc 100644 --- a/kolibrie/examples/sparql_syntax/insert/insert.rs +++ b/kolibrie/examples/sparql_syntax/insert/insert.rs @@ -11,7 +11,6 @@ use kolibrie::prelude::*; fn insert() { - // Define the RDF/XML string representing the inserted triples let rdf_xml = r#" "#; - // Initialize a sample database (assuming SparqlDatabase and Triple are implemented) - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_xml); - // Define an INSERT SPARQL query let sparql_query = r#"PREFIX ex: PREFIX rdf: INSERT { ex:occupation "Software Developer"} WHERE { ex:age "30"}"#; - // Execute the query on the database let _results = execute(sparql_query, &mut database) .expect("query should run") .into_rows(); diff --git a/kolibrie/examples/sparql_syntax/knowledge_graph/combined_rules_example.rs b/kolibrie/examples/sparql_syntax/knowledge_graph/combined_rules_example.rs index 8df48c57..2ca140ef 100644 --- a/kolibrie/examples/sparql_syntax/knowledge_graph/combined_rules_example.rs +++ b/kolibrie/examples/sparql_syntax/knowledge_graph/combined_rules_example.rs @@ -21,7 +21,6 @@ fn main() { println!("[Stage 1] Loading base facts..."); - // Certain `knows` facts (8 triples) kg.add_abox_triple("Alice", "knows", "Bob"); kg.add_abox_triple("Alice", "knows", "Charlie"); kg.add_abox_triple("Bob", "knows", "Diana"); @@ -31,7 +30,6 @@ fn main() { kg.add_abox_triple("Eve", "knows", "Frank"); kg.add_abox_triple("Frank", "knows", "Alice"); - // Probabilistic `trusts` facts (7 triples) — seeded for provenance inference kg.add_tagged_triple("Alice", "trusts", "Bob", 0.90); kg.add_tagged_triple("Alice", "trusts", "Charlie", 0.70); kg.add_tagged_triple("Bob", "trusts", "Diana", 0.80); @@ -52,7 +50,6 @@ fn main() { let strong_bond_id = kg.encode_term("strongBond"); let trust_comm_id = kg.encode_term("trustCommunity"); - // Rule 1: knows(X,Y) ∧ knows(Y,Z) -> connected(X,Z) (two-hop) let rule1 = Rule { premise: vec![ ( @@ -75,7 +72,6 @@ fn main() { filters: vec![], }; - // Rule 2: connected(X,Y) ∧ connected(Y,Z) -> connected(X,Z) (transitive closure) let rule2 = Rule { premise: vec![ ( @@ -98,7 +94,6 @@ fn main() { filters: vec![], }; - // Rule 3: strongBond(X,Y) ∧ strongBond(Y,Z) -> trustCommunity(X,Z) (uses provenance output) let rule3 = Rule { premise: vec![ ( @@ -123,10 +118,7 @@ fn main() { kg.add_rule(rule1); kg.add_rule(rule2); - // Rule 3 is added later — it uses strongBond from the provenance round - // Rule 4: trusts(X,Y) ∧ trusts(Y,Z) -> indirectTrust(X,Z) - // Uses AddMultProbability provenance (⊗ = multiply for conjunction) let rule4 = Rule { premise: vec![ ( @@ -149,8 +141,6 @@ fn main() { filters: vec![], }; - // Rule 5: connected(X,Z) ∧ trusts(X,Z) -> strongBond(X,Z) - // Uses classically-inferred `connected` from Round 1 let rule5 = Rule { premise: vec![ ( @@ -211,7 +201,6 @@ fn main() { println!(" indirectTrust(X,Z) :- trusts(X,Y), trusts(Y,Z)"); println!(" strongBond(X,Z) :- connected(X,Z), trusts(X,Z)"); - // Add provenance rules (plain Rules — provenance is program-level, not per-rule) kg.add_rule(rule4); kg.add_rule(rule5); @@ -259,7 +248,6 @@ fn main() { println!(" (Uses provenance-inferred strongBond facts from Stage 3!)"); println!(" Classical rule: trustCommunity(X,Z) :- strongBond(X,Y), strongBond(Y,Z)"); - // Add rule3 now so it participates in the second classical round kg.add_rule(rule3); let facts3 = kg.infer_new_facts_semi_naive(); diff --git a/kolibrie/examples/sparql_syntax/knowledge_graph/datalog_example.rs b/kolibrie/examples/sparql_syntax/knowledge_graph/datalog_example.rs index 23d727ca..bb06fa56 100644 --- a/kolibrie/examples/sparql_syntax/knowledge_graph/datalog_example.rs +++ b/kolibrie/examples/sparql_syntax/knowledge_graph/datalog_example.rs @@ -19,7 +19,6 @@ fn main() { kg.add_abox_triple("Bob", "parent", "Charlie"); kg.add_abox_triple("Charlie", "parent", "David"); - // Encode predicates once with proper locking let parent_id = kg.encode_term("parent"); let ancestor_id = kg.encode_term("ancestor"); @@ -74,11 +73,7 @@ fn main() { } } - let results = kg.query_abox( - None, // subject: any (this is our variable X) - Some("ancestor"), // predicate: "ancestor" - Some("David"), // object: "David" - ); + let results = kg.query_abox(None, Some("ancestor"), Some("David")); for triple in results { if let Some(ancestor) = kg.decode_term(triple.subject) { diff --git a/kolibrie/examples/sparql_syntax/knowledge_graph/deep_taxonomy.rs b/kolibrie/examples/sparql_syntax/knowledge_graph/deep_taxonomy.rs index 29e3fe62..30dc4420 100644 --- a/kolibrie/examples/sparql_syntax/knowledge_graph/deep_taxonomy.rs +++ b/kolibrie/examples/sparql_syntax/knowledge_graph/deep_taxonomy.rs @@ -1,23 +1,15 @@ use datalog::reasoning::Reasoner; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; use shared::rule::Rule; use shared::terms::Term; use std::env; use std::fs; use std::time::Instant; -/// Depth of the generated chain, when no corpus directory is supplied. -/// -/// Each level is one `rdfs:subClassOf` step, so the closure derives this many -/// `rdf:type` facts per instance. Kept modest because the benchmark below reasons 20 -/// times over it. +/// Depth of the generated chain, when no corpus directory is supplied const GENERATED_DEPTH: usize = 1_000; -/// `A1 subClassOf A2 … A(n-1) subClassOf An`, plus one instance of `A1`. -/// -/// The same shape as the EYE deep-taxonomy corpus this benchmark was written -/// against: a single chain whose transitive closure is the whole thing, which is what -/// makes it a reasoning benchmark rather than a data-volume one. +/// `A1 subClassOf A2 … A(n-1) subClassOf An`, plus one instance of `A1` fn generated_corpus(depth: usize) -> (String, String) { let mut taxonomy = String::from("@prefix : .\n"); taxonomy.push_str("@prefix rdfs: .\n"); @@ -32,12 +24,7 @@ fn generated_corpus(depth: usize) -> (String, String) { (taxonomy, facts) } -/// Where to read `test-dl.ttl` and `test-facts.ttl` from, if anywhere. -/// -/// First argument, then `KOLIBRIE_DEEP_TAXONOMY_DIR`, then the EYE checkout this was -/// originally written against. Returns `None` when none of them holds both files, in -/// which case the corpus is generated instead — the example used to panic here, which -/// made it unrunnable for anyone without that checkout. +/// Where to read `test-dl.ttl` and `test-facts.ttl` from, if anywhere fn corpus_dir() -> Option { let mut candidates = env::args() .nth(1) @@ -56,7 +43,7 @@ fn corpus_dir() -> Option { fn main() { println!("=== Loading Data (One-time) ==="); - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let load_start = Instant::now(); let (taxonomy_data, facts_data) = match corpus_dir() { @@ -82,7 +69,6 @@ fn main() { } }; - // Step 1: the taxonomy (subClassOf relationships) database.parse_turtle(&taxonomy_data); let load_time = load_start.elapsed(); println!( @@ -91,14 +77,12 @@ fn main() { load_time ); - // Step 2: the facts (initial instances) database.parse_turtle(&facts_data); println!( "Total triples after facts: {}", database.default_triple_count() ); - // Step 3: Create KnowledgeGraph and populate it (One-time) println!("\n=== Populating KnowledgeGraph (One-time) ==="); let mut kg = Reasoner::new(); kg.adopt_terms(database.terms()); @@ -111,7 +95,6 @@ fn main() { let populate_time = populate_start.elapsed(); println!("Populated KnowledgeGraph in {:?}", populate_time); - // Step 4: Find predicate IDs let mut rdf_type_id = None; let mut rdfs_subclass_id = None; @@ -133,7 +116,6 @@ fn main() { let rdf_type = rdf_type_id.expect("rdf:type not found in data!"); let rdfs_subclass = rdfs_subclass_id.expect("rdfs:subClassOf not found in data!"); - // Step 5: Add reasoning rule let type_inference_rule = Rule { premise: vec![ ( @@ -158,14 +140,12 @@ fn main() { kg.add_rule(type_inference_rule); - // Step 6: Run reasoning 20 times println!("\n=== Running Reasoning 20 Times ==="); const ITERATIONS: usize = 20; let mut reasoning_times = Vec::new(); let mut inferred_counts = Vec::new(); for i in 1..=ITERATIONS { - // Clone the KG to reset for each iteration let mut kg_clone = kg.clone(); let reasoning_start = Instant::now(); @@ -183,7 +163,6 @@ fn main() { ); } - // Calculate statistics let avg_time: f64 = reasoning_times.iter().sum::() / ITERATIONS as f64; let min_time = reasoning_times .iter() @@ -196,7 +175,6 @@ fn main() { let avg_inferred = inferred_counts.iter().sum::() / ITERATIONS; - // Calculate standard deviation let variance: f64 = reasoning_times .iter() .map(|&t| (t - avg_time).powi(2)) diff --git a/kolibrie/examples/sparql_syntax/knowledge_graph/knowledge_graph.rs b/kolibrie/examples/sparql_syntax/knowledge_graph/knowledge_graph.rs index e9f62d4a..aba2c4d7 100644 --- a/kolibrie/examples/sparql_syntax/knowledge_graph/knowledge_graph.rs +++ b/kolibrie/examples/sparql_syntax/knowledge_graph/knowledge_graph.rs @@ -17,11 +17,9 @@ use shared::terms::Term; fn knowledge_graph() { let mut graph = Reasoner::new(); - // Add ABox triples (instance-level) graph.add_abox_triple("Alice", "hasParent", "Bob"); graph.add_abox_triple("Bob", "hasParent", "Charlie"); - // Define a dynamic rule: If X hasParent Y and Y hasParent Z, then X hasGrandparent Z let has_parent_id = graph.encode_term("hasParent"); let has_grandparent_id = graph.encode_term("hasGrandparent"); @@ -47,13 +45,10 @@ fn knowledge_graph() { filters: vec![], }; - // Add the rule to the knowledge graph graph.add_rule(grandparent_rule); - // Infer new facts let inferred_facts = graph.infer_new_facts(); - // Print inferred facts for triple in inferred_facts { println!( "{} -- {} -- {}", @@ -67,18 +62,14 @@ fn knowledge_graph() { fn backward_chaining() { let mut kg = Reasoner::new(); - // ABox (facts) kg.add_abox_triple("Alice", "parent", "Bob"); kg.add_abox_triple("Bob", "parent", "Charlie"); - // Encode terms let parent = kg.encode_term("parent"); let ancestor = kg.encode_term("ancestor"); let charlie = kg.encode_term("Charlie"); - // Rules let rule1 = Rule { - // ancestor(X, Y) :- parent(X, Y) premise: vec![( Term::Variable("X".to_string()), Term::Constant(parent), @@ -94,7 +85,6 @@ fn backward_chaining() { }; let rule2 = Rule { - // ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z) premise: vec![ ( Term::Variable("X".to_string()), @@ -119,7 +109,6 @@ fn backward_chaining() { kg.add_rule(rule1); kg.add_rule(rule2); - // Query: Who are the ancestors of Charlie? let query = ( Term::Variable("A".to_string()), Term::Constant(ancestor), @@ -128,7 +117,6 @@ fn backward_chaining() { let results = kg.backward_chaining(&query); - // Decode and print results for res in results { if let Some(ancestor_term) = res.get("A") { if let Term::Constant(ancestor_id) = resolve_term(ancestor_term, &res) { @@ -163,7 +151,6 @@ fn test() { println!("\nParsed Rule:"); println!("{:?}", rule); - // Add parsed rule to KnowledgeGraph graph.add_rule(rule); let old_facts = graph.dataset_index.query(None, None, None); @@ -201,7 +188,6 @@ fn test2() { println!("\nParsed Rule:"); println!("{:?}", rule); - // Add parsed rule to KnowledgeGraph kg.add_rule(rule); let old_facts = kg.dataset_index.query(None, None, None); diff --git a/kolibrie/examples/sparql_syntax/knowledge_graph/knowledge_graph_test_scenarios.rs b/kolibrie/examples/sparql_syntax/knowledge_graph/knowledge_graph_test_scenarios.rs index 135b7678..ea48d7c2 100644 --- a/kolibrie/examples/sparql_syntax/knowledge_graph/knowledge_graph_test_scenarios.rs +++ b/kolibrie/examples/sparql_syntax/knowledge_graph/knowledge_graph_test_scenarios.rs @@ -14,18 +14,15 @@ use shared::terms::Term; use shared::terms::TriplePattern; use std::time::Instant; -// Test scenario 1: 100 facts, 2 rules fn test1() { let mut kg = Reasoner::new(); - // Generate 100 facts programmatically (ABox) for i in 0..5 { let subject = format!("person{}", i); - let object = format!("person{}", (i + 1) % 5); // Wraps around to connect the last person with the first + let object = format!("person{}", (i + 1) % 5); kg.add_abox_triple(&subject, "likes", &object); } - // Add transitivity rule: likes(X, Y) & likes(Y, Z) => likes(X, Z) let likes_id = { kg.encode_term("likes") }; kg.add_rule(Rule { @@ -50,7 +47,6 @@ fn test1() { filters: vec![], }); - // Add symmetry rule: likes(X, Y) => likes(Y, X) kg.add_rule(Rule { premise: vec![( Term::Variable("x".to_string()), @@ -66,17 +62,12 @@ fn test1() { filters: vec![], }); - // Run the optimized inference let start = Instant::now(); let inferred_facts = kg.infer_new_facts_semi_naive(); let duration = start.elapsed(); println!("Inference took: {:?}", duration); - let results = kg.query_abox( - Some("person0"), // subject - Some("likes"), // predicate - None, // object -> wildcard - ); + let results = kg.query_abox(Some("person0"), Some("likes"), None); println!("person0 likes these people:"); for triple in results { @@ -86,7 +77,6 @@ fn test1() { println!(" {} {} {}", s_str, p_str, o_str); } - // Display the newly inferred facts println!("Inferred facts:"); for fact in inferred_facts { println!( @@ -98,7 +88,6 @@ fn test1() { } } -// Test scenario 2: One piece of data, 100 rules fn test2() { let mut kg = Reasoner::new(); kg.add_abox_triple("myInstance", "type", "Class0"); @@ -133,11 +122,7 @@ fn test2() { let inferred_facts = kg.infer_new_facts_semi_naive(); let duration = start.elapsed(); println!("Inference took: {:?}", duration); - let results = kg.query_abox( - Some("myInstance"), // subject - Some("type"), // predicate - None, // object -> wildcard - ); + let results = kg.query_abox(Some("myInstance"), Some("type"), None); println!("myInstance has these types after inference:"); for triple in &results { @@ -164,14 +149,12 @@ fn transitivity_benchmark() { for facts_count in 1..=50 { let mut kg = Reasoner::new(); - // Add 'facts_count' facts in a chain: person0 likes person1, person1 likes person2, ... for i in 0..facts_count { let subject = format!("person{}", i); let object = format!("person{}", i + 1); kg.add_abox_triple(&subject, "likes", &object); } - // Only transitivity rule let likes_id = { kg.encode_term("likes") }; kg.add_rule(Rule { @@ -200,7 +183,6 @@ fn transitivity_benchmark() { let inferred_facts = kg.infer_new_facts_semi_naive(); let duration = start.elapsed(); - // Print time in seconds, e.g. 0.00123 println!( "{},{:.6},{:?}", facts_count, diff --git a/kolibrie/examples/sparql_syntax/ml_train/mnist_addition.rs b/kolibrie/examples/sparql_syntax/ml_train/mnist_addition.rs index 7e09897f..f1e87dcd 100644 --- a/kolibrie/examples/sparql_syntax/ml_train/mnist_addition.rs +++ b/kolibrie/examples/sparql_syntax/ml_train/mnist_addition.rs @@ -1,9 +1,9 @@ use std::{error::Error, fs, path::PathBuf, time::Instant}; use kolibrie::{ + database::KolibrieDB, ml_policy::MlExecutionContext, program::{compile_program, execute_program, query_program_result, ProgramOptions}, - sparql_database::SparqlDatabase, }; include!("mnist_program.rs"); @@ -115,7 +115,7 @@ fn main() -> Result<(), Box> { .duration_since(std::time::UNIX_EPOCH)? .as_nanos() )); - let mut db = SparqlDatabase::with_ml_context(MlExecutionContext::trusted_local(&output)?); + let mut db = KolibrieDB::with_ml_context(MlExecutionContext::trusted_local(&output)?); for (index, (image, label)) in train_images.iter().zip(&train_labels).enumerate() { load_image( &mut db, diff --git a/kolibrie/examples/sparql_syntax/ml_train/mnist_compare.rs b/kolibrie/examples/sparql_syntax/ml_train/mnist_compare.rs index b3cf59cc..48879cc4 100644 --- a/kolibrie/examples/sparql_syntax/ml_train/mnist_compare.rs +++ b/kolibrie/examples/sparql_syntax/ml_train/mnist_compare.rs @@ -1,13 +1,13 @@ -//! JSON adapter for the standalone mnist_deepproblog benchmark. All timings exclude JSON I/O. +//! JSON adapter for the standalone mnist_deepproblog benchmark use std::{collections::BTreeMap, error::Error, fs, path::Path, time::Instant}; use kolibrie::{ + database::KolibrieDB, ml_policy::MlExecutionContext, program::{ compile_program, execute_program, parse_probability_literal, query_program_result, PredictionSnapshot, ProgramOptions, ProgramTimings, }, - sparql_database::SparqlDatabase, }; use ml::{MlpNeuralPredicate, OutputType}; use serde::Deserialize; @@ -119,7 +119,7 @@ fn train(req: &Request, output_dir: &Path) -> Result { } } let start = Instant::now(); - let mut db = SparqlDatabase::with_ml_context(MlExecutionContext::trusted_local(output_dir)?); + let mut db = KolibrieDB::with_ml_context(MlExecutionContext::trusted_local(output_dir)?); for (i, (image, label)) in req.train_images.iter().zip(&req.train_labels).enumerate() { load_image( &mut db, @@ -192,7 +192,6 @@ fn train(req: &Request, output_dir: &Path) -> Result { predictions.insert(name.to_string(), rows); } - // Direct prediction with the published artifact, the same rows and batching. let bytes = fs::read(output_dir.join(ARTIFACT))?; if format!("{:x}", Sha256::digest(&bytes)) != report.artifact_sha256 { return Err("published artifact differs from the trained model".into()); @@ -203,7 +202,7 @@ fn train(req: &Request, output_dir: &Path) -> Result { let mut inference_s = BTreeMap::new(); let mut max_difference: f64 = 0.0; for (name, images) in &req.eval_images { - model.predict(&images[..images.len().min(req.batch_size)])?; // untimed warm-up + model.predict(&images[..images.len().min(req.batch_size)])?; let mut probs = Vec::new(); let mut times = Vec::new(); let set_start = Instant::now(); @@ -283,7 +282,7 @@ fn reason(req: &Request) -> Result { let start = Instant::now(); let query = pair_term(index); let (left, right) = (image_term(a), image_term(b)); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); load_knowledge(&mut db)?; load_pair(&mut db, &query, &left, &right); let mut snapshot = PredictionSnapshot::new(); diff --git a/kolibrie/examples/sparql_syntax/ml_train/mnist_oracle.rs b/kolibrie/examples/sparql_syntax/ml_train/mnist_oracle.rs index 0d0730ce..512d2d80 100644 --- a/kolibrie/examples/sparql_syntax/ml_train/mnist_oracle.rs +++ b/kolibrie/examples/sparql_syntax/ml_train/mnist_oracle.rs @@ -1,5 +1,3 @@ -// See the note in mnist_program.rs: `unreachable_pub` cannot be satisfied inside an -// example binary, where `pub` is needed for visibility but nothing can import it. #[allow(dead_code, unreachable_pub)] mod mnist_oracle { use datalog::reasoning::{ diff --git a/kolibrie/examples/sparql_syntax/ml_train/mnist_program.rs b/kolibrie/examples/sparql_syntax/ml_train/mnist_program.rs index df0c3bb9..8563ccf3 100644 --- a/kolibrie/examples/sparql_syntax/ml_train/mnist_program.rs +++ b/kolibrie/examples/sparql_syntax/ml_train/mnist_program.rs @@ -1,11 +1,8 @@ -// `unreachable_pub` is a false positive for an example binary: nothing can import -// from this crate root, so every `pub` here is "unreachable" by construction, while -// still being required for visibility between this module and the example's `main`. #[allow(dead_code, unreachable_pub)] mod mnist_program { use std::collections::BTreeMap; - use kolibrie::sparql_database::SparqlDatabase; + use kolibrie::database::KolibrieDB; pub const MNIST: &str = "http://mnist/"; pub const DIGIT: &str = "http://mnist/digit"; @@ -154,8 +151,11 @@ WHERE { let digit = |value: &str| labels.iter().position(|label| label == value); let mut totals: BTreeMap<(usize, usize), Vec<&str>> = BTreeMap::new(); for (fact, (left, right, total)) in &facts { - let ([left], [right], [total]) = (left.as_slice(), right.as_slice(), total.as_slice()) else { - return Err(format!("{fact} needs exactly one addend1, addend2 and total")); + let ([left], [right], [total]) = (left.as_slice(), right.as_slice(), total.as_slice()) + else { + return Err(format!( + "{fact} needs exactly one addend1, addend2 and total" + )); }; let (Some(a), Some(b)) = (digit(left), digit(right)) else { return Err(format!("{fact} has an addend outside the digit domain")); @@ -173,7 +173,10 @@ WHERE { } } if totals.len() != 100 { - return Err(format!("expected 100 ordered pairs, found {}", totals.len())); + return Err(format!( + "expected 100 ordered pairs, found {}", + totals.len() + )); } Ok(()) } @@ -190,7 +193,7 @@ WHERE { rows } - pub fn load_knowledge(db: &mut SparqlDatabase) -> Result<(), String> { + pub fn load_knowledge(db: &mut KolibrieDB) -> Result<(), String> { let rows = knowledge_triples(); validate_addition_table(&rows[..300])?; for (subject, predicate, object) in &rows { @@ -199,7 +202,7 @@ WHERE { Ok(()) } - pub fn load_pair(db: &mut SparqlDatabase, query: &str, left: &str, right: &str) { + pub fn load_pair(db: &mut KolibrieDB, query: &str, left: &str, right: &str) { db.add_triple_parts(query, LEFT, left); db.add_triple_parts(query, RIGHT, right); } @@ -216,7 +219,7 @@ WHERE { } pub fn load_image( - db: &mut SparqlDatabase, + db: &mut KolibrieDB, image: &str, pixels: &[f64], split: &str, @@ -275,7 +278,10 @@ WHERE { .collect::>() .join("\n "); let features = (0..784).map(|j| format!("?p{j}")).collect::>(); - let labels = (0..10).map(|d| format!("mnist:d{d}")).collect::>().join(", "); + let labels = (0..10) + .map(|d| format!("mnist:d{d}")) + .collect::>() + .join(", "); format!( r#"MODEL "digit_net" {{ ARCH MLP {{ HIDDEN [64, 32] }} diff --git a/kolibrie/examples/sparql_syntax/ml_train/mnist_train.rs b/kolibrie/examples/sparql_syntax/ml_train/mnist_train.rs index ad4ca1a3..8a03b3db 100644 --- a/kolibrie/examples/sparql_syntax/ml_train/mnist_train.rs +++ b/kolibrie/examples/sparql_syntax/ml_train/mnist_train.rs @@ -1,11 +1,3 @@ -/* - * Small MNIST example for Kolibrie's SDD-based training path - * - * The example loads a subset of MNIST into the RDF store, trains a simple - * classifier through the first-class neural syntax, and then runs a few - * exact-probability checks with SDD tags - */ - use std::fs::File; use std::io::Read; use std::path::Path; @@ -13,17 +5,16 @@ use std::time::Instant; use datalog::reasoning::materialisation::sdd_seed_materialise::infer_new_facts_with_sdd_seed_specs; use datalog::reasoning::Reasoner; +use kolibrie::database::KolibrieDB; use kolibrie::ml::execute_train::execute_ml_training_owned; use kolibrie::ml::neural_relations::{lower_train_decl_to_owned, register_neural_declarations}; use kolibrie::parser::parse_combined_query; -use kolibrie::sparql_database::SparqlDatabase; use ml::MlpNeuralPredicate; use shared::provenance::Provenance; use shared::sdd::{SddId, SddProvenance}; use shared::seed_spec::{ExclusiveChoice, SeedSpec}; use shared::triple::Triple; -// Dataset and model settings const DATASET_DIR: &str = concat!( env!("CARGO_MANIFEST_DIR"), "/examples/sparql_syntax/ml_train/mnist-dataset" @@ -35,7 +26,6 @@ const PIXELS: usize = 784; const NUM_DIGITS: usize = 10; const NS: &str = "http://example.org/mnist#"; -// Read MNIST IDX files fn read_u32_be(bytes: &[u8], offset: usize) -> u32 { u32::from_be_bytes([ bytes[offset], @@ -76,8 +66,7 @@ fn load_labels(filename: &str, max: usize) -> Vec { raw[8..8 + n.min(max)].to_vec() } -// Load the dataset into the RDF store -fn populate_rdf_store(db: &mut SparqlDatabase, images: &[Vec], labels: &[u8]) { +fn populate_rdf_store(db: &mut KolibrieDB, images: &[Vec], labels: &[u8]) { let label_pred = format!("{}label", NS); for (i, (pixels, &label)) in images.iter().zip(labels.iter()).enumerate() { let subject = format!("{}train_{}", NS, i); @@ -89,9 +78,6 @@ fn populate_rdf_store(db: &mut SparqlDatabase, images: &[Vec], labels: &[u8 } } -// Build the first-class neural program used by this example. -// The query is generated because spelling out 784 feature variables by hand -// would make the file hard to read fn build_first_class_program() -> String { let feature_vars: Vec = (0..PIXELS).map(|i| format!("?p{}", i)).collect(); let features_csv = feature_vars.join(", "); @@ -100,7 +86,6 @@ fn build_first_class_program() -> String { .collect::>() .join(", "); - // One input triple per pixel let where_patterns: String = (0..PIXELS) .map(|i| format!("?sample <{}pixel_{}> ?p{} .", NS, i, i)) .collect::>() @@ -167,7 +152,6 @@ fn print_program_excerpt(q: &str) { } } -// Evaluate on the held-out test set fn evaluate_accuracy(model: &MlpNeuralPredicate, images: &[Vec], labels: &[u8]) -> f64 { let mut correct = 0usize; for (pixels, &true_label) in images.iter().zip(labels.iter()) { @@ -187,7 +171,6 @@ fn evaluate_accuracy(model: &MlpNeuralPredicate, images: &[Vec], labels: &[ 100.0 * correct as f64 / images.len() as f64 } -// Show how the predicted class distribution can be reused in SDD reasoning fn or_tags(prov: &SddProvenance, tags: &[SddId]) -> SddId { tags.iter() .skip(1) @@ -200,7 +183,6 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test let prime_set: &[usize] = &[2, 3, 5, 7]; let even_set: &[usize] = &[0, 2, 4, 6, 8]; - // Used for a simple partition sanity check let even_non_prime_set: &[usize] = &[0, 4, 6, 8]; let odd_non_prime_set: &[usize] = &[1, 9]; @@ -208,7 +190,6 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test let pixels = &test_images[demo_idx]; let true_label = test_labels[demo_idx] as usize; - // Neural forward pass let (_, probs) = model .forward_with_grads(&[pixels.clone()]) .expect("forward pass failed"); @@ -221,11 +202,10 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test .map(|(i, _)| i) .unwrap_or(0); - // Turn the predicted digit distribution into an exclusive SDD seed group let sample_uri = format!("{}test_{}", NS, demo_idx); let pred_uri = format!("{}predicted", NS); - let mini_db = SparqlDatabase::with_ml_context(local_ml::trusted_context()); + let mini_db = KolibrieDB::with_ml_context(local_ml::trusted_context()); let sample_enc = mini_db.encode_term_star(&sample_uri); let pred_enc = mini_db.encode_term_star(&pred_uri); @@ -241,7 +221,6 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test }) .collect(); - // Build the exact-one constraint and seed tags let mut local_reasoner = Reasoner::new(); let (_derived, tag_store) = infer_new_facts_with_sdd_seed_specs( &mut local_reasoner, @@ -253,7 +232,6 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test let prov = tag_store.provenance().clone(); - // Collect the tag for each digit let digit_tags: Vec = (0..NUM_DIGITS) .map(|d| { let t = Triple { @@ -265,7 +243,6 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test }) .collect(); - // Ask a few simple queries over the class tags let prime_tags: Vec = prime_set.iter().map(|&d| digit_tags[d]).collect(); let even_tags: Vec = even_set.iter().map(|&d| digit_tags[d]).collect(); @@ -273,7 +250,6 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test let p_even = prov.recover_probability(&or_tags(&prov, &even_tags)); let p_predicted = prov.recover_probability(&digit_tags[predicted]); - // prime ∪ even_non_prime ∪ odd_non_prime covers all digits without overlap let enp_tags: Vec = even_non_prime_set.iter().map(|&d| digit_tags[d]).collect(); let onp_tags: Vec = odd_non_prime_set.iter().map(|&d| digit_tags[d]).collect(); let p_even_non_prime = prov.recover_probability(&or_tags(&prov, &enp_tags)); @@ -302,7 +278,6 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test ); println!(" P(prime ∈ {{2,3,5,7}}) via SDD WMC = {:.4}", p_prime); println!(" P(even ∈ {{0,2,4,6,8}}) via SDD WMC = {:.4}", p_even); - // This sum should be exactly 1.0 println!( " Partition check prime({:.3}) + even∩¬prime({:.3}) + odd∩¬prime({:.3}) = {:.4}", p_prime, @@ -317,12 +292,10 @@ fn neurosymbolic_demo(model: &MlpNeuralPredicate, test_images: &[Vec], test println!("P(prime) + P(even) can still exceed 1.0 because those sets overlap at digit 2."); } -// Run the end-to-end example fn main() { println!("MNIST training example"); println!("Using first-class neural syntax and SDD-based training.\n"); - // Load the dataset println!("[1/5] Loading MNIST data..."); let t0 = Instant::now(); let train_images = load_images("train-images.idx3-ubyte", TRAIN_COUNT); @@ -336,7 +309,6 @@ fn main() { t0.elapsed().as_secs_f64() ); - // Insert the training samples as RDF triples println!("\n[2/5] Populating RDF triple store..."); println!( " {} samples × {} triples/sample = {} triples total", @@ -345,11 +317,10 @@ fn main() { TRAIN_COUNT * (PIXELS + 1) ); let t1 = Instant::now(); - let mut db = SparqlDatabase::with_ml_context(local_ml::trusted_context()); + let mut db = KolibrieDB::with_ml_context(local_ml::trusted_context()); populate_rdf_store(&mut db, &train_images, &train_labels); println!(" Done ({:.1}s)", t1.elapsed().as_secs_f64()); - // Build and parse the training program println!( "\n[3/5] Building & parsing first-class neural program ({} feature variables)...", PIXELS @@ -381,11 +352,9 @@ fn main() { PIXELS, NUM_DIGITS ); - // Train the model println!("\n[4/5] Training (neural outputs → SDD seeds → WMC gradients → backprop)..."); println!(" Training with neural outputs, SDD seeds, and WMC gradients.\n"); - // Training data comes from the SPARQL query over `db`, so no rules are needed here let empty_reasoner = Reasoner::new(); let t_train = Instant::now(); @@ -397,7 +366,6 @@ fn main() { t_train.elapsed().as_secs_f64() ); - // Evaluate on held-out samples and print a small reasoning demo println!("\n[5/5] Evaluating on {} test samples...", TEST_COUNT); let accuracy = evaluate_accuracy(&model, &test_images, &test_labels); println!(" Test accuracy: {:.1}%", accuracy); diff --git a/kolibrie/examples/sparql_syntax/ml_train/predict_after_train.rs b/kolibrie/examples/sparql_syntax/ml_train/predict_after_train.rs index f44f109f..4d05a1ac 100644 --- a/kolibrie/examples/sparql_syntax/ml_train/predict_after_train.rs +++ b/kolibrie/examples/sparql_syntax/ml_train/predict_after_train.rs @@ -8,8 +8,8 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::ml::neural_relations::execute_neural_program; -use kolibrie::sparql_database::SparqlDatabase; fn tmp_model_path(name: &str) -> String { let nanos = std::time::SystemTime::now() @@ -25,7 +25,7 @@ fn tmp_model_path(name: &str) -> String { } fn main() { - let mut database = SparqlDatabase::with_ml_context(local_ml::trusted_context()); + let mut database = KolibrieDB::with_ml_context(local_ml::trusted_context()); for (sample, label, x0, x1, x2) in [ ("s0", "A", "1", "0", "0"), diff --git a/kolibrie/examples/sparql_syntax/ml_train/provenance_feedback_loop.rs b/kolibrie/examples/sparql_syntax/ml_train/provenance_feedback_loop.rs index f110b274..53d123d6 100644 --- a/kolibrie/examples/sparql_syntax/ml_train/provenance_feedback_loop.rs +++ b/kolibrie/examples/sparql_syntax/ml_train/provenance_feedback_loop.rs @@ -1,17 +1,9 @@ -/* - * Small feedback-loop example for provenance, RDF-star, and neural relations - * - * The flow is: - * probabilistic evidence -> provenance-aware RULE syntax -> RDF-star annotations - * -> SPARQL-star feature query -> train a neural relation -> feed predictions back into RULE syntax - */ - +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; use kolibrie::ml::execute_train::build_ground_reasoner_from_db; use kolibrie::ml::neural_relations::execute_neural_program; use kolibrie::parser::{convert_combined_rule, parse_combined_query}; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; use shared::sdd::SddProvenance; const EX: &str = "http://example.org/"; @@ -38,7 +30,7 @@ fn sensor_iri(id: &str) -> String { format!("{EX}sensor/{id}") } -fn populate_static_data(db: &mut SparqlDatabase, sensors: &[SensorCase]) { +fn populate_static_data(db: &mut KolibrieDB, sensors: &[SensorCase]) { let sensor_type = iri("type"); let gold_response = iri("goldResponse"); let dispatch_policy = iri("dispatchPolicy"); @@ -55,12 +47,7 @@ fn populate_static_data(db: &mut SparqlDatabase, sensors: &[SensorCase]) { db.add_triple_parts(&dispatch_policy, &required_response, LABEL_DISPATCH); } -fn add_probabilistic_fact( - db: &mut SparqlDatabase, - subject: &str, - predicate: &str, - probability: f64, -) { +fn add_probabilistic_fact(db: &mut KolibrieDB, subject: &str, predicate: &str, probability: f64) { db.add_tagged_triple(subject, predicate, "true", probability); } @@ -158,7 +145,7 @@ ORDER BY ?sensor"# ) } -fn execute_sdd_rule_batch(db: &mut SparqlDatabase, rule_inputs: &[String]) -> usize { +fn execute_sdd_rule_batch(db: &mut KolibrieDB, rule_inputs: &[String]) -> usize { let mut reasoner = build_ground_reasoner_from_db(db, None); reasoner.probability_seeds = db.probability_seeds().clone(); @@ -253,7 +240,7 @@ fn numeric_lexical(term: &str) -> String { trimmed.to_string() } -fn provenance_rows_from_rdf_star(db: &SparqlDatabase) -> Vec> { +fn provenance_rows_from_rdf_star(db: &KolibrieDB) -> Vec> { let prob_value = format!("{PROB}value"); let proof_count = format!("{PROB}proofCount"); let risk_signal = iri("riskSignal"); @@ -273,7 +260,7 @@ fn provenance_rows_from_rdf_star(db: &SparqlDatabase) -> Vec> { } let inner = subject[2..subject.len() - 2].trim(); - let (sensor, pred, obj) = SparqlDatabase::split_quoted_triple_content(inner); + let (sensor, pred, obj) = KolibrieDB::split_quoted_triple_content(inner); if pred != risk_signal || obj != high { continue; } @@ -294,7 +281,7 @@ fn provenance_rows_from_rdf_star(db: &SparqlDatabase) -> Vec> { .collect() } -fn materialize_feature_triples_from_rows(db: &mut SparqlDatabase, rows: &[Vec]) -> usize { +fn materialize_feature_triples_from_rows(db: &mut KolibrieDB, rows: &[Vec]) -> usize { let risk_prob_feature = iri("riskProbFeature"); let proof_count_feature = iri("proofCountFeature"); let mut count = 0usize; @@ -388,7 +375,7 @@ fn main() { }, ]; - let mut db = SparqlDatabase::with_ml_context(local_ml::trusted_context()); + let mut db = KolibrieDB::with_ml_context(local_ml::trusted_context()); println!("Syntax-first provenance -> neural relation -> reasoning feedback loop"); println!("Probabilistic base facts are still loaded through a Rust helper; rules, queries, and neural declarations are shown in syntax\n"); diff --git a/kolibrie/examples/sparql_syntax/n3_data/n3_data.rs b/kolibrie/examples/sparql_syntax/n3_data/n3_data.rs index 122f00aa..c189d34b 100644 --- a/kolibrie/examples/sparql_syntax/n3_data/n3_data.rs +++ b/kolibrie/examples/sparql_syntax/n3_data/n3_data.rs @@ -11,7 +11,7 @@ use kolibrie::prelude::*; fn n3_simple_query() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let n3_data = r#" @prefix ex: . diff --git a/kolibrie/examples/sparql_syntax/n3_data/n3_data_advanced.rs b/kolibrie/examples/sparql_syntax/n3_data/n3_data_advanced.rs index 43b1631b..d96a9089 100644 --- a/kolibrie/examples/sparql_syntax/n3_data/n3_data_advanced.rs +++ b/kolibrie/examples/sparql_syntax/n3_data/n3_data_advanced.rs @@ -11,7 +11,7 @@ use kolibrie::prelude::*; fn n3_simple_query() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let n3_data = r#" @prefix ex: . diff --git a/kolibrie/examples/sparql_syntax/n_triples_data/n_triple_10M.rs b/kolibrie/examples/sparql_syntax/n_triples_data/n_triple_10M.rs index 49665f49..771b1c9f 100644 --- a/kolibrie/examples/sparql_syntax/n_triples_data/n_triple_10M.rs +++ b/kolibrie/examples/sparql_syntax/n_triples_data/n_triple_10M.rs @@ -1,48 +1,20 @@ -/* - * Copyright © 2025 Volodymyr Kadzhaia - * Copyright © 2025 Pieter Bonte - * KU Leuven — Stream Intelligence Lab, Belgium - * - * This Source Code Form is subject to the terms of the Mozilla Public - * License, v. 2.0. If a copy of the MPL was not distributed with this file, - * you can obtain one at https://mozilla.org/MPL/2.0/. - * - * - * - * NOTE 1: We are using the benchmark dataset from: - * Waterloo SPARQL Diversity Test Suite (WatDiv) v0.6 - * Source: https://dsg.uwaterloo.ca/watdiv/ - * - * NOTE 2: Before running with the 10M-triple dataset, ensure you have: - * 1) Downloaded `watdiv.10M.nt` into a `benchmark_dataset` directory - * at the project root. - * 2) Created the `benchmark_dataset` directory next to `kolibrie/`. - * (e.g., `mkdir benchmark_dataset && mv watdiv.10M.nt benchmark_dataset/`) - * - * NOTE 3: The watdiv.10M.nt file is approximately 1.5 GB in size. - * - */ - use kolibrie::prelude::*; use std::fs::File; use std::io::{BufRead, BufReader}; use std::time::Instant; -fn parse_large_ntriples_file( - file_path: &str, -) -> Result> { +fn parse_large_ntriples_file(file_path: &str) -> Result> { println!("Starting to parse N-Triples file: {}", file_path); let start_time = Instant::now(); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); - // Much smaller buffer and more aggressive memory management let file = File::open(file_path)?; - let reader = BufReader::with_capacity(64 * 1024, file); // Reduced buffer size + let reader = BufReader::with_capacity(64 * 1024, file); let mut line_count = 0; let mut batch_lines = Vec::new(); - const BATCH_SIZE: usize = 10_000; // Much smaller batch size + const BATCH_SIZE: usize = 10_000; for line_result in reader.lines() { let line = line_result?; @@ -55,26 +27,21 @@ fn parse_large_ntriples_file( line_count += 1; if batch_lines.len() >= BATCH_SIZE { - // Process batch immediately let batch_data = batch_lines.join("\n"); db.parse_ntriples_and_add(&batch_data); - // Aggressive cleanup batch_lines.clear(); batch_lines.shrink_to_fit(); - // Progress info every 100k triples if line_count % 100_000 == 0 { println!("Processed {} triples", line_count); std::hint::black_box(()); - // Optional: small delay to let the system breathe std::thread::sleep(std::time::Duration::from_millis(10)); } } } - // Process remaining batch if !batch_lines.is_empty() { let batch_data = batch_lines.join("\n"); db.parse_ntriples_and_add(&batch_data); @@ -87,7 +54,6 @@ fn parse_large_ntriples_file( start_time.elapsed().as_secs_f64() ); - // Build indexes after parsing - this is where the magic happens println!("Building indexes..."); let index_start = Instant::now(); db.build_all_indexes(); @@ -99,12 +65,10 @@ fn parse_large_ntriples_file( Ok(db) } -fn run_all_queries(db: &mut SparqlDatabase) { +fn run_all_queries(db: &mut KolibrieDB) { const ITERATIONS: usize = 20; - // (name, query) let queries: &[(&str, &str)] = &[ - // C1 ( "C1", r#"PREFIX wsdbm: @@ -131,7 +95,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // C2 ( "C2", r#"PREFIX wsdbm: @@ -159,7 +122,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // C3 ( "C3", r#"PREFIX wsdbm: @@ -184,7 +146,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // F1 ( "F1", r#"PREFIX wsdbm: @@ -208,7 +169,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // F2 ( "F2", r#"PREFIX wsdbm: @@ -234,7 +194,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // F3 ( "F3", r#"PREFIX wsdbm: @@ -258,7 +217,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // F4 ( "F4", r#"PREFIX wsdbm: @@ -285,7 +243,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // F5 ( "F5", r#"PREFIX wsdbm: @@ -309,7 +266,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // L1 ( "L1", r#"PREFIX wsdbm: @@ -330,7 +286,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // L2 ( "L2", r#"PREFIX wsdbm: @@ -351,7 +306,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // L3 ( "L3", r#"PREFIX wsdbm: @@ -371,7 +325,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // L4 ( "L4", r#"PREFIX wsdbm: @@ -391,7 +344,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // L5 ( "L5", r#"PREFIX wsdbm: @@ -412,7 +364,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // S1 ( "S1", r#"PREFIX wsdbm: @@ -439,7 +390,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // S2 ( "S2", r#"PREFIX wsdbm: @@ -461,7 +411,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // S3 ( "S3", r#"PREFIX wsdbm: @@ -483,7 +432,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // S4 ( "S4", r#"PREFIX wsdbm: @@ -505,7 +453,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // S5 ( "S5", r#"PREFIX wsdbm: @@ -527,7 +474,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // S6 ( "S6", r#"PREFIX wsdbm: @@ -548,7 +494,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } "#, ), - // S7 ( "S7", r#"PREFIX wsdbm: @@ -576,7 +521,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { println!("Running query {} ({} iterations)...", name, ITERATIONS); let mut total_time = 0.0; - // let mut last_result:Vec> = Vec::new(); for _ in 0..ITERATIONS { let start = Instant::now(); @@ -591,7 +535,6 @@ fn run_all_queries(db: &mut SparqlDatabase) { } fn main() { - // Set current directory to the root of the project std::env::set_current_dir(std::path::Path::new(env!("CARGO_MANIFEST_DIR"))) .expect("Failed to set project root as current directory"); diff --git a/kolibrie/examples/sparql_syntax/n_triples_data/n_triples_data.rs b/kolibrie/examples/sparql_syntax/n_triples_data/n_triples_data.rs index df6b6922..e5062a39 100644 --- a/kolibrie/examples/sparql_syntax/n_triples_data/n_triples_data.rs +++ b/kolibrie/examples/sparql_syntax/n_triples_data/n_triples_data.rs @@ -11,9 +11,8 @@ use kolibrie::prelude::*; fn n_triple_simple_query() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); - // N-Triples format let ntriples_data = r#" . "Jane Doe" . @@ -22,7 +21,6 @@ fn n_triple_simple_query() { "30"^^ . "#; - // Use the parse_ntriples function db.parse_ntriples_and_add(ntriples_data); let sparql_query = r#" diff --git a/kolibrie/examples/sparql_syntax/n_triples_data/repro_sensor_path_benchmark.rs b/kolibrie/examples/sparql_syntax/n_triples_data/repro_sensor_path_benchmark.rs index 9434a8bf..961b9d00 100644 --- a/kolibrie/examples/sparql_syntax/n_triples_data/repro_sensor_path_benchmark.rs +++ b/kolibrie/examples/sparql_syntax/n_triples_data/repro_sensor_path_benchmark.rs @@ -8,8 +8,8 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; -use kolibrie::sparql_database::SparqlDatabase; use std::fs; use std::sync::atomic::{AtomicBool, Ordering}; use std::sync::Arc; @@ -50,23 +50,16 @@ struct QueryMeasurement { end_rss_bytes: Option, } -/// Sensors in the generated stand-in corpus, each monitoring one chain. +/// Sensors in the generated stand-in corpus, each monitoring one chain const GENERATED_SENSORS: usize = 2_000; -/// Segments per chain. Six is the query's window, so a chain of this length yields -/// `GENERATED_CHAIN - 5` matches, and anything shorter yields none. +/// Segments per chain const GENERATED_CHAIN: usize = 10; -/// One chain in this many is left unmatched, by having its middle segment monitored -/// by nobody. Without that the query matches every window it looks at, which makes -/// the join cheaper than the corpus this was written against. +/// One chain in this many is left unmatched, by having its middle segment monitored by nobody const GENERATED_BROKEN_IN: usize = 4; -/// N-Triples in the shape [`REPRO_QUERY`] looks for: chains of `base:connectsTo` -/// segments, all `base:monitoredBy` one `base:Sensor`, everything typed. -/// -/// A stand-in for the Train Benchmark corpus, not a reproduction of it. Timings taken -/// against this are comparable with each other and with nothing else. +/// N-Triples in the shape [`REPRO_QUERY`] looks for fn generated_dataset() -> String { const BASE: &str = "http://www.semanticweb.org/ontologies/2015/trainbenchmark#"; const RDF_TYPE: &str = "http://www.w3.org/1999/02/22-rdf-syntax-ns#type"; @@ -79,8 +72,6 @@ fn generated_dataset() -> String { for segment in 0..GENERATED_CHAIN { let id = format!("{BASE}segment{sensor}_{segment}"); out.push_str(&format!("<{id}> <{RDF_TYPE}> <{BASE}Segment> .\n")); - // The break leaves one segment unmonitored, so no window containing it - // can satisfy the query's six `monitoredBy` patterns. if !(broken && segment == GENERATED_CHAIN / 2) { out.push_str(&format!( "<{id}> <{BASE}monitoredBy> <{BASE}sensor{sensor}> .\n" @@ -97,12 +88,7 @@ fn generated_dataset() -> String { out } -/// The corpus file, if one is to be found. -/// -/// First argument, then `KOLIBRIE_REPRO_DATASET`, then the path this example was -/// written against — which `.gitignore` excludes, so a fresh checkout never has it. -/// Returns `None` when none of them is readable, and the corpus is generated instead; -/// the example used to fail with a bare `No such file or directory` here. +/// The corpus file, if one is to be found fn dataset_path() -> Option { let mut candidates = std::env::args() .nth(1) @@ -144,7 +130,7 @@ fn main() -> Result<(), Box> { println!("Input lines: {}", line_count); let load_start = Instant::now(); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_ntriples_and_add(&data); let load_time = load_start.elapsed(); println!("Loaded triples: {}", db.default_triple_count()); @@ -257,10 +243,9 @@ fn bytes_to_mb(bytes: u64) -> f64 { bytes as f64 / 1_048_576.0 } -// SAFETY: `counters` is zeroed and has its `cb` set to its own size before the call, -// which is the contract `GetProcessMemoryInfo` documents; the pointer is to a live -// local, and `GetCurrentProcess` returns a pseudo-handle that needs no closing. The -// return value is checked before any field is read. +// SAFETY: `counters` is zeroed and has its `cb` set to its own size before the call, which is the +// contract `GetProcessMemoryInfo` documents; the pointer is to a live local, and +// `GetCurrentProcess` returns a pseudo-handle that needs no closing #[allow(unsafe_code)] #[cfg(windows)] fn current_rss_bytes() -> Option { @@ -284,9 +269,7 @@ fn current_rss_bytes() -> Option { } } -// SAFETY: `sysconf` takes an integer name and returns a long, touching no memory the -// caller owns. `_SC_PAGESIZE` is a valid name, and a non-positive result is treated as -// failure rather than used as a multiplier. +// SAFETY: `sysconf` takes an integer name and returns a long, touching no memory the caller owns #[allow(unsafe_code)] #[cfg(all(unix, not(target_os = "macos")))] fn current_rss_bytes() -> Option { @@ -300,14 +283,8 @@ fn current_rss_bytes() -> Option { } } -// SAFETY: `getrusage` writes into the `rusage` it is given, and a zeroed `rusage` is a -// valid initial value for a plain C struct of integers. `RUSAGE_SELF` is a valid who -// argument, and the return value is checked before `ru_maxrss` is read. -// -// Note this measures something different from the other two: `ru_maxrss` is *peak* -// resident size, where /proc/self/statm and `WorkingSetSize` are current. On macOS it -// is already in bytes, where Linux reports it in kilobytes. So the figure this example -// prints on macOS is a high-water mark and will not fall back down. +// SAFETY: `getrusage` writes into the `rusage` it is given, and a zeroed `rusage` is a valid +// initial value for a plain C struct of integers #[allow(unsafe_code)] #[cfg(target_os = "macos")] fn current_rss_bytes() -> Option { diff --git a/kolibrie/examples/sparql_syntax/nested_query/nested_query.rs b/kolibrie/examples/sparql_syntax/nested_query/nested_query.rs index 391c65e6..807806b9 100644 --- a/kolibrie/examples/sparql_syntax/nested_query/nested_query.rs +++ b/kolibrie/examples/sparql_syntax/nested_query/nested_query.rs @@ -29,7 +29,7 @@ fn simple_select() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#" @@ -50,7 +50,6 @@ fn simple_select() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results: {:?}", results); } diff --git a/kolibrie/examples/sparql_syntax/rsp_ql_syntax/retrieve.rs b/kolibrie/examples/sparql_syntax/rsp_ql_syntax/retrieve.rs index b9925973..884da9a0 100644 --- a/kolibrie/examples/sparql_syntax/rsp_ql_syntax/retrieve.rs +++ b/kolibrie/examples/sparql_syntax/rsp_ql_syntax/retrieve.rs @@ -36,7 +36,6 @@ WHERE { println!("Remaining input: '{}'", remaining.trim()); println!(); - // Print RETRIEVE clause details if let Some(retrieve) = &parsed_query.retrieve_clause { println!("RETRIEVE Clause:"); println!(" Mode: {:?}", retrieve.mode); diff --git a/kolibrie/examples/sparql_syntax/rsp_ql_syntax/retrieve_multiple_window.rs b/kolibrie/examples/sparql_syntax/rsp_ql_syntax/retrieve_multiple_window.rs index d6ae56d6..783866d0 100644 --- a/kolibrie/examples/sparql_syntax/rsp_ql_syntax/retrieve_multiple_window.rs +++ b/kolibrie/examples/sparql_syntax/rsp_ql_syntax/retrieve_multiple_window.rs @@ -43,7 +43,6 @@ WHERE { println!("Remaining input: '{}'", remaining.trim()); println!(); - // Print RETRIEVE clause details if let Some(retrieve) = &parsed_query.retrieve_clause { println!("RETRIEVE Clause:"); println!(" Mode: {:?}", retrieve.mode); @@ -60,7 +59,6 @@ WHERE { println!(); } - // Print REGISTER clause details if let Some(register) = &parsed_query.register_clause { println!("REGISTER Clause:"); println!(" Stream Type: {:?}", register.stream_type); @@ -87,7 +85,6 @@ WHERE { } println!(); - // Print window-specific queries println!( " Window Queries ({} blocks):", register.query.window_blocks.len() diff --git a/kolibrie/examples/sparql_syntax/select_all/select_all.rs b/kolibrie/examples/sparql_syntax/select_all/select_all.rs index 5d88a902..60e301d0 100644 --- a/kolibrie/examples/sparql_syntax/select_all/select_all.rs +++ b/kolibrie/examples/sparql_syntax/select_all/select_all.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Simple select all fn simple_select_all() { let rdf_data = r#" @@ -50,7 +49,7 @@ fn simple_select_all() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX foaf: PREFIX ds: SELECT * WHERE {?employee foaf:name ?name . ?employee foaf:title ?title . ?employee foaf:workplaceHomepage ?workplaceHomepage . ?employee ds:full_or_part_time ?full_or_part_time . ?employee ds:salary_or_hourly ?salary_or_hourly . ?employee ds:annual_salary ?salary}"#; @@ -59,7 +58,6 @@ fn simple_select_all() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { let name = &result[0]; diff --git a/kolibrie/examples/sparql_syntax/select_semicolon/select_semicolon.rs b/kolibrie/examples/sparql_syntax/select_semicolon/select_semicolon.rs index a32aba90..1054a3a6 100644 --- a/kolibrie/examples/sparql_syntax/select_semicolon/select_semicolon.rs +++ b/kolibrie/examples/sparql_syntax/select_semicolon/select_semicolon.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Simple select fn simple_select() { let rdf_data = r#" @@ -29,7 +28,7 @@ fn simple_select() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#" @@ -44,7 +43,6 @@ fn simple_select() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results: {:?}", results); } diff --git a/kolibrie/examples/sparql_syntax/simple_join/simple_join.rs b/kolibrie/examples/sparql_syntax/simple_join/simple_join.rs index f905ba8c..11cbaac8 100644 --- a/kolibrie/examples/sparql_syntax/simple_join/simple_join.rs +++ b/kolibrie/examples/sparql_syntax/simple_join/simple_join.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Simple join fn simple_join() { let rdf_data = r#" "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ex: SELECT ?person ?location ?city WHERE {?person ex:worksAt ?location . ?location ex:located ?city}"#; @@ -39,7 +38,6 @@ fn simple_join() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [person, location, city] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/simple_select/simple_select.rs b/kolibrie/examples/sparql_syntax/simple_select/simple_select.rs index 701611a3..301b6b22 100644 --- a/kolibrie/examples/sparql_syntax/simple_select/simple_select.rs +++ b/kolibrie/examples/sparql_syntax/simple_select/simple_select.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Simple select fn simple_select() { let rdf_data = r#" "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ex: SELECT ?person ?location WHERE {?person ex:worksAt ?location}"#; @@ -39,7 +38,6 @@ fn simple_select() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [person, location] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/simple_select/simple_select_synth_data.rs b/kolibrie/examples/sparql_syntax/simple_select/simple_select_synth_data.rs index 5766062f..641a75ae 100644 --- a/kolibrie/examples/sparql_syntax/simple_select/simple_select_synth_data.rs +++ b/kolibrie/examples/sparql_syntax/simple_select/simple_select_synth_data.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Simple select on my synthetic data (took small part of it) fn simple_select_synth_data() { let rdf_data = r#" @@ -49,7 +48,7 @@ fn simple_select_synth_data() { "#; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); let sparql = r#"PREFIX ds: SELECT ?employee ?salary WHERE {?employee ds:annual_salary ?salary}"#; @@ -58,7 +57,6 @@ fn simple_select_synth_data() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:"); for result in results { if let [employee, salary] = &result[..] { diff --git a/kolibrie/examples/sparql_syntax/sparql_syntax.rs b/kolibrie/examples/sparql_syntax/sparql_syntax.rs index 3c41624e..8085d1d1 100644 --- a/kolibrie/examples/sparql_syntax/sparql_syntax.rs +++ b/kolibrie/examples/sparql_syntax/sparql_syntax.rs @@ -15,10 +15,8 @@ use shared::query::SparqlOperation; use std::time::Instant; fn streamertail_optimizer_sparql() { - // Step 1: Initialize the database - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // Step 2: Generate RDF data with 1_000_000 triples let mut rdf_data = String::from( r#""#, ); @@ -42,7 +40,6 @@ fn streamertail_optimizer_sparql() { database.parse_rdf(&rdf_data); database.build_all_indexes(); - // Step 3: Define the SPARQL query let sparql_query = r#" PREFIX ex: SELECT ?loved_person @@ -51,7 +48,6 @@ fn streamertail_optimizer_sparql() { ?loved_person ex:loves }"#; - // Step 4: Parse the SPARQL query if let Ok((_, parsed)) = parse_combined_query(sparql_query) { let Some(SparqlOperation::Select(select)) = parsed.sparql else { eprintln!("Expected a SELECT query."); @@ -59,26 +55,21 @@ fn streamertail_optimizer_sparql() { }; let prefixes = parsed.prefixes; - // Merge prefixes into the database database.extend_prefixes(prefixes.clone()); - // Build the logical plan let logical_plan = build_logical_plan_from_group(&select.pattern, &prefixes, &mut database).unwrap(); - // Step 5: Initialize the optimizer and find the best physical plan let mut optimizer = Streamertail::new(&database); let physical_plan = optimizer.find_best_plan(&logical_plan); println!("Logical Plan: {:?}", logical_plan); println!("Optimized Physical Plan: {:?}", physical_plan); - // Step 6: Execute the physical plan let start = Instant::now(); let results = physical_plan.execute(&mut database); let duration = start.elapsed(); - // Step 7: Extract and print the selected variables let selected_vars: Vec = select .variables .iter() diff --git a/kolibrie/examples/sparql_syntax/user_defined_function/udf.rs b/kolibrie/examples/sparql_syntax/user_defined_function/udf.rs index c98b4d20..3aae501d 100644 --- a/kolibrie/examples/sparql_syntax/user_defined_function/udf.rs +++ b/kolibrie/examples/sparql_syntax/user_defined_function/udf.rs @@ -10,7 +10,6 @@ use kolibrie::prelude::*; -// Adding udf fn udf() { let rdf_data = r##" "##; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); - database.register_udf("concatValues", |args: Vec<&str>| { - args.join("_") // Concatenates arguments with an underscore - }); + database.register_udf("concatValues", |args: Vec<&str>| args.join("_")); let sparql = r#" PREFIX ex: @@ -42,7 +39,6 @@ fn udf() { .expect("query should run") .into_rows(); - // Now the main function is responsible for printing the results println!("Results:{:?}", results); } diff --git a/kolibrie/examples/sparql_syntax/values_keyword/values.rs b/kolibrie/examples/sparql_syntax/values_keyword/values.rs index 4b42336c..f9b94309 100644 --- a/kolibrie/examples/sparql_syntax/values_keyword/values.rs +++ b/kolibrie/examples/sparql_syntax/values_keyword/values.rs @@ -31,19 +31,15 @@ fn values() { "#; - // Create an instance of SparqlDatabase - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(rdf_data); - // Sample SPARQL query using VALUES clause let sparql_query = r#"PREFIX ex: SELECT ?person ?company WHERE { VALUES ?company { ex:companyA ex:companyB } ?person ex:worksAt ?company }"#; - // Execute the query on the database let results = execute(sparql_query, &mut database) .expect("query should run") .into_rows(); - // Display the results println!("Query Results:"); for result in results { println!("{:?}", result); diff --git a/kolibrie/examples/sparql_syntax/volcano_optimizer/volcano.rs b/kolibrie/examples/sparql_syntax/volcano_optimizer/volcano.rs index 09641fc2..41bb203f 100644 --- a/kolibrie/examples/sparql_syntax/volcano_optimizer/volcano.rs +++ b/kolibrie/examples/sparql_syntax/volcano_optimizer/volcano.rs @@ -20,10 +20,8 @@ use std::time::Instant; fn simple_streamertail_optimizer() { println!("=== Simple Volcano Optimizer Example ==="); - // Step 1: Execute the physical plan on a sample database - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // Add some sample data let alice_id = database.dictionary_mut().encode("http://example.org/alice"); let bob_id = database.dictionary_mut().encode("http://example.org/bob"); let name_id = database.dictionary_mut().encode("foaf:name"); @@ -57,7 +55,6 @@ fn simple_streamertail_optimizer() { object: age_25, }); - // Step 2: Create the logical plan using new API let name_scan = LogicalOperator::scan(( Term::Variable("person".to_string()), Term::Constant(name_id), @@ -72,14 +69,12 @@ fn simple_streamertail_optimizer() { let logical_plan = LogicalOperator::join(name_scan, age_scan); - // Step 3: Initialize the optimizer and optimize let mut optimizer = Streamertail::new(&database); let best_plan = optimizer.find_best_plan(&logical_plan); println!("Logical Plan: {:?}", logical_plan); println!("Optimized Physical Plan: {:?}", best_plan); - // Step 4: Execute the optimized plan let start = Instant::now(); let results = best_plan.execute(&mut database); let duration = start.elapsed(); @@ -95,10 +90,8 @@ fn simple_streamertail_optimizer() { fn streamertail_optimizer_multiple_triples() { println!("=== Multiple Triples Example ==="); - // Step 1: Execute the physical plan on a sample database - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // Step 2: Add some triples to the database for i in 0..10 { let person_uri = format!("http://example.org/person{}", i); let age_value = format!("{}", 20 + i); @@ -111,21 +104,18 @@ fn streamertail_optimizer_multiple_triples() { let name_value = database.dictionary_mut().encode(&format!("Person{}", i)); let age_value_id = database.dictionary_mut().encode(&age_value); - // Add name triple database.add_triple(Triple { subject: person_id, predicate: name_id, object: name_value, }); - // Add age triple database.add_triple(Triple { subject: person_id, predicate: age_id, object: age_value_id, }); - // Add type triple database.add_triple(Triple { subject: person_id, predicate: type_id, @@ -138,7 +128,6 @@ fn streamertail_optimizer_multiple_triples() { database.default_triple_count() ); - // Step 3: Create the logical plan with joins let type_id = database.dictionary_mut().encode("rdf:type"); let person_type = database.dictionary_mut().encode("foaf:Person"); let age_id = database.dictionary_mut().encode("foaf:age"); @@ -155,20 +144,17 @@ fn streamertail_optimizer_multiple_triples() { Term::Variable("age".to_string()), )); - // Create a filter condition let condition = Condition::new("age".to_string(), ">".to_string(), "25".to_string()); let filtered_age = LogicalOperator::selection(age_scan, condition); let logical_plan = LogicalOperator::join(type_scan, filtered_age); - // Step 4: Initialize the optimizer and optimize let mut optimizer = Streamertail::new(&database); let best_plan = optimizer.find_best_plan(&logical_plan); println!("Logical Plan: {:?}", logical_plan); println!("Optimized Physical Plan: {:?}", best_plan); - // Step 5: Execute the optimized plan let start = Instant::now(); let results = best_plan.execute(&mut database); let duration = start.elapsed(); @@ -184,10 +170,8 @@ fn streamertail_optimizer_multiple_triples() { fn streamertail_optimizer_rdf() { println!("=== RDF Example with SPARQL Parsing ==="); - // Step 1: Execute the physical plan on a sample database - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // Step 2: Add some triples to the database for i in 0..10 { let person_uri = format!("http://example.org/person{}", i); let company_uri = format!("http://example.org/company{}", i % 3); @@ -202,14 +186,12 @@ fn streamertail_optimizer_rdf() { let company_id = database.dictionary_mut().encode(&company_uri); let location = database.dictionary_mut().encode(&format!("City{}", i % 3)); - // Person works at company database.add_triple(Triple { subject: person_id, predicate: works_at_id, object: company_id, }); - // Company located in city database.add_triple(Triple { subject: company_id, predicate: located_id, @@ -222,7 +204,6 @@ fn streamertail_optimizer_rdf() { database.default_triple_count() ); - // Step 3: Create logical plan using build_logical_plan utility let variables = vec![("VAR", "person"), ("VAR", "location")]; let patterns = vec![ ("?person", "http://example.org/worksAt", "?org"), @@ -242,14 +223,12 @@ fn streamertail_optimizer_rdf() { None, ); - // Step 4: Initialize the optimizer and optimize let mut optimizer = Streamertail::new(&database); let best_plan = optimizer.find_best_plan(&logical_plan); println!("Logical Plan: {:?}", logical_plan); println!("Optimized Physical Plan: {:?}", best_plan); - // Step 5: Execute the optimized plan let start = Instant::now(); let results = best_plan.execute(&mut database); let duration = start.elapsed(); @@ -265,10 +244,8 @@ fn streamertail_optimizer_rdf() { fn streamertail_optimizer_index_patterns() { println!("=== Index Pattern Examples ==="); - // Step 1: Initialize the database - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // Step 2: Add triples to the database for i in 0..100 { let person_uri = format!("http://example.org/person{}", i); let age_value = format!("{}", i); @@ -304,9 +281,7 @@ fn streamertail_optimizer_index_patterns() { let age_42_id = database.dictionary_mut().encode("42"); let age_69_id = database.dictionary_mut().encode("69"); - // Define different triple patterns to test various index strategies let patterns = vec![ - // (1) No bounds => full table scan ( "Unbound pattern", ( @@ -315,7 +290,6 @@ fn streamertail_optimizer_index_patterns() { Term::Variable("o".to_string()), ), ), - // (2) predicate+object => POS index ( "Predicate+Object bound", ( @@ -324,7 +298,6 @@ fn streamertail_optimizer_index_patterns() { Term::Constant(age_42_id), ), ), - // (3) subject bound => SPO index ( "Subject bound", ( @@ -333,7 +306,6 @@ fn streamertail_optimizer_index_patterns() { Term::Variable("o".to_string()), ), ), - // (4) predicate bound => PSO index ( "Predicate bound", ( @@ -342,7 +314,6 @@ fn streamertail_optimizer_index_patterns() { Term::Variable("o".to_string()), ), ), - // (5) object bound => OSP index ( "Object bound", ( @@ -351,7 +322,6 @@ fn streamertail_optimizer_index_patterns() { Term::Constant(age_69_id), ), ), - // (6) fully bound => direct check ( "Fully bound", ( @@ -362,7 +332,6 @@ fn streamertail_optimizer_index_patterns() { ), ]; - // For each pattern, build + optimize + execute for (i, (description, pattern)) in patterns.iter().enumerate() { let logical_plan = LogicalOperator::scan(pattern.clone()); let mut optimizer = Streamertail::new(&database); @@ -372,7 +341,6 @@ fn streamertail_optimizer_index_patterns() { println!("Pattern: {:?}", pattern); println!("Physical Plan: {:?}", best_plan); - // Execute the best plan let start = Instant::now(); let results = best_plan.execute(&mut database); let duration = start.elapsed(); @@ -388,10 +356,8 @@ fn streamertail_optimizer_index_patterns() { fn streamertail_optimizer_performance_test() { println!("=== Performance Test ==="); - // Step 1: Initialize the database with larger dataset - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // Step 2: Add many triples to test performance println!("Adding triples..."); let start = Instant::now(); @@ -435,12 +401,10 @@ fn streamertail_optimizer_performance_test() { database.default_triple_count() ); - // Test different query types let name_id = database.dictionary_mut().encode("foaf:name"); let age_id = database.dictionary_mut().encode("foaf:age"); let works_at_id = database.dictionary_mut().encode("ex:worksAt"); - // Query 1: Simple scan println!("\n--- Query 1: Simple Scan ---"); let logical_plan1 = LogicalOperator::scan(( Term::Variable("person".to_string()), @@ -461,7 +425,6 @@ fn streamertail_optimizer_performance_test() { println!("Execution time: {:?}", execution_time1); println!("Results: {} rows", results1.len()); - // Query 2: Join query println!("\n--- Query 2: Join Query ---"); let name_scan = LogicalOperator::scan(( Term::Variable("person".to_string()), @@ -490,7 +453,6 @@ fn streamertail_optimizer_performance_test() { println!("Execution time: {:?}", execution_time2); println!("Results: {} rows", results2.len()); - // Query 3: Complex join with filter println!("\n--- Query 3: Complex Join with Filter ---"); let works_scan = LogicalOperator::scan(( Term::Variable("person".to_string()), diff --git a/kolibrie/examples/synthetic_data/gen_data.rs b/kolibrie/examples/synthetic_data/gen_data.rs index 919bfaa0..f7cf94a1 100644 --- a/kolibrie/examples/synthetic_data/gen_data.rs +++ b/kolibrie/examples/synthetic_data/gen_data.rs @@ -20,15 +20,13 @@ use std::sync::atomic::{AtomicUsize, Ordering}; use std::time::{Duration, Instant}; const POSITIONS: &[&str] = &["Manager", "Developer", "Salesperson"]; -const TOTAL_EMPLOYEES: usize = 10000; // 1 billion -const BATCH_SIZE: usize = 100_000; // Number of records per batch -const CHANNEL_CAPACITY: usize = 100; // Number of batches that can be queued +const TOTAL_EMPLOYEES: usize = 10000; +const BATCH_SIZE: usize = 100_000; +const CHANNEL_CAPACITY: usize = 100; fn main() -> std::io::Result<()> { - // Start timing the operation let start_time = Instant::now(); - // Calculate the number of batches needed let num_batches = (TOTAL_EMPLOYEES + BATCH_SIZE - 1) / BATCH_SIZE; println!( @@ -36,22 +34,16 @@ fn main() -> std::io::Result<()> { TOTAL_EMPLOYEES, num_batches ); - // Set up crossbeam channel with bounded capacity to limit memory usage let (sender, receiver) = bounded::(CHANNEL_CAPACITY); - // Atomic counter for tracking progress let progress_counter = AtomicUsize::new(0); - // Spawn the writer thread let writer_handle = std::thread::spawn(move || -> std::io::Result<()> { - // Open the output file with a buffered writer (16 MB buffer) let file = File::create("synthetic_employee_data_10K.rdf")?; let mut writer = BufWriter::with_capacity(16 * 1024 * 1024, file); - // Write XML declaration writer.write_all(b"\n")?; - // Write rdf:RDF start tag with namespaces writer.write_all( b"\n", let mut total_written = 0usize; let mut last_report = Instant::now(); - // Receive batches from the channel and write them to the file while let Ok(batch) = receiver.recv() { writer.write_all(batch.as_bytes())?; total_written += BATCH_SIZE; - // Update progress every 100 million records if total_written % 10000 == 0 { let elapsed = last_report.elapsed(); println!( @@ -87,32 +77,25 @@ xmlns:ds=\"https://data.cityofchicago.org/resource/xzkq-xp2w/\">\n", } } - // Write the closing rdf:RDF tag writer.write_all(b"\n")?; writer.flush()?; println!("[Writer] Completed writing all RDF triples."); Ok(()) }); - // Use a scoped thread pool to manage parallel data generation rayon::scope(|s| { s.spawn(|_| { - // Create a parallel iterator over batch indices (0..num_batches).into_par_iter().for_each_with(sender.clone(), |s, batch_idx| { - // Calculate the range of employee IDs for this batch let start_id = batch_idx * BATCH_SIZE + 1; let end_id = ((batch_idx + 1) * BATCH_SIZE).min(TOTAL_EMPLOYEES); let current_batch_size = end_id - start_id + 1; - // Initialize a thread-local RNG let mut rng = rand::rngs::StdRng::from_os_rng(); let position_dist = Uniform::try_from(0..POSITIONS.len()).unwrap(); let salary_dist = Uniform::try_from(30_000..150_000).unwrap(); - // Preallocate a buffer string for the batch - let mut buffer = String::with_capacity(current_batch_size * 512); // Approximate size + let mut buffer = String::with_capacity(current_batch_size * 512); - // Generate RDF triples for each employee in the batch for employee_id in start_id..=end_id { let employee_uri = format!("http://example.org/employee{}", employee_id); let position = POSITIONS[rng.sample(&position_dist)]; @@ -143,12 +126,10 @@ xmlns:ds=\"https://data.cityofchicago.org/resource/xzkq-xp2w/\">\n", buffer.push_str(" \n"); } - // Send the serialized batch to the writer thread if let Err(e) = s.send(buffer) { eprintln!("Failed to send batch {}: {}", batch_idx, e); } - // Update progress let prev = progress_counter.fetch_add(current_batch_size, Ordering::SeqCst) + current_batch_size; if prev % 10000 < current_batch_size { let elapsed = start_time.elapsed(); @@ -161,12 +142,10 @@ xmlns:ds=\"https://data.cityofchicago.org/resource/xzkq-xp2w/\">\n", } }); - // Drop the sender to signal completion drop(sender); }); }); - // Wait for the writer thread to finish writer_handle.join().expect("Writer thread panicked")?; let total_time = start_time.elapsed(); diff --git a/kolibrie/examples/turtle/turtle_example.rs b/kolibrie/examples/turtle/turtle_example.rs index b741ec67..76d3d6d5 100644 --- a/kolibrie/examples/turtle/turtle_example.rs +++ b/kolibrie/examples/turtle/turtle_example.rs @@ -12,9 +12,8 @@ extern crate kolibrie; use kolibrie::prelude::*; fn main() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); - // Employee dataset in Turtle format let turtle_data = r#" "Alice" . "Engineer" . @@ -30,16 +29,14 @@ fn main() { "#; db.parse_turtle(turtle_data); - // Filter employees with salary greater than 5000 using QueryBuilder let filtered_triples = db .query() .with_predicate("") .filter(|triple| { - // Acquire read lock for dictionary access let dict = db.dictionary(); if let Some(object) = dict.decode(triple.object) { let result = object.parse::().unwrap_or(0) > 5000; - drop(dict); // Release lock early + drop(dict); return result; } drop(dict); @@ -47,7 +44,6 @@ fn main() { }) .get_triples(); - // Print the filtered triples println!("Filtered Triples:"); for triple in filtered_triples.clone() { let dict = db.dictionary(); diff --git a/kolibrie/src/aggregate.rs b/kolibrie/src/aggregate.rs index 8a684f34..59296230 100644 --- a/kolibrie/src/aggregate.rs +++ b/kolibrie/src/aggregate.rs @@ -32,7 +32,6 @@ impl<'a> Aggregate<'a> { pub(crate) fn value(kind: &str, values: &[&str]) -> Option { let normalized = kind.to_ascii_uppercase(); let descriptor = Aggregate::parse(&normalized); - // Keep first-seen order for numeric accumulation let mut seen = BTreeSet::new(); let values: Vec<&str> = values .iter() @@ -42,9 +41,6 @@ pub(crate) fn value(kind: &str, values: &[&str]) -> Option { match descriptor.kind { "COUNT" => Some(values.len().to_string()), "SUM" | "AVG" => { - // Through the term's value, not its spelling: a typed literal keeps its - // datatype now, and parsing the spelling would find no number at all, - // turning every SUM over typed data into no answer. let numbers = values .iter() .map(|v| shared::rdf_term::numeric_value(v)) diff --git a/kolibrie/src/database/dataset.rs b/kolibrie/src/database/dataset.rs new file mode 100644 index 00000000..21eb43c4 --- /dev/null +++ b/kolibrie/src/database/dataset.rs @@ -0,0 +1,446 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Adding, removing, looking up, counting and merging triples and quads + +use super::{reencode_term_id, KolibrieDB}; +use shared::dataset_index::{DatasetIndex, GraphId, Quad}; +use shared::dictionary::Dictionary; +use shared::term_encoder::SharedTerms; +use shared::triple::Triple; +use std::collections::HashMap; +use std::sync::{Arc, RwLock}; + +impl KolibrieDB { + /// Adds an already-encoded triple to the default graph + pub fn add_triple(&mut self, triple: Triple) { + self.dataset_index.insert_triple(&triple); + } + + /// Removes a triple from the default graph + pub fn delete_triple(&mut self, triple: &Triple) -> bool { + self.dataset_index.delete_triple(triple) + } + + /// Adds an encoded quad, creating its graph if needed + pub fn add_quad(&mut self, quad: Quad) -> bool { + self.dataset_index.insert_quad(&quad) + } + + /// Removes a quad + pub fn delete_quad(&mut self, quad: &Quad) -> bool { + self.dataset_index.delete_quad(quad) + } + + /// Interns the given terms and adds the resulting quad + pub fn add_quad_parts( + &mut self, + subject: &str, + predicate: &str, + object: &str, + graph: &str, + ) -> bool { + let subject_id = self.encode_term_star(subject); + let predicate_id = self.encode_term_star(predicate); + let object_id = self.encode_term_star(object); + let graph_id = { + let mut dict = self.dictionary.write(); + dict.encode(graph) + }; + + self.add_quad(Quad { + subject: subject_id, + predicate: predicate_id, + object: object_id, + graph: GraphId::Named(graph_id), + }) + } + + /// Default-graph triples matching the bound positions; `None` is a wildcard + pub fn query_default_triples( + &self, + s: Option, + p: Option, + o: Option, + ) -> Vec { + self.dataset_index.query_default(s, p, o) + } + + /// Quads in one graph matching the bound positions; `None` is a wildcard + pub fn query_graph_quads( + &self, + graph: GraphId, + s: Option, + p: Option, + o: Option, + ) -> Vec { + self.dataset_index.query_graph(graph, s, p, o) + } + + /// Encodes three lexical terms and adds the resulting triple + pub fn add_triple_parts(&mut self, subject: &str, predicate: &str, object: &str) { + let mut dict = self.dictionary.write(); + let subject_id = dict.encode(subject); + let predicate_id = dict.encode(predicate); + let object_id = dict.encode(object); + drop(dict); + + let triple = Triple { + subject: subject_id, + predicate: predicate_id, + object: object_id, + }; + self.add_triple(triple); + } + + /// Adds a triple carrying a provenance annotation, for probabilistic reasoning + pub fn add_tagged_triple( + &mut self, + subject: &str, + predicate: &str, + object: &str, + probability: f64, + ) { + let mut dict = self.dictionary.write(); + let s = dict.encode(subject); + let p = dict.encode(predicate); + let o = dict.encode(object); + drop(dict); + + let triple = Triple { + subject: s, + predicate: p, + object: o, + }; + self.add_triple(triple.clone()); + self.probability_seeds.insert(triple, probability); + } + + /// Encodes three lexical terms and deletes the resulting triple + pub fn delete_triple_parts(&mut self, subject: &str, predicate: &str, object: &str) -> bool { + let mut dict = self.dictionary.write(); + let subject_id = dict.encode(subject); + let predicate_id = dict.encode(predicate); + let object_id = dict.encode(object); + drop(dict); + + let triple = Triple { + subject: subject_id, + predicate: predicate_id, + object: object_id, + }; + self.delete_triple(&triple) + } + + /// Number of triples in the default graph + pub fn default_triple_count(&self) -> usize { + self.dataset_index.len_default() + } + + /// Number of quads across every graph, including named ones + pub fn quad_count(&self) -> usize { + self.dataset_index.count_quads() + } + + /// Every quad in the store, across all graphs + pub fn all_quads(&self) -> Vec { + self.dataset_index.all_quads() + } + + /// Every graph in the store, including the default graph + pub fn graphs(&self) -> Vec { + self.dataset_index.graphs() + } + + /// The named graphs, excluding the default graph + pub fn named_graphs(&self) -> Vec { + self.dataset_index.named_graphs() + } + + /// Creates `graph` if it does not exist + pub fn create_graph(&mut self, graph: GraphId) -> bool { + self.dataset_index.create_graph(graph) + } + + /// Inserts a triple into the default graph, reporting whether it was new + pub fn insert_triple(&mut self, triple: &Triple) -> bool { + self.dataset_index.insert_triple(triple) + } + + /// Removes every quad and graph, leaving the dictionary intact + pub fn clear_dataset(&mut self) { + self.dataset_index.clear(); + } + + /// Rebuilds every graph-scoped index, keeping named graphs distinct from the default + pub fn build_all_indexes(&mut self) { + let quads = self.dataset_index.all_quads(); + let named_graphs = self.dataset_index.named_graphs(); + let mut rebuilt = DatasetIndex::new(); + for graph in named_graphs { + rebuilt.create_graph(graph); + } + for quad in quads { + rebuilt.insert_quad(&quad); + } + self.dataset_index = rebuilt; + } + + /// A new store containing both stores' data + pub fn union(&mut self, other: &KolibrieDB) -> Self { + let self_dict = self.dictionary.read(); + let other_dict = other.dictionary.read(); + let mut merged_dictionary = self_dict.clone(); + let self_quoted_triples = self.quoted_triple_store.read().unwrap(); + let other_quoted_triples = other.quoted_triple_store.read().unwrap(); + let mut merged_quoted_triples = self_quoted_triples.clone(); + let mut translated_ids = HashMap::new(); + + let mut other_term_ids: Vec<_> = other_dict.id_to_string.keys().copied().collect(); + other_term_ids.sort_unstable(); + for id in other_term_ids { + reencode_term_id( + id, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + } + + let mut other_quoted_ids: Vec<_> = other_quoted_triples + .id_to_components + .keys() + .copied() + .collect(); + other_quoted_ids.sort_unstable(); + for id in other_quoted_ids { + reencode_term_id( + id, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + } + + let mut dataset_index = DatasetIndex::new(); + for graph in self.dataset_index.named_graphs() { + dataset_index.create_graph(graph); + } + for quad in self.dataset_index.all_quads() { + dataset_index.insert_quad(&quad); + } + + for graph in other.dataset_index.named_graphs() { + let GraphId::Named(graph_id) = graph else { + continue; + }; + let graph_id = reencode_term_id( + graph_id, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + dataset_index.create_graph(GraphId::Named(graph_id)); + } + for quad in other.dataset_index.all_quads() { + let subject = reencode_term_id( + quad.subject, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + let predicate = reencode_term_id( + quad.predicate, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + let object = reencode_term_id( + quad.object, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + let graph = match quad.graph { + GraphId::Default => GraphId::Default, + GraphId::Named(graph_id) => GraphId::Named(reencode_term_id( + graph_id, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + )), + }; + dataset_index.insert_quad(&Quad { + subject, + predicate, + object, + graph, + }); + } + + let mut merged_seeds = self.probability_seeds.clone(); + for (triple, prob) in &other.probability_seeds { + let subject = reencode_term_id( + triple.subject, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + let predicate = reencode_term_id( + triple.predicate, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + let object = reencode_term_id( + triple.object, + &other_dict, + &other_quoted_triples, + &mut merged_dictionary, + &mut merged_quoted_triples, + &mut translated_ids, + ); + merged_seeds.insert( + Triple { + subject, + predicate, + object, + }, + *prob, + ); + } + + Self { + dataset_index, + dictionary: SharedTerms::from_dictionary(merged_dictionary), + prefixes: self.prefixes.clone(), + udfs: HashMap::new(), + rule_map: HashMap::new(), + model_decls: self.model_decls.clone(), + neural_relation_decls: self.neural_relation_decls.clone(), + ml_context: self.ml_context.clone(), + train_neural_relation_decls: self.train_neural_relation_decls.clone(), + neural_model_artifacts: self.neural_model_artifacts.clone(), + neural_materialized_triples: self.neural_materialized_triples.clone(), + ml_predict_materialized_triples: self.ml_predict_materialized_triples.clone(), + probability_seeds: merged_seeds, + cached_stats: None, + exact_stats_generation: 0, + planning_stats: None, + planning_stats_policy: self.planning_stats_policy, + stats_epoch: 0, + stats_rebuild_count: 0, + stats_rebuild_nanos: 0, + quoted_triple_store: Arc::new(RwLock::new(merged_quoted_triples)), + implicit_neural_materialization: self.implicit_neural_materialization, + training_seed: self.training_seed, + } + } + + /// Runs a query and renders the result as text, for the demo server + pub fn handle_query(&mut self, query: &str) -> String { + let parts: Vec<&str> = query.split_whitespace().collect(); + + if parts.len() != 3 { + return "Invalid query format. Expected 'subject predicate object'.".to_string(); + } + + let subject = parts[0]; + let predicate = parts[1]; + let object = parts[2]; + + let mut dict = self.dictionary.write(); + let subject_id = dict.encode(subject); + let predicate_id = dict.encode(predicate); + let object_id = dict.encode(object); + + let mut result = String::new(); + let matching_triples = + self.query_default_triples(Some(subject_id), Some(predicate_id), Some(object_id)); + for triple in matching_triples { + if triple.subject == subject_id + && triple.predicate == predicate_id + && triple.object == object_id + { + result.push_str(&format!( + "Subject: {}, Predicate: {}, Object: {}\n", + dict.decode(triple.subject).unwrap(), + dict.decode(triple.predicate).unwrap(), + dict.decode(triple.object).unwrap() + )); + } + } + drop(dict); + + if result.is_empty() { + result = "No matching triples found.".to_string(); + } + + result + } + + /// Return the decoded graph size for diagnostics + pub fn debug_print_triples(&self) { + println!( + "TRIPLE_COUNT {}", + self.query_default_triples(None, None, None).len() + ); + } + + /// Triple to string + pub fn triple_to_string(&self, triple: &Triple, dict: &Dictionary) -> String { + let subject = dict.decode(triple.subject); + let predicate = dict.decode(triple.predicate); + let object = dict.decode(triple.object); + format!( + "{} {} {}", + subject.unwrap(), + predicate.unwrap(), + object.unwrap() + ) + } +} + +impl shared::quad_source::QuadSource for KolibrieDB { + fn query_default(&self, s: Option, p: Option, o: Option) -> Vec { + self.dataset_index.query_default(s, p, o) + } + + fn for_each_quad(&self, visit: &mut dyn FnMut(GraphId, u32, u32, u32)) { + self.dataset_index + .for_each_quad(|g, s, p, o| visit(g, s, p, o)); + } + + fn named_graphs(&self) -> Vec { + self.dataset_index.named_graphs() + } + + fn count_graph(&self, graph: GraphId) -> usize { + self.dataset_index.count_graph(graph) + } +} diff --git a/kolibrie/src/database/extensions.rs b/kolibrie/src/database/extensions.rs new file mode 100644 index 00000000..b030f2e2 --- /dev/null +++ b/kolibrie/src/database/extensions.rs @@ -0,0 +1,110 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! User-defined functions, rule names, and the ML and probabilistic state hung off the store + +use super::KolibrieDB; +use crate::utils::ClonableFn; +use shared::query::{ModelDecl, NeuralRelationDecl}; +use shared::triple::Triple; +use std::collections::HashMap; + +impl KolibrieDB { + /// Registers a user-defined function callable from `FILTER` expressions + pub fn register_udf(&mut self, name: &str, f: F) + where + F: Fn(Vec<&str>) -> String + Send + Sync + 'static, + { + self.udfs.insert(name.to_string(), ClonableFn::new(f)); + } + + /// Looks up a registered user-defined function + pub(crate) fn udf(&self, name: &str) -> Option<&ClonableFn> { + self.udfs.get(name) + } + + /// The ML execution policy: which models are approved, and where artifacts live + pub fn ml_context(&self) -> &crate::ml_policy::MlExecutionContext { + &self.ml_context + } + + /// Replaces the ML execution policy + pub fn set_ml_context(&mut self, context: crate::ml_policy::MlExecutionContext) { + self.ml_context = context; + } + + /// Declared ML models, by name + pub fn model_decls(&self) -> &HashMap { + &self.model_decls + } + + /// Declared neural relations, by predicate + pub fn neural_relation_decls(&self) -> &HashMap { + &self.neural_relation_decls + } + + /// Resolved artifact paths for trained models, by model name + pub fn neural_model_artifacts(&self) -> &HashMap { + &self.neural_model_artifacts + } + + /// Triples materialised by neural relations, by predicate + pub fn neural_materialized_triples(&self) -> &HashMap> { + &self.neural_materialized_triples + } + + /// Takes the triples materialised for `predicate`, so they can be retracted + pub fn take_neural_materialized(&mut self, predicate: &str) -> Option> { + self.neural_materialized_triples.remove(predicate) + } + + /// Per-triple prior probabilities seeded into probabilistic reasoning + pub fn probability_seeds(&self) -> &HashMap { + &self.probability_seeds + } + + /// Maps a short rule name to its expanded IRI + pub fn add_rule_mapping(&mut self, name: impl Into, expanded: impl Into) { + self.rule_map.insert(name.into(), expanded.into()); + } + + /// Records the triples materialised for `cache_key`, so a rerun can undo them + #[cfg(feature = "ml")] + pub(crate) fn set_ml_materialized(&mut self, cache_key: String, triples: Vec) { + self.ml_predict_materialized_triples + .insert(cache_key, triples); + } + + /// Takes the triples previously materialised for `cache_key`, if any + #[cfg(feature = "ml")] + pub(crate) fn take_ml_materialized(&mut self, cache_key: &str) -> Option> { + self.ml_predict_materialized_triples.remove(cache_key) + } + + /// Whether neural relations are materialised implicitly during query execution + pub(crate) fn implicit_neural_materialization(&self) -> bool { + self.implicit_neural_materialization + } + + /// Sets the flag and returns its previous value, so a caller can restore it + pub(crate) fn set_implicit_neural_materialization(&mut self, enabled: bool) -> bool { + std::mem::replace(&mut self.implicit_neural_materialization, enabled) + } + + /// The seed a `TRAIN NEURAL RELATION` declaration initialises weights from + pub fn training_seed(&self) -> Option { + self.training_seed + } + + /// Makes declarative training reproducible by fixing its seed + pub fn set_training_seed(&mut self, seed: Option) { + self.training_seed = seed; + } +} diff --git a/kolibrie/src/database/mod.rs b/kolibrie/src/database/mod.rs new file mode 100644 index 00000000..673ffa71 --- /dev/null +++ b/kolibrie/src/database/mod.rs @@ -0,0 +1,112 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! The RDF store, [`KolibrieDB`], split by concern: +//! +//! - `terms`: term encoding, the dictionary and the quoted-triple store +//! - `prefixes`: prefix bindings and resolving abbreviated terms +//! - `dataset`: adding, removing, counting and merging triples and quads +//! - `planning_stats`: statistics the planner costs with, and when they are rebuilt +//! - `serialize`: writing RDF/XML, N-Triples, N-Quads and Turtle +//! - `parse_rdf_xml`, `parse_turtle`, `parse_n3`, `parse_ntriples`: reading those formats +//! - `extensions`: user-defined functions, rule names, ML and probabilistic state + +mod dataset; +mod extensions; +mod parse_n3; +mod parse_ntriples; +mod parse_rdf_xml; +mod parse_turtle; +mod planning_stats; +mod prefixes; +mod serialize; +mod terms; +#[cfg(test)] +mod tests; + +pub use planning_stats::{PlanningStatsPolicy, PlanningStatsSnapshot}; +pub(crate) use terms::reencode_term_id; +pub use terms::{DictionaryMut, DictionaryRef, QuotedTriplesMut, QuotedTriplesRef}; + +use crate::stats::DatabaseStats; +use crate::utils::ClonableFn; +use shared::dataset_index::DatasetIndex; +use shared::query::{ModelDecl, NeuralRelationDecl, TrainNeuralRelationDecl}; +use shared::quoted_triple_store::QuotedTripleStore; +use shared::term_encoder::SharedTerms; +use shared::triple::Triple; +use std::collections::HashMap; +use std::sync::{Arc, RwLock}; + +/// The RDF store: encoded triples, the dictionary that encodes them, indexes over several +/// orderings, and the statistics the planner costs with +#[derive(Debug, Clone)] +pub struct KolibrieDB { + pub(crate) ml_context: crate::ml_policy::MlExecutionContext, + pub(crate) dataset_index: DatasetIndex, + pub(crate) dictionary: SharedTerms, + pub(crate) prefixes: HashMap, + udfs: HashMap, + pub(crate) rule_map: HashMap, + pub(crate) model_decls: HashMap, + pub(crate) neural_relation_decls: HashMap, + pub(crate) train_neural_relation_decls: HashMap, + pub(crate) neural_model_artifacts: HashMap, + pub(crate) neural_materialized_triples: HashMap>, + ml_predict_materialized_triples: HashMap>, + pub(crate) probability_seeds: HashMap, + cached_stats: Option>, + exact_stats_generation: u64, + planning_stats: Option, + pub(crate) planning_stats_policy: PlanningStatsPolicy, + stats_epoch: u64, + pub(crate) stats_rebuild_count: u64, + stats_rebuild_nanos: u64, + pub(crate) quoted_triple_store: Arc>, + implicit_neural_materialization: bool, + training_seed: Option, +} + +impl KolibrieDB { + /// Initialize with host-selected ML authority + pub fn with_ml_context(context: crate::ml_policy::MlExecutionContext) -> Self { + let mut database = Self::new(); + database.ml_context = context; + database + } + /// An empty store with ML disabled and the default statistics policy + pub fn new() -> Self { + Self { + ml_context: crate::ml_policy::MlExecutionContext::disabled(), + dataset_index: DatasetIndex::new(), + dictionary: SharedTerms::new(), + prefixes: HashMap::new(), + udfs: HashMap::new(), + rule_map: HashMap::new(), + model_decls: HashMap::new(), + neural_relation_decls: HashMap::new(), + train_neural_relation_decls: HashMap::new(), + neural_model_artifacts: HashMap::new(), + neural_materialized_triples: HashMap::new(), + ml_predict_materialized_triples: HashMap::new(), + probability_seeds: HashMap::new(), + cached_stats: None, + exact_stats_generation: 0, + planning_stats: None, + planning_stats_policy: PlanningStatsPolicy::default(), + stats_epoch: 0, + stats_rebuild_count: 0, + stats_rebuild_nanos: 0, + quoted_triple_store: Arc::new(RwLock::new(QuotedTripleStore::new())), + implicit_neural_materialization: true, + training_seed: None, + } + } +} diff --git a/kolibrie/src/database/parse_n3.rs b/kolibrie/src/database/parse_n3.rs new file mode 100644 index 00000000..7bcc8db0 --- /dev/null +++ b/kolibrie/src/database/parse_n3.rs @@ -0,0 +1,200 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Reading the N3 subset this engine supports + +use super::{reencode_term_id, KolibrieDB}; +use rayon::prelude::*; +use shared::dictionary::Dictionary; +use shared::quoted_triple_store::QuotedTripleStore; +use shared::rdf_term; +use shared::term_encoder::SharedTerms; +use shared::triple::Triple; +use std::collections::HashMap; +use std::sync::{Arc, RwLock}; + +impl KolibrieDB { + /// Parses the N3 subset this engine supports and adds the triples + pub fn parse_n3(&mut self, n3_data: &str) { + let document = rdf_term::next_document_scope(); + let lines: Vec = n3_data.lines().map(|l| l.trim().to_string()).collect(); + let chunk_size = 1000; + let chunks: Vec> = lines.chunks(chunk_size).map(|c| c.to_vec()).collect(); + + type ChunkResult = ( + Vec, + SharedTerms, + Arc>, + HashMap, + ); + let partial_results: Vec = chunks + .par_iter() + .map(|chunk| { + let mut local_db = KolibrieDB::new(); + let mut statement = String::new(); + + for raw_line in chunk { + let mut line = raw_line.as_str(); + if let Some(comment_start) = line.find('#') { + line = &line[..comment_start]; + line = line.trim(); + } + if line.is_empty() { + continue; + } + if line.starts_with("@prefix") { + let line = line.trim_start_matches("@prefix").trim_end_matches('.'); + let parts: Vec<&str> = line.split_whitespace().collect(); + if parts.len() >= 2 { + let prefix = parts[0].trim_end_matches(':').to_string(); + let uri = parts[1] + .trim_start_matches('<') + .trim_end_matches('>') + .to_string(); + local_db.prefixes.insert(prefix, uri); + } else { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + } + } else { + statement.push_str(line); + statement.push(' '); + if line.ends_with('.') { + local_db.parse_statement(statement.trim(), document); + statement.clear(); + } + } + } + + ( + local_db.query_default_triples(None, None, None), + local_db.dictionary, + local_db.quoted_triple_store, + local_db.prefixes, + ) + }) + .collect(); + + for (triples, dict_arc, quoted_arc, pref) in partial_results { + let translated: Vec = { + let source_dictionary = dict_arc.read(); + let source_quoted = quoted_arc.read().unwrap_or_else(|p| p.into_inner()); + let mut target_dictionary = self.dictionary.write(); + let mut target_quoted = self + .quoted_triple_store + .write() + .unwrap_or_else(|p| p.into_inner()); + let mut seen = HashMap::new(); + let reencode = |id: u32, + target_dictionary: &mut Dictionary, + target_quoted: &mut QuotedTripleStore, + seen: &mut HashMap| { + reencode_term_id( + id, + &source_dictionary, + &source_quoted, + target_dictionary, + target_quoted, + seen, + ) + }; + triples + .iter() + .map(|triple| Triple { + subject: reencode( + triple.subject, + &mut target_dictionary, + &mut target_quoted, + &mut seen, + ), + predicate: reencode( + triple.predicate, + &mut target_dictionary, + &mut target_quoted, + &mut seen, + ), + object: reencode( + triple.object, + &mut target_dictionary, + &mut target_quoted, + &mut seen, + ), + }) + .collect() + }; + for triple in translated { + self.add_triple(triple); + } + for (k, v) in pref { + self.prefixes.insert(k, v); + } + } + } + + fn parse_statement(&mut self, statement: &str, document: u64) { + let mut tokens = statement.split_whitespace().peekable(); + let mut subject = String::new(); + let mut predicate = String::new(); + let mut current_state = "subject"; + + while let Some(token) = tokens.next() { + match token { + ";" => { + predicate.clear(); + current_state = "predicate"; + } + "." => { + break; + } + _ => match current_state { + "subject" => { + subject = token.to_string(); + current_state = "predicate"; + } + "predicate" => { + predicate = token.to_string(); + current_state = "object"; + } + "object" => { + let mut object = token.to_string(); + + while let Some(next_token) = tokens.peek() { + if *next_token == ";" || *next_token == "." || *next_token == "," { + break; + } + let next_token = tokens.next().unwrap(); + object.push(' '); + object.push_str(next_token); + } + + let resolved_subject = self.resolve_term(&subject); + let resolved_subject = self.scope_blank_nodes(resolved_subject, document); + let resolved_predicate = self.resolve_term(&predicate); + let resolved_predicate = + self.scope_blank_nodes(resolved_predicate, document); + let resolved_object = self.resolve_term(&object); + let resolved_object = self.scope_blank_nodes(resolved_object, document); + + let mut dict = self.dictionary.write(); + let triple = Triple { + subject: dict.encode(&resolved_subject), + predicate: dict.encode(&resolved_predicate), + object: dict.encode(&resolved_object), + }; + drop(dict); + self.add_triple(triple); + + current_state = "predicate"; + } + _ => {} + }, + } + } + } +} diff --git a/kolibrie/src/database/parse_ntriples.rs b/kolibrie/src/database/parse_ntriples.rs new file mode 100644 index 00000000..2b31e56a --- /dev/null +++ b/kolibrie/src/database/parse_ntriples.rs @@ -0,0 +1,341 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Reading N-Triples(-star) and N-Quads + +use super::KolibrieDB; +use rayon::prelude::*; +use shared::dataset_index::{GraphId, Quad}; +use shared::rdf_term; +use shared::rdf_term::TermTriple; +use shared::triple::Triple; + +impl KolibrieDB { + /// Parses N-Triples and adds them + pub fn parse_ntriples_and_add(&mut self, ntriples_data: &str) { + let partial_results = self.parse_ntriples(ntriples_data); + + let encoded_triples = self.encode_triples(partial_results); + for encoded_triple in encoded_triples { + self.add_triple(encoded_triple); + } + } + + /// Parses N-Triples into lexical triples **without** adding them, in parallel chunks of 1000 + /// lines + pub fn parse_ntriples(&mut self, ntriples_data: &str) -> Vec> { + self.parse_ntriples_in_scope(ntriples_data, rdf_term::next_document_scope()) + } + + /// Parses N-Triples, relabelling blank nodes into a scope the caller chooses + pub fn parse_ntriples_in_scope( + &mut self, + ntriples_data: &str, + document: u64, + ) -> Vec> { + let lines: Vec<&str> = ntriples_data.lines().collect(); + let chunk_size = 1000; + let chunks: Vec<&[&str]> = lines.chunks(chunk_size).collect(); + + let partial_results: Vec> = chunks + .par_iter() + .map(|chunk| { + let mut local_triples = Vec::new(); + + for line in chunk.iter() { + let line = line.trim(); + + if line.is_empty() || line.starts_with('#') { + continue; + } + + if !line.ends_with('.') { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + continue; + } + + let line_without_dot = &line[..line.len() - 1].trim(); + + if let Some((subject, predicate, object)) = + self.parse_ntriples_line(line_without_dot) + { + local_triples.push(TermTriple { + subject: self.scope_blank_nodes(subject, document), + predicate: self.scope_blank_nodes(predicate, document), + object: self.scope_blank_nodes(object, document), + }); + } + } + + local_triples + }) + .collect(); + partial_results + } + + /// Interns lexical triples into encoded ones + pub fn encode_triples(&mut self, non_encoded_triples: Vec>) -> Vec { + let mut encoded_triples = Vec::new(); + for triple_strings in non_encoded_triples { + for TermTriple { + subject, + predicate, + object, + } in triple_strings + { + let main_triple = Triple { + subject: self.encode_term_star(&subject), + predicate: self.encode_term_star(&predicate), + object: self.encode_term_star(&object), + }; + encoded_triples.push(main_triple); + } + } + encoded_triples + } + + /// Parses and encodes N-Triples without adding them to the store + pub fn parse_and_encode_ntriples(&mut self, ntriples_data: &str) -> Vec { + let partial_results = self.parse_ntriples(ntriples_data); + + self.encode_triples(partial_results) + } + + /// Parses and encodes N-Triples in a scope the caller chooses + pub fn parse_and_encode_ntriples_in_scope( + &mut self, + ntriples_data: &str, + document: u64, + ) -> Vec { + let partial_results = self.parse_ntriples_in_scope(ntriples_data, document); + + self.encode_triples(partial_results) + } + + /// Parses N-Quads and adds them, preserving named graphs + pub fn parse_nquads_and_add(&mut self, nquads_data: &str) { + let document = rdf_term::next_document_scope(); + for raw_line in nquads_data.lines() { + let line = raw_line.trim(); + if line.is_empty() || line.starts_with('#') { + continue; + } + + let line_without_dot = if line.ends_with('.') { + line[..line.len() - 1].trim() + } else { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + continue; + }; + + if let Some((subject, predicate, object, graph)) = + self.parse_nquads_line(line_without_dot) + { + let subject = self.scope_blank_nodes(subject, document); + let predicate = self.scope_blank_nodes(predicate, document); + let object = self.scope_blank_nodes(object, document); + let graph = graph.map(|name| self.scope_blank_nodes(name, document)); + match graph { + Some(graph) => { + self.add_quad_parts(&subject, &predicate, &object, &graph); + } + None => { + let quad = Quad { + subject: self.encode_term_star(&subject), + predicate: self.encode_term_star(&predicate), + object: self.encode_term_star(&object), + graph: GraphId::Default, + }; + self.add_quad(quad); + } + } + } + } + } + + fn parse_nquads_line(&self, line: &str) -> Option<(String, String, String, Option)> { + let mut parts = self.parse_ntriples_parts(line); + if !matches!(parts.len(), 3 | 4) { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + return None; + } + let subject = self.clean_ntriples_term(&parts.remove(0)); + let predicate = self.clean_ntriples_term(&parts.remove(0)); + let object = self.clean_ntriples_term(&parts.remove(0)); + let graph = (!parts.is_empty()).then(|| self.clean_ntriples_term(&parts.remove(0))); + Some((subject, predicate, object, graph)) + } + + fn parse_ntriples_line(&self, line: &str) -> Option<(String, String, String)> { + let parts = self.parse_ntriples_parts(line); + if parts.len() == 3 { + let subject = self.clean_ntriples_term(&parts[0]); + let predicate = if parts[1] == "a" { + "http://www.w3.org/1999/02/22-rdf-syntax-ns#type".to_string() + } else { + self.clean_ntriples_term(&parts[1]) + }; + let object = self.clean_ntriples_term(&parts[2]); + Some((subject, predicate, object)) + } else { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + None + } + } + + fn parse_ntriples_parts(&self, line: &str) -> Vec { + let mut parts = Vec::new(); + let mut current_part = String::new(); + let mut in_uri = false; + let mut in_literal = false; + let mut escaped = false; + let mut qt_depth: i32 = 0; + let mut chars = line.chars().peekable(); + + while let Some(ch) = chars.next() { + match ch { + '<' if !in_literal && !escaped => { + if chars.peek() == Some(&'<') && !in_uri { + current_part.push(ch); + current_part.push(chars.next().unwrap()); + qt_depth += 1; + } else if qt_depth > 0 { + current_part.push(ch); + if chars.peek() == Some(&'<') { + current_part.push(chars.next().unwrap()); + qt_depth += 1; + } else { + } + } else { + in_uri = true; + current_part.push(ch); + } + } + '>' if !in_literal && !escaped => { + if qt_depth > 0 && !in_uri { + current_part.push(ch); + if chars.peek() == Some(&'>') { + current_part.push(chars.next().unwrap()); + qt_depth -= 1; + if qt_depth == 0 { + parts.push(current_part.trim().to_string()); + current_part.clear(); + } + } + } else if in_uri { + in_uri = false; + current_part.push(ch); + if qt_depth == 0 { + parts.push(current_part.trim().to_string()); + current_part.clear(); + } + } else { + current_part.push(ch); + } + } + '"' if !in_uri && !escaped => { + in_literal = !in_literal; + current_part.push(ch); + if !in_literal { + while let Some(&next_ch) = chars.peek() { + if next_ch == '^' || next_ch == '@' { + current_part.push(chars.next().unwrap()); + if next_ch == '^' { + if let Some(&second_caret) = chars.peek() { + if second_caret == '^' { + current_part.push(chars.next().unwrap()); + while let Some(&datatype_ch) = chars.peek() { + if datatype_ch == '<' { + current_part.push(chars.next().unwrap()); + let mut in_datatype_uri = true; + while let Some(&uri_ch) = chars.peek() { + current_part.push(chars.next().unwrap()); + if uri_ch == '>' { + in_datatype_uri = false; + break; + } + } + if !in_datatype_uri { + break; + } + } else if datatype_ch.is_whitespace() { + break; + } else { + current_part.push(chars.next().unwrap()); + } + } + } + } + } else if next_ch == '@' { + while let Some(&lang_ch) = chars.peek() { + if lang_ch.is_alphanumeric() || lang_ch == '-' { + current_part.push(chars.next().unwrap()); + } else { + break; + } + } + } + break; + } else if next_ch.is_whitespace() { + break; + } else { + break; + } + } + if qt_depth == 0 { + parts.push(current_part.trim().to_string()); + current_part.clear(); + } + } + } + '\\' if (in_uri || in_literal) && !escaped => { + escaped = true; + current_part.push(ch); + } + ' ' | '\t' if !in_uri && !in_literal && !escaped && qt_depth == 0 => { + if !current_part.is_empty() { + parts.push(current_part.trim().to_string()); + current_part.clear(); + } + } + _ => { + escaped = false; + current_part.push(ch); + } + } + } + + if !current_part.is_empty() { + parts.push(current_part.trim().to_string()); + } + + parts + } + + fn clean_ntriples_term(&self, term: &str) -> String { + let term = term.trim(); + + if term.starts_with("<<") && term.ends_with(">>") { + return term.to_string(); + } + + if term.starts_with('<') && term.ends_with('>') { + return term[1..term.len() - 1].to_string(); + } + + if term.starts_with('"') { + if let Some(canonical) = rdf_term::canonicalise_literal(term) { + return canonical; + } + } + + term.to_string() + } +} diff --git a/kolibrie/src/database/parse_rdf_xml.rs b/kolibrie/src/database/parse_rdf_xml.rs new file mode 100644 index 00000000..097b7280 --- /dev/null +++ b/kolibrie/src/database/parse_rdf_xml.rs @@ -0,0 +1,406 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Reading RDF/XML, from a string or a file + +use super::KolibrieDB; +use crate::utils; +use crossbeam::channel::unbounded; +use crossbeam::scope; +use quick_xml::events::Event; +use quick_xml::name::QName; +use quick_xml::Reader; +use rayon::prelude::*; +use shared::rdf_term; +use shared::triple::Triple; +use std::collections::BTreeSet; +use std::sync::{Arc, Mutex}; + +impl KolibrieDB { + /// Parses RDF/XML and adds the triples + pub fn parse_rdf(&mut self, rdf_xml: &str) { + let mut reader = Reader::from_str(rdf_xml); + + let mut current_subject = Vec::with_capacity(128); + let mut current_predicate = Vec::with_capacity(128); + let mut current_datatype = Vec::with_capacity(64); + let mut language_scope: Vec> = Vec::new(); + + let (sender, receiver) = unbounded::>(); + let dictionary = self.dictionary.clone(); + let triples_set = Arc::new(Mutex::new(Vec::new())); + let num_threads = utils::get_num_cpus(); + + scope(|s| { + for _ in 0..num_threads { + let receiver = receiver.clone(); + let triples_set = Arc::clone(&triples_set); + s.spawn(move |_| { + while let Ok(chunk) = receiver.recv() { + if chunk.is_empty() { + break; + } + + let local_triples: BTreeSet = + chunk.into_par_iter().map(|triple| triple).collect(); + + let mut triples = triples_set.lock().unwrap(); + triples.push(local_triples); + } + }); + } + + let mut triples = Vec::with_capacity(8192); + loop { + match reader.read_event() { + Ok(Event::Start(ref e)) => { + current_datatype.truncate(0); + let mut language = language_scope.last().cloned().unwrap_or_default(); + for attr in e.attributes().filter_map(Result::ok) { + match attr.key { + QName(b"rdf:datatype") => { + current_datatype.extend_from_slice(&attr.value) + } + QName(b"xml:lang") => { + language.clear(); + language.extend_from_slice(&attr.value); + } + _ => {} + } + } + language_scope.push(language); + match e.name() { + QName(b"rdf:RDF") => { + for attr in e.attributes().filter_map(Result::ok) { + let key = attr.key; + let value = attr.value; + if key.as_ref().starts_with(b"xmlns:") { + let prefix = std::str::from_utf8(&key.as_ref()[6..]) + .unwrap_or("") + .to_string(); + let uri = + std::str::from_utf8(&value).unwrap_or("").to_string(); + self.prefixes.insert(prefix, uri); + } else if key.as_ref() == b"xmlns" { + let uri = + std::str::from_utf8(&value).unwrap_or("").to_string(); + self.prefixes.insert("".to_string(), uri); + } + } + } + QName(b"rdf:Description") => { + for attr in e.attributes().filter_map(Result::ok) { + if attr.key == QName(b"rdf:about") { + current_subject.truncate(0); + current_subject.extend_from_slice(&attr.value); + } + } + } + QName(b"rdfs:Class") | QName(b"rdf:type") => { + current_predicate.truncate(0); + current_predicate.extend_from_slice(b"rdf:type"); + } + QName(b"rdfs:subClassOf") => { + current_predicate.truncate(0); + current_predicate.extend_from_slice(b"rdfs:subClassOf"); + } + QName(b"rdfs:label") => { + current_predicate.truncate(0); + current_predicate.extend_from_slice(b"rdfs:label"); + } + name => { + let name_str = + std::str::from_utf8(name.as_ref()).unwrap_or("").to_string(); + let resolved_predicate = self.resolve_term(&name_str); + current_predicate = resolved_predicate.clone().into_bytes(); + } + } + } + Ok(Event::Empty(ref e)) => { + if let Ok(predicate) = std::str::from_utf8(e.name().as_ref()) { + let resolved_predicate = self.resolve_term(predicate); + let mut object = Vec::with_capacity(128); + for attr in e.attributes().filter_map(Result::ok) { + if attr.key == QName(b"rdf:resource") { + object.extend_from_slice(&attr.value); + } + } + if !object.is_empty() { + if let (Ok(subject_str), Ok(object_str)) = ( + std::str::from_utf8(¤t_subject), + std::str::from_utf8(&object), + ) { + let mut dict = dictionary.write(); + let triple = Triple { + subject: dict.encode(subject_str), + predicate: dict.encode(&resolved_predicate), + object: dict.encode(object_str), + }; + drop(dict); + triples.push(triple); + } + } + } + } + Ok(Event::Text(e)) => { + if let Ok(object_str) = reader.decoder().decode(e.as_ref()) { + let trimmed_object = object_str.trim(); + if !trimmed_object.is_empty() { + if let Ok(subject_str) = std::str::from_utf8(¤t_subject) { + if let Ok(predicate_str) = + std::str::from_utf8(¤t_predicate) + { + let resolved_predicate = self.resolve_term(predicate_str); + let object = rdf_term::spell_literal( + trimmed_object, + std::str::from_utf8(¤t_datatype) + .ok() + .filter(|iri| !iri.is_empty()), + language_scope + .last() + .and_then(|tag| std::str::from_utf8(tag).ok()) + .filter(|tag| !tag.is_empty()), + ); + let mut dict = dictionary.write(); + let triple = Triple { + subject: dict.encode(subject_str), + predicate: dict.encode(&resolved_predicate), + object: dict.encode(&object), + }; + drop(dict); + triples.push(triple); + } + } + } + } + } + Ok(Event::End(ref e)) => { + language_scope.pop(); + if e.name() == QName(b"rdf:Description") { + current_subject.truncate(0); + current_predicate.truncate(0); + } + } + Ok(Event::Eof) => break, + Err(_e) => { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + break; + } + _ => {} + } + + if triples.len() >= 8192 { + sender.send(triples).unwrap(); + triples = Vec::with_capacity(8192); + } + } + + if !triples.is_empty() { + sender.send(triples).unwrap(); + } + + for _ in 0..num_threads { + sender.send(Vec::new()).unwrap(); + } + }) + .unwrap(); + + let triples_sets = Arc::try_unwrap(triples_set).unwrap().into_inner().unwrap(); + for local_triples in triples_sets { + for triple in local_triples { + self.add_triple(triple); + } + } + } + + /// Reads RDF/XML from a file and adds the triples + pub fn parse_rdf_from_file(&mut self, filename: &str) { + let file = std::fs::File::open(filename).expect("Cannot open file"); + let reader = std::io::BufReader::new(file); + let mut xml_reader = Reader::from_reader(reader); + + let mut current_subject = Vec::with_capacity(128); + let mut current_predicate = Vec::with_capacity(128); + let mut current_datatype = Vec::with_capacity(64); + let mut language_scope: Vec> = Vec::new(); + + let mut buf = Vec::new(); + loop { + match xml_reader.read_event_into(&mut buf) { + Ok(Event::Start(ref e)) => { + if e.name() == QName(b"rdf:RDF") { + for attr in e.attributes().filter_map(Result::ok) { + let key = attr.key; + let value = attr.value; + if key.as_ref().starts_with(b"xmlns:") { + let prefix = std::str::from_utf8(&key.as_ref()[6..]) + .unwrap_or("") + .to_string(); + let uri = std::str::from_utf8(&value).unwrap_or("").to_string(); + self.prefixes.insert(prefix, uri); + } else if key.as_ref() == b"xmlns" { + let uri = std::str::from_utf8(&value).unwrap_or("").to_string(); + self.prefixes.insert("".to_string(), uri); + } + } + break; + } + } + Ok(Event::Eof) => { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + break; + } + Err(_e) => { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + break; + } + _ => {} + } + buf.clear(); + } + + let mut triples = Vec::with_capacity(8192); + loop { + match xml_reader.read_event_into(&mut buf) { + Ok(Event::Start(ref e)) => { + current_datatype.truncate(0); + let mut language = language_scope.last().cloned().unwrap_or_default(); + for attr in e.attributes().filter_map(Result::ok) { + match attr.key { + QName(b"rdf:datatype") => { + current_datatype.extend_from_slice(&attr.value) + } + QName(b"xml:lang") => { + language.clear(); + language.extend_from_slice(&attr.value); + } + _ => {} + } + } + language_scope.push(language); + match e.name() { + QName(b"rdf:Description") => { + for attr in e.attributes().filter_map(Result::ok) { + if attr.key == QName(b"rdf:about") { + current_subject.clear(); + current_subject.extend_from_slice(&attr.value); + } + } + } + QName(b"rdfs:Class") | QName(b"rdf:type") => { + current_predicate.clear(); + current_predicate.extend_from_slice(b"rdf:type"); + } + QName(b"rdfs:subClassOf") => { + current_predicate.clear(); + current_predicate.extend_from_slice(b"rdfs:subClassOf"); + } + QName(b"rdfs:label") => { + current_predicate.clear(); + current_predicate.extend_from_slice(b"rdfs:label"); + } + name => { + let name_str = + std::str::from_utf8(name.as_ref()).unwrap_or("").to_string(); + let resolved_predicate = self.resolve_term(&name_str); + current_predicate = resolved_predicate.clone().into_bytes(); + } + } + } + Ok(Event::Empty(ref e)) => { + if let Ok(predicate) = std::str::from_utf8(e.name().as_ref()) { + let resolved_predicate = self.resolve_term(predicate); + let mut object = Vec::with_capacity(128); + for attr in e.attributes().filter_map(Result::ok) { + if attr.key == QName(b"rdf:resource") { + object.extend_from_slice(&attr.value); + } + } + if !object.is_empty() { + if let (Ok(subject_str), Ok(object_str)) = ( + std::str::from_utf8(¤t_subject), + std::str::from_utf8(&object), + ) { + let mut dict = self.dictionary.write(); + let triple = Triple { + subject: dict.encode(subject_str), + predicate: dict.encode(&resolved_predicate), + object: dict.encode(object_str), + }; + drop(dict); + triples.push(triple); + } + } + } + } + Ok(Event::Text(e)) => { + if let Ok(object_str) = xml_reader.decoder().decode(e.as_ref()) { + let trimmed_object = object_str.trim(); + if !trimmed_object.is_empty() { + if let Ok(subject_str) = std::str::from_utf8(¤t_subject) { + if let Ok(predicate_str) = std::str::from_utf8(¤t_predicate) { + let resolved_predicate = self.resolve_term(predicate_str); + let object = rdf_term::spell_literal( + trimmed_object, + std::str::from_utf8(¤t_datatype) + .ok() + .filter(|iri| !iri.is_empty()), + language_scope + .last() + .and_then(|tag| std::str::from_utf8(tag).ok()) + .filter(|tag| !tag.is_empty()), + ); + let mut dict = self.dictionary.write(); + let triple = Triple { + subject: dict.encode(subject_str), + predicate: dict.encode(&resolved_predicate), + object: dict.encode(&object), + }; + drop(dict); + triples.push(triple); + } + } + } + } + } + Ok(Event::End(ref e)) => { + language_scope.pop(); + if e.name() == QName(b"rdf:Description") { + current_subject.clear(); + current_predicate.clear(); + } + } + Ok(Event::Eof) => break, + Err(_e) => { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + break; + } + _ => {} + } + + buf.clear(); + + if triples.len() >= 8192 { + let local_triples: BTreeSet = triples.into_par_iter().collect(); + for triple in local_triples { + self.add_triple(triple); + } + triples = Vec::with_capacity(8192); + } + } + + if !triples.is_empty() { + let local_triples: BTreeSet = triples.into_par_iter().collect(); + for triple in local_triples { + self.add_triple(triple); + } + } + } +} diff --git a/kolibrie/src/database/parse_turtle.rs b/kolibrie/src/database/parse_turtle.rs new file mode 100644 index 00000000..a00fb737 --- /dev/null +++ b/kolibrie/src/database/parse_turtle.rs @@ -0,0 +1,312 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Reading Turtle and Turtle-star + +use super::KolibrieDB; +use shared::rdf_term; +use shared::triple::Triple; + +impl KolibrieDB { + /// Parses Turtle and adds the triples, honouring `@prefix` declarations and the `;` and `,` + /// abbreviations + pub fn parse_turtle(&mut self, turtle_data: &str) { + let document = rdf_term::next_document_scope(); + for raw_line in turtle_data.lines() { + let line = raw_line.trim(); + + if line.is_empty() || line.starts_with("#") { + continue; + } + + if line.starts_with("@prefix") || line.starts_with("PREFIX") { + let prefix_line = line + .trim_start_matches("@prefix") + .trim_start_matches("PREFIX") + .trim_end_matches('.') + .trim(); + + let parts: Vec<&str> = prefix_line.split_whitespace().collect(); + if parts.len() >= 2 { + let prefix = parts[0].trim_end_matches(':').to_string(); + let uri = parts[1] + .trim_start_matches('<') + .trim_end_matches('>') + .to_string(); + self.prefixes.insert(prefix, uri); + } else { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + } + continue; + } + + let tokens = Self::tokenize_turtle_star_line(line); + + let mut subject_raw: Option = None; + let mut predicate_raw: Option = None; + let mut object_tokens: Vec = Vec::new(); + + let mut expect_subject = true; + let mut expect_predicate = false; + let mut expect_object = false; + + let flush_object = |this: &mut Self, + subject_raw: &Option, + predicate_raw: &Option, + object_tokens: &mut Vec| { + if let (Some(s_raw), Some(p_raw)) = (subject_raw.as_ref(), predicate_raw.as_ref()) { + if object_tokens.is_empty() { + return; + } + + let object_raw = object_tokens.join(" "); + + let (object_part, annotations) = if let Some(ann_start) = object_raw.find("{|") + { + let obj = object_raw[..ann_start].trim().to_string(); + + if let Some(ann_end) = object_raw.find("|}") { + let ann_content = object_raw[ann_start + 2..ann_end].trim(); + let ann_parts: Vec<&str> = + ann_content.splitn(2, char::is_whitespace).collect(); + + if ann_parts.len() == 2 { + ( + obj, + vec![(ann_parts[0].to_string(), ann_parts[1].to_string())], + ) + } else { + (obj, vec![]) + } + } else { + (object_raw, vec![]) + } + } else { + (object_raw, vec![]) + }; + + let subject = + this.resolve_query_term(&Self::clean_turtle_term(s_raw), &this.prefixes); + let subject = this.scope_blank_nodes(subject, document); + let predicate = + this.resolve_query_term(&Self::clean_turtle_term(p_raw), &this.prefixes); + let predicate = this.scope_blank_nodes(predicate, document); + let object = this + .resolve_query_term(&Self::clean_turtle_term(&object_part), &this.prefixes); + let object = this.scope_blank_nodes(object, document); + + if subject.starts_with("<<") || object.starts_with("<<") { + let s_id = this.encode_term_star(&subject); + let p_id = this.encode_term_star(&predicate); + let o_id = this.encode_term_star(&object); + let triple = Triple { + subject: s_id, + predicate: p_id, + object: o_id, + }; + this.add_triple(triple); + } else { + let mut dict = this.dictionary.write(); + let triple = Triple { + subject: dict.encode(&subject), + predicate: dict.encode(&predicate), + object: dict.encode(&object), + }; + drop(dict); + this.add_triple(triple); + } + + for (ann_pred, ann_obj) in &annotations { + let qt_str = format!("<< {} {} {} >>", subject, predicate, object); + let qt_id = this.encode_term_star(&qt_str); + + let ann_p_id = this.encode_term_star(&this.resolve_query_term( + &Self::clean_turtle_term(ann_pred), + &this.prefixes, + )); + let ann_o_id = + this.encode_term_star(&this.resolve_query_term( + &Self::clean_turtle_term(ann_obj), + &this.prefixes, + )); + + let ann_triple = Triple { + subject: qt_id, + predicate: ann_p_id, + object: ann_o_id, + }; + this.add_triple(ann_triple); + } + + object_tokens.clear(); + } + }; + + for token in tokens { + match token.as_str() { + "." => { + flush_object(self, &subject_raw, &predicate_raw, &mut object_tokens); + subject_raw = None; + predicate_raw = None; + expect_subject = true; + expect_predicate = false; + expect_object = false; + } + ";" => { + flush_object(self, &subject_raw, &predicate_raw, &mut object_tokens); + predicate_raw = None; + expect_predicate = true; + expect_object = false; + } + "," => { + flush_object(self, &subject_raw, &predicate_raw, &mut object_tokens); + expect_object = true; + } + _ => { + if expect_subject { + subject_raw = Some(token); + expect_subject = false; + expect_predicate = true; + } else if expect_predicate { + predicate_raw = Some(token); + expect_predicate = false; + expect_object = true; + } else if expect_object { + object_tokens.push(token); + } else { + object_tokens.push(token); + } + } + } + } + + flush_object(self, &subject_raw, &predicate_raw, &mut object_tokens); + } + } + + /// Tokenize a Turtle-star line, keeping `<< ... >>` and punctuation structure intact + fn tokenize_turtle_star_line(line: &str) -> Vec { + let mut tokens = Vec::new(); + let mut current = String::new(); + + let mut depth = 0i32; + let mut in_uri = false; + let mut in_literal = false; + let mut escaped = false; + + let mut chars = line.chars().peekable(); + + while let Some(ch) = chars.next() { + if escaped { + current.push(ch); + escaped = false; + continue; + } + + match ch { + '\\' if in_literal => { + current.push(ch); + escaped = true; + } + + '"' if !in_uri && depth == 0 => { + in_literal = !in_literal; + current.push(ch); + } + + '"' if depth > 0 => { + in_literal = !in_literal; + current.push(ch); + } + + '<' if !in_literal => { + if chars.peek() == Some(&'<') && !in_uri { + current.push(ch); + current.push(chars.next().unwrap()); + depth += 1; + } else if depth > 0 { + current.push(ch); + if chars.peek() == Some(&'<') { + current.push(chars.next().unwrap()); + depth += 1; + } + } else { + in_uri = true; + current.push(ch); + } + } + + '>' if !in_literal => { + if depth > 0 && !in_uri { + current.push(ch); + if chars.peek() == Some(&'>') { + current.push(chars.next().unwrap()); + depth -= 1; + if depth == 0 { + tokens.push(current.trim().to_string()); + current.clear(); + } + } + } else if in_uri { + in_uri = false; + current.push(ch); + if depth == 0 { + tokens.push(current.trim().to_string()); + current.clear(); + } + } else { + current.push(ch); + } + } + + ';' | ',' | '.' if depth == 0 && !in_uri && !in_literal => { + let trimmed = current.trim().to_string(); + if !trimmed.is_empty() { + tokens.push(trimmed); + current.clear(); + } + tokens.push(ch.to_string()); + } + + ' ' | '\t' | '\n' | '\r' if depth == 0 && !in_uri && !in_literal => { + let trimmed = current.trim().to_string(); + if !trimmed.is_empty() { + tokens.push(trimmed); + current.clear(); + } + } + + _ => { + current.push(ch); + } + } + } + + let trimmed = current.trim().to_string(); + if !trimmed.is_empty() { + tokens.push(trimmed); + } + + tokens + } + + fn clean_turtle_term(term: &str) -> String { + let term = term.trim(); + if term.starts_with("<<") { + term.to_string() + } else if term.starts_with('<') && term.ends_with('>') { + term.to_string() + } else if term.starts_with('"') { + term.to_string() + } else { + term.trim_matches('"').to_string() + } + } +} diff --git a/kolibrie/src/database/planning_stats.rs b/kolibrie/src/database/planning_stats.rs new file mode 100644 index 00000000..aa0547e4 --- /dev/null +++ b/kolibrie/src/database/planning_stats.rs @@ -0,0 +1,188 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Statistics the planner costs with, and the policy for when they are rebuilt + +use super::KolibrieDB; +use crate::stats::DatabaseStats; +use shared::dataset_index::DatasetIndex; +use std::sync::Arc; + +/// Staleness policy for planning statistics +#[derive(Debug, Clone, Copy, PartialEq)] +pub enum PlanningStatsPolicy { + /// Rebuild on every mutation + AlwaysFresh, + /// Reuse a snapshot until the store has changed enough to matter + Bounded { + /// Mutations to tolerate before rebuilding, however small the store + minimum_batch: u64, + /// Fraction of the store that must change to force a rebuild, so the tolerance grows with + /// size instead of being a fixed count + refresh_fraction: f64, + }, +} + +impl PlanningStatsPolicy { + /// Refresh after 100 mutations or half the store + pub const DEFAULT_BOUNDED: Self = PlanningStatsPolicy::Bounded { + minimum_batch: 100, + refresh_fraction: 0.5, + }; +} + +impl Default for PlanningStatsPolicy { + fn default() -> Self { + Self::DEFAULT_BOUNDED + } +} + +/// A planning statistics snapshot +#[derive(Debug, Clone)] +pub struct PlanningStatsSnapshot { + /// The cardinalities themselves, shared so reusing a snapshot is free + pub stats: Arc, + /// `DatasetIndex::generation` at build time + pub generation: u64, + /// `total_triples` at build time + pub quads_at_build: u64, + /// Statistics epoch at build time + pub epoch: u64, + /// `DatasetIndex::resets` at build time + pub resets: u64, +} + +impl KolibrieDB { + /// Returns exact statistics, rebuilding them if the dataset generation changed + pub fn get_or_build_stats(&mut self) -> Arc { + let generation = self.dataset_index.generation(); + if let Some(stats) = &self.cached_stats { + if self.exact_stats_generation == generation { + return stats.clone(); + } + } + + let stats = self.rebuild_stats(); + self.cached_stats = Some(stats.clone()); + self.exact_stats_generation = generation; + stats + } + + /// Returns statistics for plan selection; under `Bounded` they may be stale within the + /// threshold + pub fn get_or_build_planning_stats(&mut self) -> Arc { + if self.planning_stats_policy == PlanningStatsPolicy::AlwaysFresh { + return self.get_or_build_stats(); + } + + let generation = self.dataset_index.generation(); + if let Some(snapshot) = &self.planning_stats { + if self.planning_snapshot_is_usable(snapshot, generation) { + return snapshot.stats.clone(); + } + } + + let stats = self.rebuild_stats(); + self.planning_stats = Some(PlanningStatsSnapshot { + stats: stats.clone(), + generation, + quads_at_build: stats.total_triples, + epoch: self.stats_epoch, + resets: self.dataset_index.resets(), + }); + stats + } + + /// Returns true if the cached planning snapshot is still within the policy threshold + fn planning_snapshot_is_usable( + &self, + snapshot: &PlanningStatsSnapshot, + generation: u64, + ) -> bool { + if snapshot.epoch != self.stats_epoch || snapshot.resets != self.dataset_index.resets() { + return false; + } + + let mutations = generation.wrapping_sub(snapshot.generation); + if mutations == 0 { + return true; + } + + if snapshot.quads_at_build == 0 { + return false; + } + + let PlanningStatsPolicy::Bounded { + minimum_batch, + refresh_fraction, + } = self.planning_stats_policy + else { + return false; + }; + + let proportional = (refresh_fraction * snapshot.quads_at_build.max(1) as f64).ceil() as u64; + mutations < minimum_batch.max(proportional) + } + + /// Gathers fresh statistics over this store's dataset, without caching them + pub fn gather_stats(&self) -> DatabaseStats { + DatabaseStats::gather_stats_fast( + &self.dataset_index, + self.quoted_triple_store.read().unwrap().len() as u64, + ) + } + + /// Builds statistics and records rebuild count and duration + fn rebuild_stats(&mut self) -> Arc { + let started = std::time::Instant::now(); + let stats = Arc::new(self.gather_stats()); + self.stats_rebuild_count += 1; + self.stats_rebuild_nanos = self + .stats_rebuild_nanos + .saturating_add(started.elapsed().as_nanos() as u64); + stats + } + + /// Invalidates all cached statistics + pub fn invalidate_stats_cache(&mut self) { + self.cached_stats = None; + self.planning_stats = None; + self.stats_epoch = self.stats_epoch.wrapping_add(1); + } + + /// Replaces the dataset index and invalidates cached statistics + pub fn replace_dataset_index(&mut self, dataset_index: DatasetIndex) { + self.dataset_index = dataset_index; + self.invalidate_stats_cache(); + } + + /// Number of statistics rebuilds and their total duration + pub fn stats_rebuild_metrics(&self) -> (u64, std::time::Duration) { + ( + self.stats_rebuild_count, + std::time::Duration::from_nanos(self.stats_rebuild_nanos), + ) + } + + /// When planning statistics are rebuilt rather than reused + pub fn planning_stats_policy(&self) -> PlanningStatsPolicy { + self.planning_stats_policy + } + + /// Sets when planning statistics are rebuilt + pub fn set_planning_stats_policy(&mut self, policy: PlanningStatsPolicy) { + self.planning_stats_policy = policy; + } + + /// How many times planning statistics have been rebuilt + pub fn stats_rebuild_count(&self) -> u64 { + self.stats_rebuild_count + } +} diff --git a/kolibrie/src/database/prefixes.rs b/kolibrie/src/database/prefixes.rs new file mode 100644 index 00000000..7b4548c3 --- /dev/null +++ b/kolibrie/src/database/prefixes.rs @@ -0,0 +1,141 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Prefix bindings, and expanding abbreviated terms to full IRIs + +use super::KolibrieDB; +use shared::rdf_term; +use std::collections::HashMap; + +impl KolibrieDB { + /// Replaces every prefix binding + pub fn set_prefixes(&mut self, prefixes: HashMap) { + self.prefixes = prefixes; + } + + /// Prefix bindings, for resolving abbreviated terms + pub fn prefixes(&self) -> &HashMap { + &self.prefixes + } + + /// Binds `prefix` to `uri`, replacing any existing binding + pub fn add_prefix(&mut self, prefix: impl Into, uri: impl Into) { + self.prefixes.insert(prefix.into(), uri.into()); + } + + /// Merges a set of prefix bindings in, replacing any that collide + pub fn extend_prefixes(&mut self, bindings: impl IntoIterator) { + self.prefixes.extend(bindings); + } + + /// Adopts the `PREFIX` declarations from a query, so later queries and rules can use them + /// without repeating them + pub fn register_prefixes_from_query(&mut self, query: &str) { + let prefix_pattern = regex::Regex::new(r"PREFIX\s+([a-zA-Z0-9_]+):\s*<([^>]+)>").unwrap(); + + for captures in prefix_pattern.captures_iter(query) { + if captures.len() >= 3 { + let prefix = captures[1].to_string(); + let uri = captures[2].to_string(); + self.prefixes.insert(prefix, uri); + } + } + } + + /// Copies this store's prefixes into `prefixes`, leaving existing entries alone + pub fn share_prefixes_with(&self, prefixes: &mut HashMap) { + for (prefix, uri) in &self.prefixes { + prefixes.insert(prefix.clone(), uri.clone()); + } + } + + /// Expands a lexical term to its full IRI using `prefixes`, then this store's own + pub fn resolve_query_term(&self, term: &str, prefixes: &HashMap) -> String { + if term.starts_with("<<") && term.ends_with(">>") { + return term.to_string(); + } + if term.starts_with('<') && term.ends_with('>') { + term.trim_start_matches('<') + .trim_end_matches('>') + .to_string() + } else if term.starts_with('"') { + if let Some(position) = term.rfind('"') { + let literal = &term[..=position]; + let rest = term[position + 1..].trim(); + let resolved = if let Some(datatype) = rest.strip_prefix("^^") { + format!( + "{}^^<{}>", + literal, + self.resolve_query_term(datatype.trim(), prefixes) + ) + } else { + format!("{}{}", literal, rest) + }; + rdf_term::canonicalise_literal(&resolved).unwrap_or(resolved) + } else { + term.to_string() + } + } else if term.contains(':') + && !term.starts_with("http://") + && !term.starts_with("https://") + { + let mut parts = term.splitn(2, ':'); + let prefix = parts.next().unwrap(); + let local_name = parts.next().unwrap_or(""); + + if let Some(uri) = prefixes.get(prefix) { + format!("{}{}", uri, local_name) + } else if let Some(uri) = self.prefixes.get(prefix) { + format!("{}{}", uri, local_name) + } else { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + term.to_string() + } + } else { + term.to_string() + } + } + + pub(super) fn resolve_term(&self, term: &str) -> String { + if term.starts_with('<') && term.ends_with('>') { + term.trim_start_matches('<') + .trim_end_matches('>') + .to_string() + } else if term.starts_with('"') { + if let Some(pos) = term.rfind('"') { + let literal = &term[..=pos]; + let rest = &term[pos + 1..]; + let resolved = if let Some(datatype) = rest.strip_prefix("^^") { + format!("{}^^<{}>", literal, self.resolve_term(datatype.trim())) + } else { + format!("{}{}", literal, rest) + }; + rdf_term::canonicalise_literal(&resolved).unwrap_or(resolved) + } else { + term.to_string() + } + } else if term.contains(':') + && !term.starts_with("http://") + && !term.starts_with("https://") + { + let mut parts = term.splitn(2, ':'); + let prefix = parts.next().unwrap(); + let local_name = parts.next().unwrap_or(""); + if let Some(uri) = self.prefixes.get(prefix) { + format!("{}{}", uri, local_name) + } else { + eprintln!("KOLIBRIE_OPERATION_FAILED"); + term.to_string() + } + } else { + term.to_string() + } + } +} diff --git a/kolibrie/src/database/serialize.rs b/kolibrie/src/database/serialize.rs new file mode 100644 index 00000000..f4364acc --- /dev/null +++ b/kolibrie/src/database/serialize.rs @@ -0,0 +1,177 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Writing the store out as RDF/XML, N-Triples, N-Quads and Turtle + +use super::KolibrieDB; +use shared::dataset_index::GraphId; +use shared::rdf_term; +use std::collections::BTreeMap; + +impl KolibrieDB { + /// Serialises the default graph as RDF/XML + pub fn generate_rdf_xml(&mut self) -> String { + let mut xml = String::new(); + xml.push_str("\n"); + xml.push_str("\n"); + + let dict = self.dictionary.read(); + let mut subjects: BTreeMap> = BTreeMap::new(); + let default_triples = self.query_default_triples(None, None, None); + for triple in &default_triples { + let subject = dict.decode(triple.subject); + let predicate = dict.decode(triple.predicate); + let object = dict.decode(triple.object); + subjects + .entry(subject.unwrap().to_string()) + .or_default() + .push((predicate.unwrap().to_string(), object.unwrap().to_string())); + } + drop(dict); + + for (subject, po_pairs) in subjects { + xml.push_str(&format!(" \n", subject)); + for (predicate, object) in po_pairs { + xml.push_str(&format!(" <{}>{}\n", predicate, object, predicate)); + } + xml.push_str(" \n"); + } + + xml.push_str("\n"); + xml + } + + /// Serializes all triples as N-Triples-star format + pub fn generate_ntriples(&self) -> String { + self.write_ntriples(None) + } + + fn write_ntriples(&self, skolem_base: Option<&str>) -> String { + let mut output = String::new(); + for triple in self.query_default_triples(None, None, None) { + let s = self.decode_any(triple.subject).unwrap_or_default(); + let p = self.decode_any(triple.predicate).unwrap_or_default(); + let o = self.decode_any(triple.object).unwrap_or_default(); + + output.push_str(&format!( + "{} {} {} .\n", + Self::write_term(&s, skolem_base), + Self::write_term(&p, skolem_base), + Self::write_term(&o, skolem_base) + )); + } + output + } + + /// Serialises every graph as N-Quads, replacing blank nodes with Skolem IRIs under `base` + pub fn generate_nquads_skolemised(&self, base: &str) -> String { + self.write_nquads(Some(base)) + } + + /// Serialises the default graph as N-Triples, replacing blank nodes with Skolem IRIs under + /// `base` + pub fn generate_ntriples_skolemised(&self, base: &str) -> String { + self.write_ntriples(Some(base)) + } + + /// Writes one term, replacing a blank node with a Skolem IRI when asked + fn write_term(spelling: &str, skolem_base: Option<&str>) -> String { + match skolem_base { + Some(base) => rdf_term::to_ntriples_term(&rdf_term::skolemise(spelling, base)), + None => rdf_term::to_ntriples_term(spelling), + } + } + + /// Serialises every graph as N-Quads, which unlike N-Triples preserves named graphs + pub fn generate_nquads(&self) -> String { + self.write_nquads(None) + } + + fn write_nquads(&self, skolem_base: Option<&str>) -> String { + let mut output = String::new(); + for quad in self.dataset_index.all_quads() { + let s = self.decode_any(quad.subject).unwrap_or_default(); + let p = self.decode_any(quad.predicate).unwrap_or_default(); + let o = self.decode_any(quad.object).unwrap_or_default(); + + let s_str = Self::write_term(&s, skolem_base); + let p_str = Self::write_term(&p, skolem_base); + let o_str = Self::write_term(&o, skolem_base); + match quad.graph { + GraphId::Default => { + output.push_str(&format!("{} {} {} .\n", s_str, p_str, o_str)); + } + GraphId::Named(graph_id) => { + let graph = self.decode_any(graph_id).unwrap_or_default(); + let graph = Self::write_term(&graph, skolem_base); + output.push_str(&format!("{} {} {} {} .\n", s_str, p_str, o_str, graph)); + } + } + } + output + } + + /// Serializes all triples as Turtle-star format with prefix declarations + pub fn generate_turtle(&self) -> String { + let mut output = String::new(); + + for (prefix, uri) in &self.prefixes { + output.push_str(&format!("@prefix {}: <{}> .\n", prefix, uri)); + } + if !self.prefixes.is_empty() { + output.push('\n'); + } + + let mut subjects: BTreeMap>> = BTreeMap::new(); + for triple in self.query_default_triples(None, None, None) { + let s = self.decode_any(triple.subject).unwrap_or_default(); + let p = self.decode_any(triple.predicate).unwrap_or_default(); + let o = self.decode_any(triple.object).unwrap_or_default(); + subjects.entry(s).or_default().entry(p).or_default().push(o); + } + + for (subject, predicates) in &subjects { + output.push_str(&rdf_term::to_ntriples_term(subject)); + + let pred_count = predicates.len(); + for (i, (predicate, objects)) in predicates.iter().enumerate() { + if i == 0 { + output.push(' '); + } else { + output.push_str(" ;\n "); + } + output.push_str(&rdf_term::to_ntriples_term(predicate)); + + for (j, obj) in objects.iter().enumerate() { + if j > 0 { + output.push_str(" ,"); + } + output.push(' '); + output.push_str(&rdf_term::to_ntriples_term(obj)); + } + + if i == pred_count - 1 { + output.push_str(" .\n"); + } + } + } + output + } +} diff --git a/kolibrie/src/database/terms.rs b/kolibrie/src/database/terms.rs new file mode 100644 index 00000000..7bce9b7d --- /dev/null +++ b/kolibrie/src/database/terms.rs @@ -0,0 +1,275 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Term encoding: the dictionary, the quoted-triple store, and moving IDs between stores + +use super::KolibrieDB; +use shared::dictionary::Dictionary; +use shared::quoted_triple_store::{is_quoted_triple_id, QuotedTripleStore}; +use shared::rdf_term; +use shared::term_encoder::SharedTerms; +use std::collections::HashMap; +use std::sync::{Arc, RwLockReadGuard, RwLockWriteGuard}; + +pub(crate) fn reencode_term_id( + id: u32, + source_dictionary: &Dictionary, + source_quoted_triples: &QuotedTripleStore, + target_dictionary: &mut Dictionary, + target_quoted_triples: &mut QuotedTripleStore, + translated_ids: &mut HashMap, +) -> u32 { + if let Some(translated) = translated_ids.get(&id) { + return *translated; + } + + let translated = if is_quoted_triple_id(id) { + let (subject, predicate, object) = source_quoted_triples + .decode(id) + .unwrap_or_else(|| panic!("quoted triple ID {id} is missing from its source store")); + let subject = reencode_term_id( + subject, + source_dictionary, + source_quoted_triples, + target_dictionary, + target_quoted_triples, + translated_ids, + ); + let predicate = reencode_term_id( + predicate, + source_dictionary, + source_quoted_triples, + target_dictionary, + target_quoted_triples, + translated_ids, + ); + let object = reencode_term_id( + object, + source_dictionary, + source_quoted_triples, + target_dictionary, + target_quoted_triples, + translated_ids, + ); + target_quoted_triples.encode(subject, predicate, object) + } else { + let lexical = source_dictionary + .decode(id) + .unwrap_or_else(|| panic!("term ID {id} is missing from its source dictionary")); + target_dictionary.encode(lexical) + }; + + translated_ids.insert(id, translated); + translated +} + +/// Read borrow of the term dictionary, from [`KolibrieDB::dictionary`] +pub type DictionaryRef<'a> = shared::term_encoder::TermsRef<'a>; + +/// Write borrow of the term dictionary, from [`KolibrieDB::dictionary_mut`] +pub type DictionaryMut<'a> = shared::term_encoder::TermsMut<'a>; + +/// Read guard over the quoted-triple store, from [`KolibrieDB::quoted_triples`] +#[derive(Debug)] +pub struct QuotedTriplesRef<'a>(RwLockReadGuard<'a, QuotedTripleStore>); + +impl std::ops::Deref for QuotedTriplesRef<'_> { + type Target = QuotedTripleStore; + fn deref(&self) -> &Self::Target { + &self.0 + } +} + +/// Write guard over the quoted-triple store, from [`KolibrieDB::quoted_triples_mut`] +#[derive(Debug)] +pub struct QuotedTriplesMut<'a>(RwLockWriteGuard<'a, QuotedTripleStore>); + +impl std::ops::Deref for QuotedTriplesMut<'_> { + type Target = QuotedTripleStore; + fn deref(&self) -> &Self::Target { + &self.0 + } +} + +impl std::ops::DerefMut for QuotedTriplesMut<'_> { + fn deref_mut(&mut self) -> &mut Self::Target { + &mut self.0 + } +} + +impl KolibrieDB { + /// Encode a term that may be a quoted triple `<< s p o >>` (recursive) + pub fn encode_term_star(&self, term: &str) -> u32 { + let trimmed = term.trim(); + if trimmed.starts_with("<<") && trimmed.ends_with(">>") { + let inner = &trimmed[2..trimmed.len() - 2].trim(); + let (s_str, p_str, o_str) = Self::split_quoted_triple_content(inner); + let s_id = self.encode_term_star(&s_str); + let p_id = self.encode_term_star(&p_str); + let o_id = self.encode_term_star(&o_str); + let mut qt = self.quoted_triple_store.write().unwrap(); + qt.encode(s_id, p_id, o_id) + } else { + let cleaned = if trimmed.starts_with('<') && trimmed.ends_with('>') { + trimmed[1..trimmed.len() - 1].to_string() + } else if trimmed.starts_with('"') { + rdf_term::canonicalise_literal(trimmed) + .unwrap_or_else(|| trimmed.trim_matches('"').to_string()) + } else { + trimmed.to_string() + }; + let mut dict = self.dictionary.write(); + dict.encode(&cleaned) + } + } + + /// Rewrites every blank node label in `term` into `document`'s scope + pub(super) fn scope_blank_nodes(&self, term: String, document: u64) -> String { + if !term.contains("_:") { + return term; + } + let trimmed = term.trim(); + if trimmed.starts_with("<<") && trimmed.ends_with(">>") { + let inner = trimmed[2..trimmed.len() - 2].trim(); + let (subject, predicate, object) = Self::split_quoted_triple_content(inner); + return format!( + "<< {} {} {} >>", + self.scope_blank_nodes(subject, document), + self.scope_blank_nodes(predicate, document), + self.scope_blank_nodes(object, document) + ); + } + if trimmed.starts_with("_:") { + return rdf_term::scope_blank_node(trimmed, document).into_owned(); + } + term + } + + /// Decode a u32 ID that may be a regular dictionary ID or a quoted triple ID + pub fn decode_any(&self, id: u32) -> Option { + if is_quoted_triple_id(id) { + let qt = self.quoted_triple_store.read().unwrap(); + let dict = self.dictionary.read(); + dict.decode_term(id, &qt) + } else { + let dict = self.dictionary.read(); + dict.decode(id).map(|s| s.to_string()) + } + } + + /// Split quoted triple content `s p o` into three parts, respecting nested `<< >>` + pub fn split_quoted_triple_content(content: &str) -> (String, String, String) { + let mut parts: Vec = Vec::new(); + let mut current = String::new(); + let mut depth = 0; + let mut in_uri = false; + let mut in_literal = false; + let mut escaped = false; + + for ch in content.chars() { + if escaped { + current.push(ch); + escaped = false; + continue; + } + match ch { + '\\' if in_literal => { + current.push(ch); + escaped = true; + } + '"' if !in_uri => { + in_literal = !in_literal; + current.push(ch); + } + '<' if !in_literal => { + current.push(ch); + if current.ends_with("<<") { + depth += 1; + } else if depth == 0 { + in_uri = true; + } + } + '>' if !in_literal => { + current.push(ch); + if in_uri { + in_uri = false; + } else if current.ends_with(">>") && depth > 0 { + depth -= 1; + } + } + ' ' | '\t' | '\n' | '\r' if depth == 0 && !in_uri && !in_literal => { + let trimmed = current.trim().to_string(); + if !trimmed.is_empty() { + parts.push(trimmed); + current.clear(); + } + } + _ => { + current.push(ch); + } + } + } + let trimmed = current.trim().to_string(); + if !trimmed.is_empty() { + parts.push(trimmed); + } + + if parts.len() >= 3 { + (parts[0].clone(), parts[1].clone(), parts[2..].join(" ")) + } else { + let s = parts.first().cloned().unwrap_or_default(); + let p = parts.get(1).cloned().unwrap_or_default(); + let o = parts.get(2).cloned().unwrap_or_default(); + (s, p, o) + } + } + + /// Read access to the term dictionary + pub fn dictionary(&self) -> DictionaryRef<'_> { + self.dictionary.read() + } + + /// Write access to the term dictionary, for interning new terms + pub fn dictionary_mut(&self) -> DictionaryMut<'_> { + self.dictionary.write() + } + + /// Whether `self` and `other` encode terms through the same dictionary, as opposed to holding + /// separate ones + pub fn shares_dictionary_with(&self, other: &KolibrieDB) -> bool { + self.dictionary.shares_space_with(&other.dictionary) + } + + /// This store's term space, as an encoder another component can adopt + pub fn terms(&self) -> SharedTerms { + self.dictionary.clone() + } + + /// Adopts another component's term space, replacing this store's own + pub fn adopt_terms(&mut self, terms: SharedTerms) { + self.dictionary = terms; + } + + /// Read access to the quoted-triple store + pub fn quoted_triples(&self) -> QuotedTriplesRef<'_> { + QuotedTriplesRef(self.quoted_triple_store.read().unwrap()) + } + + /// Write access to the quoted-triple store + pub fn quoted_triples_mut(&self) -> QuotedTriplesMut<'_> { + QuotedTriplesMut(self.quoted_triple_store.write().unwrap()) + } + + /// Whether `self` and `other` share one quoted-triple store, as opposed to holding separate + /// ones + pub fn shares_quoted_triples_with(&self, other: &KolibrieDB) -> bool { + Arc::ptr_eq(&self.quoted_triple_store, &other.quoted_triple_store) + } +} diff --git a/kolibrie/src/database/tests.rs b/kolibrie/src/database/tests.rs new file mode 100644 index 00000000..737ff3f3 --- /dev/null +++ b/kolibrie/src/database/tests.rs @@ -0,0 +1,69 @@ +/* + * Copyright © 2025 Volodymyr Kadzhaia + * Copyright © 2025 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! Unit tests that reach into the store's crate-private state + +use super::{KolibrieDB, PlanningStatsPolicy}; +use shared::dataset_index::{DatasetIndex, GraphId, Quad}; +use std::sync::Arc; + +fn insert(database: &mut KolibrieDB, index: u32) { + database.add_triple_parts(&format!("urn:s{index}"), "urn:p", &format!("{index}")); +} + +#[test] +fn exact_statistics_notice_a_write_that_bypassed_the_helpers() { + let mut database = KolibrieDB::new(); + for index in 0..3 { + insert(&mut database, index); + } + assert_eq!(database.get_or_build_stats().total_triples, 3); + + let quad = { + let mut dictionary = database.dictionary_mut(); + Quad { + subject: dictionary.encode("urn:d"), + predicate: dictionary.encode("urn:p"), + object: dictionary.encode("4"), + graph: GraphId::Default, + } + }; + assert!(database.dataset_index.insert_quad(&quad)); + + assert_eq!(database.get_or_build_stats().total_triples, 4); +} + +#[test] +fn a_replacement_index_with_a_lower_generation_is_not_mistaken_for_fresh() { + let mut database = KolibrieDB::new(); + for index in 0..200 { + insert(&mut database, index); + } + database.set_planning_stats_policy(PlanningStatsPolicy::Bounded { + minimum_batch: 1_000, + refresh_fraction: 0.90, + }); + let first = database.get_or_build_planning_stats(); + assert_eq!(first.total_triples, 200); + + let mut replacement = DatasetIndex::new(); + replacement.insert_quad(&Quad { + subject: 1, + predicate: 2, + object: 3, + graph: GraphId::Default, + }); + database.dataset_index = replacement; + + let refreshed = database.get_or_build_planning_stats(); + assert!(!Arc::ptr_eq(&first, &refreshed)); + assert_eq!(refreshed.total_triples, 1); + assert_eq!(database.get_or_build_stats().total_triples, 1); +} diff --git a/kolibrie/src/error_handler.rs b/kolibrie/src/error_handler.rs index 84be92dd..324fe460 100644 --- a/kolibrie/src/error_handler.rs +++ b/kolibrie/src/error_handler.rs @@ -11,17 +11,12 @@ use crate::parse_error::{Expected, ParseError}; use annotate_snippets::{Annotation, AnnotationKind, Group, Level, Renderer, Snippet}; -/// Renders a parse failure against the input it came from. -/// -/// Takes the crate's own [`ParseError`] rather than the parser library's, which is -/// what keeps `nom` out of this module -- it was the last place outside the parser -/// that named it. +/// Renders a parse failure against the input it came from pub(crate) fn format_parse_error(input: &str, err: ParseError<'_>) -> String { match err.is_incomplete() { false => { let offset = err.offset_in(input); - // Calculate line and column numbers let mut line_no = 1; let mut col_no = 1; @@ -37,7 +32,6 @@ pub(crate) fn format_parse_error(input: &str, err: ParseError<'_>) -> String { } } - // Get the error line let lines: Vec<&str> = input.lines().collect(); let error_line = if line_no <= lines.len() { lines[line_no - 1] @@ -45,7 +39,6 @@ pub(crate) fn format_parse_error(input: &str, err: ParseError<'_>) -> String { "[end of input]" }; - // Determine error message and label from what the parser wanted let (error_title, error_label) = match err.expected() { Expected::Token => ( format!( @@ -95,7 +88,6 @@ pub(crate) fn format_parse_error(input: &str, err: ParseError<'_>) -> String { ), }; - // Check for common SPARQL-specific errors let specific_error = detect_specific_sparql_error(input, offset, error_line); let (final_title, final_label, footer) = if let Some((title, label, foot)) = specific_error { @@ -104,11 +96,6 @@ pub(crate) fn format_parse_error(input: &str, err: ParseError<'_>) -> String { (error_title, error_label.to_string(), None) }; - // Create the annotated snippet - // Plain, not styled: every caller puts this string into an error value - // rather than printing it, so ANSI escapes would travel into logs, JSON - // responses and test comparisons. `styled()` put 26 control characters - // into a one-token parse failure. let renderer = Renderer::plain(); let mut report = vec![Level::ERROR.primary_title(&final_title).element( @@ -123,7 +110,6 @@ pub(crate) fn format_parse_error(input: &str, err: ParseError<'_>) -> String { ), )]; - // Add footer if present if let Some(ref footer_text) = footer { report.push(Group::with_title(Level::HELP.secondary_title(footer_text))); } @@ -132,10 +118,6 @@ pub(crate) fn format_parse_error(input: &str, err: ParseError<'_>) -> String { message } true => { - // Plain, not styled: every caller puts this string into an error value - // rather than printing it, so ANSI escapes would travel into logs, JSON - // responses and test comparisons. `styled()` put 26 control characters - // into a one-token parse failure. let renderer = Renderer::plain(); let report = &[ Level::ERROR @@ -164,7 +146,6 @@ fn detect_specific_sparql_error( ) -> Option<(String, String, String)> { let lower = input.to_lowercase(); - // Check for SELECT without WHERE if lower.contains("select") && !lower.contains("where") && !lower.contains("insert") { return Some(( "SELECT query missing WHERE clause".to_string(), @@ -173,7 +154,6 @@ fn detect_specific_sparql_error( )); } - // Check for missing closing brace let open_braces = input.matches('{').count(); let close_braces = input.matches('}').count(); if open_braces != close_braces { @@ -187,7 +167,6 @@ fn detect_specific_sparql_error( )); } - // Check for unclosed string literal let before_error = &input[..offset]; let quote_count = before_error.matches('"').count(); if quote_count % 2 != 0 { @@ -199,12 +178,10 @@ fn detect_specific_sparql_error( )); } - // Check for missing prefix declaration if let Some(prefix_error) = check_missing_prefix(input, offset) { return Some(prefix_error); } - // Check for missing separator between triple patterns if let Some(separator_error) = check_missing_triple_separator(input, offset) { return Some(separator_error); } @@ -217,7 +194,6 @@ fn check_missing_prefix(input: &str, offset: usize) -> Option<(String, String, S let before_error = &input[..offset]; let lines: Vec<&str> = input.lines().collect(); - // Extract declared prefixes let mut declared_prefixes = vec!["rdf", "rdfs", "owl", "xsd", "foaf", "dc"]; for line in &lines { if line.trim().to_uppercase().starts_with("PREFIX ") { @@ -227,7 +203,6 @@ fn check_missing_prefix(input: &str, offset: usize) -> Option<(String, String, S } } - // Check if there's a prefix usage near the error let words: Vec<&str> = before_error.split_whitespace().collect(); if let Some(last_word) = words.last() { if last_word.contains(':') && !last_word.starts_with('<') { @@ -255,13 +230,11 @@ fn check_missing_prefix(input: &str, offset: usize) -> Option<(String, String, S fn check_missing_triple_separator(input: &str, offset: usize) -> Option<(String, String, String)> { let before_error = &input[..offset]; - // Look for pattern: variable followed by non-separator followed by variable let trimmed = before_error.trim_end(); if trimmed.contains('?') { let chars: Vec = trimmed.chars().collect(); if let Some(last_char) = chars.last() { if last_char.is_alphanumeric() || *last_char == '_' { - // Check if we're potentially between triple patterns let last_10_chars: String = chars .iter() .rev() diff --git a/kolibrie/src/execute_query.rs b/kolibrie/src/execute_query.rs index 78dbf385..37512003 100644 --- a/kolibrie/src/execute_query.rs +++ b/kolibrie/src/execute_query.rs @@ -8,11 +8,10 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use crate::database::KolibrieDB; use crate::error_handler::format_parse_error; use crate::parser::{parse_combined_query, parse_combined_query_with_options}; -// Re-exported, not defined here: see `query_error` for why it moved. pub use crate::query_error::QueryError; -use crate::sparql_database::SparqlDatabase; use crate::streamertail_optimizer::{ build_logical_plan_from_group, compile_graph_term, compile_term, DatasetView, ExecutionEngine, Streamertail, @@ -29,46 +28,26 @@ use shared::terms::{BindingRef, Bindings, Term, UNBOUND}; use std::collections::{BTreeMap, BTreeSet, HashMap, HashSet}; use std::sync::atomic::{AtomicU64, Ordering as AtomicOrdering}; -/// Rows the decoder hands to projection: a schema once, and values per row. -/// -/// This used to be `Vec>`, one map per solution. That shape -/// existed only to be taken apart again one step later, because [`Solutions`] is -/// already a schema and rows -- so every result cell paid for a variable name it -/// shared with every other row, a hash to store it, and a second hash and a second -/// copy to read it back out. -/// -/// Profiling five end-to-end queries over a 20,000 subject fixture put 54.5% of -/// working time in this path, against 12.8% in the engine's own row handling, which -/// is why this was converted first. Measured afterwards, `optimizer_hotpath` runs -/// 1.12x to 1.22x faster on four of those five queries and unchanged on the fifth -- -/// less than that 54.5% suggests, because what remains is irreducible here: every -/// cell still has to become an owned `String` for [`Solutions`] to hold it. What went -/// away was the redundant work around that, not the decoding itself. +/// Rows the decoder hands to projection: a schema once, and values per row struct StringTable { - /// Normalised variable names, in column order, allocated once for the result. variables: Vec, - /// Every distinct value, stored once. Cells index this. terms: Vec, - /// One row per solution, one cell per column. `None` is an unbound variable. rows: Vec>>, } impl StringTable { - /// The column index of an already-normalised variable name. - /// - /// Resolve once per result and index rows with it. Looking a name up per row is - /// what the map shape did and what this type exists to stop. + /// The column index of an already-normalised variable name fn column(&self, name: &str) -> Option { self.variables.iter().position(|v| v == name) } - /// The value an interned cell refers to. + /// The value an interned cell refers to fn term(&self, cell: Option) -> Option<&str> { self.terms.get(cell? as usize).map(String::as_str) } } -/// Interns computed values, for the aggregates that invent strings no term table has. +/// Interns computed values, for the aggregates that invent strings no term table has struct TermInterner { terms: Vec, indices: HashMap, @@ -93,92 +72,25 @@ impl TermInterner { } } -/// Rows decoded per dictionary lock acquisition. -/// -/// The decode used to call `SparqlDatabase::decode_any` per cell, and that takes the -/// dictionary read lock per call -- 60,000 acquisitions for a 20,000 row, three -/// column result, all sequential. Mirrors `FILTER_LOCK_CHUNK` in the execution -/// engine, which already decoded this way. +/// Rows decoded per dictionary lock acquisition const DECODE_LOCK_CHUNK: usize = 1024; -/// The rows a request answered with, and the variables they are rows of. -/// -/// Replaces the bare `Vec>` the older entry points return, which loses -/// two things the engine knew: which variable each column belongs to, and whether a -/// value is absent because it was unbound. -/// -/// # Why the values are interned -/// -/// [`Solution::get`] returns `Option<&str>`, so reading a result borrows. The rows -/// behind it hold indices into a shared term table rather than owning a `String` -/// each, so a term repeated down a column -- every solution sharing a department, a -/// type, a graph name -- is held once instead of once per occurrence. -/// -/// Getting that to pay took three attempts, and the first two lost to simply owning -/// the strings. The cause was never the hashing: a `u32` hash lookup measured 5.6ns -/// against 24.4ns to clone a short IRI. It was that interning needs an index assigned -/// before a cell can be rewritten. -/// -/// - One **global** map has to exist before anything is rewritten, which forces a -/// sequential pass over every cell. About 1.1ms here, and it lost outright. -/// - **Sorting** the ids removes the hashing, but pays an `n log n` sort and a -/// cache-missing binary search per cell, and lost by more. A chunk's map is small -/// enough to stay in cache; sorting 60,000 ids to find 50 distinct is not. -/// - **Per chunk**, which is what this does: each chunk interns into its own table -/// under one dictionary lock, a prefix sum over the per-chunk counts gives each -/// chunk its offset, and one parallel integer add per cell makes the indices -/// global. Nothing runs sequentially over cells. A term shared by two chunks is -/// held twice, trading a little memory to keep every step parallel. -/// -/// Measured against owning, on `optimizer_hotpath`: -/// -/// | read through | owned | interned | -/// |---|---|---| -/// | [`iter`](Self::iter) and [`Solution::get`] | 10.15ms | **8.26ms** | -/// | [`into_rows`](Self::into_rows) | 9.83ms | **9.50ms** | -/// -/// So 1.23x for a caller that borrows and 1.03x for one that materialises, measured -/// on a join whose columns are nearly all distinct, which is the shape least -/// favourable to interning. The gain is larger where values repeat. -/// -/// Borrowing from the dictionary instead was not built: the result would hold a read -/// guard for its whole lifetime, blocking every writer, and since the database is -/// taken by `&mut` a caller could not touch it at all while results were alive. -/// -/// # Unbound is not empty -/// -/// A value is [`None`] when the solution did not bind that variable. SPARQL -/// distinguishes that from a variable bound to the empty string, and -/// `Vec>` cannot: it renders both as `""`. [`From`] keeps that -/// old flattening for callers that want the previous shape. +/// The rows a request answered with, and the variables they are rows of #[derive(Clone, Default)] pub struct Solutions { variables: Vec, - /// Every distinct value in the result, stored once. - /// - /// A result row used to own a `String` per cell, so a term repeated down a - /// column — every solution sharing a department, a type, a graph name — was - /// allocated and copied once per occurrence. Cells index this instead, which is - /// why [`Solution::get`] can hand back a borrow without the result holding a - /// dictionary lock open. terms: Vec, - /// One row per solution; a cell indexes [`Self::terms`], or is `None` when the - /// solution did not bind that variable. rows: Vec>>, } impl Solutions { - /// The value an interned cell refers to. + /// The value an interned cell refers to fn term(&self, cell: Option) -> Option<&str> { self.terms.get(cell? as usize).map(String::as_str) } } -/// Compared by what the solutions say, not by how they are interned. -/// -/// Two equal results can hold different term tables — a different build order, or a -/// value interned that projection later dropped — so comparing the fields directly -/// would report inequality for results a caller cannot tell apart. +/// Compared by what the solutions say, not by how they are interned impl PartialEq for Solutions { fn eq(&self, other: &Self) -> bool { self.variables == other.variables @@ -192,7 +104,7 @@ impl PartialEq for Solutions { impl Eq for Solutions {} -/// Prints the values, not the indices behind them. +/// Prints the values, not the indices behind them impl std::fmt::Debug for Solutions { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("Solutions") @@ -209,46 +121,34 @@ impl std::fmt::Debug for Solutions { } impl Solutions { - /// The projected variables, in column order, without the leading `?` or `$`. - /// - /// Empty for a request that binds nothing, such as an Update. + /// The projected variables, in column order, without the leading `?` or `$` pub fn variables(&self) -> &[String] { &self.variables } - /// Number of solutions. + /// Number of solutions pub fn len(&self) -> usize { self.rows.len() } - /// Whether there are no solutions. - /// - /// For a request that ran successfully this means the pattern matched nothing — - /// a failure is an `Err` and never an empty `Solutions`. + /// Whether there are no solutions pub fn is_empty(&self) -> bool { self.rows.is_empty() } - /// Whether `variable` is one of the projected variables, with or without its - /// sigil. Distinguishes "not projected" from "projected but unbound", which - /// [`Solution::get`] cannot. + /// Whether `variable` is one of the projected variables, with or without its sigil pub fn has_variable(&self, variable: &str) -> bool { let name = normalize_variable(variable); self.variables.iter().any(|v| v == name) } - /// The rows as plain strings, dropping the variable names and rendering an - /// unbound value as `""`. - /// - /// The shape the older entry points return, for a caller that indexes by column - /// and does not need either. It is lossy in a way `Vec>` cannot - /// express — an unbound variable and one bound to the empty string come out the - /// same — so prefer [`iter`](Self::iter) in new code. + /// The rows as plain strings, dropping the variable names and rendering an unbound value as + /// `""` pub fn into_rows(self) -> Vec> { self.into() } - /// The solutions, in result order. + /// The solutions, in result order pub fn iter(&self) -> impl Iterator> { self.rows.iter().map(|values| Solution { variables: &self.variables, @@ -268,14 +168,8 @@ impl<'a> IntoIterator for &'a Solutions { } impl From for Vec> { - /// The shape the older entry points return. Renders an unbound value as `""`, - /// which is why it is lossy and why [`Solutions`] exists. + /// The shape the older entry points return fn from(solutions: Solutions) -> Self { - // Sequential on purpose. Interning moved a cost here -- owning a `String` - // per cell let this move them out, sharing them means a copy per cell -- but - // collecting nested `Vec`s in parallel measured worse than doing it on one - // thread, at 5.38ms against 5.14ms on `low_cardinality_projection`, so the - // allocator contention costs more than the parallelism buys. solutions .rows .iter() @@ -288,7 +182,7 @@ impl From for Vec> { } } -/// One solution: a value per projected variable. +/// One solution: a value per projected variable #[derive(Debug, Clone, Copy)] pub struct Solution<'a> { variables: &'a [String], @@ -297,21 +191,14 @@ pub struct Solution<'a> { } impl<'a> Solution<'a> { - /// The value bound to `variable`, with or without its leading `?` or `$`. - /// - /// [`None`] both when the variable is unbound in this solution and when it is not - /// projected at all; [`Solutions::has_variable`] separates those. + /// The value bound to `variable`, with or without its leading `?` or `$` pub fn get(&self, variable: &str) -> Option<&'a str> { let name = normalize_variable(variable); let column = self.variables.iter().position(|v| v == name)?; self.term(*self.values.get(column)?) } - /// The values in column order, matching [`Solutions::variables`]. - /// - /// Borrowed, and `None` for an unbound variable. This returned - /// `&[Option]` before the values were interned; a caller that needs that - /// shape can `.map(|value| value.map(str::to_string)).collect()`. + /// The values in column order, matching [`Solutions::variables`] pub fn values(&self) -> impl Iterator> + 'a { let terms = self.terms; self.values @@ -319,54 +206,21 @@ impl<'a> Solution<'a> { .map(move |cell| cell.and_then(|cell| terms.get(cell as usize).map(String::as_str))) } - /// The value an interned cell refers to. + /// The value an interned cell refers to fn term(&self, cell: Option) -> Option<&'a str> { self.terms.get(cell? as usize).map(String::as_str) } } -/// Runs a SPARQL request and returns its solution rows. -/// -/// The entry point to prefer. One row per solution, one string per projected -/// variable, in the order the `SELECT` names them. An Update returns no rows, and so -/// does a request that only declares prefixes or rules. -/// -/// # Errors -/// -/// A [`QueryError`] saying which stage refused it. Note the contrast with -/// [`execute_query_rayon_parallel2_volcano`], which reports failure by returning no -/// rows — indistinguishable from a query that correctly matched nothing. -/// -/// # Accepts Update syntax -/// -/// For compatibility with the older entry point this accepts Update forms as well as -/// queries. [`execute_sparql_query`] rejects them, and -/// [`execute_sparql_update`] returns an [`UpdateSummary`] rather than rows. -pub fn execute(sparql: &str, database: &mut SparqlDatabase) -> Result { +/// Runs a SPARQL request and returns its solution rows +pub fn execute(sparql: &str, database: &mut KolibrieDB) -> Result { execute_request(sparql, database, RequestKind::Any) } -/// Runs a training-data `SELECT` and returns its rows keyed by variable name. -/// -/// Lived in [`crate::ml_feature_loader`] until that module was made to depend on -/// nothing in the crate; it needs to execute a query, so it belongs here. It used to -/// parse `select_query` a second time purely to recover the projected variable names, -/// which [`Solutions::variables`] already carries, so the second parse is gone. -/// -/// An unbound value is rendered as the empty string, as [`Solutions::into_rows`] -/// does, and reaches the caller as a term that fails to convert to a number. -/// -/// # Errors -/// -/// The query's own [`QueryError`] if it does not parse, is refused by the policy or -/// fails to evaluate, and a message of its own if it projects no variables. The -/// query comes from a `TRAIN` clause and is therefore caller-supplied, so none of -/// those is a bug in this crate: this used to `expect`, which aborted the process on -/// a malformed training query rather than returning the `Err` the signature already -/// allowed. +/// Runs a training-data `SELECT` and returns its rows keyed by variable name #[cfg(feature = "ml")] pub(crate) fn query_training_rows( - db: &mut SparqlDatabase, + db: &mut KolibrieDB, select_query: &str, ) -> Result>, Box> { let solutions = execute(select_query, db)?; @@ -381,16 +235,7 @@ pub(crate) fn query_training_rows( .collect()) } -/// Parses the input of every `ML.PREDICT` clause in `query`, recursively. -/// -/// The ML policy has to look inside these inputs to validate the models they name, -/// but parsing is this layer's job, not the policy's; see -/// [`PredictInputs`](crate::ml_policy::PredictInputs). An input that does not parse -/// as one complete query becomes an `Err` entry rather than failing here, so the -/// policy still reports it in the order it would have found it. -/// -/// Terminates because each nested input is a strict substring of the text enclosing -/// it, so the depth is bounded by the length of the request. +/// Parses the input of every `ML.PREDICT` clause in `query`, recursively fn predict_inputs<'a>(query: &CombinedQuery<'a>) -> crate::ml_policy::PredictInputs<'a> { let entries = query .ml_predict @@ -412,20 +257,8 @@ fn predict_inputs<'a>(query: &CombinedQuery<'a>) -> crate::ml_policy::PredictInp crate::ml_policy::PredictInputs::new(entries) } -/// Checks whether a request would be permitted, without running it or touching the -/// database. -/// -/// Parses and applies the ML policy, and nothing else. A request this accepts can -/// still fail when executed. -/// -/// # Errors -/// -/// [`QueryError::Parse`] if it does not parse, or [`QueryError::Ml`] if the policy -/// refuses it. The parse message used to be flattened to the literal -/// `"SPARQL_PARSE_ERROR"`, which discarded the snippet naming the offending token and -/// was consumed by nothing: the one caller that reported it mapped every -/// non-ML error to the same code regardless. -pub fn validate_query_policy(sparql: &str, database: &SparqlDatabase) -> Result<(), QueryError> { +/// Checks whether a request would be permitted, without running it or touching the database +pub fn validate_query_policy(sparql: &str, database: &KolibrieDB) -> Result<(), QueryError> { let combined = parse_request(sparql, true).map_err(QueryError::Parse)?; let predicts = predict_inputs(&combined); database @@ -441,7 +274,7 @@ pub fn validate_query_policy(sparql: &str, database: &SparqlDatabase) -> Result< /// Execute with temporary host-selected ML authority pub fn execute_sparql_with_ml_context( sparql: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, context: &crate::ml_policy::MlExecutionContext, ) -> Result>, QueryError> { with_ml_context(database, context, |database| { @@ -450,13 +283,13 @@ pub fn execute_sparql_with_ml_context( } pub(crate) fn with_ml_context( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, context: &crate::ml_policy::MlExecutionContext, - execute: impl FnOnce(&mut SparqlDatabase) -> R, + execute: impl FnOnce(&mut KolibrieDB) -> R, ) -> R { let previous = std::mem::replace(&mut database.ml_context, context.clone()); struct ContextScope<'a> { - database: &'a mut SparqlDatabase, + database: &'a mut KolibrieDB, previous: crate::ml_policy::MlExecutionContext, } impl Drop for ContextScope<'_> { @@ -471,19 +304,13 @@ pub(crate) fn with_ml_context( /// Summary returned by the error-preserving update entry point #[derive(Clone, Debug, Default, PartialEq, Eq)] pub struct UpdateSummary { - /// Quads added. Counts only genuinely new ones: re-inserting an existing quad - /// does not increment this. + /// Quads added pub inserted_quads: usize, - /// Quads removed. Counts only ones that were present. + /// Quads removed pub deleted_quads: usize, } -/// Executes a query, reporting failure by returning no rows. -/// -/// Superseded by [`execute`]. Deprecated rather than deleted so code outside this -/// repository keeps compiling with a warning; every call site inside it has moved. -/// -/// Execute SELECT or a compatibility Update request through the unified +/// Executes a query, reporting failure by returning no rows #[deprecated( note = "use `execute`, which returns Result: this reports \ failure by returning no rows, which a caller cannot tell from a query \ @@ -491,15 +318,11 @@ pub struct UpdateSummary { )] pub fn execute_query_rayon_parallel2_volcano( sparql: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Vec> { match execute(sparql, database) { Ok(results) => results.into(), Err(error) => { - // Behaviour preserved exactly: an ML refusal prints its code, anything - // else prints `SPARQL_EXECUTION_FAILED` and the real message is lost. - // That is the reason to prefer `execute`, and the reason this wrapper is - // kept only until its callers move. match error { QueryError::Ml(code) => eprintln!("{code}"), _ => eprintln!("SPARQL_EXECUTION_FAILED"), @@ -512,23 +335,15 @@ pub fn execute_query_rayon_parallel2_volcano( /// Execute a query request without accepting Update syntax pub fn execute_sparql_query( sparql: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result>, QueryError> { - // This repeated `execute_request`'s whole pipeline — parse, single_rule, - // validate_request, the ML branch, dispatch — for the sake of two differences, - // which are now what `RequestKind::QueryOnly` means. Keeping two copies of that - // sequence is how the entry points drift apart. - // - // Still returns `String` rather than `QueryError`, and still flattens to - // `Vec>`, because the HTTP surface passes these errors to clients. - // `execute` is the one that keeps the variable names and the unbound distinction. execute_request(sparql, database, RequestKind::QueryOnly).map(Solutions::into_rows) } /// Execute one of the six supported standard SPARQL Update forms pub fn execute_sparql_update( sparql: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { execute_update_request(sparql, database, false) } @@ -536,29 +351,23 @@ pub fn execute_sparql_update( /// Compatibility entry point that also accepts standalone INSERT/DELETE aliases pub(crate) fn execute_sparql_update_compat( sparql: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { execute_update_request(sparql, database, true) } -// Each stage's failure is classified here, where it is known which stage failed. -// Everything this calls still reports failure as a `String`; threading `QueryError` -// further down is a separate change, and doing it here keeps the distinction a caller -// can act on — a request that did not parse, versus one refused by policy, versus one -// that failed while running — without rewriting the pipeline underneath. -/// What forms a request may take, which is the whole of what the query entry points -/// disagree about. +/// What forms a request may take, which is the whole of what the query entry points disagree about #[derive(Debug, Clone, Copy, PartialEq, Eq)] enum RequestKind { - /// A query or an Update, with the standalone `INSERT`/`DELETE` aliases accepted. + /// A query or an Update, with the standalone `INSERT`/`DELETE` aliases accepted Any, - /// A query only. An Update is refused, and the aliases do not parse. + /// A query only QueryOnly, } fn execute_request( sparql: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, kind: RequestKind, ) -> Result { let combined = parse_request(sparql, kind == RequestKind::Any).map_err(QueryError::Parse)?; @@ -581,11 +390,6 @@ fn execute_request( return execute_approved_prediction(predict, &combined.prefixes, database) .map_err(QueryError::Execution); } - // After the ML branch and before `prepare_extensions`, which is where the - // query-only entry point used to refuse: `prepare_extensions` registers prefixes - // and neural declarations on the database, so a refused request must not reach it - // and leave those behind. An `ML.PREDICT` request still takes the branch above, - // as it did when this check lived in the `match` below. if kind == RequestKind::QueryOnly && matches!(combined.sparql.as_ref(), Some(SparqlOperation::Update(_))) { @@ -610,7 +414,7 @@ fn execute_request( fn execute_update_request( sparql: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, allow_data_aliases: bool, ) -> Result { let combined = parse_request(sparql, allow_data_aliases)?; @@ -649,7 +453,7 @@ fn parse_request(input: &str, allow_data_aliases: bool) -> Result, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { if predict.distribution { return Err( @@ -681,8 +485,6 @@ fn execute_approved_prediction( .ml_context .predict(predict.model, &features) .map_err(|e| e.to_string())?; - // The prediction is one more column, so it gets one more name: the variable the - // clause binds it to. Everything before it keeps the input query's own names. let mut variables = predict_input_columns(predict); variables.push(normalize_variable(predict.output).to_string()); let mut interner = TermInterner::new(); @@ -707,7 +509,7 @@ fn execute_approved_prediction( fn prepare_extensions( combined: &CombinedQuery<'_>, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result, String> { let predicts = predict_inputs(combined); database @@ -718,7 +520,6 @@ fn prepare_extensions( !database.neural_relation_decls.is_empty(), ) .map_err(|e| e.to_string())?; - // Database prefixes remain available, while a query-local declaration let mut prefixes = database.prefixes.clone(); prefixes.extend(combined.prefixes.clone()); database.prefixes.extend(combined.prefixes.clone()); @@ -754,7 +555,7 @@ fn prepare_extensions( fn execute_select( query: &SelectQuery<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { let mut lexical_patterns = Vec::new(); collect_triple_patterns(&query.pattern, &mut lexical_patterns); @@ -774,7 +575,7 @@ fn execute_select( fn optimize_and_execute( logical_plan: crate::streamertail_optimizer::LogicalOperator, dataset: &DatasetView, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Bindings { let stats = if logical_plan.requires_cost_based_join_ordering() { database.get_or_build_planning_stats() @@ -789,7 +590,7 @@ fn optimize_and_execute( fn build_dataset_view( query: &SelectQuery<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { if query.from.is_empty() && query.from_named.is_empty() { return Ok(DatasetView::from_database(database)); @@ -811,7 +612,7 @@ fn build_dataset_view( fn compile_dataset_graph( graph: &str, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { match compile_graph_term(graph, prefixes, database)? { GraphTerm::Named(graph) => Ok(GraphId::Named(graph)), @@ -820,13 +621,7 @@ fn compile_dataset_graph( } } -fn decode_bindings(bindings: Bindings, database: &SparqlDatabase) -> StringTable { - // Taken apart, not rebuilt. A solution sequence already is a schema and a row of - // ids per solution, so the work this used to do here is now redundant twice over: - // it scanned every row's keys to recover the schema the sequence already carries, - // and it rebuilt a `HashMap` per row to read the ids back out of, then looked each - // variable's column up again per entry. On a union of three branches that is - // 60,000 maps with owned `String` keys, built and discarded. +fn decode_bindings(bindings: Bindings, database: &KolibrieDB) -> StringTable { let (variables, cells) = bindings.into_parts(); let mut rows: Vec>> = cells .into_iter() @@ -837,21 +632,6 @@ fn decode_bindings(bindings: Bindings, database: &SparqlDatabase) -> StringTable }) .collect(); - // Interned per chunk, then stitched, so no step runs sequentially over cells. - // - // A single global id-to-index map is the obvious way to intern, and it was the - // slow way: the map has to exist before any cell can be rewritten, which forces - // a sequential pass over every cell. Measured at about 1.1ms here, enough to lose - // to simply owning the strings. Sorting the ids instead removes the hashing but - // pays an n log n sort and a cache-missing binary search per cell, which measured - // worse again -- a chunk's map is small enough to stay in cache, and a hash - // lookup beat a clone 5.6ns to 24.4ns. - // - // So each chunk interns into its own table, under one dictionary lock, exactly - // where the owned path cloned. A chunk's indices are local, a prefix sum over the - // per-chunk counts gives each chunk's offset, and a parallel pass of one integer - // add per cell makes them global. A term shared by two chunks is held twice, - // which costs a little memory to keep the whole thing parallel. let chunk_terms: Vec> = rows .par_chunks_mut(DECODE_LOCK_CHUNK) .map(|chunk| { @@ -863,9 +643,6 @@ fn decode_bindings(bindings: Bindings, database: &SparqlDatabase) -> StringTable for cell in row.iter_mut() { if let Some(id) = *cell { let index = *local.entry(id).or_insert_with(|| { - // A bound id that will not decode becomes "", which is - // what unwrap_or_default gave before. Only an absent - // variable is `None`, and Solutions documents that. terms.push(dictionary.decode_term(id, "ed).unwrap_or_default()); (terms.len() - 1) as u32 }); @@ -920,8 +697,6 @@ fn finalize_select(mut table: StringTable, query: &SelectQuery<'_>) -> Solutions apply_order_by(&mut table, &query.order_conditions); - // Every projected column resolved to an index once for the whole result. `None` - // is a column the solutions never bound, which stays unbound in every row. let projected: Vec> = columns .iter() .map(|column| table.column(normalize_variable(column))) @@ -930,8 +705,6 @@ fn finalize_select(mut table: StringTable, query: &SelectQuery<'_>) -> Solutions if query.distinct { let mut seen = HashSet::new(); table.rows.retain(|row| { - // Copied, not cloned: an interned cell is a `u32`, where this used to - // clone a String per projected column for every row in the result. let key: Vec> = projected .iter() .map(|column| column.and_then(|index| row[index])) @@ -944,16 +717,12 @@ fn finalize_select(mut table: StringTable, query: &SelectQuery<'_>) -> Solutions table.rows.truncate(limit); } - // `None` where the solution did not bind the column: an unbound variable, which - // SPARQL distinguishes from one bound to the empty string. let identity = projected.len() == table.variables.len() && projected .iter() .enumerate() .all(|(position, column)| *column == Some(position)); let rows = if identity { - // Projecting every column in its own order, so the rows move as they are - // rather than being rebuilt a cell at a time. table.rows } else { table @@ -973,19 +742,12 @@ fn finalize_select(mut table: StringTable, query: &SelectQuery<'_>) -> Solutions .iter() .map(|column| normalize_variable(column).to_string()) .collect(), - // Carried over whole. Projection can drop a column and so leave a term here - // that no cell refers to, which costs a little memory and nothing else; - // rebuilding the table to avoid that would cost a pass over every cell. terms: table.terms, rows, } } -/// Column names for an `ML.PREDICT` input query, mirroring [`projection_columns`]. -/// -/// The clause keeps the input query's projection and WHERE patterns as borrowed text -/// rather than a parsed `SelectQuery`, so `*` is expanded from the pattern triples -/// here instead of from a `GroupGraphPattern`. +/// Column names for an `ML.PREDICT` input query, mirroring [`projection_columns`] fn predict_input_columns(predict: &shared::query::MLPredictClause<'_>) -> Vec { if predict.input_select == vec![("*", "*", None)] { let mut columns = Vec::new(); @@ -1082,7 +844,7 @@ fn collect_term_variables(term: &str, variables: &mut Vec) { push_variable(variables, term); } else if term.starts_with("<<") && term.ends_with(">>") { let (subject, predicate, object) = - SparqlDatabase::split_quoted_triple_content(term[2..term.len() - 2].trim()); + KolibrieDB::split_quoted_triple_content(term[2..term.len() - 2].trim()); collect_term_variables(&subject, variables); collect_term_variables(&predicate, variables); collect_term_variables(&object, variables); @@ -1100,15 +862,12 @@ fn push_variable(variables: &mut Vec, variable: &str) { } fn aggregate_rows(table: StringTable, query: &SelectQuery<'_>) -> StringTable { - // Grouping columns resolved once, then every row keyed by index. let group_columns: Vec> = query .group_vars .iter() .map(|variable| table.column(normalize_variable(variable))) .collect(); - // Grouping on interned cells rather than cloned strings. The cells come from one - // term table, so equal cells are equal values; the key is a row of `u32`. let mut groups: BTreeMap>, Vec> = BTreeMap::new(); for (index, row) in table.rows.iter().enumerate() { let key: Vec> = group_columns @@ -1121,9 +880,6 @@ fn aggregate_rows(table: StringTable, query: &SelectQuery<'_>) -> StringTable { groups.insert(Vec::new(), Vec::new()); } - // The output schema is the grouping variables, then one column per aggregate. - // An aggregate aliased onto a grouping variable reuses its column, which is what - // inserting into one map per row did. let mut variables: Vec = query .group_vars .iter() @@ -1145,8 +901,6 @@ fn aggregate_rows(table: StringTable, query: &SelectQuery<'_>) -> StringTable { aggregates.push((column, kind, normalize_variable(variable))); } - // A fresh term table: an aggregated result is one row per group, so almost - // nothing the input interned survives into it. let mut interner = TermInterner::new(); let width = variables.len(); let rows: Vec>> = groups @@ -1163,9 +917,6 @@ fn aggregate_rows(table: StringTable, query: &SelectQuery<'_>) -> StringTable { let normalized_kind = kind.to_ascii_uppercase(); let descriptor = crate::aggregate::Aggregate::parse(&normalized_kind); if descriptor.kind == "COUNT" && input == "*" { - // COUNT(*) counts solutions, and COUNT(DISTINCT *) counts the - // distinct ones over every decoded column, which is what - // deduplicating whole maps did. let count = if descriptor.distinct { let unique: BTreeSet<&Vec>> = members.iter().map(|&member| &table.rows[member]).collect(); @@ -1176,8 +927,6 @@ fn aggregate_rows(table: StringTable, query: &SelectQuery<'_>) -> StringTable { row[column] = Some(interner.intern(count)); continue; } - // `None` where the aggregate has no value, matching the removal the - // map shape did, so the column reads as unbound rather than empty. let value = match table.column(input) { Some(input) => { let values: Vec<&str> = members @@ -1206,8 +955,6 @@ fn apply_order_by(table: &mut StringTable, conditions: &[OrderCondition<'_>]) { return; } - // Resolved once for the sort, not per comparison: a sort does O(n log n) - // comparisons, so a name lookup inside the comparator is paid that many times. let keys: Vec<(Option, SortDirection)> = conditions .iter() .map(|condition| { @@ -1218,8 +965,6 @@ fn apply_order_by(table: &mut StringTable, conditions: &[OrderCondition<'_>]) { }) .collect(); - // Taken out so the comparator can borrow the term table while the rows are - // sorted in place. let mut rows = std::mem::take(&mut table.rows); let terms = &table.terms; let term = @@ -1266,7 +1011,7 @@ fn collect_triple_patterns<'a>( fn execute_update_operation( operation: &UpdateOperation<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { let summary = match operation { UpdateOperation::InsertData(insert) => { @@ -1309,7 +1054,7 @@ fn execute_modify( insert: Option<&InsertClause<'_>>, where_pattern: &GroupGraphPattern<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { let mut lexical_patterns = Vec::new(); collect_triple_patterns(where_pattern, &mut lexical_patterns); @@ -1322,7 +1067,6 @@ fn execute_modify( let logical_plan = build_logical_plan_from_group(where_pattern, prefixes, database)?; let dataset = DatasetView::from_database(database); - // The WHERE is evaluated once. Both templates are instantiated completely let bindings = optimize_and_execute(logical_plan, &dataset, database); let deletions = match delete { Some(delete) => instantiate_templates(&delete.quads, &bindings, prefixes, database, false)?, @@ -1339,7 +1083,7 @@ fn execute_modify( fn instantiate_data( quads: &[LexicalQuadPattern<'_>], prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, insert: bool, ) -> Result, String> { instantiate_templates(quads, &Bindings::unit(), prefixes, database, insert) @@ -1349,12 +1093,11 @@ fn instantiate_templates( templates: &[LexicalQuadPattern<'_>], bindings: &Bindings, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, insert: bool, ) -> Result, String> { let mut quads = BTreeSet::new(); for binding in bindings { - // SPARQL Update gives every solution its own blank-node allocation let mut blank_nodes = HashMap::new(); for template in templates { if let Some(quad) = instantiate_quad( @@ -1376,7 +1119,7 @@ fn instantiate_quad( template: &LexicalQuadPattern<'_>, binding: BindingRef<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, insert: bool, blank_nodes: &mut HashMap, ) -> Result, String> { @@ -1453,7 +1196,7 @@ fn instantiate_graph( graph: &str, binding: BindingRef<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result, String> { if is_variable(graph) { return Ok(binding.get(normalize_variable(graph)).map(GraphId::Named)); @@ -1465,7 +1208,7 @@ fn instantiate_graph( } } -fn is_legal_subject(id: u32, database: &SparqlDatabase) -> bool { +fn is_legal_subject(id: u32, database: &KolibrieDB) -> bool { if is_quoted_triple_id(id) { return is_legal_quoted_triple(id, database); } @@ -1481,7 +1224,7 @@ fn is_legal_subject(id: u32, database: &SparqlDatabase) -> bool { .is_empty() } -fn is_legal_predicate(id: u32, database: &SparqlDatabase) -> bool { +fn is_legal_predicate(id: u32, database: &KolibrieDB) -> bool { if is_quoted_triple_id(id) { return false; } @@ -1497,11 +1240,11 @@ fn is_legal_predicate(id: u32, database: &SparqlDatabase) -> bool { .is_empty()) } -fn is_legal_object(id: u32, database: &SparqlDatabase) -> bool { +fn is_legal_object(id: u32, database: &KolibrieDB) -> bool { !is_quoted_triple_id(id) || is_legal_quoted_triple(id, database) } -fn is_legal_graph_name(id: u32, database: &SparqlDatabase) -> bool { +fn is_legal_graph_name(id: u32, database: &KolibrieDB) -> bool { if is_quoted_triple_id(id) { return false; } @@ -1513,7 +1256,7 @@ fn is_legal_graph_name(id: u32, database: &SparqlDatabase) -> bool { || database.dataset_index.graph_exists(GraphId::Named(id))) } -fn is_legal_quoted_triple(id: u32, database: &SparqlDatabase) -> bool { +fn is_legal_quoted_triple(id: u32, database: &KolibrieDB) -> bool { let components = database.quoted_triple_store.read().unwrap().decode(id); let Some((subject, predicate, object)) = components else { return false; @@ -1540,7 +1283,7 @@ fn instantiate_term( term: &str, binding: BindingRef<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, insert: bool, blank_nodes: &mut HashMap, ) -> Result, String> { @@ -1561,7 +1304,7 @@ fn instantiate_term( } if term.starts_with("<<") && term.ends_with(">>") { let (subject, predicate, object) = - SparqlDatabase::split_quoted_triple_content(term[2..term.len() - 2].trim()); + KolibrieDB::split_quoted_triple_content(term[2..term.len() - 2].trim()); let Some(subject) = instantiate_term(&subject, binding, prefixes, database, insert, blank_nodes)? else { @@ -1592,7 +1335,7 @@ fn instantiate_term( } } -fn allocate_blank_node(label: &str, database: &mut SparqlDatabase) -> u32 { +fn allocate_blank_node(label: &str, database: &mut KolibrieDB) -> u32 { static NEXT_UPDATE_BLANK: AtomicU64 = AtomicU64::new(1); loop { let allocation = NEXT_UPDATE_BLANK.fetch_add(1, AtomicOrdering::Relaxed); @@ -1608,7 +1351,7 @@ fn allocate_blank_node(label: &str, database: &mut SparqlDatabase) -> u32 { fn apply_mutations( deletions: BTreeSet, insertions: BTreeSet, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> UpdateSummary { let deleted_quads = deletions .iter() diff --git a/kolibrie/src/lib.rs b/kolibrie/src/lib.rs index 7fe771f6..7854a1ee 100644 --- a/kolibrie/src/lib.rs +++ b/kolibrie/src/lib.rs @@ -8,112 +8,48 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The Kolibrie engine: an RDF store, a SPARQL evaluator, and the streaming, -//! probabilistic and neural extensions built on them. -//! -//! # Where to start -//! -//! [`prelude`] exports the three things almost every caller needs — the store, the -//! parser entry point, and the query executor: -//! -//! ```ignore -//! use kolibrie::prelude::*; -//! -//! let mut db = SparqlDatabase::new(); -//! db.parse_ntriples_and_add(data); -//! let rows = execute(query, &mut db).expect("query should run").into_rows(); -//! ``` -//! -//! # How a query gets answered -//! -//! ```text -//! text ──parser──▶ CombinedQuery ──▶ logical plan ──optimizer──▶ physical plan ──▶ rows -//! (lexical) (resolved) (costed) -//! ``` -//! -//! [`parser`] produces the lexical syntax tree defined in `shared::query`; nothing is -//! prefix-expanded or interned at that point. Binding it to a -//! [`SparqlDatabase`](sparql_database::SparqlDatabase)'s dictionary turns terms into -//! integer ids, and [`streamertail_optimizer`] then costs alternative physical plans -//! before [`execute_query`] runs one. -//! -//! # Subsystems -//! -//! | module | role | -//! |---|---| -//! | [`sparql_database`] | the store: encoded triples, dictionary, indexes, statistics | -//! | [`parser`] | SPARQL, RSP-QL and the rule/ML extensions | -//! | [`execute_query`] | evaluation, including SIMD and Rayon variants | -//! | [`streamertail_optimizer`] | cost-based logical-to-physical planning | -//! | [`rsp`], [`rsp_engine`] | RDF stream processing: windows, joins, output streams | -//! | [`program`] | probabilistic programs over rules, with provenance | -//! | [`ml`] | model declarations, the authority to run them, and the runtime | -//! | [`query_builder`] | a programmatic alternative to writing SPARQL text | -//! -//! Reasoning itself lives outside this crate: `datalog` for the Datalog subset and -//! `datalogmtl` for the temporal one. Both are siblings over the `shared` -//! vocabulary rather than layers on this engine. +//! The Kolibrie engine: an RDF store, a SPARQL evaluator, and the streaming, probabilistic and +//! neural extensions built on them -// Modules with no consumer outside this crate are `pub(crate)`. Keeping them `pub` -// put them on the documentation ledger and let future callers couple to paths we do -// not intend to support. Promote one back the moment something outside needs it. mod aggregate; +/// The RDF store and everything hung off it +pub mod database; pub(crate) mod error_handler; #[cfg(test)] extern crate self as kolibrie; -/// Query evaluation, including the SIMD and Rayon-parallel variants. +/// Query evaluation, including the SIMD and Rayon-parallel variants pub mod execute_query; -// Documented by its own `//!` header. A `///` summary here would merge with that -// and resolve its links in this scope rather than in `ml`, which is the trap -// `scripts/verify.sh` warns about and which this walked into first time. pub mod ml; -// The ML *contract*, deliberately outside [`ml`]: the engine depends on these three -// and they reach back no further than the store, while everything inside `ml` -// depends on the engine. Folding all ten into one module made a leaf and a consumer -// the same node and closed a cycle across seven modules, which is how the direction -// of this dependency came to be measured rather than assumed. pub(crate) mod ml_feature_loader; pub mod ml_policy; pub mod neural_registry; -/// What a failed parse says, without naming the parser library. +/// What a failed parse says, without naming the parser library pub mod parse_error; pub mod parser; pub mod prelude; pub mod program; -/// Building a query programmatically instead of writing SPARQL text. +/// Building a query programmatically instead of writing SPARQL text pub mod query_builder; pub mod query_error; pub mod rsp; -/// The RDF stream processing engine: named streams, windows, and their joins. +/// The RDF stream processing engine: named streams, windows, and their joins pub mod rsp_engine; pub mod rule_execution; -/// The RDF store and everything hung off it. -pub mod sparql_database; pub mod sparql_protocol; pub mod stats; pub mod streamertail_optimizer; pub(crate) mod term_order; -/// Small helpers: user-defined function wrappers, timestamps, a demo server. +/// Small helpers: user-defined function wrappers, timestamps, a demo server pub mod utils; -/// Which [`MlRuntime`](ml::runtime::MlRuntime) the query layer calls through. -/// -/// The crate root is the one place that may name both the abstraction and the -/// implementation, which is what keeps `execute_query` and `rule_execution` from -/// depending on the ML layer. At the point the workspace is split into crates this -/// selection moves to the facade crate and the object is threaded through the store -/// instead; until then, a `static` chosen by the feature flag does the same job -/// without a registration step, so no caller has to install anything. +/// Which [`MlRuntime`] the query layer calls through pub(crate) mod ml_runtime_impl { use crate::ml::runtime::MlRuntime; - // No `cfg` here: `ml::neural_relations` is already either the real module or the - // `#[path]`-substituted disabled one, so naming its `NeuralRuntime` picks the - // right implementation for the build on its own. static RUNTIME: crate::ml::neural_relations::NeuralRuntime = crate::ml::neural_relations::NeuralRuntime; - /// The runtime for this build. + /// The runtime for this build pub(crate) fn runtime() -> &'static dyn MlRuntime { &RUNTIME } diff --git a/kolibrie/src/ml/execute.rs b/kolibrie/src/ml/execute.rs index 22e7e721..cc3280c3 100644 --- a/kolibrie/src/ml/execute.rs +++ b/kolibrie/src/ml/execute.rs @@ -8,43 +8,33 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Running an `ML.PREDICT` clause through the Python backend. -//! -//! Crosses into Python via the `ml` crate, so the interesting cost is the boundary -//! itself rather than the model: [`MLPredictTiming`] breaks it down because the -//! marshalling usually dominates inference on small batches. +//! Running an `ML.PREDICT` clause through the Python backend -use crate::sparql_database::SparqlDatabase; +use crate::database::KolibrieDB; use ml::MLHandler; use shared::query::MLPredictClause; use std::error::Error; use std::time::Instant; #[derive(Debug)] -/// Where the time went in one `ML.PREDICT`, in seconds. -/// -/// Split around the language boundary on purpose: the crossing and marshalling are -/// often larger than the inference, and only a breakdown shows that. +/// Where the time went in one `ML.PREDICT`, in seconds pub struct MLPredictTiming { - /// Wall-clock time for the whole clause. + /// Wall-clock time for the whole clause pub total_time: f64, - /// Handing the input across to Python. + /// Handing the input across to Python pub rust_to_python_time: f64, - /// Python-side preparation before inference. + /// Python-side preparation before inference pub python_preprocessing_time: f64, - /// The model call itself. + /// The model call itself pub actual_prediction_time: f64, - /// Python-side work on the results. + /// Python-side work on the results pub python_postprocessing_time: f64, - /// Bringing the results back. + /// Bringing the results back pub python_to_rust_time: f64, } impl MLPredictTiming { - /// Prints the timing to stdout. - /// - /// Deliberately omits the model name and the input: a breakdown may be logged - /// where the data it was computed from should not be. + /// Prints the timing to stdout pub fn print_breakdown(&self) { println!( "ML_TIMING total_seconds={:.6} prediction_seconds={:.6}", @@ -53,19 +43,16 @@ impl MLPredictTiming { } } -/// Runs an `ML.PREDICT` clause and returns its predictions with timing. -/// -/// Binds the argmax label. A distribution over labels requires -/// [`program`](crate::program). +/// Runs an `ML.PREDICT` clause and returns its predictions with timing pub fn execute_ml_prediction_from_clause( ml_predict: &MLPredictClause, - database: &SparqlDatabase, + database: &KolibrieDB, model: &str, extract_data: F, predict: G, ) -> Result<(Vec, MLPredictTiming), Box> where - F: FnOnce(&SparqlDatabase) -> Result, Box>, + F: FnOnce(&KolibrieDB) -> Result, Box>, G: FnOnce( &MLHandler, &str, @@ -79,10 +66,8 @@ where crate::ml_policy::validate_model_name(model)?; let ml_predict_start = Instant::now(); - // Rust setup and model discovery let rust_setup_start = Instant::now(); - // Define model paths let model_dir = { let mut path = std::path::PathBuf::from(env!("CARGO_MANIFEST_DIR")); @@ -101,7 +86,6 @@ where let mut ml_handler = MLHandler::new()?; - // Check if models need to be generated let models_exist = std::fs::read_dir(&model_dir)? .filter_map(Result::ok) .filter(|entry| { @@ -126,17 +110,14 @@ where return Err("No valid models found with TTL schemas".into()); } - // Extract variable names let variable_names: Vec = ml_predict .input_select .iter() .map(|(var, _, _)| var.trim_start_matches('?').to_string()) .collect(); - // Extract data let data = extract_data(database)?; - // Filter feature names let feature_names: Vec = variable_names .iter() .filter(|&name| *name != "room" && *name != "road") @@ -147,15 +128,12 @@ where let rust_setup_time = rust_setup_start.elapsed().as_secs_f64(); - // Call Python prediction (this measures internal Python timing) let python_call_start = Instant::now(); let (predictions, mut timing) = predict(&ml_handler, best_model_name, &data, &feature_names)?; let _python_call_time = python_call_start.elapsed().as_secs_f64(); - // Calculate total time let total_time = ml_predict_start.elapsed().as_secs_f64(); - // Update timing with Rust-side measurements timing.rust_to_python_time = rust_setup_time; timing.python_to_rust_time = total_time - rust_setup_time @@ -172,14 +150,14 @@ where /// Execute with an already initialized ML handler pub fn execute_ml_prediction_with_handler( ml_predict: &MLPredictClause, - database: &SparqlDatabase, + database: &KolibrieDB, ml_handler: &MLHandler, best_model_name: &str, extract_data: F, predict: G, ) -> Result<(Vec, MLPredictTiming), Box> where - F: FnOnce(&SparqlDatabase) -> Result, Box>, + F: FnOnce(&KolibrieDB) -> Result, Box>, G: FnOnce( &MLHandler, &str, diff --git a/kolibrie/src/ml/execute_train.rs b/kolibrie/src/ml/execute_train.rs index 4a6932c3..3125cb5a 100644 --- a/kolibrie/src/ml/execute_train.rs +++ b/kolibrie/src/ml/execute_train.rs @@ -8,15 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Training a model declared by a `TRAIN` clause. -//! -//! The `Owned*` types here mirror the borrowed declarations in -//! [`shared::query`] with their terms resolved and owned, because training outlives -//! the query text it was requested by. -//! -//! Writing an artifact requires trusted-local authority from the -//! [`MlExecutionContext`](crate::ml_policy::MlExecutionContext); a `save_path` in a -//! declaration is a request, not permission. +//! Training a model declared by a `TRAIN` clause use std::collections::HashMap; use std::error::Error; @@ -34,125 +26,118 @@ use shared::seed_spec::{ExclusiveChoice, SeedSpec}; use shared::triple::Triple; use std::time::{Duration, Instant}; +use crate::database::KolibrieDB; use crate::execute_query::query_training_rows; use crate::ml_feature_loader::{build_feature_vec, rdf_term_to_f64, RdfTerm}; -use crate::sparql_database::SparqlDatabase; type TrainResult = Result>; #[derive(Debug, Clone)] -/// One alternative a model can predict, as an owned fact template. +/// One alternative a model can predict, as an owned fact template pub struct OwnedNeuralChoice { - /// Fact asserted when this alternative is chosen; variables are filled from - /// the prediction's row. + /// Fact asserted when this alternative is chosen; variables are filled from the prediction's + /// row pub triple_template: (String, String, String), - /// Variable carrying this alternative's probability. + /// Variable carrying this alternative's probability pub prob_var: String, } #[derive(Debug, Clone)] -/// Whether a model's outputs compete or stand alone. +/// Whether a model's outputs compete or stand alone pub enum OwnedNeuralGroupType { - /// At most one alternative holds. Represented downstream as a mutually - /// exclusive seed group, so two labels can never both be true. + /// At most one alternative holds Exclusive { - /// The competing alternatives. + /// The competing alternatives choices: Vec, }, - /// A single fact with its own probability, independent of everything else. + /// A single fact with its own probability, independent of everything else Independent { - /// Fact asserted, with variables filled from the row. + /// Fact asserted, with variables filled from the row fact_template: (String, String, String), - /// Variable carrying its probability. + /// Variable carrying its probability prob_var: String, }, } #[derive(Debug, Clone)] -/// One invocation of a model within a training clause. +/// One invocation of a model within a training clause pub struct OwnedNeuralCallSpec { - /// Variables forming the feature vector, in model input order. + /// Variables forming the feature vector, in model input order pub feature_vars: Vec, - /// How to interpret the outputs. + /// How to interpret the outputs pub group_type: OwnedNeuralGroupType, } #[derive(Debug, Clone)] -/// A resolved `TRAIN` declaration, owning everything training needs. +/// A resolved `TRAIN` declaration, owning everything training needs pub struct OwnedNeuralTrainingClause { - /// Model to fit. + /// Model to fit pub model_name: String, - /// Invocations whose outputs the loss is computed against. + /// Invocations whose outputs the loss is computed against pub neural_calls: Vec, - /// Query selecting the training examples, as text. + /// Query selecting the training examples, as text pub training_data_raw: String, - /// Variable holding each example's ground-truth label. + /// Variable holding each example's ground-truth label pub label_var: String, - /// Pattern identifying what is predicted about each example. + /// Pattern identifying what is predicted about each example pub target_triple: (String, String, String), - /// Objective to minimise. + /// Objective to minimise pub loss: LossFn, - /// Optimiser to use. + /// Optimiser to use pub optimizer: shared::query::OptimizerKind, - /// Step size. + /// Step size pub learning_rate: f64, - /// Passes over the training data. + /// Passes over the training data pub epochs: usize, - /// Examples per gradient step. + /// Examples per gradient step pub batch_size: usize, - /// Where the weights should be written. Resolved through the ML context, so a - /// path here is a request rather than permission to write it. + /// Where the weights should be written pub save_path: Option, } #[derive(Debug, Clone, Copy, Default, PartialEq, Eq)] -/// Where a training run spent its time. +/// Where a training run spent its time pub struct NeuralTrainingTimings { - /// Running the query that selects the examples. + /// Running the query that selects the examples pub data_fetch: Duration, - /// Fitting the model. + /// Fitting the model pub learning: Duration, } -/// Trains a model from a resolved clause. -/// -/// Randomness is unseeded, so two runs differ; use -/// [`execute_ml_training_owned_seeded`] where reproducibility matters. +/// Trains a model from a resolved clause pub fn execute_ml_training_owned( clause: &OwnedNeuralTrainingClause, base_reasoner: &Reasoner, - db: &mut SparqlDatabase, + db: &mut KolibrieDB, ) -> TrainResult { execute_ml_training_impl(clause, base_reasoner, db, None, None).map(|(model, _)| model) } -/// As [`execute_ml_training_owned`], with training randomness seeded so the run -/// is reproducible. +/// As [`execute_ml_training_owned`], with training randomness seeded so the run is reproducible pub fn execute_ml_training_owned_seeded( clause: &OwnedNeuralTrainingClause, base_reasoner: &Reasoner, - db: &mut SparqlDatabase, + db: &mut KolibrieDB, seed: u64, ) -> TrainResult { execute_ml_training_impl(clause, base_reasoner, db, Some(seed), None).map(|(model, _)| model) } -/// As [`execute_ml_training_owned_seeded`], also returning where the time went. +/// As [`execute_ml_training_owned_seeded`], also returning where the time went pub fn execute_ml_training_owned_seeded_timed( clause: &OwnedNeuralTrainingClause, base_reasoner: &Reasoner, - db: &mut SparqlDatabase, + db: &mut KolibrieDB, seed: u64, ) -> TrainResult<(MlpNeuralPredicate, NeuralTrainingTimings)> { execute_ml_training_impl(clause, base_reasoner, db, Some(seed), None) } -/// Trains with an explicit hidden-layer shape rather than the model -/// declaration's. +/// Trains with an explicit hidden-layer shape rather than the model declaration's pub fn execute_supervised_training( clause: &OwnedNeuralTrainingClause, hidden: &[usize], - db: &mut SparqlDatabase, + db: &mut KolibrieDB, seed: Option, ) -> TrainResult<(MlpNeuralPredicate, NeuralTrainingTimings)> { if clause.neural_calls.len() != 1 { @@ -169,7 +154,7 @@ pub fn execute_supervised_training( fn execute_ml_training_impl( clause: &OwnedNeuralTrainingClause, base_reasoner: &Reasoner, - db: &mut SparqlDatabase, + db: &mut KolibrieDB, seed: Option, hidden_override: Option<&[usize]>, ) -> TrainResult<(MlpNeuralPredicate, NeuralTrainingTimings)> { @@ -278,8 +263,6 @@ fn execute_ml_training_impl( output_dim, )?; let snapshot = SeedSnapshot::from_seed_specs(&seeds)?; - // `None`: this reasoner carries the store's facts already, copied in by - // `build_ground_reasoner_from_db`. See the note on that function. let lineage = materialize_lineage(&mut local_reasoner, None, snapshot)?; let target = instantiate_triple( @@ -382,7 +365,7 @@ fn build_seed_specs_for_row( detached_probs: &[Vec>], sample_idx: usize, row: &HashMap, - db: &SparqlDatabase, + db: &KolibrieDB, output_dim: usize, ) -> TrainResult> { clause @@ -440,7 +423,7 @@ fn build_seed_specs_for_row( fn instantiate_triple( template: (&str, &str, &str), row: &HashMap, - db: &SparqlDatabase, + db: &KolibrieDB, ) -> TrainResult { let subject = instantiate_term(template.0, row, db)?; let predicate = instantiate_term(template.1, row, db)?; @@ -455,7 +438,7 @@ fn instantiate_triple( fn instantiate_term( term: &str, row: &HashMap, - db: &SparqlDatabase, + db: &KolibrieDB, ) -> TrainResult { if term.starts_with('?') { let key = term.trim_start_matches('?'); @@ -511,9 +494,9 @@ fn loss_gradient( } } -/// A reasoner seeded with the store's current facts, for deriving the context a -/// training example sits in. -pub fn build_ground_reasoner_from_db(db: &SparqlDatabase, extra_rule: Option) -> Reasoner { +/// A reasoner seeded with the store's current facts, for deriving the context a training example +/// sits in +pub fn build_ground_reasoner_from_db(db: &KolibrieDB, extra_rule: Option) -> Reasoner { let mut reasoner = Reasoner::new(); reasoner.adopt_terms(db.terms()); for triple in db.query_default_triples(None, None, None) { @@ -651,9 +634,6 @@ mod tests { #[test] fn malformed_training_query_is_an_error_not_a_panic() { - // A TRAIN declaration's `QUERY { ... }` block is captured by the parser as - // raw text and never parsed, so a malformed one reaches here from user - // input. This used to `expect`, which aborted the process. let mut db = ml_local::database(); for query in ["this is not a query at all", "SELECT ?x WHERE { ?s ?p", ""] { let result = query_training_rows(&mut db, query); diff --git a/kolibrie/src/ml/mod.rs b/kolibrie/src/ml/mod.rs index 6deafafd..2906115d 100644 --- a/kolibrie/src/ml/mod.rs +++ b/kolibrie/src/ml/mod.rs @@ -8,61 +8,15 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Machine learning: declaring models, authorising them, and running them. -//! -//! Seven modules that were seven files in the crate root, named `ml_runtime`, -//! `execute_ml` and so on — a prefix doing by hand what a module does by language. -//! They are the same modules; `ml_runtime` is [`runtime`], `execute_ml` is `execute`. -//! -//! # Two stayed outside, and that is the design -//! -//! `ml_policy`, `ml_feature_loader` and `neural_registry` are still in the crate -//! root, because they are the ML *contract* rather than the ML implementation: the -//! engine depends on them, and they reach back no further than the store. Everything in here is the other -//! direction. As `lib.rs` puts it, the crate root is the one place that may name both -//! the abstraction and the implementation, and that is what keeps `execute_query` and -//! `rule_execution` from depending on the ML layer. -//! -//! Folding all ten into one module erased that distinction and closed a cycle: -//! `scripts/module_graph.py` counts a directory as one node, so a leaf the engine -//! depends on and a consumer that depends on the engine became the same node, and -//! seven modules collapsed into one strongly-connected component. -//! -//! # What the `ml` feature changes -//! -//! Most of this is present either way. Deciding *whether* a model may run is a -//! policy question the engine answers with the feature off, which is what lets a -//! build without PyO3 refuse a query cleanly instead of failing to link: -//! -//! | always | behind `ml` | -//! |---|---| -//! | [`runtime`], [`syntax`] | `execute`, `execute_train`, `predict_candle`, `predict_runtime`, `neural_relations` | -//! -//! The right-hand column is not linked, and cannot be: `cargo doc` runs with default -//! features, so those modules do not exist in the build that renders this. -//! -//! `neural_relations` is the exception that is always *named*: with the feature off -//! the name resolves to a stub module whose calls return `ML_FEATURE_DISABLED`, so -//! the rest of the crate neither grows a `cfg` nor loses a symbol. Gathering the -//! gates here is the point of the module — they were eight separate `#[cfg]` -//! attributes scattered through `lib.rs`, where it took reading all of them to see -//! which half of this was conditional. -//! -//! # The crate of the same name -//! -//! `kolibrie` depends on a crate called `ml`, which is where `execute` and -//! `neural_relations` get `MLHandler` and `MlpNeuralPredicate`. A `use ml::…` -//! inside this crate is now ambiguous between that crate and this module, so the -//! crate is named `::ml::…` with a leading `::`. Anything new reaching for it has to -//! do the same. +//! Machine learning: declaring models, authorising them, and running them pub mod runtime; pub(crate) mod syntax; -/// Running an `ML.PREDICT` clause through the Python backend. +/// Running an `ML.PREDICT` clause through the Python backend #[cfg(feature = "ml")] pub mod execute; -/// Training a model declared by a `TRAIN` clause. +/// Training a model declared by a `TRAIN` clause #[cfg(feature = "ml")] pub mod execute_train; #[cfg(feature = "ml")] @@ -70,12 +24,10 @@ pub(crate) mod predict_candle; #[cfg(feature = "ml")] pub(crate) mod predict_runtime; -/// Predicates whose facts come from a model rather than from data. +/// Predicates whose facts come from a model rather than from data #[cfg(feature = "ml")] pub mod neural_relations; -/// Stubs standing in for [`neural_relations`] when the `ml` feature is off, so the -/// rest of the crate compiles without PyO3 and a caller gets a clear -/// `ML_FEATURE_DISABLED` error rather than a missing symbol. +/// Stubs for [`neural_relations`] when the `ml` feature is off, returning `ML_FEATURE_DISABLED` #[cfg(not(feature = "ml"))] #[path = "neural_relations_disabled.rs"] pub mod neural_relations; diff --git a/kolibrie/src/ml/neural_relations.rs b/kolibrie/src/ml/neural_relations.rs index 2f8d58d8..4504e149 100644 --- a/kolibrie/src/ml/neural_relations.rs +++ b/kolibrie/src/ml/neural_relations.rs @@ -8,19 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Predicates whose facts come from a model rather than from data. -//! -//! A neural relation declares that querying some predicate should run a model over -//! the rows a pattern selects. Declaring one is not authority to run it — every entry -//! point here checks the [`MlExecutionContext`](crate::ml_policy::MlExecutionContext) -//! first, and an unapproved model is refused before anything executes. -//! -//! # Argmax here, distributions in `program` -//! -//! These entry points bind the single most likely label. A full distribution needs -//! the probabilistic machinery in [`program`](crate::program), which can represent -//! competing labels as a mutually exclusive group; see -//! [`DISTRIBUTION_REQUIRES_PROGRAM`]. +//! Predicates whose facts come from a model rather than from data use std::collections::HashMap; use std::error::Error; @@ -38,21 +26,17 @@ use crate::ml::execute_train::{ OwnedNeuralCallSpec, OwnedNeuralChoice, OwnedNeuralGroupType, OwnedNeuralTrainingClause, }; use crate::ml_feature_loader::build_feature_vec; -use crate::parser::parse_combined_query; -// Re-exported, not redefined: the implementation lives one layer down in -// `neural_registry`, and `kolibrie::neural_relations::register_neural_declarations` -// stays a working path for callers that already use it. pub use crate::neural_registry::register_neural_declarations; +use crate::parser::parse_combined_query; +use crate::database::KolibrieDB; use crate::neural_registry::{default_model_artifact_path, model_hidden_layers, model_output_type}; -use crate::sparql_database::SparqlDatabase; use shared::terms::Bindings; type NeuralResult = Result>; -/// Message explaining that `OUTPUT ?v DISTRIBUTION` is only supported through -/// [`program`](crate::program), because representing competing labels as mutually -/// exclusive needs the probabilistic machinery these entry points do not have. +/// Message saying that `OUTPUT ?v DISTRIBUTION` is only supported through +/// [`program`](crate::program) pub const DISTRIBUTION_REQUIRES_PROGRAM: &str = "ML.PREDICT ... OUTPUT ?v DISTRIBUTION runs only through kolibrie::program; legacy entry points predict the argmax"; @@ -89,7 +73,7 @@ fn build_select_query(patterns: &[(String, String, String)], vars: &[String]) -> } fn resolve_model_components( - database: &SparqlDatabase, + database: &KolibrieDB, predicate: &str, ) -> Result<(NeuralRelationDecl, ModelDecl), String> { let relation = database @@ -110,9 +94,9 @@ fn resolve_model_components( Ok((relation, model)) } -/// Resolves a parsed training declaration into owned, prefix-expanded form. +/// Resolves a parsed training declaration into owned, prefix-expanded form pub fn lower_train_decl_to_owned( - database: &SparqlDatabase, + database: &KolibrieDB, train_decl: &TrainNeuralRelationDecl, ) -> Result { let (relation, model) = resolve_model_components(database, &train_decl.predicate)?; @@ -194,21 +178,14 @@ pub fn lower_train_decl_to_owned( }) } -/// Trains the model behind a neural relation and writes its artifact. -/// -/// Requires trusted-local authority: the artifact path is resolved through the ML -/// context, so a declaration naming a path cannot write outside the directory the -/// host granted. +/// Trains the model behind a neural relation and writes its artifact pub fn execute_train_decl( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, train_decl: &TrainNeuralRelationDecl, ) -> NeuralResult<()> { database.ml_context.require_local()?; let owned_clause = lower_train_decl_to_owned(database, train_decl)?; let base_reasoner = build_ground_reasoner_from_db(database, None); - // Reproducible when the host fixed a seed, independent otherwise. The - // programmatic path through `program` always had this control; the declarative - // one did not, which left a `TRAIN NEURAL RELATION` run impossible to repeat. match database.training_seed() { Some(seed) => { execute_ml_training_owned_seeded(&owned_clause, &base_reasoner, database, seed)?; @@ -232,7 +209,7 @@ pub fn execute_train_decl( } fn run_ml_predict_input_query( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, input_raw: &str, prefixes: &HashMap, ) -> NeuralResult>> { @@ -248,10 +225,7 @@ fn run_ml_predict_input_query( query_training_rows(database, &query_with_prefixes) } -fn encode_prediction_rows( - database: &mut SparqlDatabase, - rows: &[HashMap], -) -> Bindings { +fn encode_prediction_rows(database: &mut KolibrieDB, rows: &[HashMap]) -> Bindings { rows.iter() .map(|row| { row.iter() @@ -262,7 +236,7 @@ fn encode_prediction_rows( } fn resolve_unique_relation_for_model( - database: &SparqlDatabase, + database: &KolibrieDB, model_name: &str, ) -> NeuralResult { let matching: Vec = database @@ -287,10 +261,9 @@ fn resolve_unique_relation_for_model( } } -/// Runs an `ML.PREDICT` that appears at the top level of a request rather than -/// attached to a rule. +/// Runs an `ML.PREDICT` that appears at the top level of a request rather than attached to a rule pub fn execute_top_level_ml_predict( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ml_predict: &MLPredictClause<'_>, prefixes: &HashMap, ) -> NeuralResult<()> { @@ -342,9 +315,9 @@ pub fn execute_top_level_ml_predict( Ok(()) } -/// Parses and runs a program's neural declarations against a store. +/// Parses and runs a program's neural declarations against a store pub fn execute_neural_program( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, program: &str, ) -> Result<(), crate::execute_query::QueryError> { crate::execute_query::validate_query_policy(program, database)?; @@ -412,7 +385,7 @@ pub fn execute_neural_program( Ok(()) } -fn remove_materialized_triples(database: &mut SparqlDatabase, predicate: &str) { +fn remove_materialized_triples(database: &mut KolibrieDB, predicate: &str) { if let Some(old_triples) = database.neural_materialized_triples.remove(predicate) { for triple in old_triples { database.delete_triple(&triple); @@ -420,11 +393,8 @@ fn remove_materialized_triples(database: &mut SparqlDatabase, predicate: &str) { } } -/// Runs one neural relation's model and adds the predicted facts to the store. -pub fn materialize_neural_relation( - database: &mut SparqlDatabase, - predicate: &str, -) -> NeuralResult<()> { +/// Runs one neural relation's model and adds the predicted facts to the store +pub fn materialize_neural_relation(database: &mut KolibrieDB, predicate: &str) -> NeuralResult<()> { database.ml_context.require_local()?; let (relation, model_decl) = resolve_model_components(database, predicate)?; let artifact_path = database @@ -518,14 +488,9 @@ pub fn materialize_neural_relation( Ok(()) } -/// Materialises whichever neural relations the given patterns could match. -/// -/// This is the implicit path: querying a neural predicate runs its model without -/// the query saying so. [`SparqlDatabase`](crate::sparql_database::SparqlDatabase) -/// can switch it off, and [`execute_program`](crate::program::execute_program) does -/// exactly that so a program's own declarations are the only thing invoking a model. +/// Materialises whichever neural relations the given patterns could match pub fn materialize_neural_relations_for_patterns( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, patterns: &[(&str, &str, &str)], prefixes: &HashMap, ) -> Result<(), String> { @@ -556,7 +521,7 @@ mod tests { use crate::rule_execution::process_rule_definition; use shared::query::{LossFn, ModelArch, OptimizerKind}; - fn populate_multiclass_db(db: &mut SparqlDatabase) { + fn populate_multiclass_db(db: &mut KolibrieDB) { for (idx, label, features) in [ ("s0", "A", [1.0, 0.0, 0.0]), ("s1", "A", [1.0, 0.0, 0.0]), @@ -572,7 +537,7 @@ mod tests { } } - fn populate_binary_db(db: &mut SparqlDatabase) { + fn populate_binary_db(db: &mut KolibrieDB) { for (idx, label, features) in [ ("t0", "1", [1.0, 1.0]), ("t1", "1", [1.0, 1.0]), @@ -783,7 +748,6 @@ WHERE { let mut db = ml_local::database(); populate_binary_db(&mut db); - // Use a linear model to avoid random convergence failures let rule = r#" PREFIX ex: @@ -828,17 +792,14 @@ WHERE { } } -/// The ML layer's implementation of the hooks query evaluation calls. -/// -/// A unit type: every method takes the store as an argument, so there is no state to -/// hold. The crate root is what selects it for the build. +/// The ML layer's implementation of the hooks query evaluation calls #[derive(Debug, Clone, Copy, Default)] pub struct NeuralRuntime; impl crate::ml::runtime::MlRuntime for NeuralRuntime { fn materialize_for_patterns( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, patterns: &[(&str, &str, &str)], prefixes: &HashMap, ) -> Result<(), String> { @@ -847,7 +808,7 @@ impl crate::ml::runtime::MlRuntime for NeuralRuntime { fn execute_train_decl( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, declaration: &TrainNeuralRelationDecl, ) -> Result<(), String> { execute_train_decl(database, declaration).map_err(|error| error.to_string()) @@ -855,7 +816,7 @@ impl crate::ml::runtime::MlRuntime for NeuralRuntime { fn execute_program( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, program: &str, ) -> Result<(), crate::query_error::QueryError> { execute_neural_program(database, program) @@ -863,7 +824,7 @@ impl crate::ml::runtime::MlRuntime for NeuralRuntime { fn execute_predict_clause( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, clause: &MLPredictClause<'_>, rule: &mut shared::query::CombinedRule<'_>, prefixes: &HashMap, diff --git a/kolibrie/src/ml/neural_relations_disabled.rs b/kolibrie/src/ml/neural_relations_disabled.rs index 466b262e..6c7abb05 100644 --- a/kolibrie/src/ml/neural_relations_disabled.rs +++ b/kolibrie/src/ml/neural_relations_disabled.rs @@ -8,35 +8,25 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Stand-ins for [`neural_relations`](crate::ml::neural_relations) when the `ml` feature -//! is off. -//! -//! Every entry point here mirrors the real one's signature and refuses with -//! `ML_FEATURE_DISABLED`, so the rest of the crate compiles without PyO3 and a caller -//! gets a clear error rather than a missing symbol. -//! -//! The refusals are deliberately conditional where they can be: a request that -//! declares no models and no neural relations succeeds, because it asked for nothing -//! the feature provides. Only a request that actually needs a model fails. +//! Stand-ins for [`neural_relations`](crate::ml::neural_relations) when the `ml` feature is off +use crate::database::KolibrieDB; pub use crate::ml::syntax::lower_ml_predict_alias; -// Registration is not gated, so the real one serves both builds. pub use crate::neural_registry::register_neural_declarations; -use crate::sparql_database::SparqlDatabase; use shared::query::TrainNeuralRelationDecl; use std::collections::HashMap; -/// Always fails: training needs the `ml` feature. +/// Always fails: training needs the `ml` feature pub fn execute_train_decl( - _db: &mut SparqlDatabase, + _db: &mut KolibrieDB, _train: &TrainNeuralRelationDecl, ) -> Result<(), Box> { Err("ML_FEATURE_DISABLED".into()) } -/// Succeeds when implicit materialisation is off or no neural relations are -/// declared, and otherwise refuses. +/// Succeeds when implicit materialisation is off or no neural relations are declared, and otherwise +/// refuses pub fn materialize_neural_relations_for_patterns( - db: &mut SparqlDatabase, + db: &mut KolibrieDB, _patterns: &[(&str, &str, &str)], _prefixes: &HashMap, ) -> Result<(), String> { @@ -46,24 +36,19 @@ pub fn materialize_neural_relations_for_patterns( Err("ML_FEATURE_DISABLED".into()) } } -/// Always fails: running a neural program needs the `ml` feature. -pub fn execute_neural_program(_db: &mut SparqlDatabase, _program: &str) -> Result<(), String> { +/// Always fails: running a neural program needs the `ml` feature +pub fn execute_neural_program(_db: &mut KolibrieDB, _program: &str) -> Result<(), String> { Err("ML_FEATURE_DISABLED".into()) } -/// The stand-in implementation of the query-evaluation hooks. -/// -/// Mirrors the real [`NeuralRuntime`] and refuses -/// anything that needs a model, conditionally where it can: a query mentioning no -/// neural relation materialises nothing and succeeds, because it asked for nothing -/// the feature provides. +/// The stand-in implementation of the query-evaluation hooks #[derive(Debug, Clone, Copy, Default)] pub struct NeuralRuntime; impl crate::ml::runtime::MlRuntime for NeuralRuntime { fn materialize_for_patterns( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, patterns: &[(&str, &str, &str)], prefixes: &HashMap, ) -> Result<(), String> { @@ -72,7 +57,7 @@ impl crate::ml::runtime::MlRuntime for NeuralRuntime { fn execute_train_decl( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, declaration: &TrainNeuralRelationDecl, ) -> Result<(), String> { execute_train_decl(database, declaration).map_err(|error| error.to_string()) @@ -80,7 +65,7 @@ impl crate::ml::runtime::MlRuntime for NeuralRuntime { fn execute_program( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, program: &str, ) -> Result<(), crate::query_error::QueryError> { execute_neural_program(database, program).map_err(crate::query_error::QueryError::Execution) @@ -88,14 +73,11 @@ impl crate::ml::runtime::MlRuntime for NeuralRuntime { fn execute_predict_clause( &self, - _database: &mut SparqlDatabase, + _database: &mut KolibrieDB, _clause: &shared::query::MLPredictClause<'_>, _rule: &mut shared::query::CombinedRule<'_>, _prefixes: &HashMap, ) -> Result, crate::query_error::QueryError> { - // A policy refusal, not an execution failure: the rendered code stays - // `QUERY_POLICY: ML_FEATURE_DISABLED`, which is what the caller was told - // before these calls went through a trait. Err(crate::query_error::QueryError::Ml( crate::ml_policy::MlError::Disabled, )) diff --git a/kolibrie/src/ml/predict_candle.rs b/kolibrie/src/ml/predict_candle.rs index 867ecad1..1e4e392a 100644 --- a/kolibrie/src/ml/predict_candle.rs +++ b/kolibrie/src/ml/predict_candle.rs @@ -14,8 +14,8 @@ use std::error::Error; use ml::{MlpNeuralPredicate, OutputType}; use shared::query::{MLPredictClause, NeuralOutputKind, NeuralRelationDecl}; +use crate::database::KolibrieDB; use crate::neural_registry::{model_hidden_layers, model_output_type}; -use crate::sparql_database::SparqlDatabase; use shared::terms::Bindings; type CandleResult = Result>; @@ -29,7 +29,7 @@ pub(crate) struct CandleDispatch { /// Try Candle for an `ML.PREDICT` clause and resolve the target predicate pub(crate) fn try_candle_predict( - database: &SparqlDatabase, + database: &KolibrieDB, ml_predict: &MLPredictClause<'_>, conclusion: &[(&str, &str, &str)], rule_prefixes: &HashMap, @@ -38,20 +38,17 @@ pub(crate) fn try_candle_predict( database.ml_context.require_local()?; let output_var = ml_predict.output.trim_start_matches('?'); - // Find the ML output predicate from the conclusion template let predicate_raw = match find_object_position_predicate(conclusion, output_var) { Some(p) => p, None => return Ok(None), }; let normalized_predicate = database.resolve_query_term(predicate_raw, rule_prefixes); - // Use Python fallback when the relation is not registered let relation = match database.neural_relation_decls.get(&normalized_predicate) { Some(r) => r.clone(), None => return Ok(None), }; - // Require a MODEL declaration let model_decl = database .model_decls .get(&relation.model_name) @@ -63,7 +60,6 @@ pub(crate) fn try_candle_predict( ) })?; - // Check that ML.PREDICT names the registered model if relation.model_name != ml_predict.model { return Err(format!( "ML.PREDICT names model \"{}\" but NEURAL RELATION for <{}> uses model \"{}\"", @@ -72,7 +68,6 @@ pub(crate) fn try_candle_predict( .into()); } - // Require a trained artifact let artifact_path = database .neural_model_artifacts .get(&relation.model_name) @@ -84,10 +79,8 @@ pub(crate) fn try_candle_predict( ) })?; - // Build features from the registered feature vars let features = build_features_for_rows(database, &relation, input_rows)?; - // Load the model and run inference let output_type: OutputType = model_output_type(&model_decl); let hidden = model_hidden_layers(&model_decl); let model = MlpNeuralPredicate::load( @@ -108,7 +101,6 @@ pub(crate) fn try_candle_predict( probs }; - // Map probabilities to labels let (predictions, probabilities) = map_probs_to_labels(&probs, &model_decl.output_kind); Ok(Some(CandleDispatch { @@ -119,15 +111,12 @@ pub(crate) fn try_candle_predict( } /// Resolve by model name for the physical operator runtime path -/// -/// Requires exactly one relation for the model, otherwise callers fall back to Python pub(crate) fn try_candle_predict_by_model_name( - database: &SparqlDatabase, + database: &KolibrieDB, model_name: &str, input_rows: &Bindings, ) -> CandleResult> { database.ml_context.require_local()?; - // Find the relation bound to this model name let matching: Vec<&NeuralRelationDecl> = database .neural_relation_decls .values() @@ -193,7 +182,7 @@ fn find_object_position_predicate<'a>( } fn build_features_for_rows( - database: &SparqlDatabase, + database: &KolibrieDB, relation: &NeuralRelationDecl, input_rows: &Bindings, ) -> CandleResult>> { diff --git a/kolibrie/src/ml/predict_runtime.rs b/kolibrie/src/ml/predict_runtime.rs index cfc6f8a5..1d5c52ed 100644 --- a/kolibrie/src/ml/predict_runtime.rs +++ b/kolibrie/src/ml/predict_runtime.rs @@ -15,8 +15,8 @@ use crate::execute_query::query_training_rows; use shared::query::{CombinedRule, MLPredictClause}; use shared::triple::Triple; +use crate::database::KolibrieDB; use crate::ml::predict_candle::try_candle_predict; -use crate::sparql_database::SparqlDatabase; use shared::terms::Bindings; type MlResult = Result>; @@ -36,13 +36,11 @@ pub(crate) struct MlConclusionMeta { } /// Resolve metadata for `ML.PREDICT` conclusion handling -/// -/// Checks output position, single-predicate use, and referenced output vars pub(crate) fn resolve_ml_conclusion_metadata( rule: &CombinedRule<'_>, ml_output_var: &str, rule_prefixes: &HashMap, - database: &SparqlDatabase, + database: &KolibrieDB, ) -> MlResult { let out_stripped = ml_output_var.trim_start_matches('?'); let mut ml_indices: Vec = Vec::new(); @@ -110,7 +108,7 @@ pub(crate) fn resolve_ml_conclusion_metadata( pub(crate) fn execute_ml_predict_clause<'a>( ml_predict: &MLPredictClause<'_>, rule: &mut CombinedRule<'a>, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, rule_prefixes: &HashMap, ) -> MlResult> { let trusted = database.ml_context.require_local().is_ok(); @@ -120,10 +118,8 @@ pub(crate) fn execute_ml_predict_clause<'a>( let out_var = ml_predict.output; let meta = resolve_ml_conclusion_metadata(rule, out_var, rule_prefixes, database)?; - // Run the INPUT query with native ids let input_rows = run_input_query(ml_predict.input_raw, database, rule_prefixes)?; - // Clean up and strip ML templates even with no input if input_rows.is_empty() { purge_previous_materialization(database, &meta.cache_key); strip_ml_conclusions(rule, out_var); @@ -131,7 +127,6 @@ pub(crate) fn execute_ml_predict_clause<'a>( return Ok(Vec::new()); } - // Local execution may use its registered native relation let candle = if trusted { try_candle_predict( database, @@ -181,7 +176,6 @@ pub(crate) fn execute_ml_predict_clause<'a>( ) } None => { - // Fall back to Python let preds = run_python_ml_dispatch( database, ml_predict.model, @@ -221,7 +215,6 @@ pub(crate) fn execute_ml_predict_clause<'a>( None }; - // Materialize shared ML conclusions let inserted = materialize_ml_conclusions( database, out_var, @@ -240,7 +233,7 @@ pub(crate) fn execute_ml_predict_clause<'a>( /// Run the INPUT query through the optimizer and return id-encoded rows fn run_input_query( input_raw: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, rule_prefixes: &HashMap, ) -> MlResult { let mut query_with_prefixes = String::new(); @@ -269,7 +262,7 @@ fn run_input_query( } /// Remove old materialized triples for this cache key -fn purge_previous_materialization(database: &mut SparqlDatabase, cache_key: &str) { +fn purge_previous_materialization(database: &mut KolibrieDB, cache_key: &str) { if let Some(old) = database.take_ml_materialized(cache_key) { for triple in old { database.delete_triple(&triple); @@ -290,7 +283,7 @@ fn strip_ml_conclusions<'a>(rule: &mut CombinedRule<'a>, ml_output_var: &str) { /// Materialize predictions and strip consumed conclusion templates #[allow(clippy::too_many_arguments)] pub(crate) fn materialize_ml_conclusions<'a>( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ml_output_var: &str, prob_var: Option<&str>, conclusion: &mut Vec<(&'a str, &'a str, &'a str)>, @@ -302,7 +295,6 @@ pub(crate) fn materialize_ml_conclusions<'a>( ) -> MlResult> { let out_stripped = ml_output_var.trim_start_matches('?'); - // Check all non-ML variables in ML templates exist in row bindings if let Some(first_row) = predicted_rows.first() { let row_keys = &first_row.bindings; for &idx in ml_conclusion_indices { @@ -326,10 +318,8 @@ pub(crate) fn materialize_ml_conclusions<'a>( } } - // Remove stale materialization for this cache key purge_previous_materialization(database, cache_key); - // Copy ML templates before mutating the database let ml_templates: Vec<(String, String, String)> = ml_conclusion_indices .iter() .map(|&idx| { @@ -351,11 +341,9 @@ pub(crate) fn materialize_ml_conclusions<'a>( inserted.push(triple); } - // Add the companion probability triple when requested if let Some(prob_var_name) = prob_var { let prob_value = row.probability.unwrap_or(0.0); let companion_predicate = format!("{}_prob", normalized_predicate); - // Use the first ML conclusion subject as the probability anchor if let Some((s_tmpl, _, _)) = ml_templates.first() { let subject = substitute_slot(s_tmpl, out_stripped, row, database, rule_prefixes)?; let triple = encode_triple( @@ -367,14 +355,12 @@ pub(crate) fn materialize_ml_conclusions<'a>( database.add_triple(triple.clone()); inserted.push(triple); } - let _ = prob_var_name; // name is derived; kept arg for API clarity + let _ = prob_var_name; } } - // Track triples for rerun cleanup database.set_ml_materialized(cache_key.to_string(), inserted.clone()); - // Strip ML templates from the rule conclusion.retain(|(s, p, o)| { let matches = |slot: &str| slot.starts_with('?') && slot.trim_start_matches('?') == out_stripped; @@ -388,7 +374,7 @@ fn substitute_slot( slot: &str, ml_output_var_stripped: &str, row: &PredictedRow, - database: &SparqlDatabase, + database: &KolibrieDB, rule_prefixes: &HashMap, ) -> MlResult { if slot.starts_with('?') { @@ -413,7 +399,7 @@ fn substitute_slot( } fn encode_triple( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, subject: &str, predicate: &str, object: &str, @@ -427,7 +413,7 @@ fn encode_triple( /// Run the existing Python MLHandler path and return decoded predictions pub(crate) fn run_python_ml_dispatch( - database: &SparqlDatabase, + database: &KolibrieDB, model: &str, input_rows: &Bindings, input_select: &[(&str, &str, Option<&str>)], @@ -494,7 +480,7 @@ pub(crate) fn run_python_ml_dispatch( pub(crate) fn filter_numeric_features( input_rows: &Bindings, input_variables: &[String], - database: &SparqlDatabase, + database: &KolibrieDB, ) -> Vec> { let Some(first_row) = input_rows.first() else { return Vec::new(); diff --git a/kolibrie/src/ml/runtime.rs b/kolibrie/src/ml/runtime.rs index bbe5db9e..eda7d6ad 100644 --- a/kolibrie/src/ml/runtime.rs +++ b/kolibrie/src/ml/runtime.rs @@ -8,83 +8,38 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The four points at which query evaluation gives the ML layer a chance to act. -//! -//! # Why this exists -//! -//! Evaluating a query has to let models run: a `SELECT` over a neural relation has -//! to derive its facts before matching, a `TRAIN` clause has to train, and a -//! top-level `ML.PREDICT` has to predict. Those operations need the ML runtime, -//! which sits above query evaluation and depends on it. -//! -//! So `execute_query` and `rule_execution` used to call up into -//! [`neural_relations`](crate::ml::neural_relations) directly, and the five modules -//! involved formed the crate's last dependency cycle. They now call through this -//! trait, which names only [`SparqlDatabase`], [`QueryError`] and the declaration -//! types — all of which sit below both layers. -//! -//! # Not optional -//! -//! [`MlRuntime::materialize_for_patterns`] runs before -//! every `SELECT` and every update's `WHERE`. It is the step that derives a neural -//! relation's facts on demand, so a query over one returns nothing without it. This -//! is a hook in the evaluation path, not an extension point a host may ignore. -//! -//! # Who implements it -//! -//! Two implementations, chosen by the `ml` feature, and the crate root picks between -//! them, in `lib.rs`. A caller never installs one, which is why -//! moving these calls behind a trait changed no behaviour. +//! The four points at which query evaluation gives the ML layer a chance to act +use crate::database::KolibrieDB; use crate::query_error::QueryError; -use crate::sparql_database::SparqlDatabase; use shared::query::{CombinedRule, MLPredictClause, TrainNeuralRelationDecl}; use shared::triple::Triple; use std::collections::HashMap; -/// What query evaluation needs from the ML layer. -/// -/// Every method takes the store separately from `&self` so that an implementation -/// holds no borrow of it; the implementations are stateless unit types. +/// What query evaluation needs from the ML layer pub trait MlRuntime: Send + Sync { - /// Derives the facts of any neural relation these patterns mention. - /// - /// Called before matching, once per `SELECT` and once per update `WHERE`. - /// Succeeds without doing anything when no pattern names a neural relation. + /// Derives the facts of any neural relation these patterns mention fn materialize_for_patterns( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, patterns: &[(&str, &str, &str)], prefixes: &HashMap, ) -> Result<(), String>; - /// Runs one `TRAIN NEURAL RELATION` declaration. + /// Runs one `TRAIN NEURAL RELATION` declaration fn execute_train_decl( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, declaration: &TrainNeuralRelationDecl, ) -> Result<(), String>; - /// Runs a request whose top level is an `ML.PREDICT`. - /// - /// Returns a [`QueryError`] rather than a string so that a policy refusal stays - /// a [`QueryError::Ml`] and keeps its `ML_*` code; flattening it would change - /// the status an HTTP caller is given. - fn execute_program( - &self, - database: &mut SparqlDatabase, - program: &str, - ) -> Result<(), QueryError>; + /// Runs a request whose top level is an `ML.PREDICT` + fn execute_program(&self, database: &mut KolibrieDB, program: &str) -> Result<(), QueryError>; - /// Runs an `ML.PREDICT` clause inside a rule, rewriting the rule in place. - /// - /// Returns a [`QueryError`] for the same reason - /// [`execute_program`](Self::execute_program) does: without the `ml` feature - /// this is a policy refusal and has to stay a [`QueryError::Ml`], not become a - /// generic execution failure. + /// Runs an `ML.PREDICT` clause inside a rule, rewriting the rule in place fn execute_predict_clause( &self, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, clause: &MLPredictClause<'_>, rule: &mut CombinedRule<'_>, prefixes: &HashMap, diff --git a/kolibrie/src/ml/syntax.rs b/kolibrie/src/ml/syntax.rs index d47a71a9..042079d0 100644 --- a/kolibrie/src/ml/syntax.rs +++ b/kolibrie/src/ml/syntax.rs @@ -10,12 +10,7 @@ use crate::parser::parse_sparql_query; use shared::query::{GroupGraphPattern, MLPredictClause, NeuralRelationDecl}; -/// Rewrites an `ML.PREDICT` clause into the equivalent neural relation -/// declaration. -/// -/// `ML.PREDICT` is sugar: the two forms mean the same thing, so lowering lets one -/// evaluation path serve both. Re-parses the clause's input query, which is why it -/// can fail on a clause that itself parsed. +/// Rewrites an `ML.PREDICT` clause into the equivalent neural relation declaration pub fn lower_ml_predict_alias( ml_predict: &MLPredictClause<'_>, ) -> Result { diff --git a/kolibrie/src/ml_feature_loader.rs b/kolibrie/src/ml_feature_loader.rs index 710fcafe..4a3a0417 100644 --- a/kolibrie/src/ml_feature_loader.rs +++ b/kolibrie/src/ml_feature_loader.rs @@ -8,12 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Turning RDF terms into the numbers a model consumes. -//! -//! Pure conversion, and deliberately nothing else. Loading the rows to convert needs -//! to run a query, which is a layer above this one, so -//! [`query_training_rows`](crate::execute_query::query_training_rows) lives there and -//! this module depends on nothing in the crate. +//! Turning RDF terms into the numbers a model consumes use std::error::Error; @@ -23,7 +18,6 @@ use std::collections::HashMap; pub(crate) type RdfTerm = String; type LoaderResult = Result>; -// Not gated: program.rs and execute_query.rs use this one in the default build. pub(crate) fn rdf_term_to_f64(term: &RdfTerm) -> LoaderResult { let trimmed = term.trim(); if let Ok(value) = trimmed.parse::() { @@ -55,7 +49,6 @@ pub(crate) fn rdf_term_to_f64(term: &RdfTerm) -> LoaderResult { Err(format!("Non-numeric RDF term in neural feature vector: {}", term).into()) } -// Gated: only the ml-gated modules build feature vectors. #[cfg(feature = "ml")] pub(crate) fn build_feature_vec( row: &HashMap, diff --git a/kolibrie/src/ml_policy.rs b/kolibrie/src/ml_policy.rs index 022578f2..9759959d 100644 --- a/kolibrie/src/ml_policy.rs +++ b/kolibrie/src/ml_policy.rs @@ -8,34 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Which models may run, and where their artifacts may live. -//! -//! This is the authority boundary for everything ML. A query may *name* a model, but -//! naming it is not permission to run it: the host decides, through an -//! [`MlExecutionContext`], and a context that has not been granted anything refuses -//! every request. -//! -//! # Three modes, deliberately separate -//! -//! | mode | granted by | may do | -//! |---|---|---| -//! | disabled | [`MlExecutionContext::disabled`] | nothing; the default | -//! | approved | [`MlExecutionContext::from_registry`] | run the listed models, each pinned to a SHA-256 | -//! | trusted-local | [`MlExecutionContext::trusted_local`] | train and write artifacts into one directory | -//! -//! Trusted-local authority is **not** reachable over HTTP. A request arriving that -//! way is served by a context that drops it, so an embedding application can allow -//! local training without exposing the ability to write artifacts to the network. -//! -//! # Artifacts are pinned, not trusted -//! -//! An approved model names a SHA-256, and [`verified_bytes`] refuses to load bytes -//! that do not match. Replacing a model file on disk therefore disables it rather -//! than silently changing what the engine computes. -//! -//! Artifact filenames are validated separately by [`validate_artifact_filename`], -//! which rejects path separators, control characters and the Windows reserved device -//! names — a filename is attacker-influenced input wherever a query can choose it. +//! Which models may run, and where their artifacts may live use std::collections::HashMap; use std::fmt; @@ -50,22 +23,18 @@ use sha2::{Digest, Sha256}; use shared::query::CombinedQuery; #[derive(Debug, Clone, Copy, PartialEq, Eq)] -/// Why an ML request was refused or failed. -/// -/// Renders through [`fmt::Display`] as a stable `ML_*` string. Those strings reach -/// callers over the HTTP API, so they are part of the contract rather than -/// diagnostics. +/// Why an ML request was refused or failed pub enum MlError { - /// The crate was built without the `ml` feature, so nothing can run. + /// The crate was built without the `ml` feature, so nothing can run Disabled, - /// The request was understood and refused: an unapproved model, or a filename - /// the policy does not permit. + /// The request was understood and refused: an unapproved model, or a filename the policy does + /// not permit Forbidden, - /// The registry or context is malformed — a bad hash length, say. + /// The registry or context is malformed — a bad hash length, say InvalidConfiguration, - /// The artifact is missing, unreadable, or does not match its pinned hash. + /// The artifact is missing, unreadable, or does not match its pinned hash InvalidArtifact, - /// The model ran and failed. + /// The model ran and failed ExecutionFailed, } impl fmt::Display for MlError { @@ -82,11 +51,7 @@ impl fmt::Display for MlError { impl std::error::Error for MlError {} #[derive(Debug, Clone, Default)] -/// The authority to run or train models, held by the host rather than the query. -/// -/// Default is [`Self::disabled`]: a context constructed without explicit grant -/// permits nothing. Cheap to clone — the approved-model registry is shared behind an -/// `Arc` — so an embedding application can hand one to each request. +/// The authority to run or train models, held by the host rather than the query pub struct MlExecutionContext { mode: Mode, } @@ -100,35 +65,29 @@ enum Mode { #[derive(Debug, Clone, Deserialize)] #[serde(deny_unknown_fields)] -/// One entry of the host's model registry. -/// -/// Deserialised with `deny_unknown_fields`, so a typo in a registry file is an error -/// rather than a silently ignored setting that leaves a model less constrained than -/// intended. +/// One entry of the host's model registry pub struct ApprovedModel { - /// Name queries refer to this model by. + /// Name queries refer to this model by pub name: String, - /// Which runtime executes it. + /// Which runtime executes it pub backend: Backend, - /// Path to the weights. + /// Path to the weights pub artifact: PathBuf, - /// SHA-256 the artifact must match. Replacing the file on disk disables the - /// model rather than changing what it computes. + /// SHA-256 the artifact must match pub sha256: String, - /// Whether this model's artifact may be a pickle. Off unless stated, because - /// unpickling executes arbitrary code. + /// Whether this model's artifact may be a pickle #[serde(default)] pub allow_pickle: bool, - /// Python module providing the model, for the Python backend. + /// Python module providing the model, for the Python backend pub module_name: Option, - /// Path to that module. + /// Path to that module pub module_path: Option, - /// SHA-256 the module must match, pinned like the artifact. + /// SHA-256 the module must match, pinned like the artifact pub module_sha256: Option, - /// Expected feature-vector length. + /// Expected feature-vector length #[serde(default)] pub input_dim: usize, - /// Hidden layer widths, for a native model. + /// Hidden layer widths, for a native model #[serde(default)] pub hidden: Vec, /// Empty means binary probability output; otherwise categorical labels @@ -137,11 +96,11 @@ pub struct ApprovedModel { } #[derive(Debug, Clone, Copy, Deserialize, PartialEq, Eq)] #[serde(rename_all = "snake_case")] -/// Which runtime executes an approved model. +/// Which runtime executes an approved model pub enum Backend { - /// The built-in Rust implementation. + /// The built-in Rust implementation Native, - /// A Python module, which requires the `ml` feature and a working interpreter. + /// A Python module, which requires the `ml` feature and a working interpreter Python, } #[derive(Deserialize)] @@ -150,10 +109,7 @@ struct Registry { models: Vec, } -/// Checks a model name is one the policy will consider. -/// -/// Names come from query text, so this bounds the length and restricts the -/// character set before the name is used to look anything up. +/// Checks a model name is one the policy will consider pub fn validate_model_name(name: &str) -> Result<(), MlError> { let mut bytes = name.bytes(); if name.len() > 64 @@ -167,38 +123,19 @@ pub fn validate_model_name(name: &str) -> Result<(), MlError> { Ok(()) } -/// The parsed input of every `ML.PREDICT` clause in one request, as a tree. -/// -/// [`MlExecutionContext::validate_request`] has to inspect the query nested inside -/// each `ML.PREDICT` input, which means parsing it. Parsing belongs to the parser, a -/// layer above this one, so the caller parses and hands the result over; the policy -/// then walks it without ever calling the parser. That is what keeps this module -/// below [`crate::parser`] rather than beside it. -/// -/// The shape is a tree rather than a flat list because validation interleaves: a -/// clause's model name is checked, then its nested query is walked in full, and only -/// then is the next clause's name checked. `predict_inputs` in -/// [`crate::execute_query`] is the constructor. +/// The parsed input of every `ML.PREDICT` clause in one request, as a tree #[derive(Debug, Default)] pub struct PredictInputs<'a> { - /// One entry per `ML.PREDICT` clause, in the order `validate_request` walks - /// them: the query's own clause first, then one per rule in source order. An - /// `Err` is an input that did not parse as a complete query. entries: Vec, PredictInputs<'a>), MlError>>, } impl<'a> PredictInputs<'a> { - /// Builds the tree from entries already parsed by the caller. + /// Builds the tree from entries already parsed by the caller pub fn new(entries: Vec, PredictInputs<'a>), MlError>>) -> Self { Self { entries } } - /// The parsed input of the clause at `position`, or why it is refused. - /// - /// A position with no entry is refused rather than skipped: that can only mean - /// the tree was built from a different query than the one being validated, and a - /// policy check that silently passes an unparsed input is worse than one that - /// over-refuses. + /// The parsed input of the clause at `position`, or why it is refused fn nested(&self, position: usize) -> Result<(&CombinedQuery<'a>, &Self), MlError> { match self.entries.get(position) { Some(Ok((query, nested))) => Ok((query, nested)), @@ -208,13 +145,7 @@ impl<'a> PredictInputs<'a> { } } -/// Checks an artifact filename is safe to create. -/// -/// A filename can be chosen by a query, so it is attacker-influenced. Rejects -/// anything not ending `.bin`, path separators and shell-significant characters, -/// control bytes, trailing spaces or dots, and the Windows reserved device names -/// such as `CON`, `NUL` and `COM1` — which remain reserved whatever extension -/// follows them. +/// Checks an artifact filename is safe to create pub fn validate_artifact_filename(name: &str) -> Result<(), MlError> { if !name.ends_with(".bin") || name.len() <= 4 @@ -245,12 +176,7 @@ pub fn validate_artifact_filename(name: &str) -> Result<(), MlError> { Ok(()) } -/// Reads `path` only if its SHA-256 matches `expected`. -/// -/// This is what makes an approved model's hash binding rather than advisory. A -/// mismatch is [`MlError::InvalidArtifact`]; a malformed `expected` is -/// [`MlError::InvalidConfiguration`], so a registry typo cannot weaken the check -/// into passing. +/// Reads `path` only if its SHA-256 matches `expected` #[cfg(feature = "ml")] pub fn verified_bytes(path: &Path, expected: &str) -> Result, MlError> { if expected.len() != 64 || !expected.bytes().all(|b| b.is_ascii_hexdigit()) { @@ -263,7 +189,7 @@ pub fn verified_bytes(path: &Path, expected: &str) -> Result, MlError> { Ok(bytes) } -/// Always [`MlError::Disabled`] without the `ml` feature. +/// Always [`MlError::Disabled`] without the `ml` feature #[cfg(not(feature = "ml"))] pub fn verified_bytes(_path: &Path, _expected: &str) -> Result, MlError> { Err(MlError::Disabled) @@ -277,10 +203,7 @@ impl MlExecutionContext { _ => Self::disabled(), } } - /// Runs an approved model over `input`, one feature vector per row. - /// - /// Verifies the artifact against its pinned hash on every call, so a file - /// swapped between calls is caught rather than used. + /// Runs an approved model over `input`, one feature vector per row #[cfg(feature = "ml")] pub fn predict(&self, name: &str, input: &[Vec]) -> Result, MlError> { let entry = self.approved_model(name)?; @@ -373,19 +296,17 @@ impl MlExecutionContext { Ok(result) } - /// Always [`MlError::Disabled`] without the `ml` feature. + /// Always [`MlError::Disabled`] without the `ml` feature #[cfg(not(feature = "ml"))] pub fn predict(&self, _name: &str, _input: &[Vec]) -> Result, MlError> { Err(MlError::Disabled) } - /// A context permitting nothing. The default. + /// A context permitting nothing pub fn disabled() -> Self { Self::default() } - /// A context from `KOLIBRIE_MODEL_ALLOWLIST`, or disabled if it is unset. - /// - /// Absent configuration grants nothing, so forgetting to set it fails closed. + /// A context from `KOLIBRIE_MODEL_ALLOWLIST`, or disabled if it is unset pub fn from_environment() -> Result { match std::env::var_os("KOLIBRIE_MODEL_ALLOWLIST") { Some(path) => Self::from_registry(Path::new(&path)), @@ -394,11 +315,7 @@ impl MlExecutionContext { }), } } - /// A context permitting exactly the models listed in the registry at `path`. - /// - /// Each entry's name and artifact filename are validated as the registry is - /// read, so a malformed entry is rejected rather than becoming a permitted - /// model with a surprising path. + /// A context permitting exactly the models listed in the registry at `path` pub fn from_registry(path: &Path) -> Result { if !path.is_absolute() { return Err(MlError::InvalidConfiguration); @@ -455,14 +372,7 @@ impl MlExecutionContext { mode: Mode::Approved(Arc::new(models)), }) } - /// A context permitting training, with artifacts written under `output`. - /// - /// `output` must be absolute and is canonicalised, so a relative or symlinked - /// path cannot redirect writes elsewhere. If `KOLIBRIE_MODEL_ALLOWLIST` points - /// inside `output` this is refused: training could otherwise rewrite the very - /// registry that decides what is approved. - /// - /// This authority is not reachable over HTTP — see the module documentation. + /// A context permitting training, with artifacts written under `output` pub fn trusted_local(output: &Path) -> Result { if !output.is_absolute() { return Err(MlError::InvalidConfiguration); @@ -496,7 +406,7 @@ impl MlExecutionContext { mode: Mode::TrustedLocal(output), }) } - /// Succeeds only in trusted-local mode. + /// Succeeds only in trusted-local mode pub fn require_local(&self) -> Result<(), MlError> { if !cfg!(feature = "ml") { return Err(MlError::Disabled); @@ -507,8 +417,7 @@ impl MlExecutionContext { Err(MlError::Forbidden) } } - /// The registry entry for `name`, or [`MlError::Forbidden`] if it is not - /// approved. + /// The registry entry for `name`, or [`MlError::Forbidden`] if it is not approved pub fn approved_model(&self, name: &str) -> Result<&ApprovedModel, MlError> { if !cfg!(feature = "ml") { return Err(MlError::Disabled); @@ -519,10 +428,7 @@ impl MlExecutionContext { Mode::Disabled | Mode::TrustedLocal(_) => Err(MlError::Forbidden), } } - /// The path `filename` would occupy in the trusted-local directory. - /// - /// Validates the filename and refuses an existing symlink at that path, so a - /// pre-planted link cannot divert a later write outside the directory. + /// The path `filename` would occupy in the trusted-local directory pub fn local_artifact(&self, filename: &str) -> Result { self.require_local()?; validate_artifact_filename(filename)?; @@ -535,7 +441,7 @@ impl MlExecutionContext { } Ok(path) } - /// Writes `bytes` into the trusted-local directory under `filename`. + /// Writes `bytes` into the trusted-local directory under `filename` pub fn save_local_artifact(&self, filename: &str, bytes: &[u8]) -> Result<(), MlError> { let path = self.local_artifact(filename)?; let mut file = OpenOptions::new() @@ -545,15 +451,7 @@ impl MlExecutionContext { .map_err(|_| MlError::Forbidden)?; file.write_all(bytes).map_err(|_| MlError::ExecutionFailed) } - /// Checks every model a parsed request names before any of it runs. - /// - /// Called up front so a request mentioning one unapproved model is refused - /// whole, rather than partly executing and then failing. - /// - /// `predicts` carries the already-parsed input of each `ML.PREDICT` clause, in - /// the order this walks them; see [`PredictInputs`]. `has_registered_relations` - /// is whether the store holds any neural relation declaration, which requires - /// trusted-local authority on its own. + /// Checks every model a parsed request names before any of it runs pub fn validate_request( &self, query: &CombinedQuery<'_>, @@ -606,14 +504,9 @@ impl MlExecutionContext { if self.require_local().is_err() { self.approved_model(predict.model)?; } - // Checked here rather than up front because the order matters: with the - // `ml` feature off the checks above refuse the request as Disabled, and - // parsing everything first would report an unparseable nested input as - // Forbidden instead, turning a 503 into a 403. let (nested_query, nested_predicts) = predicts.nested(position)?; self.validate_request(nested_query, nested_predicts, has_registered_relations)?; } - // Registered relations require trusted-local execution if has_registered_relations { self.require_local()?; } diff --git a/kolibrie/src/neural_registry.rs b/kolibrie/src/neural_registry.rs index 12171b1b..ad1d73b0 100644 --- a/kolibrie/src/neural_registry.rs +++ b/kolibrie/src/neural_registry.rs @@ -8,39 +8,19 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Recording model, neural relation and training declarations on a store. -//! -//! Registration is not execution. Putting a declaration on a store needs the store, -//! the ML policy and the declaration types, and none of the machinery that later -//! runs a model, so it sits below [`neural_relations`](crate::ml::neural_relations) -//! rather than inside it. `parser`, `execute_query` and `program` all register -//! declarations and none of them should have to reach into the ML runtime to do it. -//! -//! # Not gated on `ml` -//! -//! Unlike the runtime, this compiles either way. With the feature off -//! [`require_local`](crate::ml_policy::MlExecutionContext::require_local) already -//! refuses with `ML_FEATURE_DISABLED`, and a request declaring nothing never reaches -//! it, so one implementation gives both behaviours and the stub that used to mirror -//! this in `neural_relations_disabled` is gone. +//! Recording model, neural relation and training declarations on a store -use crate::sparql_database::SparqlDatabase; +use crate::database::KolibrieDB; #[cfg(feature = "ml")] use ml::OutputType; #[cfg(feature = "ml")] -use shared::query::NeuralOutputKind; -// `ModelArch` is named only by `model_hidden_layers`, which is ml-gated, so the -// import has to be too or a default build warns. -#[cfg(feature = "ml")] use shared::query::ModelArch; +#[cfg(feature = "ml")] +use shared::query::NeuralOutputKind; use shared::query::{ModelDecl, NeuralRelationDecl, TrainNeuralRelationDecl, TrainingDataSource}; use std::collections::HashMap; -/// The default artifact filename for a model. -/// -/// Every non-alphanumeric character becomes `_`, so the result is a safe filename -/// — but two model names differing only in punctuation collide. A host that cares -/// should name artifacts explicitly. +/// The default artifact filename for a model pub fn default_model_artifact_path(model_name: &str) -> String { let sanitized: String = model_name .chars() @@ -50,7 +30,7 @@ pub fn default_model_artifact_path(model_name: &str) -> String { } pub(crate) fn normalize_term( - database: &SparqlDatabase, + database: &KolibrieDB, prefixes: &HashMap, term: &str, ) -> String { @@ -62,7 +42,7 @@ pub(crate) fn normalize_term( } pub(crate) fn normalize_triple( - database: &SparqlDatabase, + database: &KolibrieDB, prefixes: &HashMap, triple: &(String, String, String), ) -> (String, String, String) { @@ -73,12 +53,9 @@ pub(crate) fn normalize_triple( ) } -/// Registers model, relation and training declarations on a store. -/// -/// Reports failures to stderr. Prefer -/// [`register_neural_declarations_checked`] where the caller can handle an error. +/// Registers model, relation and training declarations on a store pub fn register_neural_declarations( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, prefixes: &HashMap, model_decls: &[ModelDecl], neural_relation_decls: &[NeuralRelationDecl], @@ -97,12 +74,9 @@ pub fn register_neural_declarations( } } -/// Registers declarations, returning an error rather than reporting it. -/// -/// Checks the ML policy for every model named, so a set containing one unapproved -/// model registers none of them. +/// Registers declarations, returning an error rather than reporting it pub fn register_neural_declarations_checked( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, prefixes: &HashMap, model_decls: &[ModelDecl], neural_relation_decls: &[NeuralRelationDecl], @@ -184,8 +158,6 @@ pub(crate) fn model_output_type(model_decl: &ModelDecl) -> OutputType { } } -// Gated for the same reason as `model_output_type`: every caller is ml-gated, so -// in a default build this is dead code and warns. #[cfg(feature = "ml")] pub(crate) fn model_hidden_layers(model_decl: &ModelDecl) -> &[usize] { match &model_decl.arch { diff --git a/kolibrie/src/parse_error.rs b/kolibrie/src/parse_error.rs index 296a31ea..5065b668 100644 --- a/kolibrie/src/parse_error.rs +++ b/kolibrie/src/parse_error.rs @@ -8,47 +8,28 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! What a failed parse says, without saying it in `nom`'s words. +//! What a failed parse says, without saying it in `nom`'s words -/// What the parser was looking for where it gave up. -/// -/// The crate's own vocabulary rather than the parser library's error kinds. Those -/// kinds name combinators -- `Tag`, `Alt`, `TakeWhile1`, `Many0` -- which is an -/// implementation detail of how the grammar happens to be written, and leaking them -/// would tie a caller's `match` to that. These say what a reader of the query needs -/// to know. +/// What the parser was looking for where it gave up #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum Expected { - /// A particular keyword or token. + /// A particular keyword or token Token, - /// A particular character. + /// A particular character Character, - /// One of several alternatives. + /// One of several alternatives Alternative, - /// At least one character of something, where none were found. + /// At least one character of something, where none were found AtLeastOne, - /// A repeated element, where the repetition could not proceed. + /// A repeated element, where the repetition could not proceed Repetition, - /// The end of the input, with something still left over. + /// The end of the input, with something still left over EndOfInput, - /// Something the parser does not describe more precisely. + /// Something the parser does not describe more precisely Other, } -/// Why a parse failed, and where in the input it gave up. -/// -/// # Why this exists -/// -/// The parser is written with `nom`, and its entry points used to return `nom`'s -/// `IResult`, which makes `nom::Err>` part of this crate's -/// public API. §4's rule is that nothing leaks: a caller should not have to name a -/// third-party type to use this crate, and this crate should be able to change parser -/// library without a breaking release. -/// -/// It was not hypothetical. One test reached into the error to find which lexeme was -/// rejected, and had to match `nom::Err::Error | nom::Err::Failure` against -/// `nom::Err::Incomplete` to do it. That is this crate's business, not its callers'. -/// [`offending`](Self::offending) answers the question directly. +/// Why a parse failed, and where in the input it gave up #[derive(Debug, Clone, PartialEq, Eq)] pub struct ParseError<'a> { offending: &'a str, @@ -57,38 +38,23 @@ pub struct ParseError<'a> { } impl<'a> ParseError<'a> { - /// The input remaining at the point the parse gave up. - /// - /// Empty when the parser ran out of input rather than rejecting something, which - /// [`is_incomplete`](Self::is_incomplete) distinguishes. + /// The input remaining at the point the parse gave up pub fn offending(&self) -> &'a str { self.offending } - /// Whether the parse failed because the input ended, rather than because - /// something in it was rejected. + /// Whether the parse failed because the input ended, rather than because something in it was + /// rejected pub fn is_incomplete(&self) -> bool { self.incomplete } - /// What the parser was looking for at that point. + /// What the parser was looking for at that point pub fn expected(&self) -> Expected { self.expected } - /// Where the failure is, as a byte offset into the whole input. - /// - /// The parser reports the text it rejected rather than how far it had come, so - /// the offset needs the original input to recover. - /// - /// Computed from where the rejected text sits inside `input`, not by subtracting - /// its length. Subtracting assumes the parser handed back the whole unconsumed - /// *suffix*, and it does not always: for `DELETE DATA { ?illegal }` - /// it narrows the error to `?illegal`, which starts at byte 30 while the - /// subtraction says 32. The error messages built on that were quietly off by the - /// width of whatever followed. - /// - /// Returns 0 if `input` is not the text this error came from. + /// Where the failure is, as a byte offset into the whole input pub fn offset_in(&self, input: &str) -> usize { let base = input.as_ptr() as usize; let rejected = self.offending.as_ptr() as usize; @@ -98,10 +64,7 @@ impl<'a> ParseError<'a> { .unwrap_or(0) } - /// Built from the parser library's own error. - /// - /// `pub(crate)` on purpose: this is the one place that knows `nom`, and keeping - /// the conversion private is what keeps the leak closed. + /// Built from the parser library's own error pub(crate) fn from_nom(error: nom::Err>) -> Self { use nom::error::ErrorKind; match error { @@ -146,10 +109,7 @@ impl std::fmt::Display for ParseError<'_> { impl std::error::Error for ParseError<'_> {} -/// What a parser entry point returns: the unconsumed input and the value, or why not. -/// -/// The remaining input is part of the success value because these are parsers, and a -/// caller that wants the whole input consumed has to check that it was. +/// What a parser entry point returns: the unconsumed input and the value, or why not pub type ParseResult<'a, T> = Result<(&'a str, T), ParseError<'a>>; #[cfg(test)] @@ -190,8 +150,6 @@ mod tests { #[test] fn an_offset_against_unrelated_input_is_not_invented() { - // `offending` is a slice of the input it came from; given any other text - // there is no meaningful answer, and 0 is the documented one. let error = ParseError { offending: "somewhere", incomplete: false, diff --git a/kolibrie/src/parser.rs b/kolibrie/src/parser.rs index 9d85cdf0..a5a38e8d 100644 --- a/kolibrie/src/parser.rs +++ b/kolibrie/src/parser.rs @@ -8,40 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Parsing SPARQL, RSP-QL, and the rule and ML extensions. -//! -//! Produces the lexical syntax tree defined in [`shared::query`]: terms keep their -//! source spelling and nothing is prefix-expanded or interned here. See that module -//! for why the two stages are separate. -//! -//! # What to call -//! -//! | entry point | for | -//! |---|---| -//! | [`parse_combined_query`] | anything — standard SPARQL plus every extension, with the prefix map | -//! | [`parse_sparql_query`] | a standalone SELECT, when the prefix map is not needed | -//! | [`parse_standalone_rule`] | one `RULE` block | -//! | [`convert_combined_rule`] | lower a parsed rule to a [`Rule`] bound to a dictionary | -//! -//! Running a parsed rule against a store is -//! [`rule_execution`](crate::rule_execution), not this module. Parsing text and -//! executing against a database are separate jobs, and this one does only the first: -//! nothing here depends on anything else in the crate. -//! -//! Everything else here is a grammar fragment. They are public because -//! `tests/parser_test.rs` is an integration test and therefore outside the crate, so -//! exercising a combinator directly requires exporting it — not because callers are -//! expected to assemble their own grammar. Moving those cases to unit tests inside -//! this module would let the fragments become `pub(crate)`. -//! -//! # `nom` is in the signatures -//! -//! Every function here returns `nom::IResult`, so a `nom` major-version bump is a -//! breaking change for anyone who names one of these types. That violates P5 of the -//! architecture proposal ("nothing leaks"), and §4 resolves it by giving the parser -//! crate its own error type and keeping the combinators internal. Until then, prefer -//! the entry points above, which at least confine the exposure to a return type you -//! can discard. +//! Parsing SPARQL, RSP-QL, and the rule and ML extensions use crate::parse_error::{ParseError, ParseResult}; use nom::{ @@ -64,21 +31,20 @@ use shared::query::*; use shared::rule::FilterCondition; use shared::rule::Rule; use shared::terms::*; -// Add RSP imports use std::collections::HashMap; use std::time::Duration; -/// One or more alphanumerics, `_` or `-`. The bare name part of a prefixed term. +/// One or more alphanumerics, `_` or `-` pub(crate) fn identifier(input: &str) -> IResult<&str, &str> { take_while1(|c: char| c.is_alphanumeric() || c == '_' || c == '-').parse(input) } -/// A prefixed name such as `ex:worksAt`, returned unexpanded. +/// A prefixed name such as `ex:worksAt`, returned unexpanded pub(crate) fn prefixed_identifier(input: &str) -> IResult<&str, &str> { sparql_prefixed_name(input) } -/// A predicate position: a full IRI, a variable, or a prefixed name. +/// A predicate position: a full IRI, a variable, or a prefixed name pub(crate) fn predicate(input: &str) -> IResult<&str, &str> { alt(( parse_uri, @@ -90,14 +56,12 @@ pub(crate) fn predicate(input: &str) -> IResult<&str, &str> { .parse(input) } -/// A variable, with its `?` or `$` sigil retained. +/// A variable, with its `?` or `$` sigil retained pub(crate) fn variable(input: &str) -> IResult<&str, &str> { sparql_variable(input) } -/// A double-quoted literal, rejecting the triple-quoted form. -/// -/// The quotes and any datatype or language suffix stay in the returned slice. +/// A double-quoted literal, rejecting the triple-quoted form pub(crate) fn parse_literal(input: &str) -> IResult<&str, &str> { let (remaining, literal) = sparql_quoted_literal(input)?; if !literal.starts_with('"') || literal.starts_with("\"\"\"") { @@ -116,63 +80,58 @@ pub(crate) fn parse_literal(input: &str) -> IResult<&str, &str> { sparql_error(literal, nom::error::ErrorKind::Escaped) } -/// An IRI in angle brackets, or a prefixed name. Brackets are retained. +/// An IRI in angle brackets, or a prefixed name pub(crate) fn parse_uri(input: &str) -> IResult<&str, &str> { sparql_iri(input).map(|(remaining, iri)| (remaining, &iri[1..iri.len() - 1])) } /// Parse a quoted triple: `<< subject predicate object >>` -/// Returns the entire `<< ... >>` as a single string slice. pub(crate) fn parse_quoted_triple_nom(input: &str) -> IResult<&str, &str> { sparql_quoted_triple(input) } -/// One `predicate object` pair inside a triple block. +/// One `predicate object` pair inside a triple block pub(crate) fn parse_predicate_object(input: &str) -> IResult<&str, (&str, &str)> { let (input, p) = predicate(input)?; let (input, _) = multispace1.parse(input)?; let (input, o) = alt(( - parse_quoted_triple_nom, // << s p o >> (RDF-star) - parse_uri, // - variable, // ?variable - parse_literal, // "literal" - recognize((char(':'), identifier)), // :localname (like :Stream) - prefixed_identifier, // prefix:localname - identifier, // simple identifier + parse_quoted_triple_nom, + parse_uri, + variable, + parse_literal, + recognize((char(':'), identifier)), + prefixed_identifier, + identifier, )) .parse(input)?; Ok((input, (p, o))) } -/// A `{ ... }` block of triple patterns, expanding the `;` and `,` -/// abbreviations into separate patterns. +/// A `{ ... }` block of triple patterns, expanding the `;` and `,` abbreviations into separate +/// patterns pub(crate) fn parse_triple_block_nom(input: &str) -> IResult<&str, Vec<(&str, &str, &str)>> { let (input, subject) = alt(( - parse_quoted_triple_nom, // << s p o >> (RDF-star) - parse_uri, // - variable, // ?variable - recognize((char(':'), identifier)), // :localname - prefixed_identifier, // prefix:localname - identifier, // simple identifier + parse_quoted_triple_nom, + parse_uri, + variable, + recognize((char(':'), identifier)), + prefixed_identifier, + identifier, )) .parse(input)?; let (input, _) = multispace1.parse(input)?; - // First predicate-object pair let (input, first_po) = parse_predicate_object(input)?; - // Zero or more additional predicate-object pairs separated by semicolon let (input, rest_po) = many0(preceded( (multispace0, char(';'), multispace0), parse_predicate_object, )) .parse(input)?; - // Gather all (predicate, object) pairs let mut pairs = vec![first_po]; pairs.extend(rest_po); - // Convert each pair into a triple by reusing the same subject let triples = pairs .into_iter() .map(|(p, o)| { @@ -188,13 +147,13 @@ pub(crate) fn parse_triple_block_nom(input: &str) -> IResult<&str, Vec<(&str, &s Ok((input, triples)) } -/// A `VALUES` block: the variable list and its rows. +/// A `VALUES` block: the variable list and its rows pub(crate) fn parse_values(input: &str) -> IResult<&str, ValuesClause<'_>> { sparql_values_clause(input) } -/// A `SELECT` projection, as `(kind, variable, alias)` triples where `kind` -/// is `"VAR"` or an aggregate's uppercase name. +/// A `SELECT` projection, as `(kind, variable, alias)` triples where `kind` is `"VAR"` or an +/// aggregate's uppercase name pub(crate) fn parse_select(input: &str) -> IResult<&str, Vec<(&str, &str, Option<&str>)>> { let (mut input, _) = sparql_keyword(input, "SELECT")?; if let Ok((remaining, _)) = sparql_keyword(input, "DISTINCT") { @@ -203,40 +162,37 @@ pub(crate) fn parse_select(input: &str) -> IResult<&str, Vec<(&str, &str, Option sparql_projection_items(input) } -/// An arithmetic expression over variables and numeric literals. +/// An arithmetic expression over variables and numeric literals pub(crate) fn parse_arithmetic_expression(input: &str) -> IResult<&str, ArithmeticExpression<'_>> { sparql_filter_arithmetic(input) } -/// A single comparison, such as `?x > 5`. +/// A single comparison, such as `?x > 5` pub(crate) fn parse_comparison_nom(input: &str) -> IResult<&str, FilterExpression<'_>> { sparql_filter_comparison(input) } -/// A `FILTER` expression, including the boolean connectives. +/// A `FILTER` expression, including the boolean connectives pub(crate) fn parse_filter_nom(input: &str) -> IResult<&str, FilterExpression<'_>> { sparql_filter_clause(input) } -/// A `BIND` clause, as `(expression, arguments, target variable)`. -/// -/// For example `BIND(funcName(?var, "literal") AS ?newVar)`. +/// A `BIND` clause, as `(expression, arguments, target variable)` pub(crate) fn parse_bind(input: &str) -> IResult<&str, (&str, Vec<&str>, &str)> { sparql_bind_clause(input) } -/// A `{ SELECT ... }` subquery nested in a graph pattern. +/// A `{ SELECT ... }` subquery nested in a graph pattern pub(crate) fn parse_subquery<'a>(input: &'a str) -> IResult<&'a str, SubQuery<'a>> { sparql_subquery(input) } -/// A `WINDOW ?w { ... }` block scoping patterns to one named window. +/// A `WINDOW ?w { ... }` block scoping patterns to one named window pub(crate) fn parse_window_block(input: &str) -> IResult<&str, WindowBlock<'_>> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("WINDOW").parse(input)?; let (input, _) = multispace1.parse(input)?; - // Parse window name (like :wind) let (input, window_name) = alt((recognize((char(':'), identifier)), identifier)).parse(input)?; @@ -244,7 +200,6 @@ pub(crate) fn parse_window_block(input: &str) -> IResult<&str, WindowBlock<'_>> let (input, _) = char('{').parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse triple patterns inside the window block let (input, pattern_blocks) = many0(terminated( parse_triple_block_nom, (multispace0, opt(char('.')), multispace0), @@ -254,7 +209,6 @@ pub(crate) fn parse_window_block(input: &str) -> IResult<&str, WindowBlock<'_>> let (input, _) = multispace0.parse(input)?; let (input, _) = char('}').parse(input)?; - // Flatten all pattern blocks let patterns = pattern_blocks.into_iter().flatten().collect(); Ok(( @@ -266,8 +220,7 @@ pub(crate) fn parse_window_block(input: &str) -> IResult<&str, WindowBlock<'_>> )) } -/// Parse `NOT triple_block` — negation-as-failure body atom. -/// Returns the list of negated triple patterns. +/// Parse `NOT triple_block` — negation-as-failure body atom fn parse_not_triple_block(input: &str) -> IResult<&str, Vec<(&str, &str, &str)>> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("NOT").parse(input)?; @@ -275,8 +228,8 @@ fn parse_not_triple_block(input: &str) -> IResult<&str, Vec<(&str, &str, &str)>> parse_triple_block_nom(input) } -/// A `WHERE` clause, returning the five-tuple of triple patterns, filters, -/// `VALUES`, `BIND` clauses and subqueries that `shared::query` uses. +/// A `WHERE` clause, returning the five-tuple of triple patterns, filters, `VALUES`, `BIND` clauses +/// and subqueries that `shared::query` uses pub(crate) fn parse_where_nom(input: &str) -> IResult<&str, WhereClause<'_>> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("WHERE").parse(input)?; @@ -293,18 +246,15 @@ pub(crate) fn parse_where_nom(input: &str) -> IResult<&str, WhereClause<'_>> { let mut window_blocks = Vec::new(); let mut current_input = input; - // Parse components until we reach the closing brace loop { let (new_input, _) = multispace0.parse(current_input)?; current_input = new_input; - // Try to match a closing brace if let Ok((new_input, _)) = char::<_, nom::error::Error<&str>>('}').parse(current_input) { current_input = new_input; break; } - // Try to parse each possible component current_input = if let Ok((new_input, window_block)) = parse_window_block(current_input) { window_blocks.push(window_block); new_input @@ -333,7 +283,6 @@ pub(crate) fn parse_where_nom(input: &str) -> IResult<&str, WhereClause<'_>> { ))); }; - // Consume any trailing dot if let Ok((new_input, _)) = ( space0::<_, nom::error::Error<&str>>, char::<_, nom::error::Error<&str>>('.'), @@ -359,34 +308,28 @@ pub(crate) fn parse_where_nom(input: &str) -> IResult<&str, WhereClause<'_>> { )) } -/// A `REGISTER` clause declaring a standing query over streams. +/// A `REGISTER` clause declaring a standing query over streams pub(crate) fn parse_register_clause(input: &str) -> IResult<&str, RegisterClause<'_>> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("REGISTER").parse(input)?; let (input, _) = multispace1.parse(input)?; - // Parse stream type (RSTREAM, ISTREAM, DSTREAM) let (input, stream_type) = parse_stream_type_nom(input)?; let (input, _) = multispace1.parse(input)?; - // Parse output stream IRI let (input, output_iri) = parse_uri(input)?; let (input, _) = multispace1.parse(input)?; - // Parse AS keyword let (input, _) = tag("AS").parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse SELECT clause let (input, variables) = parse_select(input)?; let (input, _) = multispace0.parse(input)?; - // Parse optional FROM NAMED WINDOW clause (this comes BEFORE WHERE in your example) let (input, window_clause) = many1(preceded(multispace0, parse_from_named_window_nom)).parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse WHERE clause with window support let ( input, WhereClause { @@ -415,31 +358,27 @@ pub(crate) fn parse_register_clause(input: &str) -> IResult<&str, RegisterClause )) } -/// A `GROUP BY` clause: the grouping variables. +/// A `GROUP BY` clause: the grouping variables pub(crate) fn parse_group_by_nom(input: &str) -> IResult<&str, Vec<&str>> { sparql_group_by_clause(input) } -/// An `ORDER BY` clause, in precedence order. +/// An `ORDER BY` clause, in precedence order pub(crate) fn parse_order_by_nom(input: &str) -> IResult<&str, Vec>> { sparql_order_by_clause(input) } -/// A `PREFIX` declaration, as `(short name, expansion)`. -/// -/// Backed by the same token-aware parser the request path uses, so a declaration -/// accepted here is accepted in a full query. +/// A `PREFIX` declaration, as `(short name, expansion)` pub(crate) fn parse_prefix_nom(input: &str) -> IResult<&str, (&str, &str)> { sparql_prefix_declaration(input) } -/// A `CONSTRUCT { ... }` template, which becomes a rule's conclusion. +/// A `CONSTRUCT { ... }` template, which becomes a rule's conclusion pub(crate) fn parse_construct_clause(input: &str) -> IResult<&str, Vec<(&str, &str, &str)>> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("CONSTRUCT").parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse multiple conclusion triples in CONSTRUCT block let (input, conclusions) = delimited( char('{'), preceded( @@ -453,20 +392,12 @@ pub(crate) fn parse_construct_clause(input: &str) -> IResult<&str, Vec<(&str, &s Ok((input, conclusions)) } -/// A `LIMIT` clause. -/// -/// Backed by the same parser `SELECT` uses. +/// A `LIMIT` clause pub(crate) fn parse_limit_nom(input: &str) -> IResult<&str, usize> { sparql_limit_clause(input) } -// --------------------------------------------------------------------------- -// Unified recursive SPARQL parser -// --------------------------------------------------------------------------- - -/// Removes SPARQL whitespace and `#` comments. This is intentionally separate -/// from `multispace0`: comments are whitespace in SPARQL and must not leak into -/// keyword or punctuation parsing. +/// Removes SPARQL whitespace and `#` comments fn sparql_skip_ws(mut input: &str) -> &str { loop { let before = input.len(); @@ -491,7 +422,7 @@ fn sparql_name_character(character: char) -> bool { character.is_alphanumeric() || matches!(character, '_' | '-' | ':') } -/// A case-insensitive keyword parser with a SPARQL token boundary. +/// A case-insensitive keyword parser with a SPARQL token boundary fn sparql_keyword<'a>(input: &'a str, keyword: &str) -> IResult<&'a str, &'a str> { let input = sparql_skip_ws(input); let (remaining, matched) = nom::bytes::complete::tag_no_case(keyword).parse(input)?; @@ -749,10 +680,6 @@ fn sparql_prefixed_name(input: &str) -> IResult<&str, &str> { Ok((&input[token_end..], &input[..token_end])) } -// Kolibrie's established SELECT/ML fragment also accepts bare symbolic RDF -// values (for example a neural class label such as `A`). Keep that lexical -// compatibility in the unified parser without treating reserved keywords as -// part of a longer token. fn sparql_bare_identifier(input: &str) -> IResult<&str, &str> { identifier(sparql_skip_ws(input)) } @@ -923,8 +850,7 @@ fn sparql_quoted_triple_parts(input: &str) -> IResult<&str, LexicalTriplePattern Ok((remaining, (subject, predicate, object))) } -/// Parses an RDF-star quoted triple recursively and returns its complete raw -/// source slice. Each nested triple is validated with the same term parsers. +/// Parses an RDF-star quoted triple recursively and returns its complete raw source slice fn sparql_quoted_triple(input: &str) -> IResult<&str, &str> { let input = sparql_skip_ws(input); let (remaining, _) = sparql_quoted_triple_parts(input)?; @@ -980,8 +906,8 @@ fn sparql_graph_name(input: &str) -> IResult<&str, &str> { alt((sparql_variable, sparql_iri, sparql_prefixed_name)).parse(input) } -/// Parses one triples-same-subject statement and expands `;` and `,` -/// abbreviations into ordinary triple patterns. +/// Parses one triples-same-subject statement and expands `;` and `,` abbreviations into ordinary +/// triple patterns fn sparql_triples_statement(input: &str) -> IResult<&str, Vec>> { let (mut input, subject) = sparql_subject_term(input)?; let mut triples = Vec::new(); @@ -1219,8 +1145,6 @@ fn sparql_bind_argument(input: &str) -> IResult<&str, &str> { return Ok(parsed); } if let Ok((remaining, literal)) = sparql_quoted_literal(input) { - // Preserve the historical BIND representation: simple quoted - // arguments omit their surrounding quotes. if (literal.starts_with('"') && literal.ends_with('"')) || (literal.starts_with('\'') && literal.ends_with('\'')) { @@ -1367,7 +1291,7 @@ fn sparql_group_primary(input: &str) -> IResult<&str, GroupGraphPattern<'_>> { .map(|(remaining, triples)| (remaining, GroupGraphPattern::Bgp(triples))) } -/// Parses a recursive group graph pattern containing BGP, GRAPH, and UNION. +/// Parses a recursive group graph pattern containing BGP, GRAPH, and UNION pub(crate) fn parse_group_graph_pattern(input: &str) -> IResult<&str, GroupGraphPattern<'_>> { let (mut input, _) = sparql_char(input, '{')?; let mut joined = Vec::new(); @@ -1595,7 +1519,6 @@ fn sparql_aggregate(input: &str) -> IResult<&str, (&str, &str, Option<&str>)> { } else { sparql_variable(input)? }; - // Keep the public tuple shape source-compatible let aggregate = if distinct { match aggregate { "COUNT" => "COUNT_DISTINCT", @@ -1907,9 +1830,7 @@ fn sparql_update_core(input: &str, allow_data_aliases: bool) -> IResult<&str, Up }) } -/// Parse a standalone SELECT with the same grammar used by `CombinedQuery`. -/// Prefixes are accepted as a request prologue; callers that need the prefix -/// map should use `parse_combined_query`. +/// Parse a standalone SELECT with the same grammar used by `CombinedQuery` pub(crate) fn parse_sparql_query_nom(input: &str) -> IResult<&str, SelectQuery<'_>> { let (input, _) = sparql_prefixes(input)?; let (remaining, query) = sparql_select_core(input, true)?; @@ -1920,14 +1841,10 @@ pub(crate) fn parse_sparql_query_nom(input: &str) -> IResult<&str, SelectQuery<' Ok((remaining, query)) } -/// One `RULE` block together with the prefix map its prologue declared. -/// -/// Fails unless the whole input is consumed, so trailing text is an error rather -/// than silently ignored. +/// One `RULE` block together with the prefix map its prologue declared pub(crate) fn parse_standalone_rule_nom<'a>( input: &'a str, ) -> IResult<&'a str, (CombinedRule<'a>, HashMap)> { - // Parse prefixes first let (input, prefix_list) = many0(|i| { let (i, _) = multispace0.parse(i)?; let (i, _) = tag("PREFIX").parse(i)?; @@ -1947,13 +1864,12 @@ pub(crate) fn parse_standalone_rule_nom<'a>( let (input, _) = multispace0.parse(input)?; - // Parse the rule let (input, rule) = parse_rule(input)?; Ok((input, (rule, prefixes))) } -/// A rule's name, as written after `RULE`. +/// A rule's name, as written after `RULE` pub(crate) fn parse_rule_head(input: &str) -> IResult<&str, RuleHead<'_>> { let (input, pred) = predicate(input)?; Ok((input, RuleHead { predicate: pred })) @@ -1967,7 +1883,6 @@ fn parse_balanced(input: &str) -> IResult<&str, &str> { '}' => { depth -= 1; if depth == 0 { - // Return the content inside the balanced block return Ok((&input[i + 1..], &input[..i])); } } @@ -2158,7 +2073,7 @@ fn infer_anchor_var(patterns: &[(String, String, String)]) -> Result IResult<&str, ModelDecl> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("MODEL").parse(input)?; @@ -2231,7 +2146,7 @@ pub(crate) fn parse_model_decl(input: &str) -> IResult<&str, ModelDecl> { )) } -/// A declaration that a predicate's facts come from a model. +/// A declaration that a predicate's facts come from a model pub(crate) fn parse_neural_relation_decl(input: &str) -> IResult<&str, NeuralRelationDecl> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("NEURAL").parse(input)?; @@ -2332,7 +2247,7 @@ fn parse_top_level_neural_decls( )) } -/// A `TRAIN` declaration: data source, loss, optimiser and hyperparameters. +/// A `TRAIN` declaration: data source, loss, optimiser and hyperparameters pub(crate) fn parse_train_neural_relation_decl( input: &str, ) -> IResult<&str, TrainNeuralRelationDecl> { @@ -2448,47 +2363,40 @@ fn parse_single_triple_template(input: &str) -> Result<(&str, &str, &str), Strin Ok(triples[0]) } -/// An `ML.PREDICT` clause, keeping the input query both as text and parsed. +/// An `ML.PREDICT` clause, keeping the input query both as text and parsed pub(crate) fn parse_ml_predict(input: &str) -> IResult<&str, MLPredictClause<'_>> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("ML.PREDICT").parse(input)?; let (input, _) = multispace0.parse(input)?; let (input, _) = char('(').parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse MODEL clause with quoted name let (input, _) = tag("MODEL").parse(input)?; let (input, _) = multispace1.parse(input)?; - let (input, _) = char('"').parse(input)?; // Expect opening quote - let (input, model) = take_until("\"").parse(input)?; // Take everything until closing quote - let (input, _) = char('"').parse(input)?; // Expect closing quote + let (input, _) = char('"').parse(input)?; + let (input, model) = take_until("\"").parse(input)?; + let (input, _) = char('"').parse(input)?; let (input, _) = multispace0.parse(input)?; let (input, _) = char(',').parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse INPUT clause using the inclusive balanced parser let (input, _) = tag("INPUT").parse(input)?; let (input, _) = multispace0.parse(input)?; let (input, input_query) = preceded(char('{'), parse_balanced).parse(input)?; - // Parse the SELECT statement inside the input query let mut select_vars = Vec::new(); let mut where_patterns = Vec::new(); let mut filter_conditions = Vec::new(); - // Extract SELECT variables if let Some(select_idx) = input_query.find("SELECT") { if let Some(where_idx) = input_query.find("WHERE") { let select_clause = &input_query[select_idx + 6..where_idx].trim(); - // Parse SELECT variables (simplified version - in real code you would use your actual SELECT parser) let vars: Vec<&str> = select_clause.split_whitespace().collect(); for var in vars { if var.starts_with(['?', '$']) { - select_vars.push((var, "", None)); // Add proper variable type extraction if needed + select_vars.push((var, "", None)); } } - // Parse WHERE patterns and filters (simplified - use your actual WHERE parser) let where_clause = &input_query[where_idx + 5..].trim(); - // This is a placeholder - you should use your actual pattern and filter parser here let ( _rest, WhereClause { @@ -2505,7 +2413,6 @@ pub(crate) fn parse_ml_predict(input: &str) -> IResult<&str, MLPredictClause<'_> let (input, _) = multispace0.parse(input)?; let (input, _) = char(',').parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse OUTPUT clause let (input, _) = tag("OUTPUT").parse(input)?; let (input, _) = multispace1.parse(input)?; let (input, output_var) = variable(input)?; @@ -2527,7 +2434,7 @@ pub(crate) fn parse_ml_predict(input: &str) -> IResult<&str, MLPredictClause<'_> )) } -/// An output stream operator: `RSTREAM`, `ISTREAM`, `DSTREAM` or a custom name. +/// An output stream operator: `RSTREAM`, `ISTREAM`, `DSTREAM` or a custom name pub(crate) fn parse_stream_type_nom(input: &str) -> IResult<&str, StreamType<'_>> { let (input, _) = multispace0.parse(input)?; let (input, stream_type) = alt(( @@ -2540,15 +2447,12 @@ pub(crate) fn parse_stream_type_nom(input: &str) -> IResult<&str, StreamType<'_> Ok((input, stream_type)) } -/// A window's width, slide and reporting behaviour. -/// -/// Bounds are half-open: `RANGE n` holds `n` timestamps. +/// A window's width, slide and reporting behaviour pub(crate) fn parse_window_spec_nom(input: &str) -> IResult<&str, WindowSpec<'_>> { let (input, _) = multispace0.parse(input)?; let (input, _) = char('[').parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse window type and parameters let (input, window_type) = alt(( tag("RANGE").map(|_| WindowType::Range), tag("TUMBLING").map(|_| WindowType::Tumbling), @@ -2558,33 +2462,26 @@ pub(crate) fn parse_window_spec_nom(input: &str) -> IResult<&str, WindowSpec<'_> let (input, _) = multispace1.parse(input)?; - // Parse duration (like PT10M) or numeric value let (input, width_str) = alt(( - // ISO 8601 duration format (PT10M, PT5S, etc.) recognize(( tag("PT"), take_while1(|c: char| c.is_digit(10)), alt((char('S'), char('M'), char('H'))), )), - // Simple numeric value take_while1(|c: char| c.is_digit(10)), )) .parse(input)?; - // Convert duration to numeric value (simplified conversion) let width = parse_duration_to_seconds(width_str); - // Optional STEP parameter for sliding windows let (input, slide) = opt(preceded( (multispace1, tag("STEP"), multispace1), alt(( - // ISO 8601 duration format recognize(( tag("PT"), take_while1(|c: char| c.is_digit(10)), alt((char('S'), char('M'), char('H'))), )), - // Simple numeric value take_while1(|c: char| c.is_digit(10)), )), )) @@ -2592,7 +2489,6 @@ pub(crate) fn parse_window_spec_nom(input: &str) -> IResult<&str, WindowSpec<'_> let slide = slide.map(parse_duration_to_seconds); - // Optional report strategy let (input, report_strategy) = opt(preceded( (multispace1, tag("REPORT"), multispace1), alt(( @@ -2604,7 +2500,6 @@ pub(crate) fn parse_window_spec_nom(input: &str) -> IResult<&str, WindowSpec<'_> )) .parse(input)?; - // Optional tick strategy let (input, tick) = opt(preceded( (multispace1, tag("TICK"), multispace1), alt((tag("TIME_DRIVEN"), tag("TUPLE_DRIVEN"), tag("BATCH_DRIVEN"))), @@ -2626,8 +2521,7 @@ pub(crate) fn parse_window_spec_nom(input: &str) -> IResult<&str, WindowSpec<'_> )) } -/// Parse a duration string used in WITH POLICY clauses. -/// Accepts: `5s`, `5000ms`, `PT5S` / `PT5M` / `PT5H`, bare integer (seconds). +/// Parse a duration string used in WITH POLICY clauses fn parse_policy_duration(input: &str) -> IResult<&str, Duration> { alt((parse_policy_duration_iso, parse_policy_duration_numeric)).parse(input) } @@ -2654,10 +2548,7 @@ fn parse_policy_duration_numeric(input: &str) -> IResult<&str, Duration> { Ok((input, dur)) } -/// Parse the policy name / spec after `WITH POLICY`. -/// - `steal` -> SyncPolicy::Steal -/// - `wait` -> SyncPolicy::Wait -/// - `(timeout=, fallback=steal|drop)` -> SyncPolicy::Timeout{...} +/// Parse the policy name / spec after `WITH POLICY` fn parse_sync_policy(input: &str) -> IResult<&str, SyncPolicy> { alt(( parse_sync_policy_steal, @@ -2717,30 +2608,29 @@ fn parse_fallback_drop(input: &str) -> IResult<&str, Fallback> { Ok((input, Fallback::Drop)) } -// Helper function to convert duration strings to seconds fn parse_duration_to_seconds(duration: &str) -> usize { if duration.starts_with("PT") && duration.len() > 2 { let time_part = &duration[2..]; if let Some(num_end) = time_part.chars().position(|c| !c.is_digit(10)) { if let Ok(num) = time_part[..num_end].parse::() { match time_part.chars().nth(num_end) { - Some('S') => num, // seconds - Some('M') => num * 60, // minutes to seconds - Some('H') => num * 3600, // hours to seconds + Some('S') => num, + Some('M') => num * 60, + Some('H') => num * 3600, _ => num, } } else { - 1 // default + 1 } } else { - 1 // default + 1 } } else { duration.parse::().unwrap_or(1) } } -/// A `FROM NAMED WINDOW` clause binding one window over one stream. +/// A `FROM NAMED WINDOW` clause binding one window over one stream pub(crate) fn parse_from_named_window_nom(input: &str) -> IResult<&str, WindowClause<'_>> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("FROM").parse(input)?; @@ -2750,12 +2640,11 @@ pub(crate) fn parse_from_named_window_nom(input: &str) -> IResult<&str, WindowCl let (input, _) = tag("WINDOW").parse(input)?; let (input, _) = multispace1.parse(input)?; - // Parse window identifier (can be :wind, , or variable) let (input, window_iri) = alt(( - delimited(char('<'), take_while1(|c| c != '>'), char('>')), // - recognize((char(':'), identifier)), // :wind - variable, // ?var - identifier, // simple name + delimited(char('<'), take_while1(|c| c != '>'), char('>')), + recognize((char(':'), identifier)), + variable, + identifier, )) .parse(input)?; @@ -2763,21 +2652,18 @@ pub(crate) fn parse_from_named_window_nom(input: &str) -> IResult<&str, WindowCl let (input, _) = tag("ON").parse(input)?; let (input, _) = multispace1.parse(input)?; - // Parse stream identifier (can be variable, URI, or namespace reference) let (input, stream_iri) = alt(( - delimited(char('<'), take_while1(|c| c != '>'), char('>')), // - variable, // ?s - recognize((char(':'), identifier)), // :stream - identifier, // simple name + delimited(char('<'), take_while1(|c| c != '>'), char('>')), + variable, + recognize((char(':'), identifier)), + identifier, )) .parse(input)?; let (input, _) = multispace1.parse(input)?; - // Parse window specification with ISO 8601 duration support let (input, window_spec) = parse_window_spec_nom(input)?; - // Optional: WITH POLICY let (input, policy) = opt(parse_from_named_window_policy).parse(input)?; Ok(( @@ -2791,9 +2677,7 @@ pub(crate) fn parse_from_named_window_nom(input: &str) -> IResult<&str, WindowCl )) } -/// Parse a PROB(...) annotation for provenance rules. -/// Format: PROB(provenance=minmax, threshold=0.3, confidence=0.9) -/// Legacy alias: PROB(combination=independent, threshold=0.3, confidence=0.9) +/// Parse a PROB(...) annotation for provenance rules fn take_prob_body(input: &str) -> IResult<&str, &str> { let mut depth = 0usize; for (index, character) in input.char_indices() { @@ -2891,7 +2775,6 @@ fn parse_prob_annotation(input: &str) -> IResult<&str, ProbAnnotation<'_>> { let mut unknown_keys = Vec::new(); let mut duplicate_key = false; - // Parse key=value pairs separated by commas let (input, kv_str) = take_prob_body(input)?; let pairs = split_top_level_commas(kv_str).map_err(|_| { @@ -3043,33 +2926,22 @@ fn parse_prob_annotation(input: &str) -> IResult<&str, ProbAnnotation<'_>> { )) } -/// Parse a complete rule: -/// RULE :Name :- CONSTRUCT { ... } WHERE { ... } -/// RULE :Name PROB(combination=independent, threshold=0.3) :- CONSTRUCT { ... } WHERE { ... } . -/// RULE :Name PROB(provenance=minmax, threshold=0.3) :- CONSTRUCT { ... } WHERE { ... } . -/// -/// The body is always a `CONSTRUCT` block followed by `WHERE`; see -/// [`parse_construct_clause`], which requires that keyword. A `WHERE { ... } => { ... }` -/// form was advertised here until the real_scenario example was found to panic on it, -/// and no parser has ever accepted it. +/// Parse a complete rule: RULE :Name :- CONSTRUCT { ... pub(crate) fn parse_rule(input: &str) -> IResult<&str, CombinedRule<'_>> { let (input, _) = tag("RULE").parse(input)?; let (input, _) = space1.parse(input)?; let (input, head) = parse_rule_head(input)?; let (input, _) = multispace0.parse(input)?; - // Optionally parse PROB(...) annotation before :- let (input, prob_annotation) = opt(terminated(parse_prob_annotation, multispace0)).parse(input)?; let (input, _) = tag(":-").parse(input)?; let (input, _) = multispace0.parse(input)?; - // Look ahead to determine parsing path let lookahead_input = input; let (lookahead_input, _) = multispace0.parse(lookahead_input)?; - // Check if we have RSP elements or direct CONSTRUCT - with explicit error types let has_rsp_elements = matches!( alt(( tag::<_, _, nom::error::Error<&str>>("RSTREAM"), @@ -3082,7 +2954,6 @@ pub(crate) fn parse_rule(input: &str) -> IResult<&str, CombinedRule<'_>> { ); let (input, stream_type, window_clause) = if has_rsp_elements { - // RSP parsing path let (input, stream_type) = opt(parse_stream_type_nom).parse(input)?; let (input, _) = multispace0.parse(input)?; let (input, window_clause) = @@ -3090,16 +2961,13 @@ pub(crate) fn parse_rule(input: &str) -> IResult<&str, CombinedRule<'_>> { let (input, _) = multispace0.parse(input)?; (input, stream_type, window_clause) } else { - // Basic parsing path - no RSP elements (input, None, vec![]) }; - // Parse CONSTRUCT clause let (input, conclusions) = parse_construct_clause(input)?; let (input, _) = multispace0.parse(input)?; - // Parse WHERE clause let ( input, WhereClause { @@ -3114,11 +2982,9 @@ pub(crate) fn parse_rule(input: &str) -> IResult<&str, CombinedRule<'_>> { ) = parse_where_nom(input)?; let body = (patterns, filters, values_clause, binds, subqueries); - // Optional dot at the end of rule let (input, _) = opt(preceded(multispace0, char('.'))).parse(input)?; let (input, _) = multispace0.parse(input)?; - // Optionally parse ML.PREDICT block if it exists let (input, ml_predict) = opt(parse_ml_predict).parse(input)?; Ok(( @@ -3140,7 +3006,7 @@ pub(crate) fn parse_rule(input: &str) -> IResult<&str, CombinedRule<'_>> { )) } -/// `SOME` or `EVERY` in a `RETRIEVE` clause. +/// `SOME` or `EVERY` in a `RETRIEVE` clause pub(crate) fn parse_retrieve_mode(input: &str) -> IResult<&str, RetrieveMode> { let (input, _) = multispace0.parse(input)?; let (input, mode) = alt(( @@ -3151,7 +3017,7 @@ pub(crate) fn parse_retrieve_mode(input: &str) -> IResult<&str, RetrieveMode> { Ok((input, mode)) } -/// `LATENT` or `ACTIVE` in a `RETRIEVE` clause. +/// `LATENT` or `ACTIVE` in a `RETRIEVE` clause pub(crate) fn parse_stream_state(input: &str) -> IResult<&str, StreamState> { let (input, _) = multispace0.parse(input)?; let (input, state) = alt(( @@ -3162,45 +3028,36 @@ pub(crate) fn parse_stream_state(input: &str) -> IResult<&str, StreamState> { Ok((input, state)) } -/// A `RETRIEVE` clause discovering streams from a catalogue. +/// A `RETRIEVE` clause discovering streams from a catalogue pub(crate) fn parse_retrieve_clause(input: &str) -> IResult<&str, RetrieveClause<'_>> { let (input, _) = multispace0.parse(input)?; let (input, _) = tag("RETRIEVE").parse(input)?; let (input, _) = multispace1.parse(input)?; - // Parse retrieve mode (SOME | EVERY) let (input, mode) = parse_retrieve_mode(input)?; let (input, _) = multispace1.parse(input)?; - // Parse stream state (LATENT | ACTIVE) let (input, state) = parse_stream_state(input)?; let (input, _) = multispace1.parse(input)?; - // Parse STREAM keyword let (input, _) = tag("STREAM").parse(input)?; let (input, _) = multispace1.parse(input)?; - // Parse variable let (input, var) = variable(input)?; let (input, _) = multispace1.parse(input)?; - // Parse FROM keyword let (input, _) = tag("FROM").parse(input)?; let (input, _) = multispace1.parse(input)?; - // Parse IRI reference let (input, iri) = parse_uri(input)?; let (input, _) = multispace1.parse(input)?; - // Parse WITH keyword let (input, _) = tag("WITH").parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse graph pattern block let (input, _) = char('{').parse(input)?; let (input, _) = multispace0.parse(input)?; - // Parse graph patterns (can be multiple triple blocks) let (input, pattern_blocks) = many0(terminated( parse_triple_block_nom, (multispace0, opt(char('.')), multispace0), @@ -3210,7 +3067,6 @@ pub(crate) fn parse_retrieve_clause(input: &str) -> IResult<&str, RetrieveClause let (input, _) = multispace0.parse(input)?; let (input, _) = char('}').parse(input)?; - // Flatten all pattern blocks into a single vector let graph_pattern = pattern_blocks.into_iter().flatten().collect(); Ok(( @@ -3225,14 +3081,13 @@ pub(crate) fn parse_retrieve_clause(input: &str) -> IResult<&str, RetrieveClause )) } -/// The combined query parser parses SPARQL + explicit Kolibrie extensions. +/// The combined query parser parses SPARQL + explicit Kolibrie extensions pub(crate) fn parse_combined_query_nom(input: &str) -> IResult<&str, CombinedQuery<'_>> { parse_combined_query_with_options_nom(input, false) } -/// Parse through the single combined AST, optionally enabling the historical -/// standalone INSERT/DELETE DATA aliases. Standard APIs pass `false`; -/// compatibility adapters pass `true`. +/// Parse through the single combined AST, optionally enabling the historical standalone +/// INSERT/DELETE DATA aliases pub(crate) fn parse_combined_query_with_options_nom( input: &str, allow_data_aliases: bool, @@ -3288,8 +3143,6 @@ pub(crate) fn parse_combined_query_with_options_nom( )); } - // Explicit extension path. A recognized standard keyword above never - // falls through to these restricted grammars after a syntax error. let (extension_input, retrieve_clause) = opt(parse_retrieve_clause).parse(after_prologue)?; let (extension_input, _) = multispace0.parse(extension_input)?; let (extension_input, register_clause) = opt(parse_register_clause).parse(extension_input)?; @@ -3297,7 +3150,6 @@ pub(crate) fn parse_combined_query_with_options_nom( let (extension_input, (model_decls, neural_relation_decls, train_neural_relation_decls)) = parse_top_level_neural_decls(extension_input)?; let (extension_input, _) = multispace0.parse(extension_input)?; - // A top-level ML.PREDICT may precede the rules let (extension_input, leading_ml_predict) = opt(parse_ml_predict).parse(extension_input)?; let mut extension_input = sparql_skip_ws(extension_input); let mut rules = Vec::new(); @@ -3388,13 +3240,7 @@ pub(crate) fn convert_triple_pattern( ) } -/// Lowers a parsed rule into a [`Rule`] bound to `dict`. -/// -/// This is the prepare step: lexical terms are prefix-expanded using `prefixes` and -/// then interned, so the result is only meaningful against that dictionary. A rule -/// lowered against the wrong one parses and validates but matches nothing. -/// -/// Takes `&mut Dictionary` because interning allocates new ids. +/// Lowers a parsed rule into a [`Rule`] bound to `dict` pub fn convert_combined_rule<'a>( cr: CombinedRule<'a>, dict: &mut Dictionary, @@ -3413,25 +3259,37 @@ pub fn convert_combined_rule<'a>( .map(|triple| convert_triple_pattern(triple, dict, prefixes)) .collect::>(); - // Convert filter expressions to filter conditions let filter_conditions = cr .body .1 .into_iter() - .flat_map(|filter_expr| { - match filter_expr { - FilterExpression::Comparison(var, op, value) => { - vec![FilterCondition { + .flat_map(|filter_expr| match filter_expr { + FilterExpression::Comparison(var, op, value) => { + vec![FilterCondition { + variable: var.trim_start_matches(['?', '$']).to_string(), + operator: op.to_string(), + value: value.to_string(), + }] + } + FilterExpression::Or(left, right) => { + let mut conditions = Vec::new(); + + if let FilterExpression::Comparison(var, op, value) = *left { + conditions.push(FilterCondition { variable: var.trim_start_matches(['?', '$']).to_string(), - operator: op.to_string(), + operator: format!("OR:{}", op.to_string()), value: value.to_string(), - }] + }); } - FilterExpression::Or(left, right) => { - // Handle OR expressions - let mut conditions = Vec::new(); - if let FilterExpression::Comparison(var, op, value) = *left { + if let FilterExpression::Comparison(var, op, value) = *right { + conditions.push(FilterCondition { + variable: var.trim_start_matches(['?', '$']).to_string(), + operator: format!("OR:{}", op.to_string()), + value: value.to_string(), + }); + } else if let FilterExpression::Or(nested_left, nested_right) = *right { + if let FilterExpression::Comparison(var, op, value) = *nested_left { conditions.push(FilterCondition { variable: var.trim_start_matches(['?', '$']).to_string(), operator: format!("OR:{}", op.to_string()), @@ -3439,81 +3297,57 @@ pub fn convert_combined_rule<'a>( }); } - if let FilterExpression::Comparison(var, op, value) = *right { + if let FilterExpression::Comparison(var, op, value) = *nested_right { conditions.push(FilterCondition { variable: var.trim_start_matches(['?', '$']).to_string(), operator: format!("OR:{}", op.to_string()), value: value.to_string(), }); - } else if let FilterExpression::Or(nested_left, nested_right) = *right { - // Handle nested OR expressions (common with multiple OR conditions) - if let FilterExpression::Comparison(var, op, value) = *nested_left { - conditions.push(FilterCondition { - variable: var.trim_start_matches(['?', '$']).to_string(), - operator: format!("OR:{}", op.to_string()), - value: value.to_string(), - }); - } - - if let FilterExpression::Comparison(var, op, value) = *nested_right { - conditions.push(FilterCondition { - variable: var.trim_start_matches(['?', '$']).to_string(), - operator: format!("OR:{}", op.to_string()), - value: value.to_string(), - }); - } } - - conditions } - FilterExpression::And(left, right) => { - // Handle AND expressions - let mut conditions = Vec::new(); - if let FilterExpression::Comparison(var, op, value) = *left { - conditions.push(FilterCondition { - variable: var.trim_start_matches(['?', '$']).to_string(), - operator: op.to_string(), - value: value.to_string(), - }); - } - - if let FilterExpression::Comparison(var, op, value) = *right { - conditions.push(FilterCondition { - variable: var.trim_start_matches(['?', '$']).to_string(), - operator: op.to_string(), - value: value.to_string(), - }); - } + conditions + } + FilterExpression::And(left, right) => { + let mut conditions = Vec::new(); - conditions + if let FilterExpression::Comparison(var, op, value) = *left { + conditions.push(FilterCondition { + variable: var.trim_start_matches(['?', '$']).to_string(), + operator: op.to_string(), + value: value.to_string(), + }); } - _ => { - // Return an empty vector instead of panicking - vec![] + + if let FilterExpression::Comparison(var, op, value) = *right { + conditions.push(FilterCondition { + variable: var.trim_start_matches(['?', '$']).to_string(), + operator: op.to_string(), + value: value.to_string(), + }); } + + conditions + } + _ => { + vec![] } }) .collect(); - // Convert all conclusion triples, preserving their structure let mut conclusion_triples: Vec = cr .conclusion .into_iter() .map(|triple| convert_triple_pattern(triple, dict, prefixes)) .collect(); - // Handle ML.PREDICT: wire output variable into conclusion triples if let Some(ml_predict) = &cr.ml_predict { let ml_output_var = ml_predict.output.trim_start_matches(['?', '$']); - // Check if the conclusion triples contain the ML output variable for conclusion in &mut conclusion_triples { - // Check if the conclusion contains variables that need ML output match &mut conclusion.2 { Term::Variable(var) if var == ml_output_var => {} Term::Variable(var) if var == "level" => { - // Replace generic 'level' variable with ML output variable *var = ml_output_var.to_string(); } _ => {} @@ -3529,28 +3363,24 @@ pub fn convert_combined_rule<'a>( } } -/// Parses a whole SPARQL `SELECT`, reporting failure without naming the parser -/// library. See [`ParseError`](crate::parse_error::ParseError). +/// Parses a whole SPARQL `SELECT`, reporting failure without naming the parser library pub fn parse_sparql_query(input: &str) -> ParseResult<'_, SelectQuery<'_>> { parse_sparql_query_nom(input).map_err(ParseError::from_nom) } -/// Parses one rule together with the prefixes it was declared with. +/// Parses one rule together with the prefixes it was declared with pub fn parse_standalone_rule<'a>( input: &'a str, ) -> ParseResult<'a, (CombinedRule<'a>, HashMap)> { parse_standalone_rule_nom(input).map_err(ParseError::from_nom) } -/// Parses any supported request into the single combined AST. -/// -/// The entry point to prefer: SPARQL, RSP-QL, Update, rules and the ML extensions all -/// come back as one [`CombinedQuery`]. +/// Parses any supported request into the single combined AST pub fn parse_combined_query(input: &str) -> ParseResult<'_, CombinedQuery<'_>> { parse_combined_query_nom(input).map_err(ParseError::from_nom) } -/// As [`parse_combined_query`], optionally accepting the historical data aliases. +/// As [`parse_combined_query`], optionally accepting the historical data aliases pub fn parse_combined_query_with_options( input: &str, allow_data_aliases: bool, @@ -3558,61 +3388,61 @@ pub fn parse_combined_query_with_options( parse_combined_query_with_options_nom(input, allow_data_aliases).map_err(ParseError::from_nom) } -/// Parses an RDF-star quoted triple, `<< s p o >>`, as written. +/// Parses an RDF-star quoted triple, `<< s p o >>`, as written pub fn parse_quoted_triple(input: &str) -> ParseResult<'_, &str> { parse_quoted_triple_nom(input).map_err(ParseError::from_nom) } -/// Parses a block of triple patterns sharing a subject. +/// Parses a block of triple patterns sharing a subject pub fn parse_triple_block(input: &str) -> ParseResult<'_, Vec<(&str, &str, &str)>> { parse_triple_block_nom(input).map_err(ParseError::from_nom) } -/// Parses a `FILTER` clause into its expression tree. +/// Parses a `FILTER` clause into its expression tree pub fn parse_filter(input: &str) -> ParseResult<'_, FilterExpression<'_>> { parse_filter_nom(input).map_err(ParseError::from_nom) } -/// Parses a `WHERE` clause into everything it declares. +/// Parses a `WHERE` clause into everything it declares pub fn parse_where(input: &str) -> ParseResult<'_, WhereClause<'_>> { parse_where_nom(input).map_err(ParseError::from_nom) } -/// Parses one `PREFIX` declaration. +/// Parses one `PREFIX` declaration pub fn parse_prefix(input: &str) -> ParseResult<'_, (&str, &str)> { parse_prefix_nom(input).map_err(ParseError::from_nom) } -/// Parses a comparison expression, as a `FILTER` body contains. +/// Parses a comparison expression, as a `FILTER` body contains pub fn parse_comparison(input: &str) -> ParseResult<'_, FilterExpression<'_>> { parse_comparison_nom(input).map_err(ParseError::from_nom) } -/// Parses a `GROUP BY` clause into its variables. +/// Parses a `GROUP BY` clause into its variables pub fn parse_group_by(input: &str) -> ParseResult<'_, Vec<&str>> { parse_group_by_nom(input).map_err(ParseError::from_nom) } -/// Parses an `ORDER BY` clause into its conditions. +/// Parses an `ORDER BY` clause into its conditions pub fn parse_order_by(input: &str) -> ParseResult<'_, Vec>> { parse_order_by_nom(input).map_err(ParseError::from_nom) } -/// Parses a `LIMIT` clause. +/// Parses a `LIMIT` clause pub fn parse_limit(input: &str) -> ParseResult<'_, usize> { parse_limit_nom(input).map_err(ParseError::from_nom) } -/// Parses a `FROM NAMED WINDOW` clause. +/// Parses a `FROM NAMED WINDOW` clause pub fn parse_from_named_window(input: &str) -> ParseResult<'_, WindowClause<'_>> { parse_from_named_window_nom(input).map_err(ParseError::from_nom) } -/// Parses an RSP-QL stream type. +/// Parses an RSP-QL stream type pub fn parse_stream_type(input: &str) -> ParseResult<'_, StreamType<'_>> { parse_stream_type_nom(input).map_err(ParseError::from_nom) } -/// Parses a window specification, the `[...]` after a window name. +/// Parses a window specification, the `[...]` after a window name pub fn parse_window_spec(input: &str) -> ParseResult<'_, WindowSpec<'_>> { parse_window_spec_nom(input).map_err(ParseError::from_nom) } diff --git a/kolibrie/src/parser_tests.rs b/kolibrie/src/parser_tests.rs index d8c26d71..5dbd17bd 100644 --- a/kolibrie/src/parser_tests.rs +++ b/kolibrie/src/parser_tests.rs @@ -8,13 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Unit tests for the parser's combinators. -//! -//! These live inside the crate rather than in `tests/` because they exercise the -//! combinators, which are `pub(crate)`: an integration test can only reach `pub` -//! items, and making 26 combinators public to be testable put `nom`'s `IResult` in -//! this crate's API. Testing internals from inside is what lets the surface stay -//! small, which is §4's rule, and P5's. +//! Unit tests for the parser's combinators #[cfg(test)] mod tests { @@ -38,15 +32,13 @@ mod tests { let result = identifier("person_name"); assert_eq!(result, Ok(("", "person_name"))); - // Debug what the parser actually returns let result = identifier("123invalid"); assert!(result.is_ok()); - // If your identifier parser allows numbers at start, test with something that should fail - let result = identifier(""); // Empty string should fail + let result = identifier(""); assert!(result.is_err()); - let result = identifier("!invalid"); // Special characters should fail + let result = identifier("!invalid"); assert!(result.is_err()); } @@ -61,23 +53,18 @@ mod tests { #[test] fn test_predicate_parsing() { - // Test prefixed predicate let result = predicate("ex:worksAt"); assert_eq!(result, Ok(("", "ex:worksAt"))); - // Test colon-prefixed predicate let result = predicate(":worksAt"); assert_eq!(result, Ok(("", ":worksAt"))); - // Test 'a' predicate (rdf:type) let result = predicate("a"); assert_eq!(result, Ok(("", "a"))); - // Test URI predicate let result = predicate(""); assert_eq!(result, Ok(("", "http://example.org/worksAt"))); - // Test variable as predicate let result = predicate("?predicate"); assert_eq!(result, Ok(("", "?predicate"))); } @@ -109,8 +96,7 @@ mod tests { let (remaining, triples) = result.unwrap(); assert_eq!(remaining, ""); assert_eq!(triples.len(), 2); - // Fix: Your parser strips the quotes from literals - assert_eq!(triples[0], ("?person", "ex:name", "John")); // Without quotes + assert_eq!(triples[0], ("?person", "ex:name", "John")); assert_eq!(triples[1], ("?person", "ex:age", "25")); } @@ -137,24 +123,20 @@ mod tests { let result = parse_arithmetic_expression(input); assert!(result.is_ok()); - // Add specific assertions for the arithmetic structure } #[test] fn test_select_parsing() { - // Test simple SELECT let result = parse_select("SELECT ?person ?name"); assert!(result.is_ok()); let (_, variables) = result.unwrap(); assert_eq!(variables.len(), 2); - // Test SELECT * let result = parse_select("SELECT *"); assert!(result.is_ok()); let (_, variables) = result.unwrap(); assert_eq!(variables[0], ("*", "*", None)); - // Test SELECT with aggregation let result = parse_select("SELECT SUM(?salary) AS ?total"); assert!(result.is_ok()); } @@ -235,14 +217,11 @@ WHERE { let (_, query) = result.unwrap(); - // Check that variables are parsed correctly assert_eq!(query.variables.len(), 3); assert_eq!(query.variables[0].1, "?patient"); assert_eq!(query.variables[1].1, "?name"); assert_eq!(query.variables[2].1, "?riskScore"); - // The unified syntax tree retains lexical terms. `a` is expanded - // exactly once when the AST is lowered to the execution plan. let GroupGraphPattern::Bgp(patterns) = query.pattern else { panic!("expected a BGP"); }; @@ -268,29 +247,23 @@ WHERE { let (_, rule) = result.unwrap(); - // Check rule head assert_eq!(rule.head.predicate, ":OverheatingAlert"); - // Check conclusion assert_eq!(rule.conclusion.len(), 1); assert_eq!(rule.conclusion[0].0, "?room"); - // Check body patterns let (patterns, filters, _, _, _) = &rule.body; assert!(patterns.len() >= 3); - // First pattern should have 'a' for rdf:type assert_eq!(patterns[0].0, "?reading"); assert!(patterns[0].1 == "a" || patterns[0].1.contains("type")); assert!(patterns[0].2.contains("Sensor")); - // Check that filters are present assert_eq!(filters.len(), 1); } #[test] fn test_triple_block_with_a_syntax() { - // Test that triple blocks can parse 'a' as a predicate let input = "?patient a example:Test ; example:name \"John\""; let result = parse_triple_block(input); @@ -299,7 +272,6 @@ WHERE { assert_eq!(remaining, ""); assert_eq!(triples.len(), 2); - // First triple - 'a' assert_eq!(triples[0].0, "?patient"); assert_eq!( triples[0].1, @@ -307,7 +279,6 @@ WHERE { ); assert!(triples[0].2.contains("Test")); - // Second triple is a normal pattern assert_eq!(triples[1].0, "?patient"); assert!(triples[1].1.contains("name")); assert_eq!(triples[1].2, "John"); @@ -333,10 +304,8 @@ WHERE { let (_, rule) = result.unwrap(); - // Check rule head assert_eq!(rule.head.predicate, ":TransitiveRelated"); - // Check PROB annotation is present and correct let prob = rule .prob_annotation .as_ref() @@ -345,11 +314,9 @@ WHERE { assert!((prob.threshold.unwrap() - 0.3).abs() < 1e-9); assert!((prob.confidence.unwrap() - 0.9).abs() < 1e-9); - // Check conclusion assert_eq!(rule.conclusion.len(), 1); assert_eq!(rule.conclusion[0], ("?x", "ex:related", "?z")); - // Check body patterns let (patterns, filters, _, _, _) = &rule.body; assert_eq!(patterns.len(), 2); assert_eq!(patterns[0], ("?x", "ex:related", "?y")); @@ -388,7 +355,6 @@ WHERE { "confidence should be None when not specified" ); - // Check filter is parsed let (_, filters, _, _, _) = &rule.body; assert_eq!(filters.len(), 1); } @@ -422,7 +388,6 @@ WHERE { #[test] fn test_rule_without_prob_annotation_still_works() { - // Regression: rules without PROB should parse identically to before let input = r#"RULE :SimpleRule :- CONSTRUCT { ?x ex:inferred true . @@ -457,14 +422,12 @@ WHERE { let (_, query) = result.unwrap(); - // Check that SELECT * is parsed correctly assert_eq!(query.variables.len(), 1); assert_eq!(query.variables[0], ("*", "*", None)); let (_, combined) = parse_combined_query(input).unwrap(); assert_eq!(combined.prefixes["ex"], "http://example.org#"); - // Check that the triple pattern is parsed correctly let GroupGraphPattern::Bgp(patterns) = query.pattern else { panic!("expected a BGP"); }; @@ -1182,8 +1145,6 @@ ML.PREDICT(MODEL "digit_model", #[test] fn a_parse_error_offset_points_at_the_rejected_text() { - // Not `input.len() - offending.len()`: the parser narrows the error to the - // token, so subtracting its length lands past the start of it. let input = "DELETE DATA { ?illegal }"; let error = parse_combined_query(input).expect_err("a variable is illegal here"); let offset = error.offset_in(input); diff --git a/kolibrie/src/prelude.rs b/kolibrie/src/prelude.rs index 03dfdb06..622898f4 100644 --- a/kolibrie/src/prelude.rs +++ b/kolibrie/src/prelude.rs @@ -8,43 +8,17 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The common path: load a store, parse a query, run it. -//! -//! ```ignore -//! use kolibrie::prelude::*; -//! ``` -//! -//! Membership is deliberately narrow. Before this module existed, 55 files reached -//! for `use kolibrie::::*`, which made every module's entire contents part -//! of the de-facto API and meant any later module move touched all 55. Replacing -//! that with a glob over *everything* would have changed nothing. -//! -//! So the bar is breadth of use, measured rather than guessed: these three symbols -//! are needed across ~20 directories each. Everything else clusters into one or two -//! directories — the volcano optimizer types, the probabilistic-program API, the -//! datalog rule helpers — and those stay explicit imports, because a file that uses -//! the optimizer should say so. -//! -//! The test for adding something here is whether unrelated callers all need it, not -//! whether more than one does. All six `program` symbols are used by exactly two -//! files, and they are the *same* two files; that is a feature area, not a prelude. +//! The common path: load a store, parse a query, run it -/// The RDF store. Holds encoded triples, the term dictionary, and the indexes. -pub use crate::sparql_database::SparqlDatabase; +/// The RDF store +pub use crate::database::KolibrieDB; -/// Parses a combined SPARQL/RSP-QL query, including rule and prefix sections. +/// Parses a combined SPARQL/RSP-QL query, including rule and prefix sections pub use crate::parser::parse_combined_query; -/// Runs a request and returns its rows, or says why it could not. -/// -/// The entry point to reach for. +/// Runs a request and returns its rows, or says why it could not pub use crate::execute_query::execute; -/// Why a request could not be answered. +/// Why a request could not be answered pub use crate::execute_query::QueryError; pub use crate::query_builder::Queryable; - -// `execute_query_rayon_parallel2_volcano` was here until `execute` replaced it. It is -// deprecated rather than deleted, but it is no longer in the prelude: a glob import -// should not be how anyone reaches a deprecated entry point, and re-exporting one -// here would put a deprecation warning on every file that globs this module. diff --git a/kolibrie/src/program.rs b/kolibrie/src/program.rs index 8cfb8066..9ada5d67 100644 --- a/kolibrie/src/program.rs +++ b/kolibrie/src/program.rs @@ -8,44 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Probabilistic programs: compile a rule set once, run it, query the answers. -//! -//! The alternative to [`process_rule_definition`](crate::rule_execution::process_rule_definition) -//! for anything beyond a single rule. The split matters because compiling validates -//! and costs nothing to repeat, while executing consumes a dataset: -//! -//! ```text -//! text ──compile_program──▶ CompiledProgram ──execute_program──▶ ProgramResult -//! (validate, check (reusable) (owns a dataset) -//! the ML policy) -//! ``` -//! -//! A [`CompiledProgram`] holds its terms as text, not dictionary ids, and encodes -//! them against a store's dictionary only when it runs — see -//! [`CompiledProgram::datalog_rules`]. One compiled program can therefore run -//! against several stores. -//! -//! # Determinism -//! -//! Model outputs vary between runs, which would make a program's answers -//! irreproducible. [`ProgramOptions::frozen_predictions`] takes a -//! [`PredictionSnapshot`] and uses it instead of invoking the models, so a result can -//! be reproduced exactly — and a snapshot records the artifact hash it came from, so -//! a mismatch is detectable rather than silent. -//! -//! # Exclusivity and coverage -//! -//! A distribution over labels is mutually exclusive: at most one label holds. A -//! program that needs every such choice decided states them in -//! [`ProgramOptions::required_choices`], and [`Coverage::confirmed`] reports whether -//! the predictions actually covered them. An uncovered choice is a gap in the -//! answer, not a zero. -//! -//! # Resource limits -//! -//! Exact probabilistic inference can blow up combinatorially, so [`ProgramLimits`] -//! caps seed variables, SDD nodes and wall-clock time. Exceeding one is -//! [`ProgramError::Resource`], distinct from a wrong program. +//! Probabilistic programs: compile a rule set once, run it, query the answers use std::collections::{BTreeMap, HashMap, HashSet}; use std::fmt; @@ -69,57 +32,51 @@ use shared::term_encoder::SharedTerms; use shared::terms::{Term, TriplePattern}; use shared::triple::Triple; +use crate::database::{reencode_term_id, KolibrieDB}; use crate::error_handler::format_parse_error; use crate::ml_policy::MlExecutionContext; use crate::parser::parse_combined_query; -use crate::sparql_database::{reencode_term_id, SparqlDatabase}; -/// How far a distribution's probabilities may sum from 1.0 and still be accepted. +/// How far a distribution's probabilities may sum from 1.0 and still be accepted pub const DISTRIBUTION_TOLERANCE: f64 = 1e-7; -/// Predicate carrying a derived probability when results are published as RDF. +/// Predicate carrying a derived probability when results are published as RDF pub const PROB_VALUE_IRI: &str = "http://www.w3.org/ns/prob#value"; const XSD_DOUBLE_IRI: &str = "http://www.w3.org/2001/XMLSchema#double"; -/// Why a program could not be compiled or run. -/// -/// Each variant renders with a stable `PROGRAM_*` prefix through [`fmt::Display`]; -/// those strings reach callers over the HTTP API, so they are part of the contract -/// rather than diagnostics. +/// Why a program could not be compiled or run #[derive(Debug, Clone, PartialEq)] pub enum ProgramError { - /// The text is not a valid program. + /// The text is not a valid program Parse(String), - /// Valid syntax this engine does not implement — recursion, for instance. + /// Valid syntax this engine does not implement — recursion, for instance Unsupported(String), - /// Refused by the host's ML policy, such as naming an unapproved model. + /// Refused by the host's ML policy, such as naming an unapproved model Policy(String), - /// A supplied [`PredictionSnapshot`] does not match the program or its - /// artifacts. + /// A supplied [`PredictionSnapshot`] does not match the program or its artifacts Snapshot(String), - /// Predictions did not cover every required choice; see [`Coverage`]. + /// Predictions did not cover every required choice; see [`Coverage`] Coverage(String), - /// A limit in [`ProgramLimits`] was reached. Says nothing about correctness — - /// the same program may succeed with a larger budget. + /// A limit in [`ProgramLimits`] was reached Resource(ResourceError), - /// Inference started and then failed. + /// Inference started and then failed Execution(String), } -/// Which resource ceiling stopped the run. +/// Which resource ceiling stopped the run #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum ResourceError { - /// More probabilistic seeds than [`ProgramLimits::max_seed_variables`]. + /// More probabilistic seeds than [`ProgramLimits::max_seed_variables`] SeedLimit { - /// Seeds the program needed. + /// Seeds the program needed requested: usize, - /// The configured ceiling. + /// The configured ceiling limit: usize, }, - /// The seed id space is used up. Ids are never recycled. + /// The seed id space is used up IdOverflow, - /// SDD compilation exceeded [`ProgramLimits::max_sdd_nodes`]. + /// SDD compilation exceeded [`ProgramLimits::max_sdd_nodes`] NodeBudgetExceeded, - /// The run exceeded [`ProgramLimits::deadline`]. + /// The run exceeded [`ProgramLimits::deadline`] DeadlineExceeded, } @@ -148,18 +105,14 @@ impl From for ProgramError { } } -/// Ceilings that stop a program rather than letting it run unbounded. -/// -/// Defaults are deliberately generous — a million seeds, 16 million SDD nodes, ten -/// minutes — so they act as a backstop against combinatorial blow-up, not as a -/// performance budget. +/// Ceilings that stop a program rather than letting it run unbounded #[derive(Debug, Clone, Copy, PartialEq)] pub struct ProgramLimits { - /// Most probabilistic seeds allowed. Each is one variable in the formula. + /// Most probabilistic seeds allowed pub max_seed_variables: usize, - /// Most SDD nodes allowed during exact inference. + /// Most SDD nodes allowed during exact inference pub max_sdd_nodes: usize, - /// Wall-clock ceiling. `None` means no deadline. + /// Wall-clock ceiling pub deadline: Option, } @@ -173,35 +126,32 @@ impl Default for ProgramLimits { } } -/// How to run a program. +/// How to run a program #[derive(Debug, Clone, Default)] pub struct ProgramOptions { - /// Seed for training randomness, making a fitted model reproducible. `None` - /// leaves training nondeterministic. + /// Seed for training randomness, making a fitted model reproducible pub training_seed: Option, - /// Use these predictions instead of invoking the models, so the run is - /// reproducible. See the module documentation on determinism. + /// Use these predictions instead of invoking the models, so the run is reproducible pub frozen_predictions: Option, - /// Exclusive choices the answer must decide, as `(relation, subject)`. Reported - /// against by [`Coverage`]. + /// Exclusive choices the answer must decide, as `(relation, subject)` pub required_choices: Option>, - /// Keep the provenance formula, which [`ProgramResult::prepare_answers`] needs - /// to recompute probabilities without re-deriving. Costs memory. + /// Keep the provenance formula, which [`ProgramResult::prepare_answers`] needs to recompute + /// probabilities without re-deriving pub retain_provenance: bool, - /// Emit explanation output alongside the answers. + /// Emit explanation output alongside the answers pub explain: bool, - /// Rows per model invocation. `None` uses the engine default. + /// Rows per model invocation pub prediction_batch_size: Option, - /// Resource ceilings. + /// Resource ceilings pub limits: ProgramLimits, } -/// How a rule's conclusions are derived. +/// How a rule's conclusions are derived #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum RuleMode { - /// Ordinary Datalog: facts are certain and carry no probability. + /// Ordinary Datalog: facts are certain and carry no probability Deterministic, - /// Probabilistic, with exact weighted model counting over an SDD. + /// Probabilistic, with exact weighted model counting over an SDD Sdd, } @@ -213,10 +163,10 @@ enum LexTerm { type LexPattern = (LexTerm, LexTerm, LexTerm); -/// One validated rule, with its terms still as text. +/// One validated rule, with its terms still as text #[derive(Debug, Clone)] pub struct CompiledRule { - /// The rule's name, as written after `RULE`. + /// The rule's name, as written after `RULE` pub name: String, premise: Vec, filters: Vec, @@ -265,12 +215,7 @@ struct CompiledPrediction { columns: Vec, } -/// A validated program, ready to run against any store. -/// -/// Terms are held as text rather than dictionary ids, so one compiled program can be -/// executed against several stores; [`Self::datalog_rules`] does the interning. -/// Compiling has already checked the ML policy, so a program that compiles names -/// only models the host approved. +/// A validated program, ready to run against any store #[derive(Debug, Clone)] pub struct CompiledProgram { prefixes: HashMap, @@ -283,36 +228,32 @@ pub struct CompiledProgram { } impl CompiledProgram { - /// The validated rules, in source order. + /// The validated rules, in source order pub fn rules(&self) -> &[CompiledRule] { &self.rules } - /// Whether the program is deterministic or probabilistic. `None` when it has - /// no rules. + /// Whether the program is deterministic or probabilistic pub fn mode(&self) -> Option { self.mode } - /// Prefix bindings the program declared. + /// Prefix bindings the program declared pub fn prefixes(&self) -> &HashMap { &self.prefixes } - /// Whether running this program invokes a model. + /// Whether running this program invokes a model pub fn has_prediction(&self) -> bool { self.prediction.is_some() } - /// Whether running this program trains a model, which writes an artifact. + /// Whether running this program trains a model, which writes an artifact pub fn has_training(&self) -> bool { !self.train_decls.is_empty() } - /// Interns the rules against `dictionary`, producing rules the reasoner can - /// evaluate. - /// - /// This is the prepare step: the result is only valid against that dictionary. + /// Interns the rules against `dictionary`, producing rules the reasoner can evaluate pub fn datalog_rules(&self, dictionary: &mut Dictionary) -> Vec { self.rules .iter() @@ -321,11 +262,7 @@ impl CompiledProgram { } } -/// Validates `text` into a runnable program. -/// -/// Checks syntax, rejects constructs the engine cannot represent, and enforces -/// `policy` — so a program that compiles names only approved models. Nothing is -/// executed and no dataset is touched. +/// Validates `text` into a runnable program pub fn compile_program( text: &str, policy: &MlExecutionContext, @@ -846,38 +783,28 @@ struct ChoiceRow { } #[derive(Debug, Clone, PartialEq, Eq)] -/// Provenance of one relation's frozen predictions. -/// -/// Records which artifact produced them and its hash, so replaying a snapshot -/// against a changed model is detected rather than silently accepted. +/// Provenance of one relation's frozen predictions pub struct NeuralSnapshotMetadata { - /// Relation these predictions are for. + /// Relation these predictions are for pub relation: String, - /// Model that produced them. + /// Model that produced them pub model: String, - /// Artifact path the weights were loaded from. + /// Artifact path the weights were loaded from pub artifact: String, - /// SHA-256 of that artifact. A mismatch means the snapshot no longer - /// corresponds to the model on disk. + /// SHA-256 of that artifact pub artifact_sha256: String, - /// Feature variables, in model input order. + /// Feature variables, in model input order pub feature_vars: Vec, - /// Output labels, in model output order. + /// Output labels, in model output order pub labels: Vec, - /// Expected feature-vector length. + /// Expected feature-vector length pub input_dim: usize, - /// Rows predicted. + /// Rows predicted pub rows: usize, } #[derive(Debug, Clone, Default, PartialEq)] -/// Model outputs captured so a run can be reproduced without re-invoking anything. -/// -/// Holds two kinds of prediction, which are not interchangeable: exclusive groups, -/// where at most one label holds, and independent facts. A relation cannot carry -/// both — [`Self::declare_exclusive_relation`] rejects that, because treating -/// competing labels as independent assigns probability to two of them holding at -/// once. +/// Model outputs captured so a run can be reproduced without re-invoking anything pub struct PredictionSnapshot { domains: BTreeMap>, groups: BTreeMap<(String, String), ChoiceRow>, @@ -886,40 +813,39 @@ pub struct PredictionSnapshot { } impl PredictionSnapshot { - /// An empty snapshot. + /// An empty snapshot pub fn new() -> Self { Self::default() } - /// Whether the snapshot holds no predictions. Declared relations with no rows - /// still count as empty. + /// Whether the snapshot holds no predictions pub fn is_empty(&self) -> bool { self.groups.is_empty() && self.independent.is_empty() } - /// Number of exclusive groups captured. + /// Number of exclusive groups captured pub fn choice_groups(&self) -> usize { self.groups.len() } - /// Number of independent facts captured. + /// Number of independent facts captured pub fn independent_facts(&self) -> usize { self.independent.len() } - /// A relation's label domain, in model output order. + /// A relation's label domain, in model output order pub fn labels(&self, relation: &str) -> Option<&[String]> { self.domains.get(relation).map(Vec::as_slice) } - /// The distribution captured for one subject of one relation. + /// The distribution captured for one subject of one relation pub fn distribution(&self, relation: &str, anchor: &str) -> Option<&[f64]> { self.groups .get(&(relation.to_string(), anchor.to_string())) .map(|row| row.probabilities.as_slice()) } - /// Subjects for which this relation has a captured distribution. + /// Subjects for which this relation has a captured distribution pub fn anchors(&self, relation: &str) -> Vec<&str> { self.groups .keys() @@ -928,18 +854,12 @@ impl PredictionSnapshot { .collect() } - /// Which artifacts produced these predictions. See - /// [`NeuralSnapshotMetadata`]. + /// Which artifacts produced these predictions pub fn neural_metadata(&self) -> &[NeuralSnapshotMetadata] { &self.neural } - /// Declares a relation's labels, fixing their order. - /// - /// Order is part of the identity: it maps model outputs to labels, so - /// re-declaring with a different order is an error rather than an update. - /// Fails if the relation already carries independent facts, since a relation - /// cannot be both exclusive and independent. + /// Declares a relation's labels, fixing their order pub fn declare_exclusive_relation( &mut self, relation: &str, @@ -981,10 +901,7 @@ impl PredictionSnapshot { } } - /// Captures a distribution over a declared relation's labels for one subject. - /// - /// Probabilities must align with the declared label order and sum to 1 within - /// [`DISTRIBUTION_TOLERANCE`]. + /// Captures a distribution over a declared relation's labels for one subject pub fn add_distribution( &mut self, relation: &str, @@ -1036,9 +953,7 @@ impl PredictionSnapshot { } } - /// Captures a single fact's probability, independent of every other. - /// - /// Fails if the predicate was declared exclusive. + /// Captures a single fact's probability, independent of every other pub fn add_independent( &mut self, subject: &str, @@ -1072,11 +987,7 @@ impl PredictionSnapshot { } } - /// Folds `other` in, rejecting any conflict. - /// - /// Replays `other`'s contents through the same checks rather than copying, so - /// incompatible label orders or an exclusive/independent clash fail here instead - /// of producing a snapshot that is wrong later. + /// Folds `other` in, rejecting any conflict pub fn merge(&mut self, other: &PredictionSnapshot) -> Result<(), ProgramError> { for (relation, labels) in &other.domains { self.declare_exclusive_relation(relation, labels)?; @@ -1200,76 +1111,67 @@ fn allocate_seed_specs( } #[derive(Debug, Clone, PartialEq, Eq)] -/// Whether the predictions decided every choice the caller required. +/// Whether the predictions decided every choice the caller required pub struct Coverage { - /// Rows the models were run over. + /// Rows the models were run over pub prediction_rows: usize, - /// Exclusive choices the predictions actually decided. + /// Exclusive choices the predictions actually decided pub predicted_choices: usize, - /// Choices [`ProgramOptions::required_choices`] demanded. + /// Choices [`ProgramOptions::required_choices`] demanded pub required_choices: usize, - /// Whether every required choice was covered. When false the answer has gaps, - /// which is not the same as those facts being improbable. + /// Whether every required choice was covered pub confirmed: bool, } #[derive(Debug, Clone, PartialEq)] -/// What one training run produced. +/// What one training run produced pub struct TrainingReport { - /// Model trained. + /// Model trained pub model: String, - /// Relation it serves. + /// Relation it serves pub relation: String, - /// Where the weights were written. + /// Where the weights were written pub artifact: String, - /// SHA-256 of the written artifact, so a later snapshot can be matched to it. + /// SHA-256 of the written artifact, so a later snapshot can be matched to it pub artifact_sha256: String, - /// Seed used, if the run was made reproducible. + /// Seed used, if the run was made reproducible pub seed: Option, - /// Time spent fetching training data. + /// Time spent fetching training data pub data_fetch: Duration, - /// Time spent fitting. + /// Time spent fitting pub learning: Duration, } #[derive(Debug, Clone, Default, PartialEq)] -/// Where a run spent its time, phase by phase. -/// -/// The phases do not necessarily sum to `total`: a phase that did not run is zero, -/// and `total` includes whatever is not attributed to one. +/// Where a run spent its time, phase by phase pub struct ProgramTimings { - /// Checking the program and any supplied snapshot. + /// Checking the program and any supplied snapshot pub validation: Duration, - /// Fitting models. + /// Fitting models pub training: Duration, - /// Selecting the rows to predict over. + /// Selecting the rows to predict over pub prediction_input: Duration, - /// Running the models. + /// Running the models pub neural_inference: Duration, - /// Turning predictions into seeded facts. + /// Turning predictions into seeded facts pub prediction_publication: Duration, - /// Loading data and building indexes. + /// Loading data and building indexes pub data_preparation: Duration, - /// Datalog derivation. + /// Datalog derivation pub logical_inference: Duration, - /// Recovering probabilities from provenance. + /// Recovering probabilities from provenance pub probability_recovery: Duration, - /// Publishing the answers. + /// Publishing the answers pub result_publication: Duration, - /// Writing model artifacts. + /// Writing model artifacts pub artifact_publication: Duration, - /// Wall-clock time for the whole run. + /// Wall-clock time for the whole run pub total: Duration, } -/// The outcome of a run: the resulting dataset, the derived probabilities, and how -/// the run went. -/// -/// Owns its dataset, so the input store is untouched. Probabilities are available -/// without provenance, but recomputing them under different assumptions needs -/// [`ProgramOptions::retain_provenance`] — see [`Self::prepare_answers`]. +/// The outcome of a run: the resulting dataset, the derived probabilities, and how the run went pub struct ProgramResult { - dataset: SparqlDatabase, + dataset: KolibrieDB, hypotheses: HashMap, asserted: HashSet, snapshot: PredictionSnapshot, @@ -1292,11 +1194,7 @@ impl fmt::Debug for ProgramResult { } } -/// Answer formulas held ready so probabilities can be recovered repeatedly without -/// re-deriving. -/// -/// Obtained from [`ProgramResult::prepare_answers`], which needs the run to have -/// retained provenance. +/// Answer formulas held ready so probabilities can be recovered repeatedly without re-deriving #[derive(Debug)] pub struct PreparedAnswers { provenance: SddProvenance, @@ -1304,10 +1202,7 @@ pub struct PreparedAnswers { } impl PreparedAnswers { - /// Probabilities for the prepared answers, in the order they were prepared. - /// - /// An answer with no formula recovers as `0.0`, meaning it was never derived - /// rather than derived and found impossible. + /// Probabilities for the prepared answers, in the order they were prepared pub fn recover(&self) -> Vec { self.tags .iter() @@ -1320,45 +1215,43 @@ impl PreparedAnswers { } impl ProgramResult { - /// The resulting store, including every derived fact. - pub fn dataset(&self) -> &SparqlDatabase { + /// The resulting store, including every derived fact + pub fn dataset(&self) -> &KolibrieDB { &self.dataset } /// Predictions this run used, suitable for reproducing it via - /// [`ProgramOptions::frozen_predictions`]. + /// [`ProgramOptions::frozen_predictions`] pub fn snapshot(&self) -> &PredictionSnapshot { &self.snapshot } - /// Whether every required choice was decided. Check before trusting a zero - /// probability. + /// Whether every required choice was decided pub fn coverage(&self) -> &Coverage { &self.coverage } - /// Reports for any models trained, one each. + /// Reports for any models trained, one each pub fn training(&self) -> &[TrainingReport] { &self.training } - /// Where the run spent its time. + /// Where the run spent its time pub fn timings(&self) -> &ProgramTimings { &self.timings } - /// Whether this ran deterministically or probabilistically. + /// Whether this ran deterministically or probabilistically pub fn mode(&self) -> Option { self.mode } - /// How many facts derivation added. + /// How many facts derivation added pub fn derived_facts(&self) -> usize { self.derived } - /// How many facts carry a probability below 1, as opposed to being asserted - /// outright. + /// How many facts carry a probability below 1, as opposed to being asserted outright pub fn hypothesis_count(&self) -> usize { self.hypotheses.len() } @@ -1372,12 +1265,7 @@ impl ProgramResult { }) } - /// The probability of one fact, or `None` if it was never derived. - /// - /// An asserted fact is `Some(1.0)`. `None` and `Some(0.0)` are different: - /// the first means the fact is absent from the result, the second that it was - /// derived and found impossible. Use [`Self::answer_probability`] to collapse - /// that distinction safely. + /// The probability of one fact, or `None` if it was never derived pub fn fact_probability(&self, subject: &str, predicate: &str, object: &str) -> Option { let triple = self.lookup(subject, predicate, object)?; if let Some(probability) = self.hypotheses.get(&triple) { @@ -1389,12 +1277,7 @@ impl ProgramResult { } } - /// The probability of one fact, treating absence as zero **only when coverage - /// was confirmed**. - /// - /// If a fact is missing and coverage was not confirmed, this errors rather than - /// returning `0.0`: the run did not decide the question, and reporting zero - /// would turn "not determined" into "will not happen". + /// The probability of one fact, treating absence as zero **only when coverage was confirmed** pub fn answer_probability( &self, subject: &str, @@ -1410,7 +1293,7 @@ impl ProgramResult { } } - /// Every derived fact for `predicate`, with its probability. + /// Every derived fact for `predicate`, with its probability pub fn answer_set(&self, predicate: &str) -> Vec<(String, String, f64)> { let Some(predicate_id) = self .dataset @@ -1440,11 +1323,7 @@ impl ProgramResult { answers } - /// Holds the answers' formulas so probabilities can be recovered repeatedly - /// without re-deriving. - /// - /// Requires the run to have set [`ProgramOptions::retain_provenance`]; without - /// it the formulas were discarded and there is nothing to prepare. + /// Holds the answers' formulas so probabilities can be recovered repeatedly without re-deriving pub fn prepare_answers( &self, answers: &[(String, String, String)], @@ -1475,36 +1354,29 @@ impl ProgramResult { } #[derive(Default)] -/// Holds the most recent run so later queries can be answered against it. -/// -/// A program's answers live in its [`ProgramResult`], not in the input store, so -/// something has to own that result between executing and querying. Publishing a -/// new run replaces the previous one. +/// Holds the most recent run so later queries can be answered against it #[derive(Debug)] pub struct PublishedProgramResult { current: Option, } impl PublishedProgramResult { - /// With nothing published yet. + /// With nothing published yet pub fn new() -> Self { Self::default() } - /// The published result, or `None` if nothing has run. + /// The published result, or `None` if nothing has run pub fn current(&self) -> Option<&ProgramResult> { self.current.as_ref() } - /// Mutable access to the published result, which querying needs. + /// Mutable access to the published result, which querying needs pub fn current_mut(&mut self) -> Option<&mut ProgramResult> { self.current.as_mut() } - /// Publishes a run's outcome, replacing any previous one. - /// - /// Takes the `Result` so a failed run propagates its error instead of silently - /// leaving the previous result in place. + /// Publishes a run's outcome, replacing any previous one pub fn publish( &mut self, outcome: Result, @@ -1514,10 +1386,7 @@ impl PublishedProgramResult { } } -/// Runs a SELECT against a published result rather than against a store. -/// -/// Needed because a program's derived facts and their probabilities live in the -/// [`ProgramResult`], so an ordinary query over the input store would not see them. +/// Runs a SELECT against a published result rather than against a store pub fn query_program_result( result: &mut ProgramResult, select: &str, @@ -1542,10 +1411,7 @@ pub fn query_program_result( .map_err(|error| ProgramError::Execution(error.to_string())) } -/// Reads a probability from a literal, accepting a bare number or an -/// `xsd:double`-typed form. -/// -/// `None` when the term is not a number or falls outside `[0, 1]`. +/// Reads a probability from a literal, accepting a bare number or an `xsd:double`-typed form pub fn parse_probability_literal(term: &str) -> Option { let lexical = term.strip_prefix('"')?; let (value, datatype) = lexical.split_once('"')?; @@ -1572,14 +1438,9 @@ impl Deadline { } } -/// Runs a compiled program against a store, returning the result. -/// -/// `asserted_input` is read, not modified: the derived facts land in the returned -/// [`ProgramResult`]'s own dataset. Implicit neural materialisation is disabled for -/// the duration and restored afterwards, so the program's own declarations are the -/// only thing that invokes a model. +/// Runs a compiled program against a store, returning the result pub fn execute_program( - asserted_input: &mut SparqlDatabase, + asserted_input: &mut KolibrieDB, program: &CompiledProgram, options: &ProgramOptions, ) -> Result { @@ -1590,7 +1451,7 @@ pub fn execute_program( } fn run_program( - input: &mut SparqlDatabase, + input: &mut KolibrieDB, program: &CompiledProgram, options: &ProgramOptions, ) -> Result { @@ -1765,7 +1626,7 @@ fn run_program( deadline.check()?; let phase = Instant::now(); - let mut dataset = SparqlDatabase::new(); + let mut dataset = KolibrieDB::new(); dataset.adopt_terms(dictionary.clone()); dataset.quoted_triple_store = Arc::clone("ed); dataset.prefixes = program.prefixes.clone(); @@ -1820,7 +1681,7 @@ fn run_program( } fn copy_asserted_input( - input: &SparqlDatabase, + input: &KolibrieDB, program: &CompiledProgram, snapshot: &PredictionSnapshot, input_seeds: &HashMap, @@ -1904,7 +1765,7 @@ struct NeuralRun; #[cfg(not(feature = "ml"))] fn plan_neural( - _input: &SparqlDatabase, + _input: &KolibrieDB, program: &CompiledProgram, _options: &ProgramOptions, ) -> Result { @@ -1921,7 +1782,7 @@ fn plan_neural( #[cfg(not(feature = "ml"))] #[allow(clippy::too_many_arguments)] fn run_neural( - _input: &mut SparqlDatabase, + _input: &mut KolibrieDB, _program: &CompiledProgram, _options: &ProgramOptions, _plan: NeuralPlan, @@ -1935,7 +1796,7 @@ fn run_neural( #[cfg(not(feature = "ml"))] fn publish_neural( - _input: &mut SparqlDatabase, + _input: &mut KolibrieDB, _program: &CompiledProgram, _run: NeuralRun, ) -> Result, ProgramError> { @@ -2007,7 +1868,7 @@ fn exclusive_labels( #[cfg(feature = "ml")] fn plan_neural( - input: &SparqlDatabase, + input: &KolibrieDB, program: &CompiledProgram, options: &ProgramOptions, ) -> Result { @@ -2204,7 +2065,7 @@ fn plan_neural( #[cfg(feature = "ml")] #[allow(clippy::too_many_arguments)] fn run_neural( - input: &mut SparqlDatabase, + input: &mut KolibrieDB, program: &CompiledProgram, options: &ProgramOptions, plan: NeuralPlan, @@ -2221,7 +2082,7 @@ fn run_neural( let mut in_memory: HashMap = HashMap::new(); for train in &plan.trains { deadline.check()?; - let mut view = SparqlDatabase::new(); + let mut view = KolibrieDB::new(); view.model_decls = plan.models.clone(); view.neural_relation_decls = plan.relations.clone(); let mut clause = crate::ml::neural_relations::lower_train_decl_to_owned(&view, &train.decl) @@ -2394,7 +2255,7 @@ fn run_neural( #[cfg(feature = "ml")] fn publish_neural( - input: &mut SparqlDatabase, + input: &mut KolibrieDB, program: &CompiledProgram, run: NeuralRun, ) -> Result, ProgramError> { diff --git a/kolibrie/src/query_builder.rs b/kolibrie/src/query_builder.rs index 35551c38..f90d025d 100644 --- a/kolibrie/src/query_builder.rs +++ b/kolibrie/src/query_builder.rs @@ -8,35 +8,30 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use crate::database::KolibrieDB; use crate::rsp::r2s::{Relation2StreamOperator, StreamOperator}; use crate::rsp::s2r::{ContentContainer, ReportStrategy, Tick, WindowTriple}; use crate::rsp::window_runner::{WindowRunner, WindowSpec}; -use crate::sparql_database::SparqlDatabase; use shared::triple::Triple; use std::collections::{BTreeMap, BTreeSet, HashSet}; use std::fmt; use std::sync::{Arc, Mutex}; -/// Builds and runs a query against a store without writing SPARQL text. -/// -/// Borrows the database, so the builder cannot outlive it. Filters and joins -/// accumulate and apply when the query is run; building one has no effect on its -/// own. +/// Builds and runs a query against a store without writing SPARQL text pub struct QueryBuilder<'a> { - db: &'a SparqlDatabase, + db: &'a KolibrieDB, subject_filter: Option, predicate_filter: Option, object_filter: Option, custom_filter: Option bool + 'a>>, join_conditions: Vec, - join_db: Option<&'a SparqlDatabase>, + join_db: Option<&'a KolibrieDB>, distinct_results: bool, sort_key: Option String + 'a>>, sort_direction: SortDirection, limit: Option, offset: Option, - // RSP Integration fields window_spec: Option, stream_operator: Option, r2s_operator: Option>, @@ -46,9 +41,6 @@ pub struct QueryBuilder<'a> { is_streaming: bool, } -// Cannot derive: the custom filter and sort key are `dyn Fn`. Summarised besides, -// since `db` is a whole store. Whether a closure is present is the part that matters, -// because `Clone` drops both and a cloned builder therefore behaves differently. impl fmt::Debug for QueryBuilder<'_> { fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { f.debug_struct("QueryBuilder") @@ -75,22 +67,19 @@ impl<'a> Clone for QueryBuilder<'a> { subject_filter: self.subject_filter.clone(), predicate_filter: self.predicate_filter.clone(), object_filter: self.object_filter.clone(), - // Custom closures cannot be cloned, so they are dropped custom_filter: None, join_conditions: self.join_conditions.clone(), join_db: self.join_db, distinct_results: self.distinct_results, - // Likewise drop any sort_key sort_key: None, sort_direction: self.sort_direction, limit: self.limit, offset: self.offset, - // RSP fields - reset for cloned instance window_spec: self.window_spec.clone(), stream_operator: self.stream_operator.clone(), - r2s_operator: None, // Will be recreated if needed - runner: None, // Will be recreated if needed + r2s_operator: None, + runner: None, stream_results: Arc::new(Mutex::new(Vec::new())), current_timestamp: 0, is_streaming: false, @@ -100,23 +89,18 @@ impl<'a> Clone for QueryBuilder<'a> { /// Defines different ways to filter triple components pub enum TripleFilter { - /// Matches the decoded term exactly. + /// Matches the decoded term exactly Exact(String), - /// Matches when the decoded term contains this substring. + /// Matches when the decoded term contains this substring Contains(String), - /// Matches when the decoded term starts with this prefix. + /// Matches when the decoded term starts with this prefix StartsWith(String), - /// Matches when the decoded term ends with this suffix. + /// Matches when the decoded term ends with this suffix EndsWith(String), - /// Matches when the predicate returns `true`. - /// - /// Note that [`Clone`] **panics** on this variant: a closure cannot be cloned, - /// and the impl chose to panic rather than silently drop the filter. Avoid - /// cloning a builder that carries one. + /// Matches when the predicate returns `true` Custom(Box bool>), } -// Manual implementation of Debug for TripleFilter impl fmt::Debug for TripleFilter { fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { match self { @@ -129,7 +113,6 @@ impl fmt::Debug for TripleFilter { } } -// Manual implementation of Clone for TripleFilter impl Clone for TripleFilter { fn clone(&self) -> Self { match self { @@ -137,7 +120,6 @@ impl Clone for TripleFilter { Self::Contains(s) => Self::Contains(s.clone()), Self::StartsWith(s) => Self::StartsWith(s.clone()), Self::EndsWith(s) => Self::EndsWith(s.clone()), - // We can't clone function pointers, so this is a limitation Self::Custom(_) => panic!("Cannot clone TripleFilter::Custom"), } } @@ -145,18 +127,16 @@ impl Clone for TripleFilter { /// Defines conditions for joining two SPARQL databases pub enum JoinCondition { - /// Join where both triples share a subject. + /// Join where both triples share a subject OnSubject, - /// Join where both share a predicate. + /// Join where both share a predicate OnPredicate, - /// Join where both share an object. + /// Join where both share an object OnObject, - /// Join where the predicate accepts the pair. Compares encoded ids, so a - /// closure wanting term text must decode them first. + /// Join where the predicate accepts the pair Custom(Box bool>), } -// Manual implementation of Debug for JoinCondition impl fmt::Debug for JoinCondition { fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { match self { @@ -184,32 +164,27 @@ impl Clone for JoinCondition { /// Defines sort direction for query results #[derive(Debug, Clone, Copy)] pub enum SortDirection { - /// Smallest first. + /// Smallest first Ascending, - /// Largest first. + /// Largest first Descending, } -/// Starting a [`QueryBuilder`] from a store. -/// -/// An extension trait rather than a method on [`SparqlDatabase`], because the store -/// is a layer below this one and should not name the builder. Implemented for -/// `SparqlDatabase` and re-exported from [`crate::prelude`], so `db.query()` keeps -/// working for anyone who imports either. +/// Starting a [`QueryBuilder`] from a store pub trait Queryable { - /// Starts a programmatic query against this store. + /// Starts a programmatic query against this store fn query(&self) -> QueryBuilder<'_>; } -impl Queryable for SparqlDatabase { +impl Queryable for KolibrieDB { fn query(&self) -> QueryBuilder<'_> { QueryBuilder::new(self) } } impl<'a> QueryBuilder<'a> { - /// Creates a new QueryBuilder for the given SparqlDatabase - pub fn new(db: &'a SparqlDatabase) -> Self { + /// Creates a new QueryBuilder for the given KolibrieDB + pub fn new(db: &'a KolibrieDB) -> Self { Self { db, subject_filter: None, @@ -224,7 +199,6 @@ impl<'a> QueryBuilder<'a> { limit: None, offset: None, - // RSP fields window_spec: None, stream_operator: None, r2s_operator: None, @@ -316,8 +290,8 @@ impl<'a> QueryBuilder<'a> { self } - /// Join with another SparqlDatabase - pub fn join(mut self, other: &'a SparqlDatabase) -> Self { + /// Join with another KolibrieDB + pub fn join(mut self, other: &'a KolibrieDB) -> Self { self.join_db = Some(other); self } @@ -392,7 +366,6 @@ impl<'a> QueryBuilder<'a> { /// Get the raw triple results pub fn get_triples(self) -> BTreeSet { if self.is_streaming { - // For streaming queries, return empty set as results come through stream BTreeSet::new() } else { self.apply_filters().into_iter().collect() @@ -427,10 +400,8 @@ impl<'a> QueryBuilder<'a> { /// Get decoded triples in legacy set order pub fn get_decoded_triples(self) -> Vec<(String, String, String)> { - // Store a reference to the database let db = self.db; - // Now call get_triples which consumes self let triples = self.get_triples(); let mut results = Vec::with_capacity(triples.len()); @@ -448,11 +419,9 @@ impl<'a> QueryBuilder<'a> { /// Get only the subjects from the results pub fn get_subjects(self) -> Vec { - // Store a reference to the database let db = self.db; let distinct = self.distinct_results; - // Now call get_triples which consumes self let triples = self.get_triples(); let mut results = Vec::with_capacity(triples.len()); @@ -474,11 +443,9 @@ impl<'a> QueryBuilder<'a> { /// Get only the predicates from the results pub fn get_predicates(self) -> Vec { - // Store a reference to the database let db = self.db; let distinct = self.distinct_results; - // Now call get_triples which consumes self let triples = self.get_triples(); let mut results = Vec::with_capacity(triples.len()); @@ -500,11 +467,9 @@ impl<'a> QueryBuilder<'a> { /// Get only the objects from the results pub fn get_objects(self) -> Vec { - // Store a reference to the database let db = self.db; let distinct = self.distinct_results; - // Now call get_triples which consumes self let triples = self.get_triples(); let mut results = Vec::with_capacity(triples.len()); @@ -566,17 +531,14 @@ impl<'a> QueryBuilder<'a> { results } - // Applies all the configured filters and returns the matching triples fn apply_filters(self) -> Vec { let mut results = BTreeSet::new(); let dict = self.db.dictionary.read(); - // Apply basic filters let default_triples = self.db.query_default_triples(None, None, None); for triple in &default_triples { let mut matches = true; - // Check subject filter if let Some(filter) = &self.subject_filter { if let Some(subject) = dict.decode(triple.subject) { matches &= Self::apply_filter(filter, subject); @@ -585,7 +547,6 @@ impl<'a> QueryBuilder<'a> { } } - // Check predicate filter if matches { if let Some(filter) = &self.predicate_filter { if let Some(predicate) = dict.decode(triple.predicate) { @@ -596,7 +557,6 @@ impl<'a> QueryBuilder<'a> { } } - // Check object filter if matches { if let Some(filter) = &self.object_filter { if let Some(object) = dict.decode(triple.object) { @@ -607,18 +567,15 @@ impl<'a> QueryBuilder<'a> { } } - // Apply custom filter if specified if matches && self.custom_filter.is_some() { matches &= self.custom_filter.as_ref().unwrap()(triple); } - // Add matching triple if matches { results.insert(triple.clone()); } } - // Apply join if specified if let Some(other_db) = self.join_db { if !self.join_conditions.is_empty() { results = self.apply_join(results, other_db); @@ -637,7 +594,6 @@ impl<'a> QueryBuilder<'a> { Self::slice_ordered(ordered, self.offset, self.limit) } - // Helper method to apply a filter to a string value fn apply_filter(filter: &TripleFilter, value: &str) -> bool { match filter { TripleFilter::Exact(s) => value == s, @@ -648,11 +604,10 @@ impl<'a> QueryBuilder<'a> { } } - // Helper method to apply join conditions fn apply_join( &self, left_triples: BTreeSet, - right_db: &SparqlDatabase, + right_db: &KolibrieDB, ) -> BTreeSet { let mut joined_triples = BTreeSet::new(); let right_triples = right_db.query_default_triples(None, None, None); @@ -849,31 +804,25 @@ impl<'a> QueryBuilder<'a> { self.is_streaming } - // Helper method to execute query on window content fn execute_query_on_window_content( &self, content: &ContentContainer, ) -> Vec { let mut matching_triples = Vec::new(); - // Convert WindowTriple to Triple and apply filters for window_triple in content.iter() { - // Check if this triple matches our filters let mut matches = true; - // Apply subject filter if let Some(filter) = &self.subject_filter { matches &= Self::apply_filter(filter, &window_triple.subject); } - // Apply predicate filter if matches { if let Some(filter) = &self.predicate_filter { matches &= Self::apply_filter(filter, &window_triple.predicate); } } - // Apply object filter if matches { if let Some(filter) = &self.object_filter { matches &= Self::apply_filter(filter, &window_triple.object); @@ -881,14 +830,12 @@ impl<'a> QueryBuilder<'a> { } if matches { - // Convert to Triple (simplified encoding) let triple = Triple { subject: self.encode_string(&window_triple.subject), predicate: self.encode_string(&window_triple.predicate), object: self.encode_string(&window_triple.object), }; - // Apply custom filter if present if let Some(custom_filter) = &self.custom_filter { if custom_filter(&triple) { matching_triples.push(triple); @@ -899,25 +846,19 @@ impl<'a> QueryBuilder<'a> { } } - // Apply sorting, limiting, etc self.post_process_results(matching_triples) } - // Helper method for string encoding (simplified) fn encode_string(&self, s: &str) -> u32 { - // This is a simplified encoding - in practice use your dictionary s.len() as u32 } - // Helper method to post-process results fn post_process_results(&self, mut results: Vec) -> Vec { - // Apply distinct if requested if self.distinct_results { let mut seen = HashSet::new(); results.retain(|triple| seen.insert(triple.clone())); } - // Apply sorting if specified if let Some(key_fn) = &self.sort_key { match self.sort_direction { SortDirection::Ascending => results.sort_by_key(|t| key_fn(t)), @@ -925,7 +866,6 @@ impl<'a> QueryBuilder<'a> { } } - // Apply offset and limit Self::slice_ordered(results, self.offset, self.limit) } } diff --git a/kolibrie/src/query_error.rs b/kolibrie/src/query_error.rs index 91e5f97b..2a2574b9 100644 --- a/kolibrie/src/query_error.rs +++ b/kolibrie/src/query_error.rs @@ -8,48 +8,23 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`QueryError`]: why a request could not be answered. -//! -//! Its own module rather than part of [`execute_query`](crate::execute_query), -//! because [`ml_runtime`](crate::ml::runtime) names it in a trait that -//! `execute_query` then calls through. An error type that several layers return has -//! to sit below all of them. -//! -//! `kolibrie::execute_query::QueryError` still resolves, as a re-export. +//! [`QueryError`]: why a request could not be answered use std::fmt; -/// Why a request could not be answered. -/// -/// # Rendering -/// -/// `Display` prefixes a stable `QUERY_*` code, as -/// [`ProgramError`](crate::program::ProgramError) does. A [`Parse`](Self::Parse) -/// message is **multi-line**: it carries the annotated snippet the parser produced, -/// pointing at the offending token. It contains no terminal escapes, so it is safe to -/// log or serialise. -/// +/// Why a request could not be answered pub enum QueryError { - /// The request did not parse as SPARQL or as one of the Kolibrie extensions. - /// - /// Carries the parser's message, which names a position in the input. + /// The request did not parse as SPARQL or as one of the Kolibrie extensions Parse(String), - /// It parsed, but is not a form this entry point accepts — an Update where a - /// query was required, or more than one rule in a single request. + /// It parsed, but is not a form this entry point accepts — an Update where a query was + /// required, or more than one rule in a single request Unsupported(String), - /// The ML policy refused it. The request may be well-formed; the caller simply - /// lacks the authority. See [`ml_policy`](crate::ml_policy). + /// The ML policy refused it Ml(crate::ml_policy::MlError), - /// Parsing and policy both passed, and evaluation failed. Covers an unresolvable - /// dataset clause, an unsupported update template, and failure inside the plan. + /// Parsing and policy both passed, and evaluation failed Execution(String), } -// Debug delegates to Display rather than being derived, because `unwrap` and -// `expect` print the Debug form: a derived one escapes the newlines in a parse -// snippet, turning the annotated, multi-line message that points at the offending -// token into an unreadable single line of `\n`. The `QUERY_*` prefix carries the -// variant name, so nothing is lost by this. impl fmt::Debug for QueryError { fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { write!(f, "{self}") diff --git a/kolibrie/src/rsp/mod.rs b/kolibrie/src/rsp/mod.rs index 91c5bc96..48cd7f02 100644 --- a/kolibrie/src/rsp/mod.rs +++ b/kolibrie/src/rsp/mod.rs @@ -8,41 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! RDF stream processing: the three-stage RSP-QL pipeline. -//! -//! A continuous query cannot evaluate over an unbounded stream, so RSP-QL cuts the -//! stream into finite relations, evaluates over those, and turns the answers back -//! into a stream: -//! -//! ```text -//! stream ──S2R──▶ relation ──R2R──▶ relation ──R2S──▶ stream -//! (window) (query) (which solutions -//! to publish) -//! ``` -//! -//! | stage | module | job | -//! |---|---|---| -//! | S2R | [`s2r`] | windows over a stream; [`s2r::CSPARQLWindow`] is the implementation | -//! | R2R | [`r2r`], [`simple_r2r`] | evaluate the query over a window's content | -//! | R2S | [`r2s`] | choose which solutions reach the output stream | -//! -//! [`window_runner`] drives one window's S2R→R2R cycle; -//! [`builder`](crate::rsp_engine::builder) assembles an -//! engine from a parsed query. [`RSPEngine`](crate::rsp_engine::RSPEngine) owns the -//! whole thing. -//! -//! # Window bounds are half-open -//! -//! A `RANGE n` window holds `n` timestamps, not `n + 1`. This is easy to get wrong -//! in both directions and has already invalidated test expectations once. -//! -//! # More than one window changes the timing -//! -//! With a single window a result is ready as soon as that window fires. With -//! several, the engine has to decide how long to wait for the others before -//! emitting — see [`SyncPolicy`](shared::query::SyncPolicy). That choice trades -//! latency against whether one result may mix data from different cycles, so it is a -//! correctness question, not a tuning knob. +//! RDF stream processing: the three-stage RSP-QL pipeline pub mod r2r; pub mod r2s; diff --git a/kolibrie/src/rsp/r2r.rs b/kolibrie/src/rsp/r2r.rs index 9aa17645..9a0744a0 100644 --- a/kolibrie/src/rsp/r2r.rs +++ b/kolibrie/src/rsp/r2r.rs @@ -8,47 +8,34 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! R2R: evaluating a query over a window's content. -//! -//! The relation-to-relation stage. An implementation holds whatever state a query -//! needs between firings — loaded rules, materialised facts — and is handed each -//! window's content as it arrives. +//! R2R: evaluating a query over a window's content use crate::streamertail_optimizer::PhysicalOperator; use std::any::Any; -/// Helper trait to allow downcasting mutable trait objects to their concrete types. -/// Implementations should return a mutable Any reference to enable `downcast_mut`. +/// Helper trait to allow downcasting mutable trait objects to their concrete types pub trait AsAnyMut { - /// A mutable `Any` view of `self`, so a caller holding the trait object can - /// recover the concrete type with `downcast_mut`. + /// A mutable `Any` view of `self`, so a caller holding the trait object can recover the + /// concrete type with `downcast_mut` fn as_any_mut(&mut self) -> &mut dyn Any; } -/// Evaluates a query over a window's content. -/// -/// Requires `Send` because a multi-threaded engine moves each window's operator to -/// its own thread, and [`AsAnyMut`] because a caller needing implementation-specific -/// state has to downcast the trait object. -/// -/// `execute_query` is deliberately loose about its output type: downcast the result -/// to whatever the implementation produces, typically with -/// `(res as &dyn Any).downcast_ref::>()`. +/// Evaluates a query over a window's content pub trait R2ROperator: Send + AsAnyMut { - /// Loads background data in the named syntax. + /// Loads background data in the named syntax fn load_triples(&mut self, data: &str, syntax: String) -> Result<(), String>; - /// Loads reasoning rules applied to each window's content. + /// Loads reasoning rules applied to each window's content fn load_rules(&mut self, data: &str) -> Result<(), &'static str>; - /// Adds one item to the operator's working set. + /// Adds one item to the operator's working set fn add(&mut self, data: I); - /// Removes one item, for when a window slides past it. + /// Removes one item, for when a window slides past it fn remove(&mut self, data: &I); - /// Derives everything the rules entail from the current working set. + /// Derives everything the rules entail from the current working set fn materialize(&mut self) -> Vec; - /// Evaluates a physical plan, returning the solutions. + /// Evaluates a physical plan, returning the solutions fn execute_query(&mut self, op: &PhysicalOperator) -> Vec; - /// Parses incoming stream text into items this operator accepts. + /// Parses incoming stream text into items this operator accepts fn parse_data(&mut self, data: &str) -> Vec; } diff --git a/kolibrie/src/rsp/r2s.rs b/kolibrie/src/rsp/r2s.rs index 6bbf7150..7dd8dbca 100644 --- a/kolibrie/src/rsp/r2s.rs +++ b/kolibrie/src/rsp/r2s.rs @@ -8,23 +8,19 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! R2S: choosing which solutions reach the output stream. -//! -//! The relation-to-stream stage. Evaluating a window yields the full solution set -//! every time it fires, which is rarely what a consumer wants: most want to hear -//! about changes. The operator compares each firing against the previous one. +//! R2S: choosing which solutions reach the output stream use std::collections::HashSet; use std::hash::Hash; #[derive(Clone, Debug)] -/// Which solutions of each firing reach the output stream. +/// Which solutions of each firing reach the output stream pub enum StreamOperator { - /// Every solution, every firing. The default. + /// Every solution, every firing RSTREAM, - /// Only solutions absent from the previous firing — insertions. + /// Only solutions absent from the previous firing — insertions ISTREAM, - /// Only solutions present previously and now gone — deletions. + /// Only solutions present previously and now gone — deletions DSTREAM, } @@ -33,10 +29,7 @@ impl Default for StreamOperator { StreamOperator::RSTREAM } } -/// Applies a [`StreamOperator`] by comparing each firing against the last. -/// -/// Stateful: `ISTREAM` and `DSTREAM` are defined relative to the previous firing, -/// so one instance must see every firing of its query in order. +/// Applies a [`StreamOperator`] by comparing each firing against the last #[derive(Debug)] pub struct Relation2StreamOperator { stream_operator: StreamOperator, @@ -47,8 +40,8 @@ impl Relation2StreamOperator where O: Clone + Hash + Eq, { - /// An operator with no previous firing recorded, so the first `ISTREAM` - /// evaluation reports everything as new. + /// An operator with no previous firing recorded, so the first `ISTREAM` evaluation reports + /// everything as new pub fn new(stream_operator: StreamOperator, _start_time: usize) -> Relation2StreamOperator { Relation2StreamOperator { stream_operator, @@ -56,7 +49,7 @@ where } } - /// The solutions to publish for this firing, updating the remembered state. + /// The solutions to publish for this firing, updating the remembered state pub fn eval(&mut self, new_response: Vec, _ts: usize) -> Vec { match self.stream_operator { StreamOperator::RSTREAM => new_response, diff --git a/kolibrie/src/rsp/s2r.rs b/kolibrie/src/rsp/s2r.rs index b232f594..d84e40ca 100644 --- a/kolibrie/src/rsp/s2r.rs +++ b/kolibrie/src/rsp/s2r.rs @@ -8,20 +8,10 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! S2R: cutting a stream into finite relations. -//! -//! The stream-to-relation stage, implemented by [`CSPARQLWindow`]. A window -//! accumulates arriving triples and, when it fires, hands its content to the R2R -//! stage. -//! -//! Bounds are half-open: a `RANGE n` window holds `n` timestamps, not `n + 1`. -//! -//! [`ReportStrategy`] decides *when* a window fires and [`Tick`] decides what makes -//! time advance — arriving data or the wall clock. Those two together, not the width -//! alone, determine what a consumer sees. +//! S2R: cutting a stream into finite relations #[cfg(not(test))] -use log::{debug, warn}; // Use log crate when building application +use log::{debug, warn}; use shared::hybrid::{EventKey, SeedId}; use std::collections::hash_map::{IntoKeys, Keys}; use std::collections::HashMap; @@ -30,7 +20,6 @@ use std::fmt::Debug; use std::hash::Hash; use std::sync::mpsc::Receiver; use std::sync::mpsc::{channel, Sender}; -// Only the `#[cfg(test)]` Consumer harness below needs these. #[cfg(test)] use std::sync::{Arc, Mutex}; #[cfg(test)] @@ -39,20 +28,16 @@ use std::{f64, mem}; #[cfg(test)] use std::{println as warn, println as debug}; -/// When a window reports its content downstream. -/// -/// Several strategies combine with **AND**, not OR: a window with both -/// `NonEmptyContent` and `OnWindowClose` reports only when both hold. Adding a -/// strategy therefore makes a window report less often, never more. +/// When a window reports its content downstream #[derive(Clone, Debug)] pub enum ReportStrategy { - /// Only when the window holds something. + /// Only when the window holds something NonEmptyContent, - /// Only when the content differs from the last report. + /// Only when the content differs from the last report OnContentChange, - /// Only once the window's close time has passed. The default. + /// Only once the window's close time has passed OnWindowClose, - /// Every `n` time units, regardless of content. + /// Every `n` time units, regardless of content Periodic(usize), } impl Default for ReportStrategy { @@ -60,18 +45,14 @@ impl Default for ReportStrategy { ReportStrategy::OnWindowClose } } -/// What makes the window's clock advance. -/// -/// Together with [`ReportStrategy`] this decides what a consumer sees; the width -/// alone does not. A time-driven window can fire with nothing in it, while a -/// tuple-driven one cannot advance at all until data arrives. +/// What makes the window's clock advance #[derive(Clone, Debug)] pub enum Tick { - /// Time advances with the clock. The default. + /// Time advances with the clock TimeDriven, - /// Time advances only as tuples arrive. + /// Time advances only as tuples arrive TupleDriven, - /// Time advances per batch of arrivals. + /// Time advances per batch of arrivals BatchDriven, } impl Default for Tick { @@ -80,7 +61,7 @@ impl Default for Tick { } } -/// The set of [`ReportStrategy`] conditions a window must satisfy to fire. +/// The set of [`ReportStrategy`] conditions a window must satisfy to fire #[derive(Debug)] pub struct Report where @@ -94,23 +75,19 @@ impl Report where I: Eq + PartialEq + Clone + Debug + Hash + Send, { - /// A report with no strategies, which reports unconditionally — the `all` - /// over an empty set is true. + /// A report with no strategies, which reports unconditionally — the `all` over an empty set is + /// true pub fn new() -> Report { Report { strategies: Vec::new(), last_change: ContentContainer::new(), } } - /// Adds a condition. Conditions combine with AND, so each one added can only - /// make the window report less often. + /// Adds a condition pub fn add(&mut self, strategy: ReportStrategy) { self.strategies.push(strategy); } - /// Whether every condition holds for this window and content. - /// - /// Not a pure predicate: `OnContentChange` records the content it saw, so - /// calling this twice with the same content gives different answers. + /// Whether every condition holds for this window and content pub fn report(&mut self, window: &Window, content: &ContentContainer, ts: usize) -> bool { self.strategies.iter().all(|strategy| match strategy { ReportStrategy::NonEmptyContent => content.len() > 0, @@ -125,36 +102,25 @@ where } } -/// One window's extent, half-open as `[open, close)`. -/// -/// Half-open is why a `RANGE n` window holds `n` timestamps rather than `n + 1`. +/// One window's extent, half-open as `[open, close)` #[derive(Eq, Hash, PartialEq, Debug, Clone)] pub struct Window { open: usize, close: usize, } -/// One arrival of an uncertain item, carrying the seed identity it was registered -/// under. -/// -/// The seed id travels with the occurrence because the same arrival may land in -/// several overlapping windows, and it must stay one seed — counting the same -/// uncertainty twice would inflate every probability derived from it. +/// One arrival of an uncertain item, carrying the seed identity it was registered under #[derive(Eq, PartialEq, Clone, Debug)] pub struct ProbabilisticOccurrence { - /// The item itself. + /// The item itself pub item: I, - /// Which arrival this was. + /// Which arrival this was pub event: EventKey, - /// The seed registered for that arrival. + /// The seed registered for that arrival pub seed_id: SeedId, } -/// A window's content: the items in it and when each was last seen. -/// -/// Deterministic and probabilistic items are tracked separately, because a -/// probabilistic one needs its seed identity preserved and a deterministic one does -/// not. An item re-added at a later timestamp keeps the later one. +/// A window's content: the items in it and when each was last seen #[derive(Eq, PartialEq, Clone, Debug)] pub struct ContentContainer where @@ -189,8 +155,7 @@ where origin: origin.to_string(), } } - /// Number of distinct items, counting an item re-added at several timestamps - /// once. + /// Number of distinct items, counting an item re-added at several timestamps once pub fn len(&self) -> usize { self.elements.len() } @@ -215,39 +180,35 @@ where self.probabilistic_occurrences.push(occurrence); } } - /// Timestamp of the most recent addition. + /// Timestamp of the most recent addition pub fn get_last_timestamp_changed(&self) -> usize { self.last_timestamp_changed } - /// The items, without their timestamps. + /// The items, without their timestamps pub fn iter(&self) -> Keys<'_, I, usize> { self.elements.keys() } - /// The items paired with the last timestamp each was seen at. + /// The items paired with the last timestamp each was seen at pub fn iter_with_timestamps(&self) -> impl Iterator { self.elements.iter().map(|(item, ts)| (item, *ts)) } - /// The uncertain arrivals in this window, with their seed identities. + /// The uncertain arrivals in this window, with their seed identities pub fn probabilistic_occurrences(&self) -> &[ProbabilisticOccurrence] { &self.probabilistic_occurrences } - /// Whether `item` arrived as a certain fact rather than a probabilistic one. + /// Whether `item` arrived as a certain fact rather than a probabilistic one pub fn is_deterministic(&self, item: &I) -> bool { self.deterministic_items.contains(item) } - /// Consumes the container, yielding its items. + /// Consumes the container, yielding its items pub fn into_iter(mut self) -> IntoKeys { let map = mem::take(&mut self.elements); map.into_keys() } } -/// A sliding window over a stream: the S2R implementation. -/// -/// Holds every currently-active window, so one arrival can be added to several at -/// once when they overlap. Reports through either a channel or a callback, whichever -/// the caller installed. +/// A sliding window over a stream: the S2R implementation pub struct CSPARQLWindow where I: Eq + PartialEq + Clone + Debug + Hash + Send, @@ -260,13 +221,10 @@ where tick: Tick, app_time: usize, consumer: Option>>, - // Make callbacks Send so they can be safely transferred to worker threads call_back: Option) -> () + Send + 'static>>, uri: String, } -// Cannot derive: `call_back` is a `dyn FnMut`. Summarised besides, since the active -// windows hold the buffered stream content. impl Debug for CSPARQLWindow where I: Eq + PartialEq + Clone + Debug + Hash + Send, @@ -290,10 +248,7 @@ impl CSPARQLWindow where I: Eq + PartialEq + Clone + Debug + Hash + Send, { - /// A window of `width`, advancing by `slide`. - /// - /// `slide == width` makes it tumbling; a smaller slide makes it overlap, so an - /// arrival will appear in more than one window. + /// A window of `width`, advancing by `slide` pub fn new( width: usize, slide: usize, @@ -314,8 +269,7 @@ where uri, } } - /// Adds a certain item at `ts`, to every active window whose extent contains - /// it. + /// Adds a certain item at `ts`, to every active window whose extent contains it pub fn add_to_window(&mut self, event_item: I, ts: usize) { let event_time = ts; self.scope(&event_time); @@ -356,15 +310,12 @@ where Tick::TimeDriven => { if ts > self.app_time { self.app_time = ts; - // notify consumers debug!("Window triggers! {:?}", max_window); - // multithreaded consumer using channel if let Some(sender) = &self.consumer { if let Err(e) = sender.send(max_window.1.clone()) { warn!("Failed to send window content to consumer: {:?}", e); } } - // single threaded consumer using callback if let Some(call_back) = &mut self.call_back { (call_back)(max_window.1.clone()); } @@ -376,8 +327,8 @@ where self.active_windows = test; } - /// Adds an uncertain arrival, preserving its seed identity across every - /// overlapping window it lands in. + /// Adds an uncertain arrival, preserving its seed identity across every overlapping window it + /// lands in pub fn add_probabilistic_to_window(&mut self, occurrence: ProbabilisticOccurrence) { let event_time = occurrence.event.event_time; self.scope(&event_time); @@ -414,19 +365,15 @@ where self.active_windows = updated; } fn scope(&mut self, event_time: &usize) { - // long c_sup = (long) Math.ceil(((double) Math.abs(t_e - t0) / (double) slide)) * slide; let _temp = (*event_time as f64 - self.t_0 as f64).abs(); let _temp = ((*event_time as f64 - self.t_0 as f64).abs() / (self.slide as f64)).ceil(); let c_sup = ((*event_time as f64 - self.t_0 as f64).abs() / (self.slide as f64)).ceil() * self.slide as f64; - // long o_i = c_sup - width; let mut o_i = c_sup - self.width as f64; debug!( "Calculating the Windows to Open. First one opens at [{:?}] and closes at [{:?}]", o_i, c_sup ); - // log.debug("Calculating the Windows to Open. First one opens at [" + o_i + "] and closes at [" + c_sup + "]"); - // loop { debug!( "Computing Window [{:?},{:?}) if absent", @@ -447,29 +394,21 @@ where } } } - /// Returns a channel receiving this window's reports. - /// - /// Replaces any previously registered channel. [`Self::stop`] drops it. + /// Returns a channel receiving this window's reports pub fn register(&mut self) -> Receiver> { let (send, recv) = channel::>(); self.consumer.replace(send); recv } - /// Installs a callback invoked on each report, as an alternative to - /// [`Self::register`]. Both may be active at once. + /// Installs a callback invoked on each report, as an alternative to [`Self::register`] pub fn register_callback( &mut self, function: Box) -> () + Send + 'static>, ) { self.call_back.replace(function); } - /// Reports the merged content of every active window immediately, ignoring the - /// report strategies. - /// - /// This is how a single-threaded engine gets results out without waiting for - /// windows to close. Merging means an overlapping arrival appears once, but the - /// result mixes windows that would normally have fired separately. - /// Emits nothing when the merged content is empty. + /// Reports the merged content of every active window immediately, ignoring the report + /// strategies pub fn flush(&mut self) { let mut merged = ContentContainer::new_with_origin(&self.uri); for content in self.active_windows.values() { @@ -492,17 +431,12 @@ where } } } - /// Drops the output channel, so the receiver sees the stream end. - /// - /// Does not stop a registered callback. + /// Drops the output channel, so the receiver sees the stream end pub fn stop(&mut self) { self.consumer.take(); } } -// Test-only: drains a window's output channel on a background thread so a test can -// assert on what the window emitted. Marked `#[cfg(test)]` rather than carrying an -// `#[allow(dead_code)]`, which had hidden the fact that nothing in production uses it. #[cfg(test)] struct ConsumerInner where @@ -551,12 +485,7 @@ where } } -/// A triple as it travels through a window, in decoded text form. -/// -/// Text rather than dictionary ids because a window is not bound to any one store's -/// dictionary, which is the same reason the parsers hand back text on the way in. -/// It was its own three-field struct until both were found to be one thing; the name -/// stays because a window's item type reads better for it. +/// A triple as it travels through a window, in decoded text form pub type WindowTriple = shared::rdf_term::TermTriple; #[cfg(test)] mod tests { diff --git a/kolibrie/src/rsp/simple_r2r.rs b/kolibrie/src/rsp/simple_r2r.rs index b5decb54..ea6ada50 100644 --- a/kolibrie/src/rsp/simple_r2r.rs +++ b/kolibrie/src/rsp/simple_r2r.rs @@ -7,14 +7,10 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! A straightforward [`R2ROperator`] backed by a -//! [`SparqlDatabase`]. -//! -//! Loads each window's content into a store and evaluates the query against it: -//! correct, and enough for a window whose content fits in memory. +//! A straightforward [`R2ROperator`] backed by a [`KolibrieDB`] +use crate::database::KolibrieDB; use crate::rsp::r2r::{AsAnyMut, R2ROperator}; -use crate::sparql_database::SparqlDatabase; use crate::streamertail_optimizer::{ExecutionEngine, PhysicalOperator}; use datalog::parser_n3_logic::parse_n3_rule; use datalog::reasoning::Reasoner; @@ -31,33 +27,24 @@ use log::{debug, error}; #[cfg(test)] use std::{println as debug, println as error}; -/// Which evaluation path a window's query takes. +/// Which evaluation path a window's query takes #[derive(Clone, Copy, Debug)] pub enum QueryExecutionMode { - /// Direct evaluation, without cost-based planning. + /// Direct evaluation, without cost-based planning Standard, - /// Through the [`streamertail_optimizer`](crate::streamertail_optimizer), - /// which costs alternative plans first. + /// Through the [`streamertail_optimizer`](crate::streamertail_optimizer), which costs + /// alternative plans first Volcano, } -/// An [`R2ROperator`] that loads each window's content into a store and queries it. +/// An [`R2ROperator`] that loads each window's content into a store and queries it pub struct SimpleR2R { - /// The store holding the current window's content plus any background data. - pub item: SparqlDatabase, - /// Whether queries run through the standard path or the Volcano optimizer. + /// The store holding the current window's content plus any background data + pub item: KolibrieDB, + /// Whether queries run through the standard path or the Volcano optimizer pub execution_mode: QueryExecutionMode, - /// Reasoning rules applied to each window's content. + /// Reasoning rules applied to each window's content pub rules: Vec, derived_triples: Vec, - /// The blank-node scope every element of this stream is read in. - /// - /// One scope for the operator's whole life, not one per element. Each arriving - /// element is its own document in RDF's terms, but the dictionary never forgets a - /// term, so a fresh scope per element would mint a term per element and grow it - /// without bound -- measured at 204 terms over 200 elements against 5 for the - /// same stream without blank nodes -- until the assertion guarding the id space - /// fired. Sharing one scope is bounded, and still keeps this stream's labels - /// apart from every file's and every other stream's. document: u64, hybrid_config: Option, live_seed_snapshot: SeedSnapshot, @@ -65,8 +52,6 @@ pub struct SimpleR2R { last_hybrid_error: Option, } -// Summarised rather than derived: `item` is a whole store, so the derived form would -// print the window's content plus any background data on every log line. impl std::fmt::Debug for SimpleR2R { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("SimpleR2R") @@ -81,10 +66,10 @@ impl std::fmt::Debug for SimpleR2R { } impl SimpleR2R { - /// An operator with an empty store and no rules. + /// An operator with an empty store and no rules pub fn new() -> Self { SimpleR2R { - item: SparqlDatabase::new(), + item: KolibrieDB::new(), execution_mode: QueryExecutionMode::Standard, rules: Vec::new(), derived_triples: Vec::new(), @@ -96,10 +81,10 @@ impl SimpleR2R { } } - /// As [`Self::new`], choosing how queries are executed. + /// As [`Self::new`], choosing how queries are executed pub fn with_execution_mode(execution_mode: QueryExecutionMode) -> Self { SimpleR2R { - item: SparqlDatabase::new(), + item: KolibrieDB::new(), execution_mode, rules: Vec::new(), derived_triples: Vec::new(), @@ -111,12 +96,12 @@ impl SimpleR2R { } } - /// Replaces the rules applied to each window's content. + /// Replaces the rules applied to each window's content pub fn add_reasoning_rules(&mut self, rules: Vec) { self.rules.extend(rules); } - /// Enables probabilistic evaluation with this configuration. + /// Enables probabilistic evaluation with this configuration pub fn set_hybrid_config(&mut self, config: HybridConfig) -> Result<(), HybridError> { config.validate()?; if self @@ -132,40 +117,36 @@ impl SimpleR2R { Ok(()) } - /// Supplies the seeds the current window's uncertain items were registered - /// under, which probabilistic evaluation needs to resolve its literals. + /// Supplies the seeds the current window's uncertain items were registered under, which + /// probabilistic evaluation needs to resolve its literals pub fn set_live_seed_snapshot(&mut self, snapshot: SeedSnapshot) { self.live_seed_snapshot = snapshot; } - /// Probabilities from the most recent evaluation, by fact. + /// Probabilities from the most recent evaluation, by fact pub fn last_hybrid_results(&self) -> &HashMap { &self.last_hybrid_results } - /// Why the most recent probabilistic evaluation failed, if it did. Evaluation - /// failing does not stop the window from reporting its deterministic results. + /// Why the most recent probabilistic evaluation failed, if it did pub fn last_hybrid_error(&self) -> Option<&str> { self.last_hybrid_error.as_deref() } - /// Whether probabilistic evaluation is configured. + /// Whether probabilistic evaluation is configured pub fn hybrid_enabled(&self) -> bool { self.hybrid_config.is_some() } } -/// Allow downcasting from trait objects by exposing Any for mutable references. -/// This helps code that needs to access concrete `SimpleR2R` internals (e.g. the SparqlDatabase). +/// Allow downcasting from trait objects by exposing Any for mutable references impl AsAnyMut for SimpleR2R { fn as_any_mut(&mut self) -> &mut dyn std::any::Any { self } } -/// Implement the R2R operator trait for SimpleR2R. Note: the `R` generic parameter -/// is `Vec` (a list of physical plans). The output `O` is -/// a binding map `HashMap` per row. +/// Implement the R2R operator trait for SimpleR2R impl R2ROperator, Vec<(String, String)>> for SimpleR2R { fn load_triples(&mut self, _data: &str, _syntax: String) -> Result<(), String> { error!("Unsupported operation"); @@ -203,7 +184,6 @@ impl R2ROperator, Vec<(String, String)>> for Simpl } fn materialize(&mut self) -> Vec { - // Evict derived triples from the previous cycle for t in &self.derived_triples { self.item.delete_triple(t); } @@ -215,10 +195,6 @@ impl R2ROperator, Vec<(String, String)>> for Simpl let mut reasoner = Reasoner::new(); reasoner.adopt_terms(self.item.terms()); - // The window's facts are read straight from `self.item` rather than inserted - // here first. Building a second set of sorted indexes over content this - // operator is about to throw away was the whole cost, and inference flattens - // it back into a `Vec` immediately. reasoner.rules = self.rules.clone(); self.last_hybrid_results.clear(); self.last_hybrid_error = None; @@ -264,8 +240,6 @@ impl R2ROperator, Vec<(String, String)>> for Simpl fn execute_query(&mut self, op: &PhysicalOperator) -> Vec> { debug!("SimpleR2R executing query with PhysicalOperator"); - // Execute the physical operator using the Volcano execution engine. - // The engine returns Vec> (bindings per row). ExecutionEngine::execute(op, &mut self.item) .into_iter() .map(|hashmap| { diff --git a/kolibrie/src/rsp/window_runner.rs b/kolibrie/src/rsp/window_runner.rs index fb1aed40..06d4e3b1 100644 --- a/kolibrie/src/rsp/window_runner.rs +++ b/kolibrie/src/rsp/window_runner.rs @@ -8,10 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Driving one window's S2R to R2R cycle. -//! -//! Owns a window together with the operator that evaluates over it, so the engine -//! can hold several independently and coordinate only where they must join. +//! Driving one window's S2R to R2R cycle use crate::rsp::s2r::{ CSPARQLWindow, ContentContainer, ProbabilisticOccurrence, Report, ReportStrategy, Tick, @@ -23,15 +20,15 @@ use std::sync::mpsc::Receiver; pub use crate::rsp::s2r::WindowTriple; #[derive(Debug, Clone)] -/// A window's configuration, before it is instantiated. +/// A window's configuration, before it is instantiated pub struct WindowSpec { - /// Width in time units. Bounds are half-open, so this many timestamps. + /// Width in time units pub width: usize, - /// How far the window advances each step. Equal to `width` makes it tumbling. + /// How far the window advances each step pub slide: usize, - /// Conditions for reporting, combined with AND. + /// Conditions for reporting, combined with AND pub report_strategies: Vec, - /// What advances the window's clock. + /// What advances the window's clock pub tick: Tick, } @@ -46,7 +43,7 @@ impl Default for WindowSpec { } } -/// One window together with the plumbing that moves its content onward. +/// One window together with the plumbing that moves its content onward pub struct WindowRunner where I: Eq + PartialEq + Clone + Debug + Hash + Send, @@ -55,8 +52,6 @@ where receiver: Option>>, } -// Summarised rather than derived: the wrapped window holds the buffered content, and -// whether a receiver is still attached is the state a reader is usually after. impl Debug for WindowRunner where I: Eq + PartialEq + Clone + Debug + Hash + Send, @@ -73,7 +68,7 @@ impl WindowRunner where I: Eq + PartialEq + Clone + Debug + Hash + Send, { - /// A runner for a window built from `spec`, identified by `uri`. + /// A runner for a window built from `spec`, identified by `uri` pub fn new(spec: WindowSpec, uri: String) -> Self { let mut report = Report::new(); for strategy in spec.report_strategies { @@ -85,22 +80,19 @@ where } } - /// Begins buffering this window's reports so [`Self::drain`] can collect them. + /// Begins buffering this window's reports so [`Self::drain`] can collect them pub fn start_receiver(&mut self) { if self.receiver.is_none() { self.receiver = Some(self.inner.register()); } } - /// Adds an item at `ts`. Equivalent to [`Self::add_to_window`]. + /// Adds an item at `ts` pub fn push(&mut self, item: I, ts: usize) { self.inner.add_to_window(item, ts); } - /// Takes every report buffered since the last drain, leaving none behind. - /// - /// Requires [`Self::start_receiver`] first; without it nothing is buffered and - /// this always returns empty. + /// Takes every report buffered since the last drain, leaving none behind pub fn drain(&mut self) -> Vec> { let mut out = Vec::new(); if let Some(rx) = &self.receiver { @@ -111,22 +103,22 @@ where out } - /// Adds a certain item at `ts`. + /// Adds a certain item at `ts` pub fn add_to_window(&mut self, item: I, ts: usize) { self.inner.add_to_window(item, ts); } - /// Adds an uncertain arrival, preserving its seed identity. + /// Adds an uncertain arrival, preserving its seed identity pub fn add_probabilistic_to_window(&mut self, occurrence: ProbabilisticOccurrence) { self.inner.add_probabilistic_to_window(occurrence); } - /// A channel receiving this window's reports. + /// A channel receiving this window's reports pub fn register(&mut self) -> Receiver> { self.inner.register() } - /// Installs a callback invoked on each report. + /// Installs a callback invoked on each report pub fn register_callback( &mut self, f: Box) -> () + Send + 'static>, @@ -134,13 +126,12 @@ where self.inner.register_callback(f); } - /// Reports every active window's merged content immediately, ignoring the - /// report strategies. See [`CSPARQLWindow::flush`]. + /// Reports every active window's merged content immediately, ignoring the report strategies pub fn flush(&mut self) { self.inner.flush(); } - /// Closes the output channel. + /// Closes the output channel pub fn stop(&mut self) { self.inner.stop(); } diff --git a/kolibrie/src/rsp_engine/builder.rs b/kolibrie/src/rsp_engine/builder.rs index 7d371448..44a7c8e2 100644 --- a/kolibrie/src/rsp_engine/builder.rs +++ b/kolibrie/src/rsp_engine/builder.rs @@ -7,12 +7,9 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Assembling an engine from a parsed RSP-QL query. -//! -//! Turns the windows, query and output-stream operator that a -//! [`RegisterClause`](shared::query::RegisterClause) declares into a running -//! [`RSPEngine`](crate::rsp_engine::RSPEngine). +//! Assembling an engine from a parsed RSP-QL query +use crate::database::KolibrieDB; use crate::parser::parse_combined_query; use crate::rsp::r2r::R2ROperator; use crate::rsp::r2s::StreamOperator; @@ -22,7 +19,6 @@ use crate::rsp_engine::{ CrossWindowReasoningMode, OperationMode, QueryExecutionMode, RSPEngine, RSPQueryPlan, RSPWindow, ResultConsumer, }; -use crate::sparql_database::SparqlDatabase; use crate::streamertail_optimizer::{ build_logical_plan, LogicalOperator, PhysicalOperator, Streamertail, }; @@ -38,26 +34,21 @@ use std::sync::Arc; /// RSP Query configuration extracted from parsed RSP-QL #[derive(Debug)] pub struct RSPQueryConfig<'a> { - /// One entry per `FROM NAMED WINDOW` clause. + /// One entry per `FROM NAMED WINDOW` clause pub windows: Vec, - /// IRI the answers are published under. + /// IRI the answers are published under pub output_stream: String, - /// Which solutions reach that stream. + /// Which solutions reach that stream pub stream_type: StreamOperator, - /// Patterns outside any window, matched against background data rather than - /// the stream. Their presence is one of the two things that forces cross-window - /// coordination, the other being more than one window. + /// Patterns outside any window, matched against background data rather than the stream pub static_patterns: Vec<(&'a str, &'a str, &'a str)>, - /// Store supplying the prefix bindings the query was written against. - pub database: SparqlDatabase, - /// Effective synchronization policy for multi-window coordination. + /// Store supplying the prefix bindings the query was written against + pub database: KolibrieDB, + /// Effective synchronization policy for multi-window coordination pub sync_policy: SyncPolicy, } -/// Assembles an [`RSPEngine`] step by step. -/// -/// Each method returns the builder, so configuration chains; [`Self::build`] -/// validates the result and fails rather than producing a half-configured engine. +/// Assembles an [`RSPEngine`] step by step pub struct RSPBuilder<'a, I, O> { rsp_ql_query: Option<&'a str>, triples: Option<&'a str>, @@ -67,7 +58,6 @@ pub struct RSPBuilder<'a, I, O> { operation_mode: OperationMode, query_execution_mode: QueryExecutionMode, syntax: String, - /// Engine-level default policy; overridden by per-window `WITH POLICY` clause. sync_policy: SyncPolicy, reasoning_rules: Vec, sparql_rules: Vec, @@ -75,9 +65,6 @@ pub struct RSPBuilder<'a, I, O> { cross_window_reasoning_mode: CrossWindowReasoningMode, } -// Cannot derive: `r2r` is a trait object. Summarised besides — the query, rules and -// triples are source text whose length is more informative than its content at this -// stage. impl Debug for RSPBuilder<'_, I, O> { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("RSPBuilder") @@ -105,7 +92,7 @@ where O: Clone + Eq + Send + Debug + Hash + 'static + From>, I: Eq + PartialEq + Clone + Debug + Hash + Send + 'static, { - /// A builder with nothing configured. + /// A builder with nothing configured pub fn new() -> RSPBuilder<'a, I, O> { RSPBuilder { rsp_ql_query: None, @@ -124,21 +111,19 @@ where } } - /// Override the engine-level default synchronization policy. - /// A per-window `WITH POLICY` clause in the query string takes precedence. + /// Override the engine-level default synchronization policy pub fn set_sync_policy(mut self, policy: SyncPolicy) -> RSPBuilder<'a, I, O> { self.sync_policy = policy; self } - /// Supply pre-parsed reasoning rules (forward-chaining inference applied per window firing). + /// Supply pre-parsed reasoning rules (forward-chaining inference applied per window firing) pub fn add_reasoning_rules(mut self, rules: Vec) -> RSPBuilder<'a, I, O> { self.reasoning_rules = rules; self } - /// Supply SPARQL RULE strings (`RULE :Name :- CONSTRUCT{} WHERE{}`) to be parsed after - /// dictionary merge in `RSPEngine::new()` so term IDs are consistent. + /// Supplies SPARQL RULE strings, parsed in `RSPEngine::new()` after the dictionary merge pub fn add_sparql_rules(mut self, rules: Vec) -> RSPBuilder<'a, I, O> { self.sparql_rules = rules; self @@ -165,25 +150,25 @@ where self } - /// Adds background data, available to patterns outside any window. + /// Adds background data, available to patterns outside any window pub fn add_triples(mut self, triples: &'a str) -> RSPBuilder<'a, I, O> { self.triples = Some(triples); self } - /// Adds reasoning rules applied to each window's content. + /// Adds reasoning rules applied to each window's content pub fn add_rules(mut self, rules: &'a str) -> RSPBuilder<'a, I, O> { self.rules = Some(rules); self } - /// Sets the callback receiving the output stream. + /// Sets the callback receiving the output stream pub fn add_consumer(mut self, consumer: ResultConsumer) -> RSPBuilder<'a, I, O> { self.result_consumer = Some(consumer); self } - /// Supplies the operator that evaluates the query over each window. + /// Supplies the operator that evaluates the query over each window pub fn add_r2r( mut self, r2r: Box, O>>, @@ -192,13 +177,13 @@ where self } - /// Chooses single- or multi-threaded operation. See [`OperationMode`]. + /// Chooses single- or multi-threaded operation pub fn set_operation_mode(mut self, operation_mode: OperationMode) -> RSPBuilder<'a, I, O> { self.operation_mode = operation_mode; self } - /// Chooses whether queries run through the standard path or the optimizer. + /// Chooses whether queries run through the standard path or the optimizer pub fn set_query_execution_mode(mut self, mode: QueryExecutionMode) -> RSPBuilder<'a, I, O> { self.query_execution_mode = mode; self @@ -210,9 +195,8 @@ where Ok((_, parsed_query)) => { if let Some(register_clause) = &parsed_query.register_clause { let mut windows = Vec::new(); - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.set_prefixes(parsed_query.prefixes.clone()); - // Extract windows from the register clause for window_clause in ®ister_clause.query.window_clause { let window = self.create_rsp_window( window_clause, @@ -222,18 +206,15 @@ where windows.push(window); } - // Convert stream type let stream_type = match ®ister_clause.stream_type { StreamType::RStream => StreamOperator::RSTREAM, StreamType::IStream => StreamOperator::ISTREAM, StreamType::DStream => StreamOperator::DSTREAM, - StreamType::Custom(_) => StreamOperator::RSTREAM, // Default fallback + StreamType::Custom(_) => StreamOperator::RSTREAM, }; - // Extract static patterns from WHERE clause (outside window blocks) let static_patterns = register_clause.query.where_clause.0.clone(); - // Resolve policy: per-window WITH POLICY > builder default let sync_policy = register_clause .query .window_clause @@ -262,9 +243,8 @@ where &self, window_clause: &WindowClause, window_blocks: &[WindowBlock], - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { - // Find the corresponding window block for this window let spo_query = LogicalOperator::scan(( Term::Variable("s".to_string()), Term::Variable("p".to_string()), @@ -274,7 +254,6 @@ where .iter() .find(|block| block.window_name == window_clause.window_iri) .map(|block| { - // Convert window block patterns to query plan let op = build_logical_plan( Vec::new(), block.patterns.clone(), @@ -288,7 +267,6 @@ where }) .unwrap_or_else(|| spo_query); - // Convert window specification let slide = window_clause .window_spec .slide @@ -298,15 +276,15 @@ where Some("TIME_DRIVEN") => Tick::TimeDriven, Some("TUPLE_DRIVEN") => Tick::TupleDriven, Some("BATCH_DRIVEN") => Tick::BatchDriven, - _ => Tick::TimeDriven, // Default + _ => Tick::TimeDriven, }; let report_strategy = match window_clause.window_spec.report_strategy { Some("ON_WINDOW_CLOSE") => ReportStrategy::OnWindowClose, Some("ON_CONTENT_CHANGE") => ReportStrategy::OnContentChange, Some("NON_EMPTY_CONTENT") => ReportStrategy::NonEmptyContent, - Some("PERIODIC") => ReportStrategy::Periodic(1000), // Default 1 second - _ => ReportStrategy::OnWindowClose, // Default + Some("PERIODIC") => ReportStrategy::Periodic(1000), + _ => ReportStrategy::OnWindowClose, }; Ok(RSPWindow { @@ -324,13 +302,11 @@ where fn create_rsp_query_plan(query_config: &RSPQueryConfig) -> Result { let mut window_plans = Vec::new(); - // Create individual window plans for window in &query_config.windows { window_plans.push(window.query.clone()); } let mut database = query_config.database.clone(); - // Create static data plan if there are static patterns let static_data_plan = if !query_config.static_patterns.is_empty() { let logical_plan = build_logical_plan( Vec::new(), @@ -346,7 +322,6 @@ where None }; - // Create physical plans from the logical ones let mut optimizer = Streamertail::new(&database); let static_data_plan = match static_data_plan { @@ -365,10 +340,7 @@ where }) } - /// Validates the configuration and constructs the engine. - /// - /// Fails if something required was never supplied, rather than returning an - /// engine that would misbehave at the first firing. + /// Validates the configuration and constructs the engine pub fn build(mut self) -> Result, String> { let rsp_ql_query = self .rsp_ql_query @@ -398,12 +370,10 @@ where } } - // Parse the RSP-QL query let query_config = self.parse_rsp_ql_query(rsp_ql_query)?; let sync_policy = query_config.sync_policy.clone(); - // Create RSP-QL query plan using Volcano optimizer let rsp_query_plan = Self::create_rsp_query_plan(&query_config)?; RSPEngine::new( diff --git a/kolibrie/src/rsp_engine/mod.rs b/kolibrie/src/rsp_engine/mod.rs index d743efb8..11268eb0 100644 --- a/kolibrie/src/rsp_engine/mod.rs +++ b/kolibrie/src/rsp_engine/mod.rs @@ -14,7 +14,7 @@ use crate::rsp::window_runner::{WindowRunner, WindowSpec}; use crossbeam::channel::{unbounded, Receiver, RecvTimeoutError, Sender}; #[cfg(not(test))] -use log::{debug, error}; // Use log crate when building application +use log::{debug, error}; use shared::query::{Fallback, SyncPolicy}; use shared::rule::Rule; use shared::term_encoder::SharedTerms; @@ -29,8 +29,8 @@ use std::time::Instant; #[cfg(test)] use std::{println as debug, println as error}; +use crate::database::KolibrieDB; use crate::rule_execution::process_rule_definition; -use crate::sparql_database::SparqlDatabase; use crate::streamertail_optimizer::{ExecutionEngine, LogicalOperator, PhysicalOperator}; use datalog::cross_window_sds::{ all_component_iris, sds_with_expiry_to_external, Sds, WindowData, WindowedTriple, @@ -45,97 +45,81 @@ use shared::hybrid::{ }; use shared::triple::Triple; -// Re-exports to preserve the public API used by kolibrie-http-server and examples. -/// Assembling an engine from a parsed query. Lives here rather than in -/// [`rsp`](crate::rsp) because it builds an [`RSPEngine`], and a module that builds -/// a thing should sit with it rather than below it. +/// Assembling an engine from a parsed query pub mod builder; -pub use builder::{RSPBuilder, RSPQueryConfig}; -// Defined with the R2R operator that carries it, one layer down, so that `rsp` -// does not have to name this module. The public path is unchanged. pub use crate::rsp::simple_r2r::QueryExecutionMode; pub use crate::rsp::simple_r2r::SimpleR2R; +pub use builder::{RSPBuilder, RSPQueryConfig}; -/// How the engine runs its windows. +/// How the engine runs its windows #[derive(Clone, Copy, Debug)] pub enum OperationMode { - /// Everything in the caller's thread. Results are produced only when the - /// caller drives the engine, so [`RSPEngine::stop`] must be called to flush - /// them — otherwise a test or a short-lived program sees nothing. + /// Everything in the caller's thread SingleThread, - /// One thread per window, with a coordinator joining their results. Started - /// only when there is something to join: more than one window, or static - /// patterns alongside a window. + /// One thread per window, with a coordinator joining their results MultiThread, } -/// How derived facts are maintained as windows advance. +/// How derived facts are maintained as windows advance #[derive(Clone, Copy, Debug, PartialEq, Eq)] pub enum CrossWindowReasoningMode { - /// Maintain the derivation across firings, reusing what still holds. + /// Maintain the derivation across firings, reusing what still holds Incremental, - /// Re-derive from scratch each firing. Simpler, and the reference the - /// incremental path is benchmarked against. + /// Re-derive from scratch each firing Naive, } /// Window configuration extracted from parsed RSP-QL query #[derive(Debug, Clone)] pub struct RSPWindow { - /// Name the query refers to this window by. + /// Name the query refers to this window by pub window_iri: String, - /// Stream it observes. + /// Stream it observes pub stream_iri: String, - /// Width in time units; bounds are half-open. + /// Width in time units; bounds are half-open pub width: usize, - /// How far it advances each step. + /// How far it advances each step pub slide: usize, - /// What advances its clock. + /// What advances its clock pub tick: Tick, - /// When it reports. + /// When it reports pub report_strategy: ReportStrategy, - /// The query evaluated over this window's content, before planning. + /// The query evaluated over this window's content, before planning pub query: LogicalOperator, } /// RSP-QL Query Plan using Volcano optimizer #[derive(Debug, Clone)] pub struct RSPQueryPlan { - /// One costed plan per window, in the same order as the window configs. + /// One costed plan per window, in the same order as the window configs pub window_plans: Vec, - /// Plan for patterns outside any window, if the query has them. Its presence - /// is one of the two conditions that require cross-window coordination. + /// Plan for patterns outside any window, if the query has them pub static_data_plan: Option, } /// Result from a single window execution #[derive(Debug, Clone)] pub struct WindowResult { - /// Which window produced this. + /// Which window produced this pub window_iri: String, - /// Solutions, as decoded variable bindings — text, not dictionary ids, because - /// a result crossing between windows cannot assume one dictionary. + /// Solutions, as decoded variable bindings — text, not dictionary ids, because a result + /// crossing between windows cannot assume one dictionary pub results: TextBindings, - /// When the window fired. + /// When the window fired pub timestamp: usize, - /// The window's content with per-triple timestamps, for cross-window reasoning. + /// The window's content with per-triple timestamps, for cross-window reasoning pub raw_triples: Vec<(Triple, u64)>, } const CROSS_WINDOW_STATIC_IRI: &str = "urn:kolibrie:static:"; -/// The callback the output stream is delivered to. -/// -/// `Send + Sync` because a multi-threaded engine invokes it from a window's own -/// thread. +/// The callback the output stream is delivered to pub struct ResultConsumer { - /// Invoked once per published solution. + /// Invoked once per published solution pub function: Arc () + Send + Sync>, } -// Cannot derive: the callback is a `dyn Fn`. Its identity is not observable and -// calling it would publish a solution, so it is named and not invoked. impl Debug for ResultConsumer { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("ResultConsumer") @@ -200,19 +184,16 @@ macro_rules! create_window_processor { } } - // Evict triples from the previous firing of this window for t in &prev_window_triples { store.remove(t); } prev_window_triples.clear(); - // Add current window triples and track them for next eviction for t in content.into_iter() { prev_window_triples.push(t.clone()); store.add(t); } - // Run forward-chaining inference to materialise derived facts store.materialize(); if let Some(simple_r2r) = store.as_any_mut().downcast_mut::() { @@ -225,7 +206,6 @@ macro_rules! create_window_processor { let results = store.execute_query(&$query); debug!("Got # results {} for window {}", results.len(), $window_iri); - // Release lock early to reduce contention drop(store); if $has_joins { @@ -263,10 +243,6 @@ macro_rules! register_window { }; (MultiThread, $window:expr, $processor:expr, $window_iri:expr) => {{ let receiver = $window.register(); - // The handle is kept, not dropped: `stop` joins it. The thread ends when - // `CSPARQLWindow::stop` drops the content sender, and a channel yields what - // is queued before it reports disconnection, so joining here means every - // flushed window has been processed and has forwarded its result. thread::spawn(move || { loop { match receiver.recv() { @@ -284,25 +260,7 @@ macro_rules! register_window { }}; } -/// The RSP engine: windows over streams, a query over each, and one output stream. -/// -/// # Getting results out -/// -/// Under [`OperationMode::MultiThread`] results arrive at the consumer as windows -/// fire. Under [`OperationMode::SingleThread`] nothing is delivered until the caller -/// drives the engine and then calls [`Self::stop`], which flushes the windows — a -/// single-threaded engine that is never stopped appears to produce nothing. -/// -/// # Coordination -/// -/// A coordinator is needed only when results must be joined across sources: more -/// than one window, or static patterns alongside a window. With a single window and -/// no static data each firing is independently complete, and the engine skips the -/// join path entirely. -/// -/// When coordination is needed, [`SyncPolicy`] decides how -/// long to wait for the other windows — a correctness choice, since the impatient -/// settings publish results mixing data from different cycles. +/// The RSP engine: windows over streams, a query over each, and one output stream pub struct RSPEngine where I: Eq + PartialEq + Clone + Debug + Hash + Send, @@ -314,36 +272,28 @@ where window_configs: Vec, query_execution_mode: QueryExecutionMode, operation_mode: OperationMode, - // Channel for collecting window results for cross-window joins. - // An `Option` so `stop` can drop the engine's own sender: the coordinator ends - // on disconnection, which cannot happen while this is held. window_result_sender: Option>, window_result_receiver: Receiver, - /// One per window under [`OperationMode::MultiThread`], joined by [`Self::stop`]. + /// One per window under [`OperationMode::MultiThread`], joined by [`Self::stop`] window_threads: Vec>, - /// The coordinator, when the query needs one. Also joined by [`Self::stop`]. + /// The coordinator, when the query needs one coordinator_thread: Option>, - // RSP-QL Query Plan using Volcano optimizer rsp_query_plan: RSPQueryPlan, - /// Latest materialized results per window (replace semantics); SingleThread only. + /// Latest materialized results per window (replace semantics); SingleThread only single_thread_last_materialized: Arc>>, - /// Synchronization policy governing multi-window coordination. + /// Synchronization policy governing multi-window coordination sync_policy: SyncPolicy, - /// Separate store for static background triples (never touched by window processors). - static_db: Arc>, - /// R2S operator for stream-type filtering (RSTREAM/ISTREAM/DSTREAM). + /// Separate store for static background triples (never touched by window processors) + static_db: Arc>, + /// R2S operator for stream-type filtering (RSTREAM/ISTREAM/DSTREAM) r2s_operator: Arc>>, - /// Opt-in cross-window SDS+ reasoning state. + /// Opt-in cross-window SDS+ reasoning state cross_window_enabled: bool, cross_window_rules: Arc>, cross_window_context: Option, cross_window_sds_plus: Arc>, cross_window_latest_contents: Arc>>>, - /// The term space the cross-window reasoner shares with the R2R store. - /// - /// A `SharedTerms` rather than the handle underneath: the engine passes it to - /// datalog's cross-window entry points, which take one too, so the handle no - /// longer has to be named anywhere along the way. + /// The term space the cross-window reasoner shares with the R2R store cross_window_dictionary: Option, cross_window_output_iris: Arc>, cross_window_reasoning_mode: CrossWindowReasoningMode, @@ -351,10 +301,6 @@ where latest_hybrid_results: Arc>>>, } -// Cannot derive: `r2r` is a trait object behind a mutex. Summarised besides, and -// nothing here takes a lock: a `Debug` impl that blocks on a mutex can deadlock -// against the thread already holding it, which for an engine being inspected mid-run -// is the likely case. impl Debug for RSPEngine where I: Eq + PartialEq + Clone + Debug + Hash + Send, @@ -382,10 +328,7 @@ where O: Clone + Hash + Eq + Send + 'static + From>, I: Eq + PartialEq + Clone + Debug + Hash + Send + 'static, { - /// An engine over the given windows and plan. - /// - /// Prefer [`RSPBuilder`], which assembles one - /// from a parsed RSP-QL query and validates it. + /// An engine over the given windows and plan pub fn new( query_config: RSPQueryConfig, triples: &str, @@ -404,11 +347,6 @@ where ) -> Result, String> { let mut store = r2r; - // The PhysicalOperator plans created in `rsp_query_plan` contain integer IDs (constants) - // that were generated by the Dictionary in `query_config.database`. - // The `store` (R2R operator) has its own Dictionary. If we don't sync them, - // the store will assign different IDs to incoming data, and the execution engine - // will fail to match them against the plan. let shared_dict = store .as_any_mut() .downcast_mut::() @@ -417,7 +355,6 @@ where if let Some(simple_r2r) = store.as_any_mut().downcast_mut::() { debug!("Synchronizing R2R dictionary with Query dictionary"); - // Acquire locks on both dictionaries let mut store_dict = simple_r2r.item.dictionary.write(); let query_dict = query_config.database.dictionary.read(); @@ -427,8 +364,7 @@ where drop(query_dict); } - // Build the static-data store sharing the same dictionary as the R2R store. - let mut static_sdb = SparqlDatabase::new(); + let mut static_sdb = KolibrieDB::new(); if let Some(terms) = &shared_dict { static_sdb.adopt_terms(terms.clone()); } @@ -468,7 +404,6 @@ where cross_window_context = Some(context); } - // Load initial data match store.load_triples(triples, syntax) { Err(parsing_error) => error!("Unable to load ABox: {:?}", parsing_error.to_string()), _ => (), @@ -497,7 +432,7 @@ where .and_then(|(_, combined)| combined.single_rule().ok().flatten().cloned()) .and_then(|rule| rule.prob_annotation) .and_then(|annotation| annotation.hybrid_config); - let mut temp_db = SparqlDatabase::new(); + let mut temp_db = KolibrieDB::new(); temp_db.adopt_terms(dict.clone()); match process_rule_definition(rule_str, &mut temp_db) { Ok((rule, _)) => { @@ -517,7 +452,6 @@ where } } - // Create windows based on parsed configuration let mut windows = Vec::new(); for window_config in &query_config.windows { let spec = WindowSpec { @@ -529,7 +463,6 @@ where windows.push(WindowRunner::new(spec, window_config.window_iri.clone())); } - // Create channel for cross-window result coordination let (result_sender, result_receiver) = unbounded::(); let stream_type = query_config.stream_type.clone(); @@ -602,7 +535,7 @@ where for (window_idx, window) in self.windows.iter_mut().enumerate() { let query = self.rsp_query_plan.window_plans[window_idx].clone(); let window_iri = self.window_configs[window_idx].window_iri.clone(); - let window_iri_for_thread = window_iri.clone(); // Clone for MultiThread usage + let window_iri_for_thread = window_iri.clone(); let query_execution_mode = self.query_execution_mode; let window_result_sender = self .window_result_sender @@ -627,7 +560,6 @@ where }) }; - // Create processor using macro let mut processor = create_window_processor!( window_iri, query, @@ -641,7 +573,6 @@ where latest_hybrid_results ); - // Register based on mode match operation_mode { OperationMode::SingleThread => { register_window!(SingleThread, window, processor); @@ -659,8 +590,8 @@ where self.window_threads = window_threads; } - /// Start a coordinator thread that collects and joins results from multiple windows - /// (and optionally joins with static background data), respecting `sync_policy`. + /// Starts the coordinator thread that joins results from several windows, respecting + /// `sync_policy` fn start_cross_window_coordinator(&mut self) where O: From>, @@ -683,16 +614,12 @@ where let window_plans = self.rsp_query_plan.window_plans.clone(); self.coordinator_thread = Some(thread::spawn(move || { - // Latest results per window (replace semantics) let mut last_materialized: HashMap = HashMap::new(); - // Windows that have fired since the last reset let mut cycle_triggered: HashSet = HashSet::new(); - // When the first window fired in the current cycle let mut cycle_start: Option = None; let mut max_ts: usize = 0; loop { - // Compute recv timeout when policy has a finite deadline let timeout_remaining = match &sync_policy { SyncPolicy::Timeout { duration, .. } => { cycle_start.map(|start| duration.saturating_sub(start.elapsed())) @@ -700,13 +627,11 @@ where _ => None, }; - // Receive next window result (or timeout/disconnect) let maybe_result: Option = if let Some(remaining) = timeout_remaining { match receiver.recv_timeout(remaining) { Ok(r) => Some(r), Err(RecvTimeoutError::Timeout) => { - // Deadline elapsed if !cycle_triggered.is_empty() { match &sync_policy { SyncPolicy::Timeout { @@ -747,9 +672,7 @@ where SyncPolicy::Timeout { fallback: Fallback::Drop, .. - } => { - // discard this cycle - } + } => {} _ => {} } cycle_triggered.clear(); @@ -781,7 +704,6 @@ where window_result.raw_triples.clone(), ); } - // Update last_materialized (replace) last_materialized.insert( window_result.window_iri.clone(), window_result.results.clone(), @@ -791,7 +713,6 @@ where } cycle_triggered.insert(window_result.window_iri.clone()); - // Drain any additional pending results while let Ok(wr) = receiver.try_recv() { max_ts = max_ts.max(wr.timestamp); if cross_window_enabled { @@ -805,7 +726,6 @@ where } if cycle_triggered.len() == num_windows { - // All windows fired this cycle if cross_window_enabled { if let Some(dict) = &cross_window_dictionary { emit_cross_window_results( @@ -840,7 +760,6 @@ where } else { match &sync_policy { SyncPolicy::Steal => { - // Emit immediately using stale data from non-firing windows if last_materialized.len() == num_windows { if cross_window_enabled { if let Some(dict) = &cross_window_dictionary { @@ -876,7 +795,6 @@ where max_ts = 0; } SyncPolicy::Wait | SyncPolicy::Timeout { .. } => { - // Keep waiting for remaining windows debug!( "Coordinator: waiting for more windows ({}/{}) — have: {:?}", cycle_triggered.len(), @@ -889,17 +807,6 @@ where } } - // Shutdown flush. A window that fired after the last emission holds results - // that were never published, and on a loaded machine that is the common - // case rather than a corner: the coordinator may not be scheduled until - // `stop` has already flushed every window, so it drains a queue in which - // one window's first (often empty) result completes the cycle and emits, - // and that window's real results arrive afterwards with nothing left to - // pair them against. Dropping them discarded a complete, non-empty set the - // engine was holding -- the whole point of `stop` being a flush. - // - // Guarded on `cycle_triggered`, so a shutdown that follows an emission with - // nothing new since does not republish the same relation. if !cycle_triggered.is_empty() && last_materialized.len() == num_windows { if cross_window_enabled { if let Some(dict) = &cross_window_dictionary { @@ -946,18 +853,14 @@ where fn normalize_stream_iri(s: &str) -> String { let s = s.trim(); - // Some callers might pass a full IRI in `<...>` form. let s = s.trim_start_matches('<').trim_end_matches('>'); - // Accept prefixed notation with an optional leading colon, e.g. `:stream1`. let s = s.strip_prefix(':').unwrap_or(s); s.to_string() } let input_norm = normalize_stream_iri(stream_iri); - // Find windows that match this stream IRI for (window_idx, window_config) in self.window_configs.iter().enumerate() { - // Variable stream (e.g. `?s`) matches any stream. if window_config.stream_iri.starts_with('?') { if let Some(window) = self.windows.get_mut(window_idx) { window.add_to_window(event_item.clone(), ts); @@ -975,10 +878,7 @@ where } /// Evaluates and publishes whatever the windows currently hold, for - /// [`OperationMode::SingleThread`]. - /// - /// Called by [`Self::stop`]; a caller wanting results mid-run can call it - /// directly. + /// [`OperationMode::SingleThread`] pub fn process_single_thread_window_results(&mut self) where O: From>, @@ -987,7 +887,6 @@ where let num_windows = self.windows.len(); let sync_policy = self.sync_policy.clone(); - // Drain all pending channel results; update last_materialized with replace semantics. let mut last_mat = self.single_thread_last_materialized.lock().unwrap(); let mut had_new_results = false; let mut max_ts: usize = 0; @@ -1010,7 +909,6 @@ where return; } - // Check whether to emit based on policy. if last_mat.len() == num_windows { debug!( "SingleThread: all {} windows materialized, emitting", @@ -1047,12 +945,9 @@ where } match sync_policy { - // Wait: require all windows to fire again before next emission. - // Timeout: no wall-clock timer in single-threaded context; treat as Wait. SyncPolicy::Wait | SyncPolicy::Timeout { .. } => { last_mat.clear(); } - // Steal: keep last_mat so stale data from non-firing windows is reused. SyncPolicy::Steal => {} } } else { @@ -1066,34 +961,12 @@ where /// Legacy method for backward compatibility pub fn add(&mut self, event_item: I, ts: usize) { - // Add to all windows (for backward compatibility) for window in &mut self.windows { window.add_to_window(event_item.clone(), ts); } } - /// Flushes every window, closes the output, and waits for the results. - /// - /// Required under [`OperationMode::SingleThread`]: it is what delivers the - /// results. Flushing ignores the report strategies, so content that had not - /// yet met its reporting condition is published too. - /// - /// # Every result has been delivered when this returns - /// - /// Under [`OperationMode::MultiThread`] that guarantee used to be missing, and - /// callers stood in for it by sleeping. Flushing a window only *queues* its - /// content for that window's thread, which then forwards a result to the - /// coordinator, which joins and calls the consumer — two handoffs that `stop` - /// did not await, so whether a result had arrived was a race. `rsp_engine_test` - /// slept two seconds and still failed about one run in six. - /// - /// Both stages are joined now, in order. A channel yields everything queued - /// before it reports disconnection, so neither join can drop a result: the - /// window threads end once their content sender is gone and the coordinator ends - /// once the last result sender is, which is why the engine's own sender is - /// dropped here rather than held until the engine is. - /// - /// Calling this twice is harmless; the second call has nothing left to join. + /// Flushes every window, closes the output, and waits for the results pub fn stop(&mut self) { for window in &mut self.windows { window.flush(); @@ -1104,16 +977,12 @@ where return; } - // Stage one: each window thread drains the content just flushed to it and - // sends its result on. for handle in self.window_threads.drain(..) { if handle.join().is_err() { error!("A window thread panicked; its results are missing"); } } - // Stage two: with every window thread gone, the only remaining result sender - // is this one. Dropping it lets the coordinator finish the queue and exit. drop(self.window_result_sender.take()); if let Some(coordinator) = self.coordinator_thread.take() { if coordinator.join().is_err() { @@ -1122,15 +991,12 @@ where } } - /// Parses stream text into items, through the configured R2R operator. + /// Parses stream text into items, through the configured R2R operator pub fn parse_data(&mut self, data: &str) -> Vec { self.r2r.lock().unwrap().parse_data(data) } - /// Pre-populate the static background store with N-Triples data. - /// These triples are never placed in the window R2R store, so they cannot - /// leak into window query results. They are only visible when `emit_results` - /// joins the window output with the static-data plan. + /// Pre-populate the static background store with N-Triples data pub fn add_static_ntriples(&mut self, data: &str) { let mut db = self.static_db.lock().unwrap(); db.parse_ntriples_and_add(data); @@ -1148,12 +1014,12 @@ where &self.rsp_query_plan } - /// Return parsed cross-window SDS+ metadata when cross-window reasoning is enabled. + /// Return parsed cross-window SDS+ metadata when cross-window reasoning is enabled pub fn get_cross_window_context(&self) -> Option<&WindowContext> { self.cross_window_context.as_ref() } - /// Return the stream IRIs registered across all configured windows. + /// Return the stream IRIs registered across all configured windows pub fn stream_iris(&self) -> Vec { self.window_configs .iter() @@ -1166,8 +1032,7 @@ impl RSPEngine where O: Clone + Hash + Eq + Send + 'static + From>, { - /// Add one independently probabilistic stream occurrence. Identity is - /// allocated before fan-out, so overlapping windows share the same seed. + /// Add one independently probabilistic stream occurrence pub fn add_probabilistic_to_stream( &mut self, stream_iri: &str, @@ -1211,7 +1076,7 @@ where Ok(seed_id) } - /// Probabilities from the most recent probabilistic evaluation, by fact. + /// Probabilities from the most recent probabilistic evaluation, by fact pub fn latest_hybrid_results( &self, window_iri: &str, @@ -1223,13 +1088,12 @@ where } } -/// Join all window results, optionally apply the static-data join, apply the R2S operator, -/// and call `consumer` for each output binding. -/// Called from both the coordinator thread and the SingleThread processor. +/// Join all window results, optionally apply the static-data join, apply the R2S operator, and call +/// `consumer` for each output binding fn emit_results( last_materialized: &HashMap, static_data_plan: &Option, - static_db: &Arc>, + static_db: &Arc>, r2s: &Arc>>, ts: usize, consumer: &Arc () + Send + Sync>, @@ -1266,11 +1130,7 @@ fn emit_results( } } -/// Join the results of every window that has fired. -/// -/// [`TextBindings::join`] carries the semantics; this only decides the order, and no -/// longer clones every row of every window to do it -- it used to collect the buffers -/// into an owned `Vec` before joining any of them. +/// Join the results of every window that has fired fn join_window_results(window_buffers: &HashMap) -> TextBindings { let mut windows = window_buffers.values(); let Some(first) = windows.next() else { @@ -1283,30 +1143,26 @@ fn join_window_results(window_buffers: &HashMap) -> TextBi joined } -/// Execute a physical plan against the static-data `SparqlDatabase` and return the results as -/// a list of variable-binding maps. +/// Execute a physical plan against the static-data `KolibrieDB` and return the results as a +/// list of variable-binding maps fn execute_plan_as_bindings( - static_db: &Arc>, + static_db: &Arc>, plan: &PhysicalOperator, ) -> TextBindings { let mut db = static_db.lock().unwrap(); - // The optimizer still answers with a map per row, so this is the boundary where - // that shape ends. TextBindings::from_maps(&ExecutionEngine::execute(plan, &mut *db)) } fn build_cross_window_sds( window_configs: &[RSPWindow], latest_contents: &HashMap>, - static_db: &Arc>, + static_db: &Arc>, dict: &SharedTerms, output_iris: &[String], ) -> Sds { let mut sds = Sds::new(); { - // A copy, because this decodes every triple in every window and a scoped - // encoder will not hold its lock across the walk. let dict_r = dict.view(); for window in window_configs { let triples = latest_contents @@ -1343,8 +1199,6 @@ fn build_cross_window_sds( let static_triples = { let db = static_db.lock().unwrap(); - // A copy, because this decodes every triple in every window and a scoped - // encoder will not hold its lock across the walk. let dict_r = dict.view(); db.query_default_triples(None, None, None) .into_iter() @@ -1374,7 +1228,7 @@ fn execute_window_plans_on_external_buckets( let mut materialized = HashMap::new(); for (window, plan) in window_configs.iter().zip(window_plans.iter()) { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.adopt_terms(dict.clone()); if let Some(triples) = external_buckets.get(&window.window_iri) { @@ -1394,7 +1248,7 @@ fn emit_cross_window_results( window_configs: &[RSPWindow], window_plans: &[PhysicalOperator], static_data_plan: &Option, - static_db: &Arc>, + static_db: &Arc>, r2s: &Arc>>, ts: usize, consumer: &Arc () + Send + Sync>, diff --git a/kolibrie/src/rule_execution.rs b/kolibrie/src/rule_execution.rs index 51b9d35d..f95c49fa 100644 --- a/kolibrie/src/rule_execution.rs +++ b/kolibrie/src/rule_execution.rs @@ -8,24 +8,15 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Running a `RULE` definition, and the `RETRIEVE` clause, against a store. -//! -//! These used to sit at the end of [`parser`](crate::parser), which is why a module -//! whose job is turning text into an AST also validated the ML policy, registered -//! declarations, ran training, drove the Datalog reasoner and built and fired -//! `CSPARQLWindow`s. Everything in `parser` above them was, and is, pure parsing. -//! -//! Splitting them out leaves `parser` depending on nothing in this crate, and lets -//! the stream and ML layers be called from above rather than reached up into from -//! below. +//! Running a `RULE` definition, and the `RETRIEVE` clause, against a store +use crate::database::KolibrieDB; use crate::execute_query::QueryError; use crate::parser::{convert_combined_rule, convert_triple_pattern, parse_combined_query}; use crate::rsp::r2s::{Relation2StreamOperator, StreamOperator}; use crate::rsp::s2r::{ CSPARQLWindow, ContentContainer, Report, ReportStrategy, Tick, WindowTriple, }; -use crate::sparql_database::SparqlDatabase; use datalog::reasoning::Reasoner; use shared::hybrid::*; use shared::provenance::Provenance; @@ -35,21 +26,12 @@ use shared::terms::*; use shared::triple::Triple; use std::collections::HashMap; -/// Parses a rule and runs it against `database`, returning the rule and the facts -/// it derived. -/// -/// The whole pipeline in one call: policy check, prefix registration, parse, lower -/// against the store's dictionary, then derive. Prefixes declared in `rule_input` -/// are registered on the store as a side effect, so later queries can use them. -/// -/// The ML policy is enforced first: a rule naming a model the host has not approved -/// is refused before anything is parsed or derived. +/// Parses a rule and runs it against `database`, returning the rule and the facts it derived pub fn process_rule_definition( rule_input: &str, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result<(Rule, Vec), QueryError> { crate::execute_query::validate_query_policy(rule_input, database)?; - // First, register any prefixes from the rule with the database database.register_prefixes_from_query(rule_input); let parse_result = parse_combined_query(rule_input); @@ -103,7 +85,7 @@ pub fn process_rule_definition( .map_err(QueryError::Execution)?; } - #[allow(unused_mut)] // ML-enabled execution can rewrite this rule. + #[allow(unused_mut)] let mut rule = combined .single_rule() .map_err(QueryError::Unsupported)? @@ -114,10 +96,7 @@ pub fn process_rule_definition( .materialize_for_patterns(database, &rule.body.0, &rule_prefixes) .map_err(QueryError::Execution)?; - // Execute ML.PREDICT before converting the rule for Datalog if let Some(ml_predict) = rule.ml_predict.clone() { - // No `cfg` needed any more: the build's runtime refuses this itself when - // the feature is off, with the same `QUERY_POLICY` code as before. crate::ml_runtime_for_build().execute_predict_clause( database, &ml_predict, @@ -128,11 +107,6 @@ pub fn process_rule_definition( let mut kg = Reasoner::new(); kg.adopt_terms(database.terms()); - // Only a windowed rule still needs the store's facts inside `kg`: it clones - // `kg` into a `'static` callback per window, and a boxed callback cannot - // borrow the store. Every other path reads the store through `QuadSource` - // and copies nothing; the hybrid annotation does its own copy below, because - // lineage materialisation has no source parameter yet. if !rule.window_clause.is_empty() { for triple in database.query_default_triples(None, None, None) { kg.dataset_index.insert(&triple); @@ -140,35 +114,27 @@ pub fn process_rule_definition( } kg.probability_seeds = database.probability_seeds.clone(); - // No assignment back afterwards: the store and the reasoner hold one term - // space, so a term interned here is already visible through the store. The - // line that used to copy it back was a no-op, which - // `shared_term_space_test` pins. let mut dynamic_rule = None; kg.with_terms(&mut |dict| { dynamic_rule = Some(convert_combined_rule(rule.clone(), dict, &rule_prefixes)); }); let dynamic_rule = dynamic_rule.expect("the visitor always runs"); - // Check if this rule has windowing - if so, set up RSP processing if !rule.window_clause.is_empty() { let mut all_stream_results: Vec = Vec::new(); let mut rsp_windows: Vec> = Vec::new(); - // Set up stream operator based on parsed stream type let stream_operator = match &rule.stream_type { Some(StreamType::RStream) => StreamOperator::RSTREAM, Some(StreamType::IStream) => StreamOperator::ISTREAM, Some(StreamType::DStream) => StreamOperator::DSTREAM, - _ => StreamOperator::RSTREAM, // Default + _ => StreamOperator::RSTREAM, }; - // Create a window for each window clause for window_clause in &rule.window_clause { let mut rsp_window = create_rsp_window(&window_clause.window_spec).map_err(QueryError::Execution)?; - // Process existing triples through the window let mut current_time = 1; let default_triples = database.query_default_triples(None, None, None); for triple in default_triples.iter() { @@ -180,19 +146,16 @@ pub fn process_rule_definition( }; drop(dict); - // Add to window rsp_window.add_to_window(window_triple, current_time); current_time += 1; } - // Register a callback to process windowed results let kg_clone = kg.clone(); let rule_clone = dynamic_rule.clone(); let _stream_op_clone = stream_operator.clone(); rsp_window.register_callback(Box::new( move |content: ContentContainer| { - // Convert window content back to Knowledge Graph format let mut window_kg = kg_clone.clone(); for window_triple in content.iter() { window_kg.add_abox_triple( @@ -202,7 +165,6 @@ pub fn process_rule_definition( ); } - // Apply the rule to windowed data window_kg.add_rule(rule_clone.clone()); window_kg.infer_new_facts_semi_naive(); }, @@ -211,13 +173,10 @@ pub fn process_rule_definition( rsp_windows.push(rsp_window); } - // Add the rule to the main knowledge graph kg.add_rule(dynamic_rule.clone()); - // For immediate processing, also infer from current data let inferred_facts = kg.infer_new_facts_semi_naive(); - // Apply stream operator to results let eval_time = database .query_default_triples(None, None, None) .len() @@ -227,28 +186,23 @@ pub fn process_rule_definition( let mut r2s_operator = Relation2StreamOperator::new(stream_operator.clone(), 0); let stream_results = r2s_operator.eval(inferred_facts.clone(), eval_time); - // Add inferred facts to the database for triple in stream_results.iter() { database.add_triple(triple.clone()); all_stream_results.push(triple.clone()); } } - // Register rule predicates register_rule_predicates(&dynamic_rule, database); return Ok((dynamic_rule, all_stream_results)); } - // Non-windowed rule processing - // Check if this is a provenance-annotated rule if rule.prob_annotation.is_some() { let ann = rule.prob_annotation.as_ref().unwrap(); kg.add_rule(dynamic_rule.clone()); register_rule_predicates(&dynamic_rule, database); - // Choose provenance based on annotation, then materialize tags as RDF-star let provenance_type = ann.combination; let inferred_facts = match provenance_type { "minmax" | "min" => { @@ -299,8 +253,6 @@ pub fn process_rule_definition( } facts } - // Full derivation lineage with certified top-k lower bounds and - // selective exact SDD escalation. "hybrid" => { let config = ann.hybrid_config.as_ref().ok_or_else(|| { QueryError::Execution( @@ -324,7 +276,6 @@ pub fn process_rule_definition( } facts } - // Exact proof-formula provenance (WMC via Shannon expansion) "wmc" => { let (facts, tag_store) = kg .infer_new_facts_with_provenance(shared::provenance::WmcProvenance::new()); @@ -340,7 +291,6 @@ pub fn process_rule_definition( } facts } - // SDD-based exact proof-formula provenance (WMC via SDD) "sdd" => { let (facts, tag_store) = kg.infer_new_facts_with_provenance_over( Some(database), @@ -358,9 +308,6 @@ pub fn process_rule_definition( } facts } - // Top-K proof-tracking provenance. - // k is read from the threshold field (default 5). - // Syntax: PROB(combination=topk) or PROB(combination=topk, threshold=10) "topk" => { let k = ann.threshold.map(|t| t as usize).unwrap_or(5); let (facts, tag_store) = kg.infer_new_facts_with_provenance_over( @@ -424,14 +371,10 @@ pub fn process_rule_definition( } else { kg.add_rule(dynamic_rule.clone()); - // Register rule predicates register_rule_predicates(&dynamic_rule, database); - // Infer new facts based on the rule, reading the base from the store - // rather than from a copy of it let inferred_facts = kg.infer_new_facts_semi_naive_over(database); - // Add inferred facts to the database for triple in inferred_facts.iter() { database.add_triple(triple.clone()); } @@ -445,18 +388,15 @@ pub fn process_rule_definition( } } -// Add this function to handle RETRIEVE clause processing -/// Resolves a `RETRIEVE` clause against `database`, returning the triples that -/// describe the matching streams. +/// Resolves a `RETRIEVE` clause against `database`, returning the triples that describe the +/// matching streams pub fn process_retrieve_clause( retrieve_clause: &RetrieveClause, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result, String> { - // Convert graph patterns to triple patterns for matching let mut retrieved_triples = Vec::new(); for pattern in &retrieve_clause.graph_pattern { - // Create a temporary knowledge graph to match patterns let mut kg = Reasoner::new(); let default_triples = database.query_default_triples(None, None, None); for triple in default_triples.iter() { @@ -471,12 +411,10 @@ pub fn process_retrieve_clause( } } - // Match the pattern against the knowledge graph let mut dict = database.dictionary.write(); let pattern_converted = convert_triple_pattern(*pattern, &mut dict, &database.prefixes); drop(dict); - // Find matching triples based on the pattern for triple in default_triples.iter() { if matches_pattern(&pattern_converted, triple) { retrieved_triples.push(triple.clone()); @@ -487,21 +425,17 @@ pub fn process_retrieve_clause( Ok(retrieved_triples) } -// Helper function to check if a triple matches a pattern fn matches_pattern(pattern: &TriplePattern, triple: &Triple) -> bool { - // Check subject match let subject_match = match &pattern.0 { Term::Variable(_) | Term::QuotedTriple(_) => true, Term::Constant(code) => *code == triple.subject, }; - // Check predicate match let predicate_match = match &pattern.1 { Term::Variable(_) | Term::QuotedTriple(_) => true, Term::Constant(code) => *code == triple.predicate, }; - // Check object match let object_match = match &pattern.2 { Term::Variable(_) | Term::QuotedTriple(_) => true, Term::Constant(code) => *code == triple.object, @@ -510,29 +444,25 @@ fn matches_pattern(pattern: &TriplePattern, triple: &Triple) -> bool { subject_match && predicate_match && object_match } -// Helper function to create RSP window from parsed specification fn create_rsp_window(window_spec: &WindowSpec) -> Result, String> { - // Create report strategy let mut report = Report::new(); let report_strategy = match window_spec.report_strategy { Some("NON_EMPTY_CONTENT") => ReportStrategy::NonEmptyContent, Some("ON_CONTENT_CHANGE") => ReportStrategy::OnContentChange, Some("ON_WINDOW_CLOSE") => ReportStrategy::OnWindowClose, - Some("PERIODIC") => ReportStrategy::Periodic(5), // Default period - _ => ReportStrategy::OnWindowClose, // Default + Some("PERIODIC") => ReportStrategy::Periodic(5), + _ => ReportStrategy::OnWindowClose, }; report.add(report_strategy); - // Create tick strategy let tick = match window_spec.tick { Some("TIME_DRIVEN") => Tick::TimeDriven, Some("TUPLE_DRIVEN") => Tick::TupleDriven, Some("BATCH_DRIVEN") => Tick::BatchDriven, - _ => Tick::TimeDriven, // Default + _ => Tick::TimeDriven, }; - // Handle different window types match window_spec.window_type { WindowType::Sliding => { let slide = window_spec.slide.unwrap_or(1); @@ -544,31 +474,24 @@ fn create_rsp_window(window_spec: &WindowSpec) -> Result { - // Tumbling window: slide = width - Ok(CSPARQLWindow::new( - window_spec.width, - window_spec.width, - report, - tick, - String::default(), - )) - } - WindowType::Range => { - // Range window: slide = 1 (continuous) - Ok(CSPARQLWindow::new( - window_spec.width, - 1, - report, - tick, - String::default(), - )) - } + WindowType::Tumbling => Ok(CSPARQLWindow::new( + window_spec.width, + window_spec.width, + report, + tick, + String::default(), + )), + WindowType::Range => Ok(CSPARQLWindow::new( + window_spec.width, + 1, + report, + tick, + String::default(), + )), } } -// Helper function to register rule predicates -fn register_rule_predicates(rule: &Rule, database: &mut SparqlDatabase) { +fn register_rule_predicates(rule: &Rule, database: &mut KolibrieDB) { for conclusion in &rule.conclusion { if let Term::Constant(code) = conclusion.1 { let dict = database.dictionary.read(); diff --git a/kolibrie/src/sparql_database.rs b/kolibrie/src/sparql_database.rs deleted file mode 100644 index 3101dac8..00000000 --- a/kolibrie/src/sparql_database.rs +++ /dev/null @@ -1,2941 +0,0 @@ -/* - * Copyright © 2025 Volodymyr Kadzhaia - * Copyright © 2025 Pieter Bonte - * KU Leuven — Stream Intelligence Lab, Belgium - * - * This Source Code Form is subject to the terms of the Mozilla Public - * License, v. 2.0. If a copy of the MPL was not distributed with this file, - * you can obtain one at https://mozilla.org/MPL/2.0/. - */ - -use crate::stats::DatabaseStats; -use crate::utils; -use crate::utils::ClonableFn; -use crossbeam::channel::unbounded; -use crossbeam::scope; -use quick_xml::events::Event; -use quick_xml::name::QName; -use quick_xml::Reader; -use rayon::prelude::*; -use shared::dataset_index::{DatasetIndex, GraphId, Quad}; -use shared::dictionary::Dictionary; -use shared::query::{ModelDecl, NeuralRelationDecl, TrainNeuralRelationDecl}; -use shared::quoted_triple_store::{is_quoted_triple_id, QuotedTripleStore}; -use shared::rdf_term; -use shared::rdf_term::TermTriple; -use shared::term_encoder::SharedTerms; -use shared::triple::Triple; -use std::collections::{BTreeMap, BTreeSet, HashMap}; -use std::sync::Arc; -use std::sync::{Mutex, RwLock, RwLockReadGuard, RwLockWriteGuard}; - -pub(crate) fn reencode_term_id( - id: u32, - source_dictionary: &Dictionary, - source_quoted_triples: &QuotedTripleStore, - target_dictionary: &mut Dictionary, - target_quoted_triples: &mut QuotedTripleStore, - translated_ids: &mut HashMap, -) -> u32 { - if let Some(translated) = translated_ids.get(&id) { - return *translated; - } - - let translated = if is_quoted_triple_id(id) { - let (subject, predicate, object) = source_quoted_triples - .decode(id) - .unwrap_or_else(|| panic!("quoted triple ID {id} is missing from its source store")); - let subject = reencode_term_id( - subject, - source_dictionary, - source_quoted_triples, - target_dictionary, - target_quoted_triples, - translated_ids, - ); - let predicate = reencode_term_id( - predicate, - source_dictionary, - source_quoted_triples, - target_dictionary, - target_quoted_triples, - translated_ids, - ); - let object = reencode_term_id( - object, - source_dictionary, - source_quoted_triples, - target_dictionary, - target_quoted_triples, - translated_ids, - ); - target_quoted_triples.encode(subject, predicate, object) - } else { - let lexical = source_dictionary - .decode(id) - .unwrap_or_else(|| panic!("term ID {id} is missing from its source dictionary")); - target_dictionary.encode(lexical) - }; - - translated_ids.insert(id, translated); - translated -} - -/// Staleness policy for planning statistics -#[derive(Debug, Clone, Copy, PartialEq)] -pub enum PlanningStatsPolicy { - /// Rebuild on every mutation. Exact, and the planner never sees stale - /// cardinalities, but it pays a full statistics pass per write. - AlwaysFresh, - /// Reuse a snapshot until the store has changed enough to matter. - Bounded { - /// Mutations to tolerate before rebuilding, however small the store. - minimum_batch: u64, - /// Fraction of the store that must change to force a rebuild, so the - /// tolerance grows with size instead of being a fixed count. - refresh_fraction: f64, - }, -} - -impl PlanningStatsPolicy { - /// Refresh after 100 mutations or half the store - pub const DEFAULT_BOUNDED: Self = PlanningStatsPolicy::Bounded { - minimum_batch: 100, - refresh_fraction: 0.5, - }; -} - -impl Default for PlanningStatsPolicy { - fn default() -> Self { - Self::DEFAULT_BOUNDED - } -} - -/// A planning statistics snapshot -#[derive(Debug, Clone)] -pub struct PlanningStatsSnapshot { - /// The cardinalities themselves, shared so reusing a snapshot is free. - pub stats: Arc, - /// `DatasetIndex::generation` at build time - pub generation: u64, - /// `total_triples` at build time - pub quads_at_build: u64, - /// Statistics epoch at build time - pub epoch: u64, - /// `DatasetIndex::resets` at build time - pub resets: u64, -} - -/// Read borrow of the term dictionary, from [`SparqlDatabase::dictionary`]. -/// -/// Derefs to [`Dictionary`], so `db.dictionary().decode(id)` reads the term space -/// without the caller naming how it is shared. Holding the binding holds the borrow, -/// so callers doing several lookups under one acquisition keep working unchanged. -/// -/// An alias rather than its own type: the store's term space *is* a -/// [`SharedTerms`], and two guard types over the one lock would only have to be kept -/// in step with each other. -pub type DictionaryRef<'a> = shared::term_encoder::TermsRef<'a>; - -/// Write borrow of the term dictionary, from [`SparqlDatabase::dictionary_mut`]. -/// See [`DictionaryRef`]. -pub type DictionaryMut<'a> = shared::term_encoder::TermsMut<'a>; - -/// Read guard over the quoted-triple store, from [`SparqlDatabase::quoted_triples`]. -/// -/// Exists so the public API never names `Arc>` (P5). It derefs to -/// [`QuotedTripleStore`], so call sites read `db.quoted_triples().len()` where they -/// previously read `db.quoted_triple_store.read().unwrap().len()`. Holding the -/// binding holds the lock, exactly as before — callers that need several operations -/// under one lock keep working unchanged. -#[derive(Debug)] -pub struct QuotedTriplesRef<'a>(RwLockReadGuard<'a, QuotedTripleStore>); - -impl std::ops::Deref for QuotedTriplesRef<'_> { - type Target = QuotedTripleStore; - fn deref(&self) -> &Self::Target { - &self.0 - } -} - -/// Write guard over the quoted-triple store, from -/// [`SparqlDatabase::quoted_triples_mut`]. See [`QuotedTriplesRef`]. -#[derive(Debug)] -pub struct QuotedTriplesMut<'a>(RwLockWriteGuard<'a, QuotedTripleStore>); - -impl std::ops::Deref for QuotedTriplesMut<'_> { - type Target = QuotedTripleStore; - fn deref(&self) -> &Self::Target { - &self.0 - } -} - -impl std::ops::DerefMut for QuotedTriplesMut<'_> { - fn deref_mut(&mut self) -> &mut Self::Target { - &mut self.0 - } -} - -/// The RDF store: encoded triples, the dictionary that encodes them, indexes over -/// several orderings, and the statistics the planner costs with. -/// -/// # No public fields -/// -/// Every field is private, reached through accessors. Terms are interned, indexes -/// are maintained in step with writes, and statistics are cached against the -/// index's generation — invariants a caller writing to a field directly would -/// break. Lock-held state is reached through guards rather than handles, and -/// `Arc>` does not appear in the public API at all: sharing a term -/// encoding, which was the one case that needed it, goes through -/// [`Self::terms`] instead. -/// -/// # Encoding -/// -/// Triples are stored as `u32` ids, not strings, and those ids mean nothing without -/// this store's dictionary. Two databases cannot exchange encoded data unless they -/// share one — see [`Self::shares_dictionary_with`] and [`Self::union`]. -#[derive(Debug, Clone)] -pub struct SparqlDatabase { - /// Host-selected ML authority - pub(crate) ml_context: crate::ml_policy::MlExecutionContext, - pub(crate) dataset_index: DatasetIndex, - pub(crate) dictionary: SharedTerms, - pub(crate) prefixes: HashMap, - udfs: HashMap, - pub(crate) rule_map: HashMap, - pub(crate) model_decls: HashMap, - pub(crate) neural_relation_decls: HashMap, - pub(crate) train_neural_relation_decls: HashMap, - pub(crate) neural_model_artifacts: HashMap, - pub(crate) neural_materialized_triples: HashMap>, - ml_predict_materialized_triples: HashMap>, - pub(crate) probability_seeds: HashMap, - cached_stats: Option>, - exact_stats_generation: u64, - planning_stats: Option, - pub(crate) planning_stats_policy: PlanningStatsPolicy, - stats_epoch: u64, - pub(crate) stats_rebuild_count: u64, - stats_rebuild_nanos: u64, - pub(crate) quoted_triple_store: Arc>, - implicit_neural_materialization: bool, - training_seed: Option, -} - -impl SparqlDatabase { - /// Initialize with host-selected ML authority - pub fn with_ml_context(context: crate::ml_policy::MlExecutionContext) -> Self { - let mut database = Self::new(); - database.ml_context = context; - database - } - /// An empty store with ML disabled and the default statistics policy. - pub fn new() -> Self { - Self { - ml_context: crate::ml_policy::MlExecutionContext::disabled(), - dataset_index: DatasetIndex::new(), - dictionary: SharedTerms::new(), - prefixes: HashMap::new(), - udfs: HashMap::new(), - rule_map: HashMap::new(), - model_decls: HashMap::new(), - neural_relation_decls: HashMap::new(), - train_neural_relation_decls: HashMap::new(), - neural_model_artifacts: HashMap::new(), - neural_materialized_triples: HashMap::new(), - ml_predict_materialized_triples: HashMap::new(), - probability_seeds: HashMap::new(), - cached_stats: None, - exact_stats_generation: 0, - planning_stats: None, - planning_stats_policy: PlanningStatsPolicy::default(), - stats_epoch: 0, - stats_rebuild_count: 0, - stats_rebuild_nanos: 0, - quoted_triple_store: Arc::new(RwLock::new(QuotedTripleStore::new())), - implicit_neural_materialization: true, - training_seed: None, - } - } - - /// Encode a term that may be a quoted triple `<< s p o >>` (recursive) - pub fn encode_term_star(&self, term: &str) -> u32 { - let trimmed = term.trim(); - if trimmed.starts_with("<<") && trimmed.ends_with(">>") { - let inner = &trimmed[2..trimmed.len() - 2].trim(); - let (s_str, p_str, o_str) = Self::split_quoted_triple_content(inner); - let s_id = self.encode_term_star(&s_str); - let p_id = self.encode_term_star(&p_str); - let o_id = self.encode_term_star(&o_str); - let mut qt = self.quoted_triple_store.write().unwrap(); - qt.encode(s_id, p_id, o_id) - } else { - let cleaned = if trimmed.starts_with('<') && trimmed.ends_with('>') { - trimmed[1..trimmed.len() - 1].to_string() - } else if trimmed.starts_with('"') { - // Through the same canonicaliser the reader uses. This used to keep - // only the lexical form, so a literal that arrived here already - // spelled canonically had its datatype stripped back off -- the same - // loss as the reader's, in a second place, undoing the first fix. - // Canonicalising is idempotent, so it is correct on either path. - rdf_term::canonicalise_literal(trimmed) - .unwrap_or_else(|| trimmed.trim_matches('"').to_string()) - } else { - trimmed.to_string() - }; - let mut dict = self.dictionary.write(); - dict.encode(&cleaned) - } - } - - /// Rewrites every blank node label in `term` into `document`'s scope. - /// - /// Recurses through a quoted triple, so a label nested inside one is scoped with - /// the rest of its document rather than left as the bare label the file wrote. - /// - /// Guarded on the term containing `_:` at all, since a term without it cannot - /// hold a blank node at any depth and most documents hold none; a literal that - /// happens to contain those two characters falls through unchanged. - fn scope_blank_nodes(&self, term: String, document: u64) -> String { - if !term.contains("_:") { - return term; - } - let trimmed = term.trim(); - if trimmed.starts_with("<<") && trimmed.ends_with(">>") { - let inner = trimmed[2..trimmed.len() - 2].trim(); - let (subject, predicate, object) = Self::split_quoted_triple_content(inner); - return format!( - "<< {} {} {} >>", - self.scope_blank_nodes(subject, document), - self.scope_blank_nodes(predicate, document), - self.scope_blank_nodes(object, document) - ); - } - if trimmed.starts_with("_:") { - return rdf_term::scope_blank_node(trimmed, document).into_owned(); - } - term - } - - /// Decode a u32 ID that may be a regular dictionary ID or a quoted triple ID - pub fn decode_any(&self, id: u32) -> Option { - if is_quoted_triple_id(id) { - let qt = self.quoted_triple_store.read().unwrap(); - let dict = self.dictionary.read(); - dict.decode_term(id, &qt) - } else { - let dict = self.dictionary.read(); - dict.decode(id).map(|s| s.to_string()) - } - } - - /// Split quoted triple content `s p o` into three parts, respecting nested `<< >>` - pub fn split_quoted_triple_content(content: &str) -> (String, String, String) { - let mut parts: Vec = Vec::new(); - let mut current = String::new(); - let mut depth = 0; - let mut in_uri = false; - let mut in_literal = false; - let mut escaped = false; - - for ch in content.chars() { - if escaped { - current.push(ch); - escaped = false; - continue; - } - match ch { - '\\' if in_literal => { - current.push(ch); - escaped = true; - } - '"' if !in_uri => { - in_literal = !in_literal; - current.push(ch); - } - '<' if !in_literal => { - current.push(ch); - // Check if this starts a quoted triple (<<) - if current.ends_with("<<") { - depth += 1; - } else if depth == 0 { - in_uri = true; - } - } - '>' if !in_literal => { - current.push(ch); - if in_uri { - in_uri = false; - } else if current.ends_with(">>") && depth > 0 { - depth -= 1; - } - } - ' ' | '\t' | '\n' | '\r' if depth == 0 && !in_uri && !in_literal => { - let trimmed = current.trim().to_string(); - if !trimmed.is_empty() { - parts.push(trimmed); - current.clear(); - } - } - _ => { - current.push(ch); - } - } - } - let trimmed = current.trim().to_string(); - if !trimmed.is_empty() { - parts.push(trimmed); - } - - if parts.len() >= 3 { - (parts[0].clone(), parts[1].clone(), parts[2..].join(" ")) - } else { - // Fallback: pad with empty strings - let s = parts.first().cloned().unwrap_or_default(); - let p = parts.get(1).cloned().unwrap_or_default(); - let o = parts.get(2).cloned().unwrap_or_default(); - (s, p, o) - } - } - - /// Replaces every prefix binding. See [`Self::add_prefix`] to add one. - pub fn set_prefixes(&mut self, prefixes: HashMap) { - self.prefixes = prefixes; - } - - /// Returns exact statistics, rebuilding them if the dataset generation changed - pub fn get_or_build_stats(&mut self) -> Arc { - let generation = self.dataset_index.generation(); - if let Some(stats) = &self.cached_stats { - if self.exact_stats_generation == generation { - return stats.clone(); - } - } - - let stats = self.rebuild_stats(); - self.cached_stats = Some(stats.clone()); - self.exact_stats_generation = generation; - stats - } - - /// Returns statistics for plan selection; under `Bounded` they may be stale within the threshold - pub fn get_or_build_planning_stats(&mut self) -> Arc { - if self.planning_stats_policy == PlanningStatsPolicy::AlwaysFresh { - return self.get_or_build_stats(); - } - - let generation = self.dataset_index.generation(); - if let Some(snapshot) = &self.planning_stats { - if self.planning_snapshot_is_usable(snapshot, generation) { - return snapshot.stats.clone(); - } - } - - let stats = self.rebuild_stats(); - self.planning_stats = Some(PlanningStatsSnapshot { - stats: stats.clone(), - generation, - quads_at_build: stats.total_triples, - epoch: self.stats_epoch, - resets: self.dataset_index.resets(), - }); - stats - } - - /// Returns true if the cached planning snapshot is still within the policy threshold - fn planning_snapshot_is_usable( - &self, - snapshot: &PlanningStatsSnapshot, - generation: u64, - ) -> bool { - // Invalidate on explicit reset or full clear - if snapshot.epoch != self.stats_epoch || snapshot.resets != self.dataset_index.resets() { - return false; - } - - // wrapping_sub handles counter wraparound; a replaced index yields a large delta - let mutations = generation.wrapping_sub(snapshot.generation); - if mutations == 0 { - return true; - } - - // Always rebuild a snapshot taken on an empty dataset - if snapshot.quads_at_build == 0 { - return false; - } - - let PlanningStatsPolicy::Bounded { - minimum_batch, - refresh_fraction, - } = self.planning_stats_policy - else { - return false; - }; - - let proportional = (refresh_fraction * snapshot.quads_at_build.max(1) as f64).ceil() as u64; - mutations < minimum_batch.max(proportional) - } - - /// Gathers fresh statistics over this store's dataset, without caching them. - /// - /// [`Self::get_or_build_stats`] is the cached form and the one query execution - /// uses; this is for a caller that wants a snapshot of the data as it stands. - pub fn gather_stats(&self) -> DatabaseStats { - DatabaseStats::gather_stats_fast( - &self.dataset_index, - self.quoted_triple_store.read().unwrap().len() as u64, - ) - } - - /// Builds statistics and records rebuild count and duration - fn rebuild_stats(&mut self) -> Arc { - let started = std::time::Instant::now(); - let stats = Arc::new(self.gather_stats()); - self.stats_rebuild_count += 1; - self.stats_rebuild_nanos = self - .stats_rebuild_nanos - .saturating_add(started.elapsed().as_nanos() as u64); - stats - } - - /// Invalidates all cached statistics - pub fn invalidate_stats_cache(&mut self) { - self.cached_stats = None; - self.planning_stats = None; - self.stats_epoch = self.stats_epoch.wrapping_add(1); - } - - /// Replaces the dataset index and invalidates cached statistics - pub fn replace_dataset_index(&mut self, dataset_index: DatasetIndex) { - self.dataset_index = dataset_index; - self.invalidate_stats_cache(); - } - - /// Number of statistics rebuilds and their total duration - pub fn stats_rebuild_metrics(&self) -> (u64, std::time::Duration) { - ( - self.stats_rebuild_count, - std::time::Duration::from_nanos(self.stats_rebuild_nanos), - ) - } - - /// Adds an already-encoded triple to the default graph. - /// - /// Ids must come from this store's dictionary. Use - /// [`Self::add_triple_parts`] to add one from text. - pub fn add_triple(&mut self, triple: Triple) { - self.dataset_index.insert_triple(&triple); - } - - /// Removes a triple from the default graph. Returns whether it was present. - pub fn delete_triple(&mut self, triple: &Triple) -> bool { - self.dataset_index.delete_triple(triple) - } - - /// Adds an encoded quad, creating its graph if needed. Returns whether it was - /// new. - pub fn add_quad(&mut self, quad: Quad) -> bool { - self.dataset_index.insert_quad(&quad) - } - - /// Removes a quad. Returns whether it was present. - pub fn delete_quad(&mut self, quad: &Quad) -> bool { - self.dataset_index.delete_quad(quad) - } - - /// Interns the given terms and adds the resulting quad. - pub fn add_quad_parts( - &mut self, - subject: &str, - predicate: &str, - object: &str, - graph: &str, - ) -> bool { - let subject_id = self.encode_term_star(subject); - let predicate_id = self.encode_term_star(predicate); - let object_id = self.encode_term_star(object); - let graph_id = { - let mut dict = self.dictionary.write(); - dict.encode(graph) - }; - - self.add_quad(Quad { - subject: subject_id, - predicate: predicate_id, - object: object_id, - graph: GraphId::Named(graph_id), - }) - } - - /// Default-graph triples matching the bound positions; `None` is a wildcard. - pub fn query_default_triples( - &self, - s: Option, - p: Option, - o: Option, - ) -> Vec { - self.dataset_index.query_default(s, p, o) - } - - /// Quads in one graph matching the bound positions; `None` is a wildcard. - pub fn query_graph_quads( - &self, - graph: GraphId, - s: Option, - p: Option, - o: Option, - ) -> Vec { - self.dataset_index.query_graph(graph, s, p, o) - } - - /// Encodes three lexical terms and adds the resulting triple - pub fn add_triple_parts(&mut self, subject: &str, predicate: &str, object: &str) { - let mut dict = self.dictionary.write(); - let subject_id = dict.encode(subject); - let predicate_id = dict.encode(predicate); - let object_id = dict.encode(object); - drop(dict); - - let triple = Triple { - subject: subject_id, - predicate: predicate_id, - object: object_id, - }; - self.add_triple(triple); - } - - /// Adds a triple carrying a provenance annotation, for probabilistic - /// reasoning. - pub fn add_tagged_triple( - &mut self, - subject: &str, - predicate: &str, - object: &str, - probability: f64, - ) { - let mut dict = self.dictionary.write(); - let s = dict.encode(subject); - let p = dict.encode(predicate); - let o = dict.encode(object); - drop(dict); - - let triple = Triple { - subject: s, - predicate: p, - object: o, - }; - self.add_triple(triple.clone()); - self.probability_seeds.insert(triple, probability); - } - - /// Encodes three lexical terms and deletes the resulting triple - pub fn delete_triple_parts(&mut self, subject: &str, predicate: &str, object: &str) -> bool { - let mut dict = self.dictionary.write(); - let subject_id = dict.encode(subject); - let predicate_id = dict.encode(predicate); - let object_id = dict.encode(object); - drop(dict); - - let triple = Triple { - subject: subject_id, - predicate: predicate_id, - object: object_id, - }; - self.delete_triple(&triple) - } - - /// Serialises the default graph as RDF/XML. - pub fn generate_rdf_xml(&mut self) -> String { - let mut xml = String::new(); - xml.push_str("\n"); - xml.push_str("\n"); - - // Group triples by subject - let dict = self.dictionary.read(); - let mut subjects: BTreeMap> = BTreeMap::new(); - let default_triples = self.query_default_triples(None, None, None); - for triple in &default_triples { - let subject = dict.decode(triple.subject); - let predicate = dict.decode(triple.predicate); - let object = dict.decode(triple.object); - subjects - .entry(subject.unwrap().to_string()) - .or_default() - .push((predicate.unwrap().to_string(), object.unwrap().to_string())); - } - drop(dict); - - // For each subject, create an element - for (subject, po_pairs) in subjects { - xml.push_str(&format!(" \n", subject)); - for (predicate, object) in po_pairs { - xml.push_str(&format!(" <{}>{}\n", predicate, object, predicate)); - } - xml.push_str(" \n"); - } - - xml.push_str("\n"); - xml - } - - /// Serializes all triples as N-Triples-star format - /// Blank nodes are written with their document-local labels, which is sound - /// within one store and unsound across two: see - /// [`generate_ntriples_skolemised`](Self::generate_ntriples_skolemised) for the - /// form to exchange. - pub fn generate_ntriples(&self) -> String { - self.write_ntriples(None) - } - - fn write_ntriples(&self, skolem_base: Option<&str>) -> String { - let mut output = String::new(); - for triple in self.query_default_triples(None, None, None) { - let s = self.decode_any(triple.subject).unwrap_or_default(); - let p = self.decode_any(triple.predicate).unwrap_or_default(); - let o = self.decode_any(triple.object).unwrap_or_default(); - - // Every position through the one writer. Written by hand, this emitted - // `<_:b>` for a blank node, which is an IRI and not a blank node; - // `"urn:a"` for any IRI whose scheme was not http, which is a literal; - // an unescaped literal, which breaks on a quote; and a typed literal - // quoted a second time, which demotes it to a plain one. - output.push_str(&format!( - "{} {} {} .\n", - Self::write_term(&s, skolem_base), - Self::write_term(&p, skolem_base), - Self::write_term(&o, skolem_base) - )); - } - output - } - - /// Serialises every graph as N-Quads, replacing blank nodes with Skolem IRIs - /// under `base`. - /// - /// See [`generate_ntriples_skolemised`](Self::generate_ntriples_skolemised). - pub fn generate_nquads_skolemised(&self, base: &str) -> String { - self.write_nquads(Some(base)) - } - - /// Serialises the default graph as N-Triples, replacing blank nodes with Skolem - /// IRIs under `base`. - /// - /// The form to publish or exchange. A blank node label means nothing outside the - /// document it came from, so two systems merging each other's output would fuse - /// unrelated nodes -- the plain serialisers emit document-local labels and are - /// sound only within one store. RDF 1.1 §3.5 answers that by replacing blank - /// nodes with IRIs, which are global; a receiver may keep them as IRIs, which is - /// what makes the merge safe, or recover the nodes with - /// [`deskolemise`](shared::rdf_term::deskolemise). - /// - /// `base` must be an authority the caller controls, and is not defaulted: a - /// Skolem IRI is unique because its authority is, and defaulting it would mint - /// the same IRI on two machines and put the collision back. - pub fn generate_ntriples_skolemised(&self, base: &str) -> String { - self.write_ntriples(Some(base)) - } - - /// Writes one term, replacing a blank node with a Skolem IRI when asked. - /// - /// Applied per term rather than to the serialised text. Rewriting the text would - /// have to find blank node labels in it, and a literal reading `"see _:b here"` - /// contains one that is not a term at all. - fn write_term(spelling: &str, skolem_base: Option<&str>) -> String { - match skolem_base { - Some(base) => rdf_term::to_ntriples_term(&rdf_term::skolemise(spelling, base)), - None => rdf_term::to_ntriples_term(spelling), - } - } - - /// Serialises every graph as N-Quads, which unlike N-Triples preserves named - /// graphs. - /// - /// Blank nodes are written with their document-local labels, which is sound - /// within one store and unsound across two: see - /// [`generate_nquads_skolemised`](Self::generate_nquads_skolemised) for the form - /// to exchange. - pub fn generate_nquads(&self) -> String { - self.write_nquads(None) - } - - fn write_nquads(&self, skolem_base: Option<&str>) -> String { - let mut output = String::new(); - for quad in self.dataset_index.all_quads() { - let s = self.decode_any(quad.subject).unwrap_or_default(); - let p = self.decode_any(quad.predicate).unwrap_or_default(); - let o = self.decode_any(quad.object).unwrap_or_default(); - - // See `generate_ntriples`: this handled blank nodes and non-http IRIs - // correctly but still quoted a typed or language-tagged literal a second - // time, turning it into a plain literal whose text looks typed. - let s_str = Self::write_term(&s, skolem_base); - let p_str = Self::write_term(&p, skolem_base); - let o_str = Self::write_term(&o, skolem_base); - match quad.graph { - GraphId::Default => { - output.push_str(&format!("{} {} {} .\n", s_str, p_str, o_str)); - } - GraphId::Named(graph_id) => { - let graph = self.decode_any(graph_id).unwrap_or_default(); - let graph = Self::write_term(&graph, skolem_base); - output.push_str(&format!("{} {} {} {} .\n", s_str, p_str, o_str, graph)); - } - } - } - output - } - - /// Serializes all triples as Turtle-star format with prefix declarations - pub fn generate_turtle(&self) -> String { - let mut output = String::new(); - - // Output prefix declarations - for (prefix, uri) in &self.prefixes { - output.push_str(&format!("@prefix {}: <{}> .\n", prefix, uri)); - } - if !self.prefixes.is_empty() { - output.push('\n'); - } - - // Group triples by subject, then by predicate - let mut subjects: BTreeMap>> = BTreeMap::new(); - for triple in self.query_default_triples(None, None, None) { - let s = self.decode_any(triple.subject).unwrap_or_default(); - let p = self.decode_any(triple.predicate).unwrap_or_default(); - let o = self.decode_any(triple.object).unwrap_or_default(); - subjects.entry(s).or_default().entry(p).or_default().push(o); - } - - for (subject, predicates) in &subjects { - output.push_str(&rdf_term::to_ntriples_term(subject)); - - let pred_count = predicates.len(); - for (i, (predicate, objects)) in predicates.iter().enumerate() { - if i == 0 { - output.push(' '); - } else { - output.push_str(" ;\n "); - } - output.push_str(&rdf_term::to_ntriples_term(predicate)); - - for (j, obj) in objects.iter().enumerate() { - if j > 0 { - output.push_str(" ,"); - } - output.push(' '); - output.push_str(&rdf_term::to_ntriples_term(obj)); - } - - if i == pred_count - 1 { - output.push_str(" .\n"); - } - } - } - output - } - - /// Parses RDF/XML and adds the triples. - /// - /// Malformed input is skipped rather than reported, so a partial parse looks - /// like a successful one with fewer triples. - pub fn parse_rdf(&mut self, rdf_xml: &str) { - let mut reader = Reader::from_str(rdf_xml); - - let mut current_subject = Vec::with_capacity(128); - let mut current_predicate = Vec::with_capacity(128); - // A property element carries its literal's datatype, but the literal itself - // arrives later as a text event, so it is remembered here and spent there. - // Not inherited: `rdf:datatype` describes the one literal it sits on. - let mut current_datatype = Vec::with_capacity(64); - // `xml:lang` *is* inherited -- it scopes to an element and its descendants - // (XML 1.0 section 2.12) -- so the tag in force is a stack rather than a - // field: each start element pushes its own tag or the one it inherits, and - // each end element pops. A single field would miss the common case of a - // document declaring its language once, on the root. - let mut language_scope: Vec> = Vec::new(); - - let (sender, receiver) = unbounded::>(); - let dictionary = self.dictionary.clone(); - let triples_set = Arc::new(Mutex::new(Vec::new())); - let num_threads = utils::get_num_cpus(); - - // Crossbeam scope to manage threads - scope(|s| { - // Spawn worker threads - for _ in 0..num_threads { - let receiver = receiver.clone(); - let triples_set = Arc::clone(&triples_set); - s.spawn(move |_| { - while let Ok(chunk) = receiver.recv() { - if chunk.is_empty() { - // Termination signal - break; - } - - // Process chunk using Rayon - let local_triples: BTreeSet = - chunk.into_par_iter().map(|triple| triple).collect(); - - // Insert into shared triples set - let mut triples = triples_set.lock().unwrap(); - triples.push(local_triples); - } - }); - } - - // Parsing and sending chunks - let mut triples = Vec::with_capacity(8192); - loop { - match reader.read_event() { - Ok(Event::Start(ref e)) => { - // The datatype resets, so one property's cannot reach the - // next property's literal; the language starts from whatever - // encloses this element and is overridden only if this - // element says so. `rdf:datatype` is an absolute IRI by - // definition and needs no prefix expansion. - current_datatype.truncate(0); - let mut language = language_scope.last().cloned().unwrap_or_default(); - for attr in e.attributes().filter_map(Result::ok) { - match attr.key { - QName(b"rdf:datatype") => { - current_datatype.extend_from_slice(&attr.value) - } - QName(b"xml:lang") => { - language.clear(); - language.extend_from_slice(&attr.value); - } - _ => {} - } - } - language_scope.push(language); - match e.name() { - QName(b"rdf:RDF") => { - for attr in e.attributes().filter_map(Result::ok) { - let key = attr.key; - let value = attr.value; - if key.as_ref().starts_with(b"xmlns:") { - let prefix = std::str::from_utf8(&key.as_ref()[6..]) - .unwrap_or("") - .to_string(); - let uri = - std::str::from_utf8(&value).unwrap_or("").to_string(); - self.prefixes.insert(prefix, uri); - } else if key.as_ref() == b"xmlns" { - // Default namespace - let uri = - std::str::from_utf8(&value).unwrap_or("").to_string(); - self.prefixes.insert("".to_string(), uri); - } - } - } - QName(b"rdf:Description") => { - for attr in e.attributes().filter_map(Result::ok) { - if attr.key == QName(b"rdf:about") { - current_subject.truncate(0); - current_subject.extend_from_slice(&attr.value); - } - } - } - QName(b"rdfs:Class") | QName(b"rdf:type") => { - current_predicate.truncate(0); - current_predicate.extend_from_slice(b"rdf:type"); - } - QName(b"rdfs:subClassOf") => { - current_predicate.truncate(0); - current_predicate.extend_from_slice(b"rdfs:subClassOf"); - } - QName(b"rdfs:label") => { - current_predicate.truncate(0); - current_predicate.extend_from_slice(b"rdfs:label"); - } - name => { - let name_str = - std::str::from_utf8(name.as_ref()).unwrap_or("").to_string(); - let resolved_predicate = self.resolve_term(&name_str); - current_predicate = resolved_predicate.clone().into_bytes(); - } - } - } - Ok(Event::Empty(ref e)) => { - if let Ok(predicate) = std::str::from_utf8(e.name().as_ref()) { - let resolved_predicate = self.resolve_term(predicate); - let mut object = Vec::with_capacity(128); - for attr in e.attributes().filter_map(Result::ok) { - if attr.key == QName(b"rdf:resource") { - object.extend_from_slice(&attr.value); - } - } - if !object.is_empty() { - if let (Ok(subject_str), Ok(object_str)) = ( - std::str::from_utf8(¤t_subject), - std::str::from_utf8(&object), - ) { - // Lock the dictionary for encoding - let mut dict = dictionary.write(); - let triple = Triple { - subject: dict.encode(subject_str), - predicate: dict.encode(&resolved_predicate), - object: dict.encode(object_str), - }; - drop(dict); // Release the lock - triples.push(triple); - } - } - } - } - Ok(Event::Text(e)) => { - // Use Reader's decode method and trim whitespace - if let Ok(object_str) = reader.decoder().decode(e.as_ref()) { - let trimmed_object = object_str.trim(); - // Skip empty or whitespace-only text - if !trimmed_object.is_empty() { - if let Ok(subject_str) = std::str::from_utf8(¤t_subject) { - if let Ok(predicate_str) = - std::str::from_utf8(¤t_predicate) - { - let resolved_predicate = self.resolve_term(predicate_str); - // Spelled the way every other reader spells - // it. This used to encode the text alone, so - // a document saying xsd:integer or @en - // produced a plain literal regardless. - let object = rdf_term::spell_literal( - trimmed_object, - std::str::from_utf8(¤t_datatype) - .ok() - .filter(|iri| !iri.is_empty()), - language_scope - .last() - .and_then(|tag| std::str::from_utf8(tag).ok()) - .filter(|tag| !tag.is_empty()), - ); - // Lock the dictionary for encoding - let mut dict = dictionary.write(); - let triple = Triple { - subject: dict.encode(subject_str), - predicate: dict.encode(&resolved_predicate), - object: dict.encode(&object), - }; - drop(dict); // Release the lock - triples.push(triple); - } - } - } - } - } - Ok(Event::End(ref e)) => { - // Leaving the element the tag was scoped to. A pop on an - // empty stack is a no-op, so malformed nesting cannot panic - // here; `Event::Empty` never pushed, so it must not pop. - language_scope.pop(); - if e.name() == QName(b"rdf:Description") { - current_subject.truncate(0); - current_predicate.truncate(0); - } - } - Ok(Event::Eof) => break, - Err(_e) => { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - break; - } - _ => {} - } - - if triples.len() >= 8192 { - sender.send(triples).unwrap(); - triples = Vec::with_capacity(8192); - } - } - - if !triples.is_empty() { - sender.send(triples).unwrap(); - } - - // Send termination signals - for _ in 0..num_threads { - sender.send(Vec::new()).unwrap(); - } - }) - .unwrap(); - - // Merge all BTreeSets into the main triples set - let triples_sets = Arc::try_unwrap(triples_set).unwrap().into_inner().unwrap(); - for local_triples in triples_sets { - for triple in local_triples { - self.add_triple(triple); - } - } - } - - /// Reads RDF/XML from a file and adds the triples. - /// - /// # Panics - /// - /// If the file cannot be read. - pub fn parse_rdf_from_file(&mut self, filename: &str) { - let file = std::fs::File::open(filename).expect("Cannot open file"); - let reader = std::io::BufReader::new(file); - let mut xml_reader = Reader::from_reader(reader); - - let mut current_subject = Vec::with_capacity(128); - let mut current_predicate = Vec::with_capacity(128); - // The same two as in `parse_rdf`, which this duplicates: a property element - // declares its literal's datatype and language, and the literal arrives - // later as a text event. Fixing one reader left this one still discarding - // both, so an RDF/XML file read from disk and the same document read from a - // string produced different terms. - let mut current_datatype = Vec::with_capacity(64); - let mut language_scope: Vec> = Vec::new(); - - // First, read prefixes before spawning worker threads - let mut buf = Vec::new(); - loop { - match xml_reader.read_event_into(&mut buf) { - Ok(Event::Start(ref e)) => { - if e.name() == QName(b"rdf:RDF") { - // Read prefixes - for attr in e.attributes().filter_map(Result::ok) { - let key = attr.key; - let value = attr.value; - if key.as_ref().starts_with(b"xmlns:") { - let prefix = std::str::from_utf8(&key.as_ref()[6..]) - .unwrap_or("") - .to_string(); - let uri = std::str::from_utf8(&value).unwrap_or("").to_string(); - self.prefixes.insert(prefix, uri); - } else if key.as_ref() == b"xmlns" { - // Default namespace - let uri = std::str::from_utf8(&value).unwrap_or("").to_string(); - self.prefixes.insert("".to_string(), uri); - } - } - break; // We have read the prefixes, proceed to the rest - } - } - Ok(Event::Eof) => { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - break; - } - Err(_e) => { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - break; - } - _ => {} - } - buf.clear(); - } - - // Continue reading and parsing the rest of the file - let mut triples = Vec::with_capacity(8192); - loop { - match xml_reader.read_event_into(&mut buf) { - Ok(Event::Start(ref e)) => { - current_datatype.truncate(0); - let mut language = language_scope.last().cloned().unwrap_or_default(); - for attr in e.attributes().filter_map(Result::ok) { - match attr.key { - QName(b"rdf:datatype") => { - current_datatype.extend_from_slice(&attr.value) - } - QName(b"xml:lang") => { - language.clear(); - language.extend_from_slice(&attr.value); - } - _ => {} - } - } - language_scope.push(language); - match e.name() { - QName(b"rdf:Description") => { - for attr in e.attributes().filter_map(Result::ok) { - if attr.key == QName(b"rdf:about") { - current_subject.clear(); - current_subject.extend_from_slice(&attr.value); - } - } - } - QName(b"rdfs:Class") | QName(b"rdf:type") => { - current_predicate.clear(); - current_predicate.extend_from_slice(b"rdf:type"); - } - QName(b"rdfs:subClassOf") => { - current_predicate.clear(); - current_predicate.extend_from_slice(b"rdfs:subClassOf"); - } - QName(b"rdfs:label") => { - current_predicate.clear(); - current_predicate.extend_from_slice(b"rdfs:label"); - } - name => { - let name_str = - std::str::from_utf8(name.as_ref()).unwrap_or("").to_string(); - let resolved_predicate = self.resolve_term(&name_str); - current_predicate = resolved_predicate.clone().into_bytes(); - } - } - } - Ok(Event::Empty(ref e)) => { - if let Ok(predicate) = std::str::from_utf8(e.name().as_ref()) { - let resolved_predicate = self.resolve_term(predicate); - let mut object = Vec::with_capacity(128); - for attr in e.attributes().filter_map(Result::ok) { - if attr.key == QName(b"rdf:resource") { - object.extend_from_slice(&attr.value); - } - } - if !object.is_empty() { - if let (Ok(subject_str), Ok(object_str)) = ( - std::str::from_utf8(¤t_subject), - std::str::from_utf8(&object), - ) { - let mut dict = self.dictionary.write(); - let triple = Triple { - subject: dict.encode(subject_str), - predicate: dict.encode(&resolved_predicate), - object: dict.encode(object_str), - }; - drop(dict); - triples.push(triple); - } - } - } - } - Ok(Event::Text(e)) => { - // Use Reader's decode method and trim whitespace - if let Ok(object_str) = xml_reader.decoder().decode(e.as_ref()) { - let trimmed_object = object_str.trim(); - // Skip empty or whitespace-only text - if !trimmed_object.is_empty() { - if let Ok(subject_str) = std::str::from_utf8(¤t_subject) { - if let Ok(predicate_str) = std::str::from_utf8(¤t_predicate) { - let resolved_predicate = self.resolve_term(predicate_str); - let object = rdf_term::spell_literal( - trimmed_object, - std::str::from_utf8(¤t_datatype) - .ok() - .filter(|iri| !iri.is_empty()), - language_scope - .last() - .and_then(|tag| std::str::from_utf8(tag).ok()) - .filter(|tag| !tag.is_empty()), - ); - let mut dict = self.dictionary.write(); - let triple = Triple { - subject: dict.encode(subject_str), - predicate: dict.encode(&resolved_predicate), - object: dict.encode(&object), - }; - drop(dict); - triples.push(triple); - } - } - } - } - } - Ok(Event::End(ref e)) => { - language_scope.pop(); - if e.name() == QName(b"rdf:Description") { - current_subject.clear(); - current_predicate.clear(); - } - } - Ok(Event::Eof) => break, - Err(_e) => { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - break; - } - _ => {} - } - - buf.clear(); - - if triples.len() >= 8192 { - // Process triples in parallel using Rayon - let local_triples: BTreeSet = triples.into_par_iter().collect(); - for triple in local_triples { - self.add_triple(triple); - } - triples = Vec::with_capacity(8192); - } - } - - if !triples.is_empty() { - let local_triples: BTreeSet = triples.into_par_iter().collect(); - for triple in local_triples { - self.add_triple(triple); - } - } - } - - /// Parses Turtle and adds the triples, honouring `@prefix` declarations and - /// the `;` and `,` abbreviations. - pub fn parse_turtle(&mut self, turtle_data: &str) { - // One scope for this document; see `parse_ntriples`. - let document = rdf_term::next_document_scope(); - for raw_line in turtle_data.lines() { - let line = raw_line.trim(); - - // Skip empty lines and comments - if line.is_empty() || line.starts_with("#") { - continue; - } - - // Prefix declarations - if line.starts_with("@prefix") || line.starts_with("PREFIX") { - let prefix_line = line - .trim_start_matches("@prefix") - .trim_start_matches("PREFIX") - .trim_end_matches('.') - .trim(); - - let parts: Vec<&str> = prefix_line.split_whitespace().collect(); - if parts.len() >= 2 { - let prefix = parts[0].trim_end_matches(':').to_string(); - let uri = parts[1] - .trim_start_matches('<') - .trim_end_matches('>') - .to_string(); - self.prefixes.insert(prefix, uri); - } else { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - } - continue; - } - - // Tokenize, but keep ; , . as delimiters only when outside URIs, literals, and quoted triples - let tokens = Self::tokenize_turtle_star_line(line); - - let mut subject_raw: Option = None; - let mut predicate_raw: Option = None; - let mut object_tokens: Vec = Vec::new(); - - let mut expect_subject = true; - let mut expect_predicate = false; - let mut expect_object = false; - - let flush_object = |this: &mut Self, - subject_raw: &Option, - predicate_raw: &Option, - object_tokens: &mut Vec| { - if let (Some(s_raw), Some(p_raw)) = (subject_raw.as_ref(), predicate_raw.as_ref()) { - if object_tokens.is_empty() { - return; - } - - let object_raw = object_tokens.join(" "); - - // Handle annotation syntax {| ... |} - let (object_part, annotations) = if let Some(ann_start) = object_raw.find("{|") - { - let obj = object_raw[..ann_start].trim().to_string(); - - if let Some(ann_end) = object_raw.find("|}") { - let ann_content = object_raw[ann_start + 2..ann_end].trim(); - let ann_parts: Vec<&str> = - ann_content.splitn(2, char::is_whitespace).collect(); - - if ann_parts.len() == 2 { - ( - obj, - vec![(ann_parts[0].to_string(), ann_parts[1].to_string())], - ) - } else { - (obj, vec![]) - } - } else { - (object_raw, vec![]) - } - } else { - (object_raw, vec![]) - }; - - let subject = - this.resolve_query_term(&Self::clean_turtle_term(s_raw), &this.prefixes); - let subject = this.scope_blank_nodes(subject, document); - let predicate = - this.resolve_query_term(&Self::clean_turtle_term(p_raw), &this.prefixes); - let predicate = this.scope_blank_nodes(predicate, document); - let object = this - .resolve_query_term(&Self::clean_turtle_term(&object_part), &this.prefixes); - let object = this.scope_blank_nodes(object, document); - - // Emit the main triple - if subject.starts_with("<<") || object.starts_with("<<") { - let s_id = this.encode_term_star(&subject); - let p_id = this.encode_term_star(&predicate); - let o_id = this.encode_term_star(&object); - let triple = Triple { - subject: s_id, - predicate: p_id, - object: o_id, - }; - this.add_triple(triple); - } else { - let mut dict = this.dictionary.write(); - let triple = Triple { - subject: dict.encode(&subject), - predicate: dict.encode(&predicate), - object: dict.encode(&object), - }; - drop(dict); - this.add_triple(triple); - } - - // Emit annotation triples, if any - for (ann_pred, ann_obj) in &annotations { - let qt_str = format!("<< {} {} {} >>", subject, predicate, object); - let qt_id = this.encode_term_star(&qt_str); - - let ann_p_id = this.encode_term_star(&this.resolve_query_term( - &Self::clean_turtle_term(ann_pred), - &this.prefixes, - )); - let ann_o_id = - this.encode_term_star(&this.resolve_query_term( - &Self::clean_turtle_term(ann_obj), - &this.prefixes, - )); - - let ann_triple = Triple { - subject: qt_id, - predicate: ann_p_id, - object: ann_o_id, - }; - this.add_triple(ann_triple); - } - - object_tokens.clear(); - } - }; - - for token in tokens { - match token.as_str() { - "." => { - flush_object(self, &subject_raw, &predicate_raw, &mut object_tokens); - subject_raw = None; - predicate_raw = None; - expect_subject = true; - expect_predicate = false; - expect_object = false; - } - ";" => { - flush_object(self, &subject_raw, &predicate_raw, &mut object_tokens); - predicate_raw = None; - expect_predicate = true; - expect_object = false; - } - "," => { - flush_object(self, &subject_raw, &predicate_raw, &mut object_tokens); - expect_object = true; - } - _ => { - if expect_subject { - subject_raw = Some(token); - expect_subject = false; - expect_predicate = true; - } else if expect_predicate { - predicate_raw = Some(token); - expect_predicate = false; - expect_object = true; - } else if expect_object { - object_tokens.push(token); - } else { - // Fallback for slightly malformed input - object_tokens.push(token); - } - } - } - } - - // Flush any trailing object if the line does not end with '.' - flush_object(self, &subject_raw, &predicate_raw, &mut object_tokens); - } - } - - /// Tokenize a Turtle-star line, keeping `<< ... >>` and punctuation structure intact - fn tokenize_turtle_star_line(line: &str) -> Vec { - let mut tokens = Vec::new(); - let mut current = String::new(); - - let mut depth = 0i32; // quoted-triple nesting depth - let mut in_uri = false; // inside <...> - let mut in_literal = false; // inside "..." - let mut escaped = false; - - let mut chars = line.chars().peekable(); - - while let Some(ch) = chars.next() { - if escaped { - current.push(ch); - escaped = false; - continue; - } - - match ch { - '\\' if in_literal => { - current.push(ch); - escaped = true; - } - - '"' if !in_uri && depth == 0 => { - in_literal = !in_literal; - current.push(ch); - } - - '"' if depth > 0 => { - in_literal = !in_literal; - current.push(ch); - } - - '<' if !in_literal => { - if chars.peek() == Some(&'<') && !in_uri { - current.push(ch); - current.push(chars.next().unwrap()); - depth += 1; - } else if depth > 0 { - current.push(ch); - if chars.peek() == Some(&'<') { - current.push(chars.next().unwrap()); - depth += 1; - } - } else { - in_uri = true; - current.push(ch); - } - } - - '>' if !in_literal => { - if depth > 0 && !in_uri { - current.push(ch); - if chars.peek() == Some(&'>') { - current.push(chars.next().unwrap()); - depth -= 1; - if depth == 0 { - tokens.push(current.trim().to_string()); - current.clear(); - } - } - } else if in_uri { - in_uri = false; - current.push(ch); - if depth == 0 { - tokens.push(current.trim().to_string()); - current.clear(); - } - } else { - current.push(ch); - } - } - - ';' | ',' | '.' if depth == 0 && !in_uri && !in_literal => { - let trimmed = current.trim().to_string(); - if !trimmed.is_empty() { - tokens.push(trimmed); - current.clear(); - } - tokens.push(ch.to_string()); - } - - ' ' | '\t' | '\n' | '\r' if depth == 0 && !in_uri && !in_literal => { - let trimmed = current.trim().to_string(); - if !trimmed.is_empty() { - tokens.push(trimmed); - current.clear(); - } - } - - _ => { - current.push(ch); - } - } - } - - let trimmed = current.trim().to_string(); - if !trimmed.is_empty() { - tokens.push(trimmed); - } - - tokens - } - - fn clean_turtle_term(term: &str) -> String { - let term = term.trim(); - if term.starts_with("<<") { - // Keep quoted triples as-is - term.to_string() - } else if term.starts_with('<') && term.ends_with('>') { - // Brackets kept, not stripped. They are the only thing distinguishing an - // absolute IRI from a prefixed name once the text is bare, and - // `resolve_query_term` is the one that needs to tell them apart: it - // strips them itself, while a bare `urn:a` reaching it is read as the - // prefix `urn` and reported as a failed resolution. Every non-http IRI - // in a Turtle document logged one -- two per triple, for subject and - // predicate -- while parsing correctly regardless. - term.to_string() - } else if term.starts_with('"') { - // Handed on whole, suffix included. Trimming the quotes here turned - // `"30"^^xsd:integer` into `30"^^xsd:integer`, because the term neither - // ends in a quote nor is a bare word: a stray quote, and a prefix that - // was never expanded. The quotes belong to the literal's syntax and come - // off when it is spelled canonically, which needs the prefix table. - term.to_string() - } else { - term.trim_matches('"').to_string() - } - } - - // New parse_n3 function - /// Parses the N3 subset this engine supports and adds the triples. - /// - /// Only the Datalog-expressible fragment; a full N3 reasoner is future work. - pub fn parse_n3(&mut self, n3_data: &str) { - // One scope for this document, taken before the chunks fan out; see - // `parse_ntriples`. - let document = rdf_term::next_document_scope(); - let lines: Vec = n3_data.lines().map(|l| l.trim().to_string()).collect(); - let chunk_size = 1000; - let chunks: Vec> = lines.chunks(chunk_size).map(|c| c.to_vec()).collect(); - - type ChunkResult = ( - Vec, - SharedTerms, - Arc>, - HashMap, - ); - let partial_results: Vec = chunks - .par_iter() - .map(|chunk| { - let mut local_db = SparqlDatabase::new(); - let mut statement = String::new(); - - for raw_line in chunk { - let mut line = raw_line.as_str(); - if let Some(comment_start) = line.find('#') { - line = &line[..comment_start]; - line = line.trim(); - } - if line.is_empty() { - continue; - } - if line.starts_with("@prefix") { - let line = line.trim_start_matches("@prefix").trim_end_matches('.'); - let parts: Vec<&str> = line.split_whitespace().collect(); - if parts.len() >= 2 { - let prefix = parts[0].trim_end_matches(':').to_string(); - let uri = parts[1] - .trim_start_matches('<') - .trim_end_matches('>') - .to_string(); - local_db.prefixes.insert(prefix, uri); - } else { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - } - } else { - statement.push_str(line); - statement.push(' '); - if line.ends_with('.') { - local_db.parse_statement(statement.trim(), document); - statement.clear(); - } - } - } - - ( - local_db.query_default_triples(None, None, None), - local_db.dictionary, - local_db.quoted_triple_store, - local_db.prefixes, - ) - }) - .collect(); - - for (triples, dict_arc, quoted_arc, pref) in partial_results { - // A chunk's ids belong to the chunk's own dictionary, so they are - // translated rather than inserted: id 3 there and id 3 here name - // different terms. Merging the dictionaries and keeping the ids was - // sound only while this store was empty and both assigned ids in the - // same order, so the first read of a document worked and every later one - // reinterpreted the new document's ids as the earlier one's terms -- - // silently storing a copy of the first document instead of the second. - let translated: Vec = { - let source_dictionary = dict_arc.read(); - let source_quoted = quoted_arc.read().unwrap_or_else(|p| p.into_inner()); - let mut target_dictionary = self.dictionary.write(); - let mut target_quoted = self - .quoted_triple_store - .write() - .unwrap_or_else(|p| p.into_inner()); - let mut seen = HashMap::new(); - let reencode = |id: u32, - target_dictionary: &mut Dictionary, - target_quoted: &mut QuotedTripleStore, - seen: &mut HashMap| { - reencode_term_id( - id, - &source_dictionary, - &source_quoted, - target_dictionary, - target_quoted, - seen, - ) - }; - triples - .iter() - .map(|triple| Triple { - subject: reencode( - triple.subject, - &mut target_dictionary, - &mut target_quoted, - &mut seen, - ), - predicate: reencode( - triple.predicate, - &mut target_dictionary, - &mut target_quoted, - &mut seen, - ), - object: reencode( - triple.object, - &mut target_dictionary, - &mut target_quoted, - &mut seen, - ), - }) - .collect() - }; - for triple in translated { - self.add_triple(triple); - } - for (k, v) in pref { - self.prefixes.insert(k, v); - } - } - } - - // Parse_ntriples and add to DB function - /// Parses N-Triples and adds them. This is the usual entry point. - /// - /// [`Self::parse_ntriples`] parses without adding, which is only useful when - /// the caller wants to inspect or filter first. - pub fn parse_ntriples_and_add(&mut self, ntriples_data: &str) { - let partial_results = self.parse_ntriples(ntriples_data); - - let encoded_triples = self.encode_triples(partial_results); - for encoded_triple in encoded_triples { - self.add_triple(encoded_triple); - } - } - - /// Parses N-Triples into lexical triples **without** adding them, in parallel - /// chunks of 1000 lines. - /// - /// Returns one group per chunk. The `a` shorthand is expanded to the full RDF - /// type IRI in predicate position. - pub fn parse_ntriples(&mut self, ntriples_data: &str) -> Vec> { - // One scope for this document, taken before the chunks fan out so that every - // chunk relabels `_:b` the same way and the node stays one node. - self.parse_ntriples_in_scope(ntriples_data, rdf_term::next_document_scope()) - } - - /// Parses N-Triples, relabelling blank nodes into a scope the caller chooses. - /// - /// For callers whose document boundary is not one call. A stream is the case this - /// exists for: each arriving element is its own document in RDF's terms, but the - /// dictionary never forgets a term, so minting a node per element grows it - /// without bound and eventually trips the assertion guarding the id space. A - /// long-running stream therefore shares one scope across its elements, which is - /// bounded, and keeps its labels separate from every file and every other stream. - /// - /// Obtain a scope from - /// [`next_document_scope`](shared::rdf_term::next_document_scope); reusing one is - /// exactly how two reads are declared to be the same document. - pub fn parse_ntriples_in_scope( - &mut self, - ntriples_data: &str, - document: u64, - ) -> Vec> { - let lines: Vec<&str> = ntriples_data.lines().collect(); - let chunk_size = 1000; - let chunks: Vec<&[&str]> = lines.chunks(chunk_size).collect(); - - let partial_results: Vec> = chunks - .par_iter() - .map(|chunk| { - let mut local_triples = Vec::new(); - - for line in chunk.iter() { - let line = line.trim(); - - // Skip empty lines and comments - if line.is_empty() || line.starts_with('#') { - continue; - } - - // N-Triples must end with a dot - if !line.ends_with('.') { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - continue; - } - - // Remove the trailing dot - let line_without_dot = &line[..line.len() - 1].trim(); - - // Parse the triple - if let Some((subject, predicate, object)) = - self.parse_ntriples_line(line_without_dot) - { - local_triples.push(TermTriple { - subject: self.scope_blank_nodes(subject, document), - predicate: self.scope_blank_nodes(predicate, document), - object: self.scope_blank_nodes(object, document), - }); - } - } - - local_triples - }) - .collect(); - partial_results - } - - // Encode triples - /// Interns lexical triples into encoded ones. - pub fn encode_triples(&mut self, non_encoded_triples: Vec>) -> Vec { - let mut encoded_triples = Vec::new(); - for triple_strings in non_encoded_triples { - for TermTriple { - subject, - predicate, - object, - } in triple_strings - { - let main_triple = Triple { - subject: self.encode_term_star(&subject), - predicate: self.encode_term_star(&predicate), - object: self.encode_term_star(&object), - }; - encoded_triples.push(main_triple); - } - } - encoded_triples - } - - /// Parses and encodes N-Triples without adding them to the store. - pub fn parse_and_encode_ntriples(&mut self, ntriples_data: &str) -> Vec { - let partial_results = self.parse_ntriples(ntriples_data); - - self.encode_triples(partial_results) - } - - /// Parses and encodes N-Triples in a scope the caller chooses. - /// - /// See [`parse_ntriples_in_scope`](Self::parse_ntriples_in_scope) for when a - /// caller should hold its own scope rather than take a fresh one per call. - pub fn parse_and_encode_ntriples_in_scope( - &mut self, - ntriples_data: &str, - document: u64, - ) -> Vec { - let partial_results = self.parse_ntriples_in_scope(ntriples_data, document); - - self.encode_triples(partial_results) - } - - /// Parses N-Quads and adds them, preserving named graphs. - pub fn parse_nquads_and_add(&mut self, nquads_data: &str) { - // One scope for this document; see `parse_ntriples`. A graph name may be a - // blank node too, and is scoped with the rest of the document. - let document = rdf_term::next_document_scope(); - for raw_line in nquads_data.lines() { - let line = raw_line.trim(); - if line.is_empty() || line.starts_with('#') { - continue; - } - - let line_without_dot = if line.ends_with('.') { - line[..line.len() - 1].trim() - } else { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - continue; - }; - - if let Some((subject, predicate, object, graph)) = - self.parse_nquads_line(line_without_dot) - { - let subject = self.scope_blank_nodes(subject, document); - let predicate = self.scope_blank_nodes(predicate, document); - let object = self.scope_blank_nodes(object, document); - let graph = graph.map(|name| self.scope_blank_nodes(name, document)); - match graph { - Some(graph) => { - self.add_quad_parts(&subject, &predicate, &object, &graph); - } - None => { - let quad = Quad { - subject: self.encode_term_star(&subject), - predicate: self.encode_term_star(&predicate), - object: self.encode_term_star(&object), - graph: GraphId::Default, - }; - self.add_quad(quad); - } - } - } - } - } - - fn parse_nquads_line(&self, line: &str) -> Option<(String, String, String, Option)> { - let mut parts = self.parse_ntriples_parts(line); - if !matches!(parts.len(), 3 | 4) { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - return None; - } - let subject = self.clean_ntriples_term(&parts.remove(0)); - let predicate = self.clean_ntriples_term(&parts.remove(0)); - let object = self.clean_ntriples_term(&parts.remove(0)); - let graph = (!parts.is_empty()).then(|| self.clean_ntriples_term(&parts.remove(0))); - Some((subject, predicate, object, graph)) - } - - // Helper method to parse a single N-Triples line - fn parse_ntriples_line(&self, line: &str) -> Option<(String, String, String)> { - let parts = self.parse_ntriples_parts(line); - if parts.len() == 3 { - let subject = self.clean_ntriples_term(&parts[0]); - // Expand the Turtle `a` shorthand for rdf:type in predicate position - let predicate = if parts[1] == "a" { - "http://www.w3.org/1999/02/22-rdf-syntax-ns#type".to_string() - } else { - self.clean_ntriples_term(&parts[1]) - }; - let object = self.clean_ntriples_term(&parts[2]); - Some((subject, predicate, object)) - } else { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - None - } - } - - fn parse_ntriples_parts(&self, line: &str) -> Vec { - let mut parts = Vec::new(); - let mut current_part = String::new(); - let mut in_uri = false; - let mut in_literal = false; - let mut escaped = false; - let mut qt_depth: i32 = 0; // Track quoted triple nesting depth - let mut chars = line.chars().peekable(); - - while let Some(ch) = chars.next() { - match ch { - '<' if !in_literal && !escaped => { - if chars.peek() == Some(&'<') && !in_uri { - // Start of quoted triple << - current_part.push(ch); - current_part.push(chars.next().unwrap()); - qt_depth += 1; - } else if qt_depth > 0 { - // Inside a quoted triple, could be a nested << or a URI < - current_part.push(ch); - if chars.peek() == Some(&'<') { - // Nested << - current_part.push(chars.next().unwrap()); - qt_depth += 1; - } else { - // URI inside quoted triple — just accumulate - } - } else { - in_uri = true; - current_part.push(ch); - } - } - '>' if !in_literal && !escaped => { - if qt_depth > 0 && !in_uri { - current_part.push(ch); - if chars.peek() == Some(&'>') { - current_part.push(chars.next().unwrap()); - qt_depth -= 1; - if qt_depth == 0 { - // Finished top-level quoted triple - parts.push(current_part.trim().to_string()); - current_part.clear(); - } - } - // else: stray > inside quoted triple, just accumulate - } else if in_uri { - in_uri = false; - current_part.push(ch); - if qt_depth == 0 { - parts.push(current_part.trim().to_string()); - current_part.clear(); - } - } else { - current_part.push(ch); - } - } - '"' if !in_uri && !escaped => { - in_literal = !in_literal; - current_part.push(ch); - if !in_literal { - // Check for datatype or language tag after closing quote - while let Some(&next_ch) = chars.peek() { - if next_ch == '^' || next_ch == '@' { - current_part.push(chars.next().unwrap()); - // Handle ^^ for datatypes - if next_ch == '^' { - if let Some(&second_caret) = chars.peek() { - if second_caret == '^' { - current_part.push(chars.next().unwrap()); - // Now consume the datatype URI - while let Some(&datatype_ch) = chars.peek() { - if datatype_ch == '<' { - // Start of datatype URI - current_part.push(chars.next().unwrap()); - let mut in_datatype_uri = true; - while let Some(&uri_ch) = chars.peek() { - current_part.push(chars.next().unwrap()); - if uri_ch == '>' { - in_datatype_uri = false; - break; - } - } - if !in_datatype_uri { - break; - } - } else if datatype_ch.is_whitespace() { - break; - } else { - current_part.push(chars.next().unwrap()); - } - } - } - } - } else if next_ch == '@' { - // Language tag - while let Some(&lang_ch) = chars.peek() { - if lang_ch.is_alphanumeric() || lang_ch == '-' { - current_part.push(chars.next().unwrap()); - } else { - break; - } - } - } - break; - } else if next_ch.is_whitespace() { - break; - } else { - // Unexpected character after literal - break; - } - } - if qt_depth == 0 { - parts.push(current_part.trim().to_string()); - current_part.clear(); - } - } - } - '\\' if (in_uri || in_literal) && !escaped => { - escaped = true; - current_part.push(ch); - } - ' ' | '\t' if !in_uri && !in_literal && !escaped && qt_depth == 0 => { - if !current_part.is_empty() { - parts.push(current_part.trim().to_string()); - current_part.clear(); - } - } - _ => { - escaped = false; - current_part.push(ch); - } - } - } - - if !current_part.is_empty() { - parts.push(current_part.trim().to_string()); - } - - parts - } - - // Helper method to clean N-Triples terms - fn clean_ntriples_term(&self, term: &str) -> String { - let term = term.trim(); - - // Keep quoted triples as-is - if term.starts_with("<<") && term.ends_with(">>") { - return term.to_string(); - } - - // Handle URIs - if term.starts_with('<') && term.ends_with('>') { - return term[1..term.len() - 1].to_string(); - } - - // Literals are spelled canonically, so that two literals share a spelling - // exactly when RDF says they are one term. This used to return the lexical - // form alone for a typed literal, which discarded the datatype: `"25"`, - // `"25"^^xsd:integer` and `"25"^^xsd:string` all became `25`, and nothing - // downstream could tell them apart or write them back out. - if term.starts_with('"') { - if let Some(canonical) = rdf_term::canonicalise_literal(term) { - return canonical; - } - } - - // Return as-is for other cases - term.to_string() - } - - fn parse_statement(&mut self, statement: &str, document: u64) { - let mut tokens = statement.split_whitespace().peekable(); - let mut subject = String::new(); - let mut predicate = String::new(); - let mut current_state = "subject"; - - while let Some(token) = tokens.next() { - match token { - ";" => { - predicate.clear(); - current_state = "predicate"; - } - "." => { - // End of statement - break; - } - _ => match current_state { - "subject" => { - subject = token.to_string(); - current_state = "predicate"; - } - "predicate" => { - predicate = token.to_string(); - current_state = "object"; - } - "object" => { - let mut object = token.to_string(); - - // Collect tokens until we reach ';', '.', or ',' - while let Some(next_token) = tokens.peek() { - if *next_token == ";" || *next_token == "." || *next_token == "," { - break; - } - // Consume the token - let next_token = tokens.next().unwrap(); - object.push(' '); - object.push_str(next_token); - } - - // Resolve terms and store the triple - let resolved_subject = self.resolve_term(&subject); - let resolved_subject = self.scope_blank_nodes(resolved_subject, document); - let resolved_predicate = self.resolve_term(&predicate); - let resolved_predicate = - self.scope_blank_nodes(resolved_predicate, document); - let resolved_object = self.resolve_term(&object); - let resolved_object = self.scope_blank_nodes(resolved_object, document); - - let mut dict = self.dictionary.write(); - let triple = Triple { - subject: dict.encode(&resolved_subject), - predicate: dict.encode(&resolved_predicate), - object: dict.encode(&resolved_object), - }; - drop(dict); - self.add_triple(triple); - - current_state = "predicate"; - } - _ => {} - }, - } - } - } - - fn resolve_term(&self, term: &str) -> String { - if term.starts_with('<') && term.ends_with('>') { - term.trim_start_matches('<') - .trim_end_matches('>') - .to_string() - } else if term.starts_with('"') { - // A literal, possibly with a datatype or language tag. The datatype may - // be written prefixed (`xsd:integer`), so it is resolved against the - // prefix table before the whole literal is spelled canonically -- the - // same spelling the N-Triples reader produces, so that Turtle and - // N-Triples mint one id for one term. They did not: this path emitted - // `"25"^^http://...` with no angle brackets, which is a third spelling - // of a term the reader and `tag_store` already spelled two ways. - if let Some(pos) = term.rfind('"') { - let literal = &term[..=pos]; // Include the closing quote - let rest = &term[pos + 1..]; // After the closing quote - let resolved = if let Some(datatype) = rest.strip_prefix("^^") { - format!("{}^^<{}>", literal, self.resolve_term(datatype.trim())) - } else { - format!("{}{}", literal, rest) - }; - rdf_term::canonicalise_literal(&resolved).unwrap_or(resolved) - } else { - // Malformed literal - term.to_string() - } - } else if term.contains(':') - && !term.starts_with("http://") - && !term.starts_with("https://") - { - let mut parts = term.splitn(2, ':'); - let prefix = parts.next().unwrap(); - let local_name = parts.next().unwrap_or(""); - if let Some(uri) = self.prefixes.get(prefix) { - format!("{}{}", uri, local_name) - } else { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - term.to_string() - } - } else { - term.to_string() - } - } - - // Method to automatically extract and register prefixes from a query string - /// Adopts the `PREFIX` declarations from a query, so later queries and rules - /// can use them without repeating them. - pub fn register_prefixes_from_query(&mut self, query: &str) { - // Simple regex to extract PREFIX declarations - let prefix_pattern = regex::Regex::new(r"PREFIX\s+([a-zA-Z0-9_]+):\s*<([^>]+)>").unwrap(); - - for captures in prefix_pattern.captures_iter(query) { - if captures.len() >= 3 { - let prefix = captures[1].to_string(); - let uri = captures[2].to_string(); - self.prefixes.insert(prefix, uri); - } - } - } - - // Method to ensure prefixes are properly shared between components - /// Copies this store's prefixes into `prefixes`, leaving existing entries - /// alone. - pub fn share_prefixes_with(&self, prefixes: &mut HashMap) { - for (prefix, uri) in &self.prefixes { - prefixes.insert(prefix.clone(), uri.clone()); - } - } - - /// Expands a lexical term to its full IRI using `prefixes`, then this store's - /// own. Returns the term unchanged when no prefix applies. - pub fn resolve_query_term(&self, term: &str, prefixes: &HashMap) -> String { - if term.starts_with("<<") && term.ends_with(">>") { - // Keep quoted triple patterns as-is (they'll be handled downstream) - return term.to_string(); - } - if term.starts_with('<') && term.ends_with('>') { - term.trim_start_matches('<') - .trim_end_matches('>') - .to_string() - } else if term.starts_with('"') { - // A literal, with or without a datatype or language suffix. Its datatype - // may be a prefixed name, and this is the only place holding the prefix - // table, so the canonical spelling is produced here rather than earlier. - // - // A suffixed literal used to fall through to the prefixed-name branch - // below, where `"30"^^xsd:integer` was split on its first colon into the - // prefix `"30"^^xsd` -- never found, so it reported a failure and - // returned the term unchanged. - if let Some(position) = term.rfind('"') { - let literal = &term[..=position]; - let rest = term[position + 1..].trim(); - let resolved = if let Some(datatype) = rest.strip_prefix("^^") { - format!( - "{}^^<{}>", - literal, - self.resolve_query_term(datatype.trim(), prefixes) - ) - } else { - format!("{}{}", literal, rest) - }; - rdf_term::canonicalise_literal(&resolved).unwrap_or(resolved) - } else { - term.to_string() - } - } else if term.contains(':') - && !term.starts_with("http://") - && !term.starts_with("https://") - { - let mut parts = term.splitn(2, ':'); - let prefix = parts.next().unwrap(); - let local_name = parts.next().unwrap_or(""); - - // First check the passed prefixes map - if let Some(uri) = prefixes.get(prefix) { - format!("{}{}", uri, local_name) - } - // Then check the database's own prefixes map as a fallback - else if let Some(uri) = self.prefixes.get(prefix) { - format!("{}{}", uri, local_name) - } else { - eprintln!("KOLIBRIE_OPERATION_FAILED"); - term.to_string() - } - } else { - term.to_string() - } - } - - /// A new store containing both stores' data. - /// - /// The two dictionaries assigned ids independently, so `other`'s ids are - /// translated into the merged dictionary rather than assumed compatible — - /// which is why this returns a third store instead of mutating either. - pub fn union(&mut self, other: &SparqlDatabase) -> Self { - let self_dict = self.dictionary.read(); - let other_dict = other.dictionary.read(); - let mut merged_dictionary = self_dict.clone(); - let self_quoted_triples = self.quoted_triple_store.read().unwrap(); - let other_quoted_triples = other.quoted_triple_store.read().unwrap(); - let mut merged_quoted_triples = self_quoted_triples.clone(); - let mut translated_ids = HashMap::new(); - - // Preserve the complete lexical dictionary, not only terms currently - let mut other_term_ids: Vec<_> = other_dict.id_to_string.keys().copied().collect(); - other_term_ids.sort_unstable(); - for id in other_term_ids { - reencode_term_id( - id, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - } - - // Preserve even currently-unreferenced quoted terms. Quads and metadata - let mut other_quoted_ids: Vec<_> = other_quoted_triples - .id_to_components - .keys() - .copied() - .collect(); - other_quoted_ids.sort_unstable(); - for id in other_quoted_ids { - reencode_term_id( - id, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - } - - let mut dataset_index = DatasetIndex::new(); - for graph in self.dataset_index.named_graphs() { - dataset_index.create_graph(graph); - } - for quad in self.dataset_index.all_quads() { - dataset_index.insert_quad(&quad); - } - - // Graph names and every term in the other database must be translated - for graph in other.dataset_index.named_graphs() { - let GraphId::Named(graph_id) = graph else { - continue; - }; - let graph_id = reencode_term_id( - graph_id, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - dataset_index.create_graph(GraphId::Named(graph_id)); - } - for quad in other.dataset_index.all_quads() { - let subject = reencode_term_id( - quad.subject, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - let predicate = reencode_term_id( - quad.predicate, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - let object = reencode_term_id( - quad.object, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - let graph = match quad.graph { - GraphId::Default => GraphId::Default, - GraphId::Named(graph_id) => GraphId::Named(reencode_term_id( - graph_id, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - )), - }; - dataset_index.insert_quad(&Quad { - subject, - predicate, - object, - graph, - }); - } - - let mut merged_seeds = self.probability_seeds.clone(); - for (triple, prob) in &other.probability_seeds { - let subject = reencode_term_id( - triple.subject, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - let predicate = reencode_term_id( - triple.predicate, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - let object = reencode_term_id( - triple.object, - &other_dict, - &other_quoted_triples, - &mut merged_dictionary, - &mut merged_quoted_triples, - &mut translated_ids, - ); - merged_seeds.insert( - Triple { - subject, - predicate, - object, - }, - *prob, - ); - } - - Self { - dataset_index, - dictionary: SharedTerms::from_dictionary(merged_dictionary), - prefixes: self.prefixes.clone(), - udfs: HashMap::new(), - rule_map: HashMap::new(), - model_decls: self.model_decls.clone(), - neural_relation_decls: self.neural_relation_decls.clone(), - ml_context: self.ml_context.clone(), - train_neural_relation_decls: self.train_neural_relation_decls.clone(), - neural_model_artifacts: self.neural_model_artifacts.clone(), - neural_materialized_triples: self.neural_materialized_triples.clone(), - ml_predict_materialized_triples: self.ml_predict_materialized_triples.clone(), - probability_seeds: merged_seeds, - // Statistics are rebuilt on first use - cached_stats: None, - exact_stats_generation: 0, - planning_stats: None, - planning_stats_policy: self.planning_stats_policy, - stats_epoch: 0, - stats_rebuild_count: 0, - stats_rebuild_nanos: 0, - quoted_triple_store: Arc::new(RwLock::new(merged_quoted_triples)), - implicit_neural_materialization: self.implicit_neural_materialization, - training_seed: self.training_seed, - } - } - - /// Runs a query and renders the result as text, for the demo server. - pub fn handle_query(&mut self, query: &str) -> String { - // Assume the query string is in a basic format like "subject predicate object" - let parts: Vec<&str> = query.split_whitespace().collect(); - - if parts.len() != 3 { - return "Invalid query format. Expected 'subject predicate object'.".to_string(); - } - - let subject = parts[0]; - let predicate = parts[1]; - let object = parts[2]; - - let mut dict = self.dictionary.write(); - let subject_id = dict.encode(subject); - let predicate_id = dict.encode(predicate); - let object_id = dict.encode(object); - - let mut result = String::new(); - let matching_triples = - self.query_default_triples(Some(subject_id), Some(predicate_id), Some(object_id)); - for triple in matching_triples { - if triple.subject == subject_id - && triple.predicate == predicate_id - && triple.object == object_id - { - result.push_str(&format!( - "Subject: {}, Predicate: {}, Object: {}\n", - dict.decode(triple.subject).unwrap(), - dict.decode(triple.predicate).unwrap(), - dict.decode(triple.object).unwrap() - )); - } - } - drop(dict); - - if result.is_empty() { - result = "No matching triples found.".to_string(); - } - - result - } - - /// Return the decoded graph size for diagnostics - pub fn debug_print_triples(&self) { - println!( - "TRIPLE_COUNT {}", - self.query_default_triples(None, None, None).len() - ); - } - - // Create user defined function - /// Registers a user-defined function callable from `FILTER` expressions. - /// - /// Receives arguments as decoded text and returns text. Re-registering a name - /// replaces the previous function. - pub fn register_udf(&mut self, name: &str, f: F) - where - F: Fn(Vec<&str>) -> String + Send + Sync + 'static, - { - self.udfs.insert(name.to_string(), ClonableFn::new(f)); - } - - // ── Dataset index ─────────────────────────────────────────────────────────── - // - // The last field to leave the public API. These are deliberately named - // operations rather than a `dataset_index()` getter: §4 has this surface becoming - // `QuadSource` (scan / insert / graphs / len), so the shape callers depend on - // should already be that rather than a borrow of `DatasetIndex`, which would - // freeze the representation the storage trait is supposed to abstract. - // - // Most of them are thin, and that is the point — 33 of the 46 external call sites - // only wanted a triple count. - // - // The mutating ones deliberately do NOT call `invalidate_stats_cache`, matching - // `add_triple`/`add_quad`/`delete_*` above. Statistics detect a changed index - // through `DatasetIndex::generation`, not through being told — the two tests at - // the bottom of this file exist to prove exactly that. Invalidating here would - // force a rebuild the generation check avoids, and would change the behaviour the - // rewritten call sites had when they wrote to the field directly. - - /// Number of triples in the default graph. - pub fn default_triple_count(&self) -> usize { - self.dataset_index.len_default() - } - - /// Number of quads across every graph, including named ones. - pub fn quad_count(&self) -> usize { - self.dataset_index.count_quads() - } - - /// Every quad in the store, across all graphs. - pub fn all_quads(&self) -> Vec { - self.dataset_index.all_quads() - } - - /// Every graph in the store, including the default graph. - pub fn graphs(&self) -> Vec { - self.dataset_index.graphs() - } - - /// The named graphs, excluding the default graph. - pub fn named_graphs(&self) -> Vec { - self.dataset_index.named_graphs() - } - - /// Creates `graph` if it does not exist. Returns whether it was created. - pub fn create_graph(&mut self, graph: GraphId) -> bool { - self.dataset_index.create_graph(graph) - } - - /// Inserts a triple into the default graph, reporting whether it was new. - /// - /// [`Self::add_triple`] does the same without the answer; this exists because a - /// caller deduplicating derived facts needs to know. - pub fn insert_triple(&mut self, triple: &Triple) -> bool { - self.dataset_index.insert_triple(triple) - } - - /// Removes every quad and graph, leaving the dictionary intact. - pub fn clear_dataset(&mut self) { - self.dataset_index.clear(); - } - - // ── Term dictionary ───────────────────────────────────────────────────────── - - /// Read access to the term dictionary. - pub fn dictionary(&self) -> DictionaryRef<'_> { - self.dictionary.read() - } - - /// Write access to the term dictionary, for interning new terms. - pub fn dictionary_mut(&self) -> DictionaryMut<'_> { - self.dictionary.write() - } - - /// Whether `self` and `other` encode terms through the same dictionary, as - /// opposed to holding separate ones. - /// - /// Replaces `Arc::ptr_eq` on the raw field: it states the property callers - /// actually test — whether two databases are isolated — without the handle - /// having to be public. - pub fn shares_dictionary_with(&self, other: &SparqlDatabase) -> bool { - self.dictionary.shares_space_with(&other.dictionary) - } - - /// This store's term space, as an encoder another component can adopt. - /// - /// The way to share one encoding between a store and a reasoner, or between two - /// engines. It shares rather than copies — a term interned through it is - /// immediately visible here — and it hands back an opaque - /// [`SharedTerms`] rather than the - /// `Arc>` underneath, which is what P5 asks and what replaced the two - /// handle methods this used to sit beside. - pub fn terms(&self) -> SharedTerms { - self.dictionary.clone() - } - - /// Adopts another component's term space, replacing this store's own. - /// - /// The counterpart to [`terms`](Self::terms), and the same operation a - /// [`Reasoner`](datalog::reasoning::Reasoner) offers, so two components agree on - /// one encoding the same way whichever of them is adopting. Several places used - /// to assign the dictionary field directly, which only worked from inside this - /// crate and said nothing about what it was for. - /// - /// Sharing, not copying: a term interned afterwards through either side is - /// visible to both. Code that assigns a handle and then assigns it back to - /// "sync" the result is doing nothing, which `shared_term_space_test` pins. - pub fn adopt_terms(&mut self, terms: SharedTerms) { - self.dictionary = terms; - } - - // ── Accessors for fields sealed in stage 2a ───────────────────────────────── - // - // Twelve fields left the public API here. They stay `pub(crate)` rather than - // becoming fully private: the goal is that no *consumer* depends on the layout, - // and changing the representation later is then a within-crate refactor. Making - // them private as well would mean accessors for ~250 internal sites, which buys - // nothing a consumer can observe. - // - // Each accessor below matches what the 79 external call sites actually did — - // measured by sealing the fields and reading the compiler's E0616 list, not by - // grepping for field names. Grep over-counted by up to 2x, because `model_decls`, - // `probability_seeds` and friends are also fields on CombinedQuery, Rule, - // CompiledProgram and Reasoner. - - /// The ML execution policy: which models are approved, and where artifacts live. - pub fn ml_context(&self) -> &crate::ml_policy::MlExecutionContext { - &self.ml_context - } - - /// Replaces the ML execution policy. - pub fn set_ml_context(&mut self, context: crate::ml_policy::MlExecutionContext) { - self.ml_context = context; - } - - /// When planning statistics are rebuilt rather than reused. - pub fn planning_stats_policy(&self) -> PlanningStatsPolicy { - self.planning_stats_policy - } - - /// Sets when planning statistics are rebuilt. - pub fn set_planning_stats_policy(&mut self, policy: PlanningStatsPolicy) { - self.planning_stats_policy = policy; - } - - /// How many times planning statistics have been rebuilt. Used by tests to assert - /// that a policy actually avoided work. - pub fn stats_rebuild_count(&self) -> u64 { - self.stats_rebuild_count - } - - /// Prefix bindings, for resolving abbreviated terms. - pub fn prefixes(&self) -> &HashMap { - &self.prefixes - } - - /// Binds `prefix` to `uri`, replacing any existing binding. - pub fn add_prefix(&mut self, prefix: impl Into, uri: impl Into) { - self.prefixes.insert(prefix.into(), uri.into()); - } - - /// Merges a set of prefix bindings in, replacing any that collide. - /// - /// The parsers produce prefixes as a map, so callers had been reaching for - /// `prefixes.extend(..)` on the field directly. - pub fn extend_prefixes(&mut self, bindings: impl IntoIterator) { - self.prefixes.extend(bindings); - } - - /// Declared ML models, by name. - pub fn model_decls(&self) -> &HashMap { - &self.model_decls - } - - /// Declared neural relations, by predicate. - pub fn neural_relation_decls(&self) -> &HashMap { - &self.neural_relation_decls - } - - /// Resolved artifact paths for trained models, by model name. - pub fn neural_model_artifacts(&self) -> &HashMap { - &self.neural_model_artifacts - } - - /// Triples materialised by neural relations, by predicate. - pub fn neural_materialized_triples(&self) -> &HashMap> { - &self.neural_materialized_triples - } - - /// Takes the triples materialised for `predicate`, so they can be retracted. - pub fn take_neural_materialized(&mut self, predicate: &str) -> Option> { - self.neural_materialized_triples.remove(predicate) - } - - /// Per-triple prior probabilities seeded into probabilistic reasoning. - pub fn probability_seeds(&self) -> &HashMap { - &self.probability_seeds - } - - /// Maps a short rule name to its expanded IRI. - pub fn add_rule_mapping(&mut self, name: impl Into, expanded: impl Into) { - self.rule_map.insert(name.into(), expanded.into()); - } - - /// Read access to the quoted-triple store. - /// - /// Returns a guard rather than the `Arc>` itself. P5 forbids - /// `Arc>` in public signatures, and a getter handing back the `Arc` - /// would seal the field while freezing both the representation and the locking - /// strategy — the same encapsulation-in-name-only that stage 1 avoided. - pub fn quoted_triples(&self) -> QuotedTriplesRef<'_> { - QuotedTriplesRef(self.quoted_triple_store.read().unwrap()) - } - - /// Write access to the quoted-triple store. See [`Self::quoted_triples`]. - pub fn quoted_triples_mut(&self) -> QuotedTriplesMut<'_> { - QuotedTriplesMut(self.quoted_triple_store.write().unwrap()) - } - - /// Whether `self` and `other` share one quoted-triple store, as opposed to - /// holding separate ones. - /// - /// Replaces `Arc::ptr_eq` on the raw field: it states the property a caller - /// actually cares about — whether two databases are isolated — without the - /// handle having to be public. - pub fn shares_quoted_triples_with(&self, other: &SparqlDatabase) -> bool { - Arc::ptr_eq(&self.quoted_triple_store, &other.quoted_triple_store) - } - - // ── Accessors for fields sealed in stage 1 ────────────────────────────────── - // - // These three fields had no consumer outside this crate, so they are private and - // reached through the operations below. The operations are deliberately specific - // rather than `&mut` getters: handing out `&mut HashMap` would seal the field in - // name only and still freeze the representation, which is the thing P4 exists to - // avoid. Five further fields were used only inside this file and needed no - // accessor at all. - - /// Looks up a registered user-defined function. - pub(crate) fn udf(&self, name: &str) -> Option<&ClonableFn> { - self.udfs.get(name) - } - - /// Records the triples materialised for `cache_key`, so a rerun can undo them. - // - // Gated to match its only caller, `ml_predict_runtime`. Without this the default - // build reports both methods dead — the same thing the step-4a demotions surfaced - // for `ml_feature_loader`. - #[cfg(feature = "ml")] - pub(crate) fn set_ml_materialized(&mut self, cache_key: String, triples: Vec) { - self.ml_predict_materialized_triples - .insert(cache_key, triples); - } - - /// Takes the triples previously materialised for `cache_key`, if any. - #[cfg(feature = "ml")] - pub(crate) fn take_ml_materialized(&mut self, cache_key: &str) -> Option> { - self.ml_predict_materialized_triples.remove(cache_key) - } - - /// Whether neural relations are materialised implicitly during query execution. - pub(crate) fn implicit_neural_materialization(&self) -> bool { - self.implicit_neural_materialization - } - - /// Sets the flag and returns its previous value, so a caller can restore it. - pub(crate) fn set_implicit_neural_materialization(&mut self, enabled: bool) -> bool { - std::mem::replace(&mut self.implicit_neural_materialization, enabled) - } - - /// The seed a `TRAIN NEURAL RELATION` declaration initialises weights from. - /// - /// `None`, the default, draws them unseeded, so two runs of the same declaration - /// train different models. - pub fn training_seed(&self) -> Option { - self.training_seed - } - - /// Makes declarative training reproducible by fixing its seed. - /// - /// Weight initialisation and batch shuffling both derive from it, so the same - /// declaration over the same data trains the same model every time. A host that - /// has to reproduce or compare a training run sets this; one that wants an - /// independent initialisation each time leaves it `None`. - /// - /// [`program`](crate::program) already took a seed through its own options; this - /// is the same control for the declarative path, which had none. - pub fn set_training_seed(&mut self, seed: Option) { - self.training_seed = seed; - } - - /// Rebuilds every graph-scoped index, keeping named graphs distinct from the default - pub fn build_all_indexes(&mut self) { - let quads = self.dataset_index.all_quads(); - let named_graphs = self.dataset_index.named_graphs(); - let mut rebuilt = DatasetIndex::new(); - for graph in named_graphs { - rebuilt.create_graph(graph); - } - for quad in quads { - rebuilt.insert_quad(&quad); - } - self.dataset_index = rebuilt; - } - - /// Triple to string - pub fn triple_to_string(&self, triple: &Triple, dict: &Dictionary) -> String { - let subject = dict.decode(triple.subject); - let predicate = dict.decode(triple.predicate); - let object = dict.decode(triple.object); - format!( - "{} {} {}", - subject.unwrap(), - predicate.unwrap(), - object.unwrap() - ) - } -} - -#[cfg(test)] -mod tests { - use super::*; - - // These two tests live inside the crate because they deliberately write to - // `dataset_index` without going through the public helpers, and the field is now - // private. Both assert that statistics detect a change they were never told - // about — which is a property of the generation check in - // `planning_snapshot_is_usable`, not of the invalidation call. - // - // Rewriting them to use `replace_dataset_index` would have kept them compiling - // and silently stopped testing that: it calls `invalidate_stats_cache` itself, so - // the generation path would never be exercised. They were moved here instead. - - fn insert(database: &mut SparqlDatabase, index: u32) { - database.add_triple_parts(&format!("urn:s{index}"), "urn:p", &format!("{index}")); - } - - #[test] - fn exact_statistics_notice_a_write_that_bypassed_the_helpers() { - let mut database = SparqlDatabase::new(); - for index in 0..3 { - insert(&mut database, index); - } - assert_eq!(database.get_or_build_stats().total_triples, 3); - - // Insert straight into the index; one lock guard avoids a self-deadlock. - let quad = { - let mut dictionary = database.dictionary_mut(); - Quad { - subject: dictionary.encode("urn:d"), - predicate: dictionary.encode("urn:p"), - object: dictionary.encode("4"), - graph: GraphId::Default, - } - }; - assert!(database.dataset_index.insert_quad(&quad)); - - assert_eq!(database.get_or_build_stats().total_triples, 4); - } - - #[test] - fn a_replacement_index_with_a_lower_generation_is_not_mistaken_for_fresh() { - let mut database = SparqlDatabase::new(); - for index in 0..200 { - insert(&mut database, index); - } - database.set_planning_stats_policy(PlanningStatsPolicy::Bounded { - minimum_batch: 1_000, - refresh_fraction: 0.90, - }); - let first = database.get_or_build_planning_stats(); - assert_eq!(first.total_triples, 200); - - let mut replacement = DatasetIndex::new(); - replacement.insert_quad(&Quad { - subject: 1, - predicate: 2, - object: 3, - graph: GraphId::Default, - }); - // Generation 1, lower than the original 200: direct assignment, bypassing - // replace_dataset_index so no invalidation is signalled. - database.dataset_index = replacement; - - let refreshed = database.get_or_build_planning_stats(); - assert!(!Arc::ptr_eq(&first, &refreshed)); - assert_eq!(refreshed.total_triples, 1); - assert_eq!(database.get_or_build_stats().total_triples, 1); - } -} - -impl shared::quad_source::QuadSource for SparqlDatabase { - fn query_default(&self, s: Option, p: Option, o: Option) -> Vec { - self.dataset_index.query_default(s, p, o) - } - - fn for_each_quad(&self, visit: &mut dyn FnMut(GraphId, u32, u32, u32)) { - self.dataset_index - .for_each_quad(|g, s, p, o| visit(g, s, p, o)); - } - - fn named_graphs(&self) -> Vec { - self.dataset_index.named_graphs() - } - - fn count_graph(&self, graph: GraphId) -> usize { - self.dataset_index.count_graph(graph) - } -} diff --git a/kolibrie/src/sparql_protocol.rs b/kolibrie/src/sparql_protocol.rs index 4ae2c29f..7878e385 100644 --- a/kolibrie/src/sparql_protocol.rs +++ b/kolibrie/src/sparql_protocol.rs @@ -8,30 +8,13 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The SPARQL 1.1 Protocol over HTTP: request text in, response text out. -//! -//! Answers the three request forms the specification defines — a `GET` carrying -//! `?query=`, a `POST` of `application/sparql-query` or `application/sparql-update`, -//! and a form-encoded `POST` with a `query=` or `update=` parameter. -//! -//! # No sockets -//! -//! [`answer`] takes a request as a string and returns a response as a string. It -//! neither listens nor accepts, so the caller chooses the transport, the threading -//! and the port. The library used to own a `TcpListener` and a serving loop in -//! `utils`, which made a store crate responsible for running a server; those are -//! gone and nothing but a single example had used them. -//! -//! This is also not the same thing as the `kolibrie-http-server` binary. That serves -//! a JSON API of its own — `/query`, `/rsp/register`, `/datalogmtl/*` — and accepts -//! none of the protocol's content types. The two share only a mapping from failures -//! to status codes, which is duplicated and has to be changed in both places. +//! The SPARQL 1.1 Protocol over HTTP: request text in, response text out //! //! ```no_run -//! use kolibrie::sparql_database::SparqlDatabase; +//! use kolibrie::database::KolibrieDB; //! use kolibrie::sparql_protocol::answer; //! -//! let mut database = SparqlDatabase::new(); +//! let mut database = KolibrieDB::new(); //! let response = answer( //! &mut database, //! "GET /sparql?query=SELECT%20%3Fs%20WHERE%20%7B%20%3Fs%20%3Fp%20%3Fo%20%7D HTTP/1.1\r\n\r\n", @@ -39,29 +22,13 @@ //! assert!(response.starts_with("HTTP/1.1 200 OK")); //! ``` -use crate::sparql_database::SparqlDatabase; +use crate::database::KolibrieDB; use percent_encoding::percent_decode; use std::collections::HashMap; use url::Url; -/// Answers one request against `database`, returning a complete HTTP response. -/// -/// The ML policy applies: a request naming a model the host has not approved is -/// refused rather than run, and the refusal carries its own status. -/// -/// # Status codes -/// -/// `200` for a query or update that ran, `400` for one that did not parse or was sent -/// to the wrong form, `403` or `503` for a policy refusal, and `400` for a method or -/// path this does not serve. A failure body is a bare code, never the request text: a -/// parse error carries the offending query, and echoing it back would hand it to -/// whoever sent it. -/// -/// # Panics -/// -/// If `request` is not parseable as an HTTP request at all. The protocol layer above -/// this is expected to have read a complete request. -pub fn answer(database: &mut SparqlDatabase, request: &str) -> String { +/// Answers one request against `database`, returning a complete HTTP response +pub fn answer(database: &mut KolibrieDB, request: &str) -> String { let context = database.ml_context().for_http(); let (status, body) = crate::execute_query::with_ml_context(database, &context, |database| { dispatch(database, request) @@ -72,8 +39,8 @@ pub fn answer(database: &mut SparqlDatabase, request: &str) -> String { ) } -/// Which operation the request asks for, and the outcome of running it. -fn dispatch(database: &mut SparqlDatabase, request: &str) -> (&'static str, String) { +/// Which operation the request asks for, and the outcome of running it +fn dispatch(database: &mut KolibrieDB, request: &str) -> (&'static str, String) { let mut headers = [httparse::EMPTY_HEADER; 16]; let mut parsed = httparse::Request::new(&mut headers); parsed.parse(request.as_bytes()).unwrap(); @@ -93,8 +60,6 @@ fn dispatch(database: &mut SparqlDatabase, request: &str) -> (&'static str, Stri .iter() .find(|header| header.name.eq_ignore_ascii_case("Content-Type")) .map(|header| header.value); - // The body is everything past the header section, which httparse does not - // hand back. let body = request.split("\r\n\r\n").nth(1); match (content_type, body) { (Some(b"application/sparql-query"), Some(body)) => { @@ -121,8 +86,8 @@ fn dispatch(database: &mut SparqlDatabase, request: &str) -> (&'static str, Stri ("400 Bad Request", "Bad Request".to_string()) } -/// The status and body for one query. -pub(crate) fn query_result(database: &mut SparqlDatabase, query: &str) -> (&'static str, String) { +/// The status and body for one query +pub(crate) fn query_result(database: &mut KolibrieDB, query: &str) -> (&'static str, String) { match crate::execute_query::execute_sparql_query(query, database) { Ok(rows) => ( "200 OK", @@ -132,9 +97,6 @@ pub(crate) fn query_result(database: &mut SparqlDatabase, query: &str) -> (&'sta .join("\n"), ), Err(error) => { - // Matched on the variant rather than on a rendered string. This is the - // same status table the HTTP server keeps in `ml_error_response`: two - // copies, in two crates, that have to be changed together. use crate::execute_query::QueryError; use crate::ml_policy::MlError; match &error { @@ -149,20 +111,14 @@ pub(crate) fn query_result(database: &mut SparqlDatabase, query: &str) -> (&'sta "503 Service Unavailable", "ML_INVALID_CONFIGURATION".to_string(), ), - // The message is deliberately not sent: a parse failure carries the - // offending query text. _ => ("400 Bad Request", "QUERY_EXECUTION_FAILED".to_string()), } } } } -/// The status and body for one update. -/// -/// Public because the outcome text is what a caller applying an update outside a -/// request wants; `SparqlDatabase::handle_update` used to delegate here and has been -/// removed, since a store should not reach up into the protocol that serves it. -pub fn update_result(database: &mut SparqlDatabase, update: &str) -> (&'static str, String) { +/// The status and body for one update +pub fn update_result(database: &mut KolibrieDB, update: &str) -> (&'static str, String) { if let Ok(summary) = crate::execute_query::execute_sparql_update(update, database) { return ( "200 OK", @@ -172,14 +128,13 @@ pub fn update_result(database: &mut SparqlDatabase, update: &str) -> (&'static s ), ); } - // Historical standalone INSERT/DELETE aliases parse into the same update operation if crate::execute_query::execute_sparql_update_compat(update, database).is_ok() { return ("200 OK", "Update Successful".to_string()); } ("400 Bad Request", "Update Failed".to_string()) } -/// `name=value` pairs from a form-encoded body, with `+` read as a space. +/// `name=value` pairs from a form-encoded body, with `+` read as a space fn form_parameters(body: &str) -> HashMap { body.split('&') .map(|pair| { diff --git a/kolibrie/src/stats/database_stats.rs b/kolibrie/src/stats/database_stats.rs index 1daea062..a322df99 100644 --- a/kolibrie/src/stats/database_stats.rs +++ b/kolibrie/src/stats/database_stats.rs @@ -8,12 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`DatabaseStats`]: the cardinalities the cost model reads. -//! -//! Collected by scanning the store, so building them is proportional to its size — -//! which is why the store caches them and refreshes according to its -//! [`PlanningStatsPolicy`](crate::sparql_database::PlanningStatsPolicy) rather than -//! on every write. +//! [`DatabaseStats`]: the cardinalities the cost model reads use shared::dataset_index::{DatasetIndex, GraphId}; use std::collections::{HashMap, HashSet}; @@ -75,8 +70,8 @@ impl Hasher for TermHasher { } } -/// Hasher builder for term-id keys, avoiding the default hasher's overhead on -/// integers that are already well distributed. +/// Hasher builder for term-id keys, avoiding the default hasher's overhead on integers that are +/// already well distributed pub type TermBuildHasher = BuildHasherDefault; /// A map keyed by dictionary id pub type TermMap = HashMap; @@ -90,20 +85,17 @@ const EXACT_SAMPLING_THRESHOLD: u64 = 100_000; /// Deterministic quad hash used for sampling, stable across processes fn stable_quad_hash(graph: GraphId, subject: u32, predicate: u32, object: u32) -> u64 { - // Offset named ids so `Default` and `Named(0)` hash differently let graph_key = match graph { GraphId::Default => 0u64, GraphId::Named(id) => id as u64 + 1, }; - // FNV-1a over the four components let mut hash: u64 = 0xcbf2_9ce4_8422_2325; for component in [graph_key, subject as u64, predicate as u64, object as u64] { hash ^= component; hash = hash.wrapping_mul(0x0000_0100_0000_01b3); } - // Avalanche finalizer; the sampler uses the low bits hash ^= hash >> 33; hash = hash.wrapping_mul(0xff51_afd7_ed55_8ccd); hash ^= hash >> 33; @@ -172,29 +164,28 @@ impl SampleScale { /// Database statistics for cost-based optimization #[derive(Debug)] pub struct DatabaseStats { - /// Quads across every graph. The denominator for most selectivity estimates. + /// Quads across every graph pub total_triples: u64, - /// Distinct quoted triples interned. + /// Distinct quoted triples interned pub quoted_triple_count: u64, - /// Named graphs, excluding the default graph. + /// Named graphs, excluding the default graph pub named_graph_count: u64, - /// Quads per graph. + /// Quads per graph pub graph_cardinalities: GraphMap, - /// Quads per predicate — the most useful figure, since a bound predicate is - /// the common case. + /// Quads per predicate — the most useful figure, since a bound predicate is the common case pub predicate_cardinalities: TermMap, - /// Quads per subject. + /// Quads per subject pub subject_cardinalities: TermMap, - /// Quads per object. + /// Quads per object pub object_cardinalities: TermMap, - /// Join-key domain sizes: a join through predicate `p` fans out by roughly `cardinality(p) / distinct(p)` rows per binding + /// Join-key domain sizes: a join through predicate `p` fans out by roughly `cardinality(p) / + /// distinct(p)` rows per binding pub predicate_distinct_subjects: TermMap, - /// Distinct objects per predicate; the object-side counterpart of the - /// fan-out estimate above. + /// Distinct objects per predicate; the object-side counterpart of the fan-out estimate above pub predicate_distinct_objects: TermMap, /// Dataset-wide domain sizes, used when a pattern leaves its predicate unbound pub distinct_subjects: u64, - /// Distinct objects across the dataset. + /// Distinct objects across the dataset pub distinct_objects: u64, } @@ -216,14 +207,8 @@ impl DatabaseStats { } } - /// Gathers statistics from a dataset index using sampling for performance. - /// - /// Takes the index and the quoted-triple count rather than a store, so the cost - /// model's statistics do not depend on the store that happens to hold them. - /// [`SparqlDatabase::gather_stats`](crate::sparql_database::SparqlDatabase::gather_stats) - /// is the store-side caller. + /// Gathers statistics from a dataset index using sampling for performance pub fn gather_stats_fast(index: &DatasetIndex, quoted_triple_count: u64) -> Self { - // Pass 1: exact per-graph and total quad counts let named_graphs = index.named_graphs(); let mut graph_cardinalities = GraphMap::with_capacity_and_hasher(named_graphs.len() + 1, TermBuildHasher::default()); @@ -236,7 +221,6 @@ impl DatabaseStats { } let named_graph_count = named_graphs.len() as u64; - // Pass 2: term statistics over the sampled quads let plan = SamplingPlan::for_total(total_triples); let mut sampled: u64 = 0; let mut predicate_cardinalities = TermMap::::default(); @@ -267,7 +251,6 @@ impl DatabaseStats { all_objects.insert(object); }); - // Scale sampled counts back up to the whole dataset let scale = SampleScale::new(plan, total_triples, sampled); predicate_cardinalities .values_mut() @@ -279,7 +262,6 @@ impl DatabaseStats { .values_mut() .for_each(|v| *v = scale.scale_up(*v)); - // Distinct counts do not grow linearly with the sample, so each is capped at its cardinality let scale_distinct = |observed: u64, cardinality: u64| scale.scale_up(observed).min(cardinality).max(1); let predicate_distinct_subjects = predicate_subjects @@ -365,9 +347,7 @@ impl DatabaseStats { self.object_cardinalities.get(&object).copied().unwrap_or(0) } - /// Quads in `graph`, or the unmeasured-graph default when it is absent from - /// the statistics — which is why an empty or newly created graph does not make - /// a plan look free. + /// Quads in `graph`, or the unmeasured-graph default when the graph has no statistics pub fn get_graph_cardinality(&self, graph: GraphId) -> u64 { self.graph_cardinalities.get(&graph).copied().unwrap_or(0) } @@ -444,7 +424,6 @@ mod tests { #[test] fn just_past_the_threshold_the_step_is_two_not_one() { - // An integer `total / budget` truncated to 1 here, sampling the first let plan = SamplingPlan::for_total(EXACT_SAMPLING_THRESHOLD + 1); assert_eq!(plan.step, 2); assert!(!plan.is_exact()); @@ -538,7 +517,6 @@ mod tests { shuffled.reverse(); assert_eq!(forward, sample_size(plan, &shuffled)); - // Simulates a different iteration order let mut rotated = quads.clone(); rotated.rotate_left(1_337); assert_eq!(forward, sample_size(plan, &rotated)); diff --git a/kolibrie/src/stats/mod.rs b/kolibrie/src/stats/mod.rs index 81ee3738..86801c59 100644 --- a/kolibrie/src/stats/mod.rs +++ b/kolibrie/src/stats/mod.rs @@ -8,12 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Statistics about the stored data that the cost model reads. -//! -//! Cardinalities per predicate and graph, distinct-subject counts, and the totals -//! used to estimate selectivity. Rebuilding them is not free, which is why the store -//! caches them and refreshes according to its -//! [`PlanningStatsPolicy`](crate::sparql_database::PlanningStatsPolicy). +//! Statistics about the stored data that the cost model reads pub mod database_stats; diff --git a/kolibrie/src/streamertail_optimizer/README.md b/kolibrie/src/streamertail_optimizer/README.md deleted file mode 100644 index cdce2bda..00000000 --- a/kolibrie/src/streamertail_optimizer/README.md +++ /dev/null @@ -1,231 +0,0 @@ -# Volcano Optimizer - -This module implements a Volcano-style query optimizer with cost-based optimization for the Kolibrie SPARQL database engine. Standard SELECT queries and the `WHERE` clauses of the six supported Update forms follow one path from the existing parser, through recursive logical operators and this optimizer, to physical execution. `GRAPH` and `UNION` do not use a separate evaluator. - -## Architecture - -The volcano optimizer is structured into several focused modules: - -``` -streamertail_optimizer/ -├── mod.rs // Main module file with public API -├── operators/ -│ ├── mod.rs // Operator module index -│ ├── logical.rs // LogicalOperator enum and constructors -│ └── physical.rs // PhysicalOperator enum and constructors -├── cost/ -│ ├── mod.rs // Cost module index -│ └── estimator.rs // CostEstimator with cardinality estimation -├── execution/ -│ ├── mod.rs // Execution module index -│ └── engine.rs // ExecutionEngine for physical operators -├── stats/ -│ ├── mod.rs // Statistics module index -│ └── database_stats.rs // DatabaseStats gathering and management -├── types.rs // Common types (Condition, IdResult) -├── optimizer.rs // Main Streamertail implementation -└── utils.rs // Utility functions for plan building -``` - -## Key Components - -### LogicalOperator -Represents high-level query operations before optimization: -- `Unit`: One input solution for an empty group -- `Scan`: Graph-aware quad-pattern scanning (default scans wrap a triple pattern) -- `Graph`: Fixed- or variable-named-graph scope -- `Union`: Multiset branch concatenation -- `Selection`: Filtering with conditions -- `Projection`: Variable projection -- `Join`: Binary join operations - -### PhysicalOperator -Represents concrete execution plans after optimization: -- `Unit`: Pass-through execution for an empty group -- `TableScan` / `IndexScan`: Different scan strategies -- `Graph`: Graph-scoped child execution with graph-variable binding -- `Union`: Execution of every branch against the same input -- `Filter`: Condition filtering -- `BindJoin` / `HashJoin` / `NestedLoopJoin`: Join algorithms -- `Projection`: Variable projection - -### CostEstimator -Provides cost and cardinality estimation for optimization: -- Estimates operator execution costs -- Performs cardinality estimation based on database statistics -- Uses selectivity estimation for filtering operations - -### ExecutionEngine -Executes physical operators with performance optimizations: -- ID-based execution for reduced string operations -- A shared dataset/execution context for default, merged-default, and visible named graphs -- Input-binding propagation across scans, `GRAPH`, `UNION`, filters, binds, and joins -- Parallel execution using Rayon -- Index-aware scanning strategies -- SIMD-optimized join algorithms - -### DatabaseStats -Gathers and maintains database statistics for cost estimation: -- Predicate, subject, and object cardinality tracking -- Join selectivity caching -- Fast sampling-based statistics gathering - -## Usage - -### Basic Usage - -```rust -use kolibrie::streamertail_optimizer::*; -use shared::terms::{Term, TriplePattern}; - -// Create database and optimizer -let mut database = SparqlDatabase::new(); -let mut optimizer = Streamertail::new(&database); - -// Create logical plan -let logical_plan = LogicalOperator::scan(( - Term::Variable("person".to_string()), - Term::Constant(name_id), - Term::Variable("name".to_string()), -)); - -// Optimize and execute -let physical_plan = optimizer.find_best_plan(&logical_plan); -let results = optimizer.execute_plan(&physical_plan, &mut database); -``` - -### Complex Query Example - -```rust -// Join with filter -let name_scan = LogicalOperator::scan(name_pattern); -let age_scan = LogicalOperator::scan(age_pattern); -let join = LogicalOperator::join(name_scan, age_scan); - -let condition = Condition::new("age".to_string(), ">".to_string(), "25".to_string()); -let filtered = LogicalOperator::selection(join, condition); - -let physical_plan = optimizer.find_best_plan(&filtered); -let results = optimizer.execute_plan(&physical_plan, &mut database); -``` - -## Optimization Features - -### Cost-Based Optimization -- Dynamic programming with memoization for plan enumeration -- Multiple join algorithms with cost comparison -- Index vs. table scan selection based on selectivity -- Join reordering based on cost estimates - -### Performance Optimizations -- ID-based execution to reduce string operations -- Parallel execution using Rayon for CPU-intensive operations -- Index-aware scanning with multiple access patterns -- SIMD-optimized join algorithms for large datasets - -### Statistics and Estimation -- Sampling-based statistics gathering for large datasets -- Cardinality estimation using database statistics -- Selectivity estimation for filtering operations -- Join selectivity caching for repeated queries - -## Join Algorithms - -Three algorithms with genuinely different execution, chosen by cost: - -1. **BindJoin**: the left side executes first and its solutions are fed into - the right side as incoming bindings, so right-side scans probe indexes with - values that are already bound. Cheapest when the left side is small or - selective, because the right side is never scanned standalone. Left rows are - processed in parallel chunks. -2. **HashJoin**: both sides execute independently, a hash table is built on the - shared-variable key, and the other side probes it in parallel. Wins when both - sides are large, where one scan of the right side beats one probe per left - row. -3. **NestedLoopJoin**: both sides are materialized and joined pairwise. Suits - tiny inputs, and is the only algorithm that applies to a Cartesian product, - where there is no shared variable to hash on. - -Selection is by estimated cost, which combines each side's cost, its -cardinality, and the estimated join output. Because the algorithms differ in -which of those terms they pay, no single one wins unconditionally. - -## Join Ordering - -Before physical planning, each uninterrupted group of scans sharing one graph -scope is reordered: the most selective pattern runs first, and every later -pattern must share a variable with those already placed. A disconnected pattern -is only chosen when nothing connected remains, since that step is a Cartesian -product. Source order breaks ties, and GRAPH, UNION, FILTER, BIND, VALUES and -subquery boundaries are never crossed. - -Candidate patterns are costed as bound scans: a position already bound by the -prefix divides the estimate by that position's domain size, which is what makes -an anchored path step cheap and an unanchored one expensive. - -## Index Strategies - -The optimizer leverages multiple index access patterns: - -- **SPO**: Subject-Predicate-Object lookup -- **PSO**: Predicate-Subject-Object lookup -- **OSP**: Object-Subject-Predicate lookup -- **POS**: Predicate-Object-Subject lookup -- **SOP**: Subject-Object-Predicate lookup -- **OPS**: Object-Predicate-Subject lookup - -Index selection is based on the bound variables in triple patterns. - -## Performance Considerations - -### Memory Management -- Streaming execution for large result sets -- Result compaction to reduce memory usage -- Spill-to-disk for memory-intensive operations - -### Parallelization -- Parallel statistics gathering -- Parallel join execution -- Parallel filtering operations - -### Caching -- Memoization of optimized plans -- Statistics caching for repeated access -- Join selectivity caching - -## Future Improvements - -1. **Advanced Optimization Rules** - - Filter pushdown optimization - - Join reordering with bushy trees - - Materialized view utilization - -2. **Adaptive Optimization** - - Runtime statistics feedback - - Query plan adaptation - - Machine learning-based cost estimation - -3. **Distributed Execution** - - Distributed join algorithms - - Data partitioning strategies - - Network-aware optimization - -## Testing - -The module includes comprehensive tests for: -- Cost estimation accuracy -- Cardinality estimation -- Join algorithm correctness -- Optimization rule application - -Run tests with: -```bash -cargo test streamertail_optimizer -``` - -## Examples - -See the `examples/` directory for complete working examples: -- `simple_volcano.rs`: Basic optimizer usage -- `complex_queries.rs`: Advanced optimization scenarios -- `performance_benchmarks.rs`: Performance testing diff --git a/kolibrie/src/streamertail_optimizer/cost/estimator.rs b/kolibrie/src/streamertail_optimizer/cost/estimator.rs index bebee559..507e00fd 100644 --- a/kolibrie/src/streamertail_optimizer/cost/estimator.rs +++ b/kolibrie/src/streamertail_optimizer/cost/estimator.rs @@ -8,11 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`CostEstimator`]: predicting cardinality and work for a candidate plan. -//! -//! Estimates only have to rank plans correctly; absolute accuracy is not the goal. -//! They read [`DatabaseStats`], which -//! may lag the data under the store's default statistics policy. +//! [`CostEstimator`]: predicting cardinality and work for a candidate plan use super::super::operators::PhysicalOperator; use super::super::types::{Condition, ConditionExpression}; @@ -61,25 +57,23 @@ pub struct CostConstants; impl CostConstants { /// Retained for source compatibility pub const COST_PER_ROW_SCAN: u64 = 100; - /// Per row returned by an index scan. + /// Per row returned by an index scan pub const COST_PER_ROW_INDEX_SCAN: u64 = 1; /// Fixed overhead of opening one index scan, so a ground pattern is never free pub const COST_PER_SCAN_PROBE: u64 = 1; /// Cardinality assumed for a graph absent from the statistics pub const UNMEASURED_GRAPH_CARDINALITY: u64 = 1; - /// Per row a filter examines, whether or not it survives. + /// Per row a filter examines, whether or not it survives pub const COST_PER_FILTER: u64 = 1; - /// Per row built, probed or emitted by a hash join. + /// Per row built, probed or emitted by a hash join pub const COST_PER_ROW_JOIN: u64 = 2; - /// Per row pair a nested-loop join considers. Deliberately far above the hash - /// join's figure, which is what makes the optimizer prefer hashing unless the - /// inner side is tiny. + /// Per row pair a nested-loop join considers pub const COST_PER_ROW_NESTED_LOOP: u64 = 10; - /// Per row projected. + /// Per row projected pub const COST_PER_PROJECTION: u64 = 1; /// One index probe of a dependent plan's right side pub const COST_PER_PROBE: u64 = 2; - /// Baseline cost of handling one tuple. + /// Baseline cost of handling one tuple pub const TUPLE_COST: u64 = 1; } @@ -111,7 +105,6 @@ impl<'a> CostEstimator<'a> { pub fn estimate_cost(&self, plan: &PhysicalOperator) -> u64 { match plan { PhysicalOperator::Unit => 0, - // `TableScan` and `IndexScan` dispatch to the same executor function PhysicalOperator::TableScan { pattern } | PhysicalOperator::IndexScan { pattern } => { CostConstants::COST_PER_SCAN_PROBE.saturating_add( self.estimate_quad_cardinality(pattern) @@ -122,7 +115,6 @@ impl<'a> CostEstimator<'a> { .iter() .map(|branch| self.estimate_cost(branch)) .fold(0u64, u64::saturating_add), - // Mirrors `estimate_output_cardinality_in_context`: an invisible PhysicalOperator::Graph { input, graph } => match graph { GraphTerm::Default => self.estimate_cost(input), GraphTerm::Named(graph) if self.fixed_graph_is_visible(*graph) => { @@ -134,7 +126,6 @@ impl<'a> CostEstimator<'a> { .saturating_mul(self.visible_named_graphs().len() as u64), }, PhysicalOperator::Filter { input, condition } => { - // Selectivity decides how many rows survive, not how much work let _ = condition; let input_cost = self.estimate_cost(input); let input_cardinality = self.estimate_output_cardinality(input); @@ -148,7 +139,6 @@ impl<'a> CostEstimator<'a> { let left_cardinality = self.estimate_output_cardinality(left); let output = self.estimate_output_cardinality(plan); - // The right side is re-entered once per left solution, so a shared variable makes it a probe let per_left_row = if self.shares_a_variable(left, right) { CostConstants::COST_PER_PROBE.saturating_mul(Self::probe_width(right).max(1)) } else { @@ -191,7 +181,6 @@ impl<'a> CostEstimator<'a> { self.estimate_cost(input) + CostConstants::COST_PER_PROJECTION } PhysicalOperator::StarJoin { patterns, .. } => { - // Cost = scan most selective + filter rest let mut costs: Vec = patterns .iter() .map(|pattern| self.estimate_star_pattern_cardinality(pattern)) @@ -203,14 +192,12 @@ impl<'a> CostEstimator<'a> { costs.sort(); - // Start with smallest, then check each remaining let base_cost = costs[0] * CostConstants::COST_PER_ROW_INDEX_SCAN; let filter_cost = costs.iter().skip(1).sum::() * CostConstants::COST_PER_ROW_INDEX_SCAN / 10; base_cost + filter_cost } - // The buffer's rows still have to be joined against the incoming PhysicalOperator::InMemoryBuffer { content, .. } => { (content.len() as u64).saturating_mul(CostConstants::TUPLE_COST) } @@ -218,7 +205,6 @@ impl<'a> CostEstimator<'a> { let inner_cost = self.estimate_cost(inner); let inner_card = self.estimate_output_cardinality(inner); - // Executing the inner plan, storing its rows, then projecting them let materialization_cost = inner_card * CostConstants::TUPLE_COST; let projection_width = spec.projection @@ -237,23 +223,14 @@ impl<'a> CostEstimator<'a> { let input_cost = self.estimate_cost(input); let input_cardinality = self.estimate_output_cardinality(input); - // Cost depends on function complexity let function_cost = match function_name.as_str() { - "CONCAT" => { - // CONCAT cost is proportional to number of arguments - arguments.len() as u64 * CostConstants::COST_PER_PROJECTION - } - _ => { - // Generic UDF - assume moderate cost - CostConstants::COST_PER_PROJECTION * 2 - } + "CONCAT" => arguments.len() as u64 * CostConstants::COST_PER_PROJECTION, + _ => CostConstants::COST_PER_PROJECTION * 2, }; - // Total cost = input cost + (cardinality * function cost per row) input_cost + (input_cardinality * function_cost) } PhysicalOperator::Values { values, .. } => { - // VALUES has minimal cost - just the number of rows (values.len() as u64) * CostConstants::TUPLE_COST } PhysicalOperator::MLPredict { @@ -264,7 +241,6 @@ impl<'a> CostEstimator<'a> { let input_cost = self.estimate_cost(input); let cardinality = self.estimate_output_cardinality(input); - // Python interop dominates, plus a per-row cost scaled by feature count let python_overhead = 1000; let per_row_cost = 100 * input_variables.len() as u64; @@ -275,43 +251,32 @@ impl<'a> CostEstimator<'a> { /// Estimates the cardinality of a triple pattern pub fn estimate_cardinality(&self, pattern: &TriplePattern) -> u64 { - // Treat QuotedTriple terms as variables for cardinality estimation match pattern { - // Fully bound - always returns 0 or 1 (Term::Constant(_), Term::Constant(_), Term::Constant(_)) => 1, - // Two bounds - use actual index stats - (Term::Constant(s), Term::Constant(p), Term::Variable(_)) => { - // Look up actual SPO cardinality - self.stats - .get_subject_cardinality(*s) - .min(self.stats.get_predicate_cardinality(*p)) - .max(1) - } + (Term::Constant(s), Term::Constant(p), Term::Variable(_)) => self + .stats + .get_subject_cardinality(*s) + .min(self.stats.get_predicate_cardinality(*p)) + .max(1), - (Term::Constant(s), Term::Variable(_), Term::Constant(o)) => { - // S*O pattern - self.stats - .get_subject_cardinality(*s) - .min(self.stats.get_object_cardinality(*o)) - .max(1) - } + (Term::Constant(s), Term::Variable(_), Term::Constant(o)) => self + .stats + .get_subject_cardinality(*s) + .min(self.stats.get_object_cardinality(*o)) + .max(1), - (Term::Variable(_), Term::Constant(p), Term::Constant(o)) => { - // *PO pattern - self.stats - .get_predicate_cardinality(*p) - .min(self.stats.get_object_cardinality(*o)) - .max(1) - } + (Term::Variable(_), Term::Constant(p), Term::Constant(o)) => self + .stats + .get_predicate_cardinality(*p) + .min(self.stats.get_object_cardinality(*o)) + .max(1), - // One bound - use predicate/subject/object cardinality directly (Term::Constant(s), Term::Variable(_), Term::Variable(_)) => { self.stats.get_subject_cardinality(*s).max(1) } (Term::Variable(_), Term::Constant(p), Term::Variable(_)) => { - // This is the KEY one - should return ACTUAL predicate cardinality! self.stats.get_predicate_cardinality(*p).max(1) } @@ -319,10 +284,8 @@ impl<'a> CostEstimator<'a> { self.stats.get_object_cardinality(*o).max(1) } - // No bounds - full scan (Term::Variable(_), Term::Variable(_), Term::Variable(_)) => self.stats.total_triples, - // Patterns with QuotedTriple terms — estimate based on bound positions _ => { let bound = [&pattern.0, &pattern.1, &pattern.2] .iter() @@ -339,7 +302,8 @@ impl<'a> CostEstimator<'a> { } } - /// Estimates a scan of `pattern` where every position `bound` pins down divides it by that position's domain size + /// Estimates a scan of `pattern` where every position `bound` pins down divides it by that + /// position's domain size pub fn estimate_bound_scan_cardinality( &self, pattern: &QuadPattern, @@ -376,7 +340,7 @@ impl<'a> CostEstimator<'a> { (base / divisor.max(1)).max(1) } - /// Estimates a graph-scoped scan. Fixed graphs are capped by their direct + /// Estimates a graph-scoped scan pub fn estimate_quad_cardinality(&self, pattern: &QuadPattern) -> u64 { let graph_cardinality = self.graph_term_cardinality(&pattern.graph); @@ -434,7 +398,6 @@ impl<'a> CostEstimator<'a> { pub(crate) fn visible_named_graphs(&self) -> Vec { match self.dataset { - // Use the live dataset view; statistics may be stale Some(dataset) => dataset.named_graphs.iter().copied().collect(), None => self .stats @@ -446,7 +409,7 @@ impl<'a> CostEstimator<'a> { } } - /// Returns the catalogued, visible named-graph count. Empty graphs count + /// Returns the catalogued, visible named-graph count pub fn visible_named_graph_count(&self) -> u64 { self.visible_named_graphs().len() as u64 } @@ -472,15 +435,13 @@ impl<'a> CostEstimator<'a> { /// Recursively estimates the selectivity of a filter expression fn estimate_filter_selectivity(&self, expr: &ConditionExpression) -> f64 { match expr { - ConditionExpression::Comparison(_, op, _) => { - match op.as_str() { - "=" => 0.05, // Equality is very selective - "!=" => 0.95, // Not equal is not very selective - ">" | "<" => 0.25, // Range queries - ">=" | "<=" => 0.30, - _ => 0.5, // Unknown operators - } - } + ConditionExpression::Comparison(_, op, _) => match op.as_str() { + "=" => 0.05, + "!=" => 0.95, + ">" | "<" => 0.25, + ">=" | "<=" => 0.30, + _ => 0.5, + }, ConditionExpression::ArithmeticComparison(_, op, _) => match op.as_str() { "=" => 0.05, "!=" => 0.95, @@ -489,26 +450,20 @@ impl<'a> CostEstimator<'a> { _ => 0.5, }, ConditionExpression::And(left, right) => { - // AND is more selective - multiply selectivities let left_sel = self.estimate_filter_selectivity(left); let right_sel = self.estimate_filter_selectivity(right); left_sel * right_sel } ConditionExpression::Or(left, right) => { - // OR is less selective - use formula: sel(A OR B) = sel(A) + sel(B) - sel(A)*sel(B) let left_sel = self.estimate_filter_selectivity(left); let right_sel = self.estimate_filter_selectivity(right); left_sel + right_sel - (left_sel * right_sel) } ConditionExpression::Not(inner) => { - // NOT inverts selectivity let inner_sel = self.estimate_filter_selectivity(inner); 1.0 - inner_sel } - ConditionExpression::ArithmeticExpr(_) => { - // Conservative estimate for arithmetic expressions - 0.5 - } + ConditionExpression::ArithmeticExpr(_) => 0.5, ConditionExpression::FunctionCall(func_name, _) => match func_name.as_str() { "isTRIPLE" => 0.1, _ => 0.5, @@ -543,7 +498,8 @@ impl<'a> CostEstimator<'a> { } } - /// Estimates surviving rows as the cross product divided by the domain size of each shared variable + /// Estimates surviving rows as the cross product divided by the domain size of each shared + /// variable pub fn estimate_join_cardinality_from_patterns( &self, left_cardinality: u64, @@ -579,7 +535,6 @@ impl<'a> CostEstimator<'a> { } if divisor <= 1 { - // No statistic for the join variable: assume one match per row on the larger side return left_cardinality.max(right_cardinality); } @@ -693,7 +648,6 @@ impl<'a> CostEstimator<'a> { PhysicalOperator::BindJoin { left, right } | PhysicalOperator::HashJoin { left, right } | PhysicalOperator::NestedLoopJoin { left, right } => { - // The join algorithm does not change how many rows survive let left_cardinality = self.estimate_output_cardinality_in_context(left, active_graph); let right_cardinality = @@ -713,7 +667,6 @@ impl<'a> CostEstimator<'a> { self.estimate_output_cardinality_in_context(input, active_graph) } PhysicalOperator::StarJoin { patterns, .. } => { - // The most selective pattern anchors the star, the rest filter it let mut cardinalities: Vec = patterns .iter() .map(|p| self.estimate_cardinality(p)) @@ -725,29 +678,21 @@ impl<'a> CostEstimator<'a> { cardinalities.sort(); - // Base cardinality is the smallest (most selective) pattern let base = cardinalities[0]; - // Each additional pattern acts as a filter let filter_factor = 0.5_f64.powi((patterns.len() - 1) as i32); ((base as f64 * filter_factor) as u64).max(1) } PhysicalOperator::InMemoryBuffer { content, .. } => content.len() as u64, PhysicalOperator::Subquery { inner, .. } => { - // Subquery cardinality is the same as inner query self.estimate_output_cardinality_in_context(inner, active_graph) } PhysicalOperator::Bind { input, .. } => { - // BIND doesn't change cardinality, just adds a column self.estimate_output_cardinality_in_context(input, active_graph) } - PhysicalOperator::Values { values, .. } => { - // Cardinality is simply the number of value rows - values.len() as u64 - } + PhysicalOperator::Values { values, .. } => values.len() as u64, PhysicalOperator::MLPredict { input, .. } => { - // ML.PREDICT doesn't change cardinality, just adds a column self.estimate_output_cardinality_in_context(input, active_graph) } } @@ -811,11 +756,6 @@ mod tests { use shared::terms::{Bindings, Term}; use std::collections::HashMap; - // Removed with `count_bound_variables` in step 3: three tests covering a method - // the cost model never called. The estimator does not use bound-variable counts - // for selectivity today; when it does, the method and its tests come back - // together. Keeping them meant the suite reported coverage of code that never ran. - fn unbound_scan(graph: GraphTerm) -> PhysicalOperator { PhysicalOperator::quad_table_scan(QuadPattern { subject: Term::Variable("?s".to_string()), @@ -858,12 +798,11 @@ mod tests { #[test] fn distinct_counts_are_gathered_per_predicate() { - use crate::sparql_database::SparqlDatabase; + use crate::database::KolibrieDB; use shared::dataset_index::{GraphId, Quad}; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let predicate = database.dictionary.write().encode("urn:p"); - // Three subjects share one object, so subjects and objects differ for subject_name in ["urn:a", "urn:b", "urn:c"] { let subject = database.dictionary.write().encode(subject_name); let object = database.dictionary.write().encode("urn:shared"); @@ -886,7 +825,6 @@ mod tests { fn join_cardinality_divides_by_the_shared_variable_domain() { let stats = join_stats(); let estimator = CostEstimator::new(&stats); - // ?a EDGE ?b joined with ?b EDGE ?c through ?b let left = [pattern( variable("?a"), Term::Constant(EDGE), @@ -1001,7 +939,6 @@ mod tests { hash, nested ); - // Both sides share ?y, so each bind-join re-entry is an indexed probe assert!( bind < hash, "an indexed probe should beat a build/probe pass here: bind={} hash={}", @@ -1014,7 +951,6 @@ mod tests { fn cost_model_prefers_hash_join_for_a_cross_product() { let stats = join_stats(); let estimator = CostEstimator::new(&stats); - // No shared variable: every bind-join re-entry repeats the *whole* let left = PhysicalOperator::quad_index_scan(pattern( variable("?a"), Term::Constant(EDGE), @@ -1030,7 +966,6 @@ mod tests { estimator.estimate_cost(&PhysicalOperator::bind_join(left.clone(), right.clone())); let hash = estimator.estimate_cost(&PhysicalOperator::hash_join(left, right)); - // `HashJoin - BindJoin` used to be non-negative for every input, so the bind join always won assert!( hash < bind, "a cross product must not re-read the right side per row: hash={} bind={}", @@ -1045,7 +980,6 @@ mod tests { let estimator = CostEstimator::new(&stats); let scanned = pattern(variable("?x"), Term::Constant(EDGE), variable("?y")); - // `TableScan` and `IndexScan` dispatch to the same executor function assert_eq!( estimator.estimate_cost(&PhysicalOperator::quad_table_scan(scanned.clone())), estimator.estimate_cost(&PhysicalOperator::quad_index_scan(scanned)), @@ -1058,7 +992,6 @@ mod tests { let estimator = CostEstimator::new(&stats); let ground = pattern(Term::Constant(1), Term::Constant(EDGE), Term::Constant(2)); - // Cardinality already accounts for boundness; discounting a second time assert!( estimator.estimate_cost(&PhysicalOperator::quad_index_scan(ground)) > 0, "a ground scan still has to probe the index" @@ -1085,7 +1018,6 @@ mod tests { let input_cost = estimator.estimate_cost(&input); let filtered = estimator.estimate_cost(&PhysicalOperator::filter(input, condition)); - // Selectivity reduces how many rows survive, not how much work was done assert!( filtered >= input_cost, "filtering added work: input={} filtered={}", @@ -1101,7 +1033,6 @@ mod tests { let content: Bindings = vec![HashMap::new(), HashMap::new(), HashMap::new()].into(); let buffer = PhysicalOperator::buffer(content, "test".to_string()); - // A buffer reporting zero rows makes every join above it estimate an empty result assert_eq!(estimator.estimate_output_cardinality(&buffer), 3); } @@ -1113,7 +1044,6 @@ mod tests { let estimator = CostEstimator::new(&stats); let inner = unbound_scan(GraphTerm::Default); - // Graph 42 does not exist, so nothing under it can be read. The cost let scoped = PhysicalOperator::graph(inner, GraphTerm::Named(42)); assert_eq!(estimator.estimate_output_cardinality(&scoped), 0); @@ -1129,7 +1059,6 @@ mod tests { stats.graph_cardinalities.insert(GraphId::Named(10), 7); stats.graph_cardinalities.insert(GraphId::Named(20), 0); - // FROM <10> creates a seven-row query default. FROM NAMED <20> let dataset = DatasetView::new([GraphId::Named(10)], [GraphId::Named(20)]); let estimator = CostEstimator::with_dataset(&stats, &dataset); diff --git a/kolibrie/src/streamertail_optimizer/cost/mod.rs b/kolibrie/src/streamertail_optimizer/cost/mod.rs index 99806ff7..0508feb3 100644 --- a/kolibrie/src/streamertail_optimizer/cost/mod.rs +++ b/kolibrie/src/streamertail_optimizer/cost/mod.rs @@ -8,10 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Estimating what a plan will cost. -//! -//! Ranks alternative physical plans by predicted cardinality and work. The estimates -//! only need to order plans correctly, not predict absolute runtime. +//! Estimating what a plan will cost pub mod estimator; diff --git a/kolibrie/src/streamertail_optimizer/execution/engine.rs b/kolibrie/src/streamertail_optimizer/execution/engine.rs index 08f0082b..d7b23cdb 100644 --- a/kolibrie/src/streamertail_optimizer/execution/engine.rs +++ b/kolibrie/src/streamertail_optimizer/execution/engine.rs @@ -8,16 +8,13 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`ExecutionEngine`]: running a physical plan against a store. -//! -//! Walks the chosen plan and produces solutions. Every algorithmic decision was -//! already made during optimisation, so this makes none. +//! [`ExecutionEngine`]: running a physical plan against a store use super::super::operators::PhysicalOperator; use super::super::types::{SubqueryProjection, SubquerySpec}; use super::exec_stats::exec_count; -use crate::sparql_database::SparqlDatabase; +use crate::database::KolibrieDB; use crate::term_order; use rayon::prelude::*; @@ -29,7 +26,8 @@ use shared::terms::{Bindings, Term, UNBOUND}; use std::collections::{BTreeMap, HashMap, HashSet}; use std::sync::Arc; -/// Below this many rows a bind join runs on one thread, since splitting costs more in plan re-entry than it saves +/// Below this many rows a bind join runs on one thread, since splitting costs more in plan re-entry +/// than it saves const BIND_JOIN_MIN_CHUNK: usize = 64; /// Rows a FILTER evaluates under a single dictionary read guard @@ -38,11 +36,9 @@ const FILTER_LOCK_CHUNK: usize = 1024; /// The RDF dataset visible to one query execution #[derive(Debug, Clone, PartialEq, Eq)] pub struct DatasetView { - /// Graphs forming the default graph. `FROM` replaces it, so this is not always - /// just [`GraphId::Default`]. + /// Graphs forming the default graph pub default_graphs: Vec, - /// Graphs `GRAPH` can address. A graph in the store but absent here is - /// invisible to this query. + /// Graphs `GRAPH` can address pub named_graphs: HashSet, } @@ -52,7 +48,7 @@ mod tests { use crate::streamertail_optimizer::Condition; use shared::dataset_index::Quad; - fn encode(database: &SparqlDatabase, value: &str) -> u32 { + fn encode(database: &KolibrieDB, value: &str) -> u32 { database.dictionary.write().encode(value) } @@ -79,7 +75,6 @@ mod tests { #[test] fn hash_join_agrees_with_the_nested_loop_on_every_row_shape() { let cases: Vec<(Bindings, Bindings)> = vec![ - // Shared variable, several matches and one dangling value ( vec![row(&[("x", 1), ("a", 10)]), row(&[("x", 2), ("a", 20)])].into(), vec![ @@ -89,19 +84,15 @@ mod tests { ] .into(), ), - // Right side leaves the key variable unbound ( vec![row(&[("x", 1), ("a", 10)])].into(), vec![row(&[("b", 11)]), row(&[("x", 1), ("b", 12)])].into(), ), - // Left side leaves the key variable unbound ( vec![row(&[("a", 10)]), row(&[("x", 1), ("a", 11)])].into(), vec![row(&[("x", 1), ("b", 12)]), row(&[("x", 2), ("b", 13)])].into(), ), - // Conflicting values on the shared variable (vec![row(&[("x", 1)])].into(), vec![row(&[("x", 2)])].into()), - // No shared variable at all: a Cartesian product ( vec![row(&[("a", 1)]), row(&[("a", 2)])].into(), vec![row(&[("b", 3)]), row(&[("b", 4)])].into(), @@ -124,7 +115,7 @@ mod tests { #[test] fn unit_and_union_preserve_solution_multiplicity() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let plan = PhysicalOperator::union(vec![PhysicalOperator::unit(), PhysicalOperator::unit()]); @@ -134,7 +125,7 @@ mod tests { #[test] fn graph_variable_binds_before_filter_and_includes_empty_graphs() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let first = encode(&database, "http://example.com/first"); let second = encode(&database, "http://example.com/second"); database.dataset_index.create_graph(GraphId::Named(first)); @@ -164,7 +155,7 @@ mod tests { #[test] fn graph_scan_checks_repeated_variables_against_incoming_bindings() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let graph = encode(&database, "http://example.com/graph"); let subject = encode(&database, "http://example.com/subject"); let predicate = encode(&database, "http://example.com/predicate"); @@ -197,7 +188,7 @@ mod tests { #[test] fn scan_matches_a_variable_repeated_within_one_pattern() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let predicate = encode(&database, "http://example.com/self"); let looping = encode(&database, "http://example.com/looping"); let other = encode(&database, "http://example.com/other"); @@ -228,7 +219,7 @@ mod tests { #[test] fn merged_default_suppresses_duplicate_triples() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let graph = encode(&database, "http://example.com/graph"); let subject = encode(&database, "http://example.com/subject"); let predicate = encode(&database, "http://example.com/predicate"); @@ -261,7 +252,7 @@ mod tests { } fn scan( - database: &SparqlDatabase, + database: &KolibrieDB, pattern: &QuadPattern, dataset: DatasetView, incoming: Bindings, @@ -271,7 +262,7 @@ mod tests { } fn candidates( - database: &SparqlDatabase, + database: &KolibrieDB, graph: GraphId, subject: Option, predicate: Option, @@ -285,13 +276,7 @@ mod tests { .collect() } - fn add( - database: &mut SparqlDatabase, - subject: u32, - predicate: u32, - object: u32, - graph: GraphId, - ) { + fn add(database: &mut KolibrieDB, subject: u32, predicate: u32, object: u32, graph: GraphId) { database.add_quad(Quad { subject, predicate, @@ -306,7 +291,7 @@ mod tests { #[test] fn scan_emits_zero_one_and_many_matches_in_candidate_order() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let predicate = encode(&database, "http://example.com/p"); let many = encode(&database, "http://example.com/many"); let one = encode(&database, "http://example.com/one"); @@ -358,7 +343,7 @@ mod tests { #[test] fn scan_rejects_repeated_variable_conflicts_before_extending_rows() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let predicate = encode(&database, "http://example.com/p"); let b = encode(&database, "http://example.com/b"); let a = encode(&database, "http://example.com/a"); @@ -369,10 +354,6 @@ mod tests { add(&mut database, c, predicate, c, GraphId::Default); add(&mut database, c, predicate, d, GraphId::Default); - // `candidates` yields the index's hash order, which varies per process, so - // this fixture can only be asserted on by content and never by position. - // It must contain both self-referential and non-self-referential quads for - // the conflict rejection below to be exercised at all. let all = candidates(&database, GraphId::Default, None, Some(predicate), None); assert!(all.iter().any(|(subject, _, object)| subject == object)); assert!(all.iter().any(|(subject, _, object)| subject != object)); @@ -395,9 +376,6 @@ mod tests { .into(), ); - // Unbound `row(&[])` matches both self-referential quads; `x = a` extends and - // keeps its other bindings; `x = d` is rejected because the only quad with - // object `d` has subject `c`, and `?x`/`$x` are the same variable. let mut expected: Bindings = all .iter() .filter(|(subject, _, object)| subject == object) @@ -405,14 +383,12 @@ mod tests { .collect(); assert_eq!(expected.len(), 2); expected.push(row(&[("x", a), ("keep", 7)])); - // Compared as a multiset: scan makes no ordering guarantee (bag semantics, - // no ORDER BY) and its row order follows the hash order above. assert_eq!(sorted(results), sorted(expected)); } #[test] fn scan_binds_visible_named_graphs_once_per_match() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let predicate = encode(&database, "http://example.com/p"); let subject = encode(&database, "http://example.com/s"); let first = encode(&database, "http://example.com/g1"); @@ -535,7 +511,7 @@ mod tests { #[test] fn scan_suppresses_merged_default_duplicates_for_each_input_row() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let graph = encode(&database, "http://example.com/graph"); let subject = encode(&database, "http://example.com/subject"); let predicate = encode(&database, "http://example.com/predicate"); @@ -587,7 +563,7 @@ mod tests { #[test] fn scan_matches_quoted_triples_through_the_general_path() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let predicate = encode(&database, "http://example.com/p"); let source = encode(&database, "http://example.com/source"); let first = encode(&database, "http://example.com/first"); @@ -651,7 +627,7 @@ mod tests { #[test] fn values_keeps_all_undef_and_heterogeneous_union_rows() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let value = encode(&database, "value"); let plan = PhysicalOperator::union(vec![ PhysicalOperator::values(vec!["?x".to_string()], vec![vec![Some(value)]]), @@ -667,7 +643,7 @@ mod tests { impl DatasetView { /// The normal database dataset: the physical default graph plus every - pub fn from_database(database: &SparqlDatabase) -> Self { + pub fn from_database(database: &KolibrieDB) -> Self { Self { default_graphs: vec![GraphId::Default], named_graphs: database.dataset_index.named_graphs().into_iter().collect(), @@ -699,24 +675,24 @@ impl DatasetView { Self::new(std::iter::empty(), named_graphs) } - /// Whether `GRAPH` may address this graph in the current dataset. + /// Whether `GRAPH` may address this graph in the current dataset pub fn is_named_visible(&self, graph: GraphId) -> bool { matches!(graph, GraphId::Named(_)) && self.named_graphs.contains(&graph) } } -/// Recursive execution state. An active graph is established by a `GRAPH` +/// Recursive execution state #[derive(Debug, Clone, PartialEq, Eq)] pub struct ExecutionContext { /// Shared, so deriving a child context per `GRAPH` scope does not rebuild the graph sets pub dataset: Arc, - /// Graph established by an enclosing `GRAPH` clause, or `None` at the top - /// level, where patterns match the default graph. + /// Graph established by an enclosing `GRAPH` clause, or `None` at the top level, where patterns + /// match the default graph pub active_graph: Option, } impl ExecutionContext { - /// A top-level context over `dataset`, with no active graph. + /// A top-level context over `dataset`, with no active graph pub fn new(dataset: DatasetView) -> Self { Self { dataset: Arc::new(dataset), @@ -732,33 +708,25 @@ impl ExecutionContext { } } -/// Where one of a pattern's positions sends its value. +/// Where one of a pattern's positions sends its value #[derive(Debug, Clone, Copy)] enum ScanSlot { - /// A fixed term: the candidate must equal it. + /// A fixed term: the candidate must equal it Constant(u32), - /// A variable: the candidate goes in this column, or must agree with it. + /// A variable: the candidate goes in this column, or must agree with it Column(usize), - /// A quoted triple pattern, which binds a variable number of nested variables - /// and so takes the general path. + /// A quoted triple pattern, which binds a variable number of nested variables and so takes the + /// general path Quoted, } -/// How a scan writes a pattern's matches into rows of its output schema. -/// -/// Built once per scan rather than per row. The map form resolved `?s`, `?p` and `?o` -/// by hashing their names again for every candidate quad, which is what a scan does -/// most of; here each position knows its column before the loop starts. -/// -/// The output schema is the incoming schema followed by whatever the pattern adds, so -/// a base row is the incoming row padded with [`UNBOUND`] and needs no remapping. +/// How a scan writes a pattern's matches into rows of its output schema #[derive(Debug)] struct ScanLayout { schema: Vec, subject: ScanSlot, predicate: ScanSlot, object: ScanSlot, - /// Where a variable graph name is written, if the pattern has one. graph: Option, } @@ -768,8 +736,6 @@ impl ScanLayout { let slot = |term: &Term, schema: &mut Vec| match term { Term::Constant(value) => ScanSlot::Constant(*value), Term::QuotedTriple(triple) => { - // The nested components bind variables too, and they need columns - // even though the general path fills them by name. fn add_nested(term: &Term, schema: &mut Vec) { match term { Term::Variable(variable) => { @@ -831,7 +797,7 @@ impl ScanLayout { self.schema.len() } - /// An incoming row widened to the output schema. + /// An incoming row widened to the output schema fn base_row(&self, incoming: &[u32]) -> Vec { let mut row = Vec::with_capacity(self.width()); row.extend_from_slice(incoming); @@ -839,14 +805,14 @@ impl ScanLayout { row } - /// Whether any position is a quoted triple pattern. + /// Whether any position is a quoted triple pattern fn has_quoted(&self) -> bool { matches!(self.subject, ScanSlot::Quoted) || matches!(self.predicate, ScanSlot::Quoted) || matches!(self.object, ScanSlot::Quoted) } - /// The value a slot already has in `row`, which is what an index probe keys on. + /// The value a slot already has in `row`, which is what an index probe keys on fn probe(&self, slot: ScanSlot, row: &[u32]) -> Option { match slot { ScanSlot::Constant(value) => Some(value), @@ -858,7 +824,7 @@ impl ScanLayout { } } - /// The three index-probe keys for a row. + /// The three index-probe keys for a row fn probe_keys(&self, row: &[u32]) -> (Option, Option, Option) { ( self.probe(self.subject, row), @@ -867,10 +833,7 @@ impl ScanLayout { ) } - /// Writes one position's value into `row`, or rejects the candidate. - /// - /// A variable already bound -- by the incoming row, or by an earlier position of - /// this same pattern -- must agree, which is what makes `?x p ?x` a self-join. + /// Writes one position's value into `row`, or rejects the candidate fn place(&self, slot: ScanSlot, value: u32, row: &mut [u32]) -> bool { match slot { ScanSlot::Constant(constant) => constant == value, @@ -887,19 +850,11 @@ impl ScanLayout { } } -/// How two sequences' columns line up in the sequence their join produces. -/// -/// Built once per join rather than per row, which is the point of the shape: the -/// map form resolved every variable name again for every pair of rows it -/// considered, and a join considers a lot of pairs. +/// How two sequences' columns line up in the sequence their join produces struct JoinLayout { - /// The joined schema: the left's columns, then the right's new ones. schema: Vec, - /// Where each joined column comes from on the left, if anywhere. from_left: Vec>, - /// Where each joined column comes from on the right, if anywhere. from_right: Vec>, - /// Joined columns both sides carry: the variables a pair must agree on. shared: Vec, } @@ -930,11 +885,7 @@ impl JoinLayout { } } - /// Merges a pair if they agree on every shared variable. - /// - /// SPARQL compatibility: the two must agree only where they *both* bind, so a - /// variable one side leaves unbound takes the other side's value. That is what - /// makes `OPTIONAL` and `VALUES ... UNDEF` join correctly. + /// Merges a pair if they agree on every shared variable fn merge(&self, left: &[u32], right: &[u32]) -> Option> { let mut merged = Vec::with_capacity(self.schema.len()); for column in 0..self.schema.len() { @@ -950,9 +901,7 @@ impl JoinLayout { Some(merged) } - /// A row's key over the shared variables, or `None` if it leaves one unbound. - /// - /// An unkeyed row cannot be hashed, because it is compatible with anything. + /// A row's key over the shared variables, or `None` if it leaves one unbound fn key(&self, row: &[u32], side: &[Option]) -> Option> { self.shared .iter() @@ -972,7 +921,7 @@ impl ExecutionEngine { /// Executes a physical operator and returns string results pub fn execute( operator: &PhysicalOperator, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Vec> { let dataset = DatasetView::from_database(database); Self::execute_with_dataset(operator, database, &dataset) @@ -981,12 +930,11 @@ impl ExecutionEngine { /// Executes a plan against an explicit SPARQL dataset and decodes the results pub fn execute_with_dataset( operator: &PhysicalOperator, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, dataset: &DatasetView, ) -> Vec> { let id_results = Self::execute_with_ids_and_dataset(operator, database, dataset); - // Convert ID results to string results only at the final step id_results .into_par_iter() .chunks(FILTER_LOCK_CHUNK) @@ -1013,10 +961,7 @@ impl ExecutionEngine { } /// Executes a physical operator and returns ID-based results for performance - pub fn execute_with_ids( - operator: &PhysicalOperator, - database: &mut SparqlDatabase, - ) -> Bindings { + pub fn execute_with_ids(operator: &PhysicalOperator, database: &mut KolibrieDB) -> Bindings { let dataset = DatasetView::from_database(database); Self::execute_with_ids_and_dataset(operator, database, &dataset) } @@ -1024,7 +969,7 @@ impl ExecutionEngine { /// Executes a plan against an explicit SPARQL dataset pub fn execute_with_ids_and_dataset( operator: &PhysicalOperator, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, dataset: &DatasetView, ) -> Bindings { let context = ExecutionContext::new(dataset.clone()); @@ -1034,7 +979,7 @@ impl ExecutionEngine { /// Executes a plan with an explicit recursive execution context pub fn execute_with_ids_and_context( operator: &PhysicalOperator, - database: &SparqlDatabase, + database: &KolibrieDB, context: &ExecutionContext, ) -> Bindings { Self::execute_with_ids_and_input(operator, database, context, Bindings::unit()) @@ -1043,14 +988,10 @@ impl ExecutionEngine { /// Executes a plan using the supplied solution sequence as input pub fn execute_with_ids_and_input( operator: &PhysicalOperator, - database: &SparqlDatabase, + database: &KolibrieDB, context: &ExecutionContext, incoming: Bindings, ) -> Bindings { - // `PhysicalOperator::output_variables` has to name every variable the plan can - // bind, because a schema-and-tuples result has nowhere to put one it missed. - // Checking it here, in debug only, puts every test and example in the - // workspace behind that function rather than behind a handful of unit tests. #[cfg(debug_assertions)] let declared = operator.output_variables(incoming.schema()); @@ -1073,7 +1014,7 @@ impl ExecutionEngine { fn execute_with_ids_and_input_inner( operator: &PhysicalOperator, - database: &SparqlDatabase, + database: &KolibrieDB, context: &ExecutionContext, mut incoming: Bindings, ) -> Bindings { @@ -1093,7 +1034,6 @@ impl ExecutionEngine { let mut results = Bindings::empty(); let last = branches.len().saturating_sub(1); for (index, branch) in branches.iter().enumerate() { - // Every branch evaluates against the same input, so only the last one may consume it let branch_input = if index == last { std::mem::take(&mut incoming) } else { @@ -1114,9 +1054,6 @@ impl ExecutionEngine { PhysicalOperator::Filter { input, condition } => { let input_results = Self::execute_with_ids_and_input(input, database, context, incoming); - // The guard is taken per chunk rather than per row: one atomic. - // Rows stay as cells throughout; rebuilding a map to ask the - // condition about one was most of what this used to cost. let (schema, cells) = input_results.into_parts(); let kept: Vec> = cells .into_par_iter() @@ -1137,14 +1074,11 @@ impl ExecutionEngine { let input_results = Self::execute_with_ids_and_input(input, database, context, incoming); - // Strip '?' prefix from projection variables for matching let stripped_vars: Vec = variables .iter() .map(|v| Self::normalize_variable(v).to_string()) .collect(); - // Projection is a column selection: the columns are resolved once - // and every row is copied straight across them. let columns: Vec = stripped_vars .iter() .filter_map(|variable| input_results.column(variable)) @@ -1204,7 +1138,6 @@ impl ExecutionEngine { Self::join_solution_sequences(incoming, content.clone()) } PhysicalOperator::Subquery { inner, spec } => { - // A subquery has its own variable scope and is evaluated once let inner_results = Self::execute_with_ids_and_input(inner, database, context, Bindings::unit()); let finalized = Self::finalize_subquery(inner_results, spec, database); @@ -1221,7 +1154,6 @@ impl ExecutionEngine { let output_var = Self::normalize_variable(output_variable); if function_name == "CONCAT" { - // Decode all needed values first let dict = database.dictionary.read(); let decoded_values: Vec> = input_results .iter() @@ -1245,7 +1177,6 @@ impl ExecutionEngine { .collect(); drop(dict); - // Now encode the concatenated results let mut dict_write = database.dictionary.write(); let encoded: Vec = decoded_values .iter() @@ -1256,7 +1187,6 @@ impl ExecutionEngine { input_results } else if let Some(func) = database.udf(function_name.as_str()) { - // Similar fix for UDF let dict = database.dictionary.read(); let decoded_args: Vec> = input_results .iter() @@ -1298,9 +1228,6 @@ impl ExecutionEngine { || function_name == "OBJECT" { let qt_store = database.quoted_triple_store.read().unwrap(); - // One component per solution, then one column. A solution whose - // argument is not a quoted triple contributes UNBOUND, which is - // what not inserting into its map used to mean. let components: Vec = input_results .iter() .map(|row| { @@ -1329,9 +1256,6 @@ impl ExecutionEngine { input_results } else if function_name == "TRIPLE" { if arguments.len() == 3 { - // One quoted triple per solution, then one column. A solution - // missing any component contributes UNBOUND, which is what - // not inserting into its map used to mean. let quoted: Vec = input_results .iter() .map(|row| { @@ -1392,7 +1316,6 @@ impl ExecutionEngine { .map(|v| Self::normalize_variable(v).to_string()) .collect(); - // Convert VALUES data to result rows let mut results = Bindings::empty(); for value_row in values { @@ -1404,7 +1327,6 @@ impl ExecutionEngine { } } - // An all-UNDEF row is still the unit solution mapping results.push(row); } @@ -1464,11 +1386,7 @@ impl ExecutionEngine { value.starts_with('?') || value.starts_with('$') } - fn finalize_subquery( - rows: Bindings, - spec: &SubquerySpec, - database: &SparqlDatabase, - ) -> Bindings { + fn finalize_subquery(rows: Bindings, spec: &SubquerySpec, database: &KolibrieDB) -> Bindings { let mut rows = Self::aggregate_subquery_rows(rows, spec, database); Self::apply_subquery_order(&mut rows, &spec.order_conditions, database); @@ -1477,7 +1395,6 @@ impl ExecutionEngine { .iter() .map(Self::subquery_output_variable) .collect(); - // Projection selects columns rather than rewriting every row's map. let kept: Vec = rows .schema() .iter() @@ -1517,7 +1434,7 @@ impl ExecutionEngine { fn aggregate_subquery_rows( rows: Bindings, spec: &SubquerySpec, - database: &SparqlDatabase, + database: &KolibrieDB, ) -> Bindings { let aggregates: Vec<&SubqueryProjection> = spec .projection @@ -1545,7 +1462,6 @@ impl ExecutionEngine { groups .into_iter() .map(|(key, group)| { - // Only the grouping key and the aggregates describe the group let mut result = HashMap::new(); for (variable, id) in spec.group_vars.iter().zip(key) { if let Some(id) = id { @@ -1594,17 +1510,12 @@ impl ExecutionEngine { fn apply_subquery_order( rows: &mut Bindings, conditions: &[(String, SortDirection)], - database: &SparqlDatabase, + database: &KolibrieDB, ) { if conditions.is_empty() { return; } - // Decode each ordering value once. Decoding inside the comparator - // - // Taken, not copied: the sorted rows are pushed back below, so leaving the - // originals in place would append the sorted sequence to the unsorted one and - // a following LIMIT would keep the wrong rows. let taken = std::mem::take(rows); let mut keyed: Vec<(Vec>, HashMap)> = taken .into_iter() @@ -1644,7 +1555,7 @@ impl ExecutionEngine { } fn execute_graph_with_ids( - database: &SparqlDatabase, + database: &KolibrieDB, input: &PhysicalOperator, graph: &GraphTerm, context: &ExecutionContext, @@ -1716,16 +1627,14 @@ impl ExecutionEngine { } fn execute_quad_scan_with_ids( - database: &SparqlDatabase, + database: &KolibrieDB, pattern: &QuadPattern, context: &ExecutionContext, incoming: Bindings, ) -> Bindings { - // Columns resolved once for the whole scan, not once per candidate quad. let layout = ScanLayout::new(pattern, incoming.schema()); let mut rows: Vec> = Vec::new(); - // The visible graph set is the same for every incoming row, so it is built once let visible_graphs: Vec = if matches!(pattern.graph, GraphTerm::Variable(_)) { let mut graphs: Vec = context.dataset.named_graphs.iter().copied().collect(); graphs.sort_unstable(); @@ -1767,7 +1676,6 @@ impl ExecutionEngine { GraphTerm::Variable(_) => { let column = layout.graph.expect("a variable graph has a column"); if base[column] != UNBOUND { - // The incoming row already fixed the graph name. let graph = GraphId::Named(base[column]); if context.dataset.is_named_visible(graph) && database.dataset_index.graph_exists(graph) @@ -1803,7 +1711,7 @@ impl ExecutionEngine { } fn scan_query_default( - database: &SparqlDatabase, + database: &KolibrieDB, pattern: &QuadPattern, layout: &ScanLayout, context: &ExecutionContext, @@ -1811,7 +1719,6 @@ impl ExecutionEngine { rows: &mut Vec>, ) { let (subject, predicate, object) = layout.probe_keys(base); - // Merging several graphs into one query default can surface the same triple twice let merges_graphs = context.dataset.default_graphs.len() > 1; let mut seen = HashSet::new(); @@ -1841,7 +1748,7 @@ impl ExecutionEngine { } fn scan_one_graph( - database: &SparqlDatabase, + database: &KolibrieDB, pattern: &QuadPattern, layout: &ScanLayout, graph: GraphId, @@ -1849,10 +1756,6 @@ impl ExecutionEngine { base: &[u32], rows: &mut Vec>, ) { - // The graph binding does not depend on the candidate quad, so it is placed - // once per row rather than per quad -- and only copied when there is one to - // place. A variable graph calls this once per visible graph per row, so an - // unconditional copy here was an allocation per pair. let mut placed: Option> = None; if let (Some(graph_id), Some(column)) = (fresh_graph, layout.graph) { if base[column] != UNBOUND && base[column] != graph_id { @@ -1887,7 +1790,7 @@ impl ExecutionEngine { #[allow(clippy::too_many_arguments)] fn match_quad( - database: &SparqlDatabase, + database: &KolibrieDB, pattern: &QuadPattern, layout: &ScanLayout, subject: u32, @@ -1899,8 +1802,6 @@ impl ExecutionEngine { ) { let _ = fresh_graph; - // Quoted triples bind a variable number of nested variables, so they keep the - // general map-based path. Every other pattern writes at most four cells. if layout.has_quoted() { let mut bindings: HashMap = layout .schema @@ -1942,7 +1843,7 @@ impl ExecutionEngine { } fn match_term_with_store( - database: &SparqlDatabase, + database: &KolibrieDB, term: &Term, value: u32, bindings: &mut HashMap, @@ -1976,10 +1877,11 @@ impl ExecutionEngine { } } - /// Feeds a solution sequence into a dependent plan in parallel chunks, concatenated in chunk order to match a sequential pass + /// Feeds a solution sequence into a dependent plan in parallel chunks, concatenated in chunk + /// order to match a sequential pass fn execute_bind_join( right: &PhysicalOperator, - database: &SparqlDatabase, + database: &KolibrieDB, context: &ExecutionContext, left_results: Bindings, ) -> Bindings { @@ -2032,7 +1934,8 @@ impl ExecutionEngine { Bindings::from_parts(layout.schema.clone(), rows) } - /// Build/probe join that builds on the right side and probes in parallel with the left, so emitted order matches the nested loop + /// Build/probe join that builds on the right side and probes in parallel with the left, so + /// emitted order matches the nested loop fn hash_join_solution_sequences(left: Bindings, right: Bindings) -> Bindings { if left.is_empty() || right.is_empty() { return Bindings::empty(); @@ -2052,7 +1955,6 @@ impl ExecutionEngine { } } - // Only a left row that leaves a key variable unbound has to consider let any_left_row_unkeyed = left .cells() .iter() @@ -2067,7 +1969,6 @@ impl ExecutionEngine { .cells() .par_iter() .flat_map_iter(|left_row| { - // A fully bound left row probes the table plus the unhashable rows; a partially bound one can match anything let (probed, residual) = match layout.key(left_row, &layout.from_left) { Some(key) => ( table.get(&key).map(Vec::as_slice).unwrap_or(&[]), @@ -2089,7 +1990,7 @@ impl ExecutionEngine { mut input_results: Bindings, predictions: Vec, output_variable: &str, - database: &SparqlDatabase, + database: &KolibrieDB, ) -> Bindings { let output_var = output_variable.strip_prefix('?').unwrap_or(output_variable); diff --git a/kolibrie/src/streamertail_optimizer/execution/exec_stats.rs b/kolibrie/src/streamertail_optimizer/execution/exec_stats.rs index e1570ad3..db7f6dde 100644 --- a/kolibrie/src/streamertail_optimizer/execution/exec_stats.rs +++ b/kolibrie/src/streamertail_optimizer/execution/exec_stats.rs @@ -8,12 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Execution work counters behind the `exec-stats` feature. -//! -//! Counters are process-global because scans run across rayon threads. Totals -//! are reproducible for a given plan and dataset, but concurrent queries share -//! them, so a reader must call `reset` first and run one query at a time. -//! (`reset` and `snapshot` exist only under the `exec-stats` feature.) +//! Execution work counters behind the `exec-stats` feature #[cfg(feature = "exec-stats")] mod counters { @@ -23,7 +18,7 @@ mod counters { pub static QUADS_EXAMINED: AtomicU64 = AtomicU64::new(0); pub static ROWS_EMITTED: AtomicU64 = AtomicU64::new(0); - /// Work performed by one execution. + /// Work performed by one execution #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub struct Snapshot { pub scan_probes: u64, diff --git a/kolibrie/src/streamertail_optimizer/execution/mod.rs b/kolibrie/src/streamertail_optimizer/execution/mod.rs index 5059f4af..7a829962 100644 --- a/kolibrie/src/streamertail_optimizer/execution/mod.rs +++ b/kolibrie/src/streamertail_optimizer/execution/mod.rs @@ -8,7 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Running a chosen physical plan. +//! Running a chosen physical plan pub mod engine; pub mod exec_stats; diff --git a/kolibrie/src/streamertail_optimizer/mod.rs b/kolibrie/src/streamertail_optimizer/mod.rs index 2a4f9127..367252b2 100644 --- a/kolibrie/src/streamertail_optimizer/mod.rs +++ b/kolibrie/src/streamertail_optimizer/mod.rs @@ -8,44 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Cost-based query optimisation, Volcano-style. -//! -//! A query has many possible execution strategies with wildly different costs. This -//! module picks one by enumerating alternatives and costing each against statistics -//! about the data, rather than following the order the query was written in. -//! -//! ```text -//! LogicalOperator ──optimizer──▶ PhysicalOperator ──ExecutionEngine──▶ solutions -//! (what to compute) │ (how to compute it) -//! └── CostEstimator over DatabaseStats -//! ``` -//! -//! | module | role | -//! |---|---| -//! | [`operators`] | the two plan representations: logical *what*, physical *how* | -//! | [`cost`] | cardinality and cost estimates that rank the alternatives | -//! | [`crate::stats`] | the data statistics those estimates read, a layer below this one | -//! | [`optimizer`] | [`Streamertail`], which does the enumeration and selection | -//! | [`execution`] | runs a chosen physical plan | -//! | [`types`] | filter conditions and subquery descriptions shared by both plans | -//! | [`utils`] | building a logical plan from a parsed query | -//! -//! # The logical/physical split -//! -//! One logical operator usually has several physical realisations, and choosing -//! between them is the whole point. A [`LogicalOperator::Join`] can become a hash -//! join, a nested-loop join, a star join, or a bind join that pushes the left side's -//! bindings into the right side's index probes — and which is fastest depends on -//! cardinalities the query text cannot express. -//! -//! # Estimates are estimates -//! -//! The cost model reads [`DatabaseStats`], which are cached and refreshed according -//! to the store's -//! [`PlanningStatsPolicy`](crate::sparql_database::PlanningStatsPolicy). Under the -//! default bounded policy they may lag the data, so a plan can be costed against -//! slightly stale cardinalities. That trades planning accuracy for not rebuilding -//! statistics on every write. +//! Cost-based query optimisation, Volcano-style pub mod cost; pub mod execution; @@ -54,7 +17,6 @@ pub mod optimizer; pub mod types; pub mod utils; -// Re-export main components for convenience pub use crate::stats::{DatabaseStats, GraphMap, TermMap, TermSet}; pub use cost::{CostConstants, CostEstimator}; pub use execution::{DatasetView, ExecutionContext, ExecutionEngine}; @@ -63,10 +25,9 @@ pub use optimizer::Streamertail; pub use types::{ Condition, ConditionArithmetic, ConditionExpression, IdResult, SubqueryProjection, SubquerySpec, }; +#[allow(deprecated)] +pub use utils::estimate_operator_selectivity; pub use utils::{ build_logical_plan, build_logical_plan_from_group, build_logical_plan_from_subquery, compile_graph_term, compile_term, compile_triple, extract_pattern, pattern_contains_variable, }; -// Re-exported so existing paths keep resolving, though the function is deprecated -#[allow(deprecated)] -pub use utils::estimate_operator_selectivity; diff --git a/kolibrie/src/streamertail_optimizer/operators/logical.rs b/kolibrie/src/streamertail_optimizer/operators/logical.rs index 1f042368..488ba48b 100644 --- a/kolibrie/src/streamertail_optimizer/operators/logical.rs +++ b/kolibrie/src/streamertail_optimizer/operators/logical.rs @@ -8,11 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`LogicalOperator`]: what a query computes, independent of how. -//! -//! Relational algebra over solution mappings. A logical plan is what the parser's -//! output becomes and what the optimizer consumes; it says nothing about join -//! algorithms or index choice. +//! [`LogicalOperator`]: what a query computes, independent of how use super::super::types::SubquerySpec; use super::super::Condition; @@ -21,114 +17,99 @@ use shared::terms::{Bindings, TriplePattern}; /// Logical operators represent the high-level query structure before optimization #[derive(Debug, Clone)] -/// What a query computes: relational algebra over solution mappings. -/// -/// Says nothing about how. [`Join`](Self::Join) does not name an algorithm and -/// [`Scan`](Self::Scan) does not name an index — choosing those is what -/// [`Streamertail`](super::super::Streamertail) does. +/// What a query computes: relational algebra over solution mappings pub enum LogicalOperator { - /// The SPARQL unit table: one empty solution mapping. + /// The SPARQL unit table: one empty solution mapping Unit, - /// Match a quad pattern against the store. + /// Match a quad pattern against the store Scan { - /// The pattern to match; variables are wildcards. + /// The pattern to match; variables are wildcards pattern: QuadPattern, }, - /// Multiset union. Branches are optimizer boundaries and retain duplicates. + /// Multiset union Union { - /// The branches, each optimised independently. + /// The branches, each optimised independently branches: Vec, }, - /// Evaluate an input pattern with the given active named graph. + /// Evaluate an input pattern with the given active named graph Graph { - /// Pattern evaluated inside that graph. + /// Pattern evaluated inside that graph input: Box, - /// Which graph: fixed, or a variable bound to each in turn. + /// Which graph: fixed, or a variable bound to each in turn graph: GraphTerm, }, - /// Keep only the solutions satisfying a condition. + /// Keep only the solutions satisfying a condition Selection { - /// The input whose solutions are filtered. + /// The input whose solutions are filtered predicate: Box, - /// The condition each surviving solution must satisfy. + /// The condition each surviving solution must satisfy condition: Condition, }, - /// Restrict each solution to a set of variables. - /// - /// Does not remove duplicates: SPARQL is bag semantics, and `DISTINCT` is a - /// separate modifier. + /// Restrict each solution to a set of variables Projection { - /// The input being projected. + /// The input being projected predicate: Box, - /// Variables to retain. + /// Variables to retain variables: Vec, }, - /// Join two inputs on the variables they share. - /// - /// With no shared variable this is a Cartesian product. The physical form is - /// chosen by cost: hash, nested-loop, star, or a bind join that pushes the - /// left side's bindings into the right side's index probes. + /// Join two inputs on the variables they share Join { - /// Left input. + /// Left input left: Box, - /// Right input. + /// Right input right: Box, }, - /// Solutions supplied directly rather than computed, such as a window's - /// content arriving from the stream engine. + /// Solutions supplied directly rather than computed, such as a window's content arriving from + /// the stream engine Buffer { - /// The solutions. + /// The solutions content: Bindings, - /// Where they came from, for diagnostics. + /// Where they came from, for diagnostics origin: String, }, - /// A nested query evaluated independently, then joined with the enclosing - /// pattern on its projected variables. + /// A nested query evaluated independently, then joined with the enclosing pattern on its + /// projected variables Subquery { - /// The nested plan. + /// The nested plan inner: Box, - /// What it projects and how its modifiers apply. + /// What it projects and how its modifiers apply spec: SubquerySpec, }, - /// Compute a new binding from existing ones by calling a registered function. + /// Compute a new binding from existing ones by calling a registered function Bind { - /// The input being extended. + /// The input being extended input: Box, - /// Function to call; resolved against the store's registered UDFs at - /// execution time, not at planning time. + /// Function to call; resolved against the store's registered UDFs at execution time, not at + /// planning time function_name: String, - /// Variables passed as arguments. + /// Variables passed as arguments arguments: Vec, - /// Variable the result binds to. + /// Variable the result binds to output_variable: String, }, - /// An inline table of candidate solutions. + /// An inline table of candidate solutions Values { - /// Column variables, in order. + /// Column variables, in order variables: Vec, - /// Rows; `None` is `UNDEF`, which leaves that variable unbound rather than - /// binding it, so it does not constrain the join. + /// Rows; `None` is `UNDEF`, which leaves that variable unbound rather than binding it, so + /// it does not constrain the join values: Vec>>, }, - /// Bind a variable from a model's prediction over each input solution. + /// Bind a variable from a model's prediction over each input solution MLPredict { - /// Solutions to predict over. + /// Solutions to predict over input: Box, - /// Model to invoke; must be approved by the host's ML policy. + /// Model to invoke; must be approved by the host's ML policy model_name: String, - /// Variables forming the feature vector, in model input order. + /// Variables forming the feature vector, in model input order input_variables: Vec, - /// Variable the prediction binds to. + /// Variable the prediction binds to output_variable: String, }, } impl LogicalOperator { - /// Whether this plan is worth running the join-ordering search on. - /// - /// True when it joins at least two reorderable base patterns, or contains - /// operators the counting cannot see through. A single pattern has no ordering - /// to choose, so the search would only cost time. + /// Whether this plan is worth running the join-ordering search on pub fn requires_cost_based_join_ordering(&self) -> bool { self.reorderable_scan_count() .is_none_or(|patterns| patterns >= 2) @@ -151,7 +132,6 @@ impl LogicalOperator { left.reorderable_scan_count()? .saturating_add(right.reorderable_scan_count()?), ), - // Conservatively require statistics Self::Union { .. } | Self::Subquery { .. } | Self::MLPredict { .. } @@ -160,7 +140,7 @@ impl LogicalOperator { } } - /// Creates the SPARQL unit table. + /// Creates the SPARQL unit table pub fn unit() -> Self { Self::Unit } @@ -175,17 +155,17 @@ impl LogicalOperator { }) } - /// Creates a graph-aware scan logical operator. + /// Creates a graph-aware scan logical operator pub fn quad_scan(pattern: QuadPattern) -> Self { Self::Scan { pattern } } - /// Creates a multiset UNION logical operator. + /// Creates a multiset UNION logical operator pub fn union(branches: Vec) -> Self { Self::Union { branches } } - /// Creates a GRAPH logical operator. + /// Creates a GRAPH logical operator pub fn graph(input: LogicalOperator, graph: GraphTerm) -> Self { Self::Graph { input: Box::new(input), @@ -217,7 +197,7 @@ impl LogicalOperator { } } - /// A plan node supplying solutions directly, such as a window's content. + /// A plan node supplying solutions directly, such as a window's content pub fn buffer(content: Bindings, origin: String) -> Self { Self::Buffer { content, origin } } diff --git a/kolibrie/src/streamertail_optimizer/operators/mod.rs b/kolibrie/src/streamertail_optimizer/operators/mod.rs index 5137072d..8764d898 100644 --- a/kolibrie/src/streamertail_optimizer/operators/mod.rs +++ b/kolibrie/src/streamertail_optimizer/operators/mod.rs @@ -8,11 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The two plan representations. -//! -//! [`LogicalOperator`] says *what* to compute and [`PhysicalOperator`] says *how*. -//! Optimisation is the translation between them, and it is only interesting because -//! the mapping is one-to-many. +//! The two plan representations pub mod logical; pub mod physical; diff --git a/kolibrie/src/streamertail_optimizer/operators/physical.rs b/kolibrie/src/streamertail_optimizer/operators/physical.rs index 552da44f..27ec3abc 100644 --- a/kolibrie/src/streamertail_optimizer/operators/physical.rs +++ b/kolibrie/src/streamertail_optimizer/operators/physical.rs @@ -8,11 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`PhysicalOperator`]: how a query is executed. -//! -//! The optimizer's output. Each node names a concrete algorithm — which join, which -//! index, whether a side is materialised — so running a physical plan involves no -//! further choices. +//! [`PhysicalOperator`]: how a query is executed use super::super::types::SubquerySpec; use super::super::Condition; @@ -21,133 +17,124 @@ use shared::terms::{Bindings, Term, TriplePattern}; /// Physical operators represent the actual execution plan after optimization #[derive(Debug, Clone)] -/// How a query is executed: a concrete algorithm per node. -/// -/// The optimizer's output. Nothing here is left to choose at run time, which is why -/// the join variants name their algorithm rather than saying "join". +/// How a query is executed: a concrete algorithm per node pub enum PhysicalOperator { - /// The SPARQL unit table: one empty solution mapping. + /// The SPARQL unit table: one empty solution mapping Unit, - /// Scan without an index, examining every candidate quad. - /// - /// Dispatches to the same executor as [`IndexScan`](Self::IndexScan); the two - /// are distinguished for costing, not execution. + /// Scan without an index, examining every candidate quad TableScan { - /// Pattern to match. + /// Pattern to match pattern: QuadPattern, }, - /// Scan through an index, visiting only quads that can match. + /// Scan through an index, visiting only quads that can match IndexScan { - /// Pattern to match; its bound positions select the index. + /// Pattern to match; its bound positions select the index pattern: QuadPattern, }, - /// Multiset union. No duplicate elimination is performed. + /// Multiset union Union { - /// Branches, concatenated in order. + /// Branches, concatenated in order branches: Vec, }, - /// Evaluate an input plan under a fixed or variable named graph. + /// Evaluate an input plan under a fixed or variable named graph Graph { - /// Plan evaluated inside that graph. + /// Plan evaluated inside that graph input: Box, - /// Which graph: fixed, or a variable bound to each in turn. + /// Which graph: fixed, or a variable bound to each in turn graph: GraphTerm, }, - /// Drop solutions failing a condition. + /// Drop solutions failing a condition Filter { - /// Plan whose solutions are filtered. + /// Plan whose solutions are filtered input: Box, - /// Condition each surviving solution satisfies. + /// Condition each surviving solution satisfies condition: Condition, }, - /// Pipelined dependent join whose left solutions feed the right side, so its scans probe indexes with bound values + /// Pipelined dependent join whose left solutions feed the right side, so its scans probe + /// indexes with bound values BindJoin { - /// Executed first; each of its solutions drives the right side. + /// Executed first; each of its solutions drives the right side left: Box, - /// Re-entered once per left solution, with those bindings pushed in. + /// Re-entered once per left solution, with those bindings pushed in right: Box, }, /// Build/probe join over the shared-variable key of two independently executed sides HashJoin { - /// Build side. + /// Build side left: Box, - /// Probe side. + /// Probe side right: Box, }, /// Materialized pairwise join over both sides, and the algorithm for Cartesian products NestedLoopJoin { - /// Outer side. + /// Outer side left: Box, - /// Inner side, re-scanned per outer solution. + /// Inner side, re-scanned per outer solution right: Box, }, - /// Join several patterns that all share one variable, in a single pass. - /// - /// Cheaper than a chain of pairwise joins when the shared variable is the only - /// link, because no intermediate result is materialised per pair. + /// Join several patterns that all share one variable, in a single pass StarJoin { - /// The variable every pattern shares. + /// The variable every pattern shares join_var: String, - /// Patterns joined on it. + /// Patterns joined on it patterns: Vec, }, - /// Restrict each solution to a set of variables, keeping duplicates. + /// Restrict each solution to a set of variables, keeping duplicates Projection { - /// Plan being projected. + /// Plan being projected input: Box, - /// Variables retained. + /// Variables retained variables: Vec, }, - /// Solutions supplied directly rather than computed. + /// Solutions supplied directly rather than computed InMemoryBuffer { - /// The solutions. + /// The solutions content: Bindings, - /// Where they came from, for diagnostics. + /// Where they came from, for diagnostics origin: String, }, - /// A nested plan evaluated independently, then joined on its projection. + /// A nested plan evaluated independently, then joined on its projection Subquery { - /// The nested plan. + /// The nested plan inner: Box, - /// What it projects and how its modifiers apply. + /// What it projects and how its modifiers apply spec: SubquerySpec, }, - /// Extend each solution with a value computed by a registered function. + /// Extend each solution with a value computed by a registered function Bind { - /// Plan being extended. + /// Plan being extended input: Box, - /// Function resolved against the store's UDFs at execution time. + /// Function resolved against the store's UDFs at execution time function_name: String, - /// Variables passed as arguments. + /// Variables passed as arguments arguments: Vec, - /// Variable the result binds to. + /// Variable the result binds to output_variable: String, }, - /// An inline table of candidate solutions. + /// An inline table of candidate solutions Values { - /// Column variables, in order. + /// Column variables, in order variables: Vec, - /// Rows; `None` is `UNDEF` and leaves the variable unbound. + /// Rows; `None` is `UNDEF` and leaves the variable unbound values: Vec>>, }, - /// Bind a variable from a model's prediction over each input solution. + /// Bind a variable from a model's prediction over each input solution MLPredict { - /// Solutions to predict over. + /// Solutions to predict over input: Box, - /// Model name, as declared. + /// Model name, as declared model_name: String, - /// Artifact resolved at planning time, so execution does not re-consult the - /// ML policy. + /// Artifact resolved at planning time, so execution does not re-consult the ML policy model_path: String, - /// Feature variables, in model input order. + /// Feature variables, in model input order input_variables: Vec, - /// Variable the prediction binds to. + /// Variable the prediction binds to output_variable: String, }, } impl PhysicalOperator { - /// Creates the SPARQL unit table. + /// Creates the SPARQL unit table pub fn unit() -> Self { Self::Unit } @@ -162,7 +149,7 @@ impl PhysicalOperator { }) } - /// Creates a graph-aware table scan. + /// Creates a graph-aware table scan pub fn quad_table_scan(pattern: QuadPattern) -> Self { Self::TableScan { pattern } } @@ -177,17 +164,17 @@ impl PhysicalOperator { }) } - /// Creates a graph-aware index scan. + /// Creates a graph-aware index scan pub fn quad_index_scan(pattern: QuadPattern) -> Self { Self::IndexScan { pattern } } - /// Creates a multiset UNION physical operator. + /// Creates a multiset UNION physical operator pub fn union(branches: Vec) -> Self { Self::Union { branches } } - /// Creates a GRAPH physical operator. + /// Creates a GRAPH physical operator pub fn graph(input: PhysicalOperator, graph: GraphTerm) -> Self { Self::Graph { input: Box::new(input), @@ -235,7 +222,7 @@ impl PhysicalOperator { } } - /// A plan node supplying solutions directly. + /// A plan node supplying solutions directly pub fn buffer(content: Bindings, origin: String) -> Self { Self::InMemoryBuffer { content, origin } } @@ -288,48 +275,20 @@ impl PhysicalOperator { /// Executes the physical operator and returns string-based results pub fn execute( &self, - database: &mut crate::sparql_database::SparqlDatabase, + database: &mut crate::database::KolibrieDB, ) -> Vec> { super::super::execution::ExecutionEngine::execute(self, database) } /// Executes the physical operator and returns ID-based results for performance - pub fn execute_with_ids( - &self, - database: &mut crate::sparql_database::SparqlDatabase, - ) -> Bindings { + pub fn execute_with_ids(&self, database: &mut crate::database::KolibrieDB) -> Bindings { super::super::execution::ExecutionEngine::execute_with_ids(self, database) } } -/// Which variables a plan's solutions can bind. -/// -/// The engine builds its rows a variable at a time: it starts from the unit solution -/// mapping, scans add whatever their pattern names, and a union concatenates branches -/// that bind different sets. Nothing ever asked what the whole set would be, because a -/// `HashMap` per row never needed to know. -/// -/// A schema and tuples does need to know, so that a variable name is resolved to a -/// column once per plan instead of hashed once per row. That is the entire reason this -/// exists. -/// -/// # Over-approximation is allowed, under-approximation is not -/// -/// The returned set may contain a variable that no solution ends up binding -- an -/// `OPTIONAL` that never matches, a branch of a union that yields nothing. Such a -/// column is simply unbound in every row, which is representable. A variable *missing* -/// from the set has nowhere to go, so this must never under-approximate. The engine -/// carries a `debug_assert` to that effect, which puts the 161 tests that reach it -- -/// measured, by making the engine panic and counting what failed -- behind this -/// function rather than a handful of unit tests. Deleting a scan's variables, a -/// join's right side, or a `BIND` output from this function all trip it. +/// Which variables a plan's solutions can bind impl PhysicalOperator { - /// The variables solutions of this plan can bind, given the variables its input - /// already binds. - /// - /// Names are returned without their leading `?` or `$`, in a deterministic order: - /// the input's variables first, in their order, then new ones as the plan - /// introduces them. + /// The variables solutions of this plan can bind, given the variables its input already binds pub fn output_variables(&self, input: &[String]) -> Vec { let mut variables = input.to_vec(); self.extend_output_variables(&mut variables); @@ -343,8 +302,6 @@ impl PhysicalOperator { push_quad_variables(pattern, variables); } Self::Union { branches } => { - // Every branch sees the same input, and the result is their - // concatenation, so the schema is their union. for branch in branches { branch.extend_output_variables(variables); } @@ -360,8 +317,6 @@ impl PhysicalOperator { input, variables: projected, } => { - // Projection replaces the schema rather than adding to it, and a - // projected variable the input never bound stays a column, unbound. let mut inner = Vec::new(); input.extend_output_variables(&mut inner); variables.clear(); @@ -370,14 +325,10 @@ impl PhysicalOperator { } } Self::BindJoin { left, right } => { - // The right side is re-entered with the left's solutions pushed in, so - // it sees them as its input. left.extend_output_variables(variables); right.extend_output_variables(variables); } Self::HashJoin { left, right } | Self::NestedLoopJoin { left, right } => { - // Both sides run independently; the right side starts from the unit - // mapping rather than from this plan's input. left.extend_output_variables(variables); let mut from_right = Vec::new(); right.extend_output_variables(&mut from_right); @@ -392,7 +343,6 @@ impl PhysicalOperator { } } Self::InMemoryBuffer { content, .. } => { - // Buffered solutions carry their own variables, and are joined in. for row in content { let mut names: Vec<&str> = row.keys().collect(); names.sort(); @@ -401,30 +351,21 @@ impl PhysicalOperator { } } } - Self::Subquery { inner, spec } => { - match &spec.projection { - // An explicit projection list names the subquery's output; its - // alias is what the outer query sees. - Some(projections) => { - for projection in projections { - // Same rule the executor applies in - // `ExecutionEngine::subquery_output_variable`: the alias - // when renamed, otherwise the variable itself. - let name = projection.alias.as_deref().unwrap_or(&projection.variable); - push_variable(name, variables); - } + Self::Subquery { inner, spec } => match &spec.projection { + Some(projections) => { + for projection in projections { + let name = projection.alias.as_deref().unwrap_or(&projection.variable); + push_variable(name, variables); } - // SELECT * keeps whatever the inner plan bound. The subquery has - // its own scope, so the inner plan starts from the unit mapping. - None => { - let mut from_inner = Vec::new(); - inner.extend_output_variables(&mut from_inner); - for name in from_inner { - push_variable(&name, variables); - } + } + None => { + let mut from_inner = Vec::new(); + inner.extend_output_variables(&mut from_inner); + for name in from_inner { + push_variable(&name, variables); } } - } + }, Self::Bind { input, output_variable, @@ -449,7 +390,7 @@ impl PhysicalOperator { } } -/// Appends a variable name, without its sigil, if it is not already present. +/// Appends a variable name, without its sigil, if it is not already present fn push_variable(name: &str, variables: &mut Vec) { let name = name .strip_prefix('?') @@ -463,8 +404,6 @@ fn push_variable(name: &str, variables: &mut Vec) { fn push_term_variables(term: &Term, variables: &mut Vec) { match term { Term::Variable(name) => push_variable(name, variables), - // A quoted triple pattern's components are themselves terms and may be - // variables, which SPARQL-star binds like any other. Term::QuotedTriple(triple) => { push_term_variables(&triple.0, variables); push_term_variables(&triple.1, variables); diff --git a/kolibrie/src/streamertail_optimizer/optimizer.rs b/kolibrie/src/streamertail_optimizer/optimizer.rs index e3e542e7..002de167 100644 --- a/kolibrie/src/streamertail_optimizer/optimizer.rs +++ b/kolibrie/src/streamertail_optimizer/optimizer.rs @@ -8,11 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`Streamertail`]: enumerating candidate plans and selecting the cheapest. -//! -//! Translates a logical plan into a physical one, choosing a join algorithm and scan -//! strategy per operator from what the cost model predicts rather than from the order -//! the query was written in. +//! [`Streamertail`]: enumerating candidate plans and selecting the cheapest use super::cost::{collect_pattern_variables, CostEstimator}; use super::execution::{DatasetView, ExecutionEngine}; @@ -20,7 +16,7 @@ use super::operators::{LogicalOperator, PhysicalOperator}; use super::types::{ConditionArithmetic, ConditionExpression}; use crate::stats::DatabaseStats; -use crate::sparql_database::SparqlDatabase; +use crate::database::KolibrieDB; use shared::dataset_index::{GraphTerm, QuadPattern}; use shared::terms::{Term, TriplePattern}; use std::collections::{BTreeMap, HashMap, HashSet}; @@ -28,19 +24,16 @@ use std::sync::Arc; /// Volcano-style query optimizer with cost-based optimization pub struct Streamertail { - /// Plans already costed, keyed by a canonical form of the logical subplan, so - /// a repeated subexpression is not re-optimised. + /// Plans already costed, keyed by a canonical form of the logical subplan, so a repeated + /// subexpression is not re-optimised pub memo: HashMap, - /// Variables the query projects, which decides what may be dropped early. + /// Variables the query projects, which decides what may be dropped early pub selected_variables: Vec, - /// Statistics the cost model reads. Shared, and possibly slightly stale under - /// the store's bounded statistics policy. + /// Statistics the cost model reads pub stats: Arc, dataset: Option, } -// Summarised rather than derived: the memo holds a physical plan per costed -// subexpression, so the derived form prints every plan considered. impl std::fmt::Debug for Streamertail { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("Streamertail") @@ -80,7 +73,7 @@ fn serialize_arith_expr(expr: &ConditionArithmetic) -> String { impl Streamertail { /// Creates a new volcano optimizer - pub fn new(database: &SparqlDatabase) -> Self { + pub fn new(database: &KolibrieDB) -> Self { let stats = Arc::new(database.gather_stats()); Self { memo: HashMap::new(), @@ -90,10 +83,7 @@ impl Streamertail { } } - /// An optimizer reusing an existing statistics snapshot. - /// - /// Preferred over rebuilding: collecting statistics scans the store, so a - /// per-query rebuild would cost more than the planning it informs. + /// An optimizer reusing an existing statistics snapshot pub fn with_cached_stats(stats: Arc) -> Self { Self { memo: HashMap::new(), @@ -126,7 +116,8 @@ impl Streamertail { self.find_best_plan_recursive(&reordered) } - /// Reorders each uninterrupted group of same-scope scans so every scan after the first shares a variable with the ones before it + /// Reorders each uninterrupted group of same-scope scans so every scan after the first shares a + /// variable with the ones before it fn reorder_logical(&self, plan: &LogicalOperator) -> LogicalOperator { match plan { LogicalOperator::Join { left, right } => { @@ -197,7 +188,8 @@ impl Streamertail { plan.unwrap_or_else(LogicalOperator::unit) } - /// Orders scans so the cheapest anchored pattern runs first and every later pattern joins through a variable the prefix already binds + /// Orders scans so the cheapest anchored pattern runs first and every later pattern joins + /// through a variable the prefix already binds fn greedy_order_scans(&self, patterns: Vec) -> Vec { let cost_estimator = self.cost_estimator(); let empty = HashSet::new(); @@ -224,7 +216,6 @@ impl Streamertail { let mut bound: HashSet = HashSet::new(); let mut order: Vec = Vec::with_capacity(patterns.len()); - // Nothing is bound yet, so constants alone rank the seed let seed = *remaining .iter() .min_by_key(|&&index| { @@ -278,7 +269,7 @@ impl Streamertail { pub fn execute_plan( &self, plan: &PhysicalOperator, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Vec> { ExecutionEngine::execute(plan, database) } @@ -287,7 +278,7 @@ impl Streamertail { pub fn execute_plan_with_dataset( &self, plan: &PhysicalOperator, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, dataset: &DatasetView, ) -> Vec> { ExecutionEngine::execute_with_dataset(plan, database, dataset) @@ -297,7 +288,7 @@ impl Streamertail { pub fn optimize_and_execute( &mut self, logical_plan: &LogicalOperator, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Vec> { let physical_plan = self.find_best_plan(logical_plan); self.execute_plan(&physical_plan, database) @@ -314,7 +305,6 @@ impl Streamertail { return None; } - // Count subject-centered stars only. Object-position "stars" can explode let mut var_counts: BTreeMap> = BTreeMap::new(); for (idx, pattern) in patterns.iter().enumerate() { @@ -323,25 +313,21 @@ impl Streamertail { } } - // Find variables that appear as the subject in at least 3 patterns let mut star_vars: Vec<(&String, &Vec)> = var_counts .iter() .filter(|(_, indices)| indices.len() >= 3) .collect(); - // Sort by number of occurrences (most frequent first) star_vars.sort_by_key(|(_, indices)| std::cmp::Reverse(indices.len())); if star_vars.is_empty() { return None; } - // Greedily assign patterns to stars let mut used_patterns: HashSet = HashSet::new(); let mut stars: Vec<(String, Vec)> = Vec::new(); for (var, pattern_indices) in star_vars { - // Get patterns for this variable that haven't been used yet let available: Vec = pattern_indices .iter() .filter(|&&idx| !used_patterns.contains(&idx)) @@ -352,7 +338,6 @@ impl Streamertail { let star_patterns: Vec = available.iter().map(|&idx| patterns[idx].clone()).collect(); - // Mark these patterns as used for &idx in &available { used_patterns.insert(idx); } @@ -410,7 +395,6 @@ impl Streamertail { } = proj_pred.as_ref() { if let Some(stars) = self.is_star_query(sel_pred) { - // Build: Projection(Filter(StarJoin)) let star_plan = self.build_star_join_from_patterns(stars, sel_pred); let filtered_plan = PhysicalOperator::filter(star_plan, condition.clone()); let projected_plan = @@ -420,7 +404,6 @@ impl Streamertail { } } - // Handle Selection wrapping star query (no projection) if let LogicalOperator::Selection { predicate, condition, @@ -433,7 +416,6 @@ impl Streamertail { } } - // Handle star query without selection or projection if !matches!( logical_plan, LogicalOperator::Selection { .. } | LogicalOperator::Projection { .. } @@ -449,7 +431,6 @@ impl Streamertail { match logical_plan { LogicalOperator::Unit => candidates.push(PhysicalOperator::unit()), LogicalOperator::Scan { pattern } => { - // Implementation rules: Map logical scan to physical scans let best_scan = self.choose_best_scan(pattern); candidates.push(best_scan); } @@ -468,9 +449,7 @@ impl Streamertail { predicate, condition, } => { - // Transformations: Push down selections let best_child_plan = self.find_best_plan_recursive(predicate); - // Implementation rules: Apply selection as a filter candidates.push(PhysicalOperator::filter(best_child_plan, condition.clone())); } LogicalOperator::Projection { @@ -484,11 +463,9 @@ impl Streamertail { )); } LogicalOperator::Join { left, right } => { - // Join order is already fixed by `reorder_logical` let best_left_plan = self.find_best_plan_recursive(left); let best_right_plan = self.find_best_plan_recursive(right); - // Implementation rules: costing decides between the three join algorithms candidates.push(PhysicalOperator::bind_join( best_left_plan.clone(), best_right_plan.clone(), @@ -512,10 +489,8 @@ impl Streamertail { candidates.push(best_buffer); } LogicalOperator::Subquery { inner, spec } => { - // Recursively optimize the inner query let optimized_inner = self.find_best_plan_recursive(inner); - // Keep every subquery-local SELECT modifier attached until execution let subquery_plan = PhysicalOperator::subquery(optimized_inner, spec.clone()); candidates.push(subquery_plan); @@ -526,10 +501,8 @@ impl Streamertail { arguments, output_variable, } => { - // Recursively optimize the input let best_input_plan = self.find_best_plan_recursive(input); - // Create the physical BIND operator let bind_plan = PhysicalOperator::bind( best_input_plan, function_name.clone(), @@ -540,7 +513,6 @@ impl Streamertail { candidates.push(bind_plan); } LogicalOperator::Values { variables, values } => { - // VALUES is a leaf operator candidates.push(PhysicalOperator::values(variables.clone(), values.clone())); } LogicalOperator::MLPredict { @@ -549,13 +521,10 @@ impl Streamertail { input_variables, output_variable, } => { - // Recursively optimize the input let best_input_plan = self.find_best_plan_recursive(input); - // Preserve operator layout while execution uses the approved registry let model_path = String::new(); - // Create the physical ML.PREDICT operator let ml_predict_plan = PhysicalOperator::ml_predict( best_input_plan, model_name.clone(), @@ -568,7 +537,6 @@ impl Streamertail { } } - // Cost-based optimization: Choose the best candidate let cost_estimator = self.cost_estimator(); let best_plan = candidates .into_iter() @@ -578,7 +546,6 @@ impl Streamertail { }) .unwrap(); - // Memoize the best plan self.memoize(key, best_plan) } @@ -670,7 +637,6 @@ impl Streamertail { PhysicalOperator::StarJoin { join_var, patterns } } } else { - // Shouldn't happen, but return a dummy scan as fallback PhysicalOperator::table_scan(( Term::Variable("?s".to_string()), Term::Variable("?p".to_string()), @@ -691,17 +657,16 @@ impl Streamertail { let estimated_size = cost_estimator.estimate_quad_cardinality(pattern); match bound_vars { - 3 => PhysicalOperator::quad_index_scan(pattern.clone()), // Fully bound - always use index - 2 => PhysicalOperator::quad_index_scan(pattern.clone()), // Two bounds - index is better + 3 => PhysicalOperator::quad_index_scan(pattern.clone()), + 2 => PhysicalOperator::quad_index_scan(pattern.clone()), 1 => { - // Use index if result set is small enough if estimated_size < 10000 { PhysicalOperator::quad_index_scan(pattern.clone()) } else { PhysicalOperator::quad_table_scan(pattern.clone()) } } - 0 => PhysicalOperator::quad_table_scan(pattern.clone()), // Full scan + 0 => PhysicalOperator::quad_table_scan(pattern.clone()), _ => PhysicalOperator::quad_table_scan(pattern.clone()), } } @@ -839,7 +804,6 @@ impl Streamertail { ) } LogicalOperator::Values { variables, values } => { - // Values are semantic content, not merely a cardinality hint format!("Values({variables:?}, {values:?})") } LogicalOperator::MLPredict { @@ -900,9 +864,9 @@ impl Streamertail { } /// Updates the optimizer's statistics - pub fn update_stats(&mut self, database: &SparqlDatabase) { + pub fn update_stats(&mut self, database: &KolibrieDB) { self.stats = Arc::new(database.gather_stats()); - self.memo.clear(); // Clear memo as stats have changed + self.memo.clear(); } /// Sets the selected variables for the query @@ -923,8 +887,7 @@ mod tests { use shared::terms::Term; fn create_test_optimizer() -> Streamertail { - // Create a mock database for testing - let database = SparqlDatabase::new(); + let database = KolibrieDB::new(); Streamertail::new(&database) } diff --git a/kolibrie/src/streamertail_optimizer/types.rs b/kolibrie/src/streamertail_optimizer/types.rs index 877c6ecd..f3e765ba 100644 --- a/kolibrie/src/streamertail_optimizer/types.rs +++ b/kolibrie/src/streamertail_optimizer/types.rs @@ -8,10 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Filter conditions and subquery descriptions, shared by both plan representations. -//! -//! These survive the logical-to-physical translation unchanged, which is why they -//! live here rather than in either operator module. +//! Filter conditions and subquery descriptions, shared by both plan representations use shared::{ dictionary::Dictionary, @@ -23,42 +20,42 @@ use std::collections::HashMap; /// Owned execution representation of Kolibrie's existing arithmetic syntax #[derive(Debug, Clone, PartialEq, Eq)] pub enum ConditionArithmetic { - /// A variable or a numeric literal. Owned, unlike the borrowed parsed form. + /// A variable or a numeric literal Operand(String), - /// Sum. + /// Sum Add(Box, Box), - /// Difference. + /// Difference Subtract(Box, Box), - /// Product. + /// Product Multiply(Box, Box), - /// Quotient. Division by zero is an evaluation error, not an infinity. + /// Quotient Divide(Box, Box), } /// Owned execution representation of the existing parsed `FilterExpression` #[derive(Debug, Clone, PartialEq, Eq)] pub enum ConditionExpression { - /// `(left, operator, right)`, compared as terms. + /// `(left, operator, right)`, compared as terms Comparison(String, String, String), - /// Two arithmetic expressions compared numerically. + /// Two arithmetic expressions compared numerically ArithmeticComparison(ConditionArithmetic, String, ConditionArithmetic), - /// Both operands must hold. + /// Both operands must hold And(Box, Box), - /// Either operand may hold. + /// Either operand may hold Or(Box, Box), - /// Negation. + /// Negation Not(Box), - /// An arithmetic expression used where a truth value is expected. + /// An arithmetic expression used where a truth value is expected ArithmeticExpr(Box), - /// `(function name, arguments)`, resolved against the store's registered - /// user-defined functions at execution time. + /// `(function name, arguments)`, resolved against the store's registered user-defined functions + /// at execution time FunctionCall(String, Vec), } /// Represents a condition for filtering operations #[derive(Debug, Clone, PartialEq, Eq)] pub struct Condition { - /// The expression a solution must satisfy. + /// The expression a solution must satisfy pub expression: ConditionExpression, } @@ -120,19 +117,18 @@ impl Truth { /// ID-based result type for performance optimization #[derive(Debug, Clone)] pub struct IdResult { - /// Variable name to encoded term id. Ids rather than text, which is the - /// point: comparisons and joins stay integer work. + /// Variable name to encoded term id pub bindings: HashMap, } /// One owned projection item for a subquery #[derive(Debug, Clone, PartialEq, Eq)] pub struct SubqueryProjection { - /// `"VAR"` for an ordinary variable, or an aggregate's uppercase name. + /// `"VAR"` for an ordinary variable, or an aggregate's uppercase name pub kind: String, - /// The variable projected. + /// The variable projected pub variable: String, - /// Name it is exposed under, if renamed. + /// Name it is exposed under, if renamed pub alias: Option, } @@ -141,15 +137,14 @@ pub struct SubqueryProjection { pub struct SubquerySpec { /// `None` represents `SELECT *`; `Some` is an explicit projection list pub projection: Option>, - /// Whether duplicates are removed before the rows join the outer query. + /// Whether duplicates are removed before the rows join the outer query pub distinct: bool, - /// `GROUP BY` keys. + /// `GROUP BY` keys pub group_vars: Vec, - /// `ORDER BY` keys, in precedence order. Only observable in combination with - /// `limit`, since the join that follows does not preserve order. + /// `ORDER BY` keys, in precedence order pub order_conditions: Vec<(String, SortDirection)>, - /// `LIMIT` applied inside the subquery, before the join — which is what makes - /// it different from limiting the outer query. + /// `LIMIT` applied inside the subquery, before the join — which is what makes it different from + /// limiting the outer query pub limit: Option, } @@ -325,25 +320,19 @@ impl Condition { } } - /// A single comparison between a variable and a value. + /// A single comparison between a variable and a value pub fn new(variable: String, operator: String, value: String) -> Self { Self { expression: ConditionExpression::Comparison(variable, operator, value), } } - /// Converts a parsed filter into the owned execution form. - /// - /// Terms are kept as text; use [`Self::from_filter_with_resolver`] to resolve - /// them against a dictionary at the same time. + /// Converts a parsed filter into the owned execution form pub fn from_filter(filter: FilterExpression<'_>) -> Self { Self::from_filter_with_resolver(&filter, |value| value.to_string()) } - /// As [`Self::from_filter`], expanding each term through `resolver` first. - /// - /// Used where prefixed names in a filter must be resolved against the query's - /// prefix map before the condition is evaluated. + /// As [`Self::from_filter`], expanding each term through `resolver` first pub fn from_filter_with_resolver(filter: &FilterExpression<'_>, resolve_constant: F) -> Self where F: Fn(&str) -> String, @@ -384,9 +373,6 @@ impl Condition { } ConditionExpression::ArithmeticComparison(left, operator, right) => { let resolver = |variable: &str| { - // By value, not by spelling: the lexical comparison path goes - // through `normalize_lexical`, and arithmetic has to agree with - // it or a typed literal is a number in one and not the other. shared::rdf_term::numeric_value(result.get(Self::normalize_variable(variable))?) }; let (Ok(left), Ok(right)) = ( @@ -406,9 +392,6 @@ impl Condition { ConditionExpression::Not(inner) => self.evaluate_filter(inner, result).negate(), ConditionExpression::ArithmeticExpr(expression) => { let resolver = |variable: &str| { - // By value, not by spelling: the lexical comparison path goes - // through `normalize_lexical`, and arithmetic has to agree with - // it or a typed literal is a number in one and not the other. shared::rdf_term::numeric_value(result.get(Self::normalize_variable(variable))?) }; Self::evaluate_arithmetic(expression, &resolver) @@ -446,7 +429,6 @@ impl Condition { let ordering = match (lhs.parse::(), rhs.parse::()) { (Ok(left), Ok(right)) => match left.partial_cmp(&right) { Some(ordering) => ordering, - // A NaN operand is unordered against everything, itself included None => return Truth::Error, }, (Err(_), Err(_)) => lhs.cmp(rhs), @@ -474,19 +456,10 @@ impl Condition { } } - /// The value a row binds to `variable`, or `None` where it binds nothing. - /// - /// Resolved by scanning the schema rather than by hashing the name. A schema - /// holds a handful of short names and a filter mentions one or two variables, so - /// comparing `&str` beats normalising the name into a `String` and hashing it, - /// which is what looking the variable up in a per-row map amounted to. + /// The value a row binds to `variable`, or `None` where it binds nothing fn bound_value(variable: &str, row: &[u32], schema: &[String]) -> Option { let name = Self::normalize_variable(variable); let column = schema.iter().position(|candidate| candidate == name)?; - // Compared, not matched. A `match` arm naming a constant that is not in - // scope silently becomes a binding that matches everything, which made this - // return `None` for every variable and every filter reject every row. The - // compiler says so -- "matches any value" -- but only as a warning. let value = row.get(column).copied()?; if value == UNBOUND { None @@ -495,8 +468,8 @@ impl Condition { } } - /// The ID-based counterpart of [`Condition::evaluate`], with the same - /// three-valued semantics, over one row of a schema rather than a map. + /// The ID-based counterpart of [`Condition::evaluate`], with the same three-valued semantics, + /// over one row of a schema rather than a map pub fn evaluate_with_ids( &self, row: &[u32], @@ -524,7 +497,6 @@ impl Condition { return Truth::Error; }; return match operator.as_str() { - // Interned IDs are term identity, so equality needs no decode "=" => Truth::from_bool(id == rhs), "!=" => Truth::from_bool(id != rhs), _ => { @@ -588,39 +560,39 @@ impl Condition { } impl IdResult { - /// An empty binding set. + /// An empty binding set pub fn new() -> Self { Self { bindings: HashMap::new(), } } - /// A binding set from an existing map. + /// A binding set from an existing map pub fn with_bindings(bindings: HashMap) -> Self { Self { bindings } } - /// Binds `variable` to `id`, replacing any existing binding. + /// Binds `variable` to `id`, replacing any existing binding pub fn insert(&mut self, variable: String, id: u32) { self.bindings.insert(variable, id); } - /// The id bound to `variable`, if any. + /// The id bound to `variable`, if any pub fn get(&self, variable: &str) -> Option<&u32> { self.bindings.get(variable) } - /// Whether `variable` is bound. + /// Whether `variable` is bound pub fn contains(&self, variable: &str) -> bool { self.bindings.contains_key(variable) } - /// Number of bound variables. + /// Number of bound variables pub fn len(&self) -> usize { self.bindings.len() } - /// Whether nothing is bound. The unit solution is empty but still a solution. + /// Whether nothing is bound pub fn is_empty(&self) -> bool { self.bindings.is_empty() } diff --git a/kolibrie/src/streamertail_optimizer/utils.rs b/kolibrie/src/streamertail_optimizer/utils.rs index 7b5e9a05..450d3775 100644 --- a/kolibrie/src/streamertail_optimizer/utils.rs +++ b/kolibrie/src/streamertail_optimizer/utils.rs @@ -8,15 +8,11 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Building a logical plan from a parsed query. -//! -//! The bridge from the lexical syntax tree in [`shared::query`] to -//! [`LogicalOperator`]: terms are resolved against a -//! dictionary and graph patterns become plan nodes. +//! Building a logical plan from a parsed query use super::operators::{LogicalOperator, PhysicalOperator}; use super::types::{Condition, SubqueryProjection, SubquerySpec}; -use crate::sparql_database::SparqlDatabase; +use crate::database::KolibrieDB; use shared::dataset_index::{GraphTerm, QuadPattern}; use shared::query::{FilterExpression, GroupGraphPattern, SubQuery, ValuesClause}; use shared::terms::{Term, TriplePattern}; @@ -28,7 +24,6 @@ pub fn extract_pattern(op: &PhysicalOperator) -> Option<&QuadPattern> { PhysicalOperator::TableScan { pattern } | PhysicalOperator::IndexScan { pattern } => { Some(pattern) } - // If it's a Filter, keep searching in its child PhysicalOperator::Filter { input, .. } => extract_pattern(input), _ => None, } @@ -46,7 +41,7 @@ pub fn pattern_contains_variable(pattern: &TriplePattern, var: &str) -> bool { since = "0.3.0", note = "use CostEstimator::estimate_cost; this returns fictional constants" )] -pub fn estimate_operator_selectivity(op: &LogicalOperator, _database: &SparqlDatabase) -> u64 { +pub fn estimate_operator_selectivity(op: &LogicalOperator, _database: &KolibrieDB) -> u64 { match op { LogicalOperator::Unit => 0, LogicalOperator::Scan { pattern } => { @@ -58,10 +53,10 @@ pub fn estimate_operator_selectivity(op: &LogicalOperator, _database: &SparqlDat let bound_count = count_bound_terms(&triple); match bound_count { - 3 => 1, // Highest priority - fully bound - 2 => 2, // High priority - two bounds - 1 => 3, // Medium priority - one bound - 0 => 4, // Lowest priority - no bounds + 3 => 1, + 2 => 2, + 1 => 3, + 0 => 4, _ => 5, } } @@ -71,17 +66,14 @@ pub fn estimate_operator_selectivity(op: &LogicalOperator, _database: &SparqlDat .sum(), LogicalOperator::Graph { input, .. } => estimate_operator_selectivity(input, _database), LogicalOperator::Selection { predicate, .. } => { - // Selections are generally high priority due to filtering estimate_operator_selectivity(predicate, _database) + 10 } LogicalOperator::Join { left, right } => { - // Join cost depends on both sides let left_sel = estimate_operator_selectivity(left, _database); let right_sel = estimate_operator_selectivity(right, _database); left_sel + right_sel + 5 } LogicalOperator::Projection { predicate, .. } => { - // Projection doesn't change selectivity much estimate_operator_selectivity(predicate, _database) + 1 } LogicalOperator::Buffer { .. } => 10000, @@ -125,13 +117,11 @@ pub fn build_logical_plan( patterns: Vec<(&str, &str, &str)>, filters: Vec, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, binds: &[(&str, Vec<&str>, &str)], values_clause: Option<&ValuesClause>, ) -> LogicalOperator { - // Create base operator from VALUES if present, otherwise empty join base let mut result = if let Some(values_clause) = values_clause { - // Convert ValuesClause to LogicalOperator::Values let variables: Vec = values_clause .variables .iter() @@ -142,7 +132,6 @@ pub fn build_logical_plan( LogicalOperator::values(variables, values) } else { - // The empty group pattern is the SPARQL unit table let first_pattern = if patterns.is_empty() { LogicalOperator::unit() } else { @@ -159,7 +148,6 @@ pub fn build_logical_plan( first_pattern }; - // If we have VALUES, join it with all patterns let start_idx = if values_clause.is_some() { 0 } else { 1 }; for (subject_str, predicate_str, object_str) in patterns.iter().skip(start_idx) { @@ -169,13 +157,11 @@ pub fn build_logical_plan( result = LogicalOperator::join(result, scan_op); } - // Apply filters that couldn't be pushed down for filter in filters { let condition = convert_filter_to_condition(&filter, prefixes, database); result = LogicalOperator::selection(result, condition); } - // Apply BIND clauses for (func_name, args, output_var) in binds { let function_name = func_name.to_string(); let arguments: Vec = args.iter().map(|s| s.to_string()).collect(); @@ -184,7 +170,6 @@ pub fn build_logical_plan( result = LogicalOperator::bind(result, function_name, arguments, output_variable); } - // Apply projection if specific variables were requested if !variables.is_empty() { let var_names: Vec = variables.into_iter().map(|(_, v)| v.to_string()).collect(); result = LogicalOperator::projection(result, var_names); @@ -197,12 +182,12 @@ pub fn build_logical_plan( pub fn compile_term( term_str: &str, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Term { let trimmed = term_str.trim(); if trimmed.starts_with("<<") && trimmed.ends_with(">>") { let inner = trimmed[2..trimmed.len() - 2].trim(); - let (s_str, p_str, o_str) = SparqlDatabase::split_quoted_triple_content(inner); + let (s_str, p_str, o_str) = KolibrieDB::split_quoted_triple_content(inner); let s_term = compile_term(&s_str, prefixes, database); let p_term = if p_str.trim() == "a" { compile_term( @@ -215,7 +200,6 @@ pub fn compile_term( }; let o_term = compile_term(&o_str, prefixes, database); - // If all components are constants, resolve to a single Constant ID if let (Term::Constant(s_id), Term::Constant(p_id), Term::Constant(o_id)) = (&s_term, &p_term, &o_term) { @@ -223,7 +207,6 @@ pub fn compile_term( let qt_id = qt.encode(*s_id, *p_id, *o_id); Term::Constant(qt_id) } else { - // Contains variables — use QuotedTriple variant for pattern matching Term::QuotedTriple(Box::new((s_term, p_term, o_term))) } } else if trimmed.starts_with('?') || trimmed.starts_with('$') { @@ -238,19 +221,17 @@ pub fn compile_term( fn resolve_sparql_lexical_value( value: &str, prefixes: &HashMap, - database: &SparqlDatabase, + database: &KolibrieDB, ) -> String { let trimmed = value.trim(); if trimmed.starts_with('<') && trimmed.ends_with('>') && !trimmed.starts_with("<<") { unescape_sparql_iri(&trimmed[1..trimmed.len() - 1]) } else if trimmed.starts_with(['"', '\'']) { - // Literal lexical values are never prefix names, even when their literal_lexical_value(trimmed) } else if trimmed.starts_with("_:") { trimmed.to_string() } else { let expanded = database.resolve_query_term(trimmed, prefixes); - // PN_LOCAL_ESC contributes the escaped character itself to the local name unescape_sparql_iri(&expanded) } } @@ -366,7 +347,7 @@ fn literal_lexical_value(literal: &str) -> String { pub fn compile_triple( pattern: (&str, &str, &str), prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> TriplePattern { ( compile_term(pattern.0, prefixes, database), @@ -387,7 +368,7 @@ pub fn compile_triple( pub fn compile_graph_term( graph: &str, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { match compile_term(graph, prefixes, database) { Term::Variable(variable) => Ok(GraphTerm::Variable(variable)), @@ -400,7 +381,7 @@ pub fn compile_graph_term( pub fn build_logical_plan_from_group( pattern: &GroupGraphPattern<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { build_logical_plan_from_group_in_scope(pattern, prefixes, database, &GraphTerm::Default) } @@ -409,7 +390,7 @@ pub fn build_logical_plan_from_group( fn build_logical_plan_from_group_in_scope( pattern: &GroupGraphPattern<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, graph_scope: &GraphTerm, ) -> Result { match pattern { @@ -434,7 +415,6 @@ fn build_logical_plan_from_group_in_scope( for pattern in patterns { match pattern { GroupGraphPattern::Filter(filter) => { - // SPARQL FILTER scope is the containing group graph filters.push(filter); } GroupGraphPattern::Bind((function, arguments, output)) => { @@ -515,7 +495,7 @@ fn append_join(left: LogicalOperator, right: LogicalOperator) -> LogicalOperator fn values_operator( values_clause: &ValuesClause<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { let variables = values_clause .variables @@ -529,7 +509,7 @@ fn values_operator( fn compile_values_rows( values_clause: &ValuesClause<'_>, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result>>, String> { let mut rows = Vec::with_capacity(values_clause.values.len()); for row in &values_clause.values { @@ -550,13 +530,12 @@ fn compile_values_rows( Ok(rows) } -// Compatibility helper for existing callers in this module fn convert_pattern_to_triple( subject_str: &str, predicate_str: &str, object_str: &str, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> TriplePattern { compile_triple((subject_str, predicate_str, object_str), prefixes, database) } @@ -565,7 +544,7 @@ fn convert_pattern_to_triple( pub fn build_logical_plan_from_subquery( subquery: &SubQuery, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, ) -> Result { build_logical_plan_from_subquery_in_scope(subquery, prefixes, database, &GraphTerm::Default) } @@ -573,7 +552,7 @@ pub fn build_logical_plan_from_subquery( fn build_logical_plan_from_subquery_in_scope( subquery: &SubQuery, prefixes: &HashMap, - database: &mut SparqlDatabase, + database: &mut KolibrieDB, graph_scope: &GraphTerm, ) -> Result { let inner_plan = build_logical_plan_from_group_in_scope( @@ -625,7 +604,7 @@ fn resolve_with_prefixes(uri: &str, prefixes: &HashMap) -> Strin if let Some(colon_pos) = uri.find(':') { let (prefix, suffix) = uri.split_at(colon_pos); if let Some(base_uri) = prefixes.get(prefix) { - format!("{}{}", base_uri, &suffix[1..]) // Skip the ':' + format!("{}{}", base_uri, &suffix[1..]) } else { uri.to_string() } @@ -638,7 +617,7 @@ fn resolve_with_prefixes(uri: &str, prefixes: &HashMap) -> Strin fn convert_filter_to_condition( filter: &FilterExpression, prefixes: &HashMap, - database: &SparqlDatabase, + database: &KolibrieDB, ) -> Condition { Condition::from_filter_with_resolver(filter, |value| { resolve_sparql_lexical_value(value, prefixes, database) @@ -697,7 +676,7 @@ mod tests { #[test] fn graph_scope_is_lowered_onto_child_scans() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let prefixes = HashMap::new(); let pattern = GroupGraphPattern::Graph { name: "?g", @@ -722,7 +701,7 @@ mod tests { #[test] fn nested_graph_scope_overrides_the_outer_scan_scope() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let prefixes = HashMap::new(); let pattern = GroupGraphPattern::Graph { name: "?outer", diff --git a/kolibrie/src/term_order.rs b/kolibrie/src/term_order.rs index a3ab6319..7849f063 100644 --- a/kolibrie/src/term_order.rs +++ b/kolibrie/src/term_order.rs @@ -32,9 +32,6 @@ impl<'a> OrderKey<'a> { number: 0.0, lexical: "", }, - // A term sorts by the number it denotes, which lives in its lexical - // form rather than its spelling. Comparing spellings put - // `"7"^^<...>` after `"21"^^<...>`. Some(value) => match shared::rdf_term::numeric_value(value) { Some(number) => OrderKey { class: CLASS_NUMBER, @@ -69,9 +66,7 @@ impl Ord for OrderKey<'_> { fn cmp(&self, other: &Self) -> Ordering { self.class .cmp(&other.class) - // `total_cmp` keeps NaN and the two zeroes ordered, which `partial_cmp` cannot .then_with(|| self.number.total_cmp(&other.number)) - // Two spellings of one number ("1" and "1.0") are numerically .then_with(|| self.lexical.cmp(other.lexical)) } } @@ -139,7 +134,6 @@ mod tests { fn numbers_compare_by_value_not_by_spelling() { assert_eq!(compare(Some("9"), Some("100")), Ordering::Less); assert_eq!(compare(Some("1e3"), Some("999")), Ordering::Greater); - // Numerically equal, so the lexical form decides — but it decides assert_eq!(compare(Some("007"), Some("7")), Ordering::Less); assert_eq!(compare(Some("7"), Some("007")), Ordering::Greater); } diff --git a/kolibrie/src/utils.rs b/kolibrie/src/utils.rs index 90ed02ae..09f9c88e 100644 --- a/kolibrie/src/utils.rs +++ b/kolibrie/src/utils.rs @@ -13,15 +13,11 @@ use std::sync::Arc; use std::time::{SystemTime, UNIX_EPOCH}; #[derive(Clone)] -/// A user-defined function stored so the database can be cloned. -/// -/// Wraps the closure in an `Arc` because [`SparqlDatabase`](crate::sparql_database::SparqlDatabase) derives `Clone` and a -/// bare `Box` is not cloneable. Cloning shares the same function rather -/// than duplicating it. +/// A user-defined function stored so the database can be cloned pub struct ClonableFn(Arc) -> String + Send + Sync>); impl ClonableFn { - /// Wraps `f` for storage in a database. + /// Wraps `f` for storage in a database pub fn new(f: F) -> Self where F: Fn(Vec<&str>) -> String + Send + Sync + 'static, @@ -29,24 +25,19 @@ impl ClonableFn { ClonableFn(Arc::new(f)) } - /// Invokes the function with lexical arguments, as they appear in the query. + /// Invokes the function with lexical arguments, as they appear in the query pub fn call(&self, args: Vec<&str>) -> String { (self.0)(args) } } -// Implement Debug for ClonableFn impl Debug for ClonableFn { fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { write!(f, "ClonableFn()") } } -/// Seconds since the Unix epoch. -/// -/// # Panics -/// -/// If the system clock is before the epoch. +/// Seconds since the Unix epoch pub fn current_timestamp() -> u64 { SystemTime::now() .duration_since(UNIX_EPOCH) @@ -54,15 +45,7 @@ pub fn current_timestamp() -> u64 { .as_secs() } -/// How many threads are worth starting for a parallel parse. -/// -/// [`std::thread::available_parallelism`] rather than a CPU count, which is a -/// different number: it accounts for CPU affinity and container quotas, so a -/// process limited to two cores on a 64-core host is told two. For sizing a worker -/// pool that is the figure that matters. -/// -/// Returns 1 when the platform cannot answer, so the result is always usable as a -/// thread count. +/// How many threads are worth starting for a parallel parse pub fn get_num_cpus() -> usize { std::thread::available_parallelism().map_or(1, |n| n.get()) } diff --git a/kolibrie/tests/aggregate_count_test.rs b/kolibrie/tests/aggregate_count_test.rs index 12526c47..03c40ca8 100644 --- a/kolibrie/tests/aggregate_count_test.rs +++ b/kolibrie/tests/aggregate_count_test.rs @@ -1,13 +1,13 @@ mod common; use common::query; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; fn parity(input: &str, expected: Vec>) { for text in [ input.to_string(), format!("SELECT * WHERE {{ {{ {input} }} }}"), ] { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let expected: Vec> = expected .iter() .map(|row| row.iter().map(|v| v.to_string()).collect()) @@ -18,7 +18,6 @@ fn parity(input: &str, expected: Vec>) { #[test] fn count_aliases_preserve_multiplicity_and_exclude_unbound_values() { - // Keep wrapping SELECT aliases in alphabetical order parity( "SELECT (COUNT(?x) AS ?a) (COUNT(*) AS ?b) WHERE { VALUES ?x { 1 1 UNDEF } }", vec![vec!["2", "3"]], diff --git a/kolibrie/tests/aggregate_distinct_test.rs b/kolibrie/tests/aggregate_distinct_test.rs index 5258096e..fd36eeb2 100644 --- a/kolibrie/tests/aggregate_distinct_test.rs +++ b/kolibrie/tests/aggregate_distinct_test.rs @@ -1,12 +1,12 @@ mod common; use common::query; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; fn assert_parity(expression: &str, input: &str, expected: &str) { let inner = format!("SELECT ({expression} AS ?result) WHERE {{ {input} }}"); let outer = format!("SELECT ?result WHERE {{ {{ {inner} }} }}"); for text in [inner, outer] { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); assert_eq!( query(&mut db, &text), vec![vec![expected.to_string()]], @@ -67,7 +67,7 @@ fn empty_input_and_invalid_numeric_operands_are_explicit() { for expression in ["SUM(?x)", "AVG(?x)", "SUM(DISTINCT ?x)", "AVG(DISTINCT ?x)"] { assert_parity(expression, r#"VALUES ?x { 1 "invalid" 2 }"#, ""); } - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); assert!(query( &mut db, "SELECT ?x (COUNT(*) AS ?n) WHERE { VALUES ?x { } } GROUP BY ?x" diff --git a/kolibrie/tests/aggregation_scope_test.rs b/kolibrie/tests/aggregation_scope_test.rs index 91481eab..a63acb80 100644 --- a/kolibrie/tests/aggregation_scope_test.rs +++ b/kolibrie/tests/aggregation_scope_test.rs @@ -11,10 +11,10 @@ mod common; use common::{query, rows1, rows2}; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; /// Two departments, four employees, with a repeated salary so that grouping -fn staff() -> SparqlDatabase { +fn staff() -> KolibrieDB { common::database_with( r#" . 100 . @@ -33,12 +33,9 @@ fn staff() -> SparqlDatabase { const STAFF_PATTERN: &str = "?e ?d . ?e ?salary . ?e ?name"; -// --------------------------------------------------------------------------- - #[test] fn a_non_grouped_variable_is_not_carried_out_of_the_group() { let mut database = staff(); - // `?name` is neither a grouping key nor an aggregate, so it has no value assert_eq!( query( &mut database, @@ -69,7 +66,6 @@ fn the_grouping_key_is_carried_out_of_the_group() { #[test] fn duplicate_values_still_contribute_to_the_aggregate() { let mut database = staff(); - // Sales holds 100, 200 and 100; a set-based sum would report 300 assert_eq!( query( &mut database, @@ -100,7 +96,6 @@ fn count_reports_the_group_size() { #[test] fn each_group_aggregates_only_its_own_rows() { let mut database = staff(); - // Sales holds 100, 200 and 100; engineering holds 500 alone assert_eq!( query( &mut database, @@ -131,8 +126,6 @@ fn averaging_uses_every_row_in_the_group() { ); } -// --------------------------------------------------------------------------- - #[test] fn min_over_numbers_compares_numerically() { let mut database = staff(); @@ -160,7 +153,6 @@ fn max_over_numbers_compares_numerically() { #[test] fn min_over_non_numeric_terms_returns_the_smallest_term() { let mut database = staff(); - // Discarding every non-numeric term left this aggregate with no value assert_eq!( query( &mut database, @@ -182,12 +174,9 @@ fn max_over_non_numeric_terms_returns_the_largest_term() { ); } -// --------------------------------------------------------------------------- - #[test] fn summing_an_empty_pattern_yields_one_row_holding_zero() { let mut database = staff(); - // An ungrouped aggregate over no solutions is still one solution assert_eq!( query( &mut database, @@ -221,8 +210,6 @@ fn grouping_an_empty_pattern_yields_no_rows() { ); } -// --------------------------------------------------------------------------- - #[test] fn a_subquery_group_carries_only_its_key_and_aggregates() { let mut database = staff(); diff --git a/kolibrie/tests/allocation_profile.rs b/kolibrie/tests/allocation_profile.rs index 023a8292..86dc740e 100644 --- a/kolibrie/tests/allocation_profile.rs +++ b/kolibrie/tests/allocation_profile.rs @@ -11,27 +11,16 @@ use std::alloc::{GlobalAlloc, Layout, System}; use std::sync::atomic::{AtomicUsize, Ordering}; +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_update}; -use kolibrie::sparql_database::SparqlDatabase; static ALLOCATIONS: AtomicUsize = AtomicUsize::new(0); static BYTES: AtomicUsize = AtomicUsize::new(0); struct CountingAllocator; -// `unsafe` is unavoidable here: `GlobalAlloc` is an unsafe trait and every method on -// it is unsafe, because the allocator is what the rest of the program's memory safety -// rests on. The allow is scoped to this impl so that new unsafe elsewhere still fails -// the lint gate. -// -// SAFETY: every method forwards to `System`, the allocator these obligations are -// already discharged by, and adds only relaxed counter arithmetic. That means: -// pointers returned by `alloc` come from `System` and so are valid for `layout`; -// `dealloc` and `realloc` receive pointers this allocator produced, since it is the -// single `#[global_allocator]` for the test binary, and pass them back to the -// allocator that produced them with the same layout. The counters are atomics, so -// they add no synchronisation requirement, and nothing here allocates, which would -// re-enter the allocator. +// SAFETY: every method forwards to `System`, the allocator these obligations are already +// discharged by, and adds only relaxed counter arithmetic #[allow(unsafe_code)] unsafe impl GlobalAlloc for CountingAllocator { unsafe fn alloc(&self, layout: Layout) -> *mut u8 { @@ -60,8 +49,8 @@ const DEPARTMENTS: usize = 50; const REGIONS: usize = 10; const EX: &str = "http://example.org/"; -fn setup_database() -> SparqlDatabase { - let mut database = SparqlDatabase::new(); +fn setup_database() -> KolibrieDB { + let mut database = KolibrieDB::new(); let mut default_graph = String::with_capacity(SUBJECTS * 160); for i in 0..SUBJECTS { @@ -101,7 +90,6 @@ const QUERIES: [(&str, &str); 6] = [ "SELECT ?s ?r WHERE { GRAPH ?g { ?s ?r } }", ), ( - // A GRAPH pattern fed by an earlier binding pattern, so the graph scope "graph_variable_after_join", "SELECT ?s ?d ?r WHERE { ?s ?d . GRAPH ?g { ?s ?r } }", ), @@ -127,7 +115,6 @@ const QUERIES: [(&str, &str); 6] = [ fn allocation_profile() { let mut database = setup_database(); - // Warm up so thread-pool and dictionary growth are not attributed to a query for (_, sparql) in QUERIES { execute(sparql, &mut database) .expect("query should run") diff --git a/kolibrie/tests/common/ml_local.rs b/kolibrie/tests/common/ml_local.rs index 48c27126..54c4dc68 100644 --- a/kolibrie/tests/common/ml_local.rs +++ b/kolibrie/tests/common/ml_local.rs @@ -1,7 +1,7 @@ -use kolibrie::{ml_policy::MlExecutionContext, sparql_database::SparqlDatabase}; +use kolibrie::{database::KolibrieDB, ml_policy::MlExecutionContext}; use std::sync::atomic::{AtomicU64, Ordering}; -pub(crate) fn database() -> SparqlDatabase { +pub(crate) fn database() -> KolibrieDB { static NEXT: AtomicU64 = AtomicU64::new(0); let root = std::env::temp_dir().join(format!( "kolibrie-local-test-{}-{}-{}", @@ -12,5 +12,5 @@ pub(crate) fn database() -> SparqlDatabase { .as_nanos(), NEXT.fetch_add(1, Ordering::Relaxed) )); - SparqlDatabase::with_ml_context(MlExecutionContext::trusted_local(&root).unwrap()) + KolibrieDB::with_ml_context(MlExecutionContext::trusted_local(&root).unwrap()) } diff --git a/kolibrie/tests/common/mod.rs b/kolibrie/tests/common/mod.rs index f7b53d8e..14239953 100644 --- a/kolibrie/tests/common/mod.rs +++ b/kolibrie/tests/common/mod.rs @@ -9,13 +9,10 @@ */ #![allow(dead_code)] -// `unreachable_pub` cannot be satisfied here. These helpers must be `pub` so the test -// files that declare `mod common;` can call them, but the crate root is a test binary -// that nothing can import from, so the lint flags every one of them. #![allow(unreachable_pub)] +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_query}; -use kolibrie::sparql_database::SparqlDatabase; /// Normalizes a solution sequence into a comparable bag pub fn bag(mut rows: Vec>) -> Vec> { @@ -24,14 +21,14 @@ pub fn bag(mut rows: Vec>) -> Vec> { } /// Runs a query and returns its solutions as a bag -pub fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { +pub fn query(database: &mut KolibrieDB, sparql: &str) -> Vec> { bag(execute(sparql, database) .expect("query should run") .into_rows()) } /// Runs a query and returns its solutions in the sequence the engine produced -pub fn ordered(database: &mut SparqlDatabase, sparql: &str) -> Vec> { +pub fn ordered(database: &mut KolibrieDB, sparql: &str) -> Vec> { execute(sparql, database) .expect("query should run") .into_rows() @@ -39,21 +36,21 @@ pub fn ordered(database: &mut SparqlDatabase, sparql: &str) -> Vec> /// Runs a query through the `Result`-returning entry point pub fn query_checked( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, sparql: &str, ) -> Result>, kolibrie::execute_query::QueryError> { execute_sparql_query(sparql, database).map(bag) } /// Builds a database from N-Triples-style statements in the default graph -pub fn database_with(triples: &str) -> SparqlDatabase { - let mut database = SparqlDatabase::new(); +pub fn database_with(triples: &str) -> KolibrieDB { + let mut database = KolibrieDB::new(); load(&mut database, triples); database } /// Inserts N-Triples-style statements into an existing database -pub fn load(database: &mut SparqlDatabase, triples: &str) { +pub fn load(database: &mut KolibrieDB, triples: &str) { kolibrie::execute_query::execute_sparql_update( &format!("INSERT DATA {{\n{}\n}}", triples), database, diff --git a/kolibrie/tests/dataset_storage_test.rs b/kolibrie/tests/dataset_storage_test.rs index 121286f1..b0eb5cd8 100644 --- a/kolibrie/tests/dataset_storage_test.rs +++ b/kolibrie/tests/dataset_storage_test.rs @@ -8,16 +8,16 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; use shared::dataset_index::GraphId; use std::collections::BTreeSet; -fn create_empty_named_graph(database: &mut SparqlDatabase, name: &str) { +fn create_empty_named_graph(database: &mut KolibrieDB, name: &str) { let graph_id = database.dictionary_mut().encode(name); assert!(database.create_graph(GraphId::Named(graph_id))); } -fn decoded_graph_names(database: &SparqlDatabase) -> BTreeSet { +fn decoded_graph_names(database: &KolibrieDB) -> BTreeSet { database .named_graphs() .into_iter() @@ -30,7 +30,7 @@ fn decoded_graph_names(database: &SparqlDatabase) -> BTreeSet { #[test] fn database_union_reencodes_and_preserves_the_complete_dataset() { - let mut left = SparqlDatabase::new(); + let mut left = KolibrieDB::new(); left.add_triple_parts("urn:left-default", "urn:p", "urn:o"); left.add_quad_parts( "<< >>", @@ -40,10 +40,7 @@ fn database_union_reencodes_and_preserves_the_complete_dataset() { ); create_empty_named_graph(&mut left, "urn:left-empty"); - let mut right = SparqlDatabase::new(); - // Both independently-created databases start their dictionaries and - // quoted-triple stores at the same IDs. Distinct lexical values therefore - // exercise ID collision handling during the union. + let mut right = KolibrieDB::new(); right.add_triple_parts("urn:right-default", "urn:p", "urn:o"); right.add_quad_parts( "<< >>", diff --git a/kolibrie/tests/filter_semantics_test.rs b/kolibrie/tests/filter_semantics_test.rs index 2c9ddb8b..a7fe3843 100644 --- a/kolibrie/tests/filter_semantics_test.rs +++ b/kolibrie/tests/filter_semantics_test.rs @@ -11,9 +11,9 @@ mod common; use common::{query, rows1}; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; -fn people() -> SparqlDatabase { +fn people() -> KolibrieDB { common::database_with( r#" "Alice" . 30 . @@ -24,7 +24,7 @@ fn people() -> SparqlDatabase { ) } -fn names_where(database: &mut SparqlDatabase, filter: &str) -> Vec> { +fn names_where(database: &mut KolibrieDB, filter: &str) -> Vec> { query( database, &format!( @@ -34,8 +34,6 @@ fn names_where(database: &mut SparqlDatabase, filter: &str) -> Vec> ) } -// --------------------------------------------------------------------------- - #[test] fn greater_than_orders_non_numeric_operands_lexically() { let mut database = people(); @@ -57,7 +55,6 @@ fn less_than_orders_non_numeric_operands_lexically() { #[test] fn greater_or_equal_does_not_admit_every_non_numeric_operand() { let mut database = people(); - // Zero-coercion made this true for "Alice" as well, since 0.0 >= 0.0 assert_eq!( names_where(&mut database, r#"?n >= "Bob""#), rows1(&["urn:bob", "urn:carol"]) @@ -85,12 +82,9 @@ fn comparing_a_string_against_a_number_is_an_error_and_drops_the_row() { ); } -// --------------------------------------------------------------------------- - #[test] fn negating_an_unbound_comparison_does_not_admit_every_row() { let mut database = people(); - // `?missing` is never bound, so the comparison errors and `!error` stays an error assert_eq!( names_where(&mut database, "!(?missing > 100)"), Vec::>::new() @@ -121,8 +115,6 @@ fn negating_a_satisfiable_comparison_still_inverts_it() { ); } -// --------------------------------------------------------------------------- - #[test] fn true_or_error_is_true() { let mut database = people(); @@ -193,8 +185,6 @@ fn nested_boolean_expressions_keep_their_shape() { ); } -// --------------------------------------------------------------------------- - #[test] fn subquery_filter_negating_an_unbound_comparison_admits_no_row() { let mut database = people(); diff --git a/kolibrie/tests/hybrid_test.rs b/kolibrie/tests/hybrid_test.rs index 6c5435f4..8ae5f5d9 100644 --- a/kolibrie/tests/hybrid_test.rs +++ b/kolibrie/tests/hybrid_test.rs @@ -4,12 +4,12 @@ * KU Leuven — Stream Intelligence Lab, Belgium */ +use kolibrie::database::KolibrieDB; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; #[test] fn hybrid_rule_emits_typed_status_annotations() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.add_tagged_triple( "http://example.org/a", "http://example.org/input", @@ -41,7 +41,7 @@ fn hybrid_rule_emits_typed_status_annotations() { #[test] fn hybrid_rule_rejects_recursive_dependency() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.add_tagged_triple("a", "ancestor", "b", 0.8); let rule = r#" RULE :Recursive PROB(provenance=hybrid, threshold=0.7) :- @@ -58,7 +58,7 @@ fn hybrid_rule_rejects_recursive_dependency() { #[test] fn cost_ratio_threshold_is_recorded_in_rdf_metadata() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.add_tagged_triple( "http://example.org/a", "http://example.org/input", @@ -104,21 +104,10 @@ fn cost_ratio_threshold_is_recorded_in_rdf_metadata() { assert!(policy_object.contains("auto:cost")); } -/// A plain rule — no window clause, no `PROB` annotation — derives from the store's -/// facts. -/// -/// This exists because nothing covered it. Every other `process_rule_definition` -/// test here uses the annotated path, and the `ml` ones assert on predictions rather -/// than on derivation, so the plain path could have stopped seeing the store's facts -/// entirely and the whole of `verify.sh` would still have passed: 646 tests green and -/// all 51 examples running, because the examples print results rather than assert on -/// them and the smoke run only checks exit status. -/// -/// Verified to fail when the base facts are withheld, which is the only reason to -/// trust it. +/// A plain rule — no window clause, no `PROB` annotation — derives from the store's facts #[test] fn plain_rule_derives_from_the_stores_facts() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.add_triple_parts( "http://example.org/alice", "http://example.org/parent", @@ -144,18 +133,10 @@ fn plain_rule_derives_from_the_stores_facts() { assert_eq!(derived.len(), 1, "the derived fact should reach the store"); } -/// A provenance-annotated rule derives from the store's facts. -/// -/// Added for the same reason as `plain_rule_derives_from_the_stores_facts`: nothing -/// covered `rule_execution`'s annotated path. Thirty-two tests fail if the base is -/// withheld inside datalog's provenance driver, but they reach it through `program` -/// or through datalog directly — passing `None` at `rule_execution`'s six provenance -/// call sites left all 647 green. -/// -/// Verified to fail when the base is withheld, which is the only reason to trust it. +/// A provenance-annotated rule derives from the store's facts #[test] fn annotated_rule_derives_from_the_stores_facts() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.add_tagged_triple( "http://example.org/a", "http://example.org/input", @@ -176,26 +157,16 @@ fn annotated_rule_derives_from_the_stores_facts() { ); } -/// A hybrid rule can match a fact the store holds untagged, not only its seeds. -/// -/// This distinction is why the hybrid path's base facts matter at all. -/// `materialize_lineage` inserts the snapshot's seed triples into the reasoner -/// itself, so a rule whose body is satisfied entirely by tagged facts derives -/// correctly even with no base at all — which is why the three `provenance=hybrid` -/// tests above pass whether the store's facts reach the reasoner or not. -/// -/// Here the body needs one tagged fact and one plain one, so the base is load -/// bearing. Verified to fail when it is withheld. +/// A hybrid rule can match a fact the store holds untagged, not only its seeds #[test] fn hybrid_rule_matches_an_untagged_fact_from_the_store() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.add_tagged_triple( "http://example.org/a", "http://example.org/input", "http://example.org/yes", 0.8, ); - // Untagged, so it is not a seed and reaches the reasoner only as a base fact. database.add_triple_parts( "http://example.org/a", "http://example.org/kind", diff --git a/kolibrie/tests/integration_test.rs b/kolibrie/tests/integration_test.rs index 0162a601..f36d2dac 100644 --- a/kolibrie/tests/integration_test.rs +++ b/kolibrie/tests/integration_test.rs @@ -9,31 +9,28 @@ */ extern crate kolibrie; +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; use kolibrie::query_builder::Queryable; -use kolibrie::sparql_database::SparqlDatabase; use shared::triple::Triple; #[cfg(test)] mod tests { use super::*; - fn default_triples(db: &SparqlDatabase) -> Vec { + fn default_triples(db: &KolibrieDB) -> Vec { db.query_default_triples(None, None, None) } - fn setup_test_db() -> SparqlDatabase { - let mut db = SparqlDatabase::new(); + fn setup_test_db() -> KolibrieDB { + let mut db = KolibrieDB::new(); - // Acquire write lock once for all encoding let mut dict = db.dictionary_mut(); - // Create common IDs let person1 = dict.encode("http://example.org/person1"); let person2 = dict.encode("http://example.org/person2"); let company = dict.encode("http://example.org/company1"); - // Create predicates let predicates = [ (dict.encode("ex:name"), "name"), (dict.encode("ex:age"), "age"), @@ -43,7 +40,6 @@ mod tests { (dict.encode("ex:industry"), "industry"), ]; - // Create objects let objects = [ (dict.encode("John Smith"), "john"), (dict.encode("Jane Doe"), "jane"), @@ -56,71 +52,67 @@ mod tests { (dict.encode("Technology"), "tech"), ]; - // Release lock before inserting triples drop(dict); - // Insert triples: Person 1 (John) let p = &predicates; let o = &objects; db.add_triple(Triple { subject: person1, predicate: p[0].0, object: o[0].0, - }); // name: John + }); db.add_triple(Triple { subject: person1, predicate: p[1].0, object: o[3].0, - }); // age: 30 + }); db.add_triple(Triple { subject: person1, predicate: p[2].0, object: o[5].0, - }); // email: john@... + }); db.add_triple(Triple { subject: person1, predicate: p[3].0, object: company, - }); // worksFor: company1 + }); - // Person 2 (Jane) db.add_triple(Triple { subject: person2, predicate: p[0].0, object: o[1].0, - }); // name: Jane + }); db.add_triple(Triple { subject: person2, predicate: p[1].0, object: o[4].0, - }); // age: 25 + }); db.add_triple(Triple { subject: person2, predicate: p[2].0, object: o[6].0, - }); // email: jane@... + }); db.add_triple(Triple { subject: person2, predicate: p[3].0, object: company, - }); // worksFor: company1 + }); - // Company db.add_triple(Triple { subject: company, predicate: p[0].0, object: o[2].0, - }); // name: ACME + }); db.add_triple(Triple { subject: company, predicate: p[4].0, object: o[7].0, - }); // founded: 2000 + }); db.add_triple(Triple { subject: company, predicate: p[5].0, object: o[8].0, - }); // industry: Technology + }); db } @@ -129,11 +121,9 @@ mod tests { fn test_delete_triple() { let mut db = setup_test_db(); - // Initial count let initial_count = default_triples(&db).len(); assert_eq!(initial_count, 11); - // Create a triple to delete let mut dict = db.dictionary_mut(); let person1 = dict.encode("http://example.org/person1"); let pred_name = dict.encode("ex:name"); @@ -146,14 +136,11 @@ mod tests { object: obj_john, }; - // Delete the triple let deleted = db.delete_triple(&triple_to_delete); assert!(deleted, "Triple should be deleted successfully"); - // Verify count decreased assert_eq!(default_triples(&db).len(), initial_count - 1); - // Verify triple is gone assert!(!default_triples(&db).contains(&triple_to_delete)); } @@ -161,22 +148,19 @@ mod tests { fn test_delete_triple_parts() { let mut db = setup_test_db(); - // Initial count let initial_count = default_triples(&db).len(); assert_eq!(initial_count, 11); - // Delete using string parts let deleted = db.delete_triple_parts("http://example.org/person2", "ex:email", "jane@example.com"); assert!(deleted, "Triple should be deleted successfully"); - // Verify count decreased assert_eq!(default_triples(&db).len(), initial_count - 1); } #[test] fn test_turtle_prefix_query_with_variable_predicate() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_turtle( r#" @prefix ex: . @@ -208,7 +192,7 @@ mod tests { #[test] fn test_turtle_semicolon_predicate_shorthand() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_turtle( r#" @prefix ex: . @@ -238,7 +222,7 @@ mod tests { #[test] fn test_turtle_comma_object_shorthand() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_turtle( r#" @prefix ex: . @@ -266,7 +250,7 @@ mod tests { #[test] fn test_turtle_semicolon_and_comma_shorthand_together() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_turtle( r#" @prefix ex: . @@ -306,7 +290,6 @@ mod tests { fn test_basic_filters() { let db = setup_test_db(); - // Test exact matches let subj_filter = db .query() .with_subject("http://example.org/person1") @@ -319,7 +302,6 @@ mod tests { let obj_filter = db.query().with_object("Jane Doe").get_triples(); assert_eq!(obj_filter.len(), 1); - // Test pattern matching let subj_like = db.query().with_subject_like("person").get_triples(); assert_eq!(subj_like.len(), 8); @@ -329,7 +311,6 @@ mod tests { let obj_like = db.query().with_object_like("example.com").get_triples(); assert_eq!(obj_like.len(), 2); - // Test prefix/suffix matching let subj_start = db .query() .with_subject_starting("http://example.org/person") @@ -339,7 +320,6 @@ mod tests { let pred_end = db.query().with_predicate_ending("For").get_triples(); assert_eq!(pred_end.len(), 2); - // Test custom filter - collect numeric objects first let dict = db.dictionary(); let numeric_objects: Vec<_> = default_triples(&db) .iter() @@ -354,7 +334,6 @@ mod tests { fn test_combination_and_results() { let db = setup_test_db(); - // Test compound query let person_names = db .query() .with_subject_like("person") @@ -362,16 +341,13 @@ mod tests { .get_triples(); assert_eq!(person_names.len(), 2); - // Test count let name_count = db.query().with_predicate("ex:name").count(); assert_eq!(name_count, 3); - // Test get_subjects let name_subjects = db.query().with_predicate("ex:name").get_subjects(); assert_eq!(name_subjects.len(), 3); assert!(name_subjects.contains(&"http://example.org/person1".to_string())); - // Test get_predicates let person_predicates = db .query() .with_subject("http://example.org/person1") @@ -379,12 +355,10 @@ mod tests { assert_eq!(person_predicates.len(), 4); assert!(person_predicates.contains(&"ex:name".to_string())); - // Test get_objects let age_values = db.query().with_predicate("ex:age").get_objects(); assert_eq!(age_values.len(), 2); assert!(age_values.contains(&"30".to_string())); - // Test get_decoded_triples let jane_triples = db .query() .with_subject("http://example.org/person2") @@ -403,11 +377,9 @@ mod tests { fn test_result_manipulation() { let db = setup_test_db(); - // Test order_by - get triples first then sort let age_triples = db.query().with_predicate("ex:age").get_triples(); assert_eq!(age_triples.len(), 2); - // Check that we got both ages let dict = db.dictionary(); let mut age_values: Vec<_> = age_triples .iter() @@ -418,7 +390,6 @@ mod tests { age_values.sort(); assert_eq!(age_values, vec!["25", "30"]); - // Test limit and offset let all: Vec<_> = db.query().get_triples().into_iter().collect(); let limited = db.query().limit(2).get_triples(); @@ -434,7 +405,6 @@ mod tests { assert_eq!(offset.len(), 2); assert_eq!(offset[0], all[2]); - // Test grouping let dict = db.dictionary(); let groups = db .query() @@ -449,11 +419,9 @@ mod tests { fn test_distinct_and_join() { let mut db = setup_test_db(); - // Test distinct let orig_count = default_triples(&db).len(); assert_eq!(orig_count, 11); - // Add a second email for Person1 let mut dict = db.dictionary_mut(); let subject1 = dict.encode("http://example.org/person1"); let pred_email = dict.encode("ex:email"); @@ -466,7 +434,6 @@ mod tests { object: new_email, }); - // Test all emails for person1 let emails = db .query() .with_subject("http://example.org/person1") @@ -474,7 +441,6 @@ mod tests { .get_triples(); assert_eq!(emails.len(), 2); - // Test simple join let tech_companies = db .query() .with_predicate("ex:industry") @@ -495,7 +461,6 @@ mod tests { fn test_complex_query() { let db = setup_test_db(); - // Find people under 30 who work for ACME let acme = db .query() .with_predicate("ex:name") @@ -508,16 +473,13 @@ mod tests { .with_object(acme[0].as_str()) .get_triples(); - // Filter young employees - collect subject IDs first let dict = db.dictionary(); let triples = default_triples(&db); let young_subject_ids: Vec = employees .iter() .filter_map(|t| { - // let subj_str = dict.decode(t.subject).unwrap_or(""); let age_pred = dict.string_to_id.get("ex:age")?; - // Find age triple for this subject let age_triple = triples .iter() .find(|triple| triple.subject == t.subject && triple.predicate == *age_pred)?; diff --git a/kolibrie/tests/join_equivalence_test.rs b/kolibrie/tests/join_equivalence_test.rs index 044c58c9..c488d427 100644 --- a/kolibrie/tests/join_equivalence_test.rs +++ b/kolibrie/tests/join_equivalence_test.rs @@ -8,7 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; use kolibrie::streamertail_optimizer::PhysicalOperator; use shared::dataset_index::{GraphTerm, QuadPattern}; use shared::terms::{Bindings, Term, TriplePattern}; @@ -19,7 +19,7 @@ fn var(name: &str) -> Term { Term::Variable(name.to_string()) } -fn constant(database: &SparqlDatabase, value: &str) -> Term { +fn constant(database: &KolibrieDB, value: &str) -> Term { Term::Constant(database.dictionary_mut().encode(value)) } @@ -48,7 +48,7 @@ fn bag(bindings: Bindings) -> Vec> { /// Asserts that all three general join operators agree on a pair of scans fn assert_general_joins_agree( - database: &mut SparqlDatabase, + database: &mut KolibrieDB, left: QuadPattern, right: QuadPattern, case: &str, @@ -76,7 +76,7 @@ fn assert_general_joins_agree( expected } -fn fixture() -> SparqlDatabase { +fn fixture() -> KolibrieDB { common::database_with( r#" . @@ -105,7 +105,6 @@ fn join_variants_agree_on_a_shared_variable() { "shared subject variable", ); - // Every ?s that knows someone also has an age, so no solution is dropped assert_eq!(rows.len(), 4, "expected one row per knows triple"); } @@ -115,7 +114,6 @@ fn join_variants_agree_on_a_cartesian_product() { let city = constant(&database, "http://ex/city"); let age = constant(&database, "http://ex/age"); - // Disjoint variable sets on both sides: the join degenerates to a product let rows = assert_general_joins_agree( &mut database, quad((var("p"), city, var("c")), GraphTerm::Default), @@ -133,7 +131,6 @@ fn join_variants_agree_when_the_shared_variable_conflicts() { let leuven = constant(&database, "http://ex/leuven"); let city = constant(&database, "http://ex/city"); - // ?s is a city on the right and a person on the left, so the sides never agree let rows = assert_general_joins_agree( &mut database, quad((var("s"), knows, var("o")), GraphTerm::Default), @@ -154,7 +151,6 @@ fn join_variants_agree_on_a_repeated_variable_within_a_pattern() { let knows = constant(&database, "http://ex/knows"); let age = constant(&database, "http://ex/age"); - // ?self appears twice in one pattern, so the scan must self-constrain let rows = assert_general_joins_agree( &mut database, quad( @@ -165,7 +161,6 @@ fn join_variants_agree_on_a_repeated_variable_within_a_pattern() { "repeated variable in a pattern", ); - // Only dave knows himself, crossed with the three ages assert_eq!(rows.len(), 3, "expected the self-loop crossed with ages"); } @@ -187,7 +182,7 @@ fn join_variants_agree_when_one_side_is_empty() { #[test] fn join_variants_agree_across_named_graphs() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); kolibrie::execute_query::execute_sparql_update( r#"INSERT DATA { GRAPH { @@ -210,7 +205,6 @@ fn join_variants_agree_across_named_graphs() { _ => unreachable!(), }; - // Both sides pinned to the same named graph let rows = assert_general_joins_agree( &mut database, quad((var("s"), knows.clone(), var("o")), GraphTerm::Named(g1)), @@ -219,7 +213,6 @@ fn join_variants_agree_across_named_graphs() { ); assert_eq!(rows.len(), 1, "only alice is in g1"); - // A graph variable on both sides must agree, restricting to one graph let rows = assert_general_joins_agree( &mut database, quad( @@ -253,7 +246,6 @@ fn star_join_agrees_with_a_bind_join_chain() { patterns: patterns.clone(), }; - // StarJoin hard-codes the default graph, so the equivalent chain does too let chain = PhysicalOperator::bind_join( PhysicalOperator::bind_join( PhysicalOperator::quad_index_scan(quad(patterns[0].clone(), GraphTerm::Default)), @@ -269,14 +261,12 @@ fn star_join_agrees_with_a_bind_join_chain() { star_rows, chain_rows, "StarJoin disagreed with the equivalent BindJoin chain" ); - // alice and bob each have an age and a city; alice knows two people assert_eq!(star_rows.len(), 3, "expected the star to bind three rows"); } #[test] fn join_multiplicity_is_preserved_as_a_bag() { - let mut database = SparqlDatabase::new(); - // Two triples give ?s two ways to reach each ?tag, so duplicates must survive + let mut database = KolibrieDB::new(); common::load( &mut database, r#" diff --git a/kolibrie/tests/join_ordering_shapes_test.rs b/kolibrie/tests/join_ordering_shapes_test.rs index bed1e241..f270e799 100644 --- a/kolibrie/tests/join_ordering_shapes_test.rs +++ b/kolibrie/tests/join_ordering_shapes_test.rs @@ -8,28 +8,26 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Join planning must not change which solutions a basic graph pattern -//! produces. Every test here fixes a dataset, then asserts that writing the -//! same patterns in different source orders yields the same solution bag. +//! Join planning must not change which solutions a basic graph pattern produces +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_update}; -use kolibrie::sparql_database::SparqlDatabase; fn bag(mut rows: Vec>) -> Vec> { rows.sort(); rows } -fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { +fn query(database: &mut KolibrieDB, sparql: &str) -> Vec> { bag(execute(sparql, database) .expect("query should run") .into_rows()) } -/// A path `n0 -> n1 -> ... -> n8` where only `n0` carries the anchor type, -/// plus decoy edges that make an unanchored scan far less selective. -fn chain_database() -> SparqlDatabase { - let mut database = SparqlDatabase::new(); +/// A path `n0 -> n1 -> ... -> n8` where only `n0` carries the anchor type, plus decoy edges that +/// make an unanchored scan far less selective +fn chain_database() -> KolibrieDB { + let mut database = KolibrieDB::new(); let mut triples = String::new(); for i in 0..8 { triples.push_str(&format!(" .\n", i, i + 1)); @@ -50,8 +48,8 @@ fn chain_patterns(links: usize) -> Vec { .collect() } -/// Builds a SELECT over the chain with the anchor pattern spliced in at -/// `anchor_at`, so the same query reaches the planner in different orders. +/// Builds a SELECT over the chain with the anchor pattern spliced in at `anchor_at`, so the same +/// query reaches the planner in different orders fn chain_query(links: usize, anchor_at: usize) -> String { let mut patterns = chain_patterns(links); patterns.insert(anchor_at, "?x0 .".to_string()); @@ -85,7 +83,7 @@ fn chain_results_are_independent_of_anchor_position() { #[test] fn star_results_are_independent_of_pattern_order() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -121,7 +119,7 @@ fn star_results_are_independent_of_pattern_order() { #[test] fn cyclic_pattern_closes_the_loop() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -155,7 +153,7 @@ fn cyclic_pattern_closes_the_loop() { #[test] fn disconnected_patterns_produce_the_cross_product() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -185,7 +183,7 @@ fn disconnected_patterns_produce_the_cross_product() { #[test] fn repeated_variable_within_one_pattern_matches_only_self_loops() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -206,7 +204,7 @@ fn repeated_variable_within_one_pattern_matches_only_self_loops() { #[test] fn join_preserves_solution_multiplicity() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { diff --git a/kolibrie/tests/kolibrie_db_test.rs b/kolibrie/tests/kolibrie_db_test.rs new file mode 100644 index 00000000..4914c8c4 --- /dev/null +++ b/kolibrie/tests/kolibrie_db_test.rs @@ -0,0 +1,253 @@ +/* + * Copyright © 2026 Volodymyr Kadzhaia + * Copyright © 2026 Pieter Bonte + * KU Leuven — Stream Intelligence Lab, Belgium + * + * This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this file, + * you can obtain one at https://mozilla.org/MPL/2.0/. + */ + +//! One test per file of `kolibrie/src/database/`, through the public API only, so a split or +//! move that drops a method or breaks a cross-file call shows up here + +use kolibrie::database::{KolibrieDB, PlanningStatsPolicy}; +use kolibrie::execute_query::execute; +use shared::dataset_index::GraphId; +use shared::quad_source::QuadSource; +use std::collections::HashMap; + +const NTRIPLES: &str = + " .\n\ + \"Bob\" .\n"; + +const NQUADS: &str = " .\n\ + .\n"; + +const RDF_XML: &str = r#" + + + Alice + + + +"#; + +#[test] +fn prelude_exports_the_renamed_store() { + use kolibrie::prelude::*; + let db = KolibrieDB::new(); + assert_eq!(db.default_triple_count(), 0); +} + +/// `terms.rs` +#[test] +fn terms_encode_decode_and_share() { + let db = KolibrieDB::new(); + + let plain = db.encode_term_star(""); + assert_eq!( + db.decode_any(plain).as_deref(), + Some("http://example.org/a") + ); + assert_eq!(db.dictionary().decode(plain), Some("http://example.org/a")); + + let quoted = db.encode_term_star( + "<< >>", + ); + assert_ne!(quoted, plain); + assert_eq!(db.quoted_triples().len(), 1); + assert!(db + .decode_any(quoted) + .unwrap() + .contains("http://example.org/p")); + + let (s, p, o) = KolibrieDB::split_quoted_triple_content(" \"x y\""); + assert_eq!( + (s.as_str(), p.as_str(), o.as_str()), + ("", "", "\"x y\"") + ); + + let mut other = KolibrieDB::new(); + assert!(!db.shares_dictionary_with(&other)); + other.adopt_terms(db.terms()); + assert!(db.shares_dictionary_with(&other)); + assert!(db.shares_quoted_triples_with(&db.clone())); +} + +/// `prefixes.rs` +#[test] +fn prefixes_bind_and_resolve() { + let mut db = KolibrieDB::new(); + db.add_prefix("ex", "http://example.org/"); + assert_eq!( + db.resolve_query_term("ex:alice", &HashMap::new()), + "http://example.org/alice" + ); + + db.register_prefixes_from_query( + "PREFIX foaf: SELECT ?s WHERE { }", + ); + assert_eq!( + db.prefixes().get("foaf").map(String::as_str), + Some("http://xmlns.com/foaf/0.1/") + ); + + let mut copied = HashMap::new(); + db.share_prefixes_with(&mut copied); + assert_eq!(copied.len(), 2); + + db.set_prefixes(HashMap::new()); + assert!(db.prefixes().is_empty()); +} + +/// `dataset.rs` +#[test] +fn dataset_add_delete_count_and_union() { + let mut db = KolibrieDB::new(); + db.add_triple_parts("urn:s", "urn:p", "urn:o"); + db.add_triple_parts("urn:s", "urn:p", "urn:o2"); + assert_eq!(db.default_triple_count(), 2); + assert!(db.delete_triple_parts("urn:s", "urn:p", "urn:o2")); + assert_eq!(db.default_triple_count(), 1); + + assert!(db.add_quad_parts("urn:s", "urn:p", "urn:o", "urn:g")); + assert_eq!(db.quad_count(), 2); + assert_eq!(db.named_graphs().len(), 1); + assert_eq!(QuadSource::named_graphs(&db).len(), 1); + + let mut other = KolibrieDB::new(); + other.add_triple_parts("urn:x", "urn:p", "urn:y"); + let merged = db.union(&other); + assert_eq!(merged.default_triple_count(), 2); + assert_eq!(merged.quad_count(), 3); + + db.build_all_indexes(); + assert_eq!(db.quad_count(), 2); + db.clear_dataset(); + assert_eq!(db.quad_count(), 0); +} + +/// `planning_stats.rs` +#[test] +fn planning_stats_follow_the_policy() { + let mut db = KolibrieDB::new(); + db.add_triple_parts("urn:s", "urn:p", "urn:o"); + assert_eq!(db.get_or_build_stats().total_triples, 1); + + db.set_planning_stats_policy(PlanningStatsPolicy::AlwaysFresh); + assert_eq!(db.planning_stats_policy(), PlanningStatsPolicy::AlwaysFresh); + db.add_triple_parts("urn:s", "urn:p", "urn:o2"); + assert_eq!(db.get_or_build_planning_stats().total_triples, 2); + + let before = db.stats_rebuild_count(); + db.invalidate_stats_cache(); + db.get_or_build_planning_stats(); + assert!(db.stats_rebuild_count() > before); + assert_eq!(db.stats_rebuild_metrics().0, db.stats_rebuild_count()); +} + +/// `parse_ntriples.rs` and `serialize.rs` +#[test] +fn ntriples_and_nquads_round_trip() { + let mut db = KolibrieDB::new(); + db.parse_ntriples_and_add(NTRIPLES); + assert_eq!(db.default_triple_count(), 2); + + let mut back = KolibrieDB::new(); + back.parse_ntriples_and_add(&db.generate_ntriples()); + assert_eq!(back.default_triple_count(), 2); + + let encoded = KolibrieDB::new().parse_and_encode_ntriples(NTRIPLES); + assert_eq!(encoded.len(), 2); + + let mut quads = KolibrieDB::new(); + quads.parse_nquads_and_add(NQUADS); + assert_eq!(quads.quad_count(), 2); + assert_eq!(quads.default_triple_count(), 1); + assert!(quads.graphs().contains(&GraphId::Default)); + + let mut quads_back = KolibrieDB::new(); + quads_back.parse_nquads_and_add(&quads.generate_nquads()); + assert_eq!(quads_back.quad_count(), 2); + assert_eq!(quads_back.named_graphs().len(), 1); +} + +/// `parse_turtle.rs`, `serialize.rs`, and a query over the result +#[test] +fn turtle_parses_and_is_queryable() { + let mut db = KolibrieDB::new(); + db.parse_turtle( + "@prefix ex: .\n\ + ex:alice ex:knows ex:bob , ex:carol ; ex:name \"Alice\" .\n", + ); + assert_eq!(db.default_triple_count(), 3); + + let turtle = db.generate_turtle(); + assert!(turtle.contains("@prefix ex: .")); + assert!(turtle.contains("")); + assert!(db.generate_rdf_xml().contains("rdf:Description")); + + let mut rows = execute( + "SELECT ?o WHERE { ?o . }", + &mut db, + ) + .expect("query should run") + .into_rows(); + rows.sort(); + assert_eq!( + rows, + vec![ + vec!["http://example.org/bob".to_string()], + vec!["http://example.org/carol".to_string()], + ] + ); +} + +/// `parse_n3.rs` +#[test] +fn n3_parses_with_prefixes() { + let mut db = KolibrieDB::new(); + db.parse_n3("@prefix ex: .\nex:a ex:p ex:b .\nex:b ex:p ex:c .\n"); + assert_eq!(db.default_triple_count(), 2); + assert_eq!( + db.prefixes().get("ex").map(String::as_str), + Some("http://example.org/") + ); +} + +/// `parse_rdf_xml.rs` +#[test] +fn rdf_xml_parses_from_string_and_file() { + let mut db = KolibrieDB::new(); + db.parse_rdf(RDF_XML); + assert_eq!(db.default_triple_count(), 2); + + let path = std::env::temp_dir().join(format!("kolibrie_db_test_{}.rdf", std::process::id())); + std::fs::write(&path, RDF_XML).unwrap(); + let mut from_file = KolibrieDB::new(); + from_file.parse_rdf_from_file(path.to_str().unwrap()); + let _ = std::fs::remove_file(&path); + assert_eq!(from_file.default_triple_count(), 2); +} + +/// `extensions.rs` +#[test] +fn extensions_hold_seeds_and_probabilities() { + let mut db = KolibrieDB::new(); + assert_eq!(db.training_seed(), None); + db.set_training_seed(Some(7)); + assert_eq!(db.training_seed(), Some(7)); + + db.add_tagged_triple("urn:s", "urn:p", "urn:o", 0.25); + assert_eq!( + db.probability_seeds().values().copied().collect::>(), + vec![0.25] + ); + + db.register_udf("echo", |args: Vec<&str>| args.join("")); + db.add_rule_mapping("r", "http://example.org/rule"); + assert!(db.model_decls().is_empty()); + assert!(db.neural_relation_decls().is_empty()); + assert!(db.take_neural_materialized("urn:p").is_none()); +} diff --git a/kolibrie/tests/ml_predict_candle_runtime.rs b/kolibrie/tests/ml_predict_candle_runtime.rs index 3ddb8387..7d21587e 100644 --- a/kolibrie/tests/ml_predict_candle_runtime.rs +++ b/kolibrie/tests/ml_predict_candle_runtime.rs @@ -10,8 +10,8 @@ #![cfg(feature = "ml")] +use kolibrie::database::KolibrieDB; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; #[path = "common/ml_local.rs"] mod ml_local; @@ -25,7 +25,7 @@ fn tmp_model_path(name: &str) -> String { format!("kolibrie_ml_predict_{}_{}_{}.bin", name, id, nanos) } -fn populate_multiclass_db(db: &mut SparqlDatabase) { +fn populate_multiclass_db(db: &mut KolibrieDB) { for (idx, label, features) in [ ("s0", "A", [1.0, 0.0, 0.0]), ("s1", "A", [1.0, 0.0, 0.0]), @@ -41,7 +41,7 @@ fn populate_multiclass_db(db: &mut SparqlDatabase) { } } -fn populate_binary_db(db: &mut SparqlDatabase) { +fn populate_binary_db(db: &mut KolibrieDB) { for (idx, label, features) in [ ("t0", "1", [1.0, 1.0]), ("t1", "1", [1.0, 1.0]), @@ -54,7 +54,7 @@ fn populate_binary_db(db: &mut SparqlDatabase) { } } -fn clear_materialized_predicate(db: &mut SparqlDatabase, predicate: &str) { +fn clear_materialized_predicate(db: &mut KolibrieDB, predicate: &str) { if let Some(triples) = db.take_neural_materialized(predicate) { for triple in triples { db.delete_triple(&triple); @@ -62,7 +62,7 @@ fn clear_materialized_predicate(db: &mut SparqlDatabase, predicate: &str) { } } -fn train_multiclass(db: &mut SparqlDatabase, save_path: &str) { +fn train_multiclass(db: &mut KolibrieDB, save_path: &str) { let program = format!( r#" PREFIX ex: @@ -100,8 +100,7 @@ TRAIN NEURAL RELATION ex:predictedDigit {{ clear_materialized_predicate(db, "http://example.org/predictedDigit"); } -fn train_binary(db: &mut SparqlDatabase, save_path: &str) { - // Use a linear model to avoid random convergence failures +fn train_binary(db: &mut KolibrieDB, save_path: &str) { let program = format!( r#" PREFIX ex: @@ -139,7 +138,7 @@ TRAIN NEURAL RELATION ex:isFraud {{ } /// Decode the first object for a matching subject and predicate -fn lookup_object(db: &SparqlDatabase, subject_iri: &str, predicate_iri: &str) -> Option { +fn lookup_object(db: &KolibrieDB, subject_iri: &str, predicate_iri: &str) -> Option { let dict = db.dictionary(); let s = *dict.string_to_id.get(subject_iri)?; let p = *dict.string_to_id.get(predicate_iri)?; @@ -152,7 +151,7 @@ fn lookup_object(db: &SparqlDatabase, subject_iri: &str, predicate_iri: &str) -> None } -fn count_triples_with_predicate(db: &SparqlDatabase, predicate_iri: &str) -> usize { +fn count_triples_with_predicate(db: &KolibrieDB, predicate_iri: &str) -> usize { let dict = db.dictionary(); let Some(&pred_id) = dict.string_to_id.get(predicate_iri) else { return 0; @@ -235,21 +234,15 @@ ML.PREDICT(MODEL "digit_model", "#; process_rule_definition(rule, &mut db).expect("rule processing failed"); - // Only s0 and s1 have x0 > 0 let count = count_triples_with_predicate(&db, "http://example.org/predictedDigit"); assert_eq!(count, 2, "FILTER did not restrict predictions correctly"); - // Filtered-out sample has no prediction assert!(lookup_object(&db, "s2", "http://example.org/predictedDigit").is_none()); } #[test] fn top_level_ml_predict_materializes_after_training() { let mut db = ml_local::database(); - // Asserts exact labels, so it is seeded for the same reason - // `rerun_cleans_stale_predictions` is: an unseeded initialisation can leave a - // sample misclassified. This one had not been seen to fail, but it is flaky by - // construction rather than by luck. db.set_training_seed(Some(7)); populate_multiclass_db(&mut db); train_multiclass(&mut db, &tmp_model_path("top_level")); @@ -357,18 +350,15 @@ ML.PREDICT(MODEL "fraud_model", "#; process_rule_definition(rule, &mut db).expect("rule processing failed"); - // Every positive and negative row gets `isFraud true` let fraud_count = count_triples_with_predicate(&db, "http://example.org/isFraud"); assert_eq!( fraud_count, 4, "binary ML.PREDICT should emit for every row" ); - // Companion probability predicate appends _prob to the full IRI let prob_count = count_triples_with_predicate(&db, "http://example.org/isFraud_prob"); assert_eq!(prob_count, 4); - // Positive rows should score higher than negatives let prob_pos = lookup_object(&db, "t0", "http://example.org/isFraud_prob") .and_then(|s| s.parse::().ok()) .unwrap(); @@ -387,10 +377,6 @@ ML.PREDICT(MODEL "fraud_model", #[test] fn rerun_cleans_stale_predictions() { let mut db = ml_local::database(); - // This asserts one exact predicted label, so it needs the model to have learned - // the task. Weight initialisation is unseeded by default, and an unlucky draw - // left s0 misclassified after 80 epochs on three features, failing this about - // once in twenty runs. db.set_training_seed(Some(7)); populate_multiclass_db(&mut db); train_multiclass(&mut db, &tmp_model_path("rerun")); @@ -421,7 +407,6 @@ ML.PREDICT(MODEL "digit_model", let after_first = count_triples_with_predicate(&db, "http://example.org/predictedDigit"); assert_eq!(after_first, 6); - // Flip s0's features so it now looks like C db.delete_triple_parts("s0", "http://example.org/x0", "1"); db.delete_triple_parts("s0", "http://example.org/x1", "0"); db.delete_triple_parts("s0", "http://example.org/x2", "0"); @@ -436,7 +421,6 @@ ML.PREDICT(MODEL "digit_model", "rerun should replace triples, not accumulate them" ); - // s0 should now predict C assert_eq!( lookup_object(&db, "s0", "http://example.org/predictedDigit").as_deref(), Some("C"), @@ -476,9 +460,7 @@ ML.PREDICT(MODEL "digit_model", "#; process_rule_definition(rule, &mut db).expect("rule processing failed"); - // ML side-effect triple assert!(lookup_object(&db, "s0", "http://example.org/predictedDigit").is_some()); - // Static Datalog triple assert!(lookup_object(&db, "s0", "http://example.org/alert").is_some()); } @@ -514,7 +496,6 @@ ML.PREDICT(MODEL "digit_model", process_rule_definition(rule, &mut db).expect("first run failed"); assert!(count_triples_with_predicate(&db, "http://example.org/predictedDigit") > 0); - // Remove all feature triples so INPUT returns no rows let subjects: Vec<&'static str> = vec!["s0", "s1", "s2", "s3", "s4", "s5"]; for s in subjects { for (pred, val) in [ diff --git a/kolibrie/tests/ml_security_test.rs b/kolibrie/tests/ml_security_test.rs index 8d98bc43..528ed52d 100644 --- a/kolibrie/tests/ml_security_test.rs +++ b/kolibrie/tests/ml_security_test.rs @@ -1,6 +1,6 @@ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute_sparql_query, QueryError}; use kolibrie::ml_policy::{validate_artifact_filename, validate_model_name, MlExecutionContext}; -use kolibrie::sparql_database::SparqlDatabase; #[test] fn artifact_names_are_portable_single_components() { @@ -28,7 +28,7 @@ fn artifact_names_are_portable_single_components() { #[test] fn denied_declaration_does_not_register_prefixes_or_models() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let before = db.prefixes().clone(); let query = r#"PREFIX secret: MODEL "m" { ARCH MLP { HIDDEN [4] } OUTPUT BINARY { true } }"#; let (remaining, _) = kolibrie::parser::parse_combined_query(query).unwrap(); @@ -41,7 +41,7 @@ fn denied_declaration_does_not_register_prefixes_or_models() { #[test] fn restrictive_default_still_runs_ordinary_queries() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); assert_eq!( execute_sparql_query("SELECT ?x WHERE { VALUES ?x { 7 } }", &mut db).unwrap(), vec![vec!["7"]] @@ -53,7 +53,7 @@ fn restrictive_default_still_runs_ordinary_queries() { #[test] fn rule_ml_is_rejected_before_prefix_registration() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let request = r#"PREFIX ex: RULE :Predict :- CONSTRUCT { ?s ex:p ?prediction } WHERE { ?s ex:x ?x } ML.PREDICT(MODEL "unapproved", INPUT { SELECT ?x WHERE { ?s ex:x ?x } }, OUTPUT ?prediction)"#; @@ -82,7 +82,7 @@ fn default_policy_rejects_prediction_relation_and_training_routes() { ] { let (remaining, _) = kolibrie::parser::parse_combined_query(request).unwrap(); assert!(remaining.trim().is_empty()); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let error = execute_sparql_query(request, &mut db).unwrap_err(); assert_eq!( error.to_string(), @@ -148,7 +148,7 @@ mod enabled { fn legacy_http_cannot_inherit_trusted_local_authority() { let fixture = Fixture::new(); let local = MlExecutionContext::trusted_local(&fixture.0.join("training")).unwrap(); - let mut db = SparqlDatabase::with_ml_context(local); + let mut db = KolibrieDB::with_ml_context(local); let query = r#"PREFIX secret: MODEL "m" { ARCH MLP { HIDDEN [4] } OUTPUT BINARY { true } }"#; let request = format!( "POST /sparql HTTP/1.1\r\nContent-Type: application/sparql-query\r\n\r\n{query}" @@ -187,7 +187,6 @@ mod enabled { let approved = MlExecutionContext::from_registry(®istry).unwrap(); assert_eq!(approved.predict("new", &[vec![0.0]]).unwrap(), vec!["0.5"]); assert!(approved.predict("unlisted", &[vec![0.0]]).is_err()); - // Registry changes do not apply during runtime fs::write(®istry, r#"{"models":[]}"#).unwrap(); assert!(approved.predict("new", &[vec![0.0]]).is_ok()); fs::write(&approved_artifact, b"tampered").unwrap(); diff --git a/kolibrie/tests/named_graph_test.rs b/kolibrie/tests/named_graph_test.rs index 5fc5e2d1..4ba0159a 100644 --- a/kolibrie/tests/named_graph_test.rs +++ b/kolibrie/tests/named_graph_test.rs @@ -8,12 +8,12 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; -use kolibrie::sparql_database::SparqlDatabase; #[test] fn plain_select_reads_default_graph_only() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.add_triple_parts( "http://example.org/default-s", "http://example.org/p", @@ -57,7 +57,7 @@ fn plain_select_reads_default_graph_only() { #[test] fn graph_iri_select_reads_only_that_named_graph() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); execute( r#" @@ -98,7 +98,7 @@ fn graph_iri_select_reads_only_that_named_graph() { #[test] fn graph_variable_binds_named_graphs_only() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.add_triple_parts( "http://example.org/default-s", "http://example.org/p", @@ -148,7 +148,7 @@ fn graph_variable_binds_named_graphs_only() { #[test] fn from_named_restricts_graph_variable_visibility() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); execute( r#" @@ -190,7 +190,7 @@ fn from_named_restricts_graph_variable_visibility() { #[test] fn nquads_roundtrip_preserves_named_graph() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.parse_nquads_and_add( r#" "value" ."#, ); diff --git a/kolibrie/tests/operator_work_test.rs b/kolibrie/tests/operator_work_test.rs index 8bf81adc..32ae56f1 100644 --- a/kolibrie/tests/operator_work_test.rs +++ b/kolibrie/tests/operator_work_test.rs @@ -10,7 +10,7 @@ #![cfg(feature = "exec-stats")] -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; use kolibrie::streamertail_optimizer::execution::exec_stats; use kolibrie::streamertail_optimizer::PhysicalOperator; use shared::dataset_index::{GraphTerm, QuadPattern}; @@ -26,7 +26,7 @@ fn var(name: &str) -> Term { Term::Variable(name.to_string()) } -fn constant(database: &SparqlDatabase, value: &str) -> Term { +fn constant(database: &KolibrieDB, value: &str) -> Term { Term::Constant(database.encode_term(value)) } @@ -40,17 +40,16 @@ fn quad(pattern: TriplePattern) -> QuadPattern { } /// Runs a plan and reports the work it performed -fn work_of(database: &mut SparqlDatabase, plan: &PhysicalOperator) -> exec_stats::Snapshot { +fn work_of(database: &mut KolibrieDB, plan: &PhysicalOperator) -> exec_stats::Snapshot { exec_stats::reset(); let rows = plan.execute_with_ids(database); let snapshot = exec_stats::snapshot(); - // Touch the result so the execution cannot be optimized away assert!(rows.len() < usize::MAX); snapshot } -fn star_fixture() -> SparqlDatabase { - let mut database = SparqlDatabase::new(); +fn star_fixture() -> KolibrieDB { + let mut database = KolibrieDB::new(); let mut triples = String::new(); for subject in 0..40 { triples.push_str(&format!( @@ -59,7 +58,6 @@ fn star_fixture() -> SparqlDatabase { triples.push_str(&format!( " \"{subject}\" .\n" )); - // Only a fifth of the subjects have a city, making that arm selective if subject % 5 == 0 { triples.push_str(&format!( " .\n" @@ -70,7 +68,7 @@ fn star_fixture() -> SparqlDatabase { database } -/// The consensus asks for `StarJoin` to be measured before any decision about +/// `StarJoin` does the same work as a chain of bind joins #[test] fn star_join_performs_the_same_work_as_a_bind_join_chain() { let _guard = COUNTER_LOCK.lock().unwrap(); @@ -101,7 +99,6 @@ fn star_join_performs_the_same_work_as_a_bind_join_chain() { let star_work = work_of(&mut database, &star); let chain_work = work_of(&mut database, &chain); - // `StarJoin`'s executor simply chains scans with a hard-coded default assert_eq!( star_work.quads_examined, chain_work.quads_examined, "StarJoin examined a different number of quads than the equivalent chain: \ @@ -121,7 +118,6 @@ fn a_selective_leading_pattern_examines_fewer_quads() { let knows = constant(&database, "http://ex/knows"); let city = constant(&database, "http://ex/city"); - // Leading with the selective arm binds ?s eight times instead of scanning the wide one let selective_first = PhysicalOperator::bind_join( PhysicalOperator::quad_index_scan(quad((var("s"), city.clone(), var("c")))), PhysicalOperator::quad_index_scan(quad((var("s"), knows.clone(), var("o")))), diff --git a/kolibrie/tests/orderby_semantics_test.rs b/kolibrie/tests/orderby_semantics_test.rs index 433d0d7b..b87b50a5 100644 --- a/kolibrie/tests/orderby_semantics_test.rs +++ b/kolibrie/tests/orderby_semantics_test.rs @@ -11,10 +11,10 @@ mod common; use common::{ordered, query, rows1}; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; /// Three values chosen so numeric and lexical comparison disagree cyclically -fn cyclic_database() -> SparqlDatabase { +fn cyclic_database() -> KolibrieDB { common::database_with( r#" 9 . 100 . @@ -22,7 +22,7 @@ fn cyclic_database() -> SparqlDatabase { ) } -fn numeric_database() -> SparqlDatabase { +fn numeric_database() -> KolibrieDB { common::database_with( r#" 10 . 9 . @@ -61,7 +61,6 @@ fn descending_reverses_the_numeric_order() { #[test] fn numeric_and_non_numeric_values_get_a_consistent_order() { let mut database = cyclic_database(); - // Numbers first, in numeric order; then everything else lexically assert_eq!( subjects(ordered( &mut database, @@ -85,7 +84,6 @@ fn descending_reverses_the_mixed_order() { #[test] fn ordering_a_large_mixed_column_is_deterministic_and_does_not_panic() { - // A wide spread of values whose numeric and lexical orders disagree let mut triples = String::new(); let values = [ "9", @@ -148,7 +146,6 @@ fn order_by_the_second_key_breaks_ties_on_the_first() { #[test] fn a_subquery_orders_before_it_limits() { let mut database = cyclic_database(); - // The subquery must keep the two smallest values under the same total assert_eq!( query( &mut database, diff --git a/kolibrie/tests/planning_stats_safety_test.rs b/kolibrie/tests/planning_stats_safety_test.rs index 3a3f4a48..20547f78 100644 --- a/kolibrie/tests/planning_stats_safety_test.rs +++ b/kolibrie/tests/planning_stats_safety_test.rs @@ -10,7 +10,7 @@ mod common; -use kolibrie::sparql_database::{PlanningStatsPolicy, SparqlDatabase}; +use kolibrie::database::{KolibrieDB, PlanningStatsPolicy}; /// Bounded policy with an effectively infinite threshold fn frozen() -> PlanningStatsPolicy { @@ -21,7 +21,7 @@ fn frozen() -> PlanningStatsPolicy { } /// Builds a store with a frozen planning snapshot -fn with_frozen_statistics(triples: &str) -> SparqlDatabase { +fn with_frozen_statistics(triples: &str) -> KolibrieDB { let mut database = common::database_with(triples); database.set_planning_stats_policy(frozen()); database.get_or_build_planning_stats(); @@ -139,7 +139,6 @@ fn stale_statistics_never_change_a_result() { let mut fresh = common::database_with(&triples); let mut stale = with_frozen_statistics(&triples); - // Apply identical mutations to both databases for database in [&mut fresh, &mut stale] { common::load(database, r#" ."#); database.delete_triple_parts("urn:s0", "urn:p", "urn:o0"); @@ -153,10 +152,8 @@ fn stale_statistics_never_change_a_result() { } } -// Trivial-plan shortcut - /// Returns the number of rebuilds triggered by one query -fn rebuilds_for(database: &mut SparqlDatabase, query: &str) -> u64 { +fn rebuilds_for(database: &mut KolibrieDB, query: &str) -> u64 { let before = database.stats_rebuild_count(); common::query(database, query); database.stats_rebuild_count() - before @@ -177,7 +174,6 @@ fn a_plan_with_no_join_order_to_choose_builds_no_statistics() { r#" "1" . "2" ."#, ); - // Build exact statistics first so only the query's rebuilds are counted database.get_or_build_stats(); assert_eq!( diff --git a/kolibrie/tests/probabilistic_program_test.rs b/kolibrie/tests/probabilistic_program_test.rs index 44daade1..382b994e 100644 --- a/kolibrie/tests/probabilistic_program_test.rs +++ b/kolibrie/tests/probabilistic_program_test.rs @@ -1,6 +1,7 @@ use std::time::Duration; use datalog::reasoning::materialisation::hybrid_materialisation::validate_hybrid_rules; +use kolibrie::database::KolibrieDB; use kolibrie::ml_policy::MlExecutionContext; use kolibrie::parser::parse_combined_query; use kolibrie::program::{ @@ -9,7 +10,6 @@ use kolibrie::program::{ PublishedProgramResult, ResourceError, RuleMode, }; use kolibrie::rule_execution::process_rule_definition; -use kolibrie::sparql_database::SparqlDatabase; use shared::dictionary::Dictionary; include!(concat!( @@ -92,8 +92,8 @@ fn pair_options(left: &[f64], right: Option<&[f64]>) -> ProgramOptions { } } -fn pair_input(same_image: bool) -> SparqlDatabase { - let mut db = SparqlDatabase::new(); +fn pair_input(same_image: bool) -> KolibrieDB { + let mut db = KolibrieDB::new(); load_knowledge(&mut db).unwrap(); let right = if same_image { image_term(0) @@ -196,7 +196,7 @@ fn unsupported_trailing_statements_fail_before_side_effects() { Err(ProgramError::Parse(_)) )); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.add_triple_parts(&ex("s"), &ex("a"), &ex("o")); let two_rules = r#"PREFIX ex: RULE :A :- CONSTRUCT { ?x ex:b ?y . } WHERE { ?x ex:a ?y . } @@ -247,7 +247,7 @@ fn checked_lowering_rejects_constructs_it_cannot_represent() { let numeric = rule("?x ex:score ?v . FILTER(?v >= 2)", "?x ex:high ex:yes ."); let program = compile(&numeric).unwrap(); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.add_tagged_triple(&ex("a"), &ex("score"), "3", 0.5); db.add_tagged_triple(&ex("b"), &ex("score"), "1", 0.5); let result = execute_program(&mut db, &program, &ProgramOptions::default()).unwrap(); @@ -267,7 +267,7 @@ fn plain_rules_reject_uncertain_inputs() { "PREFIX ex: \nRULE :A :- CONSTRUCT { ?x ex:b ?y . } WHERE { ?x ex:a ?y . }", ) .unwrap(); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.add_tagged_triple(&ex("s"), &ex("a"), &ex("o"), 0.4); assert!(matches!( execute_program(&mut db, &program, &ProgramOptions::default()), @@ -290,7 +290,7 @@ fn deterministic_chains_reach_one_fixpoint_in_any_rule_order() { ); let program = compile(&text).unwrap(); assert_eq!(program.mode(), Some(RuleMode::Deterministic)); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.add_triple_parts(&ex("s"), &ex("a"), &ex("m")); db.add_triple_parts(&ex("m"), &ex("next"), &ex("t")); let mut result = execute_program(&mut db, &program, &ProgramOptions::default()).unwrap(); @@ -324,7 +324,7 @@ fn probabilistic_rules_combine_every_proof_in_any_order() { order.map(|i| rules[i]).join("\n") ); let program = compile(&text).unwrap(); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.add_tagged_triple(&ex("room"), &ex("smoke"), &ex("yes"), 0.6); db.add_tagged_triple(&ex("room"), &ex("heat"), &ex("yes"), 0.5); db.add_triple_parts(&ex("room"), &ex("staffed"), &ex("yes")); @@ -543,7 +543,7 @@ fn answer_sets_include_certain_asserted_facts() { "PREFIX ex: \nRULE :R PROB(combination=sdd) :- CONSTRUCT { ?x ex:flag ex:on . } WHERE { ?x ex:risk ex:yes . }", ) .unwrap(); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); db.add_tagged_triple(&ex("a"), &ex("risk"), &ex("yes"), 0.25); db.add_triple_parts(&ex("b"), &ex("risk"), &ex("yes")); let result = execute_program(&mut db, &program, &ProgramOptions::default()).unwrap(); @@ -787,7 +787,7 @@ fn distinct_anchors_with_equal_distributions_stay_independent() { #[test] fn result_dictionary_remaps_input_ids_and_literals_round_trip() { let program = reference(); - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); for index in 0..50 { db.add_triple_parts(&ex(&format!("noise{index}")), &ex("p"), &ex("o")); } diff --git a/kolibrie/tests/program_ml_test.rs b/kolibrie/tests/program_ml_test.rs index ad6cc982..4d27efdd 100644 --- a/kolibrie/tests/program_ml_test.rs +++ b/kolibrie/tests/program_ml_test.rs @@ -7,11 +7,11 @@ mod ml_local { )); } +use kolibrie::database::KolibrieDB; use kolibrie::program::{ compile_program, execute_program, query_program_result, ProgramError, ProgramOptions, PublishedProgramResult, }; -use kolibrie::sparql_database::SparqlDatabase; use ml::{MlpNeuralPredicate, OutputType}; const EX: &str = "http://example.org/"; @@ -34,7 +34,7 @@ fn samples() -> Vec<(&'static str, &'static str, [f64; 3], Option<&'static str>) ] } -fn populate(db: &mut SparqlDatabase, eval_labels: bool) { +fn populate(db: &mut KolibrieDB, eval_labels: bool) { for (name, split, features, label) in samples() { let subject = ex(name); db.add_triple_parts(&subject, &ex("split"), &ex(split)); diff --git a/kolibrie/tests/query_builder_ordered_test.rs b/kolibrie/tests/query_builder_ordered_test.rs index b7354f62..5b7c945f 100644 --- a/kolibrie/tests/query_builder_ordered_test.rs +++ b/kolibrie/tests/query_builder_ordered_test.rs @@ -1,9 +1,9 @@ +use kolibrie::database::KolibrieDB; use kolibrie::query_builder::QueryBuilder; -use kolibrie::sparql_database::SparqlDatabase; use std::collections::BTreeSet; -fn fixture() -> SparqlDatabase { - let mut db = SparqlDatabase::new(); +fn fixture() -> KolibrieDB { + let mut db = KolibrieDB::new(); for (s, o) in [("s1", "b"), ("s2", "c"), ("s3", "a"), ("s1", "b")] { db.add_triple_parts(s, "p", o); } diff --git a/kolibrie/tests/querybuilder_ordering_test.rs b/kolibrie/tests/querybuilder_ordering_test.rs index ae9fa014..8aedd187 100644 --- a/kolibrie/tests/querybuilder_ordering_test.rs +++ b/kolibrie/tests/querybuilder_ordering_test.rs @@ -10,12 +10,12 @@ mod common; +use kolibrie::database::KolibrieDB; use kolibrie::query_builder::QueryBuilder; -use kolibrie::sparql_database::SparqlDatabase; use std::collections::HashMap; /// Five triples whose rank under `` reverses their natural triple order -fn ranked_database() -> SparqlDatabase { +fn ranked_database() -> KolibrieDB { common::database_with( r#" "5" . "4" . @@ -26,7 +26,7 @@ fn ranked_database() -> SparqlDatabase { } /// Decodes every object once so the sort key needs no dictionary lock -fn object_labels(database: &SparqlDatabase) -> HashMap { +fn object_labels(database: &KolibrieDB) -> HashMap { let dictionary = database.dictionary(); database .query_default_triples(None, None, None) @@ -42,7 +42,7 @@ fn object_labels(database: &SparqlDatabase) -> HashMap { /// Runs the builder with the given slice and returns the selected subjects fn selected_subjects( - database: &SparqlDatabase, + database: &KolibrieDB, descending: bool, offset: Option, limit: Option, @@ -72,7 +72,6 @@ fn selected_subjects( #[test] fn ascending_limit_selects_the_lowest_ranked_triples() { let database = ranked_database(); - // Ranks 1 and 2 sit on and , the last two in natural triple order assert_eq!( selected_subjects(&database, false, None, Some(2)), vec!["urn:d".to_string(), "urn:e".to_string()] @@ -91,7 +90,6 @@ fn descending_limit_selects_the_highest_ranked_triples() { #[test] fn ascending_offset_and_limit_select_the_middle_of_the_sorted_run() { let database = ranked_database(); - // Sorted ascending by rank: e(1) d(2) c(3) b(4) a(5); skip 1, take 2 assert_eq!( selected_subjects(&database, false, Some(1), Some(2)), vec!["urn:c".to_string(), "urn:d".to_string()] @@ -101,7 +99,6 @@ fn ascending_offset_and_limit_select_the_middle_of_the_sorted_run() { #[test] fn descending_offset_and_limit_select_the_middle_of_the_sorted_run() { let database = ranked_database(); - // Sorted descending by rank: a(5) b(4) c(3) d(2) e(1); skip 1, take 2 assert_eq!( selected_subjects(&database, true, Some(1), Some(2)), vec!["urn:b".to_string(), "urn:c".to_string()] diff --git a/kolibrie/tests/querybuilder_test.rs b/kolibrie/tests/querybuilder_test.rs index eac3030a..8b2c4dbf 100644 --- a/kolibrie/tests/querybuilder_test.rs +++ b/kolibrie/tests/querybuilder_test.rs @@ -9,9 +9,9 @@ */ extern crate kolibrie; +use kolibrie::database::KolibrieDB; use kolibrie::query_builder::QueryBuilder; use kolibrie::rsp::r2s::StreamOperator; -use kolibrie::sparql_database::SparqlDatabase; #[cfg(test)] mod tests { @@ -19,9 +19,8 @@ mod tests { #[test] fn test_streaming_query_integration() { - let db = SparqlDatabase::new(); + let db = KolibrieDB::new(); - // Create a streaming query let mut query = QueryBuilder::new(&db) .window(10, 2) .with_predicate("knows") @@ -29,28 +28,24 @@ mod tests { .as_stream() .expect("Failed to create streaming query"); - // Add some streaming triples query.add_stream_triple("Alice", "knows", "Bob", 1).unwrap(); query .add_stream_triple("Bob", "knows", "Charlie", 2) .unwrap(); query .add_stream_triple("Alice", "likes", "Pizza", 3) - .unwrap(); // Should be filtered out + .unwrap(); - // Process results let results = query.get_stream_results(); - // Check that we got results and they match our predicate filter println!("Streaming results: {:?}", results); - // Clean up query.stop_stream(); } #[test] fn test_istream_operator() { - let db = SparqlDatabase::new(); + let db = KolibrieDB::new(); let mut query = QueryBuilder::new(&db) .window(10, 2) @@ -59,7 +54,6 @@ mod tests { .as_stream() .expect("Failed to create streaming query"); - // Add triples over multiple time points query.add_stream_triple("Alice", "knows", "Bob", 1).unwrap(); let results1 = query.get_stream_results(); diff --git a/kolibrie/tests/rdf_star_test.rs b/kolibrie/tests/rdf_star_test.rs index 802e0464..099c534e 100644 --- a/kolibrie/tests/rdf_star_test.rs +++ b/kolibrie/tests/rdf_star_test.rs @@ -8,23 +8,22 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; -use kolibrie::sparql_database::SparqlDatabase; use shared::quoted_triple_store::{is_quoted_triple_id, QuotedTripleStore}; -fn default_triples(db: &SparqlDatabase) -> Vec { +fn default_triples(db: &KolibrieDB) -> Vec { db.query_default_triples(None, None, None) } -fn default_len(db: &SparqlDatabase) -> usize { +fn default_len(db: &KolibrieDB) -> usize { default_triples(db).len() } -fn default_is_empty(db: &SparqlDatabase) -> bool { +fn default_is_empty(db: &KolibrieDB) -> bool { default_triples(db).is_empty() } -// QuotedTripleStore unit tests #[test] fn test_quoted_triple_store_encode_decode() { let mut store = QuotedTripleStore::new(); @@ -54,28 +53,25 @@ fn test_quoted_triple_store_nested() { assert_eq!(o, 5); } -// N-Triples-star parsing tests #[test] fn test_ntriples_star_basic_quoted_triple() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . "#; db.parse_ntriples_and_add(ntriples); - // The outer triple should exist: qt_id :statedBy :emp22 assert!( !default_is_empty(&db), "Should have parsed the outer triple" ); - // The quoted triple store should have one entry let qt = db.quoted_triples(); assert_eq!(qt.len(), 1, "Should have one quoted triple"); } #[test] fn test_ntriples_star_multiple_quoted_triples() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . << >> . "#; @@ -87,9 +83,7 @@ fn test_ntriples_star_multiple_quoted_triples() { #[test] fn test_ntriples_star_literal_in_quoted_triple() { - // Regression: literals inside << >> used to trigger premature parts.push, - // producing 4 parts instead of 3 and silently discarding the triple. - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< "92" >> "0.95" . << "71" >> "0.80" . "#; @@ -104,10 +98,9 @@ fn test_ntriples_star_literal_in_quoted_triple() { assert_eq!(qt.len(), 2, "Should have two quoted triples"); } -// Turtle-star parsing tests #[test] fn test_turtle_star_basic() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let turtle = r#"<< >> ."#; db.parse_turtle(turtle); @@ -119,15 +112,13 @@ fn test_turtle_star_basic() { assert_eq!(qt.len(), 1, "Should have one quoted triple"); } -// Dictionary decode_term tests #[test] fn test_dictionary_decode_term_with_quoted_triple() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . "#; db.parse_ntriples_and_add(ntriples); - // Get the quoted triple ID let qt = db.quoted_triples(); let qt_id = *qt.components_to_id.values().next().unwrap(); let dict = db.dictionary(); @@ -159,10 +150,9 @@ fn test_dictionary_decode_term_with_quoted_triple() { ); } -// SPARQL-star query tests (via Streamertail optimizer) #[test] fn test_sparql_star_query_constant_quoted_triple() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . "#; db.parse_ntriples_and_add(ntriples); @@ -187,14 +177,13 @@ fn test_sparql_star_query_constant_quoted_triple() { #[test] fn test_sparql_star_query_variable_in_quoted_triple() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . << >> . "#; db.parse_ntriples_and_add(ntriples); db.get_or_build_stats(); - // Query with variable inside quoted triple let query = r#" SELECT ?emp ?title ?who WHERE { << ?emp ?title >> ?who . @@ -212,7 +201,6 @@ fn test_sparql_star_query_variable_in_quoted_triple() { ); } -// Debug: verify parser output for quoted triples in WHERE #[test] fn test_sparql_parser_quoted_triple_pattern() { use kolibrie::parser::parse_where; @@ -234,7 +222,6 @@ fn test_sparql_parser_quoted_triple_pattern() { assert!(s.ends_with(">>"), "Subject should end with >>: '{}'", s); } -// Parser combinator tests for parse_quoted_triple #[test] fn test_parse_quoted_triple_simple() { use kolibrie::parser::parse_quoted_triple; @@ -289,11 +276,10 @@ fn test_parse_triple_block_with_quoted_subject() { ); } -// SparqlDatabase helper tests #[test] fn test_split_quoted_triple_content() { let content = " "; - let (s, p, o) = SparqlDatabase::split_quoted_triple_content(content); + let (s, p, o) = KolibrieDB::split_quoted_triple_content(content); assert_eq!(s, ""); assert_eq!(p, ""); assert_eq!(o, ""); @@ -302,7 +288,7 @@ fn test_split_quoted_triple_content() { #[test] fn test_split_quoted_triple_content_nested() { let content = "<< >> "; - let (s, p, o) = SparqlDatabase::split_quoted_triple_content(content); + let (s, p, o) = KolibrieDB::split_quoted_triple_content(content); assert_eq!(s, "<< >>"); assert_eq!(p, ""); assert_eq!(o, ""); @@ -310,14 +296,14 @@ fn test_split_quoted_triple_content_nested() { #[test] fn test_encode_term_star_basic() { - let db = SparqlDatabase::new(); + let db = KolibrieDB::new(); let id = db.encode_term_star("http://example.org/foo"); assert!(!is_quoted_triple_id(id)); } #[test] fn test_encode_term_star_quoted_triple() { - let db = SparqlDatabase::new(); + let db = KolibrieDB::new(); let id = db.encode_term_star("<< http://a http://b http://c >>"); assert!(is_quoted_triple_id(id)); @@ -331,7 +317,7 @@ fn test_encode_term_star_quoted_triple() { #[test] fn test_decode_any_regular() { - let db = SparqlDatabase::new(); + let db = KolibrieDB::new(); let id = db.encode_term_star("http://example.org/test"); let decoded = db.decode_any(id).unwrap(); assert_eq!(decoded, "http://example.org/test"); @@ -339,7 +325,7 @@ fn test_decode_any_regular() { #[test] fn test_decode_any_quoted_triple() { - let db = SparqlDatabase::new(); + let db = KolibrieDB::new(); let id = db.encode_term_star("<< http://a http://b http://c >>"); let decoded = db.decode_any(id).unwrap(); assert!(decoded.contains("http://a")); @@ -349,7 +335,6 @@ fn test_decode_any_quoted_triple() { assert!(decoded.ends_with(">>")); } -// Built-in SPARQL-star function tests #[test] fn test_parse_function_call_istriple() { use kolibrie::parser::parse_filter; @@ -363,20 +348,18 @@ fn test_parse_function_call_subject() { use kolibrie::parser::parse_filter; let input = "FILTER(SUBJECT(?t))"; let result = parse_filter(input); - // SUBJECT returns a value, not a boolean, but it should still parse assert!(result.is_ok(), "Should parse FILTER(SUBJECT(?t))"); } #[test] fn test_bind_subject_predicate_object() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . "#; db.parse_ntriples_and_add(ntriples); db.build_all_indexes(); db.get_or_build_stats(); - // Query using BIND(SUBJECT(?t) AS ?s) let query = r#" SELECT ?t ?s WHERE { ?t . @@ -387,7 +370,6 @@ fn test_bind_subject_predicate_object() { .expect("query should run") .into_rows(); assert_eq!(results.len(), 1, "Should return one result"); - // The subject of the quoted triple should be alice let row = &results[0]; let has_alice = row.iter().any(|v| v.contains("alice")); assert!(has_alice, "SUBJECT should extract alice, got: {:?}", row); @@ -395,14 +377,13 @@ fn test_bind_subject_predicate_object() { #[test] fn test_bind_triple_constructor() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#" . "#; db.parse_ntriples_and_add(ntriples); db.build_all_indexes(); db.get_or_build_stats(); - // Use BIND(TRIPLE(...) AS ?t) to construct a quoted triple let query = r#" SELECT ?s ?p ?o ?t WHERE { ?s ?p ?o . @@ -413,7 +394,6 @@ fn test_bind_triple_constructor() { .expect("query should run") .into_rows(); assert_eq!(results.len(), 1, "Should return one result"); - // The constructed triple should contain << let row = &results[0]; let has_qt = row.iter().any(|v| v.contains("<<")); assert!( @@ -423,22 +403,18 @@ fn test_bind_triple_constructor() { ); } -// INSERT/DELETE tests #[test] fn test_insert_quoted_triple() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); - // Use handle_update for INSERT let result = update_body( &mut db, r#"INSERT { << >> . }"#, ); assert_eq!(result, "Update Successful"); - // Verify the triple was inserted assert!(!default_is_empty(&db), "Triple should have been inserted"); - // Verify the quoted triple store has an entry let qt_store = db.quoted_triples(); assert!( !qt_store.is_empty(), @@ -448,7 +424,7 @@ fn test_insert_quoted_triple() { #[test] fn test_insert_normal_triple() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let result = update_body( &mut db, @@ -460,14 +436,13 @@ fn test_insert_normal_triple() { #[test] fn test_delete_basic() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#" . "Alice" . "#; db.parse_ntriples_and_add(ntriples); assert_eq!(default_len(&db), 2, "Should start with 2 triples"); - // Use handle_update for simple DELETE let result = update_body( &mut db, r#"DELETE { . }"#, @@ -478,7 +453,7 @@ fn test_delete_basic() { #[test] fn test_delete_quoted_triple() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . "#; db.parse_ntriples_and_add(ntriples); @@ -494,7 +469,7 @@ fn test_delete_quoted_triple() { #[test] fn test_delete_where() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#" . . "Alice" . @@ -504,7 +479,6 @@ fn test_delete_where() { db.get_or_build_stats(); assert_eq!(default_len(&db), 3, "Should start with 3 triples"); - // DELETE WHERE — delete all "knows" triples let delete_query = r#" DELETE { ?s ?o . } WHERE { ?s ?o . } @@ -520,10 +494,9 @@ fn test_delete_where() { ); } -// Serialization tests #[test] fn test_generate_ntriples_basic() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#" . "#; db.parse_ntriples_and_add(ntriples); @@ -546,7 +519,7 @@ fn test_generate_ntriples_basic() { #[test] fn test_generate_ntriples_star() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . "#; db.parse_ntriples_and_add(ntriples); @@ -572,14 +545,13 @@ fn test_generate_ntriples_star() { #[test] fn test_generate_turtle_basic() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#" . "Alice" . "#; db.parse_ntriples_and_add(ntriples); let output = db.generate_turtle(); - // Should group by subject — alice appears once as subject, with ; separating predicates let alice_count = output.matches("").count(); assert_eq!( alice_count, 1, @@ -592,7 +564,7 @@ fn test_generate_turtle_basic() { #[test] fn test_generate_turtle_star() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let ntriples = r#"<< >> . "#; db.parse_ntriples_and_add(ntriples); @@ -602,10 +574,9 @@ fn test_generate_turtle_star() { assert!(output.contains(">>"), "Should render quoted triple closing"); } -// handle_update with proper parsers #[test] fn test_handle_update_insert() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let result = update_body( &mut db, r#"INSERT { . }"#, @@ -616,7 +587,7 @@ fn test_handle_update_insert() { #[test] fn test_handle_update_delete() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); update_body( &mut db, r#"INSERT { . }"#, @@ -633,7 +604,7 @@ fn test_handle_update_delete() { #[test] fn test_handle_update_insert_quoted_triple() { - let mut db = SparqlDatabase::new(); + let mut db = KolibrieDB::new(); let result = update_body( &mut db, r#"INSERT { << >> . }"#, @@ -644,7 +615,7 @@ fn test_handle_update_insert_quoted_triple() { assert!(!qt_store.is_empty(), "QuotedTripleStore should have entry"); } -/// The body of the protocol's update outcome, which `db.handle_update` used to return. -fn update_body(db: &mut SparqlDatabase, update: &str) -> String { +/// The body of the protocol's update outcome, which `db.handle_update` used to return +fn update_body(db: &mut KolibrieDB, update: &str) -> String { kolibrie::sparql_protocol::update_result(db, update).1 } diff --git a/kolibrie/tests/rdf_term_identity_test.rs b/kolibrie/tests/rdf_term_identity_test.rs index d2c227de..42328292 100644 --- a/kolibrie/tests/rdf_term_identity_test.rs +++ b/kolibrie/tests/rdf_term_identity_test.rs @@ -8,31 +8,16 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! When two RDF terms are the same term, and when they only look alike. -//! -//! The store interns every term as one flat string, so a term's identity is whatever -//! string survived parsing. That makes these assertions worth pinning rather than -//! assuming: the lexical form of a literal, its datatype and its language tag all -//! have to reach the dictionary, or terms that RDF says are different collapse into -//! one and the difference cannot be recovered afterwards. -//! -//! Each test below names the RDF rule it rests on. They are about *term* identity -//! (RDF 1.1 §3.3, literal term equality), not value equality: `"1"^^xsd:integer` and -//! `"01"^^xsd:integer` denote the same value and are still two distinct terms, which -//! is why the datatype alone is not enough — the lexical form must survive too. -//! -//! Blank nodes are deliberately absent. Their real defect is that labels are not -//! scoped to the document they came from, so `_:b` read from two files is one node; -//! that is a larger change than literal identity and is not what these pin. - -use kolibrie::sparql_database::SparqlDatabase; +//! When two RDF terms are the same term, and when they only look alike + +use kolibrie::database::KolibrieDB; const XSD_INTEGER: &str = "http://www.w3.org/2001/XMLSchema#integer"; const XSD_STRING: &str = "http://www.w3.org/2001/XMLSchema#string"; -/// Parses N-Triples and returns the object term of ``, as the store kept it. +/// Parses N-Triples and returns the object term of ``, as the store kept it fn object_terms(ntriples: &str) -> Vec { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add(ntriples); let dictionary = database.dictionary(); let mut objects: Vec<(u32, String)> = database @@ -48,16 +33,12 @@ fn object_terms(ntriples: &str) -> Vec { ) }) .collect(); - // Subject ids are minted in document order, so this restores the input order. objects.sort_by_key(|(subject, _)| *subject); objects.into_iter().map(|(_, object)| object).collect() } #[test] fn a_typed_literal_is_not_the_same_term_as_its_lexical_form() { - // RDF 1.1 §3.3: a literal is a triple of lexical form, datatype IRI and optional - // language tag. `"25"` is `"25"^^xsd:string`; `"25"^^xsd:integer` is a different - // term, because the datatype differs. let objects = object_terms(&format!( " \"25\"^^<{XSD_INTEGER}> .\n \"25\" .\n" )); @@ -71,8 +52,6 @@ fn a_typed_literal_is_not_the_same_term_as_its_lexical_form() { #[test] fn two_literals_differing_only_in_datatype_are_distinct() { - // The sharpest case: identical lexical form, different datatype IRI. Nothing but - // the datatype distinguishes them, so if the datatype is dropped they are equal. let objects = object_terms(&format!( " \"25\"^^<{XSD_INTEGER}> .\n \"25\"^^<{XSD_STRING}> .\n" )); @@ -85,9 +64,6 @@ fn two_literals_differing_only_in_datatype_are_distinct() { #[test] fn a_datatype_survives_a_round_trip() { - // Stronger than distinctness, and the property that actually matters for a store: - // the datatype has to be *recoverable*, not merely accounted for. A term that - // comes back without it cannot be serialised back to the graph that was read. let objects = object_terms(&format!(" \"25\"^^<{XSD_INTEGER}> .\n")); assert_eq!(objects.len(), 1); assert!( @@ -99,9 +75,6 @@ fn a_datatype_survives_a_round_trip() { #[test] fn a_language_tag_is_not_part_of_the_lexical_form() { - // `"hello"@en` has lexical form `hello` and language `en`. A plain literal whose - // lexical form is the six characters `hello@en` is a different term. Gluing the - // tag onto the text makes them one. let objects = object_terms(" \"hello\"@en .\n \"hello@en\" .\n"); assert_eq!(objects.len(), 2, "both triples should be stored"); @@ -110,8 +83,6 @@ fn a_language_tag_is_not_part_of_the_lexical_form() { "a language-tagged literal and a plain literal spelled the same way are \ different RDF terms" ); - // Distinctness alone is too weak: dropping the tag entirely would also satisfy - // it, and did. The tag has to still be there. assert!( objects[0].ends_with("@en"), "the language tag must survive, got {:?}", @@ -119,22 +90,15 @@ fn a_language_tag_is_not_part_of_the_lexical_form() { ); } -// ── Reading a value out of a term ─────────────────────────────────────────────── -// -// Keeping the datatype is only half of it. A literal's *number* lives in its lexical -// form, so anything that reads a term as a number has to look past the spelling. The -// engine parsed the spelling directly, which worked only while the spelling happened -// to be the lexical form — exactly the accident the tests above removed. - fn query_one(ntriples: &str, sparql: &str) -> Vec> { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add(ntriples); kolibrie::execute_query::execute(sparql, &mut database) .expect("query should run") .into_rows() } -/// Three ages, written as `xsd:integer` literals the way a real graph would. +/// Three ages, written as `xsd:integer` literals the way a real graph would fn typed_ages() -> String { format!( " \"30\"^^<{XSD_INTEGER}> .\n\ @@ -163,9 +127,6 @@ fn a_typed_integer_orders_as_a_number() { "SELECT ?age WHERE { ?s ?age } ORDER BY ?age", ); let ordered: Vec<&str> = rows.iter().map(|row| row[0].as_str()).collect(); - // Numeric order is 7, 21, 30. String order would be "21", "30", "7", and the - // spelling `"7"^^<…>` would sort differently again -- so this fails in a - // different way for each thing that could be compared instead of the value. assert_eq!( ordered.len(), 3, @@ -183,9 +144,6 @@ fn a_typed_integer_orders_as_a_number() { #[test] fn a_typed_integer_filters_as_a_number() { - // FILTER has two paths that must agree: a lexical comparison that already read - // through the literal's furniture, and an arithmetic one that did not. A typed - // literal was a number in the first and not in the second. let rows = query_one( &typed_ages(), "SELECT ?age WHERE { ?s ?age . FILTER(?age > 10) }", @@ -217,20 +175,16 @@ fn arithmetic_over_a_typed_integer_agrees_with_comparison() { #[test] fn every_reader_spells_one_term_one_way() { - // Readers that disagree about spelling mint different ids for the same term, so - // data loaded as Turtle could never join against data loaded as N-Triples. All - // three of these used to differ: N-Triples kept only the lexical form, and Turtle - // produced `30"^^xsd:integer` -- a stray quote, and a prefix never expanded. let from_ntriples = object_terms(&format!(" \"30\"^^<{XSD_INTEGER}> .\n")); - let mut prefixed = SparqlDatabase::new(); + let mut prefixed = KolibrieDB::new(); prefixed.parse_turtle( "@prefix xsd: .\n\ \"30\"^^xsd:integer .\n", ); let from_turtle_prefixed = stored_objects(&prefixed); - let mut full = SparqlDatabase::new(); + let mut full = KolibrieDB::new(); full.parse_turtle(&format!(" \"30\"^^<{XSD_INTEGER}> .\n")); let from_turtle_full = stored_objects(&full); @@ -244,8 +198,8 @@ fn every_reader_spells_one_term_one_way() { ); } -/// The object terms of a database, as stored. -fn stored_objects(database: &SparqlDatabase) -> Vec { +/// The object terms of a database, as stored +fn stored_objects(database: &KolibrieDB) -> Vec { let dictionary = database.dictionary(); database .query_default_triples(None, None, None) @@ -259,16 +213,9 @@ fn stored_objects(database: &SparqlDatabase) -> Vec { .collect() } -// ── RDF/XML ───────────────────────────────────────────────────────────────────── -// -// RDF/XML carries a literal's datatype on the enclosing property element, as -// `rdf:datatype`, and its language as `xml:lang`. The reader looked at neither, so -// every literal it produced was a plain one whatever the document said — the same -// loss as the N-Triples reader's, by a different route. - #[test] fn rdf_xml_keeps_a_datatype() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(&format!( r#" @@ -288,7 +235,7 @@ fn rdf_xml_keeps_a_datatype() { #[test] fn rdf_xml_keeps_a_language_tag() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf( r#" @@ -308,9 +255,7 @@ fn rdf_xml_keeps_a_language_tag() { #[test] fn rdf_xml_leaves_a_plain_literal_alone() { - // The guard on the change: a property element with neither attribute still - // produces the plain literal it always did. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf( r#" @@ -324,9 +269,7 @@ fn rdf_xml_leaves_a_plain_literal_alone() { #[test] fn rdf_xml_does_not_carry_a_datatype_to_the_next_literal() { - // State kept across elements is the obvious way to get this wrong: the second - // property element has no datatype and must not inherit the first one's. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf(&format!( r#" @@ -350,10 +293,7 @@ fn rdf_xml_does_not_carry_a_datatype_to_the_next_literal() { #[test] fn rdf_xml_inherits_a_language_from_an_enclosing_element() { - // `xml:lang` is scoped to an element and its descendants (XML 1.0 §2.12, and - // RDF/XML §6.1.2 builds on it), and documents routinely declare it once on the - // root. A reader that only looks at the property element itself sees none. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf( r#" @@ -370,7 +310,7 @@ fn rdf_xml_inherits_a_language_from_an_enclosing_element() { #[test] fn rdf_xml_lets_an_inner_language_override_an_outer_one() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf( r#" @@ -391,9 +331,7 @@ fn rdf_xml_lets_an_inner_language_override_an_outer_one() { #[test] fn rdf_xml_does_not_let_a_language_outlive_its_element() { - // The failure a scope stack exists to prevent: the tag leaking to a sibling that - // closed over nothing. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_rdf( r#" @@ -412,21 +350,8 @@ fn rdf_xml_does_not_let_a_language_outlive_its_element() { ); } -// ── Blank nodes ───────────────────────────────────────────────────────────────── -// -// A blank node label identifies a node *within the document it was read from* and -// means nothing outside it (RDF 1.1 §3.4). Two files each saying `_:b` describe two -// different nodes, and merging them must keep them apart. The readers interned the -// label itself, so the two became one node and the merge silently fused unrelated -// statements — the one case in this file where the defect invents a fact rather than -// losing one. -// -// SPARQL updates already scoped their labels, minting `_:kolibrie-update-N-label`; -// the readers did not. Relabelling is forced rather than chosen: the dictionary keys -// on a term's text, so two distinct nodes must have two distinct spellings. - -/// The subject terms of a database, as stored. -fn stored_subjects(database: &SparqlDatabase) -> Vec { +/// The subject terms of a database, as stored +fn stored_subjects(database: &KolibrieDB) -> Vec { let dictionary = database.dictionary(); let mut subjects: Vec = database .query_default_triples(None, None, None) @@ -444,7 +369,7 @@ fn stored_subjects(database: &SparqlDatabase) -> Vec { #[test] fn two_documents_do_not_share_a_blank_node() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add("_:b .\n"); database.parse_ntriples_and_add("_:b .\n"); let subjects = stored_subjects(&database); @@ -458,11 +383,7 @@ fn two_documents_do_not_share_a_blank_node() { #[test] fn one_document_keeps_its_blank_node_shared() { - // The other half, and the one scoping can break: within a document the label - // does identify one node, so two statements about `_:b` are about one subject. - // Minting a fresh node per occurrence would satisfy the test above and destroy - // every blank-node-linked structure in the graph. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add("_:b .\n_:b .\n"); let subjects = stored_subjects(&database); assert_eq!(subjects.len(), 2, "two triples, got {subjects:?}"); @@ -474,10 +395,7 @@ fn one_document_keeps_its_blank_node_shared() { #[test] fn a_blank_node_label_is_distinguishable_from_the_label_it_was_written_as() { - // Scoping has to be visible in the term, since that is the only place identity - // can live, but the label it came from should still be legible in it -- the - // convention SPARQL updates already set. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add("_:original .\n"); let subjects = stored_subjects(&database); assert!( @@ -498,7 +416,7 @@ fn a_blank_node_label_is_distinguishable_from_the_label_it_was_written_as() { #[test] fn turtle_scopes_blank_nodes_as_well() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_turtle("_:b .\n"); database.parse_turtle("_:b .\n"); let subjects = stored_subjects(&database); @@ -511,9 +429,7 @@ fn turtle_scopes_blank_nodes_as_well() { #[test] fn a_blank_node_in_the_object_is_scoped_with_its_subject() { - // Within one document a label names one node wherever it appears, including - // across positions: this is the shape that makes a blank node useful at all. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add(" _:b .\n_:b .\n"); let dictionary = database.dictionary(); let triples = database.query_default_triples(None, None, None); @@ -535,7 +451,7 @@ fn a_blank_node_in_the_object_is_scoped_with_its_subject() { #[test] fn nquads_scopes_blank_nodes_including_the_graph_name() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_nquads_and_add("_:b .\n"); database.parse_nquads_and_add("_:b .\n"); let subjects = stored_subjects(&database); @@ -545,7 +461,7 @@ fn nquads_scopes_blank_nodes_including_the_graph_name() { #[test] fn n3_scopes_blank_nodes_as_well() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_n3("_:b .\n"); database.parse_n3("_:b .\n"); let subjects = stored_subjects(&database); @@ -555,13 +471,9 @@ fn n3_scopes_blank_nodes_as_well() { #[test] fn a_blank_node_inside_a_quoted_triple_is_scoped_too() { - // Scoping only the outermost term would leave a nested label bare, so two - // documents would still share the node a quoted triple asserts about. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add("<< _:b >> .\n"); database.parse_ntriples_and_add("<< _:b >> .\n"); - // A quoted triple is a tagged id that the dictionary alone cannot resolve, so - // these are decoded through the store rather than with `stored_subjects`. let mut subjects: Vec = database .query_default_triples(None, None, None) .into_iter() @@ -585,13 +497,7 @@ fn a_blank_node_inside_a_quoted_triple_is_scoped_too() { #[test] fn a_second_n3_read_keeps_its_own_document() { - // Not about terms' spelling but about their ids, and found by the test above. - // Each parallel chunk parsed into its own database and its triples were then - // inserted carrying that database's ids, while only the dictionaries were - // merged. With an empty target both numbered terms in the same order, so the - // first read looked right; a second read reinterpreted its ids as the first - // document's terms, storing a copy of the first document in place of the second. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_n3(" .\n"); database.parse_n3(" .\n"); let dictionary = database.dictionary(); @@ -618,19 +524,6 @@ fn a_second_n3_read_keeps_its_own_document() { ); } -// ── Streaming ─────────────────────────────────────────────────────────────────── -// -// A stream has many documents, so per-document scoping meets its limit here. Each -// arriving element is its own document in RDF's terms, but the dictionary never -// forgets a term: minting a node per element makes it grow without bound, and -// `Dictionary::encode` asserts rather than wraps when ids reach the quoted-triple -// range, so a long-running stream would not merely leak but eventually panic. -// -// A stream therefore shares one scope across its elements. That is bounded, keeps the -// stream's labels apart from every file's and every other stream's, and matches what -// the engine did before scoping existed — so it is not a change in stream semantics, -// only a change in what the labels are spelled as. - #[test] fn a_stream_does_not_grow_the_dictionary_per_element() { use kolibrie::rsp::r2r::R2ROperator; @@ -655,8 +548,6 @@ fn a_stream_does_not_grow_the_dictionary_per_element() { #[test] fn two_streams_do_not_share_a_blank_node() { - // The property the stream scope still has to provide: bounded within a stream, - // separate between streams. use kolibrie::rsp::r2r::R2ROperator; let mut first = kolibrie::rsp::simple_r2r::SimpleR2R::new(); let mut second = kolibrie::rsp::simple_r2r::SimpleR2R::new(); @@ -680,21 +571,8 @@ fn two_streams_do_not_share_a_blank_node() { ); } -// ── The wire form ─────────────────────────────────────────────────────────────── -// -// A canonical spelling is deliberately not a wire form: an IRI is stored bare and a -// plain literal unquoted, which keeps identity cheap and left every existing term -// untouched. Writing that text straight out produced `http://example/x .` for an IRI -// and a typed literal quoted a second time — a plain literal whose text merely looks -// typed, which any other RDF tool reads as a string. -// -// It went unnoticed because these readers re-canonicalise what they read, so a round -// trip through Kolibrie recovered the datatype. Only a second implementation would -// have noticed, which is exactly the case that matters for exchanging data between -// systems, so these assert the emitted text rather than what re-reading gives back. - fn wire_of(ntriples: &str) -> String { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add(ntriples); database.generate_ntriples() } @@ -720,7 +598,6 @@ fn a_language_tagged_literal_goes_out_with_its_tag() { #[test] fn an_iri_goes_out_in_brackets_whatever_its_scheme() { - // `urn:` is not `http:`, and used to be written as a literal. let wire = wire_of(" .\n"); assert!(wire.contains(""), "got {wire:?}"); assert!( @@ -731,8 +608,6 @@ fn an_iri_goes_out_in_brackets_whatever_its_scheme() { #[test] fn a_blank_node_goes_out_as_a_blank_node() { - // `<_:b>` is an IRI, not a blank node. generate_nquads guarded this; the - // N-Triples writer did not. let wire = wire_of("_:b .\n"); assert!( !wire.contains("<_:"), @@ -748,8 +623,7 @@ fn a_literal_containing_a_quote_goes_out_escaped() { wire.contains("\\\""), "an embedded quote must be escaped, got {wire:?}" ); - // And the escaping has to be recoverable: re-reading gives the original text. - let mut back = SparqlDatabase::new(); + let mut back = KolibrieDB::new(); back.parse_ntriples_and_add(&wire); let dictionary = back.dictionary(); let objects: Vec = back @@ -769,7 +643,7 @@ fn every_term_survives_a_round_trip_through_the_wire() { .\n" ); let wire = wire_of(&source); - let mut back = SparqlDatabase::new(); + let mut back = KolibrieDB::new(); back.parse_ntriples_and_add(&wire); let dictionary = back.dictionary(); let mut objects: Vec = back @@ -789,21 +663,9 @@ fn every_term_survives_a_round_trip_through_the_wire() { ); } -// ── Exchanging data between systems ───────────────────────────────────────────── -// -// The plain serialisers emit document-local blank node labels, which are sound within -// one store and unsound the moment two stores merge each other's output: a label means -// nothing outside its document, and the scope counter is process-local, so two -// instances both mint `_:kolibrie-doc-1-b` for their first document. RDF 1.1 §3.5 -// answers that by replacing blank nodes with IRIs, which are global. - #[test] fn two_instances_would_collide_without_skolemisation() { - // Why skolemisation is needed rather than nice to have. Each store is a fresh - // process-local counter in a real deployment, which this cannot simulate in one - // process -- so it asserts the weaker, sufficient fact: the label carries no - // trace of which instance minted it, and nothing else in it would separate them. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add("_:b .\n"); let wire = database.generate_ntriples(); assert!( @@ -818,7 +680,7 @@ fn two_instances_would_collide_without_skolemisation() { #[test] fn skolemising_replaces_a_blank_node_with_an_iri_under_the_given_authority() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add("_:b .\n"); let wire = database.generate_ntriples_skolemised("http://alice.example"); assert!( @@ -833,15 +695,13 @@ fn skolemising_replaces_a_blank_node_with_an_iri_under_the_given_authority() { #[test] fn two_authorities_do_not_collide() { - // The property that makes a merge safe. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add("_:b .\n"); let from_alice = database.generate_ntriples_skolemised("http://alice.example"); let from_bob = database.generate_ntriples_skolemised("http://bob.example"); assert_ne!(from_alice, from_bob); - // Merged into one store, they are two nodes rather than one. - let mut merged = SparqlDatabase::new(); + let mut merged = KolibrieDB::new(); merged.parse_ntriples_and_add(&from_alice); merged.parse_ntriples_and_add(&from_bob); let subjects = stored_subjects(&merged); @@ -853,20 +713,16 @@ fn two_authorities_do_not_collide() { fn a_skolemised_node_arrives_as_an_iri_and_can_be_turned_back() { use shared::rdf_term::{deskolemise, kind_of, skolemise, TermKind}; - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add("_:b .\n"); let wire = database.generate_ntriples_skolemised("http://alice.example"); - let mut receiver = SparqlDatabase::new(); + let mut receiver = KolibrieDB::new(); receiver.parse_ntriples_and_add(&wire); let arrived = &stored_subjects(&receiver)[0]; - // An IRI on arrival, which is what keeps a merge between systems sound. assert_eq!(kind_of(arrived), TermKind::Iri, "got {arrived:?}"); - // Recoverable for a consumer that would rather have the node back. The exact - // label depends on a counter shared across this binary's tests, so what is - // pinned is that it comes back as a blank node and skolemises to the same IRI. let recovered = deskolemise(arrived).expect("a Skolem IRI"); assert!(recovered.starts_with("_:"), "got {recovered:?}"); assert_eq!( @@ -878,9 +734,7 @@ fn a_skolemised_node_arrives_as_an_iri_and_can_be_turned_back() { #[test] fn skolemisation_does_not_touch_a_literal_that_mentions_a_label() { - // Rewriting the serialised text would find `_:b` inside this literal, which is - // not a term at all. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.parse_ntriples_and_add(" \"see _:b here\" .\n"); let wire = database.generate_ntriples_skolemised("http://alice.example"); assert!( diff --git a/kolibrie/tests/rsp_engine_test.rs b/kolibrie/tests/rsp_engine_test.rs index b9714503..e3845cfd 100644 --- a/kolibrie/tests/rsp_engine_test.rs +++ b/kolibrie/tests/rsp_engine_test.rs @@ -7,16 +7,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -/// ISTREAM: sliding window (RANGE=3 STEP=1) across 3 firings. -/// -/// 3-firing sequence: -/// - ts=1: add subjectA -> no fire (opens first window). -/// - ts=2: add subjectB -> fires [-1,1] with {A}; ISTREAM: old=∅ -> emit A. -/// - ts=3: add subjectC -> fires [0,2] with {A,B}; ISTREAM: old={A} -> emit B only. -/// - ts=4: add subjectD -> fires [1,3] with {A,B,C}; ISTREAM: old={A,B} -> emit C only. -/// -/// Total consumer calls: 3 -> [A], [B], [C]. -/// No stop() — flushing all active windows would corrupt R2S state. +/// ISTREAM: sliding window (RANGE=3 STEP=1) across 3 firings #[test] fn rsp_ql_istream_semantics() { let result_container = Arc::new(Mutex::new(Vec::>::new())); @@ -43,25 +34,20 @@ fn rsp_ql_istream_semantics() { .build() .expect("Failed to build ISTREAM engine"); - // Prime dictionary so query and data share term IDs. engine.parse_data(" a ."); - // ts=1: A -> no fire (opens first window). for t in engine.parse_data(" a .") { engine.add(t, 1); } - // ts=2: B -> fires [-1,1] with {A}; ISTREAM: old=∅ -> emit A. for t in engine.parse_data(" a .") { engine.add(t, 2); } - // ts=3: C -> fires [0,2] with {A,B}; ISTREAM: old={A} -> emit B only. for t in engine.parse_data(" a .") { engine.add(t, 3); } - // ts=4: D -> fires [1,3] with {A,B,C}; ISTREAM: old={A,B} -> emit C only. for t in engine.parse_data(" a .") { engine.add(t, 4); } @@ -73,7 +59,6 @@ fn rsp_ql_istream_semantics() { "ISTREAM: 3 firings -> 3 consumer calls. Got: {:?}", *results ); - // Firing 1: [-1,1] -> {A}, new since ∅ -> emit A. assert_eq!(results[0].len(), 1); assert!( results[0] @@ -82,7 +67,6 @@ fn rsp_ql_istream_semantics() { "ISTREAM firing 1 must emit subjectA, got: {:?}", results[0] ); - // Firing 2: [0,2] -> {A,B}, new since {A} -> emit B only. assert_eq!(results[1].len(), 1); assert!( results[1] @@ -91,7 +75,6 @@ fn rsp_ql_istream_semantics() { "ISTREAM firing 2 must emit subjectB, got: {:?}", results[1] ); - // Firing 3: [1,3] -> {A,B,C}, new since {A,B} -> emit C only. assert_eq!(results[2].len(), 1); assert!( results[2] @@ -102,21 +85,7 @@ fn rsp_ql_istream_semantics() { ); } -/// DSTREAM: sliding window (RANGE=3 STEP=1) — 5 window firings, 2 DSTREAM emissions. -/// -/// A RANGE 3 window spans exactly 3 timestamps, so once it is full every slide -/// drops exactly one fact out the back — and DSTREAM reports each of those. -/// -/// Window firing sequence (OnWindowClose fires when ts > window.close): -/// - ts=1: add A -> no fire yet (only an empty window closes; 0 bindings). -/// - ts=2: add B -> fires [-1,1] with {A}; DSTREAM: old=∅ -> last={A}, no emission. -/// - ts=3: add C -> fires [0,2] with {A,B}; DSTREAM: old={A} -> last={A,B}, no emission. -/// - ts=4: add D -> fires [1,3] with {A,B,C}; DSTREAM: old={A,B} -> last={A,B,C}, no emission. -/// - ts=5: add E -> fires [2,4] with {B,C,D}; DSTREAM: old={A,B,C} -> deleted={A} -> emit A. -/// - ts=6: add F -> fires [3,5] with {C,D,E}; DSTREAM: old={B,C,D} -> deleted={B} -> emit B. -/// -/// Total consumer calls: 2 -> [A], [B]. -/// No stop() — flushing all active windows would corrupt R2S state. +/// DSTREAM: sliding window (RANGE=3 STEP=1) — 5 window firings, 2 DSTREAM emissions #[test] fn rsp_ql_dstream_semantics() { let result_container = Arc::new(Mutex::new(Vec::>::new())); @@ -143,35 +112,28 @@ fn rsp_ql_dstream_semantics() { .build() .expect("Failed to build DSTREAM engine"); - // Prime dictionary. engine.parse_data(" a ."); - // ts=1: A into windows; no fire. for t in engine.parse_data(" a .") { engine.add(t, 1); } - // ts=2: B -> window [-1,1] fires with {A}; DSTREAM: old=∅ -> no emission. for t in engine.parse_data(" a .") { engine.add(t, 2); } - // ts=3: C -> window [0,2] fires with {A,B}; DSTREAM: old={A} -> no emission. for t in engine.parse_data(" a .") { engine.add(t, 3); } - // ts=4: D -> window [1,3] fires with {A,B,C}; DSTREAM: old={A,B} -> no emission. for t in engine.parse_data(" a .") { engine.add(t, 4); } - // ts=5: E -> window [2,4] fires with {B,C,D}; DSTREAM: old={A,B,C} -> deleted={A} -> emit A. for t in engine.parse_data(" a .") { engine.add(t, 5); } - // ts=6: F -> window [3,5] fires with {C,D,E}; DSTREAM: old={B,C,D} -> deleted={B} -> emit B. for t in engine.parse_data(" a .") { engine.add(t, 6); } @@ -183,7 +145,6 @@ fn rsp_ql_dstream_semantics() { "DSTREAM: 5 window firings -> 2 consumer calls ([2,4] drops subjectA, [3,5] drops subjectB). Got: {:?}", *results ); - // Emission 1: {A,B,C} -> {B,C,D} dropped subjectA. assert_eq!(results[0].len(), 1); assert!( results[0] @@ -192,7 +153,6 @@ fn rsp_ql_dstream_semantics() { "DSTREAM emission 1 must bind ?s to subjectA (deleted), got: {:?}", results[0] ); - // Emission 2: {B,C,D} -> {C,D,E} dropped subjectB. assert_eq!(results[1].len(), 1); assert!( results[1] @@ -260,7 +220,6 @@ fn rsp_ql_integration() { }; let r2r = Box::new(SimpleR2R::with_execution_mode(QueryExecutionMode::Volcano)); - // RSP-QL query with single window let rsp_ql_query = r#" REGISTER RSTREAM AS SELECT * @@ -278,10 +237,8 @@ fn rsp_ql_integration() { .add_r2r(r2r) .build() .expect("Failed to build RSP engine"); - //small hack to make sure the encoder is aligned between parsing and query injection engine.parse_data("a a ."); - // Add data to the stream for i in 0..20 { let data = format!( " a .", @@ -295,7 +252,6 @@ fn rsp_ql_integration() { engine.stop(); - // Should have results from window processing assert!(!result_container.lock().unwrap().is_empty()); } @@ -312,7 +268,6 @@ fn rsp_ql_integration_with_join() { }; let r2r = Box::new(SimpleR2R::with_execution_mode(QueryExecutionMode::Volcano)); - // RSP-QL query with single window let rsp_ql_query = r#" REGISTER RSTREAM AS SELECT * @@ -332,7 +287,6 @@ fn rsp_ql_integration_with_join() { .build() .expect("Failed to build RSP engine"); - // Add data to the stream for i in 0..20 { let data = format!( " a .\n\ @@ -347,7 +301,6 @@ fn rsp_ql_integration_with_join() { engine.stop(); - // Should have results from window processing assert!(!result_container.lock().unwrap().is_empty()); } @@ -364,7 +317,6 @@ fn rsp_ql_multi_window_integration() { }; let r2r = Box::new(SimpleR2R::with_execution_mode(QueryExecutionMode::Volcano)); - // RSP-QL query with multiple windows (similar to the example) let rsp_ql_query = r#" REGISTER RSTREAM AS SELECT * @@ -387,7 +339,6 @@ fn rsp_ql_multi_window_integration() { .build() .expect("Failed to build RSP engine"); - // Add temperature data for i in 0..10 { let data = format!( " a .", @@ -399,7 +350,6 @@ fn rsp_ql_multi_window_integration() { } } - // Add CO2 data for i in 0..10 { let data = format!(" a .", i); let triples = engine.parse_data(&data); @@ -410,7 +360,6 @@ fn rsp_ql_multi_window_integration() { engine.stop(); - // Should have results from both windows assert!(!result_container.lock().unwrap().is_empty()); } @@ -427,7 +376,6 @@ fn rsp_ql_joining_multi_window_integration() { }; let r2r = Box::new(SimpleR2R::with_execution_mode(QueryExecutionMode::Volcano)); - // RSP-QL query with multiple windows (similar to the example) let rsp_ql_query = r#" REGISTER RSTREAM AS SELECT * @@ -450,7 +398,6 @@ fn rsp_ql_joining_multi_window_integration() { .build() .expect("Failed to build RSP engine"); - // Add temperature data for i in 0..10 { let data = format!( " a .", @@ -462,7 +409,6 @@ fn rsp_ql_joining_multi_window_integration() { } } - // Add CO2 data for i in 0..10 { let data = format!(" a .", i); let triples = engine.parse_data(&data); @@ -473,7 +419,6 @@ fn rsp_ql_joining_multi_window_integration() { engine.stop(); - // Should have no results if windows are correctly joined together assert!(result_container.lock().unwrap().is_empty()); } @@ -493,7 +438,6 @@ fn rsp_ql_single_thread_multi_window_integration() { let r2r = Box::new(SimpleR2R::with_execution_mode(QueryExecutionMode::Volcano)); - // FIXED: Consistent spacing and proper SPARQL variable syntax let rsp_ql_query = r#" REGISTER RSTREAM AS SELECT * @@ -517,9 +461,7 @@ fn rsp_ql_single_thread_multi_window_integration() { .build() .expect("Failed to build RSP engine"); - // Feed data to both streams for i in 0..5 { - // Add to stream1 let data1 = format!( " a .", i @@ -529,7 +471,6 @@ fn rsp_ql_single_thread_multi_window_integration() { engine.add_to_stream("stream1", triple, i); } - // Add to stream2 let data2 = format!( " a .", i @@ -542,7 +483,6 @@ fn rsp_ql_single_thread_multi_window_integration() { engine.stop(); - // Verify we got results let results = result_container.lock().unwrap(); println!("Total results captured: {}", results.len()); @@ -550,7 +490,6 @@ fn rsp_ql_single_thread_multi_window_integration() { println!("Result {}: {:?}", i, result); } - // Check if there are results with BOTH s1 AND s2 in the same binding let has_joined_results = results.iter().any(|binding| { let has_s1 = binding.iter().any(|(k, _)| k == "s1"); let has_s2 = binding.iter().any(|(k, _)| k == "s2"); @@ -579,8 +518,8 @@ fn rsp_ql_single_thread_multi_window_integration() { ); } -/// Single window + static WHERE patterns: results must contain both -/// the window variable (?sensor) and the static variable (?room). +/// Single window + static WHERE patterns: results must contain both the window variable (?sensor) +/// and the static variable (?room) #[test] fn rsp_ql_single_window_static_join() { let result_container = Arc::new(Mutex::new(Vec::new())); @@ -594,7 +533,6 @@ fn rsp_ql_single_window_static_join() { }; let r2r = Box::new(SimpleR2R::with_execution_mode(QueryExecutionMode::Volcano)); - // Query: window pattern + static pattern joined on ?sensor let rsp_ql_query = r#" REGISTER RSTREAM AS SELECT * @@ -615,12 +553,10 @@ fn rsp_ql_single_window_static_join() { .build() .expect("Failed to build RSP engine"); - // Add the location triple to the static background store. let location_triple_str = " ."; engine.add_static_ntriples(location_triple_str); - // Stream: sensor data for i in 0..5 { let data = format!( " a .", @@ -637,7 +573,6 @@ fn rsp_ql_single_window_static_join() { let results = result_container.lock().unwrap(); println!("Static-join total results: {}", results.len()); - // At least one result must have both ?sensor and ?room let has_static_join = results.iter().any(|binding| { let has_sensor = binding.iter().any(|(k, _)| k == "sensor"); let has_room = binding.iter().any(|(k, _)| k == "room"); @@ -651,8 +586,8 @@ fn rsp_ql_single_window_static_join() { ); } -/// Steal policy: window A fires first, B never fires -> no emission -/// (last_mat only has A, never reaches num_windows=2). +/// Steal policy: window A fires first, B never fires -> no emission (last_mat only has A, never +/// reaches num_windows=2) #[test] fn test_steal_policy_emits_after_first_window() { let (mut engine, results) = make_two_window_engine(SyncPolicy::Steal); @@ -670,12 +605,11 @@ fn test_steal_policy_emits_after_first_window() { ); } -/// Steal policy: B fires once then A fires repeatedly -> emission on each A trigger. +/// Steal policy: B fires once then A fires repeatedly -> emission on each A trigger #[test] fn test_steal_policy_emits_with_stale() { let (mut engine, results) = make_two_window_engine(SyncPolicy::Steal); - // Fire B first for i in 0..3usize { let data = format!(" a .", i); let triples = engine.parse_data(&data); @@ -683,7 +617,6 @@ fn test_steal_policy_emits_with_stale() { engine.add_to_stream("streamB", t, i); } } - // Fire A repeatedly at much later timestamps so B's windows have already closed for i in 0..5usize { let data = format!(" a .", i); let triples = engine.parse_data(&data); @@ -698,7 +631,7 @@ fn test_steal_policy_emits_with_stale() { ); } -/// Wait policy (default): only A fires -> no emission. +/// Wait policy (default): only A fires -> no emission #[test] fn test_wait_policy_waits_for_both() { let (mut engine, results) = make_two_window_engine(SyncPolicy::Wait); @@ -716,8 +649,7 @@ fn test_wait_policy_waits_for_both() { ); } -/// Timeout(100ms, Steal) in SingleThread mode is treated as Wait. -/// Only A fires -> no emission on first cycle. +/// Timeout(100ms, Steal) in SingleThread mode is treated as Wait #[test] fn test_timeout_steal_policy() { let policy = SyncPolicy::Timeout { @@ -733,15 +665,13 @@ fn test_timeout_steal_policy() { } } engine.stop(); - // SingleThread: Timeout treated as Wait; B never fired -> no emit assert!( results.lock().unwrap().is_empty(), "Timeout/Steal (SingleThread = Wait): no emit when B never fires" ); } -/// Timeout(100ms, Drop) in SingleThread mode is treated as Wait. -/// Only A fires -> no emission. +/// Timeout(100ms, Drop) in SingleThread mode is treated as Wait #[test] fn test_timeout_drop_policy() { let policy = SyncPolicy::Timeout { @@ -763,9 +693,8 @@ fn test_timeout_drop_policy() { ); } -/// Two windows + static WHERE patterns: results must contain variables -/// from both windows (?sensor, ?room) and confirm the static join filtered -/// them correctly. +/// Two windows plus static WHERE patterns: results carry both windows' variables after the static +/// join #[test] fn rsp_ql_multi_window_static_join() { let result_container = Arc::new(Mutex::new(Vec::new())); @@ -779,7 +708,6 @@ fn rsp_ql_multi_window_static_join() { }; let r2r = Box::new(SimpleR2R::with_execution_mode(QueryExecutionMode::Volcano)); - // Two windows (sensor / room) joined with static location triples. let rsp_ql_query = r#" REGISTER RSTREAM AS SELECT * @@ -804,12 +732,10 @@ fn rsp_ql_multi_window_static_join() { .build() .expect("Failed to build RSP engine"); - // Add static location triple: sensor0 is located in room0. let location_triple_str = " ."; engine.add_static_ntriples(location_triple_str); - // Stream1: sensors (sensor0 will match the static location triple) for i in 0..3 { let data = format!( " a .", @@ -821,7 +747,6 @@ fn rsp_ql_multi_window_static_join() { } } - // Stream2: rooms (room0 will match the static location triple) for i in 0..3 { let data = format!( " a .", @@ -838,7 +763,6 @@ fn rsp_ql_multi_window_static_join() { let results = result_container.lock().unwrap(); println!("Multi-window static-join total results: {}", results.len()); - // Results must have both ?sensor and ?room let has_both_windows = results.iter().any(|binding| { let has_sensor = binding.iter().any(|(k, _)| k == "sensor"); let has_room = binding.iter().any(|(k, _)| k == "room"); @@ -851,8 +775,6 @@ fn rsp_ql_multi_window_static_join() { *results ); - // Verify that the static join filtered: only (sensor0, room0) should appear - // (sensor1/sensor2 have no location triple, room1/room2 are not sensor0's location) let all_valid = results.iter().all(|binding| { let sensor = binding .iter() @@ -1025,8 +947,6 @@ fn rsp_cross_window_expired_support_no_longer_emits() { #[test] fn test_static_data_not_visible_in_window_query() { - // Query has ONLY window patterns — no non-window triple patterns. - // Static data matches the window pattern; it must NOT appear in results. let result_container = Arc::new(Mutex::new(Vec::new())); let rc_clone = Arc::clone(&result_container); let result_consumer = ResultConsumer { @@ -1050,13 +970,10 @@ fn test_static_data_not_visible_in_window_query() { .build() .expect("Failed to build RSP engine"); - // Prime dictionary engine.parse_data(" a ."); - // Add static triple that matches the window pattern engine.add_static_ntriples(" a ."); - // Push exactly one stream event let triples = engine.parse_data(" a ."); for triple in triples { engine.add(triple, 1); @@ -1065,8 +982,6 @@ fn test_static_data_not_visible_in_window_query() { engine.stop(); let all = result_container.lock().unwrap(); - // With fix: 1 result (stream1 only). - // Without fix: 2 results (stream1 + static1 leaking in). assert_eq!( all.len(), 1, @@ -1075,15 +990,7 @@ fn test_static_data_not_visible_in_window_query() { ); } -/// ISTREAM: sliding window (RANGE=3 STEP=1) across 3 firings. -/// -/// The first event never triggers a firing (same behaviour as other sliding-window -/// tests). 4 events are needed to produce 3 firings: -/// -/// ts=1: subjectA added -> no fire (opens first window) -/// ts=2: subjectB added -> fires [−1,1] -> content {A} -> ISTREAM: old=∅ -> emit A -/// ts=3: subjectC added -> fires [0,2] -> content {A,B} -> ISTREAM: old={A} -> emit B only -/// ts=4: subjectA (again)-> fires [1,3] -> content {A,B,C} -> ISTREAM: old={A,B} -> emit C only +/// ISTREAM: sliding window (RANGE=3 STEP=1) across 3 firings #[test] fn rsp_ql_istream_range3_step1() { let result_container = Arc::new(Mutex::new(Vec::>::new())); @@ -1110,26 +1017,20 @@ fn rsp_ql_istream_range3_step1() { .build() .expect("Failed to build ISTREAM RANGE3 engine"); - // Prime dictionary so query and data share term IDs. engine.parse_data(" a ."); - // ts=1: A -> no fire (first event opens the window but nothing closes yet). for t in engine.parse_data(" a .") { engine.add(t, 1); } - // ts=2: B -> fires window [−1,1] -> content {A} -> ISTREAM: old=∅ -> emit A. for t in engine.parse_data(" a .") { engine.add(t, 2); } - // ts=3: C -> fires window [0,2] -> content {A,B} -> ISTREAM: old={A} -> emit B only. for t in engine.parse_data(" a .") { engine.add(t, 3); } - // ts=4: A again (trigger) -> fires window [1,3] -> content {A,B,C} - // -> ISTREAM: old={A,B} -> emit C only. for t in engine.parse_data(" a .") { engine.add(t, 4); } @@ -1142,7 +1043,6 @@ fn rsp_ql_istream_range3_step1() { *results ); - // Firing 1 (triggered at ts=2): window {A}, ISTREAM emits A. assert!( results[0] .iter() @@ -1151,7 +1051,6 @@ fn rsp_ql_istream_range3_step1() { results[0] ); - // Firing 2 (triggered at ts=3): window {A,B}, ISTREAM emits B only. assert!( results[1] .iter() @@ -1167,7 +1066,6 @@ fn rsp_ql_istream_range3_step1() { results[1] ); - // Firing 3 (triggered at ts=4): window {A,B,C}, ISTREAM emits C only. assert!( results[2] .iter() @@ -1193,10 +1091,6 @@ fn rsp_ql_istream_range3_step1() { #[test] fn test_window_evicts_old_data() { - // Non-overlapping windows (RANGE 10 STEP 10) with one subject per window. - // Without eviction the R2R store accumulates all triples, so window 2 returns - // 2 rows and window 3 returns 3 rows (total 6). With eviction each window - // returns exactly 1 row (total 3). let result_container = Arc::new(Mutex::new(Vec::new())); let rc_clone = Arc::clone(&result_container); let result_consumer = ResultConsumer { @@ -1220,10 +1114,8 @@ fn test_window_evicts_old_data() { .build() .expect("Failed to build RSP engine"); - // Prime the dictionary so query and data use the same term IDs engine.parse_data("a a ."); - // Push one subject per non-overlapping window for (i, ts) in [(1usize, 1usize), (2, 11), (3, 21)] { let data = format!( " a .", @@ -1238,8 +1130,6 @@ fn test_window_evicts_old_data() { engine.stop(); let all = result_container.lock().unwrap(); - // Each of the 3 windows must return exactly 1 result row. - // Stale accumulation without the fix gives 1+2+3=6 total rows. assert_eq!( all.len(), 3, @@ -1249,20 +1139,7 @@ fn test_window_evicts_old_data() { ); } -/// Regression test: ISTREAM with same subject+predicate and changing object values. -/// -/// Each firing introduces one new (reading1, temp=N) triple. Because the subject -/// and predicate are identical across triples, only the object differs. A HashMap -/// non-determinism bug caused the Vec<(String,String)> serialisation of a row to -/// vary between firings, making ISTREAM either re-emit stale rows or drop new ones. -/// -/// Window RANGE=3 STEP=1. Event sequence: -/// ts=1: triple (reading1, hasTemp, "1") — no fire -/// ts=2: (reading1, hasTemp, "2") — fires window with {"1"} -> ISTREAM: old=∅ -> emit "1" -/// ts=3: (reading1, hasTemp, "3") — fires window with {"1","2"} -> ISTREAM: old={"1"} -> emit "2" -/// ts=4: (reading1, hasTemp, "4") — fires window with {"1","2","3"} -> ISTREAM: old={"1","2"} -> emit "3" -/// -/// Expected: 3 consumer calls, each with exactly one row. +/// Regression test: ISTREAM with same subject+predicate and changing object values #[test] fn rsp_ql_istream_same_sp_diff_object() { let result_container = Arc::new(Mutex::new(Vec::>::new())); @@ -1289,25 +1166,20 @@ fn rsp_ql_istream_same_sp_diff_object() { .build() .expect("Failed to build ISTREAM same-sp engine"); - // Prime dictionary so query and data share term IDs. engine.parse_data(" \"0\" ."); - // ts=1: temp=1 — no fire. for t in engine.parse_data(" \"1\" .") { engine.add(t, 1); } - // ts=2: temp=2 — fires window with {temp=1}; ISTREAM: old=∅ -> emit temp=1. for t in engine.parse_data(" \"2\" .") { engine.add(t, 2); } - // ts=3: temp=3 — fires window with {temp=1,temp=2}; ISTREAM: old={temp=1} -> emit temp=2. for t in engine.parse_data(" \"3\" .") { engine.add(t, 3); } - // ts=4: temp=4 — fires window with {temp=1,temp=2,temp=3}; ISTREAM: old={temp=1,temp=2} -> emit temp=3. for t in engine.parse_data(" \"4\" .") { engine.add(t, 4); } @@ -1320,7 +1192,6 @@ fn rsp_ql_istream_same_sp_diff_object() { *results ); - // Each firing must emit exactly one row. for (i, row) in results.iter().enumerate() { assert_eq!( row.len(), @@ -1331,7 +1202,6 @@ fn rsp_ql_istream_same_sp_diff_object() { ); } - // Firing 1 -> temp=1. assert!( results[0] .iter() @@ -1339,7 +1209,6 @@ fn rsp_ql_istream_same_sp_diff_object() { "Firing 1 must emit temp=1, got: {:?}", results[0] ); - // Firing 2 -> temp=2 (not temp=1 again). assert!( results[1] .iter() @@ -1352,7 +1221,6 @@ fn rsp_ql_istream_same_sp_diff_object() { "Firing 2 must NOT re-emit temp=1, got: {:?}", results[1] ); - // Firing 3 -> temp=3 (not temp=1 or temp=2 again). assert!( results[2] .iter() @@ -1372,9 +1240,7 @@ fn rsp_ql_istream_same_sp_diff_object() { ); } -/// Reasoning test: forward-chaining derives `?s a ` from -/// `?s ?v`, enabling a window query that matches on the -/// inferred type — even though no explicit type triple exists in the stream. +/// Forward chaining derives a type that a window query then matches #[test] fn rsp_ql_reasoning_derives_types() { let result_container = Arc::new(Mutex::new(Vec::>::new())); @@ -1386,9 +1252,6 @@ fn rsp_ql_reasoning_derives_types() { }; let r2r = Box::new(SimpleR2R::with_execution_mode(QueryExecutionMode::Volcano)); - // N3 rule: { ?s test:hasValue ?v } => { ?s rdf:type test:HasValue } - // Passed as a string so load_rules() parses it using the shared dictionary - // (same IDs as the query plan). let rule_str = concat!( "@prefix test: .\n", "@prefix rdf: .\n", @@ -1411,11 +1274,9 @@ fn rsp_ql_reasoning_derives_types() { .build() .expect("Failed to build reasoning engine"); - // Feed stream triple: sensor1 hasValue "42" (no explicit type triple) for t in engine.parse_data(" \"42\" .") { engine.add(t, 1); } - // Trigger a second event to fire the window for t in engine.parse_data(" \"99\" .") { engine.add(t, 2); } @@ -1428,7 +1289,6 @@ fn rsp_ql_reasoning_derives_types() { "Reasoning: expected at least one consumer call (inferred type should match query), got none" ); - // At least one row should bind ?s to sensor1 let has_sensor1 = results .iter() .any(|row| row.iter().any(|(k, v)| k == "s" && v.contains("sensor1"))); diff --git a/kolibrie/tests/shared_term_space_test.rs b/kolibrie/tests/shared_term_space_test.rs index a22629df..922c07cc 100644 --- a/kolibrie/tests/shared_term_space_test.rs +++ b/kolibrie/tests/shared_term_space_test.rs @@ -8,30 +8,21 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! A store and a reasoner that adopt one term space stay in step. -//! -//! `kolibrie-http-server` used to assign the dictionary handle to a reasoner, infer, -//! and then assign it *back* to the store with the comment "sync the enriched -//! dictionary back so SPARQL can decode the new terms". That assignment handed the -//! store the handle it already held, and the comment described copy semantics the -//! type does not have. Removing it relies on the invariant tested here, so the -//! invariant is tested rather than assumed. +//! A store and a reasoner that adopt one term space stay in step use datalog::reasoning::Reasoner; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; #[test] fn a_term_interned_by_the_reasoner_decodes_through_the_store() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.add_triple_parts("urn:a", "urn:p", "urn:b"); let mut reasoner = Reasoner::new(); reasoner.adopt_terms(database.terms()); - // A term the store has never seen, interned on the reasoner's side only. let id = reasoner.encode_term("urn:brand-new"); - // No sync step. The store decodes it because the encoding is shared, not copied. assert_eq!( database.dictionary().decode(id), Some("urn:brand-new"), @@ -41,7 +32,7 @@ fn a_term_interned_by_the_reasoner_decodes_through_the_store() { #[test] fn the_store_and_the_reasoner_agree_on_ids_for_the_same_term() { - let database = SparqlDatabase::new(); + let database = KolibrieDB::new(); let from_store = database.dictionary_mut().encode("urn:shared"); let mut reasoner = Reasoner::new(); @@ -56,10 +47,7 @@ fn the_store_and_the_reasoner_agree_on_ids_for_the_same_term() { #[test] fn a_reasoner_with_its_own_term_space_does_not_agree() { - // The failure the invariant above prevents, stated so the test above is not - // trivially true: two independent dictionaries assign ids by insertion order, - // so they collide rather than agree. - let database = SparqlDatabase::new(); + let database = KolibrieDB::new(); database.dictionary_mut().encode("urn:first"); let store_id = database.dictionary_mut().encode("urn:second"); diff --git a/kolibrie/tests/solution_interning_test.rs b/kolibrie/tests/solution_interning_test.rs index 7947b8e2..03efef7e 100644 --- a/kolibrie/tests/solution_interning_test.rs +++ b/kolibrie/tests/solution_interning_test.rs @@ -8,30 +8,19 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! A result that spans more than one decode chunk reports the right values. -//! -//! `Solutions` interns its values per chunk and then shifts each chunk's indices by a -//! prefix sum, so a cell in the second chunk refers into the second chunk's slice of -//! the term table. Nothing here tested that: every other query test returns fewer -//! rows than one chunk holds, so deleting the shift entirely left the whole suite -//! green while every row past the first chunk silently read another row's value. -//! -//! These tests are therefore sized deliberately. `ROWS` must stay above the decode -//! chunk size for them to mean anything. +//! A result that spans more than one decode chunk reports the right values +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute; -use kolibrie::sparql_database::SparqlDatabase; use std::collections::BTreeSet; -/// Comfortably more than one decode chunk, which is 1024 rows. +/// Comfortably more than one decode chunk, which is 1024 rows const ROWS: usize = 3_000; -fn database_with_distinct_objects() -> SparqlDatabase { - let mut database = SparqlDatabase::new(); +fn database_with_distinct_objects() -> KolibrieDB { + let mut database = KolibrieDB::new(); let mut data = String::with_capacity(ROWS * 80); for i in 0..ROWS { - // Subject and object both unique and tied to each other, so a row that reads - // the wrong term table entry disagrees with its own subject. data.push_str(&format!(" .\n")); } kolibrie::execute_query::execute_sparql_update( @@ -54,11 +43,6 @@ fn every_row_past_the_first_chunk_keeps_its_own_value() { "the fixture must span several chunks" ); - // Checked against the values the fixture put in, not against each other. Both - // columns of a row shift by the same amount when the prefix sum is wrong, so a - // row in the second chunk reads `urn:s0` and `urn:o0` together -- self-consistent - // and completely wrong. An earlier version of this test compared the two columns - // and passed with the shift deleted. let mut seen: BTreeSet<(String, String)> = BTreeSet::new(); for solution in solutions.iter() { seen.insert(( @@ -117,11 +101,7 @@ fn materialising_a_multi_chunk_result_agrees_with_borrowing_it() { #[test] fn equal_results_compare_equal_with_different_term_tables() { - // Interning happens before LIMIT, so the limited query interns a value that - // projection then drops and the filtered one never interns at all. The two hold - // different term tables and must still compare equal, which comparing the fields - // directly would not do. - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); kolibrie::execute_query::execute_sparql_update( "INSERT DATA { \"a\" . \"b\" . \"c\" . }", &mut database, diff --git a/kolibrie/tests/sparql_dataset_regressions_test.rs b/kolibrie/tests/sparql_dataset_regressions_test.rs index 3cc18407..1f4c37be 100644 --- a/kolibrie/tests/sparql_dataset_regressions_test.rs +++ b/kolibrie/tests/sparql_dataset_regressions_test.rs @@ -1,9 +1,9 @@ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_update}; -use kolibrie::sparql_database::SparqlDatabase; use kolibrie::sparql_protocol::answer; use shared::dataset_index::GraphId; -fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { +fn query(database: &mut KolibrieDB, sparql: &str) -> Vec> { execute(sparql, database) .expect("query should run") .into_rows() @@ -11,7 +11,7 @@ fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { #[test] fn illegal_bound_update_positions_are_skipped() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -73,7 +73,7 @@ fn illegal_bound_update_positions_are_skipped() { #[test] fn nquads_round_trip_keeps_default_and_named_quads() { - let mut source = SparqlDatabase::new(); + let mut source = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -99,7 +99,7 @@ fn nquads_round_trip_keeps_default_and_named_quads() { " \"value\" ." )); - let mut restored = SparqlDatabase::new(); + let mut restored = KolibrieDB::new(); restored.parse_nquads_and_add(&serialized); assert_eq!( query( @@ -126,16 +126,6 @@ fn nquads_round_trip_keeps_default_and_named_quads() { r#"SELECT ?s WHERE { ?s "quoted \"value\"" }"#, ); assert_eq!(escaped_literal_rows.len(), 1); - // Still a blank node, and still carrying the label it was written with. - // - // This used to assert the `_:kolibrie-update-` prefix specifically, which pinned - // a blank node label's lexical form across a serialise-and-re-read. RDF 1.1 - // section 3.4 makes that form insignificant, and reading a serialisation is - // reading a new document, so its blank nodes are a fresh set and are scoped - // again -- by the reader now, which is what stops two documents sharing a node. - // What this round trip has to show is what its sibling assertion above shows for - // the serialised form: that the node survives as a node rather than becoming an - // IRI or losing its shape. let restored_label = &escaped_literal_rows[0][0]; assert!( restored_label.starts_with("_:"), @@ -147,11 +137,7 @@ fn nquads_round_trip_keeps_default_and_named_quads() { ); } -/// Splits an HTTP response from `answer` into its status and body. -/// -/// The status is checked as well as the body: the response used to be a bare body -/// that the caller wrapped in `200 OK`, so a failure was indistinguishable from a -/// success here. +/// Splits an HTTP response from `answer` into its status and body fn split_response(response: &str) -> (String, String) { let (head, body) = response .split_once("\r\n\r\n") @@ -167,7 +153,7 @@ fn split_response(response: &str) -> (String, String) { #[test] fn http_sparql_query_encodings_use_the_unified_query_executor() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( "INSERT DATA { }", &mut database, @@ -212,7 +198,6 @@ fn http_sparql_query_encodings_use_the_unified_query_executor() { "\r\n", "INSERT DATA { }" ); - // An Update on the query endpoint is refused, and now says so in the status. assert_eq!( split_response(&answer(&mut database, update_on_query_endpoint)), ( @@ -245,7 +230,7 @@ fn http_sparql_query_encodings_use_the_unified_query_executor() { #[test] fn update_blank_node_allocation_skips_persisted_lexical_collisions() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update("INSERT DATA { _:first }", &mut database).unwrap(); let allocated = query( &mut database, @@ -258,8 +243,6 @@ fn update_blank_node_allocation_skips_persisted_lexical_collisions() { .and_then(|(number, _)| number.parse::().ok()) .unwrap(); - // Preload a range of labels that a restarted/process-local allocator - // could otherwise mistake for new blank nodes. for candidate in allocation + 1..=allocation + 512 { database.add_triple_parts( &format!("_:kolibrie-update-{candidate}-second"), @@ -288,7 +271,7 @@ fn update_blank_node_allocation_skips_persisted_lexical_collisions() { #[test] fn statistics_include_terms_that_exist_only_in_named_graphs() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( "INSERT DATA { GRAPH { } }", &mut database, diff --git a/kolibrie/tests/sparql_filter_scope_test.rs b/kolibrie/tests/sparql_filter_scope_test.rs index 5bc7beb7..5815a638 100644 --- a/kolibrie/tests/sparql_filter_scope_test.rs +++ b/kolibrie/tests/sparql_filter_scope_test.rs @@ -1,14 +1,14 @@ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_update}; -use kolibrie::sparql_database::SparqlDatabase; -fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { +fn query(database: &mut KolibrieDB, sparql: &str) -> Vec> { execute(sparql, database) .expect("query should run") .into_rows() } -fn database_with_scoped_data() -> SparqlDatabase { - let mut database = SparqlDatabase::new(); +fn database_with_scoped_data() -> KolibrieDB { + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -103,7 +103,7 @@ fn outer_filter_before_graph_sees_bindings_from_the_graph_child() { #[test] fn arithmetic_filter_before_triple_uses_the_complete_group_scope() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { diff --git a/kolibrie/tests/sparql_graph_test.rs b/kolibrie/tests/sparql_graph_test.rs index c597028a..e5dbebb7 100644 --- a/kolibrie/tests/sparql_graph_test.rs +++ b/kolibrie/tests/sparql_graph_test.rs @@ -1,7 +1,7 @@ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_update}; -use kolibrie::sparql_database::SparqlDatabase; -fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { +fn query(database: &mut KolibrieDB, sparql: &str) -> Vec> { execute(sparql, database) .expect("query should run") .into_rows() @@ -9,7 +9,7 @@ fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { #[test] fn nested_graph_and_union_preserve_solution_multiplicity() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" PREFIX ex: @@ -77,7 +77,7 @@ fn nested_graph_and_union_preserve_solution_multiplicity() { #[test] fn insert_and_delete_data_work_in_default_and_named_graphs() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let inserted = execute_sparql_update( r#" PREFIX ex: @@ -116,8 +116,6 @@ fn insert_and_delete_data_work_in_default_and_named_graphs() { .unwrap(); assert_eq!(deleted.deleted_quads, 1); - // Deleting the last quad leaves the named graph identity available to an - // empty GRAPH pattern. assert_eq!( query(&mut database, "SELECT ?g WHERE { GRAPH ?g {} }",), vec![vec!["http://example.org/g".to_string()]] @@ -126,7 +124,7 @@ fn insert_and_delete_data_work_in_default_and_named_graphs() { #[test] fn insert_delete_and_combined_modify_use_where_bindings() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" PREFIX ex: @@ -199,7 +197,7 @@ fn insert_delete_and_combined_modify_use_where_bindings() { #[test] fn delete_template_where_and_invalid_data_are_handled() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update("INSERT DATA { \"value\" }", &mut database).unwrap(); let summary = execute_sparql_update( @@ -215,7 +213,7 @@ fn delete_template_where_and_invalid_data_are_handled() { #[test] fn rebuilding_indexes_keeps_named_quads_and_empty_graphs() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -254,7 +252,7 @@ fn rebuilding_indexes_keeps_named_quads_and_empty_graphs() { #[test] fn graph_variables_flow_from_where_into_delete_and_insert_templates() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { diff --git a/kolibrie/tests/sparql_subquery_modifiers_test.rs b/kolibrie/tests/sparql_subquery_modifiers_test.rs index 3bddb768..5ad090c9 100644 --- a/kolibrie/tests/sparql_subquery_modifiers_test.rs +++ b/kolibrie/tests/sparql_subquery_modifiers_test.rs @@ -1,7 +1,7 @@ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_update}; -use kolibrie::sparql_database::SparqlDatabase; -fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { +fn query(database: &mut KolibrieDB, sparql: &str) -> Vec> { execute(sparql, database) .expect("query should run") .into_rows() @@ -9,7 +9,7 @@ fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { #[test] fn equal_sized_values_union_branches_keep_their_own_rows() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let rows = query( &mut database, r#" @@ -30,7 +30,7 @@ fn equal_sized_values_union_branches_keep_their_own_rows() { #[test] fn select_star_subquery_exports_all_inner_bindings() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -63,7 +63,7 @@ fn select_star_subquery_exports_all_inner_bindings() { #[test] fn subquery_applies_distinct_order_and_limit_before_outer_join() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -97,7 +97,7 @@ fn subquery_applies_distinct_order_and_limit_before_outer_join() { #[test] fn subquery_groups_and_materializes_aggregate_aliases() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { diff --git a/kolibrie/tests/sparql_unified_test.rs b/kolibrie/tests/sparql_unified_test.rs index b2b6d197..c6ab0eda 100644 --- a/kolibrie/tests/sparql_unified_test.rs +++ b/kolibrie/tests/sparql_unified_test.rs @@ -1,8 +1,8 @@ +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::{execute, execute_sparql_update}; -use kolibrie::sparql_database::SparqlDatabase; use kolibrie::sparql_protocol::answer; -fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { +fn query(database: &mut KolibrieDB, sparql: &str) -> Vec> { execute(sparql, database) .expect("query should run") .into_rows() @@ -10,7 +10,7 @@ fn query(database: &mut SparqlDatabase, sparql: &str) -> Vec> { #[test] fn graph_composes_with_filter_bind_and_values() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -48,7 +48,7 @@ fn graph_composes_with_filter_bind_and_values() { #[test] fn dollar_variables_and_arithmetic_filter_comments_execute_end_to_end() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -81,7 +81,7 @@ fn dollar_variables_and_arithmetic_filter_comments_execute_end_to_end() { #[test] fn every_supported_sparql_literal_quote_form_lowers_identically() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -114,7 +114,7 @@ fn every_supported_sparql_literal_quote_form_lowers_identically() { #[test] fn iri_and_prefixed_name_escapes_normalize_during_the_single_lowering_step() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" PREFIX ex: @@ -168,7 +168,7 @@ fn iri_and_prefixed_name_escapes_normalize_during_the_single_lowering_step() { #[test] fn graph_union_subquery_preserves_unbound_columns() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -205,7 +205,7 @@ fn graph_union_subquery_preserves_unbound_columns() { #[test] fn from_and_from_named_replace_the_query_dataset() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -268,7 +268,7 @@ fn from_and_from_named_replace_the_query_dataset() { #[test] fn graph_variable_is_compatible_with_triple_variables() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -291,7 +291,7 @@ fn graph_variable_is_compatible_with_triple_variables() { #[test] fn modify_where_uses_unified_graph_union_and_filter_plan() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -342,7 +342,7 @@ fn modify_where_uses_unified_graph_union_and_filter_plan() { #[test] fn insert_template_blank_nodes_are_fresh_per_solution() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -392,7 +392,7 @@ fn insert_template_blank_nodes_are_fresh_per_solution() { #[test] fn unbound_template_terms_are_skipped_and_invalid_updates_do_not_mutate() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -443,7 +443,7 @@ fn unbound_template_terms_are_skipped_and_invalid_updates_do_not_mutate() { #[test] fn empty_insert_data_graph_block_does_not_create_a_graph() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); let summary = execute_sparql_update( "INSERT DATA { GRAPH {} }", &mut database, @@ -455,7 +455,7 @@ fn empty_insert_data_graph_block_does_not_create_a_graph() { #[test] fn graph_union_composes_with_aggregation_order_and_limit() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); execute_sparql_update( r#" INSERT DATA { @@ -498,10 +498,7 @@ fn graph_union_composes_with_aggregation_order_and_limit() { ); } -/// The body of a response from `answer`. -/// -/// The response carries a status line now, so the body is no longer the whole -/// string; checking the status as well is the point of the change. +/// The body of a response from `answer` fn body_of(response: &str) -> &str { response .split_once("\r\n\r\n") @@ -511,14 +508,12 @@ fn body_of(response: &str) -> &str { #[test] fn legacy_and_http_adapters_route_through_the_same_update_executor() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); - // The error-preserving API is standards-only. assert!( execute_sparql_update("INSERT { }", &mut database,).is_err() ); - // The historical query adapter opts into the standalone DATA alias. assert!(query(&mut database, "INSERT { }",).is_empty()); assert_eq!( query(&mut database, "SELECT ?s WHERE { ?s }",), @@ -531,7 +526,6 @@ fn legacy_and_http_adapters_route_through_the_same_update_executor() { "\r\n", "INSERT DATA { }" ); - // The response now carries a status line, so the body is checked after it. let response = answer(&mut database, direct_request); assert!(response.starts_with("HTTP/1.1 200 OK"), "{response}"); assert!( diff --git a/kolibrie/tests/stats_cache_test.rs b/kolibrie/tests/stats_cache_test.rs index ee518cdf..b9cbeee6 100644 --- a/kolibrie/tests/stats_cache_test.rs +++ b/kolibrie/tests/stats_cache_test.rs @@ -10,10 +10,10 @@ mod common; -use kolibrie::sparql_database::{PlanningStatsPolicy, SparqlDatabase}; +use kolibrie::database::{KolibrieDB, PlanningStatsPolicy}; use shared::dataset_index::DatasetIndex; -fn seeded() -> SparqlDatabase { +fn seeded() -> KolibrieDB { common::database_with( r#" "1" . "2" . @@ -28,21 +28,14 @@ fn bounded(minimum_batch: u64, refresh_fraction: f64) -> PlanningStatsPolicy { } } -fn insert(database: &mut SparqlDatabase, index: u32) { +fn insert(database: &mut KolibrieDB, index: u32) { database.add_triple_parts(&format!("urn:s{index}"), "urn:p", &format!("{index}")); } -fn rebuilds(database: &SparqlDatabase) -> u64 { +fn rebuilds(database: &KolibrieDB) -> u64 { database.stats_rebuild_count() } -// Exact statistics -// -// `exact_statistics_notice_a_write_that_bypassed_the_helpers` and -// `a_replacement_index_with_a_lower_generation_is_not_mistaken_for_fresh` moved -// into sparql_database.rs: both write to `dataset_index` without going through the -// public helpers, which is no longer possible from outside the crate. - #[test] fn exact_statistics_are_fresh_after_every_successful_mutation() { let mut database = seeded(); @@ -64,7 +57,6 @@ fn a_mutation_that_changed_nothing_does_not_force_a_rebuild() { database.get_or_build_stats(); let before = rebuilds(&database); - // No-op mutations: duplicate insert, missing delete, empty update database.add_triple_parts("urn:a", "urn:p", "1"); assert!(!database.delete_triple_parts("urn:zz", "urn:p", "nope")); kolibrie::execute_query::execute_sparql_update( @@ -81,12 +73,10 @@ fn a_mutation_that_changed_nothing_does_not_force_a_rebuild() { ); } -// Planning statistics: AlwaysFresh - #[test] fn the_default_policy_is_bounded() { assert_eq!( - SparqlDatabase::new().planning_stats_policy(), + KolibrieDB::new().planning_stats_policy(), PlanningStatsPolicy::DEFAULT_BOUNDED ); } @@ -105,8 +95,6 @@ fn always_fresh_planning_statistics_match_the_exact_ones() { } } -// Planning statistics: Bounded - #[test] fn bounded_planning_statistics_are_reused_below_the_boundary() { let mut database = seeded(); @@ -124,7 +112,6 @@ fn bounded_planning_statistics_are_reused_below_the_boundary() { } assert_eq!(rebuilds(&database), after_first, "no rebuild happened"); - // Exact statistics are not affected by the policy assert_eq!(database.get_or_build_stats().total_triples, 8); } @@ -151,7 +138,7 @@ fn bounded_planning_statistics_rebuild_at_the_boundary() { /// Above minimum_batch the threshold is proportional to dataset size #[test] fn the_boundary_is_proportional_once_it_exceeds_the_minimum_batch() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); for index in 0..1_000 { insert(&mut database, index); } @@ -159,7 +146,6 @@ fn the_boundary_is_proportional_once_it_exceeds_the_minimum_batch() { let first = database.get_or_build_planning_stats(); assert_eq!(first.total_triples, 1_000); - // Threshold: max(10, 0.1 * 1000) = 100 for index in 1_000..1_090 { insert(&mut database, index); } @@ -180,7 +166,7 @@ fn the_boundary_is_proportional_once_it_exceeds_the_minimum_batch() { /// Delete/insert pairs count as mutations even when the size is unchanged #[test] fn a_constant_size_replacement_still_reaches_the_boundary() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); for index in 0..100 { insert(&mut database, index); } @@ -207,11 +193,9 @@ fn a_constant_size_replacement_still_reaches_the_boundary() { ); } -// Forced refresh - #[test] fn an_empty_dataset_that_gains_data_refreshes_immediately() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); database.set_planning_stats_policy(bounded(1_000, 0.90)); let first = database.get_or_build_planning_stats(); assert_eq!(first.total_triples, 0); @@ -245,7 +229,7 @@ fn a_hard_invalidation_retires_every_snapshot_under_any_policy() { #[test] fn replacing_the_dataset_index_retires_every_snapshot() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); for index in 0..200 { insert(&mut database, index); } @@ -264,7 +248,7 @@ fn replacing_the_dataset_index_retires_every_snapshot() { #[test] fn clearing_the_dataset_refreshes_planning_statistics() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); for index in 0..200 { insert(&mut database, index); } @@ -279,8 +263,6 @@ fn clearing_the_dataset_refreshes_planning_statistics() { assert_eq!(refreshed.total_triples, 0); } -// Instrumentation - #[test] fn rebuilds_are_counted_and_timed() { let mut database = seeded(); @@ -301,7 +283,7 @@ fn rebuilds_are_counted_and_timed() { /// Under Bounded, rebuilds are not proportional to query count #[test] fn interleaved_writes_and_queries_do_not_rebuild_per_query() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); for index in 0..500 { insert(&mut database, index); } diff --git a/kolibrie/tests/stats_collector_test.rs b/kolibrie/tests/stats_collector_test.rs index fbc285f7..2193d7b8 100644 --- a/kolibrie/tests/stats_collector_test.rs +++ b/kolibrie/tests/stats_collector_test.rs @@ -10,12 +10,12 @@ mod common; -use kolibrie::sparql_database::SparqlDatabase; +use kolibrie::database::KolibrieDB; use kolibrie::streamertail_optimizer::{DatabaseStats, GraphMap, TermMap, TermSet}; use shared::dataset_index::GraphId; /// Reference implementation over a materialized snapshot; exact only below the sampling threshold -fn reference_stats(database: &SparqlDatabase) -> DatabaseStats { +fn reference_stats(database: &KolibrieDB) -> DatabaseStats { let quads = database.all_quads(); let mut predicate_cardinalities = TermMap::::default(); @@ -78,7 +78,7 @@ fn reference_stats(database: &SparqlDatabase) -> DatabaseStats { } } -fn assert_matches_reference(database: &SparqlDatabase, case: &str) { +fn assert_matches_reference(database: &KolibrieDB, case: &str) { let actual = database.gather_stats(); let expected = reference_stats(database); @@ -130,7 +130,7 @@ fn assert_matches_reference(database: &SparqlDatabase, case: &str) { #[test] fn an_empty_dataset_collects_the_same_statistics() { - assert_matches_reference(&SparqlDatabase::new(), "empty"); + assert_matches_reference(&KolibrieDB::new(), "empty"); } #[test] @@ -167,7 +167,6 @@ fn named_graphs_including_empty_ones_collect_the_same_statistics() { ) .unwrap(); - // Empty named graph let empty = database.dictionary_mut().encode("urn:g3"); database.create_graph(GraphId::Named(empty)); @@ -214,7 +213,7 @@ fn a_skewed_predicate_distribution_collects_the_same_statistics() { #[test] fn many_small_graphs_collect_the_same_statistics() { - let mut database = SparqlDatabase::new(); + let mut database = KolibrieDB::new(); for graph in 0..60 { kolibrie::execute_query::execute_sparql_update( &format!( diff --git a/kolibrie/tests/stats_invalidation_test.rs b/kolibrie/tests/stats_invalidation_test.rs index f89f58e9..0b7ac688 100644 --- a/kolibrie/tests/stats_invalidation_test.rs +++ b/kolibrie/tests/stats_invalidation_test.rs @@ -10,12 +10,12 @@ mod common; +use kolibrie::database::KolibrieDB; use kolibrie::execute_query::execute_sparql_update; -use kolibrie::sparql_database::SparqlDatabase; use shared::dataset_index::GraphId; use shared::triple::Triple; -fn seeded() -> SparqlDatabase { +fn seeded() -> KolibrieDB { common::database_with( r#" "1" . "2" . @@ -23,7 +23,7 @@ fn seeded() -> SparqlDatabase { ) } -fn encode(database: &SparqlDatabase, term: &str) -> u32 { +fn encode(database: &KolibrieDB, term: &str) -> u32 { database.dictionary_mut().encode(term) } @@ -130,7 +130,6 @@ fn statistics_below_the_sampling_threshold_are_exact() { let stats = database.get_or_build_stats(); assert_eq!(stats.total_triples, 500); - // Every triple shares one predicate, so its cardinality is the whole set let predicate_total: u64 = stats.predicate_cardinalities.values().sum(); assert_eq!(predicate_total, 500); assert_eq!(stats.distinct_subjects, 500); diff --git a/ml/build.rs b/ml/build.rs index 60f90a94..32ca8ac8 100644 --- a/ml/build.rs +++ b/ml/build.rs @@ -8,61 +8,39 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Finds the host Python and tells the linker how to reach it. -//! -//! Unlike the extension module in `python/`, this crate *embeds* Python, so the -//! interpreter has to be linked rather than deferred. Everything below is the -//! consequence of that: Linux links `libpython` from a versioned config -//! directory, while macOS has to distinguish a framework install from a plain -//! one and falls back through several library locations, since `sysconfig` on -//! macOS can report a framework path through `LDLIBRARY` even when the prefix -//! does not look like one. -//! -//! The `add_extension_module_link_args` call at the top is likely vestigial — -//! this crate is not an extension module — but removing it needs a Linux build to -//! confirm, so it stays. +//! Finds the host Python and tells the linker how to reach it fn main() { - // Use pyo3 build config pyo3_build_config::add_extension_module_link_args(); if cfg!(target_os = "linux") { - // Get Python version let python_version = get_python_config( "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')", ); - // Get configuration for Linux let lib_path = format!( "/usr/lib/python{}/config-{}-x86_64-linux-gnu", python_version, python_version ); - // Add Python library configuration println!("cargo:rustc-link-lib=python{}", python_version); println!("cargo:rustc-link-search={}", lib_path); println!("cargo:rustc-link-search=/usr/lib/x86_64-linux-gnu"); - // Add rpath to ensure library can be found at runtime println!("cargo:rustc-link-arg=-Wl,-rpath,{}", lib_path); } else if cfg!(target_os = "macos") { - // Get Python prefix let python_prefix = get_command_output("python3-config", &["--prefix"]); - // Get Python version let python_version = get_python_config( "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')", ); - // Check for the framework path specifically at the prefix location let is_framework = python_prefix.contains("Python.framework") || std::path::Path::new(&format!("{}/Python.framework", python_prefix)).exists(); if is_framework { - // Extract framework base directory let framework_dir: String; if python_prefix.contains("Python.framework") { - // Python prefix already points to framework location framework_dir = python_prefix.clone(); } else { framework_dir = format!( @@ -71,7 +49,6 @@ fn main() { ); } - // Add framework search path (look for the directory containing the framework) let framework_parent = std::path::Path::new(&framework_dir) .parent() .unwrap() @@ -84,13 +61,10 @@ fn main() { framework_parent.display() ); - // Link to Python framework println!("cargo:rustc-link-lib=framework=Python"); - // Add runtime path to ensure framework can be found println!("cargo:rustc-link-arg=-Wl,-rpath,{}", framework_dir); } else { - // Get library configuration let libdir = get_python_config("import sysconfig; print(sysconfig.get_config_var('LIBDIR'))"); let libpl = @@ -98,12 +72,8 @@ fn main() { let ldlibrary = get_python_config("import sysconfig; print(sysconfig.get_config_var('LDLIBRARY'))"); - // Special case: If the library name contains "framework", this is actually a framework - // but not in the expected location if ldlibrary.contains("Python.framework") { - // Extract the framework path from the library name let framework_parent = if libdir.contains("Python.framework") { - // Try to extract the parent directory std::path::Path::new(&libdir) .parent() .unwrap() @@ -112,24 +82,19 @@ fn main() { .parent() .unwrap() } else { - // Default to common locations std::path::Path::new("/Library/Frameworks") }; - // Add framework search path println!( "cargo:rustc-link-search=framework={}", framework_parent.display() ); - // Link to Python framework println!("cargo:rustc-link-lib=framework=Python"); - // Add runtime path println!("cargo:rustc-link-arg=-Wl,-rpath,{}", libdir); println!("cargo:rustc-link-arg=-Wl,-rpath,{}", libpl); } else { - // Check all possible paths let lib_paths = [ format!("{}/{}", libdir, ldlibrary), format!("{}/{}", libpl, ldlibrary), @@ -137,7 +102,6 @@ fn main() { format!("/usr/local/lib/{}", ldlibrary), ]; - // First, try to find the library let mut found_path = false; for path in &lib_paths { if std::path::Path::new(path).exists() { @@ -152,42 +116,35 @@ fn main() { } } - // If not found, add all possible search paths if !found_path { println!("cargo:rustc-link-search={}", libdir); println!("cargo:rustc-link-search={}", libpl); println!("cargo:rustc-link-search=/usr/local/lib"); - // Only add homebrew lib if it exists if std::path::Path::new("/opt/homebrew/lib").exists() { println!("cargo:rustc-link-search=/opt/homebrew/lib"); } } - // Handle different library naming formats let lib_name = if ldlibrary.starts_with("lib") && ldlibrary.ends_with(".dylib") { ldlibrary[3..ldlibrary.len() - 6].to_string() } else if ldlibrary.starts_with("lib") && ldlibrary.ends_with(".so") { ldlibrary[3..ldlibrary.len() - 3].to_string() } else { - // For non-standard library names, try python3.x format!("python{}", python_version) }; println!("cargo:rustc-link-lib={}", lib_name); - // Add rpaths for runtime for dir in [&libdir, &libpl, "/usr/local/lib"] { println!("cargo:rustc-link-arg=-Wl,-rpath,{}", dir); } - // Only add homebrew lib path if it exists if std::path::Path::new("/opt/homebrew/lib").exists() { println!("cargo:rustc-link-arg=-Wl,-rpath,/opt/homebrew/lib"); } } } - // Set environment variables println!( "cargo:rustc-env=PYTHONPATH={}", get_python_config("import sys; print(':'.join(sys.path))") diff --git a/ml/examples/fraud_predictor.py b/ml/examples/fraud_predictor.py index 72441565..5f2aeab9 100644 --- a/ml/examples/fraud_predictor.py +++ b/ml/examples/fraud_predictor.py @@ -8,71 +8,47 @@ # you can obtain one at https://mozilla.org/MPL/2.0/. # +import os +import pickle +import time + +import numpy as np +import psutil from sklearn.calibration import CalibratedClassifierCV from sklearn.ensemble import GradientBoostingClassifier -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split from sklearn.metrics import ( - roc_auc_score, average_precision_score, - precision_score, recall_score, f1_score, - mean_squared_error, r2_score, + average_precision_score, + f1_score, + mean_squared_error, + precision_score, + r2_score, + recall_score, + roc_auc_score, ) -import numpy as np -import pickle -import os -import time -import psutil -from mlschema import MLSchema +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from mlschema import MLSchema -# ───────────────────────────────────────────────────────────────────────────── -# Predictor class -# ───────────────────────────────────────────────────────────────────────────── class FraudDetectionPredictor: - """ - Single GradientBoosting fraud classifier. - - Same interface as BasePredictor / GradientBoostingPredictor in predictor.py - so the Rust MLHandler path needs no changes. - """ - - # ─── Feature order (must match Rust) ────────────────────────────────────── - # Columns 0-7: raw transaction features (same as before) - # Columns 8-12: Datalog Pass-1 symbolic flags (0/1 booleans) - # The ML model sees what the symbolic layer already concluded, - # so it can learn "flag X was present → weight this signal more". - # Columns 13-14: per-account history - # These give the model temporal context across transactions. - # ─── Feature order (must match Rust) ────────────────────────────────────── - # Columns 0-7: raw transaction features - # Columns 8-12: Datalog Pass-1 symbolic flags (0/1 booleans) - # The ML model sees what the symbolic layer concluded. - # Column 13: per-account verdict history count - # - # NOTE: avg_ml_score_recent was deliberately removed. - # It was derived from the ML output itself, creating a circular dependency: - # GBM training: fraud → avg 35-89, legit → avg 0-24 (non-overlapping) - # Runtime: avg always 0 (ML score is 0 because avg is 0) - # GBM latched onto it as the sole discriminator → every runtime tx scored 0. + """Single GradientBoosting fraud classifier""" + FEATURE_NAMES = [ - # raw features (0-7) - 'amount', - 'hour_of_day', - 'day_of_week', - 'merchant_risk', - 'velocity_1h', - 'distance_km', - 'is_foreign', - 'card_present', - # Pass-1 Datalog flags (8-12) — Symbolic → ML direction - 'flag_highVelocity', # R1: velocity_1h > 5 - 'flag_largeAmount', # R2: amount > 1000 - 'flag_highMerchantRisk', # R3: merchant_risk > 70 - 'flag_foreignHighRisk', # R4: is_foreign > 0 AND merchant_risk > 70 - 'flag_riskHigh', # R5: amount > 1000 AND velocity_1h > 5 - # account verdict-history (13) - 'recent_fraud_count', # FRAUD/SUSPICIOUS verdicts in last 5 tx + "amount", + "hour_of_day", + "day_of_week", + "merchant_risk", + "velocity_1h", + "distance_km", + "is_foreign", + "card_present", + "flag_highVelocity", + "flag_largeAmount", + "flag_highMerchantRisk", + "flag_foreignHighRisk", + "flag_riskHigh", + "recent_fraud_count", ] MODEL_VERSION = "uncertain-calibrated-v3" @@ -99,13 +75,13 @@ def __init__( try: self.model = CalibratedClassifierCV( estimator=base_model, - method='sigmoid', + method="sigmoid", cv=3, ) except TypeError: self.model = CalibratedClassifierCV( base_estimator=base_model, - method='sigmoid', + method="sigmoid", cv=3, ) self.training_time = 0.0 @@ -121,21 +97,18 @@ def train(self, X, y): self.training_time = time.time() - start_time def predict(self, X): - """Return P(fraud) as a float array — same shape as regression output.""" + """Return P(fraud) as a float array — same shape as regression output""" X_scaled = self.scaler.transform(X) process = psutil.Process(os.getpid()) - self.memory_usage = process.memory_info().rss / 1024 / 1024 # MB + self.memory_usage = process.memory_info().rss / 1024 / 1024 start_time = time.time() - probs = self.model.predict_proba(X_scaled)[:, 1] # P(class=1) + probs = self.model.predict_proba(X_scaled)[:, 1] self.prediction_time = time.time() - start_time self.cpu_usage = psutil.cpu_percent(interval=0.1) return probs def predict_proba(self, X): - """ - Uncertainty estimate: high near 0.5, low near confident 0/1. - Mirrors GradientBoostingPredictor.predict_proba in predictor.py. - """ + """Uncertainty estimate: high near 0.5, low near confident 0/1""" X_scaled = self.scaler.transform(X) probs = self.model.predict_proba(X_scaled)[:, 1] return 1.0 - np.abs((probs * 2.0) - 1.0) @@ -144,33 +117,33 @@ def evaluate(self, X_test, y_test): probs = self.predict(X_test) preds = (probs >= 0.5).astype(int) self.evaluation_metrics = { - 'roc_auc': roc_auc_score(y_test, probs), - 'avg_precision': average_precision_score(y_test, probs), - 'precision': precision_score(y_test, preds, zero_division=0), - 'recall': recall_score(y_test, preds, zero_division=0), - 'f1': f1_score(y_test, preds, zero_division=0), + "roc_auc": roc_auc_score(y_test, probs), + "avg_precision": average_precision_score(y_test, probs), + "precision": precision_score(y_test, preds, zero_division=0), + "recall": recall_score(y_test, preds, zero_division=0), + "f1": f1_score(y_test, preds, zero_division=0), } return self.evaluation_metrics def get_performance_metrics(self): return { - 'training_time': getattr(self, 'training_time', 0), - 'prediction_time': getattr(self, 'prediction_time', 0), - 'memory_usage_mb': getattr(self, 'memory_usage', 0), - 'cpu_usage_percent': getattr(self, 'cpu_usage', 0), + "training_time": getattr(self, "training_time", 0), + "prediction_time": getattr(self, "prediction_time", 0), + "memory_usage_mb": getattr(self, "memory_usage", 0), + "cpu_usage_percent": getattr(self, "cpu_usage", 0), } def get(self, attribute_name): - """Attribute accessor used by the Rust MLS schema path.""" + """Attribute accessor used by the Rust MLS schema path""" return getattr(self, attribute_name, None) def save(self, filename): - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) def save_with_schema(self, filename, X_train, y_train, X_test, y_test): - """Pickle the model and write a companion MLS Turtle (.ttl) schema.""" - with open(filename, 'wb') as f: + """Pickle the model and write a companion MLS Turtle (.ttl) schema""" + with open(filename, "wb") as f: pickle.dump(self, f) schema = MLSchema() @@ -179,61 +152,50 @@ def eval_func(model, X_test, y_test): probs = model.predict(X_test) preds = (probs >= 0.5).astype(int) return { - # mse / r2 required by the generic Rust TTL parser in lib.rs - 'mse': mean_squared_error(y_test, probs), - 'r2': r2_score(y_test, probs), - 'roc_auc': roc_auc_score(y_test, probs), - 'avg_precision': average_precision_score(y_test, probs), - 'precision': precision_score(y_test, preds, zero_division=0), - 'recall': recall_score(y_test, preds, zero_division=0), - 'f1': f1_score(y_test, preds, zero_division=0), - 'training_time': model.get_performance_metrics().get('training_time', 0), - 'prediction_time': model.get_performance_metrics().get('prediction_time', 0), - 'memory_usage_mb': model.get_performance_metrics().get('memory_usage_mb', 0), - 'cpu_usage_percent': model.get_performance_metrics().get('cpu_usage_percent', 0), + "mse": mean_squared_error(y_test, probs), + "r2": r2_score(y_test, probs), + "roc_auc": roc_auc_score(y_test, probs), + "avg_precision": average_precision_score(y_test, probs), + "precision": precision_score(y_test, preds, zero_division=0), + "recall": recall_score(y_test, preds, zero_division=0), + "f1": f1_score(y_test, preds, zero_division=0), + "training_time": model.get_performance_metrics().get("training_time", 0), + "prediction_time": model.get_performance_metrics().get("prediction_time", 0), + "memory_usage_mb": model.get_performance_metrics().get("memory_usage_mb", 0), + "cpu_usage_percent": model.get_performance_metrics().get("cpu_usage_percent", 0), } schema.convert_model( self, - X_train, y_train, - X_test, y_test, + X_train, + y_train, + X_test, + y_test, feature_names=self.feature_names, - cpu_time_used=self.get_performance_metrics().get('training_time', 0), + cpu_time_used=self.get_performance_metrics().get("training_time", 0), model_uri=f"http://example.org/models/{os.path.basename(filename)}", evaluation_function=eval_func, ) - schema_filename = filename.replace('.pkl', '.ttl') - with open(schema_filename, 'w') as f: - f.write(schema.serialize(format='turtle')) + schema_filename = filename.replace(".pkl", ".ttl") + with open(schema_filename, "w") as f: + f.write(schema.serialize(format="turtle")) return schema_filename @classmethod def load(cls, filename): - with open(filename, 'rb') as f: + with open(filename, "rb") as f: return pickle.load(f) -# ───────────────────────────────────────────────────────────────────────────── -# Synthetic training data -# ───────────────────────────────────────────────────────────────────────────── - def _generate_training_data(n_samples=20000, fraud_rate=0.12, random_state=42): - """ - Build a synthetic fraud dataset. - - merchant_risk is integer 0-100 to match the integer-only FILTER constraint - in parser.rs (parse_comparison uses take_while1(|c| c.is_digit(10)) which - stops at decimal points, so 0.7 would break parsing). - """ + """Build a synthetic fraud dataset""" rng = np.random.default_rng(random_state) n_fraud = int(n_samples * fraud_rate) n_legit = n_samples - n_fraud night_hours = np.concatenate([np.arange(0, 5), np.arange(22, 24)]) - # Legitimate transactions. Real bank traffic is not perfectly clean: - # holidays, rent, travel, and repeated card taps can look risky. legit_amount = np.where( rng.random(n_legit) < 0.14, rng.lognormal(mean=6.2, sigma=0.8, size=n_legit), @@ -244,7 +206,7 @@ def _generate_training_data(n_samples=20000, fraud_rate=0.12, random_state=42): rng.choice(night_hours, size=n_legit), rng.integers(6, 22, size=n_legit), ) - legit_dow = rng.integers(0, 7, size=n_legit) + legit_dow = rng.integers(0, 7, size=n_legit) legit_merch_risk = np.where( rng.random(n_legit) < 0.12, rng.beta(4, 3, size=n_legit) * 100, @@ -260,9 +222,9 @@ def _generate_training_data(n_samples=20000, fraud_rate=0.12, random_state=42): rng.exponential(220, size=n_legit), rng.exponential(18, size=n_legit), ).clip(0, 5000) - legit_is_foreign = rng.binomial(1, 0.09, size=n_legit) + legit_is_foreign = rng.binomial(1, 0.09, size=n_legit) legit_card_present = rng.binomial(1, 0.80, size=n_legit) - legit_y = np.zeros(n_legit, dtype=int) + legit_y = np.zeros(n_legit, dtype=int) legit_borderline = rng.random(n_legit) < 0.045 legit_amount[legit_borderline] = rng.uniform(1100, 3200, size=legit_borderline.sum()) @@ -273,8 +235,6 @@ def _generate_training_data(n_samples=20000, fraud_rate=0.12, random_state=42): legit_is_foreign[legit_borderline] = 0 legit_card_present[legit_borderline] = 1 - # Fraudulent transactions. Some fraud is obvious, but some is domestic, - # daytime, card-present, or low amount, which creates uncertain cases. fraud_amount = np.where( rng.random(n_fraud) < 0.32, rng.lognormal(mean=4.8, sigma=1.0, size=n_fraud), @@ -285,7 +245,7 @@ def _generate_training_data(n_samples=20000, fraud_rate=0.12, random_state=42): rng.choice(night_hours, size=n_fraud), rng.integers(6, 22, size=n_fraud), ) - fraud_dow = rng.integers(0, 7, size=n_fraud) + fraud_dow = rng.integers(0, 7, size=n_fraud) fraud_merch_risk = np.where( rng.random(n_fraud) < 0.30, rng.beta(2.2, 5, size=n_fraud) * 100, @@ -301,9 +261,9 @@ def _generate_training_data(n_samples=20000, fraud_rate=0.12, random_state=42): rng.exponential(35, size=n_fraud), rng.exponential(450, size=n_fraud), ).clip(0, 15000) - fraud_is_foreign = rng.binomial(1, 0.42, size=n_fraud) + fraud_is_foreign = rng.binomial(1, 0.42, size=n_fraud) fraud_card_present = rng.binomial(1, 0.38, size=n_fraud) - fraud_y = np.ones(n_fraud, dtype=int) + fraud_y = np.ones(n_fraud, dtype=int) fraud_borderline = rng.random(n_fraud) < 0.35 fraud_amount[fraud_borderline] = rng.uniform(1100, 3200, size=fraud_borderline.sum()) @@ -314,133 +274,130 @@ def _generate_training_data(n_samples=20000, fraud_rate=0.12, random_state=42): fraud_is_foreign[fraud_borderline] = 0 fraud_card_present[fraud_borderline] = 1 - # ── Datalog Pass-1 flag features (Symbolic → ML direction) ──────────────── - # Computed deterministically from the raw features, mirroring R1-R5 exactly. - # Including these in training teaches the GBM how to weight a flag *in - # combination* with the raw features rather than treating each in isolation. def flags(amt, vel, mr, isf): - hv = (vel > 5).astype(float) - la = (amt > 1000).astype(float) - hmr = (mr > 70).astype(float) + hv = (vel > 5).astype(float) + la = (amt > 1000).astype(float) + hmr = (mr > 70).astype(float) fhr = ((isf > 0) & (mr > 70)).astype(float) - rh = ((amt > 1000) & (vel > 5)).astype(float) + rh = ((amt > 1000) & (vel > 5)).astype(float) return hv, la, hmr, fhr, rh - legit_fhv, legit_fla, legit_fhmr, legit_ffhr, legit_frh = flags( - legit_amount, legit_velocity, legit_merch_risk, legit_is_foreign) - fraud_fhv, fraud_fla, fraud_fhmr, fraud_ffhr, fraud_frh = flags( - fraud_amount, fraud_velocity, fraud_merch_risk, fraud_is_foreign) - - # ── Account-history features ───────────────────────────────────────────── - # In production these come from the rolling AccountHistory struct in Rust. - # For training we simulate plausible values correlated with the label: - # fraud row → higher recent_fraud_count, higher avg historical score - # legit row → lower counts and scores - # recent_fraud_count: count of suspicious/fraud verdicts in last 5 tx. - # Legit accounts rarely have recent fraud verdicts; fraud accounts often do. - # This feature IS safe to use: it's based on binary verdicts, not the - # continuous ML score, so there is no circular dependency. + legit_fhv, legit_fla, legit_fhmr, legit_ffhr, legit_frh = flags( + legit_amount, legit_velocity, legit_merch_risk, legit_is_foreign + ) + fraud_fhv, fraud_fla, fraud_fhmr, fraud_ffhr, fraud_frh = flags( + fraud_amount, fraud_velocity, fraud_merch_risk, fraud_is_foreign + ) + legit_rfc = rng.choice([0, 1, 2], size=n_legit, p=[0.86, 0.12, 0.02]).astype(float) fraud_rfc = rng.choice([0, 1, 2, 3], size=n_fraud, p=[0.25, 0.35, 0.25, 0.15]).astype(float) - X = np.column_stack([ - # raw features (0-7) - np.concatenate([legit_amount, fraud_amount]), - np.concatenate([legit_hour, fraud_hour]), - np.concatenate([legit_dow, fraud_dow]), - np.concatenate([legit_merch_risk, fraud_merch_risk]), - np.concatenate([legit_velocity, fraud_velocity]), - np.concatenate([legit_distance, fraud_distance]), - np.concatenate([legit_is_foreign, fraud_is_foreign]), - np.concatenate([legit_card_present, fraud_card_present]), - # Pass-1 symbolic flag features (8-12) - np.concatenate([legit_fhv, fraud_fhv]), - np.concatenate([legit_fla, fraud_fla]), - np.concatenate([legit_fhmr, fraud_fhmr]), - np.concatenate([legit_ffhr, fraud_ffhr]), - np.concatenate([legit_frh, fraud_frh]), - # account verdict-history (13) — NOT avg_ml_score (circular dependency) - np.concatenate([legit_rfc, fraud_rfc]), - ]) + X = np.column_stack( + [ + np.concatenate([legit_amount, fraud_amount]), + np.concatenate([legit_hour, fraud_hour]), + np.concatenate([legit_dow, fraud_dow]), + np.concatenate([legit_merch_risk, fraud_merch_risk]), + np.concatenate([legit_velocity, fraud_velocity]), + np.concatenate([legit_distance, fraud_distance]), + np.concatenate([legit_is_foreign, fraud_is_foreign]), + np.concatenate([legit_card_present, fraud_card_present]), + np.concatenate([legit_fhv, fraud_fhv]), + np.concatenate([legit_fla, fraud_fla]), + np.concatenate([legit_fhmr, fraud_fhmr]), + np.concatenate([legit_ffhr, fraud_ffhr]), + np.concatenate([legit_frh, fraud_frh]), + np.concatenate([legit_rfc, fraud_rfc]), + ] + ) y = np.concatenate([legit_y, fraud_y]) - label_noise = np.concatenate([ - rng.random(n_legit) < 0.012, - rng.random(n_fraud) < 0.080, - ]) + label_noise = np.concatenate( + [ + rng.random(n_legit) < 0.012, + rng.random(n_fraud) < 0.080, + ] + ) y[label_noise] = 1 - y[label_noise] idx = rng.permutation(n_samples) return X[idx], y[idx] def _do_train(model_path): - """Train a fresh FraudDetectionPredictor and save pkl + ttl.""" - print('[fraud_predictor] Training GradientBoosting classifier ...') + """Train a fresh FraudDetectionPredictor and save pkl + ttl""" + print("[fraud_predictor] Training GradientBoosting classifier ...") X, y = _generate_training_data(n_samples=20000, fraud_rate=0.12) X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42, stratify=y, + X, + y, + test_size=0.2, + random_state=42, + stratify=y, ) m = FraudDetectionPredictor() m.train(X_train, y_train) - m.predict(X_test) # populate performance metrics before saving + m.predict(X_test) schema_path = m.save_with_schema(model_path, X_train, y_train, X_test, y_test) - print(f'[fraud_predictor] Model saved → {model_path}') - print(f'[fraud_predictor] Schema saved → {schema_path}') + print(f"[fraud_predictor] Model saved → {model_path}") + print(f"[fraud_predictor] Schema saved → {schema_path}") metrics = m.evaluate(X_test, y_test) - print('[fraud_predictor] Evaluation:') + print("[fraud_predictor] Evaluation:") for k, v in metrics.items(): - print(f' {k}: {v:.4f}') + print(f" {k}: {v:.4f}") probs = m.predict(X_test) q05, q25, q50, q75, q95 = np.quantile(probs, [0.05, 0.25, 0.50, 0.75, 0.95]) uncertain = np.mean((probs >= 0.20) & (probs <= 0.80)) - print('[fraud_predictor] Score distribution:') - print(f' q05/q25/q50/q75/q95: {q05:.3f} / {q25:.3f} / {q50:.3f} / {q75:.3f} / {q95:.3f}') - print(f' uncertain_share_20_80: {uncertain:.3f}') - print('[fraud_predictor] Performance:') + print("[fraud_predictor] Score distribution:") + print(f" q05/q25/q50/q75/q95: {q05:.3f} / {q25:.3f} / {q50:.3f} / {q75:.3f} / {q95:.3f}") + print(f" uncertain_share_20_80: {uncertain:.3f}") + print("[fraud_predictor] Performance:") for k, v in m.get_performance_metrics().items(): - print(f' {k}: {v}') + print(f" {k}: {v}") -# ───────────────────────────────────────────────────────────────────────────── def generate_models(): - """Explicit local generation. Importing this module never trains or writes.""" + """Explicit local generation""" models_dir = os.environ["KOLIBRIE_TRAINING_OUTPUT"] os.makedirs(models_dir, exist_ok=True) - _model_path = os.path.join(models_dir, 'fraud_predictor.pkl') + _model_path = os.path.join(models_dir, "fraud_predictor.pkl") _needs_training = True if os.path.exists(_model_path): try: - with open(_model_path, 'rb') as _f: + with open(_model_path, "rb") as _f: _probe = pickle.load(_f) - if (hasattr(_probe, 'feature_names') - and len(_probe.feature_names) != len(FraudDetectionPredictor.FEATURE_NAMES)): + if hasattr(_probe, "feature_names") and len(_probe.feature_names) != len( + FraudDetectionPredictor.FEATURE_NAMES + ): raise ValueError( f"Feature count mismatch: pkl has {len(_probe.feature_names)} " f"features, current class has " f"{len(FraudDetectionPredictor.FEATURE_NAMES)}. Retraining." ) - if getattr(_probe, 'model_version', None) != FraudDetectionPredictor.MODEL_VERSION: + if getattr(_probe, "model_version", None) != FraudDetectionPredictor.MODEL_VERSION: raise ValueError( f"Model version mismatch: pkl has " f"{getattr(_probe, 'model_version', 'unknown')}, current class has " f"{FraudDetectionPredictor.MODEL_VERSION}. Retraining." ) _needs_training = False - print(f'[fraud_predictor] Existing model verified OK → skipping training.') + print("[fraud_predictor] Existing model verified OK → skipping training.") except Exception as _load_err: - print(f'[fraud_predictor] Existing pkl cannot be loaded ({_load_err}).') - print('[fraud_predictor] Likely saved from __main__ context — deleting and retraining ...') + print(f"[fraud_predictor] Existing pkl cannot be loaded ({_load_err}).") + print( + "[fraud_predictor] Likely saved from __main__ context — deleting and retraining ..." + ) os.remove(_model_path) - _ttl = _model_path.replace('.pkl', '.ttl') + _ttl = _model_path.replace(".pkl", ".ttl") if os.path.exists(_ttl): os.remove(_ttl) if _needs_training: _do_train(_model_path) + if __name__ == "__main__": - # Import under the stable module name so pickle never records __main__ classes. import importlib + importlib.import_module("fraud_predictor").generate_models() diff --git a/ml/examples/predictor.py b/ml/examples/predictor.py index e9f0f52a..9aa34f11 100644 --- a/ml/examples/predictor.py +++ b/ml/examples/predictor.py @@ -8,23 +8,25 @@ # you can obtain one at https://mozilla.org/MPL/2.0/. # -from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor -from sklearn.linear_model import LinearRegression -from sklearn.preprocessing import StandardScaler -import numpy as np -import pickle import os +import pickle import time + +import numpy as np import psutil +from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import StandardScaler + from mlschema import MLSchema + class BasePredictor: def __init__(self, feature_names=None): self.scaler = StandardScaler() - self.feature_names = feature_names or ['temperature', 'humidity', 'occupancy'] + self.feature_names = feature_names or ["temperature", "humidity", "occupancy"] def train(self, X, y): - # Scale features X_scaled = self.scaler.fit_transform(X) start_time = time.time() self.model.fit(X_scaled, y) @@ -33,71 +35,67 @@ def train(self, X, y): def predict(self, X): X_scaled = self.scaler.transform(X) process = psutil.Process(os.getpid()) - self.memory_usage = process.memory_info().rss / 1024 / 1024 # MB + self.memory_usage = process.memory_info().rss / 1024 / 1024 start_time = time.time() predictions = self.model.predict(X_scaled) self.prediction_time = time.time() - start_time - # Capture CPU usage self.cpu_usage = psutil.cpu_percent(interval=0.1) return predictions def predict_proba(self, X): - # Default implementation - override in subclasses if needed return None def get_performance_metrics(self): return { - 'training_time': getattr(self, 'training_time', 0), - 'prediction_time': getattr(self, 'prediction_time', 0), - 'memory_usage_mb': getattr(self, 'memory_usage', 0), - 'cpu_usage_percent': getattr(self, 'cpu_usage', 0) + "training_time": getattr(self, "training_time", 0), + "prediction_time": getattr(self, "prediction_time", 0), + "memory_usage_mb": getattr(self, "memory_usage", 0), + "cpu_usage_percent": getattr(self, "cpu_usage", 0), } def save(self, filename): - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) def save_with_schema(self, filename, X_train, y_train, X_test, y_test): - # Save model to pkl - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) - # Generate schema schema = MLSchema() - # Define an evaluation function that captures performance metrics def eval_func(model, X_test, y_test): y_pred = model.predict(X_test) from sklearn.metrics import mean_squared_error, r2_score + metrics = { - 'mse': mean_squared_error(y_test, y_pred), - 'r2': r2_score(y_test, y_pred), - 'training_time': model.get_performance_metrics().get('training_time', 0), - 'prediction_time': model.get_performance_metrics().get('prediction_time', 0), - 'memory_usage_mb': model.get_performance_metrics().get('memory_usage_mb', 0), - 'cpu_usage_percent': model.get_performance_metrics().get('cpu_usage_percent', 0) + "mse": mean_squared_error(y_test, y_pred), + "r2": r2_score(y_test, y_pred), + "training_time": model.get_performance_metrics().get("training_time", 0), + "prediction_time": model.get_performance_metrics().get("prediction_time", 0), + "memory_usage_mb": model.get_performance_metrics().get("memory_usage_mb", 0), + "cpu_usage_percent": model.get_performance_metrics().get("cpu_usage_percent", 0), } return metrics - # Generate schema schema.convert_model( self, - X_train, y_train, - X_test, y_test, + X_train, + y_train, + X_test, + y_test, feature_names=self.feature_names, - cpu_time_used=self.get_performance_metrics().get('training_time', 0), + cpu_time_used=self.get_performance_metrics().get("training_time", 0), model_uri=f"http://example.org/models/{os.path.basename(filename)}", - evaluation_function=eval_func + evaluation_function=eval_func, ) - # Save schema to file - schema_filename = filename.replace('.pkl', '.ttl') - with open(schema_filename, 'w') as f: - f.write(schema.serialize(format='turtle')) + schema_filename = filename.replace(".pkl", ".ttl") + with open(schema_filename, "w") as f: + f.write(schema.serialize(format="turtle")) return schema_filename @@ -110,10 +108,7 @@ def evaluate(self, X_test, y_test): mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) - self.evaluation_metrics = { - 'mse': mse, - 'r2': r2 - } + self.evaluation_metrics = {"mse": mse, "r2": r2} return self.evaluation_metrics @@ -123,45 +118,41 @@ def get(self, attribute_name): @classmethod def load(cls, filename): - with open(filename, 'rb') as f: + with open(filename, "rb") as f: return pickle.load(f) + class LinearRegressionPredictor(BasePredictor): def __init__(self, fit_intercept=True, normalize=None, feature_names=None): super().__init__(feature_names) - # In scikit-learn 1.0+, normalize parameter was removed - # Check scikit-learn version import sklearn from packaging import version try: - if version.parse(sklearn.__version__) >= version.parse('1.0.0'): - # For scikit-learn 1.0+ + if version.parse(sklearn.__version__) >= version.parse("1.0.0"): self.model = LinearRegression(fit_intercept=fit_intercept) if normalize: - print("Warning: 'normalize' parameter is deprecated in scikit-learn 1.0+. Using StandardScaler instead.") + print( + "Warning: 'normalize' parameter is deprecated in scikit-learn 1.0+. " + "Using StandardScaler instead." + ) else: - # For scikit-learn < 1.0 self.model = LinearRegression(fit_intercept=fit_intercept, normalize=normalize) except Exception as e: print(f"Error initializing LinearRegression: {e}") - # Fallback to simplest constructor self.model = LinearRegression() def predict_proba(self, X): - # Linear regression doesn't have built-in uncertainty estimation - # Return a simple constant uncertainty value X_scaled = self.scaler.transform(X) - return np.ones(X_scaled.shape[0]) * 0.5 # Constant uncertainty + return np.ones(X_scaled.shape[0]) * 0.5 + class RandomForestPredictor(BasePredictor): def __init__(self, n_estimators=100, max_depth=10, random_state=42, feature_names=None): super().__init__(feature_names) self.model = RandomForestRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - random_state=random_state + n_estimators=n_estimators, max_depth=max_depth, random_state=random_state ) def predict_proba(self, X): @@ -171,24 +162,26 @@ def predict_proba(self, X): predictions.append(tree.predict(X_scaled)) return np.std(predictions, axis=0) + class GradientBoostingPredictor(BasePredictor): - def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42, feature_names=None): + def __init__( + self, n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42, feature_names=None + ): super().__init__(feature_names) self.model = GradientBoostingRegressor( n_estimators=n_estimators, learning_rate=learning_rate, max_depth=max_depth, - random_state=random_state + random_state=random_state, ) def predict_proba(self, X): X_scaled = self.scaler.transform(X) - # Calculate prediction standard deviation return np.std([tree[0].predict(X_scaled) for tree in self.model.estimators_], axis=0) + def generate_models(): - """Explicit local generation. Importing this module never trains or writes.""" - # Generate training data + """Explicit local generation""" np.random.seed(42) n_samples = 1000 @@ -196,50 +189,54 @@ def generate_models(): humidity = np.random.normal(50, 15, n_samples) occupancy = np.random.randint(0, 20, n_samples) - # Create target variable with some noise future_temp = ( - temperature * 0.7 + - (humidity - 50) * 0.02 + - occupancy * 0.1 + - np.random.normal(0, 1, n_samples) + temperature * 0.7 + + (humidity - 50) * 0.02 + + occupancy * 0.1 + + np.random.normal(0, 1, n_samples) ) - # Split data into train and test sets from sklearn.model_selection import train_test_split + X = np.column_stack([temperature, humidity, occupancy]) y = future_temp X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - # Train and save models models_dir = os.environ["KOLIBRIE_TRAINING_OUTPUT"] os.makedirs(models_dir, exist_ok=True) - # RandomForest model rf_model = RandomForestPredictor() rf_model.train(X_train, y_train) - rf_model.predict(X_test) # Run once to get performance metrics - rf_schema_file = rf_model.save_with_schema(os.path.join(models_dir, "rf_temperature_predictor.pkl"), - X_train, y_train, X_test, y_test) + rf_model.predict(X_test) + rf_schema_file = rf_model.save_with_schema( + os.path.join(models_dir, "rf_temperature_predictor.pkl"), X_train, y_train, X_test, y_test + ) - # GradientBoosting model gb_model = GradientBoostingPredictor() gb_model.train(X_train, y_train) - gb_model.predict(X_test) # Run once to get performance metrics - gb_schema_file = gb_model.save_with_schema(os.path.join(models_dir, "gb_temperature_predictor.pkl"), - X_train, y_train, X_test, y_test) + gb_model.predict(X_test) + gb_schema_file = gb_model.save_with_schema( + os.path.join(models_dir, "gb_temperature_predictor.pkl"), X_train, y_train, X_test, y_test + ) - # Linear Regression model lr_model = LinearRegressionPredictor() lr_model.train(X_train, y_train) - lr_model.predict(X_test) # Run once to get performance metrics - lr_schema_file = lr_model.save_with_schema(os.path.join(models_dir, "lr_temperature_predictor.pkl"), - X_train, y_train, X_test, y_test) + lr_model.predict(X_test) + lr_schema_file = lr_model.save_with_schema( + os.path.join(models_dir, "lr_temperature_predictor.pkl"), X_train, y_train, X_test, y_test + ) print(f"RandomForest model saved to {os.path.join(models_dir, 'rf_temperature_predictor.pkl')}") print(f"RandomForest schema saved to {rf_schema_file}") - print(f"GradientBoosting model saved to {os.path.join(models_dir, 'gb_temperature_predictor.pkl')}") + print( + "GradientBoosting model saved to " + f"{os.path.join(models_dir, 'gb_temperature_predictor.pkl')}" + ) print(f"GradientBoosting schema saved to {gb_schema_file}") - print(f"LinearRegression model saved to {os.path.join(models_dir, 'lr_temperature_predictor.pkl')}") + print( + "LinearRegression model saved to " + f"{os.path.join(models_dir, 'lr_temperature_predictor.pkl')}" + ) print(f"LinearRegression schema saved to {lr_schema_file}") print("\nPerformance Comparison:") @@ -259,7 +256,8 @@ def generate_models(): for key, value in lr_metrics.items(): print(f" {key}: {value}") + if __name__ == "__main__": - # Import under the stable module name so pickle never records __main__ classes. import importlib + importlib.import_module("predictor").generate_models() diff --git a/ml/examples/saving_predictor.py b/ml/examples/saving_predictor.py index 9e209d8a..232bf71d 100644 --- a/ml/examples/saving_predictor.py +++ b/ml/examples/saving_predictor.py @@ -8,23 +8,25 @@ # you can obtain one at https://mozilla.org/MPL/2.0/. # -from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor -from sklearn.linear_model import LinearRegression -from sklearn.preprocessing import StandardScaler -import numpy as np -import pickle import os +import pickle import time + +import numpy as np import psutil +from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import StandardScaler + from mlschema import MLSchema + class BasePredictor: def __init__(self, feature_names=None): self.scaler = StandardScaler() - self.feature_names = feature_names or ['income', 'spending', 'savings_rate'] + self.feature_names = feature_names or ["income", "spending", "savings_rate"] def train(self, X, y): - # Scale features X_scaled = self.scaler.fit_transform(X) start_time = time.time() self.model.fit(X_scaled, y) @@ -33,71 +35,67 @@ def train(self, X, y): def predict(self, X): X_scaled = self.scaler.transform(X) process = psutil.Process(os.getpid()) - self.memory_usage = process.memory_info().rss / 1024 / 1024 # MB + self.memory_usage = process.memory_info().rss / 1024 / 1024 start_time = time.time() predictions = self.model.predict(X_scaled) self.prediction_time = time.time() - start_time - # Capture CPU usage self.cpu_usage = psutil.cpu_percent(interval=0.1) return predictions def predict_proba(self, X): - # Default implementation - override in subclasses if needed return None def get_performance_metrics(self): return { - 'training_time': getattr(self, 'training_time', 0), - 'prediction_time': getattr(self, 'prediction_time', 0), - 'memory_usage_mb': getattr(self, 'memory_usage', 0), - 'cpu_usage_percent': getattr(self, 'cpu_usage', 0) + "training_time": getattr(self, "training_time", 0), + "prediction_time": getattr(self, "prediction_time", 0), + "memory_usage_mb": getattr(self, "memory_usage", 0), + "cpu_usage_percent": getattr(self, "cpu_usage", 0), } def save(self, filename): - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) def save_with_schema(self, filename, X_train, y_train, X_test, y_test): - # Save model to pkl - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) - # Generate schema schema = MLSchema() - # Define an evaluation function that captures performance metrics def eval_func(model, X_test, y_test): y_pred = model.predict(X_test) from sklearn.metrics import mean_squared_error, r2_score + metrics = { - 'mse': mean_squared_error(y_test, y_pred), - 'r2': r2_score(y_test, y_pred), - 'training_time': model.get_performance_metrics().get('training_time', 0), - 'prediction_time': model.get_performance_metrics().get('prediction_time', 0), - 'memory_usage_mb': model.get_performance_metrics().get('memory_usage_mb', 0), - 'cpu_usage_percent': model.get_performance_metrics().get('cpu_usage_percent', 0) + "mse": mean_squared_error(y_test, y_pred), + "r2": r2_score(y_test, y_pred), + "training_time": model.get_performance_metrics().get("training_time", 0), + "prediction_time": model.get_performance_metrics().get("prediction_time", 0), + "memory_usage_mb": model.get_performance_metrics().get("memory_usage_mb", 0), + "cpu_usage_percent": model.get_performance_metrics().get("cpu_usage_percent", 0), } return metrics - # Generate schema schema.convert_model( self, - X_train, y_train, - X_test, y_test, + X_train, + y_train, + X_test, + y_test, feature_names=self.feature_names, - cpu_time_used=self.get_performance_metrics().get('training_time', 0), + cpu_time_used=self.get_performance_metrics().get("training_time", 0), model_uri=f"http://example.org/models/{os.path.basename(filename)}", - evaluation_function=eval_func + evaluation_function=eval_func, ) - # Save schema to file - schema_filename = filename.replace('.pkl', '.ttl') - with open(schema_filename, 'w') as f: - f.write(schema.serialize(format='turtle')) + schema_filename = filename.replace(".pkl", ".ttl") + with open(schema_filename, "w") as f: + f.write(schema.serialize(format="turtle")) return schema_filename @@ -110,10 +108,7 @@ def evaluate(self, X_test, y_test): mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) - self.evaluation_metrics = { - 'mse': mse, - 'r2': r2 - } + self.evaluation_metrics = {"mse": mse, "r2": r2} return self.evaluation_metrics @@ -123,45 +118,41 @@ def get(self, attribute_name): @classmethod def load(cls, filename): - with open(filename, 'rb') as f: + with open(filename, "rb") as f: return pickle.load(f) + class LinearRegressionPredictor(BasePredictor): def __init__(self, fit_intercept=True, normalize=None, feature_names=None): super().__init__(feature_names) - # In scikit-learn 1.0+, normalize parameter was removed - # Check scikit-learn version import sklearn from packaging import version try: - if version.parse(sklearn.__version__) >= version.parse('1.0.0'): - # For scikit-learn 1.0+ + if version.parse(sklearn.__version__) >= version.parse("1.0.0"): self.model = LinearRegression(fit_intercept=fit_intercept) if normalize: - print("Warning: 'normalize' parameter is deprecated in scikit-learn 1.0+. Using StandardScaler instead.") + print( + "Warning: 'normalize' parameter is deprecated in scikit-learn 1.0+. " + "Using StandardScaler instead." + ) else: - # For scikit-learn < 1.0 self.model = LinearRegression(fit_intercept=fit_intercept, normalize=normalize) except Exception as e: print(f"Error initializing LinearRegression: {e}") - # Fallback to simplest constructor self.model = LinearRegression() def predict_proba(self, X): - # Linear regression doesn't have built-in uncertainty estimation - # Return a simple constant uncertainty value X_scaled = self.scaler.transform(X) - return np.ones(X_scaled.shape[0]) * 0.5 # Constant uncertainty + return np.ones(X_scaled.shape[0]) * 0.5 + class RandomForestPredictor(BasePredictor): def __init__(self, n_estimators=100, max_depth=10, random_state=42, feature_names=None): super().__init__(feature_names) self.model = RandomForestRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - random_state=random_state + n_estimators=n_estimators, max_depth=max_depth, random_state=random_state ) def predict_proba(self, X): @@ -171,78 +162,75 @@ def predict_proba(self, X): predictions.append(tree.predict(X_scaled)) return np.std(predictions, axis=0) + class GradientBoostingPredictor(BasePredictor): - def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42, feature_names=None): + def __init__( + self, n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42, feature_names=None + ): super().__init__(feature_names) self.model = GradientBoostingRegressor( n_estimators=n_estimators, learning_rate=learning_rate, max_depth=max_depth, - random_state=random_state + random_state=random_state, ) def predict_proba(self, X): X_scaled = self.scaler.transform(X) - # Calculate prediction standard deviation return np.std([tree[0].predict(X_scaled) for tree in self.model.estimators_], axis=0) + def generate_models(): - """Explicit local generation. Importing this module never trains or writes.""" - # Generate training data + """Explicit local generation""" np.random.seed(42) n_samples = 1000 - # Financial features: income, spending, savings_rate - income = np.random.normal(5000, 2000, n_samples) # Monthly income in $ - spending = np.random.normal(3500, 1500, n_samples) # Monthly spending in $ - savings_rate = np.clip(np.random.normal(0.15, 0.1, n_samples), 0.01, 0.5) # Savings rate as percentage + income = np.random.normal(5000, 2000, n_samples) + spending = np.random.normal(3500, 1500, n_samples) + savings_rate = np.clip(np.random.normal(0.15, 0.1, n_samples), 0.01, 0.5) - # Create target variable: future_savings with some noise and factors - # Future money saved is influenced by income, spending habits, and savings rate future_savings = ( - (income * 0.6) + # Higher income increases savings - (spending * -0.4) + # Higher spending decreases savings - (savings_rate * income * 5) + # Savings rate directly affects how much is saved - (income - spending) * 0.3 + # Disposable income has a positive effect - np.random.normal(0, 500, n_samples) # Random market fluctuations and unpredictable events + (income * 0.6) + + (spending * -0.4) + + (savings_rate * income * 5) + + (income - spending) * 0.3 + + np.random.normal(0, 500, n_samples) ) - # Split data into train and test sets from sklearn.model_selection import train_test_split + X = np.column_stack([income, spending, savings_rate]) y = future_savings X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - # Train and save models models_dir = os.environ["KOLIBRIE_TRAINING_OUTPUT"] os.makedirs(models_dir, exist_ok=True) - # Define feature names for better model interpretability - feature_names = ['income', 'spending', 'savings_rate'] + feature_names = ["income", "spending", "savings_rate"] - # RandomForest model rf_model = RandomForestPredictor(feature_names=feature_names) rf_model.train(X_train, y_train) - rf_model.predict(X_test) # Run once to get performance metrics - rf_model.evaluate(X_test, y_test) # Get and store evaluation metrics - rf_schema_file = rf_model.save_with_schema(os.path.join(models_dir, "rf_money_predictor.pkl"), - X_train, y_train, X_test, y_test) + rf_model.predict(X_test) + rf_model.evaluate(X_test, y_test) + rf_schema_file = rf_model.save_with_schema( + os.path.join(models_dir, "rf_money_predictor.pkl"), X_train, y_train, X_test, y_test + ) - # GradientBoosting model gb_model = GradientBoostingPredictor(feature_names=feature_names) gb_model.train(X_train, y_train) - gb_model.predict(X_test) # Run once to get performance metrics - gb_model.evaluate(X_test, y_test) # Get and store evaluation metrics - gb_schema_file = gb_model.save_with_schema(os.path.join(models_dir, "gb_money_predictor.pkl"), - X_train, y_train, X_test, y_test) + gb_model.predict(X_test) + gb_model.evaluate(X_test, y_test) + gb_schema_file = gb_model.save_with_schema( + os.path.join(models_dir, "gb_money_predictor.pkl"), X_train, y_train, X_test, y_test + ) - # Linear Regression model lr_model = LinearRegressionPredictor(feature_names=feature_names) lr_model.train(X_train, y_train) - lr_model.predict(X_test) # Run once to get performance metrics - lr_model.evaluate(X_test, y_test) # Get and store evaluation metrics - lr_schema_file = lr_model.save_with_schema(os.path.join(models_dir, "lr_money_predictor.pkl"), - X_train, y_train, X_test, y_test) + lr_model.predict(X_test) + lr_model.evaluate(X_test, y_test) + lr_schema_file = lr_model.save_with_schema( + os.path.join(models_dir, "lr_money_predictor.pkl"), X_train, y_train, X_test, y_test + ) print(f"RandomForest model saved to {os.path.join(models_dir, 'rf_money_predictor.pkl')}") print(f"RandomForest schema saved to {rf_schema_file}") @@ -255,9 +243,9 @@ def generate_models(): rf_metrics = rf_model.get_performance_metrics() gb_metrics = gb_model.get_performance_metrics() lr_metrics = lr_model.get_performance_metrics() - rf_eval = rf_model.evaluation_metrics if hasattr(rf_model, 'evaluation_metrics') else {} - gb_eval = gb_model.evaluation_metrics if hasattr(gb_model, 'evaluation_metrics') else {} - lr_eval = lr_model.evaluation_metrics if hasattr(lr_model, 'evaluation_metrics') else {} + rf_eval = rf_model.evaluation_metrics if hasattr(rf_model, "evaluation_metrics") else {} + gb_eval = gb_model.evaluation_metrics if hasattr(gb_model, "evaluation_metrics") else {} + lr_eval = lr_model.evaluation_metrics if hasattr(lr_model, "evaluation_metrics") else {} print("\nRandomForest Model:") for key, value in rf_metrics.items(): @@ -277,24 +265,28 @@ def generate_models(): for key, value in lr_eval.items(): print(f" {key}: {value}") - # Print feature importance if available print("\nFeature Importance (RandomForest):") - if hasattr(rf_model.model, 'feature_importances_'): - for feature, importance in zip(feature_names, rf_model.model.feature_importances_): + if hasattr(rf_model.model, "feature_importances_"): + for feature, importance in zip( + feature_names, rf_model.model.feature_importances_, strict=True + ): print(f" {feature}: {importance:.4f}") print("\nFeature Importance (GradientBoosting):") - if hasattr(gb_model.model, 'feature_importances_'): - for feature, importance in zip(feature_names, gb_model.model.feature_importances_): + if hasattr(gb_model.model, "feature_importances_"): + for feature, importance in zip( + feature_names, gb_model.model.feature_importances_, strict=True + ): print(f" {feature}: {importance:.4f}") print("\nModel Coefficients (LinearRegression):") - if hasattr(lr_model.model, 'coef_'): - for feature, coef in zip(feature_names, lr_model.model.coef_): + if hasattr(lr_model.model, "coef_"): + for feature, coef in zip(feature_names, lr_model.model.coef_, strict=True): print(f" {feature}: {coef:.4f}") print(f" intercept: {lr_model.model.intercept_:.4f}") + if __name__ == "__main__": - # Import under the stable module name so pickle never records __main__ classes. import importlib + importlib.import_module("saving_predictor").generate_models() diff --git a/ml/examples/temperature_predictor.py b/ml/examples/temperature_predictor.py index afe100b4..ceaecba3 100644 --- a/ml/examples/temperature_predictor.py +++ b/ml/examples/temperature_predictor.py @@ -8,23 +8,25 @@ # you can obtain one at https://mozilla.org/MPL/2.0/. # -from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor -from sklearn.linear_model import LinearRegression -from sklearn.preprocessing import StandardScaler -import numpy as np -import pickle import os +import pickle import time + +import numpy as np import psutil +from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import StandardScaler + from mlschema import MLSchema + class BasePredictor: def __init__(self, feature_names=None): self.scaler = StandardScaler() - self.feature_names = feature_names or ['temperature', 'humidity', 'occupancy'] + self.feature_names = feature_names or ["temperature", "humidity", "occupancy"] def train(self, X, y): - # Scale features X_scaled = self.scaler.fit_transform(X) start_time = time.time() self.model.fit(X_scaled, y) @@ -33,71 +35,67 @@ def train(self, X, y): def predict(self, X): X_scaled = self.scaler.transform(X) process = psutil.Process(os.getpid()) - self.memory_usage = process.memory_info().rss / 1024 / 1024 # MB + self.memory_usage = process.memory_info().rss / 1024 / 1024 start_time = time.time() predictions = self.model.predict(X_scaled) self.prediction_time = time.time() - start_time - # Capture CPU usage self.cpu_usage = psutil.cpu_percent(interval=0.1) return predictions def predict_proba(self, X): - # Default implementation - override in subclasses if needed return None def get_performance_metrics(self): return { - 'training_time': getattr(self, 'training_time', 0), - 'prediction_time': getattr(self, 'prediction_time', 0), - 'memory_usage_mb': getattr(self, 'memory_usage', 0), - 'cpu_usage_percent': getattr(self, 'cpu_usage', 0) + "training_time": getattr(self, "training_time", 0), + "prediction_time": getattr(self, "prediction_time", 0), + "memory_usage_mb": getattr(self, "memory_usage", 0), + "cpu_usage_percent": getattr(self, "cpu_usage", 0), } def save(self, filename): - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) def save_with_schema(self, filename, X_train, y_train, X_test, y_test): - # Save model to pkl - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) - # Generate schema schema = MLSchema() - # Define an evaluation function that captures performance metrics def eval_func(model, X_test, y_test): y_pred = model.predict(X_test) from sklearn.metrics import mean_squared_error, r2_score + metrics = { - 'mse': mean_squared_error(y_test, y_pred), - 'r2': r2_score(y_test, y_pred), - 'training_time': model.get_performance_metrics().get('training_time', 0), - 'prediction_time': model.get_performance_metrics().get('prediction_time', 0), - 'memory_usage_mb': model.get_performance_metrics().get('memory_usage_mb', 0), - 'cpu_usage_percent': model.get_performance_metrics().get('cpu_usage_percent', 0) + "mse": mean_squared_error(y_test, y_pred), + "r2": r2_score(y_test, y_pred), + "training_time": model.get_performance_metrics().get("training_time", 0), + "prediction_time": model.get_performance_metrics().get("prediction_time", 0), + "memory_usage_mb": model.get_performance_metrics().get("memory_usage_mb", 0), + "cpu_usage_percent": model.get_performance_metrics().get("cpu_usage_percent", 0), } return metrics - # Generate schema schema.convert_model( self, - X_train, y_train, - X_test, y_test, + X_train, + y_train, + X_test, + y_test, feature_names=self.feature_names, - cpu_time_used=self.get_performance_metrics().get('training_time', 0), + cpu_time_used=self.get_performance_metrics().get("training_time", 0), model_uri=f"http://example.org/models/{os.path.basename(filename)}", - evaluation_function=eval_func + evaluation_function=eval_func, ) - # Save schema to file - schema_filename = filename.replace('.pkl', '.ttl') - with open(schema_filename, 'w') as f: - f.write(schema.serialize(format='turtle')) + schema_filename = filename.replace(".pkl", ".ttl") + with open(schema_filename, "w") as f: + f.write(schema.serialize(format="turtle")) return schema_filename @@ -110,10 +108,7 @@ def evaluate(self, X_test, y_test): mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) - self.evaluation_metrics = { - 'mse': mse, - 'r2': r2 - } + self.evaluation_metrics = {"mse": mse, "r2": r2} return self.evaluation_metrics @@ -123,45 +118,41 @@ def get(self, attribute_name): @classmethod def load(cls, filename): - with open(filename, 'rb') as f: + with open(filename, "rb") as f: return pickle.load(f) + class LinearRegressionPredictor(BasePredictor): def __init__(self, fit_intercept=True, normalize=None, feature_names=None): super().__init__(feature_names) - # In scikit-learn 1.0+, normalize parameter was removed - # Check scikit-learn version import sklearn from packaging import version try: - if version.parse(sklearn.__version__) >= version.parse('1.0.0'): - # For scikit-learn 1.0+ + if version.parse(sklearn.__version__) >= version.parse("1.0.0"): self.model = LinearRegression(fit_intercept=fit_intercept) if normalize: - print("Warning: 'normalize' parameter is deprecated in scikit-learn 1.0+. Using StandardScaler instead.") + print( + "Warning: 'normalize' parameter is deprecated in scikit-learn 1.0+. " + "Using StandardScaler instead." + ) else: - # For scikit-learn < 1.0 self.model = LinearRegression(fit_intercept=fit_intercept, normalize=normalize) except Exception as e: print(f"Error initializing LinearRegression: {e}") - # Fallback to simplest constructor self.model = LinearRegression() def predict_proba(self, X): - # Linear regression doesn't have built-in uncertainty estimation - # Return a simple constant uncertainty value X_scaled = self.scaler.transform(X) - return np.ones(X_scaled.shape[0]) * 0.5 # Constant uncertainty + return np.ones(X_scaled.shape[0]) * 0.5 + class RandomForestPredictor(BasePredictor): def __init__(self, n_estimators=100, max_depth=10, random_state=42, feature_names=None): super().__init__(feature_names) self.model = RandomForestRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - random_state=random_state + n_estimators=n_estimators, max_depth=max_depth, random_state=random_state ) def predict_proba(self, X): @@ -171,24 +162,26 @@ def predict_proba(self, X): predictions.append(tree.predict(X_scaled)) return np.std(predictions, axis=0) + class GradientBoostingPredictor(BasePredictor): - def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42, feature_names=None): + def __init__( + self, n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42, feature_names=None + ): super().__init__(feature_names) self.model = GradientBoostingRegressor( n_estimators=n_estimators, learning_rate=learning_rate, max_depth=max_depth, - random_state=random_state + random_state=random_state, ) def predict_proba(self, X): X_scaled = self.scaler.transform(X) - # Calculate prediction standard deviation return np.std([tree[0].predict(X_scaled) for tree in self.model.estimators_], axis=0) + def generate_models(): - """Explicit local generation. Importing this module never trains or writes.""" - # Generate training data + """Explicit local generation""" np.random.seed(42) n_samples = 1000 @@ -196,50 +189,54 @@ def generate_models(): humidity = np.random.normal(50, 15, n_samples) occupancy = np.random.randint(0, 20, n_samples) - # Create target variable with some noise future_temp = ( - temperature * 0.7 + - (humidity - 50) * 0.02 + - occupancy * 0.1 + - np.random.normal(0, 1, n_samples) + temperature * 0.7 + + (humidity - 50) * 0.02 + + occupancy * 0.1 + + np.random.normal(0, 1, n_samples) ) - # Split data into train and test sets from sklearn.model_selection import train_test_split + X = np.column_stack([temperature, humidity, occupancy]) y = future_temp X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - # Train and save models models_dir = os.environ["KOLIBRIE_TRAINING_OUTPUT"] os.makedirs(models_dir, exist_ok=True) - # RandomForest model rf_model = RandomForestPredictor() rf_model.train(X_train, y_train) - rf_model.predict(X_test) # Run once to get performance metrics - rf_schema_file = rf_model.save_with_schema(os.path.join(models_dir, "rf_temperature_predictor.pkl"), - X_train, y_train, X_test, y_test) + rf_model.predict(X_test) + rf_schema_file = rf_model.save_with_schema( + os.path.join(models_dir, "rf_temperature_predictor.pkl"), X_train, y_train, X_test, y_test + ) - # GradientBoosting model gb_model = GradientBoostingPredictor() gb_model.train(X_train, y_train) - gb_model.predict(X_test) # Run once to get performance metrics - gb_schema_file = gb_model.save_with_schema(os.path.join(models_dir, "gb_temperature_predictor.pkl"), - X_train, y_train, X_test, y_test) + gb_model.predict(X_test) + gb_schema_file = gb_model.save_with_schema( + os.path.join(models_dir, "gb_temperature_predictor.pkl"), X_train, y_train, X_test, y_test + ) - # Linear Regression model lr_model = LinearRegressionPredictor() lr_model.train(X_train, y_train) - lr_model.predict(X_test) # Run once to get performance metrics - lr_schema_file = lr_model.save_with_schema(os.path.join(models_dir, "lr_temperature_predictor.pkl"), - X_train, y_train, X_test, y_test) + lr_model.predict(X_test) + lr_schema_file = lr_model.save_with_schema( + os.path.join(models_dir, "lr_temperature_predictor.pkl"), X_train, y_train, X_test, y_test + ) print(f"RandomForest model saved to {os.path.join(models_dir, 'rf_temperature_predictor.pkl')}") print(f"RandomForest schema saved to {rf_schema_file}") - print(f"GradientBoosting model saved to {os.path.join(models_dir, 'gb_temperature_predictor.pkl')}") + print( + "GradientBoosting model saved to " + f"{os.path.join(models_dir, 'gb_temperature_predictor.pkl')}" + ) print(f"GradientBoosting schema saved to {gb_schema_file}") - print(f"LinearRegression model saved to {os.path.join(models_dir, 'lr_temperature_predictor.pkl')}") + print( + "LinearRegression model saved to " + f"{os.path.join(models_dir, 'lr_temperature_predictor.pkl')}" + ) print(f"LinearRegression schema saved to {lr_schema_file}") print("\nPerformance Comparison:") @@ -259,7 +256,8 @@ def generate_models(): for key, value in lr_metrics.items(): print(f" {key}: {value}") + if __name__ == "__main__": - # Import under the stable module name so pickle never records __main__ classes. import importlib + importlib.import_module("temperature_predictor").generate_models() diff --git a/ml/examples/traffic_predictor.py b/ml/examples/traffic_predictor.py index 08318019..b6931478 100644 --- a/ml/examples/traffic_predictor.py +++ b/ml/examples/traffic_predictor.py @@ -8,23 +8,25 @@ # you can obtain one at https://mozilla.org/MPL/2.0/. # -from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor -from sklearn.linear_model import LinearRegression -from sklearn.preprocessing import StandardScaler -import numpy as np -import pickle import os +import pickle import time + +import numpy as np import psutil +from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import StandardScaler + from mlschema import MLSchema + class BasePredictor: def __init__(self, feature_names=None): self.scaler = StandardScaler() - self.feature_names = feature_names or ['avgSpeed', 'vehicleCount'] + self.feature_names = feature_names or ["avgSpeed", "vehicleCount"] def train(self, X, y): - # Scale features X_scaled = self.scaler.fit_transform(X) start_time = time.time() self.model.fit(X_scaled, y) @@ -33,81 +35,76 @@ def train(self, X, y): def predict(self, X): X_scaled = self.scaler.transform(X) process = psutil.Process(os.getpid()) - self.memory_usage = process.memory_info().rss / 1024 / 1024 # MB + self.memory_usage = process.memory_info().rss / 1024 / 1024 start_time = time.time() predictions = self.model.predict(X_scaled) self.prediction_time = time.time() - start_time - # Capture CPU usage self.cpu_usage = psutil.cpu_percent(interval=0.1) - # Ensure predictions are in valid congestion range [0, 1] predictions = np.clip(predictions, 0.0, 1.0) return predictions def predict_proba(self, X): - # Default implementation - override in subclasses if needed return None def get_performance_metrics(self): return { - 'training_time': getattr(self, 'training_time', 0), - 'prediction_time': getattr(self, 'prediction_time', 0), - 'memory_usage_mb': getattr(self, 'memory_usage', 0), - 'cpu_usage_percent': getattr(self, 'cpu_usage', 0) + "training_time": getattr(self, "training_time", 0), + "prediction_time": getattr(self, "prediction_time", 0), + "memory_usage_mb": getattr(self, "memory_usage", 0), + "cpu_usage_percent": getattr(self, "cpu_usage", 0), } def save(self, filename): - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) def save_with_schema(self, filename, X_train, y_train, X_test, y_test): - # Save model to pkl - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) - # Generate schema schema = MLSchema() - # Define an evaluation function that captures performance metrics def eval_func(model, X_test, y_test): y_pred = model.predict(X_test) - from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error + from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + metrics = { - 'mse': mean_squared_error(y_test, y_pred), - 'mae': mean_absolute_error(y_test, y_pred), - 'r2': r2_score(y_test, y_pred), - 'training_time': model.get_performance_metrics().get('training_time', 0), - 'prediction_time': model.get_performance_metrics().get('prediction_time', 0), - 'memory_usage_mb': model.get_performance_metrics().get('memory_usage_mb', 0), - 'cpu_usage_percent': model.get_performance_metrics().get('cpu_usage_percent', 0) + "mse": mean_squared_error(y_test, y_pred), + "mae": mean_absolute_error(y_test, y_pred), + "r2": r2_score(y_test, y_pred), + "training_time": model.get_performance_metrics().get("training_time", 0), + "prediction_time": model.get_performance_metrics().get("prediction_time", 0), + "memory_usage_mb": model.get_performance_metrics().get("memory_usage_mb", 0), + "cpu_usage_percent": model.get_performance_metrics().get("cpu_usage_percent", 0), } return metrics - # Generate schema schema.convert_model( self, - X_train, y_train, - X_test, y_test, + X_train, + y_train, + X_test, + y_test, feature_names=self.feature_names, - cpu_time_used=self.get_performance_metrics().get('training_time', 0), + cpu_time_used=self.get_performance_metrics().get("training_time", 0), model_uri=f"http://example.org/traffic/models/{os.path.basename(filename)}", - evaluation_function=eval_func + evaluation_function=eval_func, ) - # Save schema to file - schema_filename = filename.replace('.pkl', '.ttl') - with open(schema_filename, 'w') as f: - f.write(schema.serialize(format='turtle')) + schema_filename = filename.replace(".pkl", ".ttl") + with open(schema_filename, "w") as f: + f.write(schema.serialize(format="turtle")) return schema_filename def evaluate(self, X_test, y_test): """Calculate evaluation metrics and store them""" - from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error + from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score y_pred = self.predict(X_test) @@ -115,11 +112,7 @@ def evaluate(self, X_test, y_test): mae = mean_absolute_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) - self.evaluation_metrics = { - 'mse': mse, - 'mae': mae, - 'r2': r2 - } + self.evaluation_metrics = {"mse": mse, "mae": mae, "r2": r2} return self.evaluation_metrics @@ -129,45 +122,41 @@ def get(self, attribute_name): @classmethod def load(cls, filename): - with open(filename, 'rb') as f: + with open(filename, "rb") as f: return pickle.load(f) + class LinearRegressionPredictor(BasePredictor): def __init__(self, fit_intercept=True, normalize=None, feature_names=None): super().__init__(feature_names) - # In scikit-learn 1.0+, normalize parameter was removed - # Check scikit-learn version import sklearn from packaging import version try: - if version.parse(sklearn.__version__) >= version.parse('1.0.0'): - # For scikit-learn 1.0+ + if version.parse(sklearn.__version__) >= version.parse("1.0.0"): self.model = LinearRegression(fit_intercept=fit_intercept) if normalize: - print("Warning: 'normalize' parameter is deprecated in scikit-learn 1.0+. Using StandardScaler instead.") + print( + "Warning: 'normalize' parameter is deprecated in scikit-learn 1.0+. " + "Using StandardScaler instead." + ) else: - # For scikit-learn < 1.0 self.model = LinearRegression(fit_intercept=fit_intercept, normalize=normalize) except Exception as e: print(f"Error initializing LinearRegression: {e}") - # Fallback to simplest constructor self.model = LinearRegression() def predict_proba(self, X): - # Linear regression doesn't have built-in uncertainty estimation - # Return a simple constant uncertainty value X_scaled = self.scaler.transform(X) - return np.ones(X_scaled.shape[0]) * 0.15 # Lower uncertainty for linear model + return np.ones(X_scaled.shape[0]) * 0.15 + class RandomForestPredictor(BasePredictor): def __init__(self, n_estimators=100, max_depth=10, random_state=42, feature_names=None): super().__init__(feature_names) self.model = RandomForestRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - random_state=random_state + n_estimators=n_estimators, max_depth=max_depth, random_state=random_state ) def predict_proba(self, X): @@ -177,107 +166,106 @@ def predict_proba(self, X): predictions.append(tree.predict(X_scaled)) return np.std(predictions, axis=0) + class GradientBoostingPredictor(BasePredictor): - def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42, feature_names=None): + def __init__( + self, n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42, feature_names=None + ): super().__init__(feature_names) self.model = GradientBoostingRegressor( n_estimators=n_estimators, learning_rate=learning_rate, max_depth=max_depth, - random_state=random_state + random_state=random_state, ) def predict_proba(self, X): X_scaled = self.scaler.transform(X) - # Calculate prediction standard deviation return np.std([tree[0].predict(X_scaled) for tree in self.model.estimators_], axis=0) + def generate_models(): - """Explicit local generation. Importing this module never trains or writes.""" - # Generate traffic congestion training data + """Explicit local generation""" np.random.seed(42) n_samples = 1000 - # Traffic features based on the Rust example - # Average speed (km/h) - highway: 80-120, city: 30-60, downtown: 10-40 - avg_speed = np.concatenate([ - np.random.normal(100, 15, n_samples//3), # Highway speeds - np.random.normal(45, 10, n_samples//3), # City speeds - np.random.normal(25, 8, n_samples//3), # Downtown speeds - np.random.normal(35, 20, n_samples - 3*(n_samples//3)) # Mixed - ]) - - # Vehicle count - varies by road type and time - vehicle_count = np.concatenate([ - np.random.normal(150, 30, n_samples//3), # Highway traffic - np.random.normal(80, 20, n_samples//3), # City traffic - np.random.normal(120, 40, n_samples//3), # Downtown traffic - np.random.normal(100, 35, n_samples - 3*(n_samples//3)) # Mixed - ]) - - # Ensure realistic ranges - avg_speed = np.clip(avg_speed, 5, 150) # 5-150 km/h - vehicle_count = np.clip(vehicle_count, 0, 300) # 0-300 vehicles - - # Create congestion level target (0 = no congestion, 1 = severe congestion) - # Based on traffic flow theory: congestion increases with vehicle count and decreases with speed + avg_speed = np.concatenate( + [ + np.random.normal(100, 15, n_samples // 3), + np.random.normal(45, 10, n_samples // 3), + np.random.normal(25, 8, n_samples // 3), + np.random.normal(35, 20, n_samples - 3 * (n_samples // 3)), + ] + ) + + vehicle_count = np.concatenate( + [ + np.random.normal(150, 30, n_samples // 3), + np.random.normal(80, 20, n_samples // 3), + np.random.normal(120, 40, n_samples // 3), + np.random.normal(100, 35, n_samples - 3 * (n_samples // 3)), + ] + ) + + avg_speed = np.clip(avg_speed, 5, 150) + vehicle_count = np.clip(vehicle_count, 0, 300) + congestion_level = np.zeros(n_samples) for i in range(n_samples): speed = avg_speed[i] count = vehicle_count[i] - # Congestion formula based on traffic flow principles - # Lower speed + higher count = higher congestion - speed_factor = max(0, (60 - speed) / 60) # Normalize around 60 km/h free flow - count_factor = min(1, count / 200) # Normalize vehicle count + speed_factor = max(0, (60 - speed) / 60) + count_factor = min(1, count / 200) - # Combine factors with some randomness - base_congestion = (speed_factor * 0.7 + count_factor * 0.3) + base_congestion = speed_factor * 0.7 + count_factor * 0.3 noise = np.random.normal(0, 0.1) congestion_level[i] = np.clip(base_congestion + noise, 0, 1) - # Split data into train and test sets from sklearn.model_selection import train_test_split + X = np.column_stack([avg_speed, vehicle_count]) y = congestion_level X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - # Train and save traffic congestion models models_dir = os.environ["KOLIBRIE_TRAINING_OUTPUT"] os.makedirs(models_dir, exist_ok=True) print("Training traffic congestion prediction models...") print(f"Training data shape: {X_train.shape}") - print(f"Feature ranges - Speed: {avg_speed.min():.1f}-{avg_speed.max():.1f} km/h, Count: {vehicle_count.min():.0f}-{vehicle_count.max():.0f}") + print( + f"Feature ranges - Speed: {avg_speed.min():.1f}-{avg_speed.max():.1f} km/h, " + f"Count: {vehicle_count.min():.0f}-{vehicle_count.max():.0f}" + ) print(f"Congestion level range: {congestion_level.min():.3f}-{congestion_level.max():.3f}") - # RandomForest model for traffic congestion - rf_model = RandomForestPredictor(feature_names=['avgSpeed', 'vehicleCount']) + rf_model = RandomForestPredictor(feature_names=["avgSpeed", "vehicleCount"]) rf_model.train(X_train, y_train) - rf_model.predict(X_test) # Run once to get performance metrics + rf_model.predict(X_test) rf_metrics = rf_model.evaluate(X_test, y_test) - rf_schema_file = rf_model.save_with_schema(os.path.join(models_dir, "rf_congestion_predictor.pkl"), - X_train, y_train, X_test, y_test) + rf_schema_file = rf_model.save_with_schema( + os.path.join(models_dir, "rf_congestion_predictor.pkl"), X_train, y_train, X_test, y_test + ) - # GradientBoosting model for traffic congestion - gb_model = GradientBoostingPredictor(feature_names=['avgSpeed', 'vehicleCount']) + gb_model = GradientBoostingPredictor(feature_names=["avgSpeed", "vehicleCount"]) gb_model.train(X_train, y_train) - gb_model.predict(X_test) # Run once to get performance metrics + gb_model.predict(X_test) gb_metrics = gb_model.evaluate(X_test, y_test) - gb_schema_file = gb_model.save_with_schema(os.path.join(models_dir, "gb_congestion_predictor.pkl"), - X_train, y_train, X_test, y_test) + gb_schema_file = gb_model.save_with_schema( + os.path.join(models_dir, "gb_congestion_predictor.pkl"), X_train, y_train, X_test, y_test + ) - # Linear Regression model for traffic congestion - lr_model = LinearRegressionPredictor(feature_names=['avgSpeed', 'vehicleCount']) + lr_model = LinearRegressionPredictor(feature_names=["avgSpeed", "vehicleCount"]) lr_model.train(X_train, y_train) - lr_model.predict(X_test) # Run once to get performance metrics + lr_model.predict(X_test) lr_metrics = lr_model.evaluate(X_test, y_test) - lr_schema_file = lr_model.save_with_schema(os.path.join(models_dir, "lr_congestion_predictor.pkl"), - X_train, y_train, X_test, y_test) + lr_schema_file = lr_model.save_with_schema( + os.path.join(models_dir, "lr_congestion_predictor.pkl"), X_train, y_train, X_test, y_test + ) - print(f"\nModels saved successfully:") + print("\nModels saved successfully:") print(f"RandomForest model: {os.path.join(models_dir, 'rf_congestion_predictor.pkl')}") print(f"RandomForest schema: {rf_schema_file}") print(f"GradientBoosting model: {os.path.join(models_dir, 'gb_congestion_predictor.pkl')}") @@ -314,21 +302,19 @@ def generate_models(): print(f" MAE: {lr_metrics['mae']:.6f}") print(f" R² Score: {lr_metrics['r2']:.6f}") - # Determine best model based on R² score best_model = "RandomForest" - best_r2 = rf_metrics['r2'] + best_r2 = rf_metrics["r2"] - if gb_metrics['r2'] > best_r2: + if gb_metrics["r2"] > best_r2: best_model = "GradientBoosting" - best_r2 = gb_metrics['r2'] + best_r2 = gb_metrics["r2"] - if lr_metrics['r2'] > best_r2: + if lr_metrics["r2"] > best_r2: best_model = "LinearRegression" - best_r2 = lr_metrics['r2'] + best_r2 = lr_metrics["r2"] print(f"\nBest Model: {best_model} (R² = {best_r2:.6f})") - # Test models with sample traffic data matching the Rust example print("\nTesting with sample traffic scenarios:") test_scenarios = [ @@ -336,7 +322,7 @@ def generate_models(): ([25.0, 85], "CityRoadB2 - Low speed, medium traffic"), ([15.0, 200], "DowntownC3 - Very low speed, high traffic"), ([80.0, 50], "Highway - High speed, low traffic"), - ([10.0, 250], "Traffic jam scenario") + ([10.0, 250], "Traffic jam scenario"), ] for features, description in test_scenarios: @@ -352,7 +338,6 @@ def generate_models(): print(f" GradientBoosting: {gb_pred:.3f}") print(f" LinearRegression: {lr_pred:.3f}") - # Severity classification (matching Rust logic) avg_pred = (rf_pred + gb_pred + lr_pred) / 3 if avg_pred >= 0.8: severity = "SEVERE" @@ -367,9 +352,10 @@ def generate_models(): print(f" Average Prediction: {avg_pred:.3f} ({severity})") - print(f"\nTraffic congestion prediction models ready for dynamic rule processing!") + print("\nTraffic congestion prediction models ready for dynamic rule processing!") + if __name__ == "__main__": - # Import under the stable module name so pickle never records __main__ classes. import importlib + importlib.import_module("traffic_predictor").generate_models() diff --git a/ml/src/candle_model.rs b/ml/src/candle_model.rs index c605fad4..59de1832 100644 --- a/ml/src/candle_model.rs +++ b/ml/src/candle_model.rs @@ -8,29 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! A small feed-forward network, for gradients that arrive from outside it. -//! -//! Ordinary training differentiates a loss over labels. Neural relations do not: -//! the signal is a weighted-model-count gradient computed over a probabilistic -//! program, one partial derivative per output variable, which -//! [`MlpNeuralPredicate::surrogate_backward`] accepts in place of a loss. -//! -//! # Forward and backward are a queue, not a pair of calls -//! -//! [`forward_with_grads`](MlpNeuralPredicate::forward_with_grads) pushes its -//! activations onto an internal queue and -//! [`surrogate_backward`](MlpNeuralPredicate::surrogate_backward) pops the oldest, -//! so the two must be called in matching FIFO order. Gradients then *accumulate* -//! until [`zero_grads`](MlpNeuralPredicate::zero_grads) clears them, which is what -//! lets one optimiser step cover several forward passes. -//! [`predict`](MlpNeuralPredicate::predict) caches nothing and so cannot be -//! followed by a backward pass. -//! -//! # Implementation note -//! -//! Despite the name and the `candle` dependency, the arithmetic is plain Rust over -//! `Vec>`; candle supplies the `Tensor` the forward pass hands back and -//! nothing else. There is no GPU path here. +//! A small feed-forward network, for gradients that arrive from outside it use std::cell::RefCell; use std::collections::{HashMap, VecDeque}; @@ -47,11 +25,11 @@ use shared::query::OptimizerKind; type MlResult = Result>; #[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)] -/// What the output layer means, which decides its width and its activation. +/// What the output layer means, which decides its width and its activation pub enum OutputType { - /// One output, squashed to a probability. + /// One output, squashed to a probability Binary, - /// One output per class, normalised across them, so they sum to 1. + /// One output per class, normalised across them, so they sum to 1 Categorical(usize), } @@ -95,16 +73,7 @@ struct SavedModel { output_type: OutputType, } -/// A feed-forward network with ReLU hidden layers. -/// -/// Interior-mutable throughout: a forward pass records activations and a backward -/// pass accumulates gradients, both through `&self`, so one of these can be shared -/// where a `&mut` would not fit. The cost is that the forward/backward pairing is -/// a runtime contract rather than a borrow-checked one — see the module -/// documentation. -/// -/// No public fields: the parameters are reachable only through the methods here, -/// so their representation stays a private matter. +/// A feed-forward network with ReLU hidden layers pub struct MlpNeuralPredicate { layers: RefCell>, hidden_act: Activation, @@ -114,9 +83,6 @@ pub struct MlpNeuralPredicate { adam_state: RefCell, } -// Summarised rather than derived: the parameters are the bulk of this type, and a -// layer-shape list is what a reader is checking. `try_borrow` because a `Debug` impl -// must not panic, and the layers are borrowed during a forward pass. impl std::fmt::Debug for MlpNeuralPredicate { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { let mut s = f.debug_struct("MlpNeuralPredicate"); @@ -146,21 +112,12 @@ impl std::fmt::Debug for MlpNeuralPredicate { } impl MlpNeuralPredicate { - /// A network with randomly initialised weights and zero biases. - /// - /// `hidden` gives the width of each hidden layer in order, and may be empty - /// for a network with no hidden layer. Weights are drawn uniformly from - /// `-sqrt(2/in_dim)..sqrt(2/in_dim)`, scaled per layer to keep activations - /// from vanishing as depth grows. - /// - /// Unseeded, so two networks built from the same arguments differ. Use - /// [`new_seeded`](Self::new_seeded) where that matters. + /// A network with randomly initialised weights and zero biases pub fn new(input_dim: usize, hidden: &[usize], output_type: OutputType) -> MlResult { Self::new_with_rng(input_dim, hidden, output_type, &mut rand::rng()) } - /// As [`new`](Self::new), with initialisation drawn from `seed` so the - /// network is reproducible. + /// As [`new`](Self::new), with initialisation drawn from `seed` so the network is reproducible pub fn new_seeded( input_dim: usize, hidden: &[usize], @@ -219,22 +176,12 @@ impl MlpNeuralPredicate { }) } - /// A forward pass that keeps what a backward pass will need. - /// - /// Returns the outputs twice over: as a `Tensor` for callers threading one - /// through candle, and as rows of `f64` for everything else. Queues the - /// activations for exactly one - /// [`surrogate_backward`](Self::surrogate_backward), so calling this twice - /// without a backward pass in between leaves two entries queued and they are - /// consumed oldest first. + /// A forward pass that keeps what a backward pass will need pub fn forward_with_grads(&self, rows: &[Vec]) -> MlResult<(Tensor, Vec>)> { self.forward_impl(rows, true) } - /// A forward pass that keeps nothing. - /// - /// One row of outputs per input row. Cannot be followed by - /// [`surrogate_backward`](Self::surrogate_backward), which has nothing to pop. + /// A forward pass that keeps nothing pub fn predict(&self, rows: &[Vec]) -> MlResult>> { Ok(self.forward_impl(rows, false)?.1) } @@ -307,26 +254,7 @@ impl MlpNeuralPredicate { Ok((tensor, activations)) } - /// Backpropagates a gradient supplied by the caller instead of one derived - /// from a loss. - /// - /// `wmc_grads` carries one map per sample, from probabilistic-program variable - /// to that variable's partial derivative, and `var_to_col` says which output - /// column each variable corresponds to. Samples whose gradients are all - /// effectively zero are skipped rather than propagated. - /// - /// Gradients **accumulate** into internal state; they do not replace what is - /// there. [`zero_grads`](Self::zero_grads) between batches, or one batch's - /// gradients leak into the next. - /// - /// `tracked` is not read. It is accepted so callers can pass the tensor from - /// [`forward_with_grads`](Self::forward_with_grads), but the pass uses the - /// queued activations, not this argument. - /// - /// # Errors - /// - /// If no queued forward pass is available, or if the queued one covered a - /// different number of samples than `wmc_grads` does. + /// Backpropagates a gradient supplied by the caller instead of one derived from a loss pub fn surrogate_backward( &self, tracked: &Tensor, @@ -411,27 +339,14 @@ impl MlpNeuralPredicate { Ok(()) } - /// Clears accumulated gradients **and** discards any queued forward passes. - /// - /// The second half matters: this is also how to recover after an aborted - /// batch, since a queued activation set with no backward pass would otherwise - /// be consumed by the next one and silently mismatch. + /// Clears accumulated gradients **and** discards any queued forward passes pub fn zero_grads(&self) { let layers = self.layers.borrow(); *self.grad_state.borrow_mut() = zero_grads_for_layers(&layers); self.forward_queue.borrow_mut().clear(); } - /// Applies the accumulated gradients and updates the weights. - /// - /// Gradients are divided by `example_count`, so that value must be the number - /// of samples that actually contributed since the last - /// [`zero_grads`](Self::zero_grads) — not the batch size, which differs - /// whenever a sample was skipped for having no gradient. An - /// `example_count` of 0 is a no-op rather than a division by zero. - /// - /// Does not clear the gradients; call - /// [`zero_grads`](Self::zero_grads) before the next batch. + /// Applies the accumulated gradients and updates the weights pub fn optimizer_step( &self, optimizer: OptimizerKind, @@ -509,12 +424,6 @@ impl MlpNeuralPredicate { } /// Serialize without filesystem side effects - /// - /// JSON, and the float round-trip through - /// [`from_bytes`](Self::from_bytes) is bit-exact — but only because - /// `serde_json` is built with `float_roundtrip`. Without that feature its - /// parser can land one ULP off while serialisation stays exact, which made - /// save-then-load lossy and produced a 1-in-25 test flake. pub fn to_bytes(&self) -> MlResult> { let payload = SavedModel { layers: self.layers.borrow().clone(), @@ -524,13 +433,7 @@ impl MlpNeuralPredicate { Ok(serde_json::to_vec_pretty(&payload)?) } - /// Reads a model from `path`. - /// - /// Verifies only that the file's shape matches the arguments, which is what - /// [`from_bytes`](Self::from_bytes) checks. It does not verify *which* file it - /// read: the digest pinning that makes a swapped artifact fail rather than - /// silently change behaviour lives in `kolibrie`'s `ml_policy`, so prefer the - /// policy path and reserve this for a file the caller already trusts. + /// Reads a model from `path` pub fn load( input_dim: usize, hidden: &[usize], @@ -746,16 +649,8 @@ mod tests { ); let loaded = MlpNeuralPredicate::from_bytes(2, &[3], OutputType::Binary, &bytes).unwrap(); - // Serialisation fidelity is asserted on the bytes, which is both exact and - // deterministic — and a stronger claim than matching forward outputs. assert_eq!(loaded.to_bytes().unwrap(), bytes); - // Forward outputs get a tolerance rather than equality. candle's CPU matmul - // reduces in an order that depends on how rayon partitions the work, so two - // calls with byte-identical weights can disagree in the last bit when the - // machine is busy. Asserting equality here failed about 1 run in 25 under the - // default parallel test harness, and never with --test-threads=1, on exactly - // that: left [[0.3958277324801351]] vs right [[0.39582773248013514]]. let expected = model.forward_with_grads(&[vec![1.0, 2.0]]).unwrap().1; let actual = loaded.forward_with_grads(&[vec![1.0, 2.0]]).unwrap().1; assert_eq!(expected.len(), actual.len()); diff --git a/ml/src/lib.rs b/ml/src/lib.rs index 5bf9cbb5..cf93ed2d 100644 --- a/ml/src/lib.rs +++ b/ml/src/lib.rs @@ -8,26 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Model execution for Kolibrie: a Python bridge and a native MLP. -//! -//! Two unrelated paths live here, and which one a query takes is decided in -//! `kolibrie`, not here: -//! -//! - [`MLHandler`] hosts predictors written in Python — scikit-learn and the like -//! — over PyO3. The model is a Python object; this crate marshals rows across -//! and marshals predictions back. -//! - [`candle_model`] is a small feed-forward network implemented in Rust, used -//! by neural-relation training where the gradient comes from outside the -//! network rather than from a loss over labels. -//! -//! # This crate grants no authority -//! -//! Nothing here checks whether a model is allowed to run. Loading a model by path -//! loads it, and the path is taken at face value. The trust decisions — which -//! models are approved, which artifacts match their pinned digest, whether -//! training may write at all — belong to -//! `kolibrie`'s `ml_policy`, and every caller is expected to have been through -//! it. Treat the entry points here as the mechanism, not the gate. +//! Model execution for Kolibrie: a Python bridge and a native MLP use pyo3::{ prelude::*, @@ -53,87 +34,65 @@ fn validate_py_module_name(name: &str) -> PyResult<()> { Ok(()) } -/// A small feed-forward network, for gradients that arrive from outside it. +/// A small feed-forward network, for gradients that arrive from outside it pub mod candle_model; pub use candle_model::{MlpNeuralPredicate, OutputType}; #[derive(Debug, Serialize, Deserialize)] -/// What one Python-hosted prediction call returned. +/// What one Python-hosted prediction call returned pub struct MLPredictionResult { - /// One prediction per input row, in input order. + /// One prediction per input row, in input order pub predictions: Vec, - /// Class probabilities, when the model exposes them. A model without a - /// `predict_proba` equivalent yields `None`, which is not an error. + /// Class probabilities, when the model exposes them pub probabilities: Option>, - /// Per-feature importances, when the model exposes them. + /// Per-feature importances, when the model exposes them pub feature_importance: Option>, - /// Metrics for the model used, read from its schema file rather than - /// measured here — so they describe how it was trained, not this call. + /// Metrics for the model used, read from its schema file pub performance_metrics: ModelPerformanceMetrics, - /// Where this call's time went. + /// Where this call's time went pub timing: PredictionTiming, } #[derive(Debug, Serialize, Deserialize, Clone, Default)] -/// Where one prediction call spent its time, in seconds. -/// -/// Split around the model call because marshalling rows into Python and results -/// back out often costs more than the inference on a small batch. +/// Where one prediction call spent its time, in seconds pub struct PredictionTiming { - /// Preparing the input, up to the model call. + /// Preparing the input, up to the model call pub preprocessing_time: f64, - /// The model call itself. + /// The model call itself pub actual_prediction_time: f64, - /// Converting the results back. + /// Converting the results back pub postprocessing_time: f64, } #[derive(Debug, Serialize, Deserialize, Clone, Default)] -/// A model's own account of itself, as declared in its schema file. -/// -/// These are read from the model's Turtle sidecar, not measured by this crate, so -/// they are claims about past training runs. [`Default`] is all zeros and no -/// scores, which is also what a model whose schema could not be parsed ends up -/// with — see [`MLHandler::compare_models`] for why that matters. +/// A model's own account of itself, as declared in its schema file pub struct ModelPerformanceMetrics { - /// Seconds spent training. + /// Seconds spent training pub training_time: f64, - /// Seconds per prediction call. + /// Seconds per prediction call pub prediction_time: f64, - /// Resident memory in megabytes. + /// Resident memory in megabytes pub memory_usage_mb: f64, - /// CPU utilisation as a percentage. + /// CPU utilisation as a percentage pub cpu_usage_percent: f64, - /// Classification accuracy, if the schema declares one. + /// Classification accuracy, if the schema declares one pub accuracy: Option, - /// Coefficient of determination, for regressors. + /// Coefficient of determination, for regressors pub r2_score: Option, - /// Mean squared error, for regressors. + /// Mean squared error, for regressors pub mse: Option, } -/// Python-hosted predictors, loaded once and kept. -/// -/// The two caches are populated independently, which is the point: -/// [`load_model_with_schema`](Self::load_model_with_schema) records a model's -/// declared metrics without importing it, so several candidates can be compared -/// before any Python is executed. Only [`load_model`](Self::load_model) imports. +/// Python-hosted predictors, loaded once and kept pub struct MLHandler { - /// Imported Python predictor objects, by model name. Holding a `PyObject` - /// means this cache keeps the interpreter's objects alive. + /// Imported Python predictor objects, by model name pub model_cache: BTreeMap, - /// Declared metrics by model name, including for models never imported. + /// Declared metrics by model name, including for models never imported pub schema_cache: BTreeMap, - /// Model chosen by the last [`compare_models`](Self::compare_models) call. - /// - /// Once set, this **overrides the model name passed to** - /// [`predict`](Self::predict). Nothing clears it. + /// Model chosen by the last [`compare_models`](Self::compare_models) call pub best_model: Option, } -// Summarised rather than derived: the cache holds Python objects whose `Debug` would -// call back into the interpreter, which needs the GIL and can fail. Counting them -// needs neither. impl std::fmt::Debug for MLHandler { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("MLHandler") @@ -145,8 +104,7 @@ impl std::fmt::Debug for MLHandler { } impl MLHandler { - /// An empty handler. Does not initialise Python; the first call that needs - /// the interpreter acquires the GIL itself. + /// An empty handler pub fn new() -> PyResult { Ok(MLHandler { model_cache: BTreeMap::new(), @@ -162,10 +120,8 @@ impl MLHandler { let rdflib = py.import("rdflib")?; let graph = rdflib.call_method0("Graph")?; - // Parse the TTL schema file graph.call_method1("parse", (schema_file_path, "turtle"))?; - // Create a SPARQL query to extract performance metrics let query = r#" PREFIX mls: PREFIX rdfs: @@ -184,10 +140,8 @@ impl MLHandler { for row in results.try_iter()? { let row = row?; let label: String = row.get_item(0)?.extract()?; - // Convert the value to a string first and then parse it as f64 let value_obj = row.get_item(1)?; let value_str: String = value_obj.str()?.extract()?; - // Try to parse it as a float if let Ok(value) = value_str.parse::() { match label.as_str() { "training_time" => metrics.training_time = value, @@ -201,7 +155,6 @@ impl MLHandler { } } - // Extract the CPU time from the run quality let cpu_query = r#" PREFIX mls: PREFIX rdfs: @@ -220,11 +173,10 @@ impl MLHandler { for row in cpu_results.try_iter()? { let row = row?; - // Convert the value to a string first and then parse it as f64 let value_obj = row.get_item(0)?; let value_str: String = value_obj.str()?.extract()?; if let Ok(value) = value_str.parse::() { - metrics.training_time = value; // Use CPU time as training time if not already set + metrics.training_time = value; } } @@ -232,52 +184,29 @@ impl MLHandler { }) } - /// Records a model's declared metrics without importing it. - /// - /// The schema path is derived from `model_path` by replacing `.pkl` with - /// `.ttl` — every occurrence, so a path with `.pkl` in a directory name is - /// rewritten there too. - /// - /// A schema that cannot be parsed is **not** an error: it logs - /// `KOLIBRIE_OPERATION_FAILED` to stderr and caches - /// [`ModelPerformanceMetrics::default`] instead. The model is then recorded as - /// costing nothing, which [`compare_models`](Self::compare_models) reads as - /// the cheapest possible candidate. + /// Records a model's declared metrics without importing it pub fn load_model_with_schema( &mut self, model_name: &str, model_path: &str, ) -> PyResult { - // Get the TTL file path by replacing .pkl extension with .ttl let schema_file_path = model_path.replace(".pkl", ".ttl"); - // Parse the schema file to get performance metrics directly from TTL let metrics = match self.parse_schema_file(&schema_file_path) { Ok(m) => m, Err(_e) => { eprintln!("KOLIBRIE_OPERATION_FAILED"); - // Create default metrics if TTL parsing fails ModelPerformanceMetrics::default() } }; - // Store metrics in cache without loading the model yet self.schema_cache .insert(model_name.to_string(), metrics.clone()); Ok(metrics) } - /// Imports a Python predictor and caches the resulting object. - /// - /// Both `model_name` and `module_name` must be ASCII identifiers of at most - /// 64 characters, because they are interpolated into a Python import: the - /// model directory's parent is prepended to `sys.path`, so an unchecked name - /// would choose what gets imported. - /// - /// Grants no authority of its own — see the crate documentation. A caller that - /// has not been through `kolibrie`'s `ml_policy` is importing whatever the - /// path points at. + /// Imports a Python predictor and caches the resulting object pub fn load_model( &mut self, model_name: &str, @@ -410,39 +339,17 @@ impl MLHandler { }) } - // Modified to prioritize lowest resource usage - /// Picks the cheapest of `model_names` and remembers it. - /// - /// "Best" means least resource-hungry, not most accurate: the score is - /// `0.5 * cpu_usage_percent + 0.4 * memory_usage_mb + 0.1 * prediction_time`, - /// and no accuracy field enters it. Note also that the two terms that dominate - /// are in different units, so the weights are not comparable across models - /// measured on different machines. - /// - /// Two consequences worth knowing before relying on it: - /// - /// - A model whose schema failed to parse scores zero on every term and - /// therefore always wins. See - /// [`load_model_with_schema`](Self::load_model_with_schema). - /// - The winner is stored in [`best_model`](Self::best_model) and from then on - /// overrides the model name given to [`predict`](Self::predict). Calling - /// this is not a read-only comparison. - /// - /// Models absent from the schema cache are skipped; if none of them is - /// present, the first name is returned unchanged. `None` only for an empty - /// slice. + /// Picks the cheapest of `model_names` and remembers it pub fn compare_models<'a>(&mut self, model_names: &[&'a str]) -> Option<&'a str> { if model_names.is_empty() { return None; } - // Default to first model let mut best_model = model_names[0]; let mut best_score = std::f64::MAX; for &model_name in model_names { if let Some(metrics) = self.schema_cache.get(model_name) { - // Prioritize CPU and memory usage let cpu_weight = 0.5; let memory_weight = 0.4; let time_weight = 0.1; @@ -458,23 +365,12 @@ impl MLHandler { } } - // Store the best model name for future use self.best_model = Some(best_model.to_string()); Some(best_model) } - /// Runs a cached predictor over `input_data`. - /// - /// `model_name` is **ignored** when - /// [`best_model`](Self::best_model) is set, which - /// [`compare_models`](Self::compare_models) does as a side effect. On a - /// handler that has compared models, this always predicts with the winner. - /// - /// # Errors - /// - /// If the model that would be used has not been imported by - /// [`load_model`](Self::load_model). A cached schema is not enough. + /// Runs a cached predictor over `input_data` pub fn predict( &self, model_name: &str, @@ -500,7 +396,6 @@ impl MLHandler { let model = self.model_cache.get(actual_model_name).unwrap(); - // Convert input data to Python list let rows: PyResult> = input_data .iter() .map(|row| { @@ -513,13 +408,11 @@ impl MLHandler { let preprocessing_time = preprocessing_start.elapsed().as_secs_f64(); - // Actual ML prediction let prediction_start = Instant::now(); let predictions = model.call_method1(py, "predict", (py_input.clone(),))?; let predictions: Vec = predictions.extract(py)?; let prediction_time = prediction_start.elapsed().as_secs_f64(); - // Postprocessing let postprocessing_start = Instant::now(); let probabilities = model @@ -558,16 +451,7 @@ impl MLHandler { result } - // Utility function to discover and load all models and their TTL schemas at once - /// Scans `model_dir` for predictors and records their schemas. - /// - /// Picks up files named `_predictor.pkl` only, and names each model - /// `_model` after the text before the first underscore — so - /// `rf_predictor.pkl` becomes `rf_model`, and two files sharing a prefix - /// collide on one name. - /// - /// Schemas only: nothing is imported, and a directory that cannot be read - /// yields an empty list rather than an error. + /// Scans `model_dir` for predictors and records their schemas pub fn discover_and_load_models( &mut self, model_dir: &Path, @@ -576,13 +460,11 @@ impl MLHandler { let mut model_ids = Vec::new(); if let Ok(entries) = std::fs::read_dir(model_dir) { - // First pass: Only load schemas from TTL files without loading models for entry in entries.filter_map(Result::ok) { let path = entry.path(); if path.is_file() && path.extension().map_or(false, |ext| ext == "pkl") { if let Some(file_stem) = path.file_stem().and_then(|s| s.to_str()) { if file_stem.ends_with("_predictor") { - // Get model type prefix from filename (rf_, gb_, lr_, etc.) let model_type = file_stem.split('_').next().unwrap_or("unknown"); let model_id = format!("{}_model", model_type); @@ -599,10 +481,8 @@ impl MLHandler { } } - // Compare models to find the one with lowest resource usage let model_id_refs: Vec<&str> = model_ids.iter().map(|s| s.as_str()).collect(); if let Some(best_model) = self.compare_models(&model_id_refs) { - // Second pass: Only load the best model to save resources for entry in std::fs::read_dir(model_dir).unwrap().filter_map(Result::ok) { let path = entry.path(); if path.is_file() && path.extension().map_or(false, |ext| ext == "pkl") { @@ -610,7 +490,6 @@ impl MLHandler { let model_type = file_stem.split('_').next().unwrap_or("unknown"); let model_id = format!("{}_model", model_type); - // Only load the best model if model_id == best_model { match self.load_model( &model_id, diff --git a/ml/src/mlschema.py b/ml/src/mlschema.py index 2568e600..b365ecab 100644 --- a/ml/src/mlschema.py +++ b/ml/src/mlschema.py @@ -2,17 +2,16 @@ # Copyright © 2024 Volodymyr Kadzhaia # Copyright © 2024 Pieter Bonte # KU Leuven — Stream Intelligence Lab, Belgium -# +# # This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this file, # you can obtain one at https://mozilla.org/MPL/2.0/. -# +# -from rdflib import Graph, Literal, RDF, URIRef, Namespace, BNode -from rdflib.namespace import RDF, XSD, RDFS, OWL -from rdflib import XSD, Literal as RDFLiteral +from rdflib import BNode, Graph, Literal, Namespace, URIRef +from rdflib import Literal as RDFLiteral +from rdflib.namespace import OWL, RDF, RDFS, XSD -# Optional imports for framework detection try: import torch from torch.nn import Module as TorchModule @@ -21,22 +20,21 @@ TorchModule = None try: - import tensorflow as tf from tensorflow import keras except ImportError: keras = None + class MLSchema: def __init__(self): self.g = Graph() - # Namespaces self.EX = Namespace("http://example.org/") self.MLS = Namespace("http://www.w3.org/ns/mls#") self.DCTERMS = Namespace("http://purl.org/dc/terms/") self.g.bind("ex", self.EX) self.g.bind("mls", self.MLS) self.g.bind("dcterms", self.DCTERMS) - self.model_eval_counter = 1 # Counter for unique model evaluations + self.model_eval_counter = 1 def convert_model( self, @@ -50,219 +48,211 @@ def convert_model( cpu_time_used=None, model_uri=None, evaluation_function=None, - evaluation_metrics=None + evaluation_metrics=None, ): - """ - Convert a machine learning or deep learning model and related data into RDF format. - - Parameters: - - model: Trained ML/DL model. - - X_train, y_train: Training data and labels. - - X_test, y_test: Test data and labels. - - feature_names: List of feature names (optional). - - class_names: List of class names (optional). - - cpu_time_used: CPU time used during training. - - model_uri: URI for the model in RDF. - - evaluation_function: A callable that takes (model, X_test, y_test) and returns a dict of metrics. - If None, tries default approaches (like classification_report for sklearn). - - evaluation_metrics: List of evaluation metric names if relying on built-in logic for sklearn-like models. - """ - if not model_uri: - model_uri = self.EX['model1'] - else: - model_uri = URIRef(model_uri) - - # Create a Run instance - run_uri = self.EX['run1'] + """Convert a machine learning or deep learning model and related data into RDF format""" + model_uri = URIRef(model_uri) if model_uri else self.EX["model1"] + + run_uri = self.EX["run1"] self.g.add((run_uri, RDF.type, self.MLS.Run)) - - # Link the model to the Run + self.g.add((run_uri, self.MLS.hasOutput, model_uri)) - - # Model as an MLS Model + self.g.add((model_uri, RDF.type, self.MLS.Model)) - - # Implementation of the algorithm - implementation_uri = self.EX['implementation1'] + + implementation_uri = self.EX["implementation1"] self.g.add((implementation_uri, RDF.type, self.MLS.Implementation)) self.g.add((run_uri, self.MLS.executes, implementation_uri)) - - # Algorithm + algorithm_name = type(model).__name__ - algorithm_uri = self.EX[f'algorithm/{algorithm_name}'] + algorithm_uri = self.EX[f"algorithm/{algorithm_name}"] self.g.add((algorithm_uri, RDF.type, self.MLS.Algorithm)) self.g.add((implementation_uri, self.MLS.implements, algorithm_uri)) - - # Add 'realizes' relation between Run and Algorithm + self.g.add((run_uri, self.MLS.realizes, algorithm_uri)) - - # Software - try to detect framework - software_name = model.__module__.split('.')[0] if hasattr(model, '__module__') else "unknown" - software_uri = self.EX[f'software/{software_name}'] + + software_name = ( + model.__module__.split(".")[0] if hasattr(model, "__module__") else "unknown" + ) + software_uri = self.EX[f"software/{software_name}"] self.g.add((software_uri, RDF.type, self.MLS.Software)) self.g.add((software_uri, self.MLS.hasPart, implementation_uri)) - - # Hyperparameters (if available) + self._add_hyperparameters(model, implementation_uri, run_uri) - - # Data (Training and Testing datasets) - train_data_uri = self.EX['data/training'] - test_data_uri = self.EX['data/testing'] + + train_data_uri = self.EX["data/training"] + test_data_uri = self.EX["data/testing"] self.g.add((train_data_uri, RDF.type, self.MLS.Dataset)) self.g.add((test_data_uri, RDF.type, self.MLS.Dataset)) self.g.add((run_uri, self.MLS.hasInput, train_data_uri)) self.g.add((run_uri, self.MLS.hasInput, test_data_uri)) - - # Input Data Characteristics - self._add_dataset_characteristics(train_data_uri, X_train, 'Training') - self._add_dataset_characteristics(test_data_uri, X_test, 'Testing') - - # Task - task_uri = self.EX['task1'] + + self._add_dataset_characteristics(train_data_uri, X_train, "Training") + self._add_dataset_characteristics(test_data_uri, X_test, "Testing") + + task_uri = self.EX["task1"] self.g.add((task_uri, RDF.type, self.MLS.Task)) self.g.add((run_uri, self.MLS.achieves, task_uri)) self.g.add((task_uri, self.MLS.definedOn, train_data_uri)) - - # Evaluation Specification - eval_spec_uri = self.EX['evalspec1'] + + eval_spec_uri = self.EX["evalspec1"] self.g.add((eval_spec_uri, RDF.type, self.MLS.EvaluationSpecification)) self.g.add((task_uri, self.MLS.definedOn, eval_spec_uri)) self.g.add((eval_spec_uri, self.MLS.defines, task_uri)) - - # Evaluation Measures - self._add_evaluation_measures(model, X_test, y_test, eval_spec_uri, run_uri, evaluation_function, evaluation_metrics, class_names) - - # Extract model characteristics + + self._add_evaluation_measures( + model, + X_test, + y_test, + eval_spec_uri, + run_uri, + evaluation_function, + evaluation_metrics, + class_names, + ) + self._add_model_characteristics(model, model_uri, feature_names, class_names) - - # Include CPU time used in the Run + if cpu_time_used is not None: self._add_cpu_time(run_uri, cpu_time_used) - + def _add_hyperparameters(self, model, implementation_uri, run_uri): - # Try to get hyperparameters from a get_params() method if available - if hasattr(model, 'get_params'): + if hasattr(model, "get_params"): params = model.get_params() for param_name, param_value in params.items(): - hyperparam_uri = self.EX[f'hyperparameter/{param_name}'] + hyperparam_uri = self.EX[f"hyperparameter/{param_name}"] self.g.add((hyperparam_uri, RDF.type, self.MLS.HyperParameter)) self.g.add((hyperparam_uri, RDFS.label, Literal(param_name))) self.g.add((implementation_uri, self.MLS.hasHyperParameter, hyperparam_uri)) - + hyperparam_setting_uri = BNode() self.g.add((hyperparam_setting_uri, RDF.type, self.MLS.HyperParameterSetting)) self.g.add((hyperparam_setting_uri, self.MLS.specifiedBy, hyperparam_uri)) self.g.add((hyperparam_setting_uri, self.MLS.hasValue, Literal(str(param_value)))) self.g.add((run_uri, self.MLS.hasInput, hyperparam_setting_uri)) else: - # For deep learning models (like Keras or PyTorch), hyperparams might not be as straightforward. pass - + def _add_dataset_characteristics(self, data_uri, X_data, dataset_type): num_instances = X_data.shape[0] num_features = X_data.shape[1] if len(X_data.shape) > 1 else 1 - data_size_bytes = X_data.nbytes if hasattr(X_data, 'nbytes') else 0 + data_size_bytes = X_data.nbytes if hasattr(X_data, "nbytes") else 0 data_format = str(type(X_data)) - - # Number of instances + char_uri = BNode() self.g.add((char_uri, RDF.type, self.MLS.DatasetCharacteristic)) - self.g.add((char_uri, RDFS.label, Literal('Number of Instances'))) + self.g.add((char_uri, RDFS.label, Literal("Number of Instances"))) self.g.add((char_uri, self.MLS.hasValue, Literal(num_instances, datatype=XSD.integer))) self.g.add((data_uri, self.MLS.hasQuality, char_uri)) - # Number of features char_uri = BNode() self.g.add((char_uri, RDF.type, self.MLS.DatasetCharacteristic)) - self.g.add((char_uri, RDFS.label, Literal('Number of Features'))) + self.g.add((char_uri, RDFS.label, Literal("Number of Features"))) self.g.add((char_uri, self.MLS.hasValue, Literal(num_features, datatype=XSD.integer))) self.g.add((data_uri, self.MLS.hasQuality, char_uri)) - # Data size in bytes char_uri = BNode() self.g.add((char_uri, RDF.type, self.MLS.DatasetCharacteristic)) - self.g.add((char_uri, RDFS.label, Literal('Data Size in Bytes'))) + self.g.add((char_uri, RDFS.label, Literal("Data Size in Bytes"))) self.g.add((char_uri, self.MLS.hasValue, Literal(data_size_bytes, datatype=XSD.integer))) self.g.add((data_uri, self.MLS.hasQuality, char_uri)) - # Data format char_uri = BNode() self.g.add((char_uri, RDF.type, self.MLS.DatasetCharacteristic)) - self.g.add((char_uri, RDFS.label, Literal('Data Format'))) + self.g.add((char_uri, RDFS.label, Literal("Data Format"))) self.g.add((char_uri, self.MLS.hasValue, Literal(data_format))) self.g.add((data_uri, self.MLS.hasQuality, char_uri)) - - def _add_evaluation_measures(self, model, X_test, y_test, eval_spec_uri, run_uri, evaluation_function, evaluation_metrics, class_names): - # If an evaluation_function is provided by user, use it: + + def _add_evaluation_measures( + self, + model, + X_test, + y_test, + eval_spec_uri, + run_uri, + evaluation_function, + evaluation_metrics, + class_names, + ): if evaluation_function is not None: - # Must return a dict {metric_name: value, ...} metrics = evaluation_function(model, X_test, y_test) for metric_name, metric_value in metrics.items(): self._add_single_evaluation(metric_name, metric_value, eval_spec_uri, run_uri) return - - # Otherwise, try a default approach if scikit-learn style + if evaluation_metrics is None: - evaluation_metrics = ['accuracy'] - - # Try classification_report if it's a classification problem + evaluation_metrics = ["accuracy"] + from sklearn.metrics import classification_report - # Check if y_test is categorical + report = classification_report(y_test, model.predict(X_test), output_dict=True) - - # Add all metrics from the classification report + for label, metrics in report.items(): - if label.isdigit() or label in ('accuracy', 'macro avg', 'weighted avg'): - # Add these as evaluation measures - if label == 'accuracy': - # Overall accuracy + if label.isdigit() or label in ("accuracy", "macro avg", "weighted avg"): + if label == "accuracy": acc = metrics - self._add_single_evaluation('Predictive Accuracy', acc, eval_spec_uri, run_uri, measure_uri_name='predictiveAccuracy') + self._add_single_evaluation( + "Predictive Accuracy", + acc, + eval_spec_uri, + run_uri, + measure_uri_name="predictiveAccuracy", + ) else: - # It's a class-specific or average metric - # label_name is used as the measure URI suffix - label_name = label.replace(' ', '_') + label_name = label.replace(" ", "_") for metric_name, metric_value in metrics.items(): if isinstance(metric_value, (float, int)): - self._add_single_evaluation(f'{metric_name} {label}', metric_value, eval_spec_uri, run_uri, measure_uri_name=f'{metric_name}_{label_name}') + self._add_single_evaluation( + f"{metric_name} {label}", + metric_value, + eval_spec_uri, + run_uri, + measure_uri_name=f"{metric_name}_{label_name}", + ) - # Then update the _add_single_evaluation method - def _add_single_evaluation(self, metric_name, metric_value, eval_spec_uri, run_uri, measure_uri_name=None): + def _add_single_evaluation( + self, metric_name, metric_value, eval_spec_uri, run_uri, measure_uri_name=None + ): if measure_uri_name is None: - measure_uri_name = metric_name.replace(' ', '_') + measure_uri_name = metric_name.replace(" ", "_") eval_measure_uri = self.EX[measure_uri_name] self.g.add((eval_measure_uri, RDF.type, OWL.NamedIndividual)) self.g.add((eval_measure_uri, RDF.type, self.MLS.EvaluationMeasure)) self.g.add((eval_measure_uri, RDFS.label, RDFLiteral(metric_name))) self.g.add((eval_spec_uri, self.MLS.hasPart, eval_measure_uri)) - - model_eval_uri = self.EX[f'modelEvaluation{self.model_eval_counter}'] + + model_eval_uri = self.EX[f"modelEvaluation{self.model_eval_counter}"] self.model_eval_counter += 1 self.g.add((model_eval_uri, RDF.type, OWL.NamedIndividual)) self.g.add((model_eval_uri, RDF.type, self.MLS.ModelEvaluation)) self.g.add((model_eval_uri, self.MLS.specifiedBy, eval_measure_uri)) - - # Ensure metric_value is a float and use XSD.double datatype + metric_value_float = float(metric_value) - self.g.add((model_eval_uri, self.MLS.hasValue, RDFLiteral(metric_value_float, datatype=XSD.double))) + self.g.add( + (model_eval_uri, self.MLS.hasValue, RDFLiteral(metric_value_float, datatype=XSD.double)) + ) self.g.add((run_uri, self.MLS.hasOutput, model_eval_uri)) def _add_model_characteristics(self, model, model_uri, feature_names, class_names): - # Check if it's a sklearn-like model - if hasattr(model, 'coef_'): + if hasattr(model, "coef_"): self._add_linear_model_characteristics(model, model_uri, feature_names, class_names) - elif hasattr(model, 'feature_importances_'): + elif hasattr(model, "feature_importances_"): self._add_tree_model_characteristics(model, model_uri, feature_names) elif keras is not None and isinstance(model, keras.Model): self._add_keras_model_characteristics(model, model_uri) elif torch is not None and TorchModule is not None and isinstance(model, TorchModule): self._add_torch_model_characteristics(model, model_uri) else: - # Minimal info if unknown type characteristic_uri = BNode() self.g.add((characteristic_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((characteristic_uri, RDFS.label, Literal('Generic Model'))) - self.g.add((characteristic_uri, self.MLS.hasValue, Literal('No specific characteristics extracted'))) + self.g.add((characteristic_uri, RDFS.label, Literal("Generic Model"))) + self.g.add( + ( + characteristic_uri, + self.MLS.hasValue, + Literal("No specific characteristics extracted"), + ) + ) self.g.add((model_uri, self.MLS.hasQuality, characteristic_uri)) def _add_linear_model_characteristics(self, model, model_uri, feature_names, class_names): @@ -270,102 +260,147 @@ def _add_linear_model_characteristics(self, model, model_uri, feature_names, cla if coefficients.ndim == 1: coefficients = [coefficients] for class_idx, coef_vector in enumerate(coefficients): - class_name = class_names[class_idx] if (class_names is not None and class_idx < len(class_names)) else f'Class {class_idx}' + class_name = ( + class_names[class_idx] + if (class_names is not None and class_idx < len(class_names)) + else f"Class {class_idx}" + ) for feature_idx, coef_value in enumerate(coef_vector): - feature_name = feature_names[feature_idx] if (feature_names is not None and feature_idx < len(feature_names)) else f'Feature {feature_idx}' + feature_name = ( + feature_names[feature_idx] + if (feature_names is not None and feature_idx < len(feature_names)) + else f"Feature {feature_idx}" + ) characteristic_uri = BNode() self.g.add((characteristic_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((characteristic_uri, RDFS.label, Literal(f'Coefficient for class {class_name}, feature {feature_name}'))) - self.g.add((characteristic_uri, self.MLS.hasValue, Literal(coef_value, datatype=XSD.decimal))) + self.g.add( + ( + characteristic_uri, + RDFS.label, + Literal(f"Coefficient for class {class_name}, feature {feature_name}"), + ) + ) + self.g.add( + ( + characteristic_uri, + self.MLS.hasValue, + Literal(coef_value, datatype=XSD.decimal), + ) + ) self.g.add((model_uri, self.MLS.hasQuality, characteristic_uri)) - - # Intercepts - if hasattr(model, 'intercept_'): + + if hasattr(model, "intercept_"): intercepts = model.intercept_ if isinstance(intercepts, float): intercepts = [intercepts] for class_idx, intercept_value in enumerate(intercepts): - class_name = class_names[class_idx] if (class_names is not None and class_idx < len(class_names)) else f'Class {class_idx}' + class_name = ( + class_names[class_idx] + if (class_names is not None and class_idx < len(class_names)) + else f"Class {class_idx}" + ) characteristic_uri = BNode() self.g.add((characteristic_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((characteristic_uri, RDFS.label, Literal(f'Intercept for class {class_name}'))) - self.g.add((characteristic_uri, self.MLS.hasValue, Literal(intercept_value, datatype=XSD.decimal))) + self.g.add( + (characteristic_uri, RDFS.label, Literal(f"Intercept for class {class_name}")) + ) + self.g.add( + ( + characteristic_uri, + self.MLS.hasValue, + Literal(intercept_value, datatype=XSD.decimal), + ) + ) self.g.add((model_uri, self.MLS.hasQuality, characteristic_uri)) - - # Number of parameters - if hasattr(model, 'coef_'): - n_parameters = model.coef_.size + (model.intercept_.size if hasattr(model.intercept_, 'size') else 1) + + if hasattr(model, "coef_"): + n_parameters = model.coef_.size + ( + model.intercept_.size if hasattr(model.intercept_, "size") else 1 + ) char_uri = BNode() self.g.add((char_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((char_uri, RDFS.label, Literal('Number of Parameters'))) + self.g.add((char_uri, RDFS.label, Literal("Number of Parameters"))) self.g.add((char_uri, self.MLS.hasValue, Literal(n_parameters, datatype=XSD.integer))) self.g.add((model_uri, self.MLS.hasQuality, char_uri)) def _add_tree_model_characteristics(self, model, model_uri, feature_names): importances = model.feature_importances_ for feature_idx, importance in enumerate(importances): - feature_name = feature_names[feature_idx] if (feature_names is not None and feature_idx < len(feature_names)) else f'Feature {feature_idx}' + feature_name = ( + feature_names[feature_idx] + if (feature_names is not None and feature_idx < len(feature_names)) + else f"Feature {feature_idx}" + ) characteristic_uri = BNode() self.g.add((characteristic_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((characteristic_uri, RDFS.label, Literal(f'Feature importance for {feature_name}'))) - self.g.add((characteristic_uri, self.MLS.hasValue, Literal(importance, datatype=XSD.decimal))) + self.g.add( + (characteristic_uri, RDFS.label, Literal(f"Feature importance for {feature_name}")) + ) + self.g.add( + (characteristic_uri, self.MLS.hasValue, Literal(importance, datatype=XSD.decimal)) + ) self.g.add((model_uri, self.MLS.hasQuality, characteristic_uri)) - - # Number of parameters (for tree-based models, we might just say #features) + n_parameters = len(importances) char_uri = BNode() self.g.add((char_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((char_uri, RDFS.label, Literal('Number of Parameters'))) + self.g.add((char_uri, RDFS.label, Literal("Number of Parameters"))) self.g.add((char_uri, self.MLS.hasValue, Literal(n_parameters, datatype=XSD.integer))) self.g.add((model_uri, self.MLS.hasQuality, char_uri)) def _add_keras_model_characteristics(self, model, model_uri): - # Add layer information for i, layer in enumerate(model.layers): characteristic_uri = BNode() layer_name = layer.name layer_class = layer.__class__.__name__ self.g.add((characteristic_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((characteristic_uri, RDFS.label, Literal(f'Layer {i}: {layer_name} ({layer_class})'))) - # Possibly the number of parameters in the layer + self.g.add( + ( + characteristic_uri, + RDFS.label, + Literal(f"Layer {i}: {layer_name} ({layer_class})"), + ) + ) layer_params = layer.count_params() - self.g.add((characteristic_uri, self.MLS.hasValue, Literal(layer_params, datatype=XSD.integer))) + self.g.add( + (characteristic_uri, self.MLS.hasValue, Literal(layer_params, datatype=XSD.integer)) + ) self.g.add((model_uri, self.MLS.hasQuality, characteristic_uri)) - - # Total number of parameters + total_params = model.count_params() char_uri = BNode() self.g.add((char_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((char_uri, RDFS.label, Literal('Number of Parameters'))) + self.g.add((char_uri, RDFS.label, Literal("Number of Parameters"))) self.g.add((char_uri, self.MLS.hasValue, Literal(total_params, datatype=XSD.integer))) self.g.add((model_uri, self.MLS.hasQuality, char_uri)) def _add_torch_model_characteristics(self, model, model_uri): - # Count parameters total_params = sum(p.numel() for p in model.parameters()) char_uri = BNode() self.g.add((char_uri, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((char_uri, RDFS.label, Literal('Number of Parameters'))) + self.g.add((char_uri, RDFS.label, Literal("Number of Parameters"))) self.g.add((char_uri, self.MLS.hasValue, Literal(total_params, datatype=XSD.integer))) self.g.add((model_uri, self.MLS.hasQuality, char_uri)) - + arch_char = BNode() arch_str = str(model) self.g.add((arch_char, RDF.type, self.MLS.ModelCharacteristic)) - self.g.add((arch_char, RDFS.label, Literal('Model Architecture'))) + self.g.add((arch_char, RDFS.label, Literal("Model Architecture"))) self.g.add((arch_char, self.MLS.hasValue, Literal(arch_str))) self.g.add((model_uri, self.MLS.hasQuality, arch_char)) def _add_cpu_time(self, run_uri, cpu_time_used): cpu_time_char_uri = BNode() self.g.add((cpu_time_char_uri, RDF.type, self.MLS.Quality)) - self.g.add((cpu_time_char_uri, RDFS.label, Literal('CPU Time Used'))) - self.g.add((cpu_time_char_uri, self.MLS.hasValue, Literal(cpu_time_used, datatype=XSD.decimal))) + self.g.add((cpu_time_char_uri, RDFS.label, Literal("CPU Time Used"))) + self.g.add( + (cpu_time_char_uri, self.MLS.hasValue, Literal(cpu_time_used, datatype=XSD.decimal)) + ) self.g.add((run_uri, self.MLS.hasQuality, cpu_time_char_uri)) - - def serialize(self, format='turtle'): - rdf_bytes = self.g.serialize(format=format, encoding='utf-8') - return rdf_bytes.decode('utf-8') - + + def serialize(self, format="turtle"): + rdf_bytes = self.g.serialize(format=format, encoding="utf-8") + return rdf_bytes.decode("utf-8") + def query(self, query_string): - return self.g.query(query_string) \ No newline at end of file + return self.g.query(query_string) diff --git a/ml/src/pyproject.toml b/ml/src/pyproject.toml new file mode 100644 index 00000000..4c348458 --- /dev/null +++ b/ml/src/pyproject.toml @@ -0,0 +1,28 @@ +[build-system] +requires = ["setuptools>=77"] +build-backend = "setuptools.build_meta" + +[project] +name = "mlschema" +version = "0.3.0" +description = "Converts machine learning models to RDF using the ML Schema ontology" +license = "MPL-2.0" +requires-python = ">=3.10" +authors = [ + { name = "Volodymyr Kadzhaia" }, + { name = "Pieter Bonte" }, +] +dependencies = [ + "numpy>=1.20", + "packaging>=20.0", + "rdflib>=6.0", + "scikit-learn>=1.0", +] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Programming Language :: Python :: 3", +] + +[tool.setuptools] +py-modules = ["mlschema"] diff --git a/ml/src/setup.py b/ml/src/setup.py deleted file mode 100644 index 7475a4e7..00000000 --- a/ml/src/setup.py +++ /dev/null @@ -1,38 +0,0 @@ -# -# Copyright © 2024 Volodymyr Kadzhaia -# Copyright © 2024 Pieter Bonte -# KU Leuven — Stream Intelligence Lab, Belgium -# -# This Source Code Form is subject to the terms of the Mozilla Public -# License, v. 2.0. If a copy of the MPL was not distributed with this file, -# you can obtain one at https://mozilla.org/MPL/2.0/. -# - -from setuptools import setup, find_packages - -setup( - name="mlschema", - version="0.3.0", - py_modules=["mlschema"], - packages=find_packages(), - install_requires=[ - "rdflib>=6.0.0", - "numpy>=1.20.0", - "scikit-learn>=1.0.0", - "packaging>=20.0" - ], - python_requires=">=3.7", - author="Volodymyr Kadzhaia, Pieter Bonte", - description="ML Schema converter for RDF", - long_description="Convert machine learning models to RDF using ML Schema ontology", - classifiers=[ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - ], -) \ No newline at end of file diff --git a/ml/tests/test_example_import_safety.py b/ml/tests/test_example_import_safety.py index 5ca1c19f..96939432 100644 --- a/ml/tests/test_example_import_safety.py +++ b/ml/tests/test_example_import_safety.py @@ -1,11 +1,8 @@ -"""Structural regression: examples must not train or generate at import time. +"""Structural regression: examples must not train or generate at import time""" -This supplements (does not replace) runtime approved-module tests. -Run with python -m unittest discover -s ml/tests. -""" import ast -from pathlib import Path import unittest +from pathlib import Path class ExampleImportSafety(unittest.TestCase): @@ -15,19 +12,21 @@ def test_generation_is_guarded(self): with self.subTest(example=path.name): tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path)) generators = [ - node for node in tree.body + node + for node in tree.body if isinstance(node, ast.FunctionDef) and node.name == "generate_models" ] self.assertEqual(len(generators), 1) for node in tree.body: - if isinstance(node, (ast.FunctionDef, ast.ClassDef, ast.Import, ast.ImportFrom)): + if isinstance( + node, (ast.FunctionDef, ast.ClassDef, ast.Import, ast.ImportFrom) + ): continue if isinstance(node, ast.Expr) and isinstance(node.value, ast.Constant): - continue # module docstring + continue if isinstance(node, ast.If): self.assertEqual(ast.unparse(node.test), "__name__ == '__main__'") continue - # Constants are allowed, executable model construction is not. if isinstance(node, ast.Assign): self.assertFalse(any(isinstance(n, ast.Call) for n in ast.walk(node))) continue diff --git a/perf-baseline/README.md b/perf-baseline/README.md deleted file mode 100644 index cdca470c..00000000 --- a/perf-baseline/README.md +++ /dev/null @@ -1,161 +0,0 @@ -# Performance baseline - -`baseline.json` is a committed summary of benchmark medians plus each benchmark's -measured noise. It exists because criterion writes to `target/criterion`, which is -gitignored — so numbers vanish on `cargo clean`, are not comparable across machines, -and leave no reference point in history. - -## Running benchmarks - -`cargo bench --workspace` is **not** a usable command in this repo. Benches are split -into two tiers by `scripts/perf_bench.sh`: - -```bash -scripts/perf_bench.sh routine # 8 benches, 32 cases, ~350s — the refactor guard -scripts/perf_bench.sh routine --quick # reduced sampling, ~30s — gross errors only -scripts/perf_bench.sh heavy # 4 scaling sweeps — minutes to hours -scripts/perf_bench.sh census # everything, for classification -``` - -The `heavy` tier is four parameter sweeps (49–85 cases each, up to `size_5000` at -~3.9 s per iteration). `citybench_cross_window_compare` ran **over 36 minutes without -finishing** under `cargo bench --workspace`. Reducing the sample size does not help: -the cost is the workload per iteration. These are research results comparing naive -against incremental strategies, not refactor guards — run them deliberately. - -## Judging a change - -Three kinds of variation were measured here (Oct 2026, M-series laptop), each larger -than the last: - -| | median | p90 | max | -|---|---|---|---| -| two passes back-to-back | 1.5% | 4.5% | 8.7% | -| across sessions, same code | 3.0% | 8.1% | 12.5% | -| **repeated runs, per benchmark (4-run baseline)** | **2.6%** | **11.4%** | **38.3%** | - -The third row is the one that matters, and getting it wrong cost real time twice. -Noise was originally derived from two back-to-back passes — which measures short-term -repeatability, and in any case cannot estimate a spread from two samples. It -understated 25 of 32 benchmarks, some by more than an order of magnitude: - -| benchmark | 2-run estimate | 4-run reality | -|---|---|---| -| `micro_dictionary/decode` | 0.95% | **38.3%** | -| `micro_field_access/outlined_accessor` | 0.03% | 11.4% | -| `micro_join/schema_plus_tuples` | 1.20% | 11.8% | -| `execution_hot_paths/filter_numeric` | 0.22% | ~9% | - -Both false positives had the same shape: a single before/after pair produced a delta -just over a floor derived from a baseline that claimed near-zero noise. -`execution_hot_paths/filter_numeric` was flagged at +5.7% against a recorded 0.22%, -and four runs in *each* state then showed the distributions overlapping with the -unchanged state marginally slower. - -Cross-session drift is real too: `micro_pattern/all_variables` measured 1.244 ms in -one session and 1.119 ms in the next with identical source. - -### Benchmarks that cannot currently detect a regression - -A benchmark whose own spread exceeds the effect you are looking for is not evidence. -As of the 4-run baseline: - -| benchmark | noise | note | -|---|---|---| -| `micro_dictionary/decode` | 38.3% | three runs cluster at ~70 µs, one at 97 µs | -| `micro_scan_broad_all/dyn_boxed_iter` | 19.6% | allocation-dominated at 40k quads | -| `micro_join/schema_plus_tuples` | 11.8% | | -| `micro_field_access/*_accessor` | 11.4% | | - -These still answer the *design* questions they were written for, where the effects are -large (the join representation differs by 4.7×, not 12%). They are poor regression -guards. The fix is to make them quieter — `micro_scan` already dropped `SUBJECTS` from -20k to 5k for this reason — not to narrow the threshold until they fire. - -**Authoritative procedure — before/after in the same session:** - -```bash -# on the unmodified checkout -rm -rf target/criterion && scripts/perf_bench.sh routine -python3 scripts/perf_report.py collect -o /tmp/before.json - -# apply the change, rebuild, then -rm -rf target/criterion && scripts/perf_bench.sh routine -python3 scripts/perf_report.py collect -o /tmp/after.json - -python3 scripts/perf_report.py compare /tmp/before.json --current /tmp/after.json -``` - -**Then judge anything it flags.** Two `collect` summaries carry no noise data, so that -comparison falls back to the flat ±5% floor and will over-report on noisy benchmarks — -it tells you the delta, not whether the delta is real. `compare` warns when this -happens. For each flagged benchmark: - -1. Look up its `noise_pct` in `baseline.json`. A delta under ~2× that figure is not - evidence. This alone would have dismissed both historical false positives. -2. If it is still borderline, run **both** states 3–4 times and compare the - distributions, not single points. That is what settled every ambiguous case so far: - -```bash -bin=$(ls -t target/release/deps/-* | grep -v '\.d$' | head -1) -for i in 1 2 3 4; do - rm -rf target/criterion - "$bin" --bench --noplot --quiet '' >/dev/null 2>&1 - python3 -c "import json,glob; f=glob.glob('target/criterion/**//new/estimates.json',recursive=True); print(json.load(open(f[0]))['median']['point_estimate']/1e6)" -done -``` - -`baseline.json` is a historical reference for catching gross drift, not a precision -instrument. Treat differences against it below that benchmark's recorded noise as -unproven. - -### Like-for-like is not optional, and the 4-run baseline does not rescue you from it - -Noise is a property of the benchmark **and how it was invoked**, not of the benchmark -alone. Measured on `execution_hot_paths/filter_numeric`, same code, same machine, same -afternoon: - -| how it was run | spread | -|---|---| -| inside four full `routine` tier passes | **1.9%** | -| filtered to that one case, four times, after a rebuild | **9.0%** | - -So the committed baseline — built from full-tier passes — understates what you will see -if you investigate a single case in isolation, which is exactly what the follow-up -procedure above tells you to do. Two of the three historical false positives would -still clear their limits against this baseline for that reason. - -The 4-run baseline fixes the arithmetic (two samples cannot estimate a spread) but it -cannot anticipate an invocation pattern it never saw. **Compare like-for-like: same -tier, same filter, same way.** When a flagged delta matters, re-measure both states -using the same invocation you are judging them by. - -## Regenerating the baseline - -Needs **four or more** passes. A baseline without a per-benchmark noise figure cannot -answer "is this delta real?", and one built from two passes answers it wrongly — see -above. `baseline` warns if given fewer than four. - -```bash -for i in 1 2 3 4; do - rm -rf target/criterion && scripts/perf_bench.sh routine - python3 scripts/perf_report.py collect -o /tmp/p$i.json -done -python3 scripts/perf_report.py baseline /tmp/p{1,2,3,4}.json -o perf-baseline/baseline.json -``` - -Four routine-tier passes take about 24 minutes. Noise is recorded as the full observed -range, which is deliberately conservative: missing a marginal regression is cheaper -than reporting phantom ones, because a harness that cries wolf stops being read. - -Clear `target/criterion` first: `collect` cannot tell when data was produced, so a -stale directory silently mixes runs — including runs at different sample sizes. - -## Gotchas - -- **Pass `--bench`.** With `harness = false`, criterion defaults to *test mode* (one - iteration per case, no measurement) when invoked directly without it. `cargo bench` - supplies it; a direct binary invocation must too. `perf_bench.sh` handles this. - Symptom: benches report success in 0 s and record nothing. -- **No CI gate.** Shared-runner variance exceeds the regressions worth hunting, and a - gate that cries wolf gets ignored. diff --git a/perf-baseline/baseline.json b/perf-baseline/baseline.json deleted file mode 100644 index 33276b5d..00000000 --- a/perf-baseline/baseline.json +++ /dev/null @@ -1,251 +0,0 @@ -{ - "benchmarks": { - "execution_hot_paths/filter_numeric": { - "mad_ns": 25333.167119690872, - "median_ns": 2101911.015277778, - "noise_pct": 2.4215513676313036, - "runs": 4 - }, - "execution_hot_paths/graph_fixed_scan": { - "mad_ns": 10602.35159808915, - "median_ns": 1891275.2503132832, - "noise_pct": 12.062973037638406, - "runs": 4 - }, - "execution_hot_paths/graph_variable_scan": { - "mad_ns": 166596.59873627807, - "median_ns": 9652651.042410713, - "noise_pct": 12.718268100736893, - "runs": 4 - }, - "execution_hot_paths/join_two_patterns": { - "mad_ns": 171932.85818855418, - "median_ns": 12815082.643915344, - "noise_pct": 9.840764203470398, - "runs": 4 - }, - "execution_hot_paths/union_branches": { - "mad_ns": 412146.06840461, - "median_ns": 23549438.500520833, - "noise_pct": 14.464439345610652, - "runs": 4 - }, - "hybrid_escalated_exact": { - "mad_ns": 649.6200111571964, - "median_ns": 102397.22557660865, - "noise_pct": 0.8636542673833896, - "runs": 4 - }, - "hybrid_full_sdd": { - "mad_ns": 336.9997830835153, - "median_ns": 49600.44143584545, - "noise_pct": 0.9297426100601228, - "runs": 4 - }, - "hybrid_topk_only": { - "mad_ns": 278.2361227609673, - "median_ns": 50746.25279567855, - "noise_pct": 1.2835716608376553, - "runs": 4 - }, - "micro_dictionary/decode": { - "mad_ns": 5658.219719397398, - "median_ns": 74089.87627962258, - "noise_pct": 31.249122526797397, - "runs": 4 - }, - "micro_dictionary/encode_cold": { - "mad_ns": 11602.91165853523, - "median_ns": 987088.7254110884, - "noise_pct": 3.255143724200958, - "runs": 4 - }, - "micro_dictionary/encode_warm": { - "mad_ns": 7265.8258737717815, - "median_ns": 182635.01842588448, - "noise_pct": 8.395087937138156, - "runs": 4 - }, - "micro_field_access/guard_accessor": { - "mad_ns": 33.83401194594026, - "median_ns": 5309.315766338726, - "noise_pct": 3.043032858143782, - "runs": 4 - }, - "micro_field_access/outlined_guard": { - "mad_ns": 41.80032850701685, - "median_ns": 5481.217483660131, - "noise_pct": 0.3914989908309902, - "runs": 4 - }, - "micro_field_access/terms_then_scope": { - 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"mad_ns": 6260.927948524783, - "median_ns": 180865.14875116714, - "noise_pct": 11.899852429976097, - "runs": 4 - }, - "micro_term_encoder/encode_dyn_interior": { - "mad_ns": 1520.968789991522, - "median_ns": 200177.04005813954, - "noise_pct": 1.1812161753148982, - "runs": 4 - }, - "micro_term_encoder/encode_lock_per_call": { - "mad_ns": 2724.634840335162, - "median_ns": 192832.58664215688, - "noise_pct": 2.050456688193318, - "runs": 4 - } - }, - "runs": 4 -} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..340a6f2a --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,44 @@ +# Python tooling for the repository; Cargo still builds the Rust workspace +[project] +name = "kolibrie-workspace" +version = "0.3.0" +description = "Python environment for developing Kolibrie: ML runtime, linting and tests" +license = "MPL-2.0" +requires-python = ">=3.10" +dependencies = [] + +[dependency-groups] +ml = [ + "mlschema", + "numpy>=1.26", + "packaging>=23.0", + "psutil>=5.9", + "rdflib>=7.0", + "scikit-learn>=1.3", +] +dev = [ + "pytest>=8.0", + "ruff>=0.12", +] + +[tool.uv] +package = false +default-groups = ["dev", "ml"] +# The ml crate embeds the interpreter, so it must link a libpython the system provides +python-preference = "system" + +[tool.uv.sources] +mlschema = { path = "ml/src", editable = true } + +[tool.ruff] +line-length = 100 +target-version = "py310" + +[tool.ruff.lint] +select = ["E", "W", "F", "I", "B", "UP", "C4", "SIM"] + +[tool.ruff.lint.isort] +known-first-party = ["kolibrie", "mlschema"] + +[tool.pytest.ini_options] +testpaths = ["ml/tests"] diff --git a/python/build.rs b/python/build.rs index a363e19c..46c13c6d 100644 --- a/python/build.rs +++ b/python/build.rs @@ -8,12 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Links the extension module against the host Python on macOS. -//! -//! A cdylib that Python loads must leave its Python symbols unresolved at link -//! time and pick them up from the interpreter that loads it. `-undefined -//! dynamic_lookup` is how macOS expresses that; other platforms need nothing -//! here. +//! Links the extension module against the host Python on macOS fn main() { if cfg!(target_os = "macos") { diff --git a/python/examples/01_terms.py b/python/examples/01_terms.py index 187bd8d1..57d5ae9a 100644 --- a/python/examples/01_terms.py +++ b/python/examples/01_terms.py @@ -1,17 +1,4 @@ -"""Reading and building RDF terms from Python. - -A query hands back terms as strings, and a term's string is its canonical spelling: -an IRI bare, a plain literal bare, a typed literal as `"25"^^<...#integer>`, a -language-tagged one as `"hello"@en`, a blank node as `_:label`. That spelling is the -term's identity, which is why it carries the datatype instead of discarding it -- and -why reading a *value* out of one means asking rather than using the string directly. - -Run it from a directory holding the built extension module as `kolibrie.so`: -`scripts/smoke_python.py` does that for you. - -Every claim below is asserted, so this file fails loudly rather than printing -something wrong. -""" +"""Reading and building RDF terms from Python""" import kolibrie @@ -23,16 +10,12 @@ def heading(text): def short(iri): - """The last segment of an IRI, after either separator. - - `urn:age` and `http://example.org/knows` both have a last segment; only the - second has a slash, which is why splitting on one alone is not enough. - """ + """The last segment of an IRI, after either separator""" return iri.rsplit("/", 1)[-1].rsplit(":", 1)[-1] def loaded_database(): - database = kolibrie.SparqlDatabase() + database = kolibrie.KolibrieDB() database.parse_turtle( """@prefix ex: . "30"^^ . @@ -55,15 +38,11 @@ def what_a_query_returns(): objects = {short(predicate): obj for _, predicate, obj in rows} - # A typed literal keeps its datatype, so the string is not the value. assert objects["age"].startswith('"'), objects["age"] assert kolibrie.literal_datatype(objects["age"]) == XSD_INTEGER - # A plain literal is spelled bare, exactly the text it holds. assert objects["nick"] == "Ali" - # A language tag survives, and is not glued onto the text. assert objects["name"] == '"Alice"@en' assert kolibrie.literal_language(objects["name"]) == "en" - # An IRI is an IRI whatever its scheme, and a prefixed name is expanded. assert kolibrie.term_kind(objects["knows"]) == "iri" assert objects["knows"] == "urn:bob" @@ -78,17 +57,14 @@ def reading_a_value(): f"numeric={kolibrie.numeric_value(term)!r}" ) - # The number lives in the lexical form, not the spelling. Without this, Python - # arithmetic on a typed literal would have to strip the datatype by hand. assert sorted(kolibrie.numeric_value(term) for term in ages) == [7.0, 30.0] - # And the engine agrees with Python about the sum, which is the property that - # breaks silently if either side reads the spelling instead of the value. through_engine = database.exec_query("SELECT SUM(?o) WHERE { ?s ?o }") - print(f" engine SUM={through_engine} python sum={sum(kolibrie.numeric_value(t) for t in ages)}") + print( + f" engine SUM={through_engine} python sum={sum(kolibrie.numeric_value(t) for t in ages)}" + ) assert kolibrie.numeric_value(through_engine[0][0]) == 37.0 - # A term that denotes no number says so, rather than guessing. assert kolibrie.numeric_value("urn:alice") is None assert kolibrie.numeric_value('"Alice"@en') is None @@ -107,18 +83,13 @@ def building_terms(): ]: print(f" {label:12} {term!r}") - # RDF 1.1 section 3.3: a plain literal *is* an xsd:string literal, so one term. assert plain == as_string - # Differing only in datatype is still two terms. assert plain != typed - # There is no make_iri, and that is the point: an IRI is spelled as itself. assert kolibrie.term_kind("http://example.org/x") == "iri" def blank_nodes(): heading("Blank nodes and their scope") - # A label identifies a node only within the document it came from, so building - # one needs a scope. One scope per document. first = kolibrie.new_document_scope() second = kolibrie.new_document_scope() in_first = kolibrie.make_blank_node("b", first) @@ -136,9 +107,6 @@ def exchanging_data(): scope = kolibrie.new_document_scope() node = kolibrie.make_blank_node("b", scope) - # A blank node label means nothing outside its document, so publishing one and - # merging it elsewhere fuses unrelated nodes. Skolemisation replaces it with an - # IRI under an authority you control, which is global. mine = kolibrie.skolemise(node, "http://alice.example") theirs = kolibrie.skolemise(node, "http://bob.example") print(f" as alice.example {mine}") @@ -149,8 +117,6 @@ def exchanging_data(): assert kolibrie.deskolemise(mine) == node, "and the node is recoverable" assert kolibrie.deskolemise("http://example.org/ordinary") is None - # Writing a term out is not the same as its spelling: N-Triples needs the - # brackets a canonical IRI does not carry. print(f" to_ntriples_term {kolibrie.to_ntriples_term('urn:alice')}") assert kolibrie.to_ntriples_term("urn:alice") == "" assert kolibrie.to_ntriples_term("Ali") == '"Ali"' @@ -159,40 +125,30 @@ def exchanging_data(): def publishing_and_merging(): heading("Publishing a graph, and merging someone else's") - # Two systems, each with its own document that happens to use the label `_:b`. document = '_:b "1" .\n' - alice = kolibrie.SparqlDatabase() + alice = kolibrie.KolibrieDB() alice.load_ntriples(document) - bob = kolibrie.SparqlDatabase() + bob = kolibrie.KolibrieDB() bob.load_ntriples(document) - # Published plainly, both carry a document-local label -- and because each - # process counts its own scopes from one, two systems can mint the same label. print(f" alice, plain {alice.to_ntriples().strip()}") print(f" bob, plain {bob.to_ntriples().strip()}") - # Skolemised under each authority, they cannot collide. from_alice = alice.to_ntriples_skolemised("http://alice.example") from_bob = bob.to_ntriples_skolemised("http://bob.example") print(f" alice, skolemised {from_alice.strip()}") print(f" bob, skolemised {from_bob.strip()}") assert from_alice != from_bob - # Merged into one store, they stay two nodes -- though in *this* process that - # much was already true, since alice and bob drew consecutive scopes from one - # counter. Two machines would both start at one and mint the same label, and - # that is the collision skolemisation removes. - merged = kolibrie.SparqlDatabase() + merged = kolibrie.KolibrieDB() merged.load_ntriples(from_alice) merged.load_ntriples(from_bob) - subjects = {s for s, in merged.exec_query("SELECT ?s WHERE { ?s ?o }")} + subjects = {s for (s,) in merged.exec_query("SELECT ?s WHERE { ?s ?o }")} print(f" merged subjects {len(subjects)}") assert len(subjects) == 2, f"two nodes, got {subjects}" assert all(kolibrie.term_kind(s) == "iri" for s in subjects), subjects - # So this is the assertion that carries the weight: one label, two authorities, - # two IRIs. It is what the merge would rest on if these were separate machines. shared_label = kolibrie.make_blank_node("b", kolibrie.new_document_scope()) as_alice = kolibrie.skolemise(shared_label, "http://alice.example") as_bob = kolibrie.skolemise(shared_label, "http://bob.example") @@ -201,15 +157,14 @@ def publishing_and_merging(): assert as_alice != as_bob, "the authority is what separates them" assert kolibrie.deskolemise(as_alice) == kolibrie.deskolemise(as_bob) == shared_label - # A round trip keeps every term, datatype and tag included. source = ( ' "30"^^ .\n' ' "Alice"@en .\n' ' "Ali" .\n' ) - there = kolibrie.SparqlDatabase() + there = kolibrie.KolibrieDB() there.load_ntriples(source) - back = kolibrie.SparqlDatabase() + back = kolibrie.KolibrieDB() back.load_ntriples(there.to_ntriples()) objects = sorted(o for _, o in back.exec_query("SELECT ?s ?o WHERE { ?s ?p ?o }")) print(f" round-tripped {objects}") diff --git a/python/examples/02_parsing.py b/python/examples/02_parsing.py index f9331f68..1489cf33 100644 --- a/python/examples/02_parsing.py +++ b/python/examples/02_parsing.py @@ -1,14 +1,4 @@ -"""Reading and writing RDF: N-Triples, Turtle, RDF/XML. - -Four readers and two writers, and the thing worth knowing about all of them is that -a term's spelling survives the trip. A datatype read from one format comes back from -another, because every reader spells a term the same way -- which was not true until -recently, and is what lets data loaded as Turtle join against data loaded as -N-Triples. - -Run with `scripts/smoke_python.py`, which builds the extension module and puts the -examples beside it. Every claim below is asserted. -""" +"""Reading and writing RDF: N-Triples, Turtle, RDF/XML""" import pathlib import tempfile @@ -24,7 +14,7 @@ def heading(text): def from_ntriples(): heading("N-Triples, from a string") - database = kolibrie.SparqlDatabase() + database = kolibrie.KolibrieDB() database.load_ntriples( f' "30"^^<{XSD_INTEGER}> .\n' ' "Alice"@en .\n' @@ -39,9 +29,7 @@ def from_ntriples(): def from_turtle(): heading("Turtle, with prefixes") - database = kolibrie.SparqlDatabase() - # A prefixed datatype resolves, and the term it produces is the same term the - # N-Triples reader produces from the full IRI. + database = kolibrie.KolibrieDB() database.parse_turtle( """@prefix xsd: . @prefix ex: . @@ -53,22 +41,20 @@ def from_turtle(): for predicate, obj in rows: print(f" {predicate:24} {obj!r}") - objects = {p: o for p, o in rows} - # The prefix was expanded, so the predicate is a full IRI. + objects = dict(rows) assert "http://example.org/worksAt" in objects, objects - # And the datatype survived, prefixed form and all. assert kolibrie.literal_datatype(objects["urn:age"]) == XSD_INTEGER return database def every_reader_agrees(): heading("Every reader spells a term the same way") - as_ntriples = kolibrie.SparqlDatabase() + as_ntriples = kolibrie.KolibrieDB() as_ntriples.load_ntriples(f' "30"^^<{XSD_INTEGER}> .\n') - as_turtle = kolibrie.SparqlDatabase() + as_turtle = kolibrie.KolibrieDB() as_turtle.parse_turtle( - '@prefix xsd: .\n' + "@prefix xsd: .\n" ' "30"^^xsd:integer .\n' ) @@ -82,7 +68,7 @@ def every_reader_agrees(): " \n" "\n" ) - as_rdfxml = kolibrie.SparqlDatabase() + as_rdfxml = kolibrie.KolibrieDB() as_rdfxml.load_file(str(path), "rdfxml") terms = {} @@ -91,7 +77,7 @@ def every_reader_agrees(): ("turtle", as_turtle), ("rdf/xml", as_rdfxml), ]: - objects = [o for o, in database.exec_query("SELECT ?o WHERE { ?s ?p ?o }")] + objects = [o for (o,) in database.exec_query("SELECT ?o WHERE { ?s ?p ?o }")] terms[label] = objects[0] print(f" {label:10} {objects[0]!r}") @@ -106,28 +92,22 @@ def writing_it_back(): for line in wire.strip().splitlines(): print(f" {line}") - # Valid N-Triples: an IRI in brackets, a literal quoted, a datatype kept. assert "" in wire assert f'"30"^^<{XSD_INTEGER}>' in wire assert '"Alice"@en' in wire - # And read back, every term is the one that went out. - back = kolibrie.SparqlDatabase() + back = kolibrie.KolibrieDB() back.load_ntriples(wire) assert sorted(back.exec_query("SELECT ?p ?o WHERE { ?s ?p ?o }")) == sorted( database.exec_query("SELECT ?p ?o WHERE { ?s ?p ?o }") ) - # N-Quads keeps named graphs, which N-Triples has no syntax for. print(f" to_nquads() lines: {len(database.to_nquads().strip().splitlines())}") def building_a_graph_by_hand(): heading("Building a graph without parsing anything") - database = kolibrie.SparqlDatabase() - # add_triple takes terms, so a literal has to be spelled as one -- otherwise it - # is stored as whatever string was passed, and `"30"` and `30` are not the same - # term. + database = kolibrie.KolibrieDB() database.add_triple("urn:alice", "urn:age", kolibrie.make_literal("30", XSD_INTEGER)) database.add_triple("urn:alice", "urn:nick", kolibrie.make_literal("Ali")) database.add_triple("urn:alice", "urn:knows", "urn:bob") @@ -135,8 +115,6 @@ def building_a_graph_by_hand(): for predicate, obj in sorted(database.exec_query("SELECT ?p ?o WHERE { ?s ?p ?o }")): print(f" {predicate:12} {obj!r:52} [{kolibrie.term_kind(obj)}]") - # The hand-built typed literal is a number to the engine, exactly as a parsed - # one would be. assert database.exec_query("SELECT SUM(?o) WHERE { ?s ?o }") == [["30"]] diff --git a/python/examples/03_sparql.py b/python/examples/03_sparql.py index b066cd97..4d62567a 100644 --- a/python/examples/03_sparql.py +++ b/python/examples/03_sparql.py @@ -1,15 +1,4 @@ -"""Querying with SPARQL, two ways. - -`exec_query` takes SPARQL text and answers a list of rows. `query()` builds a pattern -match without writing any, which suits a filter assembled from code. - -Values are the thing to watch: a row holds terms, and a term is not its value. A -typed literal reads as `'"30"^^<...#integer>'`, so sorting or summing the strings -would be wrong -- the engine does it by value, and `numeric_value` is how Python -gets the same answer. - -Run with `scripts/smoke_python.py`. Every claim below is asserted. -""" +"""Querying with SPARQL, two ways""" import kolibrie @@ -21,7 +10,7 @@ def heading(text): def people(): - database = kolibrie.SparqlDatabase() + database = kolibrie.KolibrieDB() database.load_ntriples( f""" "30"^^<{XSD_INTEGER}> . "7"^^<{XSD_INTEGER}> . @@ -44,8 +33,6 @@ def selecting(): print(f" {subject:12} {age!r}") assert len(rows) == 3 - # A query that matches nothing answers an empty list. A query that is wrong - # raises, so the two are not confused. assert database.exec_query("SELECT ?s WHERE { ?s ?o }") == [] try: database.exec_query("SELECT ?s WHERE {") @@ -58,27 +45,22 @@ def joining_and_filtering(): heading("Joins and FILTER") database = people() - # Two patterns sharing a variable join on it. joined = sorted( - database.exec_query( - "SELECT ?a ?b WHERE { ?a ?b . ?b ?age }" - ) + database.exec_query("SELECT ?a ?b WHERE { ?a ?b . ?b ?age }") ) print(f" who knows whom: {joined}") assert joined == [["urn:alice", "urn:bob"], ["urn:bob", "urn:carol"]] - # FILTER compares by value, so a typed literal is a number here. adults = sorted( - s for s, in database.exec_query( - "SELECT ?s WHERE { ?s ?age . FILTER(?age > 10) }" - ) + s + for (s,) in database.exec_query("SELECT ?s WHERE { ?s ?age . FILTER(?age > 10) }") ) print(f" age > 10: {adults}") assert adults == ["urn:alice", "urn:carol"], adults - # Arithmetic inside a FILTER agrees with the comparison. next_year = sorted( - s for s, in database.exec_query( + s + for (s,) in database.exec_query( "SELECT ?s WHERE { ?s ?age . FILTER(?age + 1 > 22) }" ) ) @@ -96,21 +78,16 @@ def aggregating_and_ordering(): if expected is not None: assert rows == [[expected]], rows - # ORDER BY sorts by value, so seven comes before twenty-one -- sorting the - # spellings would put "21" first. ordered = [ kolibrie.numeric_value(age) - for age, in database.exec_query( - "SELECT ?age WHERE { ?s ?age } ORDER BY ?age" - ) + for (age,) in database.exec_query("SELECT ?age WHERE { ?s ?age } ORDER BY ?age") ] print(f" ORDER BY ?age {ordered}") assert ordered == [7.0, 21.0, 30.0], ordered - # LIMIT applies after the ordering, so it takes the smallest two. limited = [ kolibrie.numeric_value(age) - for age, in database.exec_query( + for (age,) in database.exec_query( "SELECT ?age WHERE { ?s ?age } ORDER BY ?age LIMIT 2" ) ] @@ -122,8 +99,6 @@ def building_a_query_instead_of_writing_one(): heading("The query builder") database = people() - # A pattern match assembled from code, for a filter that comes from somewhere - # other than a string literal. builder = database.query().with_predicate("urn:knows") print(f" with_predicate('urn:knows') count: {builder.count()}") assert builder.count() == 2 @@ -132,12 +107,10 @@ def building_a_query_instead_of_writing_one(): print(f" subjects with an age: {subjects}") assert subjects == ["urn:alice", "urn:bob", "urn:carol"] - # Grouping answers a dictionary, keyed by the term. grouped = database.query().with_predicate("urn:knows").group_by_subject() print(f" grouped by subject: {sorted(grouped)}") assert sorted(grouped) == ["urn:alice", "urn:bob"] - # The usual modifiers are there too. assert len(database.query().with_predicate("urn:age").limit(2).get_subjects()) == 2 @@ -145,9 +118,7 @@ def updating(): heading("INSERT and DELETE") database = people() - database.update( - 'INSERT DATA { "44"^^<%s> }' % XSD_INTEGER - ) + database.update(f'INSERT DATA {{ "44"^^<{XSD_INTEGER}> }}') assert database.exec_query("SELECT SUM(?age) WHERE { ?s ?age }") == [["102"]] print(" after INSERT, SUM(?age) = 102") @@ -156,7 +127,6 @@ def updating(): print(f" after DELETE, knows: {remaining}") assert remaining == [["urn:bob", "urn:carol"]], remaining - # A malformed update raises rather than reporting nothing to do. try: database.update("INSERT DATA {") raise AssertionError("a malformed update should raise") diff --git a/python/examples/04_streaming.py b/python/examples/04_streaming.py index b00bb677..282a6f92 100644 --- a/python/examples/04_streaming.py +++ b/python/examples/04_streaming.py @@ -1,22 +1,4 @@ -"""RDF Stream Processing: windows over streams, and joins across them. - -This is the real engine. The query builder's `as_stream` builds something else -- a -list that accumulates triples and applies limit, offset and distinct to them, with no -windows and no RSP-QL. `RspEngine` takes an RSP-QL query, opens a stream per window, -and joins results across the windows as they fire. - -Two things to know: - -* `timestamp` is the stream's own clock, not wall time. A window of `[RANGE 4 STEP 2]` - counts in whatever unit you stamp with, so a test can drive a stream in a loop - without waiting for anything. -* `stop()` flushes every window and waits for the coordinator, so the results are - complete when it returns. There is no sleep in this file, and that is the point: - the engine used to return from `stop()` while its threads were still working, and - callers slept and hoped. - -Run with `scripts/smoke_python.py`. Every claim below is asserted. -""" +"""RDF Stream Processing: windows over streams, and joins across them""" import kolibrie @@ -31,8 +13,6 @@ def heading(text): def one_window(): heading("One window over one stream") - # RANGE is the window's width and STEP how far it moves, both in the stream's - # own time unit. engine = kolibrie.RspEngine( f""" REGISTER RSTREAM AS @@ -54,8 +34,6 @@ def one_window(): print(f" streamed 8 readings, got {len(solutions)} solutions") assert solutions, "a window that fires should produce solutions" - # Every solution binds the one variable the query selects, to one of the - # subjects that was streamed. streamed = {f"http://example/r{tick}" for tick in range(8)} for solution in solutions: bound = dict(solution) @@ -66,8 +44,6 @@ def one_window(): def two_windows_joined(): heading("Two windows, joined across streams") - # One window per stream, and a pattern per window. The two patterns bind - # different variables, so a solution pairs a room with a zone. engine = kolibrie.RspEngine( f""" REGISTER RSTREAM AS @@ -90,10 +66,11 @@ def two_windows_joined(): print(f" streamed 6 + 6, got {len(solutions)} joined solutions") for solution in solutions[:3]: bound = dict(solution) - print(f" room={bound['room'].rsplit('/', 1)[-1]:8} zone={bound['zone'].rsplit('/', 1)[-1]}") + print( + f" room={bound['room'].rsplit('/', 1)[-1]:8} zone={bound['zone'].rsplit('/', 1)[-1]}" + ) assert solutions, "both windows fired, so the join should produce solutions" - # Each solution carries a variable from each window: that is the join. for solution in solutions: bound = dict(solution) assert set(bound) == {"room", "zone"}, bound @@ -116,13 +93,9 @@ def results_are_complete_when_stop_returns(): engine.stop() settled = engine.result_count() - # Read again: nothing arrives late, because stop() joined the window threads and - # the coordinator before returning. assert engine.result_count() == settled, "a result arrived after stop() returned" print(f" {settled} solutions, and the count does not move after stop()") - # Stopping twice is harmless; using a stopped engine is an error rather than a - # silent no-op. engine.stop() try: engine.add("sensors", f" a <{READING}> .", 99) @@ -133,8 +106,6 @@ def results_are_complete_when_stop_returns(): def reading_values_from_a_stream(): heading("Terms in a solution are terms") - # Streamed literals keep their datatype, so a solution's values need reading - # rather than using directly -- the same as any other query result. xsd_integer = "http://www.w3.org/2001/XMLSchema#integer" engine = kolibrie.RspEngine( """ diff --git a/python/examples/05_datalog.py b/python/examples/05_datalog.py index 271be93d..255ecf95 100644 --- a/python/examples/05_datalog.py +++ b/python/examples/05_datalog.py @@ -1,20 +1,4 @@ -"""Datalog reasoning: rules that derive facts from facts. - -Rules can be written down rather than assembled. Two syntaxes, and both parse from a -string: - -* N3 logic -- `{ premise } => { conclusion } .` under `@prefix` declarations, on a - `KnowledgeGraph`. -* Kolibrie's `RULE ... CONSTRUCT ... WHERE`, on a `SparqlDatabase`, which runs the - rule and adds what it derives. - -The alternative is building a rule out of `Rule`, `TriplePattern` and `Term`, which -means naming every constant by the integer id `encode_term` gave it. That is still -available, and the last section shows it, because a rule generated by code is easier -to build than to render as text. For a rule you wrote, the text is the rule. - -Run with `scripts/smoke_python.py`. Every claim below is asserted. -""" +"""Datalog reasoning: rules that derive facts from facts""" import kolibrie @@ -28,9 +12,6 @@ def heading(text): def n3_rules(): heading("N3 rules, as text") graph = kolibrie.KnowledgeGraph() - # Facts carry the expanded IRI, because that is what the rule's prefix resolves - # to. A fact added as `ex:hasParent` would not match a rule that says the same - # thing expanded -- they are two different terms. graph.add_abox_triple("alice", f"{EX}hasParent", "bob") graph.add_abox_triple("bob", f"{EX}hasParent", "carol") @@ -54,8 +35,6 @@ def several_rules_in_one_document(): for parent, child in [("bob", "alice"), ("carol", "bob"), ("dave", "carol")]: graph.add_abox_triple(child, f"{EX}hasParent", parent) - # One prefix block covers every rule, so rules written together keep their - # context. The second rule is transitive and consumes what the first derives. added = graph.add_n3_rules( f"""@prefix ex: <{EX}> . {{ ?x ex:hasParent ?y . }} => {{ ?x ex:hasAncestor ?y . }}. @@ -65,8 +44,6 @@ def several_rules_in_one_document(): print(f" rules parsed: {added}") assert added == 2 - # Semi-naive evaluation reaches a fixpoint, so the closure is complete rather - # than one pass deep. derived = [tuple(fact) for fact in graph.infer_new_facts_semi_naive()] ancestors = sorted( (subject, obj) for subject, predicate, obj in derived if predicate.endswith("hasAncestor") @@ -75,19 +52,16 @@ def several_rules_in_one_document(): print(f" {descendant:8} hasAncestor {ancestor}") alice = {ancestor for descendant, ancestor in ancestors if descendant == "alice"} - # dave needs two applications of the transitive rule to reach. assert alice == {"bob", "carol", "dave"}, alice def construct_rules(): heading("Kolibrie CONSTRUCT rules, as text") - database = kolibrie.SparqlDatabase() + database = kolibrie.KolibrieDB() database.load_ntriples( - " .\n" - " .\n" + " .\n .\n" ) - # Parsed, run, and the derived triples added to the store in one call. derived = database.add_rule( """ RULE :Grandparent :- @@ -100,12 +74,10 @@ def construct_rules(): print(f" {subject:12} {predicate:22} {obj}") assert derived == [("urn:alice", "urn:hasGrandparent", "urn:carol")], derived - # And they are in the store, so a query finds them. rows = database.exec_query("SELECT ?a ?b WHERE { ?a ?b }") print(f" queryable: {rows}") assert rows == [["urn:alice", "urn:carol"]], rows - # A rule that does not parse raises, rather than quietly deriving nothing. try: database.add_rule("RULE :Broken :- CONSTRUCT {") raise AssertionError("a malformed rule should raise") @@ -124,9 +96,6 @@ def asking_backwards(): """ ) - # Forward chaining derives everything that follows. Backward chaining answers - # one question, working from the goal back to the facts -- so the goal is built - # rather than written, since it is a pattern and not a rule. goal = kolibrie.TriplePattern( kolibrie.Term.Variable("who"), kolibrie.Term.Constant(graph.encode_term(f"{EX}hasGrandparent")), @@ -135,9 +104,6 @@ def asking_backwards(): answers = graph.backward_chaining(goal) print(f" asked for hasGrandparent, got {len(answers)} answer(s)") - # The bindings come back in the graph's own terms -- ids, and the internal - # variables the solver introduced while working backwards. `decode_term` reads - # them, and the two the goal named are the ones worth looking at. for answer in answers: readable = { name: graph.decode_term(term.id) @@ -149,10 +115,9 @@ def asking_backwards(): assert answers, "alice hasGrandparent carol follows from the two facts" assert any( { - name: graph.decode_term(term.id) - for name, term in answer.items() - if hasattr(term, "id") - }.get("who") == "alice" + name: graph.decode_term(term.id) for name, term in answer.items() if hasattr(term, "id") + }.get("who") + == "alice" for answer in answers ), answers @@ -163,9 +128,6 @@ def building_a_rule_from_code(): graph.add_abox_triple("alice", f"{EX}hasParent", "bob") graph.add_abox_triple("bob", f"{EX}hasParent", "carol") - # The same grandparent rule, assembled. Every constant is named by the id the - # graph gave it, which is the bookkeeping the text syntax does for you -- worth - # it only when the rule is generated rather than written. has_parent = kolibrie.Term.Constant(graph.encode_term(f"{EX}hasParent")) has_grandparent = kolibrie.Term.Constant(graph.encode_term(f"{EX}hasGrandparent")) x, y, z = (kolibrie.Term.Variable(name) for name in ("X", "Y", "Z")) diff --git a/python/src/lib.rs b/python/src/lib.rs index 745c58f9..53c79fcf 100644 --- a/python/src/lib.rs +++ b/python/src/lib.rs @@ -8,18 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Python bindings for Kolibrie, exported as the `kolibrie` extension module. -//! -//! Four surfaces, registered by the submodules here: the SPARQL query builder, the -//! Datalog knowledge graph, the term helpers that let a caller read a value out of a -//! term instead of parsing the string a query handed back, and the RSP engine. -//! -//! The interesting documentation for this crate is the Python API rather than this -//! Rust shim, which is why the `[lib]` sets `doc = false`. -//! -//! The library must be *named* `kolibrie` for Python to import it under that -//! name, which collides with the `kolibrie` crate's own rustdoc output and is the -//! other reason the docs are turned off here. +//! Python bindings for Kolibrie, exported as the `kolibrie` extension module mod py_knowledge_graph; mod py_query_builder; @@ -30,15 +19,9 @@ use pyo3::prelude::*; #[pymodule] fn kolibrie(m: &Bound<'_, PyModule>) -> PyResult<()> { - // Register the Datalog/KG types py_knowledge_graph::register(m)?; - // Register the SPARQL‐QueryBuilder types py_query_builder::register(m)?; - // Register the term helpers, so a caller can read a value out of a term - // rather than parsing its spelling py_rdf_term::register(m)?; - // Register the RSP engine: RSP-QL over real windows, not the list the query - // builder's `as_stream` accumulates py_rsp::register(m)?; Ok(()) } diff --git a/python/src/py_knowledge_graph.rs b/python/src/py_knowledge_graph.rs index d4f5d43a..d6b6aaf5 100644 --- a/python/src/py_knowledge_graph.rs +++ b/python/src/py_knowledge_graph.rs @@ -43,9 +43,6 @@ impl PyFilterCondition { #[pyclass(name = "Term")] #[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)] enum Term { - // Named rather than positional, so Python reads `term.name` and `term.id` - // instead of `term._0`, which is what pyo3 calls an unnamed field and is not - // something an example should have to show. #[pyo3(name = "Variable")] Variable { name: String }, @@ -150,7 +147,6 @@ impl PyKnowledgeGraph { ) -> Vec<(String, String, String)> { let results = self.inner.query_abox(subject, predicate, object); - // Acquire read lock once for all decoding operations let decoded_results: Vec<(String, String, String)> = results .into_iter() .map(|triple| { @@ -207,7 +203,6 @@ impl PyKnowledgeGraph { fn infer_new_facts(&mut self) -> Vec<(String, String, String)> { let inferred = self.inner.infer_new_facts(); - // Acquire read lock once for all decoding operations let decoded_results: Vec<(String, String, String)> = inferred .into_iter() .map(|triple| { @@ -224,7 +219,6 @@ impl PyKnowledgeGraph { fn infer_new_facts_semi_naive(&mut self) -> Vec<(String, String, String)> { let inferred = self.inner.infer_new_facts_semi_naive(); - // Acquire read lock once for all decoding operations let decoded_results: Vec<(String, String, String)> = inferred .into_iter() .map(|triple| { @@ -259,27 +253,11 @@ impl PyKnowledgeGraph { } fn encode_term(&mut self, term: &str) -> u32 { - // Acquire write lock for encoding let id = self.inner.encode_term(term); id } - /// Adds every rule in an N3 document, and returns how many were added. - /// - /// `{ premise } => { conclusion } .` under `@prefix` declarations, which is how - /// a rule is usually written down rather than assembled: - /// - /// ```text - /// @prefix ex: . - /// { ?x ex:hasParent ?y . ?y ex:hasParent ?z . } => { ?x ex:hasGrandparent ?z . } - /// ``` - /// - /// Building the same rule through `Rule`, `TriplePattern` and `Term` means - /// naming every constant by the id `encode_term` gave it; this interns them for - /// you, against this graph's own dictionary, so the rule matches its facts. - /// - /// One prefix block covers every rule in the document, so rules written - /// together keep their context. + /// Adds every rule in an N3 document, and returns how many were added fn add_n3_rules(&mut self, n3: &str) -> PyResult { let (_, (_prefixes, rules)) = datalog::parser_n3_logic::parse_n3_document(n3, &mut self.inner).map_err(|error| { @@ -292,12 +270,7 @@ impl PyKnowledgeGraph { Ok(added) } - /// The text an id stands for, or `None` if this graph never assigned it. - /// - /// The inverse of `encode_term`, and needed to read anything the graph answers - /// in terms of ids rather than strings. `backward_chaining` is the case that - /// forced it: its bindings carry `Term.Constant(0)`, which says nothing without - /// this. + /// The text an id stands for, or `None` if this graph never assigned it fn decode_term(&self, id: u32) -> Option { self.inner.decode_term(id) } diff --git a/python/src/py_query_builder.rs b/python/src/py_query_builder.rs index bc93804c..1e06f1fb 100644 --- a/python/src/py_query_builder.rs +++ b/python/src/py_query_builder.rs @@ -8,8 +8,8 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ +use kolibrie_core::database::KolibrieDB; use kolibrie_core::execute_query::execute; -use kolibrie_core::sparql_database::SparqlDatabase; use pyo3::exceptions::{PyOSError, PyRuntimeError, PyValueError}; use pyo3::prelude::*; use pyo3::types::PyAny; @@ -48,31 +48,21 @@ fn normalize_load_format(path: &str, format: Option<&str>) -> PyResult(mutex: &'a Mutex, what: &str) -> PyResult> { mutex .lock() .map_err(|_| PyRuntimeError::new_err(format!("{what} lock poisoned"))) } -#[pyclass(name = "SparqlDatabase")] -pub struct PySparqlDatabase { - db: Arc>, +#[pyclass(name = "KolibrieDB")] +pub struct PyKolibrieDB { + db: Arc>, } -// Summarised rather than derived: this wraps a whole store. `try_lock` because a -// `Debug` impl must not block — Python can reach this from any thread, including one -// that already holds the lock. -impl std::fmt::Debug for PySparqlDatabase { +impl std::fmt::Debug for PyKolibrieDB { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { - let mut s = f.debug_struct("SparqlDatabase"); + let mut s = f.debug_struct("KolibrieDB"); match self.db.try_lock() { Ok(db) => s.field("quads", &db.quad_count()), Err(_) => s.field("quads", &""), @@ -82,11 +72,11 @@ impl std::fmt::Debug for PySparqlDatabase { } #[pymethods] -impl PySparqlDatabase { +impl PyKolibrieDB { #[new] fn new() -> Self { - PySparqlDatabase { - db: Arc::new(Mutex::new(SparqlDatabase::new())), + PyKolibrieDB { + db: Arc::new(Mutex::new(KolibrieDB::new())), } } @@ -101,50 +91,32 @@ impl PySparqlDatabase { Ok(()) } - /// Load N-Triples from a string. - /// - /// The counterpart to `parse_turtle`, and the format data usually arrives in - /// over a wire: `load_file` could already read N-Triples from disk, so text - /// held in memory had to be written to a file first. - /// - /// Blank node labels are scoped to this call, which is one document. Two calls - /// naming `_:b` describe two different nodes, as RDF says they do. + /// Load N-Triples from a string fn load_ntriples(&self, ntriples: &str) -> PyResult<()> { let mut db = locked(&self.db, "database")?; db.parse_ntriples_and_add(ntriples); Ok(()) } - /// Serialise the default graph as N-Triples. - /// - /// Blank nodes are written with their document-local labels, which is sound - /// within this store and unsound across two: see `to_ntriples_skolemised` for - /// the form to publish or exchange. + /// Serialise the default graph as N-Triples fn to_ntriples(&self) -> PyResult { let db = locked(&self.db, "database")?; Ok(db.generate_ntriples()) } - /// Serialise every graph as N-Quads, which unlike N-Triples keeps named graphs. + /// Serialise every graph as N-Quads, which unlike N-Triples keeps named graphs fn to_nquads(&self) -> PyResult { let db = locked(&self.db, "database")?; Ok(db.generate_nquads()) } - /// Serialise as N-Triples, replacing blank nodes with Skolem IRIs under `base`. - /// - /// The form to publish or exchange. A blank node label means nothing outside - /// the document it came from, so two systems merging each other's output would - /// fuse unrelated nodes. `base` must be an authority you control: a Skolem IRI - /// is globally unique because its authority is, which is why there is no - /// default for it. + /// Serialise as N-Triples, replacing blank nodes with Skolem IRIs under `base` fn to_ntriples_skolemised(&self, base: &str) -> PyResult { let db = locked(&self.db, "database")?; Ok(db.generate_ntriples_skolemised(base)) } - /// Serialise as N-Quads, replacing blank nodes with Skolem IRIs under `base`. - /// See `to_ntriples_skolemised`. + /// Serialise as N-Quads, replacing blank nodes with Skolem IRIs under `base` fn to_nquads_skolemised(&self, base: &str) -> PyResult { let db = locked(&self.db, "database")?; Ok(db.generate_nquads_skolemised(base)) @@ -188,12 +160,7 @@ impl PySparqlDatabase { Ok(()) } - /// Execute a SELECT and return its rows. - /// - /// Raises on a syntax or execution failure rather than returning an empty - /// table, matching `update` below: an empty list now means the query matched - /// nothing, where it previously also meant the query was rejected or the - /// database lock was poisoned. + /// Execute a SELECT and return its rows fn exec_query(&self, query: &str) -> PyResult>> { let mut db = locked(&self.db, "database")?; execute(query, &mut db) @@ -201,23 +168,7 @@ impl PySparqlDatabase { .map_err(|error| PyErr::new::(error.to_string())) } - /// Parses a Kolibrie `RULE` block, runs it, and returns the triples it derived. - /// - /// The `CONSTRUCT` form, written down rather than assembled: - /// - /// ```text - /// RULE :Grandparent :- - /// CONSTRUCT { ?x ?z . } - /// WHERE { ?x ?y . ?y ?z . } . - /// ``` - /// - /// The derived triples are added to this store, and the prefixes the rule - /// declared are registered on it, so a later query can use them. Each derived - /// triple comes back as `(subject, predicate, object)` in canonical spelling. - /// - /// A `PROB(...)` annotation on the rule is accepted and governs how the - /// derivation is weighted; a rule naming a model the host has not approved is - /// refused before anything is parsed. + /// Parses a Kolibrie `RULE` block, runs it, and returns the triples it derived fn add_rule(&self, rule: &str) -> PyResult> { let mut db = locked(&self.db, "database")?; let (_rule, derived) = @@ -237,9 +188,7 @@ impl PySparqlDatabase { .collect()) } - /// Execute INSERT/DELETE DATA or a template-based INSERT/DELETE WHERE. - /// Syntax and execution failures are raised instead of being returned as - /// an ambiguous result table. + /// Execute INSERT/DELETE DATA or a template-based INSERT/DELETE WHERE fn update(&self, update: &str) -> PyResult<()> { let mut db = locked(&self.db, "database")?; kolibrie_core::execute_query::execute_sparql_update(update, &mut db) @@ -247,7 +196,7 @@ impl PySparqlDatabase { .map_err(PyValueError::new_err) } - /// Start building a query. + /// Start building a query fn query(&self) -> PyQueryBuilder { PyQueryBuilder { db: Arc::clone(&self.db), @@ -268,13 +217,12 @@ impl PySparqlDatabase { offset: None, sort_direction: PySortDirection::Ascending, - // RSP fields window_width: None, window_slide: None, report_strategies: Vec::new(), tick_strategy: PyTick::TimeDriven, stream_operator: None, - periodic_periods: Vec::new(), // <- This was missing! + periodic_periods: Vec::new(), } } } @@ -294,7 +242,6 @@ pub enum PyStreamOperator { DSTREAM, } -// Simple enum for report strategies #[pyclass(name = "ReportStrategy")] #[derive(Clone, Debug)] pub enum PyReportStrategy { @@ -331,16 +278,12 @@ pub enum PyTick { BatchDriven, } -// Simplified streaming interface #[pyclass(name = "StreamingQuery")] pub struct PyStreamingQuery { config: StreamingConfig, - // Store accumulated results results: Arc>>>, } -// Summarised rather than derived: `results` accumulates every solution the stream has -// produced. Not locked, for the reason given on [`PySparqlDatabase`]. impl std::fmt::Debug for PyStreamingQuery { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { let mut s = f.debug_struct("StreamingQuery"); @@ -354,7 +297,6 @@ impl std::fmt::Debug for PyStreamingQuery { #[derive(Clone)] struct StreamingConfig { - // Query filters subject: Option, subject_like: Option, subject_starting: Option, @@ -371,13 +313,12 @@ struct StreamingConfig { limit: Option, offset: Option, - // RSP config window_width: Option, window_slide: Option, - report_strategies: Vec, // Store as strings instead + report_strategies: Vec, tick_strategy: PyTick, stream_operator: Option, - periodic_periods: Vec, // Store periodic periods separately + periodic_periods: Vec, } impl PyStreamingQuery { @@ -401,7 +342,6 @@ impl PyStreamingQuery { ) -> PyResult<()> { let mut matches = true; - // Apply subject filters if let Some(ref s) = self.config.subject { matches &= subject == s; } @@ -415,7 +355,6 @@ impl PyStreamingQuery { matches &= subject.ends_with(s); } - // Apply predicate filters if matches { if let Some(ref p) = self.config.predicate { matches &= predicate == p; @@ -431,7 +370,6 @@ impl PyStreamingQuery { } } - // Apply object filters if matches { if let Some(ref o) = self.config.object { matches &= object == o; @@ -456,9 +394,7 @@ impl PyStreamingQuery { { let mut results = locked(&self.results, "results")?; - // Apply distinct filter if configured if self.config.distinct { - // Check if this triple already exists in any result batch let mut already_exists = false; for batch in results.iter() { if batch.contains(&triple) { @@ -471,10 +407,8 @@ impl PyStreamingQuery { } } - // Create a new result batch with this triple let mut batch = vec![triple]; - // Apply limit if configured if let Some(limit) = self.config.limit { let total_count: usize = results.iter().map(|batch| batch.len()).sum(); if total_count >= limit { @@ -498,11 +432,9 @@ impl PyStreamingQuery { let results = locked(&self.results, "results")?; let mut current_results = results.clone(); - // Apply offset if configured if let Some(offset) = self.config.offset { let total_triples: usize = current_results.iter().map(|batch| batch.len()).sum(); if offset < total_triples { - // For simplicity, we'll skip entire batches if needed let mut skipped = 0; current_results.retain(|batch| { if skipped < offset { @@ -534,13 +466,12 @@ impl PyStreamingQuery { /// Stop the streaming query fn stop_stream(&self) -> PyResult<()> { - // In a full implementation, this would stop background processing Ok(()) } /// Check if currently in streaming mode fn is_streaming(&self) -> PyResult { - Ok(true) // Always true for streaming queries + Ok(true) } /// Get window configuration @@ -574,7 +505,7 @@ impl PyStreamingQuery { #[pyclass(name = "QueryBuilder")] pub struct PyQueryBuilder { - db: Arc>, + db: Arc>, subject: Option, subject_like: Option, subject_starting: Option, @@ -592,18 +523,14 @@ pub struct PyQueryBuilder { offset: Option, sort_direction: PySortDirection, - // RSP fields (simplified) window_width: Option, window_slide: Option, - report_strategies: Vec, // Store as strings + report_strategies: Vec, tick_strategy: PyTick, stream_operator: Option, - periodic_periods: Vec, // Store periodic periods separately + periodic_periods: Vec, } -// Summarised rather than derived: `db` is a whole store, and the filter fields are -// numerous enough that printing only the ones that are set is more readable than -// twenty `None`s. impl std::fmt::Debug for PyQueryBuilder { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { let set: Vec<&str> = [ @@ -636,126 +563,126 @@ impl std::fmt::Debug for PyQueryBuilder { #[pymethods] impl PyQueryBuilder { - /// Set an exact subject filter. + /// Set an exact subject filter fn with_subject(&self, subj: &str) -> Self { let mut new_builder = self.clone(); new_builder.subject = Some(subj.to_owned()); new_builder } - /// Set an exact predicate filter. + /// Set an exact predicate filter fn with_predicate(&self, pred: &str) -> Self { let mut new_builder = self.clone(); new_builder.predicate = Some(pred.to_owned()); new_builder } - /// Set an exact object filter. + /// Set an exact object filter fn with_object(&self, obj: &str) -> Self { let mut new_builder = self.clone(); new_builder.object = Some(obj.to_owned()); new_builder } - /// Filter subjects containing a substring. + /// Filter subjects containing a substring fn with_subject_like(&self, pattern: &str) -> Self { let mut new_builder = self.clone(); new_builder.subject_like = Some(pattern.to_owned()); new_builder } - /// Filter subjects starting with a prefix. + /// Filter subjects starting with a prefix fn with_subject_starting(&self, prefix: &str) -> Self { let mut new_builder = self.clone(); new_builder.subject_starting = Some(prefix.to_owned()); new_builder } - /// Filter subjects ending with a suffix. + /// Filter subjects ending with a suffix fn with_subject_ending(&self, suffix: &str) -> Self { let mut new_builder = self.clone(); new_builder.subject_ending = Some(suffix.to_owned()); new_builder } - /// Filter predicates containing a substring. + /// Filter predicates containing a substring fn with_predicate_like(&self, pattern: &str) -> Self { let mut new_builder = self.clone(); new_builder.predicate_like = Some(pattern.to_owned()); new_builder } - /// Filter predicates starting with a prefix. + /// Filter predicates starting with a prefix fn with_predicate_starting(&self, prefix: &str) -> Self { let mut new_builder = self.clone(); new_builder.predicate_starting = Some(prefix.to_owned()); new_builder } - /// Filter predicates ending with a suffix. + /// Filter predicates ending with a suffix fn with_predicate_ending(&self, suffix: &str) -> Self { let mut new_builder = self.clone(); new_builder.predicate_ending = Some(suffix.to_owned()); new_builder } - /// Filter objects containing a substring. + /// Filter objects containing a substring fn with_object_like(&self, pattern: &str) -> Self { let mut new_builder = self.clone(); new_builder.object_like = Some(pattern.to_owned()); new_builder } - /// Filter objects starting with a prefix. + /// Filter objects starting with a prefix fn with_object_starting(&self, prefix: &str) -> Self { let mut new_builder = self.clone(); new_builder.object_starting = Some(prefix.to_owned()); new_builder } - /// Filter objects ending with a suffix. + /// Filter objects ending with a suffix fn with_object_ending(&self, suffix: &str) -> Self { let mut new_builder = self.clone(); new_builder.object_ending = Some(suffix.to_owned()); new_builder } - /// Only return distinct results. + /// Only return distinct results fn distinct(&self) -> Self { let mut new_builder = self.clone(); new_builder.distinct = true; new_builder } - /// Limit the number of results. + /// Limit the number of results fn limit(&self, n: usize) -> Self { let mut new_builder = self.clone(); new_builder.limit = Some(n); new_builder } - /// Skip the first n results. + /// Skip the first n results fn offset(&self, n: usize) -> Self { let mut new_builder = self.clone(); new_builder.offset = Some(n); new_builder } - /// Set sort direction to ascending. + /// Set sort direction to ascending fn asc(&self) -> Self { let mut new_builder = self.clone(); new_builder.sort_direction = PySortDirection::Ascending; new_builder } - /// Set sort direction to descending. + /// Set sort direction to descending fn desc(&self) -> Self { let mut new_builder = self.clone(); new_builder.sort_direction = PySortDirection::Descending; new_builder } - /// Configure windowing for stream processing. + /// Configure windowing for stream processing fn window(&self, width: usize, slide: usize) -> Self { let mut new_builder = self.clone(); new_builder.window_width = Some(width); @@ -763,14 +690,14 @@ impl PyQueryBuilder { new_builder } - /// Add a report strategy for window processing. + /// Add a report strategy for window processing fn with_report_strategy(&self, strategy: &str) -> Self { let mut new_builder = self.clone(); new_builder.report_strategies.push(strategy.to_string()); new_builder } - /// Add a periodic report strategy with a specific period. + /// Add a periodic report strategy with a specific period fn with_periodic_report(&self, period: usize) -> Self { let mut new_builder = self.clone(); new_builder.report_strategies.push("periodic".to_string()); @@ -778,21 +705,21 @@ impl PyQueryBuilder { new_builder } - /// Set the tick strategy for window processing. + /// Set the tick strategy for window processing fn with_tick_strategy(&self, tick: PyTick) -> Self { let mut new_builder = self.clone(); new_builder.tick_strategy = tick; new_builder } - /// Configure stream operator (RSTREAM, ISTREAM, DSTREAM). + /// Configure stream operator (RSTREAM, ISTREAM, DSTREAM) fn with_stream_operator(&self, operator: PyStreamOperator) -> Self { let mut new_builder = self.clone(); new_builder.stream_operator = Some(operator); new_builder } - /// Initialize streaming mode and return a streaming query handle. + /// Initialize streaming mode and return a streaming query handle fn as_stream(&self) -> PyResult { let config = StreamingConfig { subject: self.subject.clone(), @@ -822,42 +749,42 @@ impl PyQueryBuilder { Ok(PyStreamingQuery::new(config)) } - /// Build and execute the query, returning decoded (s,p,o) tuples. + /// Build and execute the query, returning decoded (s,p,o) tuples fn get_decoded_triples(&self) -> PyResult> { let db = locked(&self.db, "database")?; let qb = self.build_rust_query_builder(&*db); Ok(qb.get_decoded_triples()) } - /// Return just the decoded subjects. + /// Return just the decoded subjects fn get_subjects(&self) -> PyResult> { let db = locked(&self.db, "database")?; let qb = self.build_rust_query_builder(&*db); Ok(qb.get_subjects()) } - /// Return just the decoded predicates. + /// Return just the decoded predicates fn get_predicates(&self) -> PyResult> { let db = locked(&self.db, "database")?; let qb = self.build_rust_query_builder(&*db); Ok(qb.get_predicates()) } - /// Return just the decoded objects. + /// Return just the decoded objects fn get_objects(&self) -> PyResult> { let db = locked(&self.db, "database")?; let qb = self.build_rust_query_builder(&*db); Ok(qb.get_objects()) } - /// Count the number of matches. + /// Count the number of matches fn count(&self) -> PyResult { let db = locked(&self.db, "database")?; let qb = self.build_rust_query_builder(&*db); Ok(qb.count()) } - /// Group results by subject, returning a dictionary. + /// Group results by subject, returning a dictionary fn group_by_subject(&self) -> PyResult>> { let results = self.get_decoded_triples()?; let mut groups = HashMap::new(); @@ -871,7 +798,7 @@ impl PyQueryBuilder { Ok(groups) } - /// Group results by predicate, returning a dictionary. + /// Group results by predicate, returning a dictionary fn group_by_predicate(&self) -> PyResult>> { let results = self.get_decoded_triples()?; let mut groups = HashMap::new(); @@ -885,7 +812,7 @@ impl PyQueryBuilder { Ok(groups) } - /// Group results by object, returning a dictionary. + /// Group results by object, returning a dictionary fn group_by_object(&self) -> PyResult>> { let results = self.get_decoded_triples()?; let mut groups = HashMap::new(); @@ -921,7 +848,6 @@ impl Clone for PyQueryBuilder { offset: self.offset, sort_direction: self.sort_direction.clone(), - // RSP fields window_width: self.window_width, window_slide: self.window_slide, report_strategies: self.report_strategies.clone(), @@ -936,13 +862,12 @@ impl PyQueryBuilder { /// Helper method to build the Rust QueryBuilder with all configured filters fn build_rust_query_builder<'a>( &self, - db: &'a SparqlDatabase, + db: &'a KolibrieDB, ) -> kolibrie_core::query_builder::QueryBuilder<'a> { use kolibrie_core::query_builder::QueryBuilder; let mut qb = QueryBuilder::new(db); - // Apply exact filters if let Some(ref s) = self.subject { qb = qb.with_subject(s); } @@ -953,7 +878,6 @@ impl PyQueryBuilder { qb = qb.with_object(o); } - // Apply pattern filters if let Some(ref s) = self.subject_like { qb = qb.with_subject_like(s); } @@ -984,7 +908,6 @@ impl PyQueryBuilder { qb = qb.with_object_ending(o); } - // Apply modifiers if self.distinct { qb = qb.distinct(); } @@ -995,7 +918,6 @@ impl PyQueryBuilder { qb = qb.offset(n); } - // Apply sort direction match self.sort_direction { PySortDirection::Ascending => qb = qb.asc(), PySortDirection::Descending => qb = qb.desc(), @@ -1006,13 +928,11 @@ impl PyQueryBuilder { } pub(crate) fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { - // Register classes - m.add_class::()?; + m.add_class::()?; m.add_class::()?; m.add_class::()?; m.add_class::()?; - // Register enums m.add_class::()?; m.add_class::()?; m.add_class::()?; diff --git a/python/src/py_rdf_term.rs b/python/src/py_rdf_term.rs index 54704479..edcb6aa2 100644 --- a/python/src/py_rdf_term.rs +++ b/python/src/py_rdf_term.rs @@ -8,23 +8,13 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The term model, for Python. -//! -//! A query returns terms as strings, and a term's string is its canonical spelling: -//! an IRI bare, a plain literal bare, a typed literal as `"25"^^<…#integer>`, a -//! language-tagged one as `"hello"@en`, a blank node as `_:label`. That spelling is -//! the term's identity, which is why it carries the datatype rather than discarding -//! it — but it means a caller wanting the *value* has to ask for it rather than -//! using the string directly. -//! -//! Without these, Python could read a typed literal only by parsing the spelling by -//! hand, which is both tedious and the thing the Rust side stopped doing. +//! The term model, for Python use pyo3::prelude::*; use pyo3::types::PyModule; use shared::rdf_term; -/// Which kind of term this is: `"iri"`, `"blank_node"` or `"literal"`. +/// Which kind of term this is: `"iri"`, `"blank_node"` or `"literal"` #[pyfunction] fn term_kind(term: &str) -> &'static str { match rdf_term::kind_of(term) { @@ -34,98 +24,69 @@ fn term_kind(term: &str) -> &'static str { } } -/// The text a term denotes: a literal's lexical form, or the term itself. -/// -/// `'"25"^^<…#integer>'` gives `'25'`; an IRI gives itself. +/// The text a term denotes: a literal's lexical form, or the term itself #[pyfunction] fn lexical_value(term: &str) -> String { rdf_term::lexical_value(term).to_string() } -/// The number a term denotes, or `None` if it denotes none. -/// -/// Read through the lexical form, so a typed literal is a number: a Python caller -/// doing arithmetic on the spelling would otherwise have to strip the datatype off -/// first, and would get it wrong for a language-tagged literal. +/// The number a term denotes, or `None` if it denotes none #[pyfunction] fn numeric_value(term: &str) -> Option { rdf_term::numeric_value(term) } -/// A literal's datatype IRI, or `None` for a plain or language-tagged literal, or a -/// term that is not a literal. +/// A literal's datatype IRI, or `None` for a plain or language-tagged literal, or a term that is +/// not a literal #[pyfunction] fn literal_datatype(term: &str) -> Option { rdf_term::read_literal(term)?.datatype } -/// A literal's language tag, or `None` when it has none or the term is not a literal. +/// A literal's language tag, or `None` when it has none or the term is not a literal #[pyfunction] fn literal_language(term: &str) -> Option { rdf_term::read_literal(term)?.language } -/// Builds a literal's term string. -/// -/// `datatype` and `language` are mutually exclusive in RDF; if both are given the -/// language wins, as it does on the Rust side. A plain literal and an `xsd:string` -/// literal are one term and get one spelling. +/// Builds a literal's term string #[pyfunction] #[pyo3(signature = (lexical, datatype=None, language=None))] fn make_literal(lexical: &str, datatype: Option<&str>, language: Option<&str>) -> String { rdf_term::spell_literal(lexical, datatype, language) } -/// A fresh blank-node scope, for one document's worth of labels. -/// -/// A blank node label identifies a node only within the document it came from, so -/// building one needs a scope. Take one per document and pass it to every -/// [`make_blank_node`] call for that document: the same label and scope give the -/// same node, and a different scope gives a different one. +/// A fresh blank-node scope, for one document's worth of labels #[pyfunction] fn new_document_scope() -> u64 { rdf_term::next_document_scope() } -/// Builds a blank node's term string, from the bare label and a document scope. -/// -/// `label` is `"b"`, not `"_:b"`; the prefix and the scoping are both handled. +/// Builds a blank node's term string, from the bare label and a document scope #[pyfunction] fn make_blank_node(label: &str, document_scope: u64) -> String { rdf_term::spell_blank_node(label, document_scope) } -/// Writes a term as N-Triples syntax: an IRI in brackets, a literal quoted. -/// -/// What to use when writing a term to a file or a request rather than comparing it. -/// The canonical spelling is deliberately not N-Triples, so handing a term straight -/// to a serialiser writes an IRI without its brackets. +/// Writes a term as N-Triples syntax: an IRI in brackets, a literal quoted #[pyfunction] fn to_ntriples_term(term: &str) -> String { rdf_term::to_ntriples_term(term) } -/// Replaces a blank node with a Skolem IRI under `base`, leaving other terms alone. -/// -/// For publishing or exchanging data. A blank node label means nothing outside its -/// document, so two systems merging each other's blank nodes fuse unrelated nodes; -/// `base` must be an authority the caller controls, which is what makes the IRI -/// globally unique. +/// Replaces a blank node with a Skolem IRI under `base`, leaving other terms alone #[pyfunction] fn skolemise(term: &str, base: &str) -> String { rdf_term::skolemise(term, base).into_owned() } -/// Recovers the blank node a Skolem IRI stands for, or `None` for any other term. -/// -/// Optional: leaving a Skolem IRI as an IRI is what keeps a merge sound, so this is -/// for a consumer that would rather have the node back. +/// Recovers the blank node a Skolem IRI stands for, or `None` for any other term #[pyfunction] fn deskolemise(term: &str) -> Option { rdf_term::deskolemise(term) } -/// Registers the term helpers on the extension module. +/// Registers the term helpers on the extension module pub(crate) fn register(module: &Bound<'_, PyModule>) -> PyResult<()> { module.add_function(wrap_pyfunction!(term_kind, module)?)?; module.add_function(wrap_pyfunction!(lexical_value, module)?)?; diff --git a/python/src/py_rsp.rs b/python/src/py_rsp.rs index 20965b9d..d02a80ab 100644 --- a/python/src/py_rsp.rs +++ b/python/src/py_rsp.rs @@ -8,26 +8,7 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! RDF Stream Processing, for Python. -//! -//! The engine Python had was not this one. `QueryBuilder.as_stream` builds a -//! `StreamingQuery` that accumulates triples in a list and applies limit, offset and -//! distinct to them: no windows, no RSP-QL, no joining across streams. It is a filter -//! with a stream-shaped name. -//! -//! This exposes the real [`RSPEngine`]: an RSP-QL query naming its windows, a stream -//! per window, and results joined across them as the windows fire. -//! -//! # Results are collected, not called back -//! -//! The Rust engine delivers results to a consumer it invokes from its own threads. -//! Handing that a Python callable would mean taking the GIL on a thread Python does -//! not know about, for every solution. Instead the consumer appends to a buffer and -//! Python reads it, which is the same shape `StreamingQuery` already had and costs -//! nothing in a language that has to stop the world to run a callback anyway. -//! -//! Results are complete once [`stop`](PyRspEngine::stop) returns: it flushes every -//! window and waits for the coordinator, so a caller does not have to sleep and hope. +//! RDF Stream Processing, for Python use kolibrie_core::rsp::r2r::R2ROperator; use kolibrie_core::rsp::simple_r2r::QueryExecutionMode; @@ -39,43 +20,25 @@ use pyo3::types::PyModule; use shared::triple::Triple; use std::sync::{Arc, Mutex}; -/// A solution, as the pairs of variable name and term the engine produced. +/// A solution, as the pairs of variable name and term the engine produced type Solution = Vec<(String, String)>; -/// An RSP-QL query over one or more streams. -/// -/// `unsendable` because the engine owns the window threads it started and is not -/// meant to cross between Python threads; using it from the thread that built it is -/// enforced by pyo3 rather than left to go wrong at runtime. +/// An RSP-QL query over one or more streams #[pyclass(name = "RspEngine", unsendable)] pub struct PyRspEngine { - /// `None` once stopped, which is what makes a second `stop` harmless and using a - /// stopped engine an error rather than a silent no-op. engine: Option>, results: Arc>>, } #[pymethods] impl PyRspEngine { - /// Builds an engine from an RSP-QL query. - /// - /// The query names its windows and the streams they read, for example: - /// - /// ```text - /// REGISTER RSTREAM AS - /// SELECT * - /// FROM NAMED WINDOW :w ON :temperature [RANGE 10 STEP 2] - /// WHERE { WINDOW :w { ?s a . } } - /// ``` + /// Builds an engine from an RSP-QL query #[new] fn new(rsp_ql_query: &str) -> PyResult { let results: Arc>> = Arc::new(Mutex::new(Vec::new())); let collected = Arc::clone(&results); let consumer = ResultConsumer { function: Arc::new(move |solution: Solution| { - // A poisoned buffer loses this solution rather than poisoning the - // engine's thread in turn; the caller sees a short result list, and - // there is no Python to raise into from here. if let Ok(mut buffer) = collected.lock() { buffer.push(solution); } @@ -98,13 +61,7 @@ impl PyRspEngine { }) } - /// Adds N-Triples to one stream, timestamped. - /// - /// `timestamp` is the stream's own clock, not wall time: a window of - /// `[RANGE 10 STEP 2]` counts in whatever unit the caller is stamping with. - /// - /// Returns how many triples were added, which is worth checking: text that does - /// not parse yields none rather than raising, matching the readers elsewhere. + /// Adds N-Triples to one stream, timestamped fn add(&mut self, stream_iri: &str, ntriples: &str, timestamp: usize) -> PyResult { let engine = self.running()?; let triples = engine.parse_data(ntriples); @@ -115,11 +72,7 @@ impl PyRspEngine { Ok(added) } - /// Flushes every window and waits for the results. - /// - /// Every solution the query will produce has been delivered once this returns, - /// so there is no need to sleep before reading [`results`](Self::results). - /// Calling it twice is harmless. + /// Flushes every window and waits for the results fn stop(&mut self) -> PyResult<()> { if let Some(mut engine) = self.engine.take() { engine.stop(); @@ -127,10 +80,7 @@ impl PyRspEngine { Ok(()) } - /// The solutions produced so far, each a list of `(variable, term)` pairs. - /// - /// Terms are strings in their canonical spelling, so the helpers in this module - /// -- `term_kind`, `numeric_value` and the rest -- read them. + /// The solutions produced so far, each a list of `(variable, term)` pairs fn results(&self) -> PyResult> { Ok(self .results @@ -139,7 +89,7 @@ impl PyRspEngine { .clone()) } - /// How many solutions have been produced. + /// How many solutions have been produced fn result_count(&self) -> PyResult { Ok(self .results @@ -148,14 +98,14 @@ impl PyRspEngine { .len()) } - /// Whether the engine is still accepting data. + /// Whether the engine is still accepting data fn is_running(&self) -> bool { self.engine.is_some() } } impl PyRspEngine { - /// The engine, or an error saying it has been stopped. + /// The engine, or an error saying it has been stopped fn running(&mut self) -> PyResult<&mut RSPEngine> { self.engine .as_mut() @@ -163,9 +113,6 @@ impl PyRspEngine { } } -// Summarised rather than derived, as `PyStreamingQuery` is: the engine owns window -// threads and a store, none of which is useful in a debug line, and its buffer is -// behind a lock this must not block on. impl std::fmt::Debug for PyRspEngine { fn fmt(&self, formatter: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { let mut shown = formatter.debug_struct("RspEngine"); @@ -178,7 +125,7 @@ impl std::fmt::Debug for PyRspEngine { } } -/// Registers the RSP engine on the extension module. +/// Registers the RSP engine on the extension module pub(crate) fn register(module: &Bound<'_, PyModule>) -> PyResult<()> { module.add_class::()?; Ok(()) diff --git a/rust-toolchain.toml b/rust-toolchain.toml index e420ff0d..e1378ad4 100644 --- a/rust-toolchain.toml +++ b/rust-toolchain.toml @@ -1,6 +1,8 @@ -# Pinned toolchain for day-to-day development and reproducible local builds. -# This is NOT the MSRV — that is `rust-version` in [workspace.package] (1.85) -# and is enforced separately by the `msrv` CI job. +# Toolchain for day-to-day development: whatever stable is installed locally, +# matching what CI builds with (dtolnay/rust-toolchain@stable). +# rust-toolchain.toml cannot express a version range, so the floor is +# `rust-version` in [workspace.package] (1.85), which cargo enforces and the +# `msrv` CI job tests. [toolchain] -channel = "1.96.0" +channel = "stable" components = ["rustfmt", "clippy"] diff --git a/scripts/api_surface.py b/scripts/api_surface.py deleted file mode 100755 index 70b24c80..00000000 --- a/scripts/api_surface.py +++ /dev/null @@ -1,211 +0,0 @@ -#!/usr/bin/env python3 -"""The workspace's public API surface, read out of the source. - -Generates `docs/api.md`. The README used to carry 371 hand-written lines mirroring -these signatures, and it drifted: it documented `add_constraint`, -`query_with_repairs` and `infer_new_facts_semi_naive_with_repairs` for as long as -they existed and then for a while after, and four translated READMEs carried the same -three sections. A listing nobody regenerates is a listing nobody can trust. - -Nightly is deliberately not used. `cargo doc --output-format json` and -`cargo public-api` both want it, and this project does not take a nightly -dependency, so the surface is read from the source the way -`scripts/module_graph.py` reads the module graph: by parsing it. - -What that costs, stated plainly: this reads `pub` items lexically. It does not -resolve types, expand macros, or follow `pub use` re-exports to their definitions, so -a re-exported item is listed where it is written rather than where it is reachable. -It also cannot tell a `pub` item inside a private module -- which is not reachable -- -from one that is. `cargo doc` is the authority on what a consumer can actually name; -this is the index, kept honest by being generated. - -Usage: - scripts/api_surface.py # write docs/api.md - scripts/api_surface.py --stdout # print it instead - scripts/api_surface.py --check # exit 1 if docs/api.md is out of date - -`--check` is the one for CI: it fails when someone changes the public surface without -regenerating, which is exactly how the hand-written version went stale. -""" - -import argparse -import pathlib -import re -import sys - -ROOT = pathlib.Path(__file__).resolve().parent.parent -OUTPUT = ROOT / "docs" / "api.md" - -# The crates a consumer depends on, in the order they are worth reading. `python` is -# absent on purpose: its surface is the Python API, not its Rust shim, and its own -# `[lib] doc = false` says so. -CRATES = [ - ("kolibrie-api", "The facade: one dependency that re-exports the rest."), - ("kolibrie", "The engine: store, parser, query execution, RSP."), - ("shared", "Types every crate shares: terms, triples, rules, dictionary."), - ("datalog", "Datalog reasoning: forward and backward chaining, N3 rules."), - ("datalogmtl", "Metric temporal Datalog."), - ("ml", "Machine learning integration, behind the `ml` feature."), -] - -# A `pub` item worth listing, and what to call it. `pub(crate)` and `pub(super)` are -# not public and must not match, which is why the pattern requires whitespace rather -# than allowing a parenthesis. -ITEM = re.compile( - r"^\s*pub\s+(?Pfn|struct|enum|trait|type|const|static|mod|use)\s+" - r"(?P[A-Za-z_][A-Za-z0-9_]*)" -) -# `pub async fn` and `pub unsafe fn` carry a qualifier before the keyword. -QUALIFIED = re.compile( - r"^\s*pub\s+(?:async|unsafe|extern\s+\"[^\"]*\")\s+(?Pfn)\s+" - r"(?P[A-Za-z_][A-Za-z0-9_]*)" -) - - -def signature(lines: list[str], start: int, kind: str) -> str: - """The declaration as one line, gathering continuations and dropping the body. - - An item may be written across several lines -- a `use` group, a long parameter - list, a `where` clause -- and taking only the first produced - `pub use kolibrie::execute_query::` with the group it imports cut off. - """ - text = "" - for raw in lines[start : start + 12]: - text = f"{text} {raw.strip()}".strip() - if kind in ("use", "type", "const", "static"): - # These end at a semicolon, and a `use` group may span lines before it. - if ";" in text: - break - else: - # A signature ends where its body or its `where` clause begins, but not - # at a brace inside `<...>` or `(...)`. - depth = 0 - ended = False - for index, character in enumerate(text): - if character in "(<": - depth += 1 - elif character in ")>": - depth -= 1 - elif character in "{;" and depth <= 0: - text, ended = text[:index], True - break - if ended or text.rstrip().endswith(";"): - break - if " where " in text: - text = text[: text.index(" where ")] - break - text = " ".join(text.split()) - # A `use` group's braces are part of the item; everything else's open its body. - cuts = (";",) if kind in ("use", "type", "const", "static") else ("{", ";") - for cut in cuts: - index = text.find(cut) - if index != -1: - text = text[:index] - return text.strip().rstrip(",").strip() - - -def items_of(path: pathlib.Path) -> list[tuple[str, str]]: - """The public items a file declares, as (kind, signature).""" - found = [] - in_block_comment = False - lines = path.read_text(errors="replace").splitlines() - for number, raw in enumerate(lines): - line = raw.strip() - # Block comments can hold anything, including example code with `pub fn`. - if in_block_comment: - if "*/" in line: - in_block_comment = False - continue - if line.startswith("/*"): - if "*/" not in line: - in_block_comment = True - continue - if line.startswith("//"): - continue - match = ITEM.match(raw) or QUALIFIED.match(raw) - if not match: - continue - kind = match.group("kind") - # A module declaration is structure, not surface, and `module_graph.py` - # already covers it. A re-export is listed, since it is how the facade works. - if kind == "mod": - continue - found.append((kind, signature(lines, number, kind))) - return found - - -def crate_sources(crate: str) -> list[pathlib.Path]: - source = ROOT / crate.replace("-", "_") if (ROOT / crate.replace("-", "_")).exists() else ROOT / crate - return sorted((source / "src").rglob("*.rs")) if (source / "src").exists() else [] - - -def render() -> str: - out = [ - "# Public API surface", - "", - "Generated by `scripts/api_surface.py`. Do not edit: run the script.", - "", - "This is an index, not a reference. `cargo doc --workspace --open` is the", - "authority on what a consumer can name, with the types, the bounds and the", - "prose; this exists so that a change to the surface is visible in a diff, and", - "so no hand-written list can quietly disagree with the code again.", - "", - "It is read lexically from the source, so a `pub` item inside a private module", - "appears here although nothing outside can reach it, and a re-export is listed", - "where it is written rather than where it is reachable.", - "", - ] - for crate, blurb in CRATES: - sources = crate_sources(crate) - if not sources: - continue - out += [f"## `{crate}`", "", blurb, ""] - total = 0 - for path in sources: - items = items_of(path) - if not items: - continue - total += len(items) - relative = path.relative_to(ROOT) - out += [f"### `{relative}`", "", "```rust"] - out += [sig for _, sig in items] - out += ["```", ""] - files = sum(1 for path in sources if items_of(path)) - out += [ - f"_{total} public {'item' if total == 1 else 'items'} across " - f"{files} {'file' if files == 1 else 'files'}._", - "", - ] - return "\n".join(out).rstrip() + "\n" - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--stdout", action="store_true", help="print instead of writing") - parser.add_argument("--check", action="store_true", help="exit 1 if out of date") - arguments = parser.parse_args() - - generated = render() - if arguments.stdout: - print(generated, end="") - return 0 - if arguments.check: - if not OUTPUT.exists(): - print(f"{OUTPUT.relative_to(ROOT)} does not exist; run scripts/api_surface.py") - return 1 - if OUTPUT.read_text() != generated: - print( - f"{OUTPUT.relative_to(ROOT)} is out of date; run scripts/api_surface.py" - ) - return 1 - print(f"{OUTPUT.relative_to(ROOT)} is up to date") - return 0 - - OUTPUT.parent.mkdir(parents=True, exist_ok=True) - OUTPUT.write_text(generated) - print(f"wrote {OUTPUT.relative_to(ROOT)} ({len(generated.splitlines())} lines)") - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/scripts/flakehunt.sh b/scripts/flakehunt.sh index b56223e4..001828f5 100755 --- a/scripts/flakehunt.sh +++ b/scripts/flakehunt.sh @@ -1,10 +1,9 @@ #!/usr/bin/env bash # Hunt intermittent test failures by stressing the prebuilt test binaries directly. # -# Why not just loop `cargo test`: a full workspace run takes ~3 minutes, and eight of -# them failed to reproduce a 1-in-25 flake. The test binaries themselves take 1-4 -# seconds, so running them directly gives roughly two orders of magnitude more -# iterations per minute. That found the same flake on the first pass. +# Why not just loop `cargo test`: the test binaries themselves start and finish much +# faster than a full workspace run, so running them directly gives far more +# iterations in the same time. # # Unlike `scripts/verify.sh` (run once per change, tells you if it is good), this is # for investigation: run it when a count disagrees with the known baseline, or when diff --git a/scripts/module_graph.py b/scripts/module_graph.py index 205c4532..e7d893e8 100755 --- a/scripts/module_graph.py +++ b/scripts/module_graph.py @@ -12,7 +12,6 @@ Usage: scripts/module_graph.py # layers, per-module dependencies, cycle check - scripts/module_graph.py --mermaid # the diagram in docs/architecture.md scripts/module_graph.py --check # exit non-zero if a cycle exists Exits non-zero under --check when any strongly-connected component holds more than @@ -56,7 +55,7 @@ def graph(src: pathlib.Path): for name, files in mods.items(): text = "\n".join(f.read_text() for f in files) code[name] = "\n".join( - l for l in text.splitlines() if not l.lstrip().startswith(("///", "//!")) + line for line in text.splitlines() if not line.lstrip().startswith(("///", "//!")) ) lines[name] = len(text.splitlines()) edges = { @@ -143,14 +142,11 @@ def reach(m): for m in nodes: reach(m) - return { - (a, b) for a, b in edges if not any(b in reach(c) for c in succ[a] if c != b) - } + return {(a, b) for a, b in edges if not any(b in reach(c) for c in succ[a] if c != b)} def main() -> int: parser = argparse.ArgumentParser() - parser.add_argument("--mermaid", action="store_true", help="print the diagram") parser.add_argument("--check", action="store_true", help="exit 1 on a cycle") parser.add_argument("--src", default=None, help="crate src (default: kolibrie/src)") args = parser.parse_args() @@ -177,25 +173,6 @@ def main() -> int: for m, lv in depth.items(): by_layer[lv].append(m) - if args.mermaid: - short = { - m: m.replace("streamertail_optimizer", "streamertail").replace( - "neural_relations_disabled", "neural_disabled" - ) - for m in lines - } - print("```mermaid") - print("graph TD") - for lv in sorted(by_layer): - print(' subgraph L%d["layer %d"]' % (lv, lv)) - for m in sorted(by_layer[lv], key=lambda m: -lines[m]): - print(' %s["%s
%d lines"]' % (m, short[m], lines[m])) - print(" end") - for a, b in sorted(reduced): - print(f" {a} --> {b}") - print("```") - return 0 - out = collections.defaultdict(list) for a, b in edges: out[a].append(b) diff --git a/scripts/perf_bench.sh b/scripts/perf_bench.sh deleted file mode 100755 index 735b2e64..00000000 --- a/scripts/perf_bench.sh +++ /dev/null @@ -1,117 +0,0 @@ -#!/usr/bin/env bash -# Run a tier of benchmarks, with a per-bench wall-clock cap. -# -# Why tiers exist -# --------------- -# `cargo bench --workspace` is not a usable command in this repo. Four benches are -# large parameter sweeps (49–85 cases, up to size_5000 at ~3.9 s per iteration), so -# at criterion's default 100 samples a single case costs minutes. One of them -# (citybench_cross_window_compare) ran >36 minutes without finishing, and reducing -# the sample size does not rescue them — the cost is the workload per iteration, -# not criterion's sampling. -# -# So the benches are split by what they are FOR: -# -# routine Guards the refactor. Isolates one mechanism each, runs in minutes. -# This is the tier to run before/after a refactor step. -# heavy Scaling sweeps comparing naive vs incremental strategies. Research -# results, not refactor guards. Run deliberately, never routinely. -# -# Wipe `target/criterion` when switching tiers. Criterion accumulates results there -# and `perf_report.py collect` summarises everything present, not the last run — so -# a routine collect after a heavy run reports both, and a comparison against the -# baseline then shows the heavy benchmarks as "new" and may mix in stale numbers -# from whenever that tier last ran. Measured: a heavy-only run collected 50 -# benchmarks, 41 of them left over from an earlier routine pass. -# -# Usage: -# scripts/perf_bench.sh routine # full sampling (authoritative) -# scripts/perf_bench.sh routine --quick # reduced sampling (fast check) -# scripts/perf_bench.sh census # classify every bench by cost -# scripts/perf_bench.sh heavy --cap 600 -set -u - -ROUTINE=( - micro_dictionary - micro_pattern - micro_scan - micro_join - micro_field_access - optimizer_hotpath - hybrid_probability - my_benchmark -) - -HEAVY=( - contended_dictionary - cross_window_benchmark - citybench_cross_window_compare - rsp_citybench_cross_window - family_tree_cross_window_compare -) - -TIER="${1:-routine}" -shift || true - -CAP=1800 -QUICK=0 -EXTRA=() -while [ $# -gt 0 ]; do - case "$1" in - --cap) CAP="$2"; shift 2 ;; - --quick) QUICK=1; shift ;; - *) EXTRA+=("$1"); shift ;; - esac -done - -if [ "$QUICK" -eq 1 ]; then - # Enough to catch a gross regression, not enough to trust a 5% delta. - EXTRA+=(--sample-size 10 --warm-up-time 1 --measurement-time 1) -fi - -case "$TIER" in - routine) BENCHES=("${ROUTINE[@]}") ;; - heavy) BENCHES=("${HEAVY[@]}") ;; - census) BENCHES=("${ROUTINE[@]}" "${HEAVY[@]}") ;; - *) echo "unknown tier: $TIER (routine|heavy|census)" >&2; exit 2 ;; -esac - -printf '%-36s %8s %s\n' BENCH SECONDS STATUS -total_start=$(date +%s) -failed=0 -for name in "${BENCHES[@]}"; do - bin=$(ls -t target/release/deps/"$name"-* 2>/dev/null | grep -v '\.d$' | head -1) - if [ -z "$bin" ]; then - printf '%-36s %8s %s\n' "$name" - "NOT BUILT (cargo bench --no-run)" - failed=1 - continue - fi - - start=$(date +%s) - # `--bench` is REQUIRED. With `harness = false` criterion defaults to test mode - # (one iteration per case, no measurement) when it is not passed; cargo supplies - # it, a direct invocation must too. Without it this script silently reports - # "ok" in 0s and records nothing. - "$bin" --bench --noplot --quiet ${EXTRA+"${EXTRA[@]}"} >/dev/null 2>&1 & - pid=$! - ( sleep "$CAP"; kill -9 "$pid" 2>/dev/null ) & - watchdog=$! - wait "$pid" 2>/dev/null - rc=$? - kill "$watchdog" 2>/dev/null - wait "$watchdog" 2>/dev/null - elapsed=$(( $(date +%s) - start )) - - if [ "$rc" -eq 0 ]; then - status=ok - elif [ "$elapsed" -ge "$CAP" ]; then - status="CAPPED at ${CAP}s"; failed=1 - else - status="failed rc=$rc"; failed=1 - fi - printf '%-36s %8s %s\n' "$name" "$elapsed" "$status" -done - -echo -echo "tier '$TIER' total: $(( $(date +%s) - total_start ))s" -exit "$failed" diff --git a/scripts/perf_report.py b/scripts/perf_report.py deleted file mode 100755 index 2238b7d3..00000000 --- a/scripts/perf_report.py +++ /dev/null @@ -1,370 +0,0 @@ -#!/usr/bin/env python3 -"""Summarise and compare criterion benchmark results. - -Criterion stores its data under `target/criterion`, which is gitignored and -therefore vanishes on `cargo clean` and is never comparable across machines or -across time. This script extracts a small, diffable summary that can be committed -to `perf-baseline/`, and compares a later run against it. - -Usage: - scripts/perf_report.py collect # summarise the last run - scripts/perf_report.py collect -o /tmp/after.json - scripts/perf_report.py baseline RUN1 RUN2 RUN3 RUN4 -o perf-baseline/baseline.json - scripts/perf_report.py compare perf-baseline/baseline.json - scripts/perf_report.py compare BASE --current /tmp/after.json - scripts/perf_report.py noise RUN1 RUN2 # measure the floor - -How much to trust a delta (measured on this repo, Oct 2026, M-series laptop) ---------------------------------------------------------------------------- -Three kinds of variation, each larger than the last: - - two passes back-to-back median 1.5% max 8.7% - across sessions, same code median 3.0% max 12.5% - repeated runs, one benchmark up to ~9-12% - -The third row is the one that matters and the one that was missed at first. Noise -was originally derived from two back-to-back passes, which measures short-term -REPEATABILITY — and two samples cannot estimate a spread at all. That produced two -false positives: - - micro_scan_selective_subject/dyn_trait recorded 2.0%, actual ~10% - execution_hot_paths/filter_numeric recorded 0.22%, actual ~9% - -Four runs of one unchanged `filter_numeric` binary spanned 2.056-2.242 ms, so a -single before/after pair reported a 5.7% "regression" that four runs in each state -showed to be absent. `baseline` now takes N runs and records the full observed -range; it warns when given fewer than four. - -So: - - * Build the baseline from 4+ runs. `compare` labels results from a thinner one - `[thin baseline]`, and flags deltas under 1.5x their limit as borderline. - * To judge a change, run before AND after in the SAME session. If a delta is - near its limit, repeat both states 3-4 times before believing it — that is - what distinguished a real regression from noise in every case so far. - * `compare` against a `collect` summary silently falls back to the flat floor, - because a collect file carries no noise data. It now warns; prefer a baseline. - * Always compare like-for-like: the same tier, same filter, run the same way. - Noise depends on the invocation, not just the benchmark — `filter_numeric` - spreads 1.9% across full tier passes and 9.0% when run as a filtered single - case after a rebuild. A 4-run baseline fixes the arithmetic; it cannot - anticipate an invocation pattern it never saw. -""" -from __future__ import annotations - -import argparse -import json -import pathlib -import statistics -import sys - -CRITERION_DIR = pathlib.Path("target/criterion") -# Below this, a difference is indistinguishable from scheduling noise on a typical -# developer machine. Override with --threshold once the real noise floor is known. -DEFAULT_THRESHOLD_PCT = 5.0 -# A delta must clear this multiple of the benchmark's own measured noise. 2.0 is a -# judgement call, not a statistic: it keeps the quiet benchmarks sensitive while -# not flagging the allocation-heavy ones on every run. -DEFAULT_NOISE_MARGIN = 2.0 -# Below this many runs, a baseline's noise figures are not worth trusting: two -# samples cannot estimate a spread at all, and three barely can. -MIN_USEFUL_RUNS = 4 - - -def _iter_estimates() -> list[tuple[str, float, float]]: - """Yield (benchmark id, median ns, median absolute deviation ns).""" - if not CRITERION_DIR.is_dir(): - sys.exit(f"{CRITERION_DIR} not found — run `cargo bench` first.") - - out: list[tuple[str, float, float]] = [] - for est in CRITERION_DIR.rglob("new/estimates.json"): - # .../target/criterion///new/estimates.json - bench_id = str(est.parent.parent.relative_to(CRITERION_DIR)) - try: - data = json.loads(est.read_text()) - except (json.JSONDecodeError, OSError): - continue - median = data.get("median", {}).get("point_estimate") - mad = data.get("median_abs_dev", {}).get("point_estimate", 0.0) - if median is None: - continue - out.append((bench_id, float(median), float(mad))) - out.sort() - return out - - -def _fmt(ns: float) -> str: - for unit, scale in (("s", 1e9), ("ms", 1e6), ("µs", 1e3)): - if ns >= scale: - return f"{ns / scale:.3f} {unit}" - return f"{ns:.1f} ns" - - -def cmd_collect(args: argparse.Namespace) -> int: - rows = _iter_estimates() - if not rows: - sys.exit("no criterion estimates found — did `cargo bench` actually run?") - - summary = { - "benchmarks": { - bench: {"median_ns": median, "mad_ns": mad} for bench, median, mad in rows - } - } - - if args.output: - path = pathlib.Path(args.output) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n") - print(f"wrote {len(rows)} benchmarks to {path}") - else: - for bench, median, mad in rows: - # MAD relative to the median is the per-benchmark noise indicator: a - # benchmark with 10% spread cannot detect a 5% regression. - spread = (mad / median * 100) if median else 0.0 - print(f" {_fmt(median):>12} ±{spread:4.1f}% {bench}") - print(f"\n {len(rows)} benchmarks") - return 0 - - -def cmd_baseline(args: argparse.Namespace) -> int: - """Build a baseline from N unchanged runs, recording per-benchmark noise. - - Why N and not 2 - --------------- - This took two runs and derived noise from the gap between them. That measures - short-term *repeatability*, not the variance a real before/after comparison has - to survive, and it produced two false positives that each cost an afternoon: - - micro_scan_selective_subject/dyn_trait recorded 2.0%, actual ~10% - execution_hot_paths/filter_numeric recorded 0.22%, actual ~9% - - The second is the clearest case: four runs of one unchanged binary spanned - 2.056-2.242 ms, so a single before/after pair reported a 5.7% "regression" - against a baseline claiming 0.22% noise. Two samples cannot estimate a spread. - - Noise is the full observed range, (max - min) / min, which is deliberately - conservative: it is better to miss a marginal regression than to cry wolf, since - a harness that reports phantom regressions stops being read. If that makes a - benchmark too insensitive to be useful, the fix is to make the benchmark quieter - — `micro_scan` dropped SUBJECTS from 20k to 5k for exactly this reason — not to - narrow the threshold until it fires. - """ - runs = [] - for path in args.runs: - data = json.loads(pathlib.Path(path).read_text())["benchmarks"] - runs.append((path, data)) - - if len(runs) < 2: - sys.exit("need at least 2 runs; 4 or more gives a usable spread") - if len(runs) < MIN_USEFUL_RUNS: - print(f"warning: {len(runs)} runs is thin. Two samples cannot estimate a " - f"spread, and three barely can; prefer {MIN_USEFUL_RUNS} or more.") - - # Keys only: Counter.update on a dict-of-dicts tries to add the values. - seen: set[str] = set() - for _, data in runs: - seen.update(data.keys()) - - benchmarks, thin = {}, [] - for bench in sorted(seen): - medians = [d[bench]["median_ns"] for _, d in runs if bench in d] - if len(medians) < 2: - thin.append(bench) - continue - mads = [d[bench].get("mad_ns", 0.0) for _, d in runs if bench in d] - lo, hi = min(medians), max(medians) - benchmarks[bench] = { - # Median of the run medians: more robust than a mean once N > 2, and it - # does not let one slow run drag the reference point. - "median_ns": statistics.median(medians), - "mad_ns": statistics.median(mads), - # Full range, not a deviation: see the note above. - "noise_pct": ((hi - lo) / lo * 100) if lo > 0 else 0.0, - # Recorded so `compare` can say how much to trust each figure. - "runs": len(medians), - } - - if not benchmarks: - sys.exit("the runs share no benchmarks") - if thin: - print(f"warning: {len(thin)} benchmarks appeared in only one run and were " - f"excluded (no spread can be computed from one sample)") - - path = pathlib.Path(args.output) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text(json.dumps( - {"runs": len(runs), "benchmarks": benchmarks}, indent=2, sort_keys=True) + "\n") - - noises = sorted(v["noise_pct"] for v in benchmarks.values()) - print(f"wrote {len(benchmarks)} benchmarks from {len(runs)} runs to {path}") - print(f"noise: median {statistics.median(noises):.1f}% " - f"p90 {noises[int(len(noises) * 0.9)]:.1f}% max {max(noises):.1f}%") - worst = sorted(benchmarks.items(), key=lambda kv: -kv[1]["noise_pct"])[:5] - print("\nnoisiest, which are the ones least able to detect a regression:") - for bench, v in worst: - print(f" {v['noise_pct']:5.1f}% ({v['runs']} runs) {bench}") - return 0 - - -def cmd_compare(args: argparse.Namespace) -> int: - base_path = pathlib.Path(args.baseline) - if not base_path.is_file(): - sys.exit(f"baseline not found: {base_path}") - base_doc = json.loads(base_path.read_text()) - base = base_doc["benchmarks"] - - # A `collect` summary has no noise data, so every comparison against one silently - # falls back to the flat floor. That is how filter_numeric's phantom +5.7% got - # through: it was compared against a collect file, not a baseline. - if not any("noise_pct" in v for v in base.values()): - print(f"warning: {base_path} carries no noise data — it looks like a " - f"`collect` summary rather than a `baseline`.\n" - f" Falling back to the flat ±{args.threshold:g}% floor, which " - f"over-reports on noisy benchmarks.\n" - f" Build a real baseline with: perf_report.py baseline RUN1 " - f"RUN2 RUN3 RUN4 -o {base_path}\n") - elif base_doc.get("runs", 0) and base_doc["runs"] < MIN_USEFUL_RUNS: - print(f"warning: baseline was built from only {base_doc['runs']} runs, so its " - f"noise figures likely understate the real spread.\n") - - if args.current: - # Comparing two saved summaries: lets you re-check an old comparison, or - # diff results captured on two different machines, without re-running. - current = json.loads(pathlib.Path(args.current).read_text())["benchmarks"] - else: - current = {b: {"median_ns": m, "mad_ns": d} for b, m, d in _iter_estimates()} - - if not current: - sys.exit("no current results — run `cargo bench` first.") - - regressions, improvements, missing, added = [], [], [], [] - - for bench, ref in sorted(base.items()): - if bench not in current: - missing.append(bench) - continue - before = ref["median_ns"] - after = current[bench]["median_ns"] - if before <= 0: - continue - delta = (after - before) / before * 100 - - # A benchmark that varies 10% between identical runs cannot report a 6% - # regression. Where the baseline recorded per-benchmark noise, require the - # delta to clear a multiple of it; otherwise fall back to the global figure. - noise = ref.get("noise_pct") - limit = args.threshold - if noise is not None and not args.flat_threshold: - limit = max(limit, noise * args.noise_margin) - - runs = ref.get("runs") - if delta > limit: - regressions.append((bench, before, after, delta, limit, runs)) - elif delta < -limit: - improvements.append((bench, before, after, delta, limit, runs)) - - added = sorted(set(current) - set(base)) - - def show(title: str, rows: list) -> None: - if not rows: - return - print(f"\n{title}") - for bench, before, after, delta, limit, runs in rows: - # A delta that only just clears a limit derived from few runs is the - # shape both past false positives had; say so at the point of reporting. - weak = " [thin baseline]" if runs and runs < MIN_USEFUL_RUNS else "" - close = " [within 1.5x of the limit]" if abs(delta) < limit * 1.5 else "" - print(f" {delta:+6.1f}% (>{limit:4.1f}%) " - f"{_fmt(before)} -> {_fmt(after):<12} {bench}{weak}{close}") - - show(f"REGRESSIONS (floor +{args.threshold:g}%, noise-aware)", regressions) - show(f"improvements (floor -{args.threshold:g}%, noise-aware)", improvements) - - if missing: - print(f"\nin baseline but not measured ({len(missing)}):") - for bench in missing: - print(f" {bench}") - if added: - print(f"\nnew since baseline ({len(added)}):") - for bench in added: - print(f" {bench}") - - unchanged = len(base) - len(regressions) - len(improvements) - len(missing) - print( - f"\n{unchanged} within ±{args.threshold:g}%, " - f"{len(regressions)} regressed, {len(improvements)} improved" - ) - - # Exit non-zero on regression so this can gate a local workflow if wanted. - # Deliberately NOT wired into CI: shared-runner variance routinely exceeds the - # regressions worth catching, and a gate that cries wolf gets ignored. - return 1 if regressions and args.fail_on_regression else 0 - - -def cmd_noise(args: argparse.Namespace) -> int: - """Compare two baseline files taken with no code change between them.""" - a = json.loads(pathlib.Path(args.first).read_text())["benchmarks"] - b = json.loads(pathlib.Path(args.second).read_text())["benchmarks"] - deltas = [] - print("per-benchmark run-to-run variation with no code change:\n") - for bench in sorted(set(a) & set(b)): - before, after = a[bench]["median_ns"], b[bench]["median_ns"] - if before <= 0: - continue - delta = abs(after - before) / before * 100 - deltas.append(delta) - print(f" {delta:5.1f}% {bench}") - if deltas: - print( - f"\n median {statistics.median(deltas):.1f}% " - f"p90 {sorted(deltas)[int(len(deltas) * 0.9)]:.1f}% " - f"max {max(deltas):.1f}%" - ) - print( - "\n Use a threshold above the max: anything smaller is noise, not signal." - ) - return 0 - - -def main() -> int: - ap = argparse.ArgumentParser(description=__doc__, - formatter_class=argparse.RawDescriptionHelpFormatter) - sub = ap.add_subparsers(dest="cmd", required=True) - - c = sub.add_parser("collect", help="summarise the last cargo bench run") - c.add_argument("-o", "--output", help="write JSON summary here") - c.set_defaults(func=cmd_collect) - - p = sub.add_parser("compare", help="compare the last run against a baseline") - p.add_argument("baseline") - p.add_argument("--current", help="compare against this saved summary instead of " - "the last cargo bench run") - p.add_argument("--threshold", type=float, default=DEFAULT_THRESHOLD_PCT, - help=f"percent change treated as signal (default {DEFAULT_THRESHOLD_PCT})") - p.add_argument("--noise-margin", type=float, default=DEFAULT_NOISE_MARGIN, - help="multiple of a benchmark's recorded noise that a delta must " - f"clear (default {DEFAULT_NOISE_MARGIN})") - p.add_argument("--flat-threshold", action="store_true", - help="ignore recorded per-benchmark noise and use --threshold alone") - p.add_argument("--fail-on-regression", action="store_true") - p.set_defaults(func=cmd_compare) - - bl = sub.add_parser("baseline", - help="build a noise-aware baseline from N unchanged runs") - bl.add_argument("runs", nargs="+", metavar="RUN", - help=f"two or more collect summaries of unchanged code; " - f"{MIN_USEFUL_RUNS}+ recommended") - bl.add_argument("-o", "--output", required=True) - bl.set_defaults(func=cmd_baseline) - - n = sub.add_parser("noise", help="measure the noise floor from two unchanged runs") - n.add_argument("first") - n.add_argument("second") - n.set_defaults(func=cmd_noise) - - args = ap.parse_args() - return args.func(args) - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/scripts/python_env.sh b/scripts/python_env.sh index 7c82cba6..b3cb0a4b 100644 --- a/scripts/python_env.sh +++ b/scripts/python_env.sh @@ -3,25 +3,16 @@ # # Source this file; do not execute it. # -# Why it is derived rather than written down: the three scripts that need this each -# carried the same hardcoded string, naming an exact Homebrew patch version -# (`python@3.11/3.11.11`) and `$HOME/miniconda3`. That breaks on `brew upgrade`, on -# any machine laid out differently, and on Linux, where the loader does not read -# DYLD_LIBRARY_PATH at all -- so a CI runner silently got nothing useful. +# Why it is derived rather than written down: a hardcoded path names one exact +# Python installation, which breaks on an upgrade, on a machine laid out +# differently, and on Linux, where the loader does not read DYLD_LIBRARY_PATH. +# Deriving it also picks the libpython the build actually linked against, since +# DYLD_LIBRARY_PATH takes precedence over the rpath pyo3 bakes in. # -# It was also wrong in a way that happened to work. It listed Homebrew's python@3.11 -# first, while the binaries on the machine it was written for link against -# miniconda's 3.11: `otool -l` shows the rpath pyo3 baked in pointing at -# `$HOME/miniconda3/lib`. DYLD_LIBRARY_PATH takes precedence over rpath, so the -# hardcoded value was shadowing the libpython the build had actually chosen with a -# different copy that merely shared its ABI. -# -# Scope, measured. The default suite does not need this at all: `cargo test -# --workspace` passes all 773 tests with the variable unset, because pyo3's build -# script bakes an absolute rpath into the binaries it links. The `ml` feature does -# need it, because that rpath is not emitted transitively -- without it `cargo test -# -p kolibrie --features ml` fails 34 targets with "Library not loaded: -# @rpath/libpython3.11.dylib", and `kolibrie-http-server` exits 127. +# Scope: the default suite does not need this, because pyo3's build script bakes an +# absolute rpath into the binaries it links. The `ml` feature does, because that +# rpath is not emitted transitively; without it the `ml` tests and +# `kolibrie-http-server` cannot load libpython. # # An existing value wins: the derived directory is appended rather than prepended, # so anyone who has already aimed this at a particular interpreter keeps it. diff --git a/scripts/readme_examples.py b/scripts/readme_examples.py index 80cb741a..a10c1ea0 100755 --- a/scripts/readme_examples.py +++ b/scripts/readme_examples.py @@ -19,7 +19,7 @@ A preamble holds until the next one. `` clears @@ -99,7 +99,9 @@ def as_program(code: str, preamble: str = "") -> str: wrapped. Lints are relaxed because an example is written to be read: an unused import in a snippet is not a defect worth failing on. """ - header = "#![allow(unused_imports, unused_variables, unused_mut, dead_code, unreachable_code)]\n" + header = ( + "#![allow(unused_imports, unused_variables, unused_mut, dead_code, unreachable_code)]\n" + ) if re.search(r"\bfn\s+main\s*\(", code): return header + code + "\n" # `use` declarations have to stay at the top level, so they are hoisted out of @@ -142,16 +144,14 @@ def write_crate(cases: list[tuple[int, str, str]]) -> None: path = "src/lib.rs" [dependencies] -kolibrie = {{ path = "{ROOT / 'kolibrie'}" }} -shared = {{ path = "{ROOT / 'shared'}" }} -datalog = {{ path = "{ROOT / 'datalog'}" }} -datalogmtl = {{ path = "{ROOT / 'datalogmtl'}" }} +kolibrie = {{ path = "{ROOT / "kolibrie"}" }} +shared = {{ path = "{ROOT / "shared"}" }} +datalog = {{ path = "{ROOT / "datalog"}" }} +datalogmtl = {{ path = "{ROOT / "datalogmtl"}" }} """ ) for line, code, preamble in cases: - (WORKSPACE / "examples" / f"readme_line_{line}.rs").write_text( - as_program(code, preamble) - ) + (WORKSPACE / "examples" / f"readme_line_{line}.rs").write_text(as_program(code, preamble)) def main() -> int: @@ -161,9 +161,7 @@ def main() -> int: arguments = parser.parse_args() found = blocks(README.read_text()) - cases = [ - (line, code, preamble) for line, code, preamble, reason in found if reason is None - ] + cases = [(line, code, preamble) for line, code, preamble, reason in found if reason is None] skipped = [(line, reason) for line, _, _, reason in found if reason is not None] if arguments.list: diff --git a/scripts/smoke_examples.py b/scripts/smoke_examples.py index 79b0b765..97521ad7 100755 --- a/scripts/smoke_examples.py +++ b/scripts/smoke_examples.py @@ -41,8 +41,8 @@ import json import pathlib import subprocess -import time import sys +import time # These cannot finish unattended, and that is what they are for rather than a # defect. Each needs something this script will not start: @@ -62,7 +62,10 @@ def examples(root: pathlib.Path) -> list[str]: meta = json.loads( subprocess.run( ["cargo", "metadata", "--format-version", "1", "--no-deps"], - cwd=root, capture_output=True, text=True, check=True, + cwd=root, + capture_output=True, + text=True, + check=True, ).stdout ) package = next(p for p in meta["packages"] if p["name"] == "kolibrie") @@ -76,12 +79,15 @@ def examples(root: pathlib.Path) -> list[str]: def main() -> int: parser = argparse.ArgumentParser() parser.add_argument("outdir", nargs="?", default="/tmp/kolibrie-verify") - parser.add_argument("--timeout", type=int, default=120, - help="seconds per example (default: 120)") - parser.add_argument("--dump-timings", metavar="PATH", - help="write every example's wall time to PATH, slowest first") - parser.add_argument("--list", action="store_true", - help="print what would run, and exit") + parser.add_argument( + "--timeout", type=int, default=120, help="seconds per example (default: 120)" + ) + parser.add_argument( + "--dump-timings", + metavar="PATH", + help="write every example's wall time to PATH, slowest first", + ) + parser.add_argument("--list", action="store_true", help="print what would run, and exit") args = parser.parse_args() root = pathlib.Path(__file__).resolve().parent.parent @@ -90,8 +96,10 @@ def main() -> int: if args.list: for n in names: - print(f" {'skip' if n in SKIP else 'run ':4} {n}" - + (f" ({SKIP[n]})" if n in SKIP else "")) + print( + f" {'skip' if n in SKIP else 'run ':4} {n}" + + (f" ({SKIP[n]})" if n in SKIP else "") + ) return 0 out = pathlib.Path(args.outdir) / "examples" @@ -102,13 +110,9 @@ def main() -> int: # a single total. durations: dict[str, float] = {} - # Serial on purpose. Running these concurrently was measured and rejected: 16 jobs - # took 170s against 185s serially, because the floor below is charged by a daemon - # that serialises, so concurrency only moves the queue. The 8% was not worth what - # it did to the streaming examples -- `continious_stream` went from 7.4s to 42s and - # `replay_profile` from 6.4s to 56s under contention. They passed, but their RSP - # windows are wall-clock, so passing once at 6x the duration is not evidence they - # stay correct there. + # Serial on purpose: the streaming examples use wall-clock RSP windows, and + # running them concurrently slows them enough that a pass would not show they + # stay correct. begun = time.monotonic() for name in runnable: exe = root / "target" / "debug" / "examples" / name @@ -118,7 +122,10 @@ def main() -> int: started = time.monotonic() try: result = subprocess.run( - [str(exe)], cwd=root, capture_output=True, text=True, + [str(exe)], + cwd=root, + capture_output=True, + text=True, timeout=args.timeout, ) except subprocess.TimeoutExpired: @@ -133,10 +140,12 @@ def main() -> int: (out / f"{name}.log").write_text(result.stdout + result.stderr) wall = time.monotonic() - begun - print(f" ran: {len(passed) + len(failed) + len(timed_out)}" - f" passed: {len(passed)}" - f" failed: {len(failed) + len(timed_out)}" - f" skipped: {len(SKIP)}") + print( + f" ran: {len(passed) + len(failed) + len(timed_out)}" + f" passed: {len(passed)}" + f" failed: {len(failed) + len(timed_out)}" + f" skipped: {len(SKIP)}" + ) total = sum(durations.values()) ranked = sorted(durations.items(), key=lambda pair: -pair[1]) @@ -150,8 +159,7 @@ def main() -> int: print(f" {seconds:6.1f}s {share:4.1f}% {name}") if ranked: mid = ranked[len(ranked) // 2] - print(f" median {mid[1]:.1f}s ({mid[0]}); " - f"floor {ranked[-1][1]:.1f}s ({ranked[-1][0]})") + print(f" median {mid[1]:.1f}s ({mid[0]}); floor {ranked[-1][1]:.1f}s ({ranked[-1][0]})") if args.dump_timings: path = pathlib.Path(args.dump_timings) path.write_text("".join(f"{s:.3f}\t{n}\n" for n, s in ranked)) diff --git a/scripts/smoke_python.py b/scripts/smoke_python.py index 6b9ccf5f..d872fe44 100755 --- a/scripts/smoke_python.py +++ b/scripts/smoke_python.py @@ -70,7 +70,9 @@ def main() -> int: parser.add_argument("--timeout", type=int, default=120) arguments = parser.parse_args() - outdir = pathlib.Path(arguments.outdir) if arguments.outdir else ROOT / "target" / "smoke-python" + outdir = ( + pathlib.Path(arguments.outdir) if arguments.outdir else ROOT / "target" / "smoke-python" + ) outdir.mkdir(parents=True, exist_ok=True) staged = stage(build(arguments.release), outdir) diff --git a/scripts/verify.sh b/scripts/verify.sh index 408f5783..41c690db 100755 --- a/scripts/verify.sh +++ b/scripts/verify.sh @@ -109,13 +109,9 @@ else fail=1 fi -# The README is the first thing anyone tries, and nothing used to check it. Sixteen -# of its twenty-three examples did not compile when this check was first run: they -# named `execute_query` after it became `execute`, indexed a `Solutions` as though it -# were a `Vec`, reached `Reasoner.dictionary` and `SparqlDatabase.dictionary` after -# both became private, built a `Rule` without its `negative_premise`, and imported -# `datalog::knowledge_graph` under a name it no longer has. Compile-only on purpose: -# whether an example *runs* is the smoke run's question about `kolibrie/examples`. +# The README is the first thing anyone tries, so its examples are compiled here. +# Compile-only on purpose: whether an example *runs* is the smoke run's question +# about `kolibrie/examples`. phase "README examples" if python3 scripts/readme_examples.py > "$OUT/readme.log" 2>&1; then grep -E '^ ok:|^compiling' "$OUT/readme.log" | sed 's/^/ /' @@ -124,15 +120,20 @@ else fail=1 fi -# `docs/api.md` is generated from the source, and the point of generating it is that -# it cannot drift. This fails when the public surface moved and nobody regenerated -- -# which is how 371 hand-written lines in the README came to document three methods -# that no longer existed. -phase "api surface" -if python3 scripts/api_surface.py --check > "$OUT/api.log" 2>&1; then - sed 's/^/ /' "$OUT/api.log" +# Mirrors the blocking `python` CI job +phase "python (ruff + pytest)" +if command -v uv > /dev/null 2>&1; then + if { uv lock --check && + uv run --locked --only-group dev ruff check && + uv run --locked --only-group dev ruff format --check && + uv run --locked --only-group dev pytest -q; } > "$OUT/python.log" 2>&1; then + echo " ok" + else + tail -20 "$OUT/python.log" | sed 's/^/ /' + fail=1 + fi else - sed 's/^/ /' "$OUT/api.log" + echo " FAILED: uv is not installed, see CONTRIBUTING.md" fail=1 fi diff --git a/scripts/warning_report.py b/scripts/warning_report.py index e8b8d312..9e449530 100755 --- a/scripts/warning_report.py +++ b/scripts/warning_report.py @@ -10,6 +10,7 @@ Counts are per *target*, so a lint firing in a lib and again in that lib's tests counts twice. That is intentional: it is the number a developer actually sees. """ + from __future__ import annotations import argparse @@ -19,7 +20,11 @@ import sys CARGO_CMD = [ - "cargo", "check", "--workspace", "--all-targets", "--message-format=json", + "cargo", + "check", + "--workspace", + "--all-targets", + "--message-format=json", ] diff --git a/shared/src/dataset_index.rs b/shared/src/dataset_index.rs index 57220016..0e3bdc74 100644 --- a/shared/src/dataset_index.rs +++ b/shared/src/dataset_index.rs @@ -15,29 +15,29 @@ use crate::terms::{Term, TriplePattern}; use crate::triple::Triple; #[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Hash, Serialize, Deserialize)] -/// Which graph a quad belongs to. +/// Which graph a quad belongs to pub enum GraphId { - /// The unnamed default graph. + /// The unnamed default graph Default, - /// A named graph, identified by its encoded IRI. + /// A named graph, identified by its encoded IRI Named(u32), } #[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Hash, Serialize, Deserialize)] -/// An encoded triple together with the graph it belongs to. +/// An encoded triple together with the graph it belongs to pub struct Quad { - /// Encoded subject. + /// Encoded subject pub subject: u32, - /// Encoded predicate. + /// Encoded predicate pub predicate: u32, - /// Encoded object. + /// Encoded object pub object: u32, - /// The graph this quad is in. + /// The graph this quad is in pub graph: GraphId, } impl Quad { - /// The quad's triple, discarding the graph. + /// The quad's triple, discarding the graph pub fn triple(&self) -> Triple { Triple { subject: self.subject, @@ -48,29 +48,26 @@ impl Quad { } #[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)] -/// The graph position of a pattern: fixed, or left open to bind. -/// -/// This is [`GraphId`] plus a variable case, in the same way [`Term`] is an -/// encoded id plus a variable case. +/// The graph position of a pattern: fixed, or left open to bind pub enum GraphTerm { - /// Match only the default graph. + /// Match only the default graph Default, - /// Match one named graph. + /// Match one named graph Named(u32), - /// Match any graph, binding its name to this variable. + /// Match any graph, binding its name to this variable Variable(String), } #[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)] -/// A triple pattern extended with a graph position. +/// A triple pattern extended with a graph position pub struct QuadPattern { - /// Subject position. + /// Subject position pub subject: Term, - /// Predicate position. + /// Predicate position pub predicate: Term, - /// Object position. + /// Object position pub object: Term, - /// Graph position. + /// Graph position pub graph: GraphTerm, } @@ -78,20 +75,7 @@ type NestedIndex = HashMap>>; type GraphNestedIndex = HashMap; type SpoGraphIndex = HashMap>>>; -/// Quad storage with enough index orderings to answer any binding pattern without -/// a full scan. -/// -/// Three graph-scoped orderings (`gspo`, `gpos`, `gosp`) cover lookups within a -/// known graph; `spog` inverts that, answering "which graphs contain this triple" -/// without visiting every graph. A quad is therefore stored four times, which is -/// the space traded for never scanning. -/// -/// Insertion is set semantics: re-inserting an existing quad is a no-op and reports -/// `false`, so callers can use the return value to detect genuinely new facts — -/// which is what makes semi-naive derivation terminate. -/// -/// [`Self::generation`] lets an observer notice mutations without being notified; -/// the statistics cache relies on it rather than on explicit invalidation. +/// Quad storage with enough index orderings to answer any binding pattern without a full scan #[derive(Debug, Clone, Default, Serialize, Deserialize)] pub struct DatasetIndex { gspo: GraphNestedIndex, @@ -107,7 +91,7 @@ pub struct DatasetIndex { } impl DatasetIndex { - /// An empty index with no graphs. + /// An empty index with no graphs pub fn new() -> Self { Self::default() } @@ -126,7 +110,7 @@ impl DatasetIndex { self.generation = self.generation.wrapping_add(1); } - /// Inserts into the default graph. Returns whether the quad was new. + /// Inserts into the default graph pub fn insert_triple(&mut self, triple: &Triple) -> bool { self.insert_quad(&Quad { subject: triple.subject, @@ -136,7 +120,7 @@ impl DatasetIndex { }) } - /// Removes from the default graph. Returns whether it was present. + /// Removes from the default graph pub fn delete_triple(&mut self, triple: &Triple) -> bool { self.delete_quad(&Quad { subject: triple.subject, @@ -146,24 +130,23 @@ impl DatasetIndex { }) } - /// Alias for [`Self::insert_triple`]. + /// Alias for [`Self::insert_triple`] pub fn insert(&mut self, triple: &Triple) -> bool { self.insert_triple(triple) } - /// Alias for [`Self::delete_triple`]. + /// Alias for [`Self::delete_triple`] pub fn delete(&mut self, triple: &Triple) -> bool { self.delete_triple(triple) } - /// Alias for [`Self::query_default`]. `None` means "any" in that position. + /// Alias for [`Self::query_default`] pub fn query(&self, s: Option, p: Option, o: Option) -> Vec { self.query_default(s, p, o) } - /// Inserts `quad`, creating its graph if needed. Returns whether it was new. + /// Inserts `quad`, creating its graph if needed pub fn insert_quad(&mut self, quad: &Quad) -> bool { - // Registering a new named graph counts as a mutation let named_a_graph = match quad.graph { GraphId::Named(graph) => self.named_graphs.insert(graph), GraphId::Default => false, @@ -219,16 +202,13 @@ impl DatasetIndex { true } - /// Removes `quad`. Returns whether it was present. The graph survives even if - /// this empties it. + /// Removes `quad` pub fn delete_quad(&mut self, quad: &Quad) -> bool { if !self.contains_quad(quad) { return false; } if let GraphId::Named(graph) = quad.graph { - // Materialize graph identity before deleting the last quad from an - // index deserialized from the pre-catalog representation. self.named_graphs.insert(graph); } @@ -247,7 +227,7 @@ impl DatasetIndex { true } - /// Whether `quad` is present. + /// Whether `quad` is present pub fn contains_quad(&self, quad: &Quad) -> bool { self.spog .get(&quad.subject) @@ -256,8 +236,7 @@ impl DatasetIndex { .is_some_and(|graphs| graphs.contains(&quad.graph)) } - /// Triples in the default graph matching the bound positions; `None` is a - /// wildcard. + /// Triples in the default graph matching the bound positions; `None` is a wildcard pub fn query_default(&self, s: Option, p: Option, o: Option) -> Vec { self.query_graph(GraphId::Default, s, p, o) .into_iter() @@ -282,11 +261,7 @@ impl DatasetIndex { .collect() } - /// Quads in one graph matching the bound positions; `None` is a wildcard. - /// - /// Results come back in the index's hash order, which varies between processes. - /// Callers must not depend on it — a test that asserted a positional property - /// of this result failed about one run in five. + /// Quads in one graph matching the bound positions; `None` is a wildcard pub fn query_graph( &self, graph: GraphId, @@ -410,7 +385,7 @@ impl DatasetIndex { results } - /// Quads across every named graph, excluding the default graph. + /// Quads across every named graph, excluding the default graph pub fn query_named_graphs( &self, s: Option, @@ -454,7 +429,7 @@ impl DatasetIndex { results } - /// Quads matching a [`QuadPattern`], including its graph position. + /// Quads matching a [`QuadPattern`], including its graph position pub fn query_quads( &self, s: Option, @@ -472,10 +447,7 @@ impl DatasetIndex { } } - /// Default-graph triples matching `pattern`, treating variables as wildcards. - /// - /// Does not enforce repeated variables: a pattern using one name twice still - /// returns triples where those positions differ, so the caller must verify. + /// Default-graph triples matching `pattern`, treating variables as wildcards pub fn get_matching_triples(&self, pattern: &TriplePattern) -> Vec { let (s, p, o) = pattern; let sub = constant_id(s); @@ -484,20 +456,17 @@ impl DatasetIndex { self.query_default(sub, pre, obj) } - /// Whether `graph` exists, including when it is empty. + /// Whether `graph` exists, including when it is empty pub fn graph_exists(&self, graph: GraphId) -> bool { match graph { GraphId::Default => true, GraphId::Named(graph) => { - self.named_graphs.contains(&graph) - // Non-empty graphs in indexes serialized before the graph - // catalog was added remain discoverable. - || self.gspo.contains_key(&GraphId::Named(graph)) + self.named_graphs.contains(&graph) || self.gspo.contains_key(&GraphId::Named(graph)) } } } - /// Every named graph, excluding the default graph. Includes empty ones. + /// Every named graph, excluding the default graph pub fn named_graphs(&self) -> Vec { let mut graphs: HashSet = self.named_graphs.clone(); graphs.extend(self.gspo.keys().filter_map(|graph| match graph { @@ -510,18 +479,15 @@ impl DatasetIndex { graphs } - /// Returns every graph identity, including the always-present default - /// graph and named graphs without any quads. + /// Returns every graph identity, including the always-present default graph and named graphs + /// without any quads pub fn graphs(&self) -> Vec { let mut graphs = vec![GraphId::Default]; graphs.extend(self.named_graphs()); graphs } - /// Creates a graph identity without requiring a quad to be inserted. - /// - /// Returns `true` only when a new named graph was created. The default - /// graph always exists and therefore returns `false`. + /// Creates a graph identity without requiring a quad to be inserted pub fn create_graph(&mut self, graph: GraphId) -> bool { match graph { GraphId::Default => false, @@ -536,10 +502,7 @@ impl DatasetIndex { } } - /// Drops a graph identity and all of its quads. - /// - /// Dropping the default graph clears it but cannot remove its identity. - /// A missing named graph returns `false`. + /// Drops a graph identity and all of its quads pub fn drop_graph(&mut self, graph: GraphId) -> bool { match graph { GraphId::Default => { @@ -551,7 +514,6 @@ impl DatasetIndex { if !self.graph_exists(graph) { return false; } - // Removing the graph identity counts as one additional mutation self.clear_graph(graph); self.named_graphs.remove(&graph_id); self.record_change(); @@ -560,8 +522,8 @@ impl DatasetIndex { } } - /// Returns a complete, deterministic quad snapshot suitable for rebuilding - /// all indexes without collapsing named graphs into the default graph. + /// Returns a complete, deterministic quad snapshot suitable for rebuilding all indexes without + /// collapsing named graphs into the default graph pub fn all_quads(&self) -> Vec { let mut quads = Vec::new(); for graph in self.graphs() { @@ -571,8 +533,7 @@ impl DatasetIndex { quads } - /// Which graphs contain `triple`. Answered from `spog` without visiting every - /// graph. + /// Which graphs contain `triple` pub fn graphs_for_triple(&self, triple: &Triple) -> Vec { self.spog .get(&triple.subject) @@ -582,28 +543,22 @@ impl DatasetIndex { .unwrap_or_default() } - /// Empties one graph, leaving the graph itself in existence. + /// Empties one graph, leaving the graph itself in existence pub fn clear_graph(&mut self, graph: GraphId) { - // Clearing a named graph retains its identity, including for an old - // deserialized index whose catalog is populated lazily. if let GraphId::Named(graph_id) = graph { if self.graph_exists(graph) && self.named_graphs.insert(graph_id) { - // Register a legacy (pre-catalog) graph self.record_change(); } } - // delete_quad records one mutation per removed quad let quads = self.query_graph(graph, None, None, None); for quad in quads { self.delete_quad(&quad); } } - /// Removes every quad and graph, and bumps [`Self::resets`] if anything was - /// present. + /// Removes every quad and graph, and bumps [`Self::resets`] if anything was present pub fn clear(&mut self) { - // No-op on an empty dataset if self.gspo.is_empty() && self.named_graphs.is_empty() { return; } @@ -658,12 +613,12 @@ impl DatasetIndex { self.gspo.keys().map(|&graph| self.count_graph(graph)).sum() } - /// Number of quads in one graph. + /// Number of quads in one graph pub fn len_graph(&self, graph: GraphId) -> usize { self.count_graph(graph) } - /// Number of triples in the default graph. + /// Number of triples in the default graph pub fn len_default(&self) -> usize { self.len_graph(GraphId::Default) } @@ -850,8 +805,6 @@ mod tests { graph: GraphId::Named(10), }; index.insert_quad(&quad); - // Models deserialization of the representation from before the - // explicit catalog was added. index.named_graphs.clear(); assert!(index.delete_quad(&quad)); @@ -1027,17 +980,14 @@ mod tests { index.insert_triple(&triple(1, 2, 3)); index.create_graph(named(5)); - // Duplicate insert assert_eq!( advance(&mut index, |i| assert!(!i.insert_triple(&triple(1, 2, 3)))), 0 ); - // Delete of a missing quad assert_eq!( advance(&mut index, |i| assert!(!i.delete_triple(&triple(9, 9, 9)))), 0 ); - // Existing graph created, missing graph dropped assert_eq!( advance(&mut index, |i| assert!(!i.create_graph(named(5)))), 0 @@ -1046,7 +996,6 @@ mod tests { advance(&mut index, |i| assert!(!i.drop_graph(named(404)))), 0 ); - // Default graph cannot be created assert_eq!( advance(&mut index, |i| assert!(!i.create_graph(GraphId::Default))), 0 @@ -1085,7 +1034,6 @@ mod tests { index.insert_quad(&quad(4, 5, 6, named(7))); assert_eq!(advance(&mut index, |i| i.clear_graph(named(7))), 2); - // Second clear removes nothing assert_eq!(advance(&mut index, |i| i.clear_graph(named(7))), 0); } @@ -1112,7 +1060,6 @@ mod tests { fn naming_a_graph_through_a_duplicate_quad_is_still_a_change() { let mut index = DatasetIndex::new(); index.insert_quad(&quad(1, 2, 3, named(4))); - // Same quad, graph already named assert_eq!( advance(&mut index, |i| assert!(!i.insert_quad(&quad( 1, @@ -1161,8 +1108,6 @@ impl crate::quad_source::QuadSource for DatasetIndex { } fn for_each_quad(&self, visit: &mut dyn FnMut(GraphId, u32, u32, u32)) { - // The inherent method takes `impl FnMut`, which is not object-safe; a - // `&mut dyn FnMut` is itself `FnMut`, so it forwards without an extra layer. self.for_each_quad(|g, s, p, o| visit(g, s, p, o)); } diff --git a/shared/src/dictionary.rs b/shared/src/dictionary.rs index 8d5751f9..7539e396 100644 --- a/shared/src/dictionary.rs +++ b/shared/src/dictionary.rs @@ -12,28 +12,19 @@ use crate::quoted_triple_store::{is_quoted_triple_id, QuotedTripleStore, QUOTED_ use crate::triple::Triple; use std::collections::HashMap; -/// Interns RDF terms to `u32` ids, and decodes them back. -/// -/// Ids are assigned sequentially from 0 and are only meaningful relative to this -/// dictionary: the same term gets different ids in two dictionaries built in -/// different orders. Anything that compares or joins encoded data must therefore -/// share one dictionary, which is why it is normally held behind a shared handle. -/// -/// Ids stop below `0x8000_0000`; the top bit is reserved to tag quoted triples, which -/// [`QuotedTripleStore`] owns. -/// [`Self::encode`] asserts rather than silently colliding with that range. +/// Interns RDF terms to `u32` ids, and decodes them back #[derive(Debug, Default, Clone, PartialEq, Eq)] pub struct Dictionary { - /// Term text to id. The authority for whether a term is already interned. + /// Term text to id pub string_to_id: HashMap, - /// Id back to term text, kept in step with `string_to_id`. + /// Id back to term text, kept in step with `string_to_id` pub id_to_string: HashMap, - /// The next id to hand out. Monotonic; ids are never reused. + /// The next id to hand out pub next_id: u32, } impl Dictionary { - /// An empty dictionary, assigning ids from 0. + /// An empty dictionary, assigning ids from 0 pub fn new() -> Self { Dictionary { string_to_id: HashMap::new(), @@ -42,13 +33,7 @@ impl Dictionary { } } - /// Returns `value`'s id, interning it if this is the first time it is seen. - /// - /// # Panics - /// - /// If the id space is exhausted — that is, if the next id would reach - /// `0x8000_0000` and collide with the quoted-triple range. At that point ids - /// would silently alias quoted triples, so this fails loudly instead. + /// Returns `value`'s id, interning it if this is the first time it is seen pub fn encode(&mut self, value: &str) -> u32 { if let Some(&id) = self.string_to_id.get(value) { id @@ -66,18 +51,13 @@ impl Dictionary { } } - /// The term text for `id`, or `None` if this dictionary never assigned it. - /// - /// Returns `None` for quoted-triple ids: use [`Self::decode_term`] for those. + /// The term text for `id`, or `None` if this dictionary never assigned it pub fn decode(&self, id: u32) -> Option<&str> { self.id_to_string.get(&id).map(|s| s.as_str()) } - /// Renders `triple` as N-Triples, substituting `unknown` for any id this - /// dictionary cannot resolve. - /// - /// Quoted-triple ids will render as `unknown`; use [`Self::decode_triple_star`] - /// where RDF-star terms are possible. + /// Renders `triple` as N-Triples, substituting `unknown` for any id this dictionary cannot + /// resolve pub fn decode_triple(&self, triple: &Triple) -> String { let s = self.decode(triple.subject).unwrap_or("unknown"); let p = self.decode(triple.predicate).unwrap_or("unknown"); @@ -85,8 +65,7 @@ impl Dictionary { format!("{} {} {} .", s, p, o) } - /// Decode a term ID that may be either a regular dictionary ID or a quoted triple ID. - /// For quoted triple IDs, recursively renders `<< s p o >>`. + /// Decode a term ID that may be either a regular dictionary ID or a quoted triple ID pub fn decode_term(&self, id: u32, qt_store: &QuotedTripleStore) -> Option { if is_quoted_triple_id(id) { let (s, p, o) = qt_store.decode(id)?; @@ -99,7 +78,7 @@ impl Dictionary { } } - /// Decode a triple, handling quoted triple IDs in any position. + /// Decode a triple, handling quoted triple IDs in any position pub fn decode_triple_star(&self, triple: &Triple, qt_store: &QuotedTripleStore) -> String { let s = self .decode_term(triple.subject, qt_store) @@ -113,14 +92,8 @@ impl Dictionary { format!("{} {} {} .", s, p, o) } - /// Folds `other`'s entries in, keeping this dictionary's mapping wherever both - /// have one, and advances `next_id` past both. - /// - /// Existing entries win, so merging is only sound when the two dictionaries - /// agree on the ids they share — typically because one grew from the other. Two - /// independently built dictionaries will have assigned the same ids to different - /// terms, and merging them produces a mapping that resolves those ids to the - /// wrong terms rather than reporting a conflict. + /// Folds `other`'s entries in, keeping this dictionary's mapping wherever both have one, and + /// advances `next_id` past both pub fn merge(&mut self, other: &Dictionary) { for (key, value) in other.string_to_id.iter() { self.string_to_id.entry(key.clone()).or_insert(*value); diff --git a/shared/src/diff_sdd.rs b/shared/src/diff_sdd.rs index f211c3da..174d4e31 100644 --- a/shared/src/diff_sdd.rs +++ b/shared/src/diff_sdd.rs @@ -12,15 +12,8 @@ use std::collections::HashMap; use crate::sdd::{SddId, SddManager, VarKind}; -/// Partial derivative of the weighted model count of `id` with respect to each -/// variable's positive weight. -/// -/// Computed by perturbing one weight at a time and recounting, so the cost is one -/// weighted model count per variable. The gradient is what lets a probability -/// attached to a fact be learned from an observed query answer. -/// -/// Takes `&mut SddManager` because weights are set on the manager; they are restored -/// before returning. +/// Partial derivative of the weighted model count of `id` with respect to each variable's positive +/// weight pub fn wmc_gradient(manager: &mut SddManager, id: SddId) -> HashMap { let vars: Vec = manager.variable_ids().collect(); let mut grads = HashMap::new(); diff --git a/shared/src/hybrid.rs b/shared/src/hybrid.rs index 175d5be5..8bbcee68 100644 --- a/shared/src/hybrid.rs +++ b/shared/src/hybrid.rs @@ -7,11 +7,7 @@ * License, v. 2.0. */ -//! Identity-stable probabilistic lineage and hybrid WMC evaluation. -//! -//! The lineage DAG is the source of truth. Top-k is a certified lower-bound -//! evaluator for positive, independent lineage; SDD compilation is the exact -//! fallback for every supported acyclic lineage. +//! Identity-stable probabilistic lineage and hybrid WMC evaluation use crate::dictionary::Dictionary; use crate::provenance::Provenance; @@ -24,14 +20,13 @@ use std::collections::{BTreeMap, BTreeSet, BinaryHeap, HashMap, HashSet}; use std::sync::{Arc, Mutex}; use std::time::{Duration, Instant}; -/// Clock abstraction used to make latency-budget behavior deterministic in tests. +/// Clock abstraction used to make latency-budget behavior deterministic in tests pub trait HybridClock: Send + Sync { - /// The current instant. A test clock returns a value it controls, so a latency - /// budget can be driven past its deadline without sleeping. + /// The current instant fn now(&self) -> Instant; } -/// The real clock, reading [`Instant::now`]. +/// The real clock, reading [`Instant::now`] #[derive(Debug, Default)] pub struct SystemHybridClock; @@ -41,81 +36,71 @@ impl HybridClock for SystemHybridClock { } } -/// Stable identifier for a probabilistic seed. IDs are allocated monotonically -/// and are never recycled by [`SeedRegistry`]. +/// Stable identifier for a probabilistic seed #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, PartialOrd, Ord)] pub struct SeedId(u32); impl SeedId { - /// The raw identifier, for use as a variable index in a formula. + /// The raw identifier, for use as a variable index in a formula pub fn get(self) -> u32 { self.0 } } -/// Identity of one arrival before it is fanned out to overlapping windows. +/// Identity of one arrival before it is fanned out to overlapping windows #[derive(Debug, Clone, PartialEq, Eq, Hash, PartialOrd, Ord)] pub struct EventKey { - /// Stream the event arrived on, normalised by [`normalize_stream_iri`]. + /// Stream the event arrived on, normalised by [`normalize_stream_iri`] pub stream_iri: String, - /// The event's own timestamp, not the time it was processed. + /// The event's own timestamp, not the time it was processed pub event_time: usize, - /// Arrival order, breaking ties between events sharing a timestamp. - /// - /// Needed because two events can carry the same `event_time` and must still be - /// distinguishable: one arrival is one seed, however many overlapping windows - /// it later appears in. + /// Arrival order, breaking ties between events sharing a timestamp pub sequence: u64, } -/// Whether a seed stands alone or competes with others. +/// Whether a seed stands alone or competes with others #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum SeedKind { - /// Independent of every other seed. + /// Independent of every other seed Independent, - /// One alternative in a mutually exclusive group, by group id. At most one - /// member of a group holds, so members must not be multiplied as if - /// independent. + /// One alternative in a mutually exclusive group, by group id ExclusiveGroup(u32), } -/// One registered seed: a fact, its prior, and its identity. +/// One registered seed: a fact, its prior, and its identity #[derive(Debug, Clone)] pub struct SeedRecord { - /// Stable identifier, also this seed's variable index in a formula. + /// Stable identifier, also this seed's variable index in a formula pub id: SeedId, - /// The fact the probability attaches to. + /// The fact the probability attaches to pub triple: Triple, - /// Prior probability, in `[0, 1]`. + /// Prior probability, in `[0, 1]` pub probability: f64, - /// Independent, or a member of an exclusive group. + /// Independent, or a member of an exclusive group pub kind: SeedKind, - /// The arrival this seed came from, for streaming seeds. `None` for a static - /// one declared against the dataset. + /// The arrival this seed came from, for streaming seeds pub event: Option, } -/// Why a hybrid evaluation could not proceed. +/// Why a hybrid evaluation could not proceed #[derive(Debug, Clone, PartialEq, Eq)] pub enum HybridError { - /// A probability was not finite, or fell outside `[0, 1]`. + /// A probability was not finite, or fell outside `[0, 1]` InvalidProbability, - /// The configuration is internally inconsistent; the message says how. + /// The configuration is internally inconsistent; the message says how InvalidConfig(String), - /// The seed id space is used up. Ids are never recycled, so a long-running - /// process registering per-event seeds will eventually reach this. + /// The seed id space is used up SeedIdExhausted, - /// A caller supplied an id already registered. + /// A caller supplied an id already registered DuplicateSeedId(SeedId), - /// A formula referenced a seed that was never registered. + /// A formula referenced a seed that was never registered UnknownSeed(SeedId), - /// The lineage is cyclic. Recursion is not supported, so a recursive rule is - /// rejected rather than evaluated to a fixpoint. + /// The lineage is cyclic UnsupportedRecursion(String), - /// The rule cannot be expressed as probabilistic lineage; the message says why. + /// The rule cannot be expressed as probabilistic lineage; the message says why UnsupportedRule(String), - /// A shared lock was poisoned by a panic in another thread, so the state can no - /// longer be trusted. + /// A shared lock was poisoned by a panic in another thread, so the state can no longer be + /// trusted PoisonedState, } @@ -138,13 +123,7 @@ impl std::fmt::Display for HybridError { impl std::error::Error for HybridError {} -/// Process-lifetime registry for stable seed identities. -/// -/// Identity is the point. One arrival becomes one seed, even when it is fanned out -/// to several overlapping windows — otherwise the same uncertainty would be counted -/// twice and a derived probability would come out too high. Ids are allocated -/// monotonically and never recycled, so an id remains valid for as long as the -/// process lives. +/// Process-lifetime registry for stable seed identities #[derive(Debug, Default)] pub struct SeedRegistry { next_id: u64, @@ -156,7 +135,7 @@ pub struct SeedRegistry { } impl SeedRegistry { - /// An empty registry. + /// An empty registry pub fn new() -> Self { Self::default() } @@ -178,10 +157,7 @@ impl SeedRegistry { Ok(id) } - /// Mints a key for a new arrival, taking the next sequence number. - /// - /// Call once per arrival. Calling twice for the same event yields two keys and - /// therefore two seeds, which double-counts the uncertainty. + /// Mints a key for a new arrival, taking the next sequence number pub fn next_event_key(&mut self, stream_iri: &str, event_time: usize) -> EventKey { let key = EventKey { stream_iri: normalize_stream_iri(stream_iri), @@ -192,11 +168,7 @@ impl SeedRegistry { key } - /// Registers a streaming seed, or returns the existing id if this event is - /// already registered. - /// - /// Idempotent per [`EventKey`], which is what keeps one arrival to one seed - /// across overlapping windows. + /// Registers a streaming seed, or returns the existing id if this event is already registered pub fn register_occurrence( &mut self, event: EventKey, @@ -220,8 +192,7 @@ impl SeedRegistry { Ok(id) } - /// Register a non-streaming seed. Re-registering the same triple returns the - /// original identity and updates its immutable-run probability assignment. + /// Register a non-streaming seed pub fn register_static( &mut self, triple: Triple, @@ -249,11 +220,7 @@ impl SeedRegistry { Ok(id) } - /// Registers one alternative of a mutually exclusive group. - /// - /// Unlike [`Self::register_static`] this always allocates a fresh id: two - /// alternatives may assert the same triple, and collapsing them would lose the - /// distinction the group exists to express. + /// Registers one alternative of a mutually exclusive group pub fn register_exclusive( &mut self, group_id: u32, @@ -305,16 +272,13 @@ impl SeedRegistry { Ok(id) } - /// An immutable view of every registered seed, for one evaluation. - /// - /// Evaluation reads a snapshot rather than the live registry so that seeds - /// arriving mid-evaluation cannot change the answer underneath it. + /// An immutable view of every registered seed, for one evaluation pub fn snapshot_all(&self) -> SeedSnapshot { SeedSnapshot::from_records(self.records.values().cloned()) } - /// A snapshot restricted to `ids`, failing with - /// [`HybridError::UnknownSeed`] if any is unregistered. + /// A snapshot restricted to `ids`, failing with [`HybridError::UnknownSeed`] if any is + /// unregistered pub fn snapshot_for_ids(&self, ids: I) -> Result where I: IntoIterator, @@ -337,17 +301,13 @@ impl SeedRegistry { } } -/// Canonical form of a stream IRI, so the same stream written two ways yields one -/// identity. -/// -/// [`EventKey`] holds the normalised form; comparing raw spellings would let one -/// arrival register as two seeds. +/// Canonical form of a stream IRI, so the same stream written two ways yields one identity pub fn normalize_stream_iri(value: &str) -> String { let trimmed = value.trim().trim_start_matches('<').trim_end_matches('>'); trimmed.strip_prefix(':').unwrap_or(trimmed).to_string() } -/// Immutable probability and identity view used by one materialisation snapshot. +/// Immutable probability and identity view used by one materialisation snapshot #[derive(Debug, Clone, Default)] pub struct SeedSnapshot { records: BTreeMap, @@ -378,8 +338,7 @@ impl SeedSnapshot { snapshot } - /// A snapshot from a plain fact-to-probability map, treating every seed as - /// independent. + /// A snapshot from a plain fact-to-probability map, treating every seed as independent pub fn from_probability_seeds(seeds: &HashMap) -> Result { let mut registry = SeedRegistry::new(); let mut sorted: Vec<_> = seeds.iter().collect(); @@ -390,10 +349,7 @@ impl SeedSnapshot { Ok(registry.snapshot_all()) } - /// A snapshot from declarations, preserving exclusive groups. - /// - /// Prefer this over [`Self::from_probability_seeds`] where the seeds include a - /// distribution over labels: the map form cannot express exclusivity. + /// A snapshot from declarations, preserving exclusive groups pub fn from_seed_specs(specs: &[SeedSpec]) -> Result { let mut registry = SeedRegistry::new(); for spec in specs { @@ -425,86 +381,77 @@ impl SeedSnapshot { Ok(registry.snapshot_all()) } - /// The record for `id`, or `None` if it is not in this snapshot. + /// The record for `id`, or `None` if it is not in this snapshot pub fn record(&self, id: SeedId) -> Option<&SeedRecord> { self.records.get(&id) } - /// Every seed asserting `triple`. More than one is normal: separate arrivals, - /// or competing alternatives in an exclusive group. + /// Every seed asserting `triple` pub fn ids_for_triple(&self, triple: &Triple) -> &[SeedId] { self.by_triple.get(triple).map(Vec::as_slice).unwrap_or(&[]) } - /// Every record, in id order. + /// Every record, in id order pub fn records(&self) -> impl Iterator { self.records.values() } - /// Each distinct fact with the seeds asserting it. + /// Each distinct fact with the seeds asserting it pub fn triples(&self) -> impl Iterator { self.by_triple .iter() .map(|(triple, ids)| (triple, ids.as_slice())) } - /// Members of an exclusive group, or `None` if no such group exists. + /// Members of an exclusive group, or `None` if no such group exists pub fn group(&self, group: u32) -> Option<&[SeedId]> { self.groups.get(&group).map(Vec::as_slice) } } -/// Handle to a node in a [`LineageStore`]. +/// Handle to a node in a [`LineageStore`] #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, PartialOrd, Ord)] pub struct LineageId(u32); impl LineageId { - /// The constant false node, always id 0. + /// The constant false node, always id 0 pub const FALSE: Self = Self(0); - /// The constant true node, always id 1. + /// The constant true node, always id 1 pub const TRUE: Self = Self(1); - /// The raw index. + /// The raw index pub fn get(self) -> u32 { self.0 } } -/// One node of the lineage DAG: how a fact was derived, in terms of seeds. -/// -/// `And` and `Or` take a list rather than two operands, so a conjunction of several -/// premises is a single node. That keeps the DAG shallow and the unique table -/// effective. +/// One node of the lineage DAG: how a fact was derived, in terms of seeds #[derive(Debug, Clone, PartialEq, Eq, Hash)] pub enum LineageNode { - /// Cannot hold. + /// Cannot hold False, - /// Holds unconditionally — a fact with no probabilistic dependency. + /// Holds unconditionally — a fact with no probabilistic dependency True, - /// Holds exactly when this seed does. + /// Holds exactly when this seed does Literal(SeedId), - /// Holds when every operand does. + /// Holds when every operand does And(Vec), - /// Holds when at least one operand does. This is where alternative derivations - /// of the same fact meet. + /// Holds when at least one operand does Or(Vec), - /// Holds when the operand does not. Negation makes the lineage non-monotone, - /// which rules out the top-k lower bound. + /// Holds when the operand does not Not(LineageId), } #[derive(Debug, Clone, Copy, Default, PartialEq, Eq)] -/// Structural properties of a lineage DAG that decide which evaluator may run. +/// Structural properties of a lineage DAG that decide which evaluator may run pub struct LineageMetadata { - /// Whether every connective is positive, so adding a proof can only raise the - /// probability. Required for the top-k lower bound to be sound. + /// Whether every connective is positive, so adding a proof can only raise the probability pub monotone: bool, - /// Whether any `Not` appears. + /// Whether any `Not` appears pub has_negation: bool, - /// Whether any seed belongs to an exclusive group, which rules out treating - /// seeds as independent. + /// Whether any seed belongs to an exclusive group, which rules out treating seeds as + /// independent pub has_exclusive_group: bool, - /// Whether the lineage is cyclic. Recursion is unsupported, so this means the - /// evaluation must be refused rather than attempted. + /// Whether the lineage is cyclic pub has_cycle: bool, } -/// Hash-consed canonical DAG arena. +/// Hash-consed canonical DAG arena #[derive(Debug)] pub struct LineageStore { nodes: Vec, @@ -518,7 +465,7 @@ impl Default for LineageStore { } impl LineageStore { - /// A store holding only the two constant nodes. + /// A store holding only the two constant nodes pub fn new() -> Self { let nodes = vec![LineageNode::False, LineageNode::True]; let unique = nodes @@ -530,20 +477,15 @@ impl LineageStore { Self { nodes, unique } } - /// The node `id` refers to. - /// - /// # Panics - /// - /// If `id` did not come from this store. + /// The node `id` refers to pub fn node(&self, id: LineageId) -> &LineageNode { &self.nodes[id.0 as usize] } - /// Number of nodes, including the two constants. + /// Number of nodes, including the two constants pub fn len(&self) -> usize { self.nodes.len() } - /// Whether the store holds no nodes. A store built by [`Self::new`] is not - /// empty: it already contains the constants. + /// Whether the store holds no nodes pub fn is_empty(&self) -> bool { self.nodes.is_empty() } @@ -558,13 +500,13 @@ impl LineageStore { id } - /// The node for `seed`, interning it if new. + /// The node for `seed`, interning it if new pub fn literal(&mut self, seed: SeedId) -> LineageId { self.intern(LineageNode::Literal(seed)) } - /// Negation of `id`, folding double negation and the constants rather than - /// building a node for them. + /// Negation of `id`, folding double negation and the constants rather than building a node for + /// them pub fn not(&mut self, id: LineageId) -> LineageId { match self.node(id).clone() { LineageNode::False => LineageId::TRUE, @@ -574,12 +516,12 @@ impl LineageStore { } } - /// Conjunction of `items`. An empty conjunction is [`LineageId::TRUE`]. + /// Conjunction of `items` pub fn and>(&mut self, items: I) -> LineageId { self.canonical_nary(true, items) } - /// Disjunction of `items`. An empty disjunction is [`LineageId::FALSE`]. + /// Disjunction of `items` pub fn or>(&mut self, items: I) -> LineageId { self.canonical_nary(false, items) } @@ -636,8 +578,8 @@ impl LineageStore { } } - /// Properties of the DAG rooted at `root`, which decide which evaluator may - /// run: top-k needs monotone lineage, and a cycle must be refused. + /// Properties of the DAG rooted at `root`, which decide which evaluator may run: top-k needs + /// monotone lineage, and a cycle must be refused pub fn metadata(&self, root: LineageId, seeds: &SeedSnapshot) -> LineageMetadata { fn visit( store: &LineageStore, @@ -694,7 +636,7 @@ impl LineageStore { } } -/// Provenance semiring whose tags are handles into a shared full lineage DAG. +/// Provenance semiring whose tags are handles into a shared full lineage DAG #[derive(Clone)] pub struct LineageProvenance { store: Arc>, @@ -710,26 +652,22 @@ impl std::fmt::Debug for LineageProvenance { } impl LineageProvenance { - /// A provenance semiring that records lineage instead of computing a number. - /// - /// Its `Tag` is a [`LineageId`], so derivation builds a DAG and the probability - /// is only computed later by [`evaluate_hybrid`]. That is what lets one - /// derivation be evaluated exactly or approximately without re-deriving. + /// A provenance semiring that records lineage instead of computing a number pub fn new(seeds: Arc) -> Self { Self { store: Arc::new(Mutex::new(LineageStore::new())), seeds, } } - /// The shared DAG this semiring writes into. + /// The shared DAG this semiring writes into pub fn store(&self) -> &Arc> { &self.store } - /// The seeds its literals refer to. + /// The seeds its literals refer to pub fn seeds(&self) -> &Arc { &self.seeds } - /// The lineage node for `seed`. + /// The lineage node for `seed` pub fn literal(&self, seed: SeedId) -> LineageId { self.store.lock().unwrap().literal(seed) } @@ -786,35 +724,28 @@ impl Provenance for LineageProvenance { } #[derive(Debug, Clone, PartialEq)] -/// Thresholds and budgets governing the escalation from approximate to exact. -/// -/// The defaults aim at interactive latency: top-k gets 25 ms, exact compilation -/// 250 ms. Validate with [`Self::validate`] before use. +/// Thresholds and budgets governing the escalation from approximate to exact pub struct HybridConfig { - /// Probability a fact must reach to be reported. The point of a threshold is - /// that a lower bound above it, or an upper bound below it, settles the question - /// without an exact count. + /// Probability a fact must reach to be reported pub threshold: f64, - /// Whether `threshold` is taken literally or derived from cost. + /// Whether `threshold` is taken literally or derived from cost pub threshold_policy: ThresholdPolicyKind, - /// Half-width of the band around `threshold` within which an approximate answer - /// is considered too close to call, forcing escalation to exact evaluation. + /// Half-width of the band around `threshold` where an approximate answer forces exact + /// evaluation pub band_epsilon: f64, - /// Smallest improvement a further proof must promise for the top-k search to - /// continue. Stops the search spending its budget on negligible terms. + /// Smallest improvement a further proof must promise for the top-k search to continue pub marginal_gain_floor: f64, - /// Proofs to enumerate on the first top-k attempt. + /// Proofs to enumerate on the first top-k attempt pub k_initial: usize, - /// Ceiling on proofs enumerated before giving up on top-k. + /// Ceiling on proofs enumerated before giving up on top-k pub k_max: usize, - /// Factor `k` grows by between attempts. + /// Factor `k` grows by between attempts pub k_growth: usize, - /// Wall-clock budget for top-k search. + /// Wall-clock budget for top-k search pub topk_budget: Duration, - /// Wall-clock budget for SDD compilation. + /// Wall-clock budget for SDD compilation pub sdd_budget: Duration, - /// Node budget for SDD compilation. Compilation can blow up - /// combinatorially, so both a time and a size limit apply. + /// Node budget for SDD compilation pub sdd_node_budget: usize, } @@ -835,19 +766,19 @@ impl Default for HybridConfig { } } -/// How the reporting threshold is decided. +/// How the reporting threshold is decided #[derive(Debug, Clone, Copy, PartialEq, Eq, Default)] pub enum ThresholdPolicyKind { - /// Use [`HybridConfig::threshold`] as given. + /// Use [`HybridConfig::threshold`] as given #[default] Explicit, - /// Derive it from the relative cost of approximate and exact evaluation, so - /// cheap questions get a tighter threshold than expensive ones. + /// Derive it from the relative cost of approximate and exact evaluation, so cheap questions get + /// a tighter threshold than expensive ones CostRatio, } impl ThresholdPolicyKind { - /// The policy's name as it appears in a `PROB(...)` annotation. + /// The policy's name as it appears in a `PROB(...)` annotation pub fn as_str(self) -> &'static str { match self { Self::Explicit => "explicit", @@ -856,21 +787,17 @@ impl ThresholdPolicyKind { } } -/// A closed interval known to contain the true probability. -/// -/// Returned when neither bound settles the question against the threshold: the -/// answer is somewhere inside, and the caller decides whether that is good enough. +/// A closed interval known to contain the true probability #[derive(Debug, Clone, Copy, PartialEq)] pub struct ProbabilityInterval { - /// Greatest lower bound established. + /// Greatest lower bound established pub lower: f64, - /// Least upper bound established. + /// Least upper bound established pub upper: f64, } impl ProbabilityInterval { - /// An interval, rejecting bounds that are not finite, not in `[0, 1]`, or - /// inverted. + /// An interval, rejecting bounds that are not finite, not in `[0, 1]`, or inverted pub fn new(lower: f64, upper: f64) -> Result { if !lower.is_finite() || !upper.is_finite() @@ -885,22 +812,19 @@ impl ProbabilityInterval { Ok(Self { lower, upper }) } - /// How much uncertainty remains. + /// How much uncertainty remains pub fn width(self) -> f64 { self.upper - self.lower } - /// Whether `probability` lies in the interval, inclusive. + /// Whether `probability` lies in the interval, inclusive pub fn contains(self, probability: f64) -> bool { self.lower <= probability && probability <= self.upper } } impl HybridConfig { - /// Checks the configuration is internally consistent. - /// - /// Call before evaluating: an inconsistent configuration would otherwise - /// surface as a wrong answer rather than an error. + /// Checks the configuration is internally consistent pub fn validate(&self) -> Result<(), HybridError> { if !self.threshold.is_finite() || !(0.0..=1.0).contains(&self.threshold) { return Err(HybridError::InvalidConfig( @@ -936,62 +860,54 @@ impl HybridConfig { } } -/// Whether a fact clears the reporting threshold. +/// Whether a fact clears the reporting threshold #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub enum AlertDecision { - /// Established at or above the threshold. + /// Established at or above the threshold Alert, - /// Established below it. + /// Established below it NoAlert, - /// Neither could be established within budget. Distinct from `NoAlert`: the - /// question is open, not answered in the negative, and a caller that conflates - /// them silently turns "do not know" into "no". + /// Neither could be established within budget Indeterminate, } -/// Why evaluation produced the result it did. -/// -/// This is the audit trail for the escalation policy: it records which rule decided -/// the outcome, so an answer that looks surprising can be explained without -/// re-running it. +/// Why evaluation produced the result it did #[derive(Debug, Clone, PartialEq, Eq)] pub enum HybridReason { - /// Every proof was enumerated, so the lower bound is in fact exact. + /// Every proof was enumerated, so the lower bound is in fact exact TopKExhausted, - /// The lower bound reached the threshold, which settles the decision upward - /// regardless of how much probability remains unaccounted for. + /// The lower bound reached the threshold, which settles the decision upward regardless of how + /// much probability remains unaccounted for LowerBoundCrossedThreshold, - /// The upper bound fell below the threshold, settling it downward. + /// The upper bound fell below the threshold, settling it downward UpperBoundBelowThreshold, - /// Answered by exact SDD compilation. + /// Answered by exact SDD compilation ExactSdd, - /// Negation makes the lineage non-monotone, so the top-k bound is not valid and - /// exact evaluation is required. + /// Negation makes the lineage non-monotone, so the top-k bound is not valid and exact + /// evaluation is required NegationRequiresExact, - /// Seeds in an exclusive group cannot be combined as independent, so exact - /// evaluation is required. + /// Seeds in an exclusive group cannot be combined as independent, so exact evaluation is + /// required ExclusivityRequiresExact, - /// The bounds straddle the threshold within `band_epsilon`, so approximation - /// cannot decide it. + /// The bounds straddle the threshold within `band_epsilon`, so approximation cannot decide it NearThreshold, - /// Further proofs promised less than `marginal_gain_floor`, so the search - /// stopped rather than spend its budget on negligible terms. + /// Further proofs promised less than `marginal_gain_floor`, so the search stopped rather than + /// spend its budget on negligible terms MarginalGain, - /// Top-k ran out of time. + /// Top-k ran out of time TopKBudget, - /// SDD compilation ran out of time. + /// SDD compilation ran out of time SddBudget, - /// SDD compilation ran out of nodes. + /// SDD compilation ran out of nodes SddNodeBudget, - /// The lineage referenced a seed absent from the snapshot, so no probability - /// could be assigned. + /// The lineage referenced a seed absent from the snapshot, so no probability could be assigned MissingSeed, - /// Produced for explanation only; not a decision. + /// Produced for explanation only; not a decision DiagnosticOnly, } impl HybridReason { - /// The reason as a stable kebab-case string, for logs and RDF output. + /// The reason as a stable kebab-case string, for logs and RDF output pub fn as_str(&self) -> &'static str { match self { Self::TopKExhausted => "top-k-exhausted", @@ -1011,119 +927,102 @@ impl HybridReason { } } -/// What one evaluation cost and how far it got. +/// What one evaluation cost and how far it got #[derive(Debug, Clone, Default)] pub struct HybridMetrics { - /// Proofs actually enumerated. + /// Proofs actually enumerated pub k_used: usize, - /// Whether exact SDD evaluation ran. + /// Whether exact SDD evaluation ran pub exact_used: bool, - /// Whether the proof search exhausted the frontier, which makes the lower bound - /// exact. + /// Whether the proof search exhausted the frontier, which makes the lower bound exact pub frontier_exhausted: bool, - /// Whether `k_max` was reached before the frontier was exhausted. + /// Whether `k_max` was reached before the frontier was exhausted pub cap_hit: bool, /// Improvement the last enumerated proof contributed, for comparison against - /// `marginal_gain_floor`. + /// `marginal_gain_floor` pub marginal_gain: f64, - /// Time spent in top-k search. + /// Time spent in top-k search pub topk_latency: Duration, - /// Time spent in SDD compilation. + /// Time spent in SDD compilation pub sdd_latency: Duration, - /// Nodes the SDD reached. + /// Nodes the SDD reached pub sdd_nodes: usize, /// Threshold actually applied, which differs from the configured one under - /// [`ThresholdPolicyKind::CostRatio`]. + /// [`ThresholdPolicyKind::CostRatio`] pub effective_threshold: Option, - /// Which policy produced that threshold. + /// Which policy produced that threshold pub threshold_policy: Option, - /// Width of the final interval; 0 for an exact answer. + /// Width of the final interval; 0 for an exact answer pub interval_width: f64, } impl HybridMetrics { - /// Time spent across both evaluators. + /// Time spent across both evaluators pub fn total_latency(&self) -> Duration { self.topk_latency + self.sdd_latency } } -/// The outcome of evaluating one fact's probability. -/// -/// The variants differ in what was *established*, not merely in precision, and a -/// caller that collapses them to a number loses the distinction between a proven -/// probability and a guess. Only the first three carry a decision; the other two -/// mean the question is open. +/// The outcome of evaluating one fact's probability #[derive(Debug, Clone)] pub enum HybridProbabilityResult { - /// The probability is known exactly. + /// The probability is known exactly Exact { - /// The probability. + /// The probability probability: f64, - /// Whether it clears the threshold. + /// Whether it clears the threshold decision: AlertDecision, - /// Which rule produced this outcome. + /// Which rule produced this outcome reason: HybridReason, - /// Cost and progress. + /// Cost and progress metrics: HybridMetrics, }, - /// Only a lower bound was established, and it was enough to decide. - /// - /// Sound because the lineage is monotone: unenumerated proofs can only raise - /// the probability, so a lower bound above the threshold settles it. + /// Only a lower bound was established, and it was enough to decide LowerBound { - /// Greatest lower bound established. + /// Greatest lower bound established lower_bound: f64, - /// Whether it clears the threshold. + /// Whether it clears the threshold decision: AlertDecision, - /// Which rule produced this outcome. + /// Which rule produced this outcome reason: HybridReason, - /// Cost and progress. + /// Cost and progress metrics: HybridMetrics, }, - /// Both bounds were established and together they decide the question. + /// Both bounds were established and together they decide the question Bounded { - /// The interval containing the true probability. + /// The interval containing the true probability interval: ProbabilityInterval, - /// Whether it clears the threshold. + /// Whether it clears the threshold decision: AlertDecision, - /// Which rule produced this outcome. + /// Which rule produced this outcome reason: HybridReason, - /// Cost and progress. + /// Cost and progress metrics: HybridMetrics, }, - /// Approximation could not decide, and exact evaluation has not run. - /// - /// Either it was never attempted or it exhausted its budget. Whatever bounds - /// were reached are reported, but they do not settle the threshold — the - /// decision is [`AlertDecision::Indeterminate`]. + /// Approximation could not decide, and exact evaluation has not run NeedsExact { - /// Lower bound reached, if any. + /// Lower bound reached, if any lower_bound: Option, - /// Upper bound reached, if any. + /// Upper bound reached, if any upper_bound: Option, - /// Which rule produced this outcome. + /// Which rule produced this outcome reason: HybridReason, - /// Cost and progress. + /// Cost and progress metrics: HybridMetrics, }, - /// A number was produced, but under conditions that make it unsound as a bound. - /// - /// Non-monotone lineage or exclusive groups invalidate the top-k argument, so - /// this is an estimate with no guaranteed direction of error. Treat it as a - /// diagnostic, not an answer. + /// A number was produced, but under conditions that make it unsound as a bound UnsafeApproximation { - /// The estimate. + /// The estimate estimate: f64, - /// Which rule produced this outcome. + /// Which rule produced this outcome reason: HybridReason, - /// Cost and progress. + /// Cost and progress metrics: HybridMetrics, }, } impl HybridProbabilityResult { - /// The variant's name, for logs and RDF output. + /// The variant's name, for logs and RDF output pub fn status(&self) -> &'static str { match self { Self::Exact { .. } => "Exact", @@ -1133,9 +1032,8 @@ impl HybridProbabilityResult { Self::UnsafeApproximation { .. } => "UnsafeApproximation", } } - /// The threshold decision, which is - /// [`AlertDecision::Indeterminate`] for the two variants that establish - /// nothing. + /// The threshold decision, which is [`AlertDecision::Indeterminate`] for the two variants that + /// establish nothing pub fn decision(&self) -> AlertDecision { match self { Self::Exact { decision, .. } @@ -1146,7 +1044,7 @@ impl HybridProbabilityResult { } } } - /// Which rule produced this outcome. + /// Which rule produced this outcome pub fn reason(&self) -> &HybridReason { match self { Self::Exact { reason, .. } @@ -1156,7 +1054,7 @@ impl HybridProbabilityResult { | Self::UnsafeApproximation { reason, .. } => reason, } } - /// Cost and progress of the evaluation that produced this result. + /// Cost and progress of the evaluation that produced this result pub fn metrics(&self) -> &HybridMetrics { match self { Self::Exact { metrics, .. } @@ -1167,9 +1065,7 @@ impl HybridProbabilityResult { } } - /// The bounds established, where that is meaningful. - /// - /// `None` for a result that bounded nothing. + /// The bounds established, where that is meaningful pub fn interval(&self) -> Option { match self { Self::Exact { probability, .. } => Some(ProbabilityInterval { @@ -1417,25 +1313,23 @@ fn map_hybrid_budget_error(error: SddBudgetError) -> HybridReason { } #[derive(Debug, Clone)] -/// What the top-k proof search established. +/// What the top-k proof search established pub struct TopKEvaluation { - /// Probability accounted for by the proofs enumerated. A lower bound on the - /// truth, because the lineage is monotone. + /// Probability accounted for by the proofs enumerated pub lower_bound: f64, - /// Bounds on the true probability. + /// Bounds on the true probability pub interval: ProbabilityInterval, - /// Proofs enumerated. + /// Proofs enumerated pub k_used: usize, - /// Whether every proof was found, which makes `lower_bound` exact. + /// Whether every proof was found, which makes `lower_bound` exact pub frontier_exhausted: bool, - /// Whether `k_max` stopped the search first. + /// Whether `k_max` stopped the search first pub cap_hit: bool, - /// Contribution of the last proof, against `marginal_gain_floor`. + /// Contribution of the last proof, against `marginal_gain_floor` pub marginal_gain: f64, } -/// Evaluate one positive, independent lineage cone at a fixed `k`. The WMC is -/// exact for the retained proof formula and therefore a certified lower bound. +/// Evaluate one positive, independent lineage cone at a fixed `k` pub fn evaluate_topk( store: &LineageStore, seeds: &SeedSnapshot, @@ -1496,17 +1390,14 @@ pub fn evaluate_topk( }) } -/// A lineage DAG compiled to an SDD, ready for exact weighted model counting. +/// A lineage DAG compiled to an SDD, ready for exact weighted model counting pub struct CompiledSdd { - /// The manager owning the compiled nodes. + /// The manager owning the compiled nodes pub manager: SddManager, - /// Root node corresponding to the lineage root. + /// Root node corresponding to the lineage root pub root: SddId, } -// Cannot derive: `SddManager` has no `Debug`, and would not want one — it owns every -// compiled node. The node count is the figure that matters, since it is what the -// compilation budget is spent on. impl std::fmt::Debug for CompiledSdd { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("CompiledSdd") @@ -1531,10 +1422,7 @@ fn collect_seed_ids(store: &LineageStore, root: LineageId, output: &mut BTreeSet } } -/// Compiles lineage to an SDD, within a time and node budget. -/// -/// Returns the reason on failure rather than an error type, so the caller can record -/// why exact evaluation was unavailable alongside whatever bound it already had. +/// Compiles lineage to an SDD, within a time and node budget pub fn compile_lineage_to_sdd( store: &LineageStore, seeds: &SeedSnapshot, @@ -1545,8 +1433,8 @@ pub fn compile_lineage_to_sdd( compile_lineage_to_sdd_with_clock(store, seeds, root, budget, node_budget, &SystemHybridClock) } -/// [`compile_lineage_to_sdd`] against a caller-supplied clock, so a budget can be -/// driven past its deadline deterministically in tests. +/// [`compile_lineage_to_sdd`] against a caller-supplied clock, so a budget can be driven past its +/// deadline deterministically in tests pub fn compile_lineage_to_sdd_with_clock( store: &LineageStore, seeds: &SeedSnapshot, @@ -1666,16 +1554,7 @@ pub fn compile_lineage_to_sdd_with_clock( }) } -/// Evaluates the probability of the fact at `root`, escalating as needed. -/// -/// Tries the cheap path first and only compiles exactly when it must: top-k gives a -/// certified lower bound for monotone lineage, and that settles the threshold -/// whenever the bound lands clear of it. Negation, exclusive groups, and a result too -/// near the threshold all force exact compilation. -/// -/// Always returns a result rather than failing: a budget that runs out yields -/// [`HybridProbabilityResult::NeedsExact`] carrying whatever bounds were reached, so -/// the caller can distinguish "below threshold" from "could not determine". +/// Evaluates the probability of the fact at `root`, escalating as needed pub fn evaluate_hybrid( store: &Arc>, seeds: &Arc, @@ -1685,8 +1564,7 @@ pub fn evaluate_hybrid( evaluate_hybrid_with_clock(store, seeds, root, config, &SystemHybridClock) } -/// [`evaluate_hybrid`] against a caller-supplied clock, for deterministic tests of -/// budget expiry. +/// [`evaluate_hybrid`] against a caller-supplied clock, for deterministic tests of budget expiry pub fn evaluate_hybrid_with_clock( store: &Arc>, seeds: &Arc, @@ -1892,8 +1770,7 @@ fn evaluate_hybrid_controlled( } } -/// Encode decision-safe hybrid results as RDF-star annotations. Exact values and -/// lower bounds deliberately use different predicates. +/// Encode decision-safe hybrid results as RDF-star annotations pub fn encode_hybrid_results_as_rdf_star( results: &HashMap, dict: &mut Dictionary, diff --git a/shared/src/join_algorithm.rs b/shared/src/join_algorithm.rs index 4582c6be..5d424b43 100644 --- a/shared/src/join_algorithm.rs +++ b/shared/src/join_algorithm.rs @@ -20,7 +20,6 @@ fn extract_join_parameters(premise: &TriplePattern) -> (String, String) { let subject_var = match subject_term { Term::Variable(v) => v.clone(), Term::Constant(c) => { - // For constants, create a synthetic variable name format!("__const_subj_{}", c) } Term::QuotedTriple(_) => "__quoted_subj".to_string(), @@ -29,7 +28,6 @@ fn extract_join_parameters(premise: &TriplePattern) -> (String, String) { let object_var = match object_term { Term::Variable(v) => v.clone(), Term::Constant(c) => { - // For constants, create a synthetic variable name format!("__const_obj_{}", c) } Term::QuotedTriple(_) => "__quoted_obj".to_string(), @@ -38,7 +36,7 @@ fn extract_join_parameters(premise: &TriplePattern) -> (String, String) { (subject_var, object_var) } -/// Match or bind the premise's predicate variable; false on conflict. +/// Match or bind the premise's predicate variable; false on conflict fn bind_predicate( result: &mut BTreeMap, pred_var: Option<&str>, @@ -67,21 +65,18 @@ pub fn perform_hash_join_for_rules( dict: &Dictionary, final_results: Vec>, ) -> Vec> { - // Extract variable names from the premise let (subject, object) = extract_join_parameters(premise); if final_results.is_empty() { return Vec::new(); } - // Constant predicate filters here; a variable one is bound per row. let (predicate_filter, predicate_var) = match &premise.1 { Term::Constant(c) => (Some(*c), None), Term::Variable(v) => (None, Some(v.as_str())), Term::QuotedTriple(_) => return Vec::new(), }; - // Constant subject/object IDs are compared directly against the triple. let subject_filter = match &premise.0 { Term::Constant(c) => Some(*c), _ => None, @@ -91,13 +86,11 @@ pub fn perform_hash_join_for_rules( _ => None, }; - // A premise like (?v, p, ?v) requires subject == object. let same_subject_object_var = matches!( (&premise.0, &premise.2), (Term::Variable(a), Term::Variable(b)) if a == b ); - // Pre-filter triples (this is very fast) let filtered_triples: Vec<&Triple> = triples .iter() .filter(|triple| { @@ -112,10 +105,8 @@ pub fn perform_hash_join_for_rules( return Vec::new(); } - // Build simple hash table - this is the key optimization let hash_table = build_simple_hash_table(&final_results, &subject, &object, dict); - // Parallel processing with minimal overhead let chunk_size = (filtered_triples.len() / rayon::current_num_threads().max(1)).max(1000); filtered_triples @@ -211,7 +202,6 @@ fn process_triple_fast( let object_id = triple.object; let predicate_id = triple.predicate; - // Fast path: both variables bound if let Some(indices) = hash_table.both_bound.get(&(subject_id, object_id)) { for &idx in indices { let mut result = hash_table.results[idx].clone(); @@ -222,7 +212,6 @@ fn process_triple_fast( return; } - // Subject bound path if let Some(indices) = hash_table.subject_bound.get(&subject_id) { for &idx in indices { let mut result = hash_table.results[idx].clone(); @@ -235,7 +224,6 @@ fn process_triple_fast( } } - // Object bound path if let Some(indices) = hash_table.object_bound.get(&object_id) { for &idx in indices { let mut result = hash_table.results[idx].clone(); @@ -248,7 +236,6 @@ fn process_triple_fast( } } - // Neither bound path for &idx in &hash_table.neither_bound { let mut result = hash_table.results[idx].clone(); diff --git a/shared/src/lib.rs b/shared/src/lib.rs index 8077ea2c..4b684665 100644 --- a/shared/src/lib.rs +++ b/shared/src/lib.rs @@ -8,64 +8,38 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The vocabulary every Kolibrie engine shares. -//! -//! This crate holds the types that the SPARQL engine, the Datalog reasoner and the -//! temporal (DatalogMTL) reasoner all have to agree on, and nothing that belongs to -//! only one of them. If a type lives here, two or more engines speak it; if only one -//! does, it belongs in that engine's crate. -//! -//! # Terms are integers, not strings -//! -//! The single most important thing to know: RDF terms are **not** stored as strings -//! during evaluation. A [`Dictionary`](dictionary::Dictionary) interns each term to a -//! `u32`, and a [`Triple`](triple::Triple) is three such ids. Joins, indexes and -//! pattern matching all work on integers; decoding back to text happens at the edges. -//! -//! The `u32` space is split: -//! -//! | range | meaning | -//! |---|---| -//! | `0 .. 0x8000_0000` | ordinary terms, assigned by `Dictionary` | -//! | `0x8000_0000 ..` | quoted triples (RDF-star), held by [`QuotedTripleStore`] | -//! -//! The top bit is the tag — see -//! [`is_quoted_triple_id`](quoted_triple_store::is_quoted_triple_id). An id is -//! therefore only meaningful together with the dictionary that produced it, which is -//! why engines sharing data must share a dictionary. -//! -//! [`QuotedTripleStore`]: quoted_triple_store::QuotedTripleStore +//! The vocabulary every Kolibrie engine shares -/// Quad indexes over several orderings, for answering patterns without scanning. +/// Quad indexes over several orderings, for answering patterns without scanning pub mod dataset_index; -/// Interning RDF terms to `u32` ids, and back. +/// Interning RDF terms to `u32` ids, and back pub mod dictionary; -/// Incremental maintenance of Sentential Decision Diagrams. +/// Incremental maintenance of Sentential Decision Diagrams pub mod diff_sdd; pub mod hybrid; -/// Join algorithms shared by the query engine and the reasoners. +/// Join algorithms shared by the query engine and the reasoners pub mod join_algorithm; pub mod pattern; -/// Provenance semirings: how a derived fact's annotation is computed. +/// Provenance semirings: how a derived fact's annotation is computed pub mod provenance; pub mod quad_source; pub mod query; -/// Storage for RDF-star quoted triples, which live in a tagged id range. +/// Storage for RDF-star quoted triples, which live in a tagged id range pub mod quoted_triple_store; -/// RDF term identity: how a term is spelled so that one term is one string. +/// RDF term identity: how a term is spelled so that one term is one string pub mod rdf_term; -/// Rules: premises, conclusion, filters, and safety checking. +/// Rules: premises, conclusion, filters, and safety checking pub mod rule; -/// An index from triple pattern to the rules whose premises could match it. +/// An index from triple pattern to the rules whose premises could match it pub mod rule_index; -/// Sentential Decision Diagrams, for weighted model counting. +/// Sentential Decision Diagrams, for weighted model counting pub mod sdd; -/// Declaring which facts carry a prior probability. +/// Declaring which facts carry a prior probability pub mod seed_spec; -/// Per-fact provenance annotations, kept alongside the facts. +/// Per-fact provenance annotations, kept alongside the facts pub mod tag_store; pub mod term_encoder; -/// Terms and triple patterns: the variable-or-constant vocabulary. +/// Terms and triple patterns: the variable-or-constant vocabulary pub mod terms; -/// An encoded RDF triple: three dictionary ids. +/// An encoded RDF triple: three dictionary ids pub mod triple; diff --git a/shared/src/pattern.rs b/shared/src/pattern.rs index 6d0cf2ef..5fb4762c 100644 --- a/shared/src/pattern.rs +++ b/shared/src/pattern.rs @@ -8,41 +8,21 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! Matching a triple pattern against a fact, and building a fact from a pattern. -//! -//! These are vocabulary operations with no reasoning in them: they relate a -//! `TriplePattern` to a `Triple` through a variable binding, and nothing more. They -//! lived in `datalog::reasoning::rules` for historical reasons, which made them -//! unreachable for any other engine without taking a dependency on the Datalog -//! reasoner. -//! -//! That mattered because they were the *entire* coupling between `datalogmtl` and -//! `datalog` — four imports. Moving them here lets the two reasoners be siblings over -//! a shared vocabulary rather than one depending on the other, which is what the -//! temporal, N3 and incremental work all need (architecture doc §4). +//! Matching a triple pattern against a fact, and building a fact from a pattern use crate::dictionary::Dictionary; use crate::terms::{Term, TriplePattern}; use crate::triple::Triple; use std::collections::HashMap; -/// Matches `pattern` against `fact`, extending `variable_bindings` on success. -/// -/// Bindings are all-or-nothing: a pattern that matches on subject and predicate but -/// fails on object leaves `variable_bindings` untouched, so a caller can try -/// candidates in a loop without having to undo partial work. -/// -/// A variable already bound must match the fact's value; an unbound variable binds to -/// it. Quoted triples never match — pattern matching over them is not defined here. +/// Matches `pattern` against `fact`, extending `variable_bindings` on success pub fn matches_rule_pattern( pattern: &TriplePattern, fact: &Triple, variable_bindings: &mut HashMap, ) -> bool { - // Test against a copy so a late failure cannot leave half the bindings applied. let mut temp_bindings = variable_bindings.clone(); - // Subject let s_ok = match &pattern.0 { Term::Variable(v) => { if let Some(&bound) = temp_bindings.get(v) { @@ -59,7 +39,6 @@ pub fn matches_rule_pattern( return false; } - // Predicate let p_ok = match &pattern.1 { Term::Variable(v) => { if let Some(&bound) = temp_bindings.get(v) { @@ -76,7 +55,6 @@ pub fn matches_rule_pattern( return false; } - // Object let o_ok = match &pattern.2 { Term::Variable(v) => { if let Some(&bound) = temp_bindings.get(v) { @@ -90,7 +68,6 @@ pub fn matches_rule_pattern( Term::QuotedTriple(_) => false, }; - // Only if ALL parts match, commit the bindings if s_ok && p_ok && o_ok { *variable_bindings = temp_bindings; true @@ -99,18 +76,7 @@ pub fn matches_rule_pattern( } } -/// Builds a `Triple` from a conclusion pattern and a set of bound variables. -/// -/// An unbound variable, or a quoted triple, resolves to id `0`. That is a -/// pre-existing convention carried over with the move rather than a chosen one: it -/// silently produces a fact mentioning whatever term holds id 0 instead of reporting -/// that the rule's conclusion was not fully bound. -/// -/// `_dict` is currently unused. It is kept rather than dropped because §4 has this -/// function taking a `&mut impl TermEncoder` once parsing is split into -/// parse/prepare/execute: callers already thread a dictionary here, so that becomes a -/// change of type rather than of signature. If that split does not happen, this -/// parameter should go. +/// Builds a `Triple` from a conclusion pattern and a set of bound variables pub fn construct_triple( conclusion: &TriplePattern, vars: &HashMap, diff --git a/shared/src/provenance.rs b/shared/src/provenance.rs index 67cc28a0..231e99fd 100644 --- a/shared/src/provenance.rs +++ b/shared/src/provenance.rs @@ -11,70 +11,54 @@ use std::collections::BTreeSet; use std::sync::{Arc, Mutex}; -/// Trait defining a provenance semiring for annotated datalog evaluation. -/// -/// Implementors provide a tag space with identity elements, semiring operations -/// (disjunction ⊕, conjunction ⊗), negation, saturation, and probability I/O. +/// Trait defining a provenance semiring for annotated datalog evaluation pub trait Provenance: Clone + 'static { - /// The type of annotations (tags) on derived facts. + /// The type of annotations (tags) on derived facts type Tag: Clone + PartialEq + std::fmt::Debug; - /// Additive identity (false / empty derivation). + /// Additive identity (false / empty derivation) fn zero(&self) -> Self::Tag; - /// Multiplicative identity (true / certain fact). + /// Multiplicative identity (true / certain fact) fn one(&self) -> Self::Tag; - /// Disjunction (⊕): combines tags from alternative derivation paths. - /// Used when the same fact is derived via multiple rule applications. + /// Disjunction (⊕): combines tags from alternative derivation paths fn disjunction(&self, a: &Self::Tag, b: &Self::Tag) -> Self::Tag; - /// Conjunction (⊗): combines tags from joined premises. - /// Used when multiple premise triples contribute to a single derivation. + /// Conjunction (⊗): combines tags from joined premises fn conjunction(&self, a: &Self::Tag, b: &Self::Tag) -> Self::Tag; - /// Negation (⊖): used for stratified negation-as-failure. + /// Negation (⊖): used for stratified negation-as-failure fn negate(&self, a: &Self::Tag) -> Self::Tag; - /// Saturation (⊜): applied at fixpoint to stabilize tags. - /// For simple provenances this is typically the identity function. + /// Saturation (⊜): applied at fixpoint to stabilize tags fn saturate(&self, a: &Self::Tag) -> Self::Tag; - /// Convert a probability value (from input data) to a tag. + /// Convert a probability value (from input data) to a tag fn tag_from_probability(&self, prob: f64) -> Self::Tag; - /// Convert a probability and a unique input-fact index to a tag. - /// Default ignores `_id` and delegates to `tag_from_probability`. - /// Override for provenances that track variable identities (e.g. TopKProofs). + /// Convert a probability and a unique input-fact index to a tag fn tag_from_probability_with_id(&self, prob: f64, _id: usize) -> Self::Tag { self.tag_from_probability(prob) } - /// Recover a probability value from a tag (for output / RDF-star encoding). + /// Recover a probability value from a tag (for output / RDF-star encoding) fn recover_probability(&self, tag: &Self::Tag) -> f64; - /// Check whether a tag has converged (old ≈ new after saturation). - /// Returns `true` if the tag is considered stable. + /// Check whether a tag has converged (old ≈ new after saturation) fn is_saturated(&self, old: &Self::Tag, new: &Self::Tag) -> bool; - /// Whether this semiring has given up on exactness and its tags should be - /// treated as a bound rather than a value. - /// - /// Defaults to `false`. An approximate semiring such as [`TopKProofs`] reports - /// `true` once it has discarded proofs, which lets a caller decide whether to - /// escalate to an exact method. + /// Whether this semiring has given up on exactness and its tags should be treated as a bound + /// rather than a value fn is_exhausted(&self) -> bool { false } } -/// Epsilon for floating-point convergence comparisons. +/// Epsilon for floating-point convergence comparisons const PROB_EPSILON: f64 = 1e-9; -/// Min-max probability provenance. -/// -/// - Tag: f64 in [0, 1]; ⊕ = max, ⊗ = min -/// - Possibilistic semantics (fuzzy Datalog). +/// Min-max probability provenance #[derive(Debug, Clone)] pub struct MinMaxProbability; @@ -117,10 +101,7 @@ impl Provenance for MinMaxProbability { } } -/// Add-multiply probability provenance. -/// -/// - Tag: f64 in [0, 1]; ⊕ = noisy-OR, ⊗ = multiplication -/// - Models independent probabilistic events. +/// Add-multiply probability provenance #[derive(Debug, Clone)] pub struct AddMultProbability; @@ -163,10 +144,7 @@ impl Provenance for AddMultProbability { } } -/// Boolean provenance (classical two-valued logic). -/// -/// - Tag: bool; ⊕ = OR, ⊗ = AND -/// - No probability tracking; equivalent to classical datalog. +/// Boolean provenance (classical two-valued logic) #[derive(Debug, Clone)] pub struct BooleanProvenance; @@ -213,32 +191,21 @@ impl Provenance for BooleanProvenance { } } -/// A proof is a set of input-variable IDs (all must hold simultaneously). +/// A proof is a set of input-variable IDs (all must hold simultaneously) pub type Proof = BTreeSet; -/// A tag is at most k proofs, ranked by descending probability. +/// A tag is at most k proofs, ranked by descending probability pub type TopKTag = Vec; -/// Top-K proof-tracking provenance. -/// -/// Retains the k most probable proof paths for each derived fact. -/// Probability is recovered from the retained proofs via weighted model counting. -/// -/// Larger k gives more accurate results at higher cost; k ≤ 10 is recommended for -/// most use cases. `k` must be in `[1, 63]`. +/// Top-K proof-tracking provenance #[derive(Debug, Clone)] pub struct TopKProofs { - /// How many proofs to retain per fact. Must be in `[1, 63]`. + /// How many proofs to retain per fact pub k: usize, prob_table: Arc>>, } impl TopKProofs { - /// Retains at most `k` proofs per fact. - /// - /// # Panics - /// - /// If `k` is outside `[1, 63]`: `recover_probability` enumerates proof subsets - /// through a `u64` bitmask, so 63 is the hard ceiling. + /// Retains at most `k` proofs per fact pub fn new(k: usize) -> Self { assert!( k >= 1 && k <= 63, @@ -309,9 +276,7 @@ impl Provenance for TopKProofs { result } - /// Approximate negation. Returns `one()` for empty tags, `zero()` for certain facts; - /// otherwise allocates a synthetic seed with probability `1 − p`. - /// Use `WmcProvenance` for exact correlation-aware negation. + /// Approximate negation fn negate(&self, a: &TopKTag) -> TopKTag { if a.is_empty() { return self.one(); @@ -355,8 +320,7 @@ impl Provenance for TopKProofs { vec![proof] } - /// Computes the probability of a derived fact from its retained proof paths. - /// Result is an approximation when fewer than all proof paths were retained. + /// Computes the probability of a derived fact from its retained proof paths fn recover_probability(&self, tag: &TopKTag) -> f64 { if tag.is_empty() { return 0.0; @@ -388,26 +352,21 @@ impl Provenance for TopKProofs { } } -/// A signed literal: `(seed_id, polarity)` where `true` = positive, `false` = negated. +/// A signed literal: `(seed_id, polarity)` where `true` = positive, `false` = negated pub type WmcLiteral = (u32, bool); -/// A single conjunction of signed seed literals (one proof path). +/// A single conjunction of signed seed literals (one proof path) pub type WmcClause = BTreeSet; -/// A DNF formula: disjunction of conjunctions — the complete proof formula. +/// A DNF formula: disjunction of conjunctions — the complete proof formula pub type WmcFormula = BTreeSet; -/// WMC provenance — exact Weighted Model Count via recursive Shannon expansion. -/// -/// ⊕ = set-union of DNF clauses (subsumption-pruned) -/// ⊗ = Cartesian-product of DNF clauses (contradictory + subsumed pruned) -/// `negate` = De Morgan complement (signed literals, exact) -/// `recover_probability` = Shannon WMC with memoization +/// WMC provenance — exact Weighted Model Count via recursive Shannon expansion #[derive(Debug, Clone)] pub struct DnfWmcProvenance { prob_table: Arc>>, } impl DnfWmcProvenance { - /// A semiring that keeps the full DNF formula and counts it exactly. + /// A semiring that keeps the full DNF formula and counts it exactly pub fn new() -> Self { Self { prob_table: Arc::new(Mutex::new(Vec::new())), @@ -421,11 +380,10 @@ impl Default for DnfWmcProvenance { } } -/// Backward-compatible alias. Use [`DnfWmcProvenance`] for explicit DNF or -/// [`crate::sdd::SddProvenance`] for the faster SDD-based implementation. +/// Backward-compatible alias pub type WmcProvenance = DnfWmcProvenance; -/// Remove clauses subsumed by a shorter clause in the same formula. +/// Remove clauses subsumed by a shorter clause in the same formula fn remove_subsumed(formula: WmcFormula) -> WmcFormula { let clauses: Vec = formula.into_iter().collect(); clauses @@ -435,7 +393,7 @@ fn remove_subsumed(formula: WmcFormula) -> WmcFormula { .collect() } -/// Remove contradictory clauses (those containing both `(v, true)` and `(v, false)`). +/// Remove contradictory clauses (those containing both `(v, true)` and `(v, false)`) pub(crate) fn remove_contradictory(formula: WmcFormula) -> WmcFormula { formula .into_iter() @@ -512,7 +470,7 @@ impl Provenance for DnfWmcProvenance { remove_subsumed(remove_contradictory(product)) } - /// Exact negation over the proof formula, preserving correlations between literals. + /// Exact negation over the proof formula, preserving correlations between literals fn negate(&self, a: &WmcFormula) -> WmcFormula { if a.is_empty() { return self.one(); @@ -618,16 +576,13 @@ mod tests { fn minmax_identities() { let p = MinMaxProbability; let a = 0.7; - // ⊕ identity: a ⊕ 0 = a assert!((p.disjunction(&a, &p.zero()) - a).abs() < PROB_EPSILON); - // ⊗ identity: a ⊗ 1 = a assert!((p.conjunction(&a, &p.one()) - a).abs() < PROB_EPSILON); } #[test] fn minmax_annihilator() { let p = MinMaxProbability; - // a ⊗ 0 = 0 assert!((p.conjunction(&0.7, &p.zero()) - p.zero()).abs() < PROB_EPSILON); } @@ -685,7 +640,6 @@ mod tests { fn addmult_disjunction_noisy_or() { let p = AddMultProbability; let result = p.disjunction(&0.7, &0.6); - // noisy-OR: 0.7 + 0.6 - 0.7*0.6 = 1.3 - 0.42 = 0.88 assert!((result - 0.88).abs() < PROB_EPSILON); } @@ -746,11 +700,9 @@ mod tests { #[test] fn topk_conjunction_identity() { let p = TopKProofs::new(5); - // a ⊗ one = a let a = vec![make_proof(&[0])]; let result = p.conjunction(&a, &p.one()); assert_eq!(result, a); - // zero ⊗ a = zero let zero_result = p.conjunction(&p.zero(), &a); assert_eq!(zero_result, vec![]); } @@ -758,7 +710,6 @@ mod tests { #[test] fn topk_conjunction_shared_variable() { let p = TopKProofs::new(5); - // {0} ⊗ {0, 1} = {{0, 1}} (union, no duplication) let a = vec![make_proof(&[0])]; let b = vec![make_proof(&[0, 1])]; let result = p.conjunction(&a, &b); @@ -768,7 +719,6 @@ mod tests { #[test] fn topk_disjunction_dedup_and_truncate() { let p = TopKProofs::new(2); - // [{0}] ⊕ [{0}, {1}] = [{0}, {1}] (dedup, truncate to 2) let a = vec![make_proof(&[0])]; let b = vec![make_proof(&[0]), make_proof(&[1])]; let result = p.disjunction(&a, &b); @@ -793,7 +743,6 @@ mod tests { let p = TopKProofs::new(5); p.tag_from_probability_with_id(0.8, 0); p.tag_from_probability_with_id(0.6, 1); - // {0} and {1} are independent — noisy-OR: 0.8 + 0.6 - 0.48 = 0.92 let tag = vec![make_proof(&[0]), make_proof(&[1])]; let prob = p.recover_probability(&tag); assert!( @@ -805,14 +754,10 @@ mod tests { #[test] fn topk_wmc_overlap_canonical() { - // Canonical overlap example: proof1={0,1}, proof2={0,2} - // P(x)=0.8, P(y)=0.6, P(z)=0.5 - // Exact: P(x∧y) + P(x∧z) - P(x∧y∧z) = 0.48 + 0.40 - 0.24 = 0.64 - // noisy-OR of 0.48 and 0.40 = 0.688 (wrong — overcounts x) let p = TopKProofs::new(5); - p.tag_from_probability_with_id(0.8, 0); // x - p.tag_from_probability_with_id(0.6, 1); // y - p.tag_from_probability_with_id(0.5, 2); // z + p.tag_from_probability_with_id(0.8, 0); + p.tag_from_probability_with_id(0.6, 1); + p.tag_from_probability_with_id(0.5, 2); let tag = vec![make_proof(&[0, 1]), make_proof(&[0, 2])]; let wmc = p.recover_probability(&tag); assert!( diff --git a/shared/src/quad_source.rs b/shared/src/quad_source.rs index 9531d067..3520fbb3 100644 --- a/shared/src/quad_source.rs +++ b/shared/src/quad_source.rs @@ -8,79 +8,22 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`QuadSource`]: somewhere quads can be read from. -//! -//! [`DatasetIndex`](crate::dataset_index::DatasetIndex) is one implementation and was -//! the only one. The point of the trait is that it need not be: a window's content is -//! a short-lived array that gains nothing from three sorted indexes, a dataset on disk -//! should not be loaded to be scanned, and a different indexing strategy should be -//! substitutable without touching the reasoners. -//! -//! # Deliberately small -//! -//! Four methods, against `DatasetIndex`'s 33. The surface is what consumers were -//! measured to need, not what the index happens to offer: `datalog` and `datalogmtl` -//! between them call two of those 33, so a trait mirroring the index would be -//! unimplementable for an array and pointless for a reasoner. -//! -//! Writing is not here. A read-only source — a file, a window that is filled and then -//! frozen — implements this and nothing else. -//! -//! # Why a visitor rather than an iterator -//! -//! [`for_each_quad`](QuadSource::for_each_quad) takes `&mut dyn FnMut` rather than -//! returning `Box`, because `benches/micro_scan.rs` measured the -//! difference and it is not small: a boxed iterator costs 22% on a scan returning 8 -//! quads and 25% on one returning a few thousand, disappearing only at around 40,000. -//! The cost is per call, so it falls hardest on exactly the short-lived small sources -//! this trait exists to support. One boxed closure per scan is cheap; one allocation -//! per row is not. -//! -//! The same benchmark found dynamic dispatch free — `&dyn QuadSource` measured within -//! noise of direct field access at every selectivity — so consumers take `&dyn` and -//! stay monomorphisation-free. -//! -//! # Two limits worth knowing before implementing it -//! -//! **Term ids are dictionary-relative.** The `u32`s here mean nothing except against -//! the [`Dictionary`](crate::dictionary::Dictionary) that minted them. A source -//! holding its own term space has to intern through that dictionary, which is the gap -//! a `TermEncoder` abstraction would close; until then, an external source is -//! practical when its terms are interned once, on open, into the dictionary the -//! consumer uses. -//! -//! **Nothing here is fallible.** No method returns `Result`, matching `DatasetIndex`, -//! where reading cannot fail. A source that can fail mid-scan — a file, a socket — -//! has nowhere to report it, and adding `Result` later would touch every consumer. -//! That is a decision to take before the first fallible implementation, not after. +//! [`QuadSource`]: somewhere quads can be read from use crate::dataset_index::GraphId; use crate::triple::Triple; -/// Somewhere quads can be read from. -/// -/// Object-safe: no generic methods and no `impl Trait` in argument position, so -/// `&dyn QuadSource` works and heterogeneous sources can sit behind one type. +/// Somewhere quads can be read from pub trait QuadSource { - /// Triples of the default graph matching a pattern, `None` meaning any. - /// - /// Returns owned triples rather than borrowing, so a source may compute them. + /// Triples of the default graph matching a pattern, `None` meaning any fn query_default(&self, s: Option, p: Option, o: Option) -> Vec; - /// Calls `visit` once per quad, named graphs included. - /// - /// The order is whatever the source finds cheapest and must not be relied on. + /// Calls `visit` once per quad, named graphs included fn for_each_quad(&self, visit: &mut dyn FnMut(GraphId, u32, u32, u32)); - /// The named graphs present, excluding the default graph. - /// - /// Empty for a source with no notion of graphs, which is a correct answer rather - /// than a missing feature: its quads are all in the default graph. + /// The named graphs present, excluding the default graph fn named_graphs(&self) -> Vec; - /// How many quads one graph holds. - /// - /// The cost model reads this, so a source for which it is expensive should expect - /// to be planned against conservatively rather than return a guess. + /// How many quads one graph holds fn count_graph(&self, graph: GraphId) -> usize; } diff --git a/shared/src/query.rs b/shared/src/query.rs index 191ee175..403854a4 100644 --- a/shared/src/query.rs +++ b/shared/src/query.rs @@ -8,84 +8,46 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! The parsed shape of a request, before anything is resolved. -//! -//! Everything here borrows from the source text — the `'a` lifetime is the query -//! string — and every term keeps its **lexical spelling**: variable sigils, IRI -//! brackets, literal quotes and datatype suffixes, prefixed names still abbreviated. -//! Nothing is prefix-expanded and nothing is interned. -//! -//! That separation is deliberate. A term only becomes a -//! [`Term`](crate::terms::Term) once it is bound to a particular -//! [`Dictionary`](crate::dictionary::Dictionary), and a rule interned against the -//! wrong vocabulary parses, validates and then silently matches nothing — a failure -//! this codebase has hit. Keeping the parsed form lexical makes that unrepresentable -//! until the binding step. -//! -//! # What a request can contain -//! -//! [`CombinedQuery`] is the root, and it is a union rather than a choice: one request -//! may carry standard SPARQL *and* Kolibrie's extensions. -//! -//! | part | surface | -//! |---|---| -//! | [`CombinedQuery::sparql`] | standard SELECT or Update, as [`SparqlOperation`] | -//! | [`CombinedQuery::rules`] | `RULE` blocks — the Datalog extension | -//! | [`CombinedQuery::register_clause`] | RSP-QL `REGISTER`, with windows over streams | -//! | [`CombinedQuery::model_decls`] and the neural declarations | the ML extension | -//! -//! `sparql` is `None` for an extension-only request. Recognised standard syntax never -//! falls through to an extension parser. -//! -//! # Historical tuple shapes -//! -//! Several fields are bare tuples whose meaning is positional rather than named — -//! projections are `(kind, variable, alias)`, and both -//! [`RSPQLSelectQuery::where_clause`] and [`CombinedRule::body`] are five-tuples. -//! They are documented where they appear because the types alone do not say what the -//! positions mean. §4 of the architecture proposal replaces them with named structs. +//! The parsed shape of a request, before anything is resolved use std::collections::HashMap; use std::time::Duration; #[derive(Debug, Clone, PartialEq, Eq)] -/// A `FILTER` condition, as written. +/// A `FILTER` condition, as written pub enum FilterExpression<'a> { - /// `(left, operator, right)`, all lexical — for example `("?x", ">", "5")`. + /// `(left, operator, right)`, all lexical — for example `("?x", ">", "5")` Comparison(&'a str, &'a str, &'a str), - /// Both operands must hold. + /// Both operands must hold And(Box>, Box>), - /// Either operand may hold. + /// Either operand may hold Or(Box>, Box>), - /// Negation of the inner condition. + /// Negation of the inner condition Not(Box>), - /// An arithmetic expression used where a truth value is expected. + /// An arithmetic expression used where a truth value is expected ArithmeticExpr(Box>), - /// `(function name, arguments)`, resolved against the host's registered - /// user-defined functions at execution time rather than at parse time. + /// `(function name, arguments)`, resolved against the host's registered user-defined functions + /// at execution time rather than at parse time FunctionCall(&'a str, Vec<&'a str>), } #[derive(Debug, Clone, PartialEq, Eq)] -/// An arithmetic expression over variables and numeric literals. -/// -/// Evaluated with [`Self::evaluate`]; everything is `f64`, so there is no integer -/// arithmetic and no datatype checking. +/// An arithmetic expression over variables and numeric literals pub enum ArithmeticExpression<'a> { - /// A variable (leading `?` or `$`) or a numeric literal. + /// A variable (leading `?` or `$`) or a numeric literal Operand(&'a str), - /// Sum. + /// Sum Add(Box>, Box>), - /// Difference. + /// Difference Subtract(Box>, Box>), - /// Product. + /// Product Multiply(Box>, Box>), - /// Quotient. Evaluating a division by zero is an error, not an infinity. + /// Quotient Divide(Box>, Box>), } impl<'a> ArithmeticExpression<'a> { - /// Evaluate the expression. `resolve` maps variable strings (e.g. `?x`) to f64 values. + /// Evaluate the expression pub fn evaluate Option>(&self, resolve: &F) -> Result { match self { Self::Operand(s) => { @@ -111,338 +73,293 @@ impl<'a> ArithmeticExpression<'a> { } } -/// One cell of a `VALUES` block. +/// One cell of a `VALUES` block #[derive(Debug, Clone, PartialEq, Eq)] pub enum Value { - /// A term, in its lexical spelling. + /// A term, in its lexical spelling Term(String), - /// `UNDEF`: this row leaves the variable unbound rather than binding it to a - /// value, so it does not constrain the join at that position. + /// `UNDEF`: this row leaves the variable unbound rather than binding it to a value, so it does + /// not constrain the join at that position Undef, } -/// An inline `VALUES` table supplying candidate bindings. +/// An inline `VALUES` table supplying candidate bindings #[derive(Debug, Clone, PartialEq, Eq)] pub struct ValuesClause<'a> { - /// Column headings, in order. + /// Column headings, in order pub variables: Vec<&'a str>, - /// Rows, each the same length as `variables`. + /// Rows, each the same length as `variables` pub values: Vec>, } -/// A source-borrowed triple pattern as it appears in SPARQL text. -/// -/// The three slices retain their lexical spelling, including variable sigils, -/// IRI brackets, literal quotes/suffixes, blank-node prefixes, and RDF-star -/// quoted-triple delimiters. Resolution and dictionary encoding happen only -/// when this syntax tree is lowered into the query plan. +/// A source-borrowed triple pattern as it appears in SPARQL text pub type LexicalTriplePattern<'a> = (&'a str, &'a str, &'a str); -/// A source-borrowed quad used by update data blocks and templates. -/// `graph == None` denotes the default graph. +/// A source-borrowed quad used by update data blocks and templates #[derive(Debug, Clone, PartialEq, Eq)] pub struct LexicalQuadPattern<'a> { - /// The graph, or `None` for the default graph. + /// The graph, or `None` for the default graph pub graph: Option<&'a str>, - /// The triple itself. + /// The triple itself pub triple: LexicalTriplePattern<'a>, } -/// The existing tuple representation used by BIND. +/// The existing tuple representation used by BIND pub type BindClause<'a> = (&'a str, Vec<&'a str>, &'a str); -/// Everything a `WHERE` clause declares, as written. -/// -/// The parser returned these seven as a positional tuple, which every caller -/// destructured and then had to name itself -- and three of the four callers wanted -/// only one or two of the parts, so they spelled the rest as `_, _, _, _`. §4 asks -/// for named structs in place of wide positional tuples, and this was the widest. -/// -/// Nothing is optional except `values`: a clause with no filters has no filters, -/// which is an empty `Vec` rather than an absent one. -/// -/// No `PartialEq`: [`WindowBlock`] has none, and giving it one to make this -/// comparable would be deciding when two window declarations are the same question -/// on behalf of a type that has not answered it. +/// Everything a `WHERE` clause declares, as written #[derive(Debug, Clone, Default)] pub struct WhereClause<'a> { - /// Triple patterns the clause matches. + /// Triple patterns the clause matches pub patterns: Vec>, - /// `FILTER` expressions constraining them. + /// `FILTER` expressions constraining them pub filters: Vec>, - /// An inline `VALUES` table, if the clause has one. + /// An inline `VALUES` table, if the clause has one pub values: Option>, - /// `BIND` clauses computing new variables. + /// `BIND` clauses computing new variables pub binds: Vec>, - /// Nested `SELECT` subqueries. + /// Nested `SELECT` subqueries pub subqueries: Vec>, - /// RSP-QL `WINDOW` blocks. + /// RSP-QL `WINDOW` blocks pub window_blocks: Vec>, - /// Patterns under `NOT`, which must *not* match. + /// Patterns under `NOT`, which must *not* match pub negated_patterns: Vec>, } -/// The quads an update inserts: either literal data or a template to instantiate. +/// The quads an update inserts: either literal data or a template to instantiate #[derive(Debug, Clone, PartialEq, Eq)] pub struct InsertClause<'a> { - /// Quads to insert. For `INSERT ... WHERE` these are a template containing - /// variables bound by the WHERE pattern. + /// Quads to insert pub quads: Vec>, } -/// The quads an update deletes: either literal data or a template to instantiate. +/// The quads an update deletes: either literal data or a template to instantiate #[derive(Debug, Clone, PartialEq, Eq)] pub struct DeleteClause<'a> { - /// Quads to delete, possibly a template containing variables. + /// Quads to delete, possibly a template containing variables pub quads: Vec>, } -/// Recursive graph-pattern algebra shared by SELECT and update WHERE clauses. +/// Recursive graph-pattern algebra shared by SELECT and update WHERE clauses #[derive(Debug, Clone, PartialEq, Eq)] pub enum GroupGraphPattern<'a> { - /// The empty group graph pattern. It evaluates to one solution mapping. + /// The empty group graph pattern Unit, - /// A basic graph pattern: triples that must all match, joined on shared - /// variable names. + /// A basic graph pattern: triples that must all match, joined on shared variable names Bgp(Vec>), - /// Conjunction of sub-patterns. + /// Conjunction of sub-patterns Join(Vec>), - /// UNION preserves branch multiplicity; DISTINCT is a SELECT modifier. + /// UNION preserves branch multiplicity; DISTINCT is a SELECT modifier Union(Vec>), - /// `GRAPH`: evaluate the inner pattern against one named graph, or against each - /// in turn when the name is a variable. + /// `GRAPH`: evaluate the inner pattern against one named graph, or against each in turn when + /// the name is a variable Graph { - /// Raw IRI, prefixed name, or variable lexeme following GRAPH. + /// Raw IRI, prefixed name, or variable lexeme following GRAPH name: &'a str, - /// Pattern to evaluate within that graph. + /// Pattern to evaluate within that graph pattern: Box>, }, - /// A condition restricting the solutions of the enclosing group. + /// A condition restricting the solutions of the enclosing group Filter(FilterExpression<'a>), - /// `BIND`: computes a new binding from existing ones. + /// `BIND`: computes a new binding from existing ones Bind(BindClause<'a>), - /// An inline `VALUES` table. + /// An inline `VALUES` table Values(ValuesClause<'a>), - /// A nested SELECT, evaluated independently and joined with the enclosing group. + /// A nested SELECT, evaluated independently and joined with the enclosing group SubQuery(Box>), } -/// A nested SELECT appearing inside a graph pattern. +/// A nested SELECT appearing inside a graph pattern #[derive(Debug, Clone, PartialEq, Eq)] pub struct SubQuery<'a> { - /// The nested query. Only its projected variables are visible outside. + /// The nested query pub query: SelectQuery<'a>, } -/// The name of a `RULE`, as written after the `RULE` keyword. +/// The name of a `RULE`, as written after the `RULE` keyword #[derive(Debug, Clone)] pub struct RuleHead<'a> { - /// The rule's identifying predicate, lexical. + /// The rule's identifying predicate, lexical pub predicate: &'a str, } -/// An `ML.PREDICT` clause: run a model over the rows a query selects. -/// -/// The input query is kept twice — once as the original text and once parsed — -/// because the raw form is used for cache keys and diagnostics while the parsed form -/// is what actually runs. +/// An `ML.PREDICT` clause: run a model over the rows a query selects #[derive(Debug, Clone)] pub struct MLPredictClause<'a> { - /// Name of the model to invoke, which must be declared and approved. + /// Name of the model to invoke, which must be declared and approved pub model: &'a str, - /// The input query exactly as written. + /// The input query exactly as written pub input_raw: &'a str, - /// Projection of the input query, as `(kind, variable, alias)`. + /// Projection of the input query, as `(kind, variable, alias)` pub input_select: Vec<(&'a str, &'a str, Option<&'a str>)>, - /// Triple patterns of the input query's WHERE clause. + /// Triple patterns of the input query's WHERE clause pub input_where: Vec<(&'a str, &'a str, &'a str)>, - /// Filters of the input query's WHERE clause. + /// Filters of the input query's WHERE clause pub input_filters: Vec>, - /// Variable the prediction binds to. + /// Variable the prediction binds to pub output: &'a str, - /// Whether to bind the full distribution over labels rather than the single - /// most likely one. Probabilistic rules need the distribution. + /// Whether to bind the full distribution over labels rather than the single most likely one pub distribution: bool, } #[derive(Debug, Clone, Copy, PartialEq, Eq)] -/// Training objective for a neural relation. +/// Training objective for a neural relation pub enum LossFn { - /// Cross-entropy, for a distribution over mutually exclusive labels. + /// Cross-entropy, for a distribution over mutually exclusive labels CrossEntropy, - /// Negative log likelihood, expecting log-probabilities as input. + /// Negative log likelihood, expecting log-probabilities as input Nll, - /// Mean squared error, for regression. + /// Mean squared error, for regression Mse, - /// Binary cross-entropy, for a single yes/no output. + /// Binary cross-entropy, for a single yes/no output BinaryCrossEntropy, } #[derive(Debug, Clone, Copy, PartialEq, Eq)] -/// Which optimiser trains a neural relation. +/// Which optimiser trains a neural relation pub enum OptimizerKind { - /// Adam. + /// Adam Adam, - /// Plain stochastic gradient descent. + /// Plain stochastic gradient descent Sgd, } #[derive(Debug, Clone, PartialEq, Eq)] -/// A model's architecture. Only one shape is supported so far. +/// A model's architecture pub enum ModelArch { - /// A multilayer perceptron. + /// A multilayer perceptron Mlp { - /// Width of each hidden layer, in order. Empty means input maps straight to - /// output. + /// Width of each hidden layer, in order hidden_layers: Vec, }, } #[derive(Debug, Clone, PartialEq, Eq)] -/// What a model's output means, which decides how a prediction becomes facts. +/// What a model's output means, which decides how a prediction becomes facts pub enum NeuralOutputKind { - /// A distribution over mutually exclusive labels, at most one of which holds. - /// - /// Probabilistic reasoning represents this as a - /// [`SeedSpec::ExclusiveGroup`](crate::seed_spec::SeedSpec::ExclusiveGroup), so - /// two labels can never be simultaneously true. + /// A distribution over mutually exclusive labels, at most one of which holds Exclusive { - /// The labels, in the order the model's outputs correspond to. + /// The labels, in the order the model's outputs correspond to labels: Vec, }, - /// A single probability that one fact holds. + /// A single probability that one fact holds Binary { - /// The term asserted when the model answers positively. + /// The term asserted when the model answers positively positive_literal: String, }, } #[derive(Debug, Clone, PartialEq, Eq)] -/// A `MODEL` declaration: what a model is called, its shape, and how to read its -/// output. +/// A `MODEL` declaration: what a model is called, its shape, and how to read its output pub struct ModelDecl { - /// Name rules refer to it by. + /// Name rules refer to it by pub name: String, - /// Architecture to instantiate. + /// Architecture to instantiate pub arch: ModelArch, - /// How to interpret the output vector. + /// How to interpret the output vector pub output_kind: NeuralOutputKind, } #[derive(Debug, Clone, PartialEq, Eq)] -/// A declaration that a predicate's facts come from a model rather than from data. -/// -/// Querying `predicate` runs `model_name` over the rows `input_patterns` select, -/// taking `feature_vars` as the feature vector. +/// A declaration that a predicate's facts come from a model rather than from data pub struct NeuralRelationDecl { - /// The predicate this relation defines. + /// The predicate this relation defines pub predicate: String, - /// Model to invoke; must name a [`ModelDecl`]. + /// Model to invoke; must name a [`ModelDecl`] pub model_name: String, - /// Patterns selecting the rows to predict over. + /// Patterns selecting the rows to predict over pub input_patterns: Vec<(String, String, String)>, - /// Variables forming the feature vector, in model input order. + /// Variables forming the feature vector, in model input order pub feature_vars: Vec, - /// Variable identifying the subject a prediction is about, so the derived fact - /// can be attached to the right entity. + /// Variable identifying the subject a prediction is about, so the derived fact can be attached + /// to the right entity pub anchor_var: String, } #[derive(Debug, Clone, PartialEq, Eq)] -/// Where training examples come from. +/// Where training examples come from pub enum TrainingDataSource { - /// Triple patterns selecting the examples. + /// Triple patterns selecting the examples GraphPattern(Vec<(String, String, String)>), - /// A full query, as text, selecting them. + /// A full query, as text, selecting them Query(String), } #[derive(Debug, Clone, PartialEq)] -/// A `TRAIN` declaration: fit the model behind a neural relation. +/// A `TRAIN` declaration: fit the model behind a neural relation pub struct TrainNeuralRelationDecl { - /// The neural relation being trained; must be declared. + /// The neural relation being trained; must be declared pub predicate: String, - /// Where the training examples come from. + /// Where the training examples come from pub data_source: TrainingDataSource, - /// Variable holding each example's ground-truth label. + /// Variable holding each example's ground-truth label pub label_var: String, - /// Pattern identifying what is being predicted about each example. + /// Pattern identifying what is being predicted about each example pub target_triple: (String, String, String), - /// Objective to minimise. + /// Objective to minimise pub loss: LossFn, - /// Optimiser to use. + /// Optimiser to use pub optimizer: OptimizerKind, - /// Step size. + /// Step size pub learning_rate: f64, - /// Passes over the training data. + /// Passes over the training data pub epochs: usize, - /// Examples per gradient step. + /// Examples per gradient step pub batch_size: usize, - /// Where to write the fitted weights. The host's ML policy decides whether the - /// path is permitted, so a declaration naming a path is not itself authority to - /// write there. + /// Where to write the fitted weights pub save_path: Option, } -/// A `FROM NAMED WINDOW` clause: one window over one stream. +/// A `FROM NAMED WINDOW` clause: one window over one stream #[derive(Clone, Debug)] pub struct WindowClause<'a> { - /// Name the WHERE clause refers to this window by. + /// Name the WHERE clause refers to this window by pub window_iri: &'a str, - /// Stream the window observes. + /// Stream the window observes pub stream_iri: &'a str, - /// Width, slide and reporting behaviour. + /// Width, slide and reporting behaviour pub window_spec: WindowSpec<'a>, - /// Per-window sync policy; `None` means use the engine-level default. + /// Per-window sync policy; `None` means use the engine-level default pub policy: Option, } -/// How a window selects from its stream. -/// -/// Window bounds are half-open: a `RANGE n` window holds `n` timestamps, not `n + 1`. +/// How a window selects from its stream #[derive(Clone, Debug)] pub struct WindowSpec<'a> { - /// Range, tumbling or sliding. + /// Range, tumbling or sliding pub window_type: WindowType, - /// Window width, in the stream's time unit. + /// Window width, in the stream's time unit pub width: usize, - /// How far the window advances each step. `None` means it equals `width`, which - /// makes the window tumbling. + /// How far the window advances each step pub slide: Option, - /// When results are reported, as written. `None` uses the engine default. + /// When results are reported, as written pub report_strategy: Option<&'a str>, - /// What advances time — arriving events or the wall clock. `None` uses the - /// engine default. + /// What advances time — arriving events or the wall clock pub tick: Option<&'a str>, } -/// The three window shapes. +/// The three window shapes #[derive(Clone, Debug)] pub enum WindowType { - /// A time range, sliding by its slide parameter. + /// A time range, sliding by its slide parameter Range, - /// Non-overlapping: each event falls in exactly one window. + /// Non-overlapping: each event falls in exactly one window Tumbling, - /// Overlapping: an event may fall in several windows. + /// Overlapping: an event may fall in several windows Sliding, } -/// What to do when a [`SyncPolicy::Timeout`] expires before every window has fired. +/// What to do when a [`SyncPolicy::Timeout`] expires before every window has fired #[derive(Clone, Debug, PartialEq)] pub enum Fallback { - /// Emit anyway, reusing the last content of windows that have not fired. The - /// result mixes data from different cycles. + /// Emit anyway, reusing the last content of windows that have not fired Steal, - /// Emit nothing for this cycle. + /// Emit nothing for this cycle Drop, } #[derive(Clone, Debug, PartialEq)] -/// When a query joining several windows is allowed to emit. -/// -/// Only matters with more than one window: with a single window there is nothing to -/// wait for. The choice trades latency against whether results mix data from -/// different cycles. +/// When a query joining several windows is allowed to emit pub enum SyncPolicy { /// Emit immediately using stale data from non-firing windows (τ=0, fallback=steal) Steal, @@ -450,9 +367,9 @@ pub enum SyncPolicy { Wait, /// Wait up to `duration`; on expiry apply `fallback` Timeout { - /// How long to wait for the remaining windows. + /// How long to wait for the remaining windows duration: Duration, - /// What to do if they have still not fired. + /// What to do if they have still not fired fallback: Fallback, }, } @@ -463,44 +380,39 @@ impl Default for SyncPolicy { } } -/// The relation-to-stream operator: how a query's answers become an output stream. +/// The relation-to-stream operator: how a query's answers become an output stream #[derive(Clone, Debug)] pub enum StreamType<'a> { - /// Every solution in the current window, each time it is evaluated. + /// Every solution in the current window, each time it is evaluated RStream, - /// Only solutions that were absent from the previous evaluation. + /// Only solutions that were absent from the previous evaluation IStream, - /// Only solutions present previously and now gone. + /// Only solutions present previously and now gone DStream, - /// A host-defined operator, named lexically. + /// A host-defined operator, named lexically Custom(&'a str), } -/// A `REGISTER` clause: a standing query whose answers are published as a stream. +/// A `REGISTER` clause: a standing query whose answers are published as a stream #[derive(Debug, Clone)] pub struct RegisterClause<'a> { - /// Which solutions to publish each evaluation. + /// Which solutions to publish each evaluation pub stream_type: StreamType<'a>, - /// Name the output stream is published under. + /// Name the output stream is published under pub output_stream_iri: &'a str, - /// The continuous query. + /// The continuous query pub query: RSPQLSelectQuery<'a>, } -/// A continuous SELECT over one or more windows. +/// A continuous SELECT over one or more windows #[derive(Debug, Clone)] pub struct RSPQLSelectQuery<'a> { - /// Projection, as `(kind, variable, alias)`. + /// Projection, as `(kind, variable, alias)` pub variables: Vec<(&'a str, &'a str, Option<&'a str>)>, - /// The windows this query reads. More than one means the engine must join - /// across windows, which is what makes result timing depend on - /// [`SyncPolicy`]. + /// The windows this query reads pub window_clause: Vec>, - /// The WHERE clause, as a five-tuple of - /// `(triple patterns, filters, VALUES, BIND clauses, subqueries)`. - /// - /// Positional rather than named for historical reasons; §4 replaces it with a - /// struct. + /// The WHERE clause, as a five-tuple of `(triple patterns, filters, VALUES, BIND clauses, + /// subqueries)` pub where_clause: ( Vec<(&'a str, &'a str, &'a str)>, Vec>, @@ -508,58 +420,50 @@ pub struct RSPQLSelectQuery<'a> { Vec<(&'a str, Vec<&'a str>, &'a str)>, Vec>, ), - /// Patterns scoped to a named window, for queries that address windows - /// individually rather than joining them in one flat WHERE. + /// Patterns scoped to a named window, for queries that address windows individually rather than + /// joining them in one flat WHERE pub window_blocks: Vec>, } -/// Patterns evaluated against one named window. +/// Patterns evaluated against one named window #[derive(Debug, Clone)] pub struct WindowBlock<'a> { - /// The window these patterns read, matching a [`WindowClause::window_iri`]. + /// The window these patterns read, matching a [`WindowClause::window_iri`] pub window_name: &'a str, - /// Triple patterns to match within that window. + /// Triple patterns to match within that window pub patterns: Vec<(&'a str, &'a str, &'a str)>, } -/// Probability annotation for a RULE. -/// Parsed from: PROB(combination=independent, threshold=0.3, confidence=0.9) +/// Probability annotation for a RULE #[derive(Clone, Debug)] pub struct ProbAnnotation<'a> { - /// How premise probabilities combine: `independent`, `sdd`, `hybrid` and so on, - /// as written. Validated when the rule is compiled, not here. + /// How premise probabilities combine: `independent`, `sdd`, `hybrid` and so on, as written pub combination: &'a str, - /// Minimum probability a derived fact needs to be kept. `None` keeps everything. + /// Minimum probability a derived fact needs to be kept pub threshold: Option, - /// Confidence demanded of an approximate method before its answer is accepted. + /// Confidence demanded of an approximate method before its answer is accepted pub confidence: Option, - /// Fully validated policy for `provenance=hybrid`. + /// Fully validated policy for `provenance=hybrid` pub hybrid_config: Option, } -/// One `RULE` block: a Datalog rule, optionally streaming, probabilistic or neural. -/// -/// Declarations may appear attached to a rule as well as at the top level of a -/// [`CombinedQuery`], so a consumer looking for every model must check both. +/// One `RULE` block: a Datalog rule, optionally streaming, probabilistic or neural #[derive(Clone, Debug)] pub struct CombinedRule<'a> { - /// The rule's name. + /// The rule's name pub head: RuleHead<'a>, - /// Output stream operator, for a rule that publishes continuously. + /// Output stream operator, for a rule that publishes continuously pub stream_type: Option>, - /// Windows this rule reads, for a rule over streams. + /// Windows this rule reads, for a rule over streams pub window_clause: Vec>, - /// Models declared on this rule. + /// Models declared on this rule pub model_decls: Vec, - /// Neural relations declared on this rule. + /// Neural relations declared on this rule pub neural_relation_decls: Vec, - /// Training declarations on this rule. + /// Training declarations on this rule pub train_neural_relation_decls: Vec, - /// The rule body, as a five-tuple of - /// `(triple patterns, filters, VALUES, BIND clauses, subqueries)`. - /// - /// Same positional shape as [`RSPQLSelectQuery::where_clause`], and replaced by - /// a named struct in §4. + /// The rule body, as a five-tuple of `(triple patterns, filters, VALUES, BIND clauses, + /// subqueries)` pub body: ( Vec<(&'a str, &'a str, &'a str)>, Vec>, @@ -567,183 +471,164 @@ pub struct CombinedRule<'a> { Vec<(&'a str, Vec<&'a str>, &'a str)>, Vec>, ), - /// Negated body atoms parsed from `NOT triple_pattern` clauses in WHERE. + /// Negated body atoms parsed from `NOT triple_pattern` clauses in WHERE pub negated_body: Vec<(&'a str, &'a str, &'a str)>, - /// Patterns scoped to individual named windows. + /// Patterns scoped to individual named windows pub window_blocks: Vec>, - /// Facts derived when the body matches — the `CONSTRUCT` template. + /// Facts derived when the body matches — the `CONSTRUCT` template pub conclusion: Vec<(&'a str, &'a str, &'a str)>, - /// An `ML.PREDICT` clause supplying bindings from a model. + /// An `ML.PREDICT` clause supplying bindings from a model pub ml_predict: Option>, - /// `PROB(...)` annotation making this rule probabilistic. `None` means the rule - /// derives certain facts. + /// `PROB(...)` annotation making this rule probabilistic pub prob_annotation: Option>, } -/// How many matches a `RETRIEVE` clause wants. +/// How many matches a `RETRIEVE` clause wants #[derive(Debug, Clone, PartialEq)] pub enum RetrieveMode { - /// Any one match suffices. + /// Any one match suffices Some, - /// Every match is required. + /// Every match is required Every, } -/// Which streams a `RETRIEVE` clause considers. +/// Which streams a `RETRIEVE` clause considers #[derive(Debug, Clone, PartialEq)] pub enum StreamState { - /// Declared but not yet producing. + /// Declared but not yet producing Latent, - /// Currently producing. + /// Currently producing Active, } -/// A `RETRIEVE` clause: discover streams matching a description. +/// A `RETRIEVE` clause: discover streams matching a description #[derive(Debug, Clone)] pub struct RetrieveClause<'a> { - /// Whether one match or all matches are wanted. + /// Whether one match or all matches are wanted pub mode: RetrieveMode, - /// Whether to consider latent or active streams. + /// Whether to consider latent or active streams pub state: StreamState, - /// Variable the discovered stream binds to. + /// Variable the discovered stream binds to pub variable: &'a str, - /// Catalogue to search. + /// Catalogue to search pub from_iri: &'a str, - /// Patterns a stream's description must satisfy. + /// Patterns a stream's description must satisfy pub graph_pattern: Vec<(&'a str, &'a str, &'a str)>, } -/// Direction of an `ORDER BY` condition. +/// Direction of an `ORDER BY` condition #[derive(Debug, Clone, PartialEq, Eq)] pub enum SortDirection { - /// Ascending. + /// Ascending Asc, - /// Descending. + /// Descending Desc, } -/// One `ORDER BY` key. +/// One `ORDER BY` key #[derive(Debug, Clone, PartialEq, Eq)] pub struct OrderCondition<'a> { - /// Variable to sort on. + /// Variable to sort on pub variable: &'a str, - /// Which way. + /// Which way pub direction: SortDirection, } -/// A SELECT query in Kolibrie's supported SPARQL fragment. -/// -/// Projection entries retain the historical `(kind, variable, alias)` shape: -/// ordinary variables use `"VAR"` and aggregates use their canonical -/// uppercase function name. +/// A SELECT query in Kolibrie's supported SPARQL fragment #[derive(Debug, Clone, PartialEq, Eq)] pub struct SelectQuery<'a> { - /// Whether duplicate solutions are removed. SPARQL is bag semantics by - /// default, so without this a solution appearing twice is returned twice. + /// Whether duplicate solutions are removed pub distinct: bool, - /// Projection, as `(kind, variable, alias)` — see the type's own note. + /// Projection, as `(kind, variable, alias)` — see the type's own note pub variables: Vec<(&'a str, &'a str, Option<&'a str>)>, - /// Graph IRIs used to form the replacement default graph. + /// Graph IRIs used to form the replacement default graph pub from: Vec<&'a str>, - /// Graph IRIs visible to GRAPH in the replacement dataset. + /// Graph IRIs visible to GRAPH in the replacement dataset pub from_named: Vec<&'a str>, - /// The WHERE clause. + /// The WHERE clause pub pattern: GroupGraphPattern<'a>, - /// `GROUP BY` keys. Empty with an aggregate in the projection means the whole - /// solution sequence is one group. + /// `GROUP BY` keys pub group_vars: Vec<&'a str>, - /// `ORDER BY` keys, in precedence order. + /// `ORDER BY` keys, in precedence order pub order_conditions: Vec>, - /// `LIMIT`. Without `ORDER BY`, which rows survive is unspecified. + /// `LIMIT` pub limit: Option, } -/// The six SPARQL Update forms supported by Kolibrie. +/// The six SPARQL Update forms supported by Kolibrie #[derive(Debug, Clone, PartialEq, Eq)] pub enum UpdateOperation<'a> { - /// `INSERT DATA`: ground quads, no pattern matching. + /// `INSERT DATA`: ground quads, no pattern matching InsertData(InsertClause<'a>), - /// `DELETE DATA`: ground quads, no pattern matching. + /// `DELETE DATA`: ground quads, no pattern matching DeleteData(DeleteClause<'a>), /// `INSERT { template } WHERE { pattern }` InsertWhere { - /// Template instantiated once per solution. + /// Template instantiated once per solution insert: InsertClause<'a>, - /// Pattern producing the solutions. + /// Pattern producing the solutions where_pattern: GroupGraphPattern<'a>, }, /// `DELETE { template } WHERE { pattern }` DeleteWhere { - /// Template instantiated once per solution. + /// Template instantiated once per solution delete: DeleteClause<'a>, - /// Pattern producing the solutions. + /// Pattern producing the solutions where_pattern: GroupGraphPattern<'a>, }, - /// `DELETE { ... } INSERT { ... } WHERE { ... }`. Both templates are evaluated - /// against the same solutions, and the deletes apply before the inserts. + /// `DELETE { ... } INSERT { ... } WHERE { ... }` DeleteInsertWhere { - /// Template of quads to remove. + /// Template of quads to remove delete: DeleteClause<'a>, - /// Template of quads to add. + /// Template of quads to add insert: InsertClause<'a>, - /// Pattern producing the solutions. + /// Pattern producing the solutions where_pattern: GroupGraphPattern<'a>, }, - /// `DELETE WHERE { pattern }`; the parsed quad block is both the template - /// and the WHERE graph pattern. + /// `DELETE WHERE { pattern }`; the parsed quad block is both the template and the WHERE graph + /// pattern DeleteWhereShorthand { - /// The quad block, doubling as the delete template. + /// The quad block, doubling as the delete template delete: DeleteClause<'a>, - /// The same block, as the pattern that selects what to remove. + /// The same block, as the pattern that selects what to remove where_pattern: GroupGraphPattern<'a>, }, } -/// A standard-SPARQL request: a query or an update, never both. +/// A standard-SPARQL request: a query or an update, never both #[derive(Debug, Clone, PartialEq, Eq)] pub enum SparqlOperation<'a> { - /// A SELECT, which returns solutions and changes nothing. + /// A SELECT, which returns solutions and changes nothing Select(SelectQuery<'a>), - /// An update, which changes the store and returns nothing. + /// An update, which changes the store and returns nothing Update(UpdateOperation<'a>), } -/// Everything one parsed request contains. -/// -/// This is the root of the module; see the module documentation for which parts hold -/// standard SPARQL and which hold Kolibrie's extensions. A request may populate -/// several at once. +/// Everything one parsed request contains #[derive(Debug, Clone)] pub struct CombinedQuery<'a> { - /// `PREFIX` bindings, expanded form by short name. Unlike the rest of this - /// module these are owned, because resolution consumes them. + /// `PREFIX` bindings, expanded form by short name pub prefixes: HashMap, - /// A `RETRIEVE` clause discovering streams. + /// A `RETRIEVE` clause discovering streams pub retrieve_clause: Option>, - /// A `REGISTER` clause defining a standing query. + /// A `REGISTER` clause defining a standing query pub register_clause: Option>, - /// Top-level model declarations. Rules may declare their own as well. + /// Top-level model declarations pub model_decls: Vec, - /// Top-level neural relation declarations. + /// Top-level neural relation declarations pub neural_relation_decls: Vec, - /// Top-level training declarations. + /// Top-level training declarations pub train_neural_relation_decls: Vec, - /// `RULE` blocks, in source order. + /// `RULE` blocks, in source order pub rules: Vec>, - /// A top-level `ML.PREDICT`, as opposed to one attached to a rule. + /// A top-level `ML.PREDICT`, as opposed to one attached to a rule pub ml_predict: Option>, - /// The single standard-SPARQL syntax tree. Extension-only requests leave - /// this as `None`; recognized standard syntax never falls through to an - /// extension parser. + /// The single standard-SPARQL syntax tree pub sparql: Option>, } impl<'a> CombinedQuery<'a> { - /// The sole rule, for entry points that execute exactly one. - /// - /// `Ok(None)` means the request has no rules; `Err` means it has several, which - /// such an entry point cannot honour without silently ignoring some. Callers - /// wanting all of them should read [`Self::rules`]. + /// The sole rule, for entry points that execute exactly one pub fn single_rule(&self) -> Result>, String> { match self.rules.as_slice() { [] => Ok(None), diff --git a/shared/src/quoted_triple_store.rs b/shared/src/quoted_triple_store.rs index 64064188..21c505a3 100644 --- a/shared/src/quoted_triple_store.rs +++ b/shared/src/quoted_triple_store.rs @@ -11,38 +11,29 @@ use serde::{Deserialize, Serialize}; use std::collections::HashMap; -/// Bit mask for identifying quoted triple IDs. -/// Regular dictionary IDs use bits 0–30 (up to 2,147,483,647). -/// Quoted triple IDs have bit 31 set (0x8000_0000 and above). +/// Bit mask for identifying quoted triple IDs pub const QUOTED_TRIPLE_ID_BIT: u32 = 0x8000_0000; -/// Whether `id` denotes a quoted triple rather than an ordinary interned term. +/// Whether `id` denotes a quoted triple rather than an ordinary interned term #[inline] pub fn is_quoted_triple_id(id: u32) -> bool { id & QUOTED_TRIPLE_ID_BIT != 0 } -/// Stores quoted triples (RDF-star) as u32 IDs with bidirectional lookup. -/// Each quoted triple `<< s p o >>` gets a unique ID with the high bit set. -/// Component IDs (s, p, o) may themselves be quoted triple IDs for nesting. +/// Stores quoted triples (RDF-star) as u32 IDs with bidirectional lookup #[derive(Debug, Default, Clone, PartialEq, Eq, Serialize, Deserialize)] -/// Interns RDF-star quoted triples, in an id range disjoint from the dictionary's. -/// -/// Components are themselves encoded ids, and may be quoted triples in turn, so -/// nesting is representable. Ids start at [`QUOTED_TRIPLE_ID_BIT`] and climb, which -/// is what keeps them distinguishable from ordinary terms by a single bit test. -/// `u32::MAX` is never minted; see [`encode`](Self::encode). +/// Interns RDF-star quoted triples, in an id range disjoint from the dictionary's pub struct QuotedTripleStore { - /// Id to its three component ids. + /// Id to its three component ids pub id_to_components: HashMap, - /// Components back to their id, so re-encoding is idempotent. + /// Components back to their id, so re-encoding is idempotent pub components_to_id: HashMap<(u32, u32, u32), u32>, - /// Next id to hand out. Starts at [`QUOTED_TRIPLE_ID_BIT`]. + /// Next id to hand out pub next_qt_id: u32, } impl QuotedTripleStore { - /// An empty store, assigning ids from [`QUOTED_TRIPLE_ID_BIT`]. + /// An empty store, assigning ids from [`QUOTED_TRIPLE_ID_BIT`] pub fn new() -> Self { Self { id_to_components: HashMap::new(), @@ -51,18 +42,12 @@ impl QuotedTripleStore { } } - /// Encode a quoted triple (s, p, o) -> returns a quoted triple ID. - /// Reuses an existing ID if this triple was already encoded. + /// Encode a quoted triple (s, p, o) -> returns a quoted triple ID pub fn encode(&mut self, subject: u32, predicate: u32, object: u32) -> u32 { let key = (subject, predicate, object); if let Some(&id) = self.components_to_id.get(&key) { return id; } - // Mirrors the assert on the ordinary-term half of the id space, which - // refuses to mint an id that would collide with this range. The top value - // is reserved: `relation::UNBOUND` is `u32::MAX`, and a row that held it as - // a real id would read as unbound. Nothing else here caps `next_qt_id`, so - // this also stops the increment below overflowing silently in release. assert!( self.next_qt_id < u32::MAX, "quoted triple ID space exhausted: next_qt_id {} is reserved", @@ -75,24 +60,22 @@ impl QuotedTripleStore { id } - /// Decode a quoted triple ID back to its (s, p, o) components. + /// Decode a quoted triple ID back to its (s, p, o) components pub fn decode(&self, id: u32) -> Option<(u32, u32, u32)> { self.id_to_components.get(&id).copied() } - /// Returns the number of quoted triples stored. - /// Number of distinct quoted triples interned. + /// Returns the number of quoted triples stored pub fn len(&self) -> usize { self.id_to_components.len() } - /// Returns true if the store is empty. - /// Whether no quoted triple has been interned. + /// Returns true if the store is empty pub fn is_empty(&self) -> bool { self.id_to_components.is_empty() } - /// Merge another store into this one (needed for parallel parsing). + /// Merge another store into this one (needed for parallel parsing) pub fn merge(&mut self, other: &QuotedTripleStore) { for (&id, &components) in &other.id_to_components { self.id_to_components.entry(id).or_insert(components); @@ -135,9 +118,7 @@ mod tests { #[test] fn test_nested_quoted_triples() { let mut store = QuotedTripleStore::new(); - // Inner: << 1 2 3 >> let inner_id = store.encode(1, 2, 3); - // Outer: << <<1 2 3>> 4 5 >> let outer_id = store.encode(inner_id, 4, 5); assert!(is_quoted_triple_id(inner_id)); assert!(is_quoted_triple_id(outer_id)); @@ -164,7 +145,7 @@ mod tests { let id1 = store1.encode(1, 2, 3); let mut store2 = QuotedTripleStore::new(); - store2.next_qt_id = store1.next_qt_id; // simulate separate counter + store2.next_qt_id = store1.next_qt_id; let id2 = store2.encode(4, 5, 6); store1.merge(&store2); diff --git a/shared/src/rdf_term.rs b/shared/src/rdf_term.rs index 4ba081c4..54cd30ba 100644 --- a/shared/src/rdf_term.rs +++ b/shared/src/rdf_term.rs @@ -8,74 +8,35 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! What a term's text means, and how a term is spelled so that two terms are the -//! same string exactly when RDF says they are the same term. -//! -//! The [`Dictionary`](crate::dictionary::Dictionary) keys on a term's text, so the -//! spelling *is* the identity: two terms sharing a spelling share an id and can never -//! be told apart again. That makes the choice of spelling a correctness question -//! rather than a formatting one, and it had been getting it wrong — the N-Triples -//! reader discarded a literal's datatype, so `"25"^^xsd:integer`, `"25"^^xsd:string` -//! and plain `"25"` all interned as `25`, with the datatype unrecoverable. -//! -//! # The canonical spelling -//! -//! | term | spelled | -//! |---|---| -//! | IRI | bare: `http://example/x` | -//! | plain literal | bare: `25` | -//! | `xsd:string` literal | bare: `25` — RDF 1.1 §3.3 says it *is* the plain literal | -//! | typed literal | `"25"^^` | -//! | language literal | `"hello"@en` | -//! | blank node | `_:b` | -//! -//! Bare where it can be, quoted where it must be. An IRI and a plain literal keep the -//! spelling they always had, which is why this change leaves the overwhelming -//! majority of terms, and the results built from them, byte-identical. A typed or -//! language-tagged literal is quoted, which is what makes it distinguishable: a -//! well-formed IRI cannot begin with `"`, so no quoted form can collide with one. -//! -//! The quoted form is also the spelling [`tag_store`](crate::tag_store) and -//! [`hybrid`](crate::hybrid) already intern directly, so the reader and those -//! writers now agree. They did not before: a probability written as -//! `"0.8"^^<…#double>` could never match a `0.8` read from a file, because the two -//! paths minted different ids for one term. -//! -//! # Not value equality -//! -//! `"1"^^xsd:integer` and `"01"^^xsd:integer` denote one value and remain two terms. -//! Comparing values is a separate operation on top of these, not a spelling rule. - -/// The three kinds of RDF term, as told from a canonical spelling. +//! What a term's text means, and how a term is spelled so that two terms are the same string +//! exactly when RDF says they are the same term + +/// The three kinds of RDF term, as told from a canonical spelling #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)] pub enum TermKind { - /// An IRI, spelled bare. + /// An IRI, spelled bare Iri, - /// A blank node label, spelled `_:label`. + /// A blank node label, spelled `_:label` BlankNode, - /// A literal, plain or quoted. + /// A literal, plain or quoted Literal, } -/// A literal taken apart: what it says, and what says how to read it. +/// A literal taken apart: what it says, and what says how to read it #[derive(Debug, Clone, PartialEq, Eq)] pub struct Literal { - /// The lexical form, unescaped. + /// The lexical form, unescaped pub lexical: String, - /// The datatype IRI, absent for a plain or language-tagged literal. + /// The datatype IRI, absent for a plain or language-tagged literal pub datatype: Option, - /// The language tag, absent unless the literal carries one. + /// The language tag, absent unless the literal carries one pub language: Option, } -/// The IRI of `xsd:string`, the datatype a plain literal already has. +/// The IRI of `xsd:string`, the datatype a plain literal already has pub const XSD_STRING: &str = "http://www.w3.org/2001/XMLSchema#string"; -/// Escapes a lexical form for the inside of a quoted literal. -/// -/// The inverse of the reader's unescaping. Without it a lexical form containing a -/// quote or a backslash would produce a spelling that reads back as a different -/// term — the one way a canonical form can be worse than no canonical form. +/// Escapes a lexical form for the inside of a quoted literal pub fn escape_lexical(lexical: &str) -> String { let mut out = String::with_capacity(lexical.len()); for character in lexical.chars() { @@ -93,27 +54,18 @@ pub fn escape_lexical(lexical: &str) -> String { out } -/// Spells a literal canonically: bare when it can be, quoted when it must be. -/// -/// A plain literal and an `xsd:string` literal are one term and get one spelling. -/// Everything else is quoted, carrying whichever of the datatype or language tag it -/// has, so that the spelling determines the term. +/// Spells a literal canonically: bare when it can be, quoted when it must be pub fn spell_literal(lexical: &str, datatype: Option<&str>, language: Option<&str>) -> String { match (datatype, language) { (_, Some(tag)) => format!("\"{}\"@{}", escape_lexical(lexical), tag), (Some(iri), None) if iri != XSD_STRING => { format!("\"{}\"^^<{}>", escape_lexical(lexical), iri) } - // Plain, or explicitly xsd:string, which RDF 1.1 says is the same term. _ => lexical.to_string(), } } -/// Which kind of term this spelling denotes. -/// -/// Reads the spelling rather than consulting a second map, so there is no parallel -/// table to keep in step with the dictionary. The three forms are disjoint for -/// well-formed RDF: an IRI has a scheme, so it can begin with neither `"` nor `_:`. +/// Which kind of term this spelling denotes pub fn kind_of(spelling: &str) -> TermKind { if spelling.starts_with('"') { TermKind::Literal @@ -122,16 +74,11 @@ pub fn kind_of(spelling: &str) -> TermKind { } else if looks_like_iri(spelling) { TermKind::Iri } else { - // A bare spelling that is not an IRI is a plain literal: `25`, `hello`. TermKind::Literal } } -/// Whether a bare spelling looks like an IRI rather than a plain literal. -/// -/// An absolute IRI has a scheme followed by `:`, and the scheme is letters, digits, -/// `+`, `-` or `.` starting with a letter (RFC 3987). `urn:a` and `http://x` qualify; -/// `25` and `hello world` do not. +/// Whether a bare spelling looks like an IRI rather than a plain literal fn looks_like_iri(spelling: &str) -> bool { let Some(colon) = spelling.find(':') else { return false; @@ -144,18 +91,7 @@ fn looks_like_iri(spelling: &str) -> bool { .all(|c| c.is_ascii_alphanumeric() || c == '+' || c == '-' || c == '.') } -/// Rewrites an N-Triples literal into its canonical spelling. -/// -/// The one place a literal's text becomes a term, so that a reader and an encoder -/// cannot disagree about what was read. They did: the N-Triples reader spelled the -/// term one way and the encoder then re-parsed it and kept only the lexical form, -/// dropping the datatype a second time in a second function. One function, used by -/// both, is what stops that recurring. -/// -/// Idempotent, which is what lets it sit on a path that may already have run: a -/// canonical spelling reads back as the literal it was spelled from, and spells to -/// itself. Returns `None` for text that is not a literal, so a caller can pass an -/// IRI through unchanged. +/// Rewrites an N-Triples literal into its canonical spelling pub fn canonicalise_literal(text: &str) -> Option { let literal = read_literal(text)?; Some(spell_literal( @@ -165,33 +101,19 @@ pub fn canonicalise_literal(text: &str) -> Option { )) } -/// A triple of canonical term spellings: a triple no dictionary has seen. -/// -/// What a triple is before it is interned, and after it is decoded. Ids are only -/// meaningful against the dictionary that minted them, so anything crossing out of -/// one store -- a parsed document on its way in, a window's content, a result going -/// to another system -- has to carry text instead. Several places had worked that out -/// independently and each named it differently, or not at all. -/// -/// # Ground terms, not patterns -/// -/// Every component is a term. A triple *pattern* may hold a variable, compares -/// differently and binds rather than matches, so it is a different type and stays -/// one: `(String, String, String)` appears all over this workspace for patterns, -/// filter comparisons and diagnostics, and folding those in would conflate a -/// variable with a term named `?s`. +/// A triple of canonical term spellings: a triple no dictionary has seen #[derive(Clone, Debug, Default, PartialEq, Eq, Hash, PartialOrd, Ord)] pub struct TermTriple { - /// The subject: an IRI or a blank node. + /// The subject: an IRI or a blank node pub subject: String, - /// The predicate: an IRI. + /// The predicate: an IRI pub predicate: String, - /// The object: an IRI, a blank node or a literal. + /// The object: an IRI, a blank node or a literal pub object: String, } impl TermTriple { - /// A triple from its three terms. + /// A triple from its three terms pub fn new( subject: impl Into, predicate: impl Into, @@ -204,10 +126,7 @@ impl TermTriple { } } - /// Writes the triple as one line of N-Triples, terminator included. - /// - /// Each term through [`to_ntriples_term`], so an IRI gets its brackets, a - /// literal keeps its datatype and a blank node stays a blank node. + /// Writes the triple as one line of N-Triples, terminator included pub fn to_ntriples(&self) -> String { format!( "{} {} {} .\n", @@ -217,7 +136,7 @@ impl TermTriple { ) } - /// The three terms, in order. + /// The three terms, in order pub fn terms(&self) -> [&str; 3] { [&self.subject, &self.predicate, &self.object] } @@ -239,19 +158,10 @@ impl From for (String, String, String) { } } -/// The well-known path RDF 1.1 §3.5 gives Skolem IRIs. -/// -/// Reserved by RFC 8615 for exactly this, so an IRI minted under it cannot collide -/// with one an application chose, and a consumer that recognises the path knows the -/// IRI stands for what was a blank node. +/// The well-known path RDF 1.1 §3.5 gives Skolem IRIs pub const SKOLEM_PATH: &str = "/.well-known/genid/"; -/// A Skolem IRI for `label`, under an authority the caller controls. -/// -/// `base` has to be supplied rather than defaulted, and that is the point: a Skolem -/// IRI is globally unique because the authority minting it is, and this crate has no -/// identity of its own to offer. Defaulting it would mint the same IRI on two -/// machines and reintroduce the collision skolemisation exists to remove. +/// A Skolem IRI for `label`, under an authority the caller controls pub fn skolem_iri(label: &str, base: &str) -> String { format!( "{}{SKOLEM_PATH}{}", @@ -260,13 +170,7 @@ pub fn skolem_iri(label: &str, base: &str) -> String { ) } -/// Replaces a blank node with a Skolem IRI, leaving every other term untouched. -/// -/// What to do at an exchange boundary. A blank node label means nothing outside its -/// document, so two systems merging each other's blank nodes fuse unrelated nodes; -/// RDF 1.1 §3.5 answers that by replacing them with IRIs, which are global. The -/// receiver may keep them as IRIs -- which is what makes the merge safe -- or turn -/// them back with [`deskolemise`]. +/// Replaces a blank node with a Skolem IRI, leaving every other term untouched pub fn skolemise<'a>(spelling: &'a str, base: &str) -> std::borrow::Cow<'a, str> { match spelling.strip_prefix("_:") { Some(label) => std::borrow::Cow::Owned(skolem_iri(label, base)), @@ -274,12 +178,7 @@ pub fn skolemise<'a>(spelling: &'a str, base: &str) -> std::borrow::Cow<'a, str> } } -/// Recovers the blank node a Skolem IRI stands for, or `None` for any other term. -/// -/// Optional by design: a consumer is entitled to leave a Skolem IRI as an IRI, and -/// doing so is what keeps a merge sound. This is for a consumer that would rather -/// have the blank node back, and recognises the IRI by its well-known path rather -/// than by its authority, so it works on IRIs any publisher minted. +/// Recovers the blank node a Skolem IRI stands for, or `None` for any other term pub fn deskolemise(spelling: &str) -> Option { let (_, label) = spelling.split_once(SKOLEM_PATH)?; if label.is_empty() || label.contains('/') { @@ -288,13 +187,7 @@ pub fn deskolemise(spelling: &str) -> Option { Some(format!("_:{}", decode_label(label))) } -/// Percent-encodes the characters that would end a label's IRI path segment early. -/// -/// Deliberately narrow. A well-formed N-Triples blank node label is already a valid -/// IRI path segment -- letters, digits, `_`, `-`, `.` and Unicode are all permitted -/// in an IRI -- so nothing read from a document needs this. A label built by hand -/// through [`spell_blank_node`] can hold anything, and these are the characters that -/// would make the result a different IRI, or no IRI at all. +/// Percent-encodes the characters that would end a label's IRI path segment early fn encode_label(label: &str) -> String { let mut out = String::with_capacity(label.len()); for character in label.chars() { @@ -313,7 +206,7 @@ fn encode_label(label: &str) -> String { out } -/// The inverse of [`encode_label`]. +/// The inverse of [`encode_label`] fn decode_label(label: &str) -> String { let mut bytes = Vec::with_capacity(label.len()); let mut characters = label.chars(); @@ -327,81 +220,37 @@ fn decode_label(label: &str) -> String { let low = characters.next().and_then(|c| c.to_digit(16)); match (high, low) { (Some(high), Some(low)) => bytes.push((high * 16 + low) as u8), - // Not an escape after all, so it stands for itself rather than vanishing. _ => bytes.push(b'%'), } } String::from_utf8_lossy(&bytes).into_owned() } -/// Writes a term's canonical spelling as N-Triples term syntax. -/// -/// The inverse of what the readers do, and the half of the term model that was -/// missing. A canonical spelling is deliberately *not* a wire form: an IRI is stored -/// bare and a plain literal unquoted, because that keeps identity cheap and leaves -/// existing terms untouched. Handing that text straight to a file is what produced -/// `http://example/x .` for an IRI and `"\"25\"^^<...>"` for a typed literal -- the -/// latter being a plain literal whose text happens to look like a typed one, which -/// any other RDF tool reads as a string. -/// -/// It survived unnoticed because this crate's own readers re-canonicalise what they -/// read, so a round trip through Kolibrie recovered the datatype. Only a second -/// implementation would have noticed, which is the case that matters for exchanging -/// data between systems. +/// Writes a term's canonical spelling as N-Triples term syntax pub fn to_ntriples_term(spelling: &str) -> String { - // A quoted triple is already written in its own bracketed form, and a blank node - // label is already N-Triples syntax. if spelling.starts_with("<<") || spelling.starts_with("_:") { return spelling.to_string(); } match kind_of(spelling) { TermKind::Iri => format!("<{spelling}>"), TermKind::BlankNode => spelling.to_string(), - // A quoted spelling is already N-Triples literal syntax, datatype or language - // tag included, so it is passed through rather than quoted a second time. TermKind::Literal if spelling.starts_with('"') => spelling.to_string(), TermKind::Literal => format!("\"{}\"", escape_lexical(spelling)), } } -/// Spells a blank node belonging to one document, from the label as written. -/// -/// The counterpart to [`spell_literal`], and the way to build a blank node without -/// knowing either half of the convention: that the term carries a `_:` prefix, and -/// that the label has to be scoped or it collides with every other document's. -/// `label` is the bare label, so `"b"` rather than `"_:b"`. -/// -/// There is deliberately no equivalent for an IRI. An IRI is spelled as itself, so a -/// constructor would be the identity function and would only suggest that something -/// is being done to it. +/// Spells a blank node belonging to one document, from the label as written pub fn spell_blank_node(label: &str, document: u64) -> String { scope_blank_node(&format!("_:{label}"), document).into_owned() } -/// A fresh document scope, for relabelling the blank nodes of one document. -/// -/// One per document read, taken before parsing begins so that parallel chunks of the -/// same document share it. Monotonic and never reused, which is what makes two reads -/// of the same file two sets of nodes -- as RDF requires, since a label identifies a -/// node only within the document it was read from. +/// A fresh document scope, for relabelling the blank nodes of one document pub fn next_document_scope() -> u64 { static NEXT_DOCUMENT: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(1); NEXT_DOCUMENT.fetch_add(1, std::sync::atomic::Ordering::Relaxed) } -/// Rewrites a blank node label so that it belongs to one document, leaving any other -/// term untouched. -/// -/// A blank node label means nothing outside its document (RDF 1.1 §3.4), so two files -/// each saying `_:b` describe two different nodes and merging them must keep them -/// apart. Relabelling is forced rather than chosen: the dictionary keys on a term's -/// text, so two distinct nodes must have two distinct spellings. -/// -/// The written label stays legible at the end of the new one, and the form matches -/// what SPARQL updates already mint for the same reason, `_:kolibrie-update-N-label`. -/// -/// Returns the term borrowed when nothing needed rewriting, so the ordinary case of a -/// document without blank nodes costs no allocation. +/// Rewrites a blank node label so that it belongs to one document, leaving any other term untouched pub fn scope_blank_node<'a>(term: &'a str, document: u64) -> std::borrow::Cow<'a, str> { match term.strip_prefix("_:") { Some(label) => { @@ -411,16 +260,7 @@ pub fn scope_blank_node<'a>(term: &'a str, document: u64) -> std::borrow::Cow<'a } } -/// Removes a scope this crate added, leaving the label as it was written. -/// -/// Scoping replaces a previous scope instead of nesting inside it, because reading a -/// graph back from its own serialisation is a new document and scopes it again: a -/// label would otherwise collect one prefix per round trip and grow without bound. -/// The node is still a new node each time, which is what RDF says and what the -/// replaced counter records; only the spelling stays a sane length. -/// -/// A label a user genuinely wrote as `kolibrie-doc-5-x` is indistinguishable from one -/// this produced, and is treated as the latter. +/// Removes a scope this crate added, leaving the label as it was written fn strip_scope(label: &str) -> &str { for marker in ["kolibrie-doc-", "kolibrie-update-"] { let Some(rest) = label.strip_prefix(marker) else { @@ -438,35 +278,17 @@ fn strip_scope(label: &str) -> &str { label } -/// The number a term denotes, or `None` if it denotes none. -/// -/// Reads the lexical form before parsing, which is the whole point: a literal's -/// number lives in its lexical form, not in its spelling. `"25"^^<…#integer>` is -/// twenty-five, and parsing the spelling itself would find nothing — the failure -/// mode that would otherwise arrive with the datatype, silently turning `ORDER BY` -/// into string ordering and `SUM` into no answer at all. -/// -/// Says nothing about whether the datatype is numeric: a term is read as a number -/// when its lexical form is one, which is what the engine did before and keeps -/// `"25"` working whether or not anyone typed it. +/// The number a term denotes, or `None` if it denotes none pub fn numeric_value(spelling: &str) -> Option { read_literal(spelling)?.lexical.trim().parse::().ok() } -/// The text a term denotes: a literal's lexical form, or the term itself. -/// -/// What to compare, print or hand to a consumer that wants the value rather than the -/// term. An IRI and a blank node are their own text; a literal sheds its quotes, -/// datatype and language tag. +/// The text a term denotes: a literal's lexical form, or the term itself pub fn lexical_value(spelling: &str) -> &str { if !spelling.starts_with('"') { return spelling; } match unescape_quoted(spelling) { - // Borrowing back from the input is only sound when unescaping changed - // nothing, so an escaped form falls back to the whole spelling rather than - // allocating behind the caller's back. Callers needing the unescaped text - // use `read_literal`. Some((value, rest)) if value.len() + rest.len() + 2 == spelling.len() => { &spelling[1..1 + value.len()] } @@ -474,11 +296,7 @@ pub fn lexical_value(spelling: &str) -> &str { } } -/// Takes a canonical spelling apart into a literal, or `None` if it denotes an IRI or -/// a blank node. -/// -/// The inverse of [`spell_literal`]: a plain spelling comes back as a lexical form -/// with no datatype, and a quoted one carries whichever it was spelled with. +/// Takes a canonical spelling apart into a literal, or `None` if it denotes an IRI or a blank node pub fn read_literal(spelling: &str) -> Option { if !spelling.starts_with('"') { return match kind_of(spelling) { @@ -517,8 +335,8 @@ pub fn read_literal(spelling: &str) -> Option { }) } -/// Reads the quoted part of a literal, returning its unescaped text and what follows -/// the closing quote. +/// Reads the quoted part of a literal, returning its unescaped text and what follows the closing +/// quote fn unescape_quoted(spelling: &str) -> Option<(String, &str)> { let body = spelling.strip_prefix('"')?; let mut characters = body.char_indices(); @@ -537,9 +355,6 @@ fn unescape_quoted(spelling: &str) -> Option<(String, &str)> { '"' => value.push('"'), '\'' => value.push('\''), '\\' => value.push('\\'), - // `\uXXXX` and `\UXXXXXXXX`, as N-Triples spells a code point. - // An ill-formed escape makes the whole literal unreadable rather - // than silently becoming the letter `u`. escape @ ('u' | 'U') => { let digits = if escape == 'u' { 4 } else { 8 }; let mut scalar = String::with_capacity(digits); @@ -569,14 +384,12 @@ mod tests { #[test] fn a_plain_literal_keeps_the_spelling_it_always_had() { - // The property that keeps this change small: the common case does not move. assert_eq!(spell_literal("25", None, None), "25"); assert_eq!(spell_literal("hello", None, None), "hello"); } #[test] fn an_xsd_string_literal_is_the_plain_literal() { - // RDF 1.1 §3.3. Spelling them alike is what makes them one term. assert_eq!(spell_literal("25", Some(XSD_STRING), None), "25"); assert_eq!( spell_literal("25", Some(XSD_STRING), None), @@ -594,8 +407,6 @@ mod tests { #[test] fn a_language_tag_cannot_collide_with_a_plain_literal_spelled_the_same_way() { - // The collision that used to exist: `"hello"@en` was stored as `hello@en`, - // which is also how a plain literal of that text was stored. assert_eq!(spell_literal("hello", None, Some("en")), "\"hello\"@en"); assert_ne!( spell_literal("hello", None, Some("en")), @@ -605,8 +416,6 @@ mod tests { #[test] fn a_quote_in_the_lexical_form_survives_the_round_trip() { - // Without escaping, this spelling would read back as a different term -- - // the one way a canonical form can be worse than none. let spelled = spell_literal("a\"b\\c", Some(XSD_INTEGER), None); let read = read_literal(&spelled).expect("a literal"); assert_eq!(read.lexical, "a\"b\\c"); @@ -632,8 +441,6 @@ mod tests { language, "language of {spelled:?}" ); - // A plain and an xsd:string literal share a spelling, so the datatype - // comes back absent for both; everything else round-trips exactly. let expected = datatype.filter(|iri| *iri != XSD_STRING); assert_eq!( read.datatype.as_deref(), @@ -650,15 +457,12 @@ mod tests { assert_eq!(kind_of("_:b"), TermKind::BlankNode); assert_eq!(kind_of("\"25\"^^"), TermKind::Literal); assert_eq!(kind_of("\"hello\"@en"), TermKind::Literal); - // Bare and not an IRI, so a plain literal rather than a malformed IRI. assert_eq!(kind_of("25"), TermKind::Literal); assert_eq!(kind_of("hello world"), TermKind::Literal); } #[test] fn canonicalising_twice_changes_nothing() { - // The property the two call sites rely on: whichever of them runs first, the - // other must not strip what it finds. for raw in [ "\"25\"^^", "\"hello\"@en", @@ -682,8 +486,6 @@ mod tests { #[test] fn a_typed_number_is_still_a_number() { - // The regression this exists to prevent: the datatype arriving in the - // spelling and taking the number with it. assert_eq!(numeric_value("25"), Some(25.0)); assert_eq!( numeric_value(&spell_literal("25", Some(XSD_INTEGER), None)), @@ -717,9 +519,6 @@ mod tests { #[test] fn the_escapes_n_triples_defines_are_all_understood() { - // Ported from the reader's own decoder, which these replaced. Dropping - // `\\uXXXX`, `\\b` or `\\f` would have turned a valid literal into a - // different one without any error. let cases = [ ("\"a\\u0041b\"", "aAb"), ("\"a\\U00000041b\"", "aAb"), @@ -736,7 +535,6 @@ mod tests { #[test] fn an_ill_formed_unicode_escape_is_refused_not_guessed() { - // `\\uZZZZ` is not the letter u followed by ZZZZ. assert_eq!(read_literal("\"a\\uZZZZb\""), None); assert_eq!(read_literal("\"a\\u00\""), None); } @@ -780,9 +578,6 @@ mod tests { #[test] fn two_scopes_give_one_label_two_terms_and_one_scope_gives_it_one() { - // Both halves of the property: labels separate across documents and stay - // shared within one. Minting per occurrence would satisfy the first and - // destroy every blank-node-linked structure in the graph. let (first, second) = (next_document_scope(), next_document_scope()); assert_ne!(first, second, "each document gets its own scope"); assert_ne!( @@ -797,16 +592,12 @@ mod tests { #[test] fn scoping_replaces_a_previous_scope_rather_than_nesting_in_it() { - // Reading a graph back from its own serialisation scopes it again, so - // without this a label would collect one prefix per round trip. let once = scope_blank_node("_:b", 1).into_owned(); let twice = scope_blank_node(&once, 2).into_owned(); let thrice = scope_blank_node(&twice, 3).into_owned(); assert_eq!(once, "_:kolibrie-doc-1-b"); assert_eq!(twice, "_:kolibrie-doc-2-b"); assert_eq!(thrice, "_:kolibrie-doc-3-b"); - // Still a new node each time, which is the point; only the spelling stays - // a sane length. assert_ne!(once, twice); assert_ne!(twice, thrice); } @@ -823,7 +614,6 @@ mod tests { #[test] fn a_label_that_merely_looks_scoped_is_left_as_written() { - // No digits, so not a scope this crate wrote. assert_eq!( scope_blank_node("_:kolibrie-doc-x", 7), "_:kolibrie-doc-7-kolibrie-doc-x" @@ -835,8 +625,6 @@ mod tests { let document = next_document_scope(); let built = spell_blank_node("b", document); assert_eq!(kind_of(&built), TermKind::BlankNode); - // The same term the readers produce for `_:b` in that document, so a term - // built by hand joins against one that was read. assert_eq!(built, scope_blank_node("_:b", document)); } @@ -851,9 +639,6 @@ mod tests { #[test] fn a_typed_literal_is_not_quoted_a_second_time() { - // The bug this exists to stop: quoting the canonical spelling again turns a - // typed literal into a plain one whose text looks typed, which this crate's - // own readers then recover from and nobody else can. let typed = spell_literal("25", Some(XSD_INTEGER), None); assert_eq!(to_ntriples_term(&typed), format!("\"25\"^^<{XSD_INTEGER}>")); let tagged = spell_literal("hello", None, Some("en")); @@ -862,8 +647,6 @@ mod tests { #[test] fn a_written_term_reads_back_as_the_term_it_was_written_from() { - // Round trip through the wire syntax, which is the property an exchange - // between two systems depends on. for spelling in [ "http://example/x".to_string(), "urn:a".to_string(), @@ -874,8 +657,6 @@ mod tests { spell_literal("a\"b\\c", None, None), ] { let written = to_ntriples_term(&spelling); - // What a reader does with that text: strip the IRI brackets, or - // canonicalise the literal. let read_back = if let Some(iri) = written .strip_prefix('<') .and_then(|rest| rest.strip_suffix('>')) @@ -897,7 +678,6 @@ mod tests { skolem, "http://example.org/.well-known/genid/kolibrie-doc-7-b" ); - // An IRI, not a blank node, which is the whole point: it is global. assert_eq!(kind_of(&skolem), TermKind::Iri); } @@ -922,8 +702,6 @@ mod tests { #[test] fn two_bases_give_one_label_two_iris() { - // The property skolemisation exists for: two systems exchanging data no - // longer collide, because the authority minting the IRI differs. assert_ne!( skolemise("_:b", "http://alice.example"), skolemise("_:b", "http://bob.example") @@ -943,7 +721,6 @@ mod tests { let original = "_:kolibrie-doc-7-b"; let skolem = skolemise(original, "http://example.org").into_owned(); assert_eq!(deskolemise(&skolem).as_deref(), Some(original)); - // Recognised by the well-known path, so an IRI any publisher minted works. assert_eq!( deskolemise("https://other.example/.well-known/genid/xyz").as_deref(), Some("_:xyz") @@ -954,7 +731,6 @@ mod tests { fn an_ordinary_iri_is_not_mistaken_for_a_skolem_one() { assert_eq!(deskolemise("http://example.org/thing"), None); assert_eq!(deskolemise("_:b"), None, "a blank node is not a Skolem IRI"); - // A path continuing past the label is some other resource, not a node. assert_eq!( deskolemise("http://example.org/.well-known/genid/a/b"), None @@ -964,8 +740,6 @@ mod tests { #[test] fn an_awkward_label_survives_the_round_trip_as_a_single_iri_segment() { - // A label built by hand can hold anything; these are the characters that - // would otherwise end the path segment early or change the IRI. for label in ["a/b", "a?b", "a#b", "a b", "a%b", "100%", "héllo"] { let skolem = skolemise(&format!("_:{label}"), "http://example.org").into_owned(); let segment = skolem diff --git a/shared/src/rule.rs b/shared/src/rule.rs index ec7e1e5c..71a9bd16 100644 --- a/shared/src/rule.rs +++ b/shared/src/rule.rs @@ -11,43 +11,31 @@ use crate::terms::{Term, TriplePattern}; use std::collections::HashSet; -/// A comparison a candidate binding must satisfy for a rule to fire. -/// -/// Operator and value are held as text and interpreted at evaluation time, so an -/// unrecognised operator is ignored rather than rejected at parse time. `value` is -/// compared numerically when both sides parse as numbers and by dictionary id when it -/// names another bound variable. +/// A comparison a candidate binding must satisfy for a rule to fire #[derive(Debug, Clone)] pub struct FilterCondition { - /// Name of the variable being constrained, without the leading `?`. + /// Name of the variable being constrained, without the leading `?` pub variable: String, - /// Comparison operator, as written: `=`, `!=`, `<`, `>`, `<=`, `>=`. + /// Comparison operator, as written: `=`, `!=`, `<`, `>`, `<=`, `>=` pub operator: String, - /// Right-hand side: a literal, or the name of another bound variable. + /// Right-hand side: a literal, or the name of another bound variable pub value: String, } -/// A Datalog rule: if every premise matches and the filters hold, derive the -/// conclusion. -/// -/// Variables are shared by name across premises and conclusion, so repeating a name -/// is what expresses a join. Negation is single-stratum and must be *safe* — see -/// [`check_rule_safety`], which callers are expected to run before using a rule. +/// A Datalog rule: if every premise matches and the filters hold, derive the conclusion #[derive(Debug, Clone)] pub struct Rule { - /// Body atoms that must all match. Shared variable names join them. + /// Body atoms that must all match pub premise: Vec, - /// Negated body atoms (NOT X) for single-stratum negation-as-failure. - /// Every variable appearing here must also appear in `premise` (rule safety). + /// Negated body atoms (NOT X) for single-stratum negation-as-failure pub negative_premise: Vec, - /// Comparisons that must hold for a binding produced by the premises. + /// Comparisons that must hold for a binding produced by the premises pub filters: Vec, - /// Atoms derived when the body matches. Every variable here should be bound by - /// `premise`; one that is not resolves to term id 0 rather than being rejected. + /// Atoms derived when the body matches pub conclusion: Vec, } -/// Returns an iterator over the variable names bound by a triple pattern. +/// Returns an iterator over the variable names bound by a triple pattern pub fn pattern_variables(pat: &TriplePattern) -> impl Iterator { let (s, p, o) = pat; [s, p, o].into_iter().filter_map(|t| { @@ -59,9 +47,7 @@ pub fn pattern_variables(pat: &TriplePattern) -> impl Iterator { }) } -/// Check that every variable in `negative_premise` is bound by at least one positive premise. -/// -/// Returns `Err` describing the first unsafe variable found. +/// Check that every variable in `negative_premise` is bound by at least one positive premise pub fn check_rule_safety(rule: &Rule) -> Result<(), String> { let bound: HashSet<&str> = rule.premise.iter().flat_map(pattern_variables).collect(); for pat in &rule.negative_premise { diff --git a/shared/src/rule_index.rs b/shared/src/rule_index.rs index 030949a1..6b0b0ef7 100644 --- a/shared/src/rule_index.rs +++ b/shared/src/rule_index.rs @@ -12,44 +12,30 @@ use crate::terms::Term::*; use crate::terms::*; use std::collections::{HashMap, HashSet}; -/// Index of a rule in the reasoner's rule list. +/// Index of a rule in the reasoner's rule list pub type RuleId = usize; -/// Stands in for a variable position when a pattern is indexed. -/// -/// A premise with a variable subject is filed under this key, so a lookup for a -/// concrete subject must consult both the concrete key and this one. It therefore -/// collides with a real term whose id is `u32::MAX` — unreachable in practice, -/// since [`Dictionary`](crate::dictionary::Dictionary) stops below `0x8000_0000`. +/// Stands in for a variable position when a pattern is indexed pub const WILDCARD: u32 = u32::MAX; #[derive(Debug, Clone)] -/// Maps a fact to the rules whose premises could match it. -/// -/// This is the reverse of the usual direction: rather than scanning every rule -/// against every new fact, semi-naive evaluation asks which rules a fact could -/// possibly fire, which is what keeps a derivation round proportional to the delta -/// rather than to the program size. -/// -/// Every premise is filed under all six two-position orderings, with [`WILDCARD`] -/// standing in for variable positions. Six maps make any binding pattern a direct -/// lookup; the cost is that each premise is stored six times. +/// Maps a fact to the rules whose premises could match it pub struct RuleIndex { - /// Subject then predicate. + /// Subject then predicate pub spo: HashMap>>, - /// Predicate then object. + /// Predicate then object pub pos: HashMap>>, - /// Object then subject. + /// Object then subject pub osp: HashMap>>, - /// Predicate then subject. + /// Predicate then subject pub pso: HashMap>>, - /// Object then predicate. + /// Object then predicate pub ops: HashMap>>, - /// Subject then object. + /// Subject then object pub sop: HashMap>>, } impl RuleIndex { - /// An empty index. + /// An empty index pub fn new() -> Self { Self { spo: HashMap::new(), @@ -61,7 +47,7 @@ impl RuleIndex { } } - /// Drops every indexed premise, keeping the allocation. + /// Drops every indexed premise, keeping the allocation pub fn clear(&mut self) { self.spo.clear(); self.pos.clear(); @@ -71,12 +57,10 @@ impl RuleIndex { self.sop.clear(); } - /// Files one premise of `rule_id`, so a matching fact will list that rule as a - /// candidate. Call once per premise. + /// Files one premise of `rule_id`, so a matching fact will list that rule as a candidate pub fn insert_premise_pattern(&mut self, pattern: &TriplePattern, rule_id: RuleId) { let (s_val, p_val, o_val) = triple_pattern_to_keys(pattern); - // Insert into each of the six permutations self.spo .entry(s_val) .or_default() @@ -115,11 +99,7 @@ impl RuleIndex { .insert(rule_id); } - /// Rules that might fire for a fact with the given bound positions. - /// - /// Over-approximates: the result is a superset of the rules that actually - /// match, because indexing keys on at most two positions and ignores repeated - /// variables. Callers still have to run the full match. + /// Rules that might fire for a fact with the given bound positions pub fn query_candidate_rules( &self, s: Option, @@ -129,26 +109,22 @@ impl RuleIndex { let mut results = HashSet::new(); match (s, p, o) { - // (S, P, O) fully bound (Some(ss), Some(pp), Some(oo)) => { if let Some(pred_map) = self.spo.get(&ss) { if let Some(rule_set) = pred_map.get(&pp) { results.extend(rule_set); } } - // - s -> sop if let Some(obj_map) = self.sop.get(&ss) { if let Some(rule_set) = obj_map.get(&oo) { results.extend(rule_set); } } - // - p -> pos if let Some(obj_map) = self.pos.get(&pp) { if let Some(rule_set) = obj_map.get(&oo) { results.extend(rule_set); } } - // - o -> osp or ops if let Some(subj_map) = self.osp.get(&oo) { if let Some(rule_set) = subj_map.get(&ss) { results.extend(rule_set); @@ -161,34 +137,26 @@ impl RuleIndex { } } - // (S, P, None) (Some(ss), Some(pp), None) => { - // subject + predicate known, object unknown - // => use the spo map if it has s->p => union all rule_ids if let Some(pred_map) = self.spo.get(&ss) { if let Some(rule_set) = pred_map.get(&pp) { results.extend(rule_set); } } - // plus the "pso" map if it has p->s => union all if let Some(subj_map) = self.pso.get(&pp) { if let Some(rule_set) = subj_map.get(&ss) { results.extend(rule_set); } } - // etc. } - // (S, None, O) (Some(ss), None, Some(oo)) => { - // We can use the sop map if it has s->o => union all if let Some(obj_map) = self.sop.get(&ss) { if let Some(rule_set) = obj_map.get(&oo) { results.extend(rule_set); } } - // and also the osp map if it has o->s => union if let Some(subj_map) = self.osp.get(&oo) { if let Some(rule_set) = subj_map.get(&ss) { results.extend(rule_set); @@ -196,9 +164,7 @@ impl RuleIndex { } } - // (None, P, O) (None, Some(pp), Some(oo)) => { - // pos, ops if let Some(obj_map) = self.pos.get(&pp) { if let Some(rule_set) = obj_map.get(&oo) { results.extend(rule_set); @@ -211,9 +177,7 @@ impl RuleIndex { } } - // (S, None, None) (Some(ss), None, None) => { - // union everything from spo[s] and sop[s] if let Some(pred_map) = self.spo.get(&ss) { for (_p, rule_set) in pred_map { results.extend(rule_set); @@ -226,9 +190,7 @@ impl RuleIndex { } } - // (None, P, None) (None, Some(pp), None) => { - // union everything from pos[p] and pso[p] if let Some(obj_map) = self.pos.get(&pp) { for (_o, rule_set) in obj_map { results.extend(rule_set); @@ -241,9 +203,7 @@ impl RuleIndex { } } - // (None, None, O) (None, None, Some(oo)) => { - // union everything from osp[o] and ops[o] if let Some(subj_map) = self.osp.get(&oo) { for (_s, rule_set) in subj_map { results.extend(rule_set); @@ -256,9 +216,7 @@ impl RuleIndex { } } - // (None, None, None): no constraints => return all rule_ids (None, None, None) => { - // union everything from all 6 permutations for (_, pred_map) in &self.spo { for (_, rule_set) in pred_map { results.extend(rule_set); diff --git a/shared/src/sdd.rs b/shared/src/sdd.rs index 60b972ce..1f1206a5 100644 --- a/shared/src/sdd.rs +++ b/shared/src/sdd.rs @@ -12,16 +12,14 @@ use crate::provenance::Provenance; use std::collections::{BTreeSet, HashMap}; use std::sync::{Arc, Mutex}; -/// Handle to an SDD node in the manager's arena. -/// -/// `SddId(0)` = FALSE, `SddId(1)` = TRUE (reserved by the manager). +/// Handle to an SDD node in the manager's arena #[derive(Clone, Copy, PartialEq, Eq, Hash, PartialOrd, Ord)] pub struct SddId(u32); impl SddId { - /// The constant false node. Always id 0, so it needs no lookup. + /// The constant false node pub const FALSE: SddId = SddId(0); - /// The constant true node. Always id 1. + /// The constant true node pub const TRUE: SddId = SddId(1); } @@ -35,17 +33,11 @@ impl std::fmt::Debug for SddId { } } -/// Handle to a vtree node. +/// Handle to a vtree node type VTreeId = u32; #[derive(Debug, Clone)] enum VTreeNode { - // Written when the leaf is created, never read back: lookups go through - // `var_to_vtree`, which holds the same mapping in the useful direction. Kept - // rather than deleted because a vtree leaf that does not record its variable - // is not a vtree leaf — the structure would no longer be interpretable on its - // own. The allow is narrowed to this field so anything else that dies in this - // enum still warns. Leaf { #[allow(dead_code)] var: u32, @@ -56,7 +48,7 @@ enum VTreeNode { }, } -/// An element in a Decision node: (prime, sub) pair. +/// An element in a Decision node: (prime, sub) pair type Element = (SddId, SddId); #[derive(Debug, Clone)] @@ -67,14 +59,14 @@ enum SddNode { var: u32, polarity: bool, }, - /// Compressed, trimmed X-partition at a vtree node. + /// Compressed, trimmed X-partition at a vtree node Decision { vtree: VTreeId, elements: Vec, }, } -/// Key for the unique table — ensures canonical sharing. +/// Key for the unique table — ensures canonical sharing #[derive(Clone, PartialEq, Eq, Hash)] enum UniqueKey { Literal { @@ -87,38 +79,30 @@ enum UniqueKey { }, } -/// Boolean operator for Apply. +/// Boolean operator for Apply #[derive(Clone, Copy, PartialEq, Eq, Hash, Debug)] pub enum BoolOp { - /// Conjunction. + /// Conjunction And, - /// Disjunction. + /// Disjunction Or, } #[derive(Debug, Clone, Copy, PartialEq, Eq)] -/// Why an interruptible SDD operation gave up. -/// -/// Compilation can blow up combinatorially, so operations run under a budget and -/// report this rather than running unbounded. A caller seeing either should treat -/// the result as unavailable and fall back to an approximate method. +/// Why an interruptible SDD operation gave up pub enum SddBudgetError { - /// The caller-supplied deadline passed. + /// The caller-supplied deadline passed DeadlineExceeded, - /// The node limit was reached. + /// The node limit was reached NodeBudgetExceeded, } -/// Per-operation guard for interruptible SDD construction. The callback must -/// return `true` while the deadline remains available. +/// Per-operation guard for interruptible SDD construction pub struct SddOperationBudget<'a> { max_nodes: usize, deadline_available: &'a mut dyn FnMut() -> bool, } -// Cannot derive: the deadline is a `&mut dyn FnMut`, which has no `Debug`. Calling it -// here would consume part of the budget being reported on, so it is named and not -// invoked. impl std::fmt::Debug for SddOperationBudget<'_> { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("SddOperationBudget") @@ -129,8 +113,7 @@ impl std::fmt::Debug for SddOperationBudget<'_> { } impl<'a> SddOperationBudget<'a> { - /// A budget of `max_nodes`, abandoned early once `deadline_available` returns - /// `false`. + /// A budget of `max_nodes`, abandoned early once `deadline_available` returns `false` pub fn new(max_nodes: usize, deadline_available: &'a mut dyn FnMut() -> bool) -> Self { Self { max_nodes, @@ -156,25 +139,17 @@ impl<'a> SddOperationBudget<'a> { } } -/// Whether a variable was registered as an independent Bernoulli or as a member -/// of an exclusive (annotated-disjunction) group. -/// -/// This determines the correct partial-derivative formula in `diff_sdd::wmc_gradient`: -/// - Independent: `∂WMC/∂p = WMC(x=1) − WMC(x=0)` (neg weight = 1−p, both perturbed) -/// - ExclusiveGroup: `∂WMC/∂p = WMC(x=1)` only (neg weight = 1.0, constant w.r.t. p) +/// Whether a variable was registered as an independent Bernoulli or as a member of an exclusive +/// (annotated-disjunction) group #[derive(Clone, Copy, PartialEq, Eq, Debug)] pub enum VarKind { - /// Independent of every other variable; its negative weight is `1 - p`. + /// Independent of every other variable; its negative weight is `1 - p` Independent, - /// One alternative in a mutually exclusive group, identified by group id. Its - /// negative weight is fixed at 1, which is what changes the gradient formula. + /// One alternative in a mutually exclusive group, identified by group id ExclusiveGroup(u32), } -/// Arena-based SDD manager with unique table and apply cache. -/// -/// Owns the vtree, all SDD nodes, and probability assignments. -/// Nodes 0 and 1 are reserved for FALSE and TRUE. +/// Arena-based SDD manager with unique table and apply cache pub struct SddManager { nodes: Vec, unique_table: HashMap, @@ -183,25 +158,17 @@ pub struct SddManager { vtree_nodes: Vec, vtree_root: Option, var_to_vtree: HashMap, - /// Positive literal weight: `wmc(Literal(v, true)) = pos_weight[v]` pos_weight: Vec, - /// Negative literal weight: `wmc(Literal(v, false)) = neg_weight[v]` - /// For Independent vars: 1.0 − pos_weight[v]. - /// For ExclusiveGroup vars: 1.0 (annotated-disjunction encoding). neg_weight: Vec, - /// Per-variable kind tag for gradient computation. var_kind: Vec, } impl SddManager { - /// A manager holding only the constant nodes. - /// - /// [`SddId::FALSE`] and [`SddId::TRUE`] are pre-seeded at indices 0 and 1, so - /// the constants are addressable without a lookup. + /// A manager holding only the constant nodes pub fn new() -> Self { let mut nodes = Vec::new(); - nodes.push(SddNode::False); // index 0 - nodes.push(SddNode::True); // index 1 + nodes.push(SddNode::False); + nodes.push(SddNode::True); Self { nodes, unique_table: HashMap::new(), @@ -216,16 +183,13 @@ impl SddManager { } } - /// Ensure variable `var` exists in the vtree with probability `prob`. - /// Registers as Independent (neg_weight = 1 − prob). - /// Extends the right-linear vtree if needed. + /// Ensure variable `var` exists in the vtree with probability `prob` pub fn ensure_variable(&mut self, var: u32, prob: f64) { let p = prob.clamp(0.0, 1.0); self.ensure_variable_weights(var, p, 1.0 - p, VarKind::Independent); } - /// Ensure variable `var` exists with explicit positive and negative literal weights. - /// Use `neg = 1.0` for exclusive-group variables (annotated-disjunction encoding). + /// Ensure variable `var` exists with explicit positive and negative literal weights pub fn ensure_variable_weights(&mut self, var: u32, pos: f64, neg: f64, kind: VarKind) { let id = var as usize; if id >= self.pos_weight.len() { @@ -237,17 +201,14 @@ impl SddManager { self.neg_weight[id] = neg.clamp(0.0, 1.0); self.var_kind[id] = kind; - // Already in vtree? if self.var_to_vtree.contains_key(&var) { return; } - // Create leaf let leaf_id = self.vtree_nodes.len() as VTreeId; self.vtree_nodes.push(VTreeNode::Leaf { var }); self.var_to_vtree.insert(var, leaf_id); - // Extend right-linear vtree match self.vtree_root { None => { self.vtree_root = Some(leaf_id); @@ -263,12 +224,7 @@ impl SddManager { } } - /// Build the "exactly one of k" SDD formula for an exclusive group. - /// - /// Recursive: `exactly_one([v]) = lit(v, true)` - /// `exactly_one([v, rest..]) = (lit(v,true) AND all_false(rest)) OR (lit(v,false) AND exactly_one(rest))` - /// - /// All variables must already be registered via `ensure_variable_weights`. + /// Build the "exactly one of k" SDD formula for an exclusive group pub fn exactly_one(&mut self, vars: &[u32]) -> SddId { match vars { [] => SddId::FALSE, @@ -276,7 +232,6 @@ impl SddManager { [v, rest @ ..] => { let lit_v_true = self.literal(*v, true); let lit_v_false = self.literal(*v, false); - // all_false(rest): conjunction of lit(r, false) for each r in rest let all_false = rest.iter().fold(SddId::TRUE, |acc, &r| { let lf = self.literal(r, false); self.apply(acc, lf, BoolOp::And) @@ -289,19 +244,17 @@ impl SddManager { } } - // --- Public accessors for diff_sdd --- - - /// Read access to positive literal weights. + /// Read access to positive literal weights pub fn pos_weight(&self) -> &[f64] { &self.pos_weight } - /// Read access to negative literal weights. + /// Read access to negative literal weights pub fn neg_weight(&self) -> &[f64] { &self.neg_weight } - /// Set the positive literal weight for variable `var`. + /// Set the positive literal weight for variable `var` pub fn set_pos_weight(&mut self, var: u32, w: f64) { let id = var as usize; if id < self.pos_weight.len() { @@ -309,7 +262,7 @@ impl SddManager { } } - /// Set the negative literal weight for variable `var`. + /// Set the negative literal weight for variable `var` pub fn set_neg_weight(&mut self, var: u32, w: f64) { let id = var as usize; if id < self.neg_weight.len() { @@ -317,12 +270,12 @@ impl SddManager { } } - /// Iterate over all registered variable IDs. + /// Iterate over all registered variable IDs pub fn variable_ids(&self) -> impl Iterator + '_ { self.var_to_vtree.keys().copied() } - /// Get the kind tag for a variable. + /// Get the kind tag for a variable pub fn var_kind(&self, var: u32) -> VarKind { self.var_kind .get(var as usize) @@ -339,7 +292,7 @@ impl SddManager { } } - /// Left child of an internal vtree node. + /// Left child of an internal vtree node fn vtree_left(&self, v: VTreeId) -> VTreeId { match &self.vtree_nodes[v as usize] { VTreeNode::Internal { left, .. } => *left, @@ -347,7 +300,7 @@ impl SddManager { } } - /// Right child of an internal vtree node. + /// Right child of an internal vtree node fn vtree_right(&self, v: VTreeId) -> VTreeId { match &self.vtree_nodes[v as usize] { VTreeNode::Internal { right, .. } => *right, @@ -369,7 +322,7 @@ impl SddManager { } } - /// Get or create a literal SDD node. + /// Get or create a literal SDD node pub fn literal(&mut self, var: u32, polarity: bool) -> SddId { let key = UniqueKey::Literal { var, polarity }; if let Some(&id) = self.unique_table.get(&key) { @@ -381,24 +334,18 @@ impl SddManager { id } - /// Create a Decision node. Applies compression and trimming. - /// Returns an existing node if one matches (unique table). + /// Create a Decision node fn unique_d(&mut self, vtree: VTreeId, mut elements: Vec) -> SddId { - // Remove elements with prime = FALSE (they contribute nothing) elements.retain(|&(p, _)| p != SddId::FALSE); - // If no elements remain, this is FALSE if elements.is_empty() { return SddId::FALSE; } - // Trimming rule 1: {(TRUE, s)} -> s if elements.len() == 1 && elements[0].0 == SddId::TRUE { return elements[0].1; } - // Trimming rule 2: {(p, TRUE), (¬p, FALSE)} -> p - // More generally: if exactly 2 elements with subs TRUE and FALSE if elements.len() == 2 { let (p1, s1) = elements[0]; let (p2, s2) = elements[1]; @@ -410,10 +357,8 @@ impl SddManager { } } - // Compression: merge elements with identical subs by OR-ing their primes elements = self.compress(elements); - // Re-check after compression if elements.is_empty() { return SddId::FALSE; } @@ -431,7 +376,6 @@ impl SddManager { } } - // Sort elements for canonical key elements.sort(); let key = UniqueKey::Decision { @@ -450,14 +394,14 @@ impl SddManager { id } - /// Compress elements: merge (p1, s) and (p2, s) into (p1 OR p2, s). + /// Compress elements: merge (p1, s) and (p2, s) into (p1 OR p2, s) fn compress(&mut self, elements: Vec) -> Vec { let mut by_sub: HashMap> = HashMap::new(); for &(prime, sub) in &elements { by_sub.entry(sub).or_default().push(prime); } if by_sub.len() == elements.len() { - return elements; // already compressed + return elements; } let mut result = Vec::new(); for (sub, primes) in by_sub { @@ -470,13 +414,7 @@ impl SddManager { result } - /// Expand an SDD to a Decision (X-partition) at internal vtree node `v`. - /// - /// Primes must respect the left subtree of `v`, subs the right subtree. - /// - Constants: `TRUE -> {(TRUE, TRUE)}`, `FALSE -> {(TRUE, FALSE)}` - /// - Decision at `v`: return elements as-is - /// - Literal/SDD in left subtree: `{(id, TRUE), (¬id, FALSE)}` - /// - Literal/SDD in right subtree: `{(TRUE, id)}` + /// Expand an SDD to a Decision (X-partition) at internal vtree node `v` fn expand(&mut self, id: SddId, vtree: VTreeId) -> Vec { match id { SddId::TRUE => vec![(SddId::TRUE, SddId::TRUE)], @@ -490,15 +428,12 @@ impl SddManager { .. } if *dv == vtree => elements.clone(), _ => { - // Literal or Decision at a descendant vtree node let left = self.vtree_left(vtree); let nv = node_vtree.unwrap(); if nv == left || self.is_descendant_of(nv, left) { - // In the left (prime) subtree let neg_id = self.negate(id); vec![(id, SddId::TRUE), (neg_id, SddId::FALSE)] } else { - // In the right (sub) subtree vec![(SddId::TRUE, id)] } } @@ -507,9 +442,8 @@ impl SddManager { } } - /// Apply a Boolean operator to two SDDs. O(|a| * |b|). + /// Apply a Boolean operator to two SDDs pub fn apply(&mut self, a: SddId, b: SddId, op: BoolOp) -> SddId { - // Terminal cases match op { BoolOp::And => { if a == SddId::FALSE || b == SddId::FALSE { @@ -541,7 +475,6 @@ impl SddManager { } } - // Complementary literals: x AND NOT x = FALSE, x OR NOT x = TRUE if let ( SddNode::Literal { var: va, @@ -561,7 +494,6 @@ impl SddManager { } } - // Cache lookup (canonicalize key: smaller id first for commutative ops) let cache_key = if a.0 <= b.0 { (a, b, op as u8) } else { @@ -583,22 +515,17 @@ impl SddManager { match (va, vb) { (None, None) => { - // Both constants — should have been caught by terminal cases unreachable!("apply_inner with two constants: {:?} {:?} {:?}", a, b, op); } (None, Some(vb_id)) => self.apply_expanded(a, b, op, vb_id), (Some(va_id), None) => self.apply_expanded(a, b, op, va_id), (Some(va), Some(vb)) if va == vb => self.apply_same_vtree(a, b, op, va), (Some(va), Some(vb)) => { - // Different vtree nodes — find the common ancestor if self.is_descendant_of(va, vb) { - // b is at a higher (or same) vtree node self.apply_different_vtree(a, b, op, vb) } else if self.is_descendant_of(vb, va) { - // a is at a higher vtree node self.apply_different_vtree(a, b, op, va) } else { - // Neither is ancestor of the other — find LCA let lca = self.find_lca(va, vb); self.apply_different_vtree(a, b, op, lca) } @@ -606,7 +533,7 @@ impl SddManager { } } - /// Apply when both SDDs are at the same vtree node (Decision-Decision cross-product). + /// Apply when both SDDs are at the same vtree node (Decision-Decision cross-product) fn apply_same_vtree(&mut self, a: SddId, b: SddId, op: BoolOp, vtree: VTreeId) -> SddId { let a_elems = self.expand(a, vtree); let b_elems = self.expand(b, vtree); @@ -626,7 +553,7 @@ impl SddManager { self.unique_d(vtree, result_elems) } - /// Apply when SDDs are at different vtree levels. Normalize to `target_vtree`. + /// Apply when SDDs are at different vtree levels fn apply_different_vtree( &mut self, a: SddId, @@ -639,54 +566,40 @@ impl SddManager { self.apply_same_vtree(a_norm, b_norm, op, target_vtree) } - /// Apply when one operand is a constant (TRUE/FALSE). + /// Apply when one operand is a constant (TRUE/FALSE) fn apply_expanded(&mut self, a: SddId, b: SddId, op: BoolOp, vtree: VTreeId) -> SddId { let a_norm = self.normalize_to(a, vtree); let b_norm = self.normalize_to(b, vtree); self.apply_same_vtree(a_norm, b_norm, op, vtree) } - /// Normalize an SDD to a Decision at vtree node `target`. - /// If the SDD already respects `target`, return as-is. - /// Otherwise, wrap it in a trivial partition. - /// - /// Uses `make_decision_raw` to avoid calling `apply` during normalization - /// (which would cause infinite recursion via compress -> apply -> normalize). + /// Normalize an SDD to a Decision at vtree node `target` fn normalize_to(&mut self, id: SddId, target: VTreeId) -> SddId { - // Constants if id == SddId::TRUE || id == SddId::FALSE { - return id; // handled by expand() in apply_same_vtree + return id; } let current_vtree = self.vtree_of(id); match current_vtree { - Some(v) if v == target => id, // already at target + Some(v) if v == target => id, Some(v) => { - // v is a descendant of target — wrap in a Decision at target let left = self.vtree_left(target); let right = self.vtree_right(target); if self.is_descendant_of(v, left) { - // SDD belongs in the left (prime) side - // Create {(id, TRUE), (NOT id, FALSE)} — already compressed & trimmed let neg_id = self.negate(id); self.make_decision_raw(target, vec![(id, SddId::TRUE), (neg_id, SddId::FALSE)]) } else if self.is_descendant_of(v, right) { - // SDD belongs in the right (sub) side - // Create {(TRUE, id)} — trimming: this just returns id via unique_d self.unique_d(target, vec![(SddId::TRUE, id)]) } else { - // Shouldn't happen if vtree is correct id } } - None => id, // constant, handled by expand + None => id, } } - /// Create a Decision node directly, without calling compress (which calls apply). - /// Used by `normalize_to` to avoid infinite recursion. - /// The caller must guarantee the elements are already compressed. + /// Create a Decision node directly, without calling compress (which calls apply) fn make_decision_raw(&mut self, vtree: VTreeId, mut elements: Vec) -> SddId { elements.retain(|&(p, _)| p != SddId::FALSE); if elements.is_empty() { @@ -722,11 +635,10 @@ impl SddManager { id } - /// Find the lowest common ancestor of two vtree nodes. + /// Find the lowest common ancestor of two vtree nodes fn find_lca(&self, a: VTreeId, b: VTreeId) -> VTreeId { let a_ancestors = self.vtree_ancestors(a); let b_ancestors = self.vtree_ancestors(b); - // Find lowest node in a's ancestor chain that is also in b's for &anc in &a_ancestors { if b_ancestors.contains(&anc) { return anc; @@ -735,10 +647,9 @@ impl SddManager { self.vtree_root.unwrap() } - /// Get ancestors of a vtree node (including itself), from node to root. + /// Get ancestors of a vtree node (including itself), from node to root fn vtree_ancestors(&self, node: VTreeId) -> Vec { let mut result = vec![node]; - // Walk up: for a right-linear tree, we search all internal nodes for (idx, vnode) in self.vtree_nodes.iter().enumerate() { if let VTreeNode::Internal { left, right } = vnode { if *left == node || *right == node { @@ -751,10 +662,7 @@ impl SddManager { result } - /// Negate an SDD. O(|SDD|). - /// - /// For Decision nodes, negate all subs (keep primes unchanged): - /// `¬{(p₁,s₁),...,(pₙ,sₙ)} = {(p₁,¬s₁),...,(pₙ,¬sₙ)}` + /// Negate an SDD pub fn negate(&mut self, id: SddId) -> SddId { match id { SddId::FALSE => SddId::TRUE, @@ -778,7 +686,7 @@ impl SddManager { } } - /// Compute the weighted model count (probability) of an SDD. O(|SDD|). + /// Compute the weighted model count (probability) of an SDD pub fn wmc(&self, id: SddId) -> f64 { let mut memo: HashMap = HashMap::new(); self.wmc_inner(id, &mut memo) @@ -816,10 +724,7 @@ impl SddManager { result } - /// Enumerate all satisfying variable assignments (proof paths) of an SDD. - /// - /// Each model is a set of (variable, polarity) pairs. - /// Called only at explanation-time, not during reasoning. + /// Enumerate all satisfying variable assignments (proof paths) of an SDD pub fn enumerate_models(&self, id: SddId) -> Vec> { match id { SddId::FALSE => vec![], @@ -853,14 +758,14 @@ impl SddManager { } } - /// Number of nodes in the manager (including TRUE/FALSE). + /// Number of nodes in the manager (including TRUE/FALSE) pub fn node_count(&self) -> usize { self.nodes.len() } } impl SddManager { - /// Interruptible counterpart of [`SddManager::literal`]. + /// Interruptible counterpart of [`SddManager::literal`] pub fn try_literal( &mut self, var: u32, @@ -879,7 +784,7 @@ impl SddManager { Ok(id) } - /// Interruptible counterpart of [`SddManager::apply`]. + /// Interruptible counterpart of [`SddManager::apply`] pub fn try_apply( &mut self, a: SddId, @@ -1216,7 +1121,7 @@ impl SddManager { Ok(result) } - /// Interruptible counterpart of [`SddManager::negate`]. + /// Interruptible counterpart of [`SddManager::negate`] pub fn try_negate( &mut self, id: SddId, @@ -1249,7 +1154,7 @@ impl SddManager { } } - /// Interruptible counterpart of [`SddManager::exactly_one`]. + /// Interruptible counterpart of [`SddManager::exactly_one`] pub fn try_exactly_one( &mut self, vars: &[u32], @@ -1276,22 +1181,19 @@ impl SddManager { } } -/// Provenance semiring backed by Sentential Decision Diagrams. -/// -/// Uses SDDs for exact WMC with polytime Apply, linear negation, -/// and linear probability recovery. Tags are compact `SddId` handles. +/// Provenance semiring backed by Sentential Decision Diagrams #[derive(Debug, Clone)] pub struct SddProvenance { manager: Arc>, guard: Option>, } -/// Ceilings applied to every operation on an [`SddProvenance`]. +/// Ceilings applied to every operation on an [`SddProvenance`] #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub struct SddLimits { - /// Largest number of nodes an operation may add before abandoning. + /// Largest number of nodes an operation may add before abandoning pub max_nodes: usize, - /// Wall-clock deadline, if any. + /// Wall-clock deadline, if any pub deadline: Option, } @@ -1302,8 +1204,7 @@ struct SddGuard { } impl SddProvenance { - /// An unbounded manager. Compilation may take arbitrarily long; prefer - /// [`Self::with_limits`] where a query has a time budget. + /// An unbounded manager pub fn new() -> Self { Self { manager: Arc::new(Mutex::new(SddManager::new())), @@ -1311,11 +1212,7 @@ impl SddProvenance { } } - /// A manager that abandons operations exceeding `limits`. - /// - /// The first breach is recorded and every later operation fails with the same - /// error, so one exhausted budget does not yield partial results that look - /// complete. + /// A manager that abandons operations exceeding `limits` pub fn with_limits(limits: SddLimits) -> Self { Self { manager: Arc::new(Mutex::new(SddManager::new())), @@ -1326,22 +1223,19 @@ impl SddProvenance { } } - /// Access the underlying manager (for explanation export). + /// Access the underlying manager (for explanation export) pub fn manager(&self) -> &Arc> { &self.manager } - /// The budget error this manager latched, if any. `None` means no operation has - /// breached its limits. + /// The budget error this manager latched, if any pub fn budget_error(&self) -> Option { self.guard .as_ref() .and_then(|guard| *guard.error.lock().unwrap()) } - /// Runs `operation` against the manager under this instance's limits. - /// - /// Fails immediately if a previous operation already breached the budget. + /// Runs `operation` against the manager under this instance's limits pub fn try_with_budget( &self, operation: impl FnOnce( @@ -1385,7 +1279,6 @@ impl Default for SddProvenance { } } -// Manual Debug for SddManager (contains non-Debug HashMap keys) impl std::fmt::Debug for SddManager { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { f.debug_struct("SddManager") @@ -1448,7 +1341,7 @@ impl Provenance for SddProvenance { self.manager.lock().unwrap().wmc(*tag).clamp(0.0, 1.0) } - /// SDD canonicity: ID equality = logical equivalence. + /// SDD canonicity: ID equality = logical equivalence fn is_saturated(&self, old: &SddId, new: &SddId) -> bool { old == new } @@ -1460,8 +1353,6 @@ mod tests { const EPS: f64 = 1e-9; - // --- Basic SddManager tests --- - #[test] fn constants() { let mgr = SddManager::new(); @@ -1502,7 +1393,6 @@ mod tests { let x = mgr.literal(0, true); let y = mgr.literal(1, true); let xy = mgr.apply(x, y, BoolOp::Or); - // P(x OR y) = 0.8 + 0.6 - 0.48 = 0.92 assert!( (mgr.wmc(xy) - 0.92).abs() < EPS, "expected 0.92, got {}", @@ -1517,14 +1407,12 @@ mod tests { let x = mgr.literal(0, true); let nx = mgr.negate(x); assert!((mgr.wmc(nx) - 0.2).abs() < EPS); - // Double negation let nnx = mgr.negate(nx); assert_eq!(nnx, x); } #[test] fn negate_conjunction() { - // NOT (x AND y) = (NOT x) OR (NOT y) let mut mgr = SddManager::new(); mgr.ensure_variable(0, 0.8); mgr.ensure_variable(1, 0.6); @@ -1532,7 +1420,6 @@ mod tests { let y = mgr.literal(1, true); let xy = mgr.apply(x, y, BoolOp::And); let neg_xy = mgr.negate(xy); - // P(NOT (x AND y)) = 1 - 0.48 = 0.52 assert!( (mgr.wmc(neg_xy) - 0.52).abs() < EPS, "expected 0.52, got {}", @@ -1542,7 +1429,6 @@ mod tests { #[test] fn complement_invariant() { - // P(f) + P(NOT f) = 1.0 let mut mgr = SddManager::new(); mgr.ensure_variable(0, 0.8); mgr.ensure_variable(1, 0.6); @@ -1550,7 +1436,6 @@ mod tests { let x = mgr.literal(0, true); let y = mgr.literal(1, true); let z = mgr.literal(2, true); - // f = (x AND y) OR (x AND z) let xy = mgr.apply(x, y, BoolOp::And); let xz = mgr.apply(x, z, BoolOp::And); let f = mgr.apply(xy, xz, BoolOp::Or); @@ -1565,9 +1450,6 @@ mod tests { #[test] fn overlap_canonical_wmc() { - // Same test case as topk_wmc_overlap_canonical - // proof1={0,1}, proof2={0,2}, P(x)=0.8, P(y)=0.6, P(z)=0.5 - // Exact: P(x∧y) + P(x∧z) - P(x∧y∧z) = 0.48 + 0.40 - 0.24 = 0.64 let mut mgr = SddManager::new(); mgr.ensure_variable(0, 0.8); mgr.ensure_variable(1, 0.6); @@ -1587,9 +1469,6 @@ mod tests { #[test] fn three_paths_wmc() { - // Three proof paths, matching wmc_no_truncation_three_paths - // {0,1}, {0,2}, {3} with P=[0.8, 0.6, 0.5, 0.4] - // Exact (via inclusion-exclusion): 0.8784 let mut mgr = SddManager::new(); mgr.ensure_variable(0, 0.8); mgr.ensure_variable(1, 0.6); @@ -1603,20 +1482,8 @@ mod tests { let xz = mgr.apply(x, z, BoolOp::And); let f1 = mgr.apply(xy, xz, BoolOp::Or); let f = mgr.apply(f1, w, BoolOp::Or); - // P = 0.64 + 0.4 - 0.64*0.4 = 1.04 - 0.256 = 0.784... actually need careful calc - // P(xy) = 0.48, P(xz) = 0.40, P(w) = 0.4 - // P(xy OR xz) = 0.64 - // P(f) = P(xy OR xz OR w) = 0.64 + 0.4 - 0.64*0.4 = 0.784 - // Wait, the expected value in the test is 0.8784. Let me recalculate. - // P(xy OR xz) = 0.64 (verified) - // P(w) = 0.4 - // Since w is independent of x,y,z: - // P(f) = 0.64 + 0.4 - 0.64*0.4 = 0.784 - // Hmm, but the existing test expects 0.8784 — let me check what that test does. - // Actually the existing test might use different structure. Let me just verify - // our WMC is correct for this specific construction. let prob = mgr.wmc(f); - let expected = 0.64 + 0.4 - 0.64 * 0.4; // = 0.784 + let expected = 0.64 + 0.4 - 0.64 * 0.4; assert!( (prob - expected).abs() < EPS, "expected {}, got {}", @@ -1647,8 +1514,6 @@ mod tests { #[test] fn naf_shared_seed_exact() { - // active = seed0 (P=0.8), blocked = seed0 (SAME seed) - // active AND NOT blocked = seed0 AND NOT seed0 = FALSE -> P = 0.0 let mut mgr = SddManager::new(); mgr.ensure_variable(0, 0.8); let active = mgr.literal(0, true); @@ -1661,8 +1526,6 @@ mod tests { #[test] fn naf_independent_seeds() { - // active = seed0 (P=0.8), blocked = seed1 (P=0.3) - // active AND NOT blocked: P = 0.8 * 0.7 = 0.56 let mut mgr = SddManager::new(); mgr.ensure_variable(0, 0.8); mgr.ensure_variable(1, 0.3); diff --git a/shared/src/seed_spec.rs b/shared/src/seed_spec.rs index 05aacc0d..b108316f 100644 --- a/shared/src/seed_spec.rs +++ b/shared/src/seed_spec.rs @@ -10,42 +10,34 @@ use crate::triple::Triple; -/// A declaration that certain facts carry a prior probability. -/// -/// Seeds are the leaves of probabilistic inference: every derived probability is a -/// function of these. Each seed owns a distinct variable in the underlying formula, -/// which is why the ids here must be unique across a program. +/// A declaration that certain facts carry a prior probability #[derive(Debug, Clone)] pub enum SeedSpec { - /// One fact, probabilistically independent of every other seed. + /// One fact, probabilistically independent of every other seed Independent { - /// The fact the probability attaches to. + /// The fact the probability attaches to triple: Triple, - /// Prior probability, in `[0, 1]`. + /// Prior probability, in `[0, 1]` prob: f64, - /// Identifies this seed's variable in the formula. Must be unique. + /// Identifies this seed's variable in the formula seed_id: u32, }, - /// A set of mutually exclusive alternatives, at most one of which holds. - /// - /// Used where a prediction is a distribution over labels: the choices are not - /// independent, so treating them as separate `Independent` seeds would assign - /// non-zero probability to two labels holding at once. + /// A set of mutually exclusive alternatives, at most one of which holds ExclusiveGroup { - /// Identifies the group. Must be unique. + /// Identifies the group group_id: u32, - /// The alternatives. Their probabilities should sum to at most 1. + /// The alternatives choices: Vec, }, } -/// One alternative within a [`SeedSpec::ExclusiveGroup`]. +/// One alternative within a [`SeedSpec::ExclusiveGroup`] #[derive(Debug, Clone)] pub struct ExclusiveChoice { - /// The fact that holds if this alternative is the one taken. + /// The fact that holds if this alternative is the one taken pub triple: Triple, - /// Probability of this alternative, in `[0, 1]`. + /// Probability of this alternative, in `[0, 1]` pub prob: f64, - /// Identifies this choice within its group. + /// Identifies this choice within its group pub choice_id: u32, } diff --git a/shared/src/tag_store.rs b/shared/src/tag_store.rs index b64b7f60..6f1aad1f 100644 --- a/shared/src/tag_store.rs +++ b/shared/src/tag_store.rs @@ -14,19 +14,17 @@ use crate::quoted_triple_store::QuotedTripleStore; use crate::triple::Triple; use std::collections::HashMap; -/// Stores provenance tags for triples, parameterized by a [`Provenance`] semiring. -/// -/// Absence of a triple in the store means it carries the `one()` tag (certain fact). +/// Stores provenance tags for triples, parameterized by a [`Provenance`] semiring #[derive(Debug, Clone)] pub struct TagStore { tags: HashMap, provenance: P, - /// Seed triples in sort order (index == seed variable ID). + /// Seed triples in sort order (index == seed variable ID) pub seed_triples: Vec, } impl TagStore

{ - /// An empty store: every triple initially carries `provenance.one()`. + /// An empty store: every triple initially carries `provenance.one()` pub fn new(provenance: P) -> Self { Self { tags: HashMap::new(), @@ -35,13 +33,12 @@ impl TagStore

{ } } - /// Get the provenance reference. - /// The semiring this store annotates with. + /// Get the provenance reference pub fn provenance(&self) -> &P { &self.provenance } - /// Get the tag for a triple. Returns `one()` if not explicitly stored (certain fact). + /// Get the tag for a triple pub fn get_tag(&self, triple: &Triple) -> P::Tag { self.tags .get(triple) @@ -49,7 +46,7 @@ impl TagStore

{ .unwrap_or_else(|| self.provenance.one()) } - /// Set the tag for a triple. If the tag equals `one()`, it is removed (implicit certainty). + /// Set the tag for a triple pub fn set_tag(&mut self, triple: &Triple, tag: P::Tag) { if tag == self.provenance.one() { self.tags.remove(triple); @@ -58,8 +55,7 @@ impl TagStore

{ } } - /// Update a triple's tag using disjunction (⊕) with a new derivation tag. - /// Returns `true` if the tag changed. + /// Update a triple's tag using disjunction (⊕) with a new derivation tag pub fn update_disjunction(&mut self, triple: &Triple, new_tag: &P::Tag) -> bool { let old_tag = self.get_tag(triple); let combined = self.provenance.disjunction(&old_tag, new_tag); @@ -71,28 +67,27 @@ impl TagStore

{ } } - /// Check if a triple has an explicit (non-one) tag stored. + /// Check if a triple has an explicit (non-one) tag stored pub fn has_explicit_tag(&self, triple: &Triple) -> bool { self.tags.contains_key(triple) } - /// Returns the number of triples with explicit tags. + /// Returns the number of triples with explicit tags pub fn len(&self) -> usize { self.tags.len() } - /// Whether no triple carries an explicit tag. Note that this is the state in - /// which every fact is certain, not the state in which there are no facts. + /// Whether no triple carries an explicit tag pub fn is_empty(&self) -> bool { self.tags.is_empty() } - /// Iterate over all (triple, tag) pairs with explicit tags. + /// Iterate over all (triple, tag) pairs with explicit tags pub fn iter(&self) -> impl Iterator { self.tags.iter() } - /// Export tags as RDF-star triples encoding probability values. + /// Export tags as RDF-star triples encoding probability values pub fn encode_as_rdf_star( &self, dict: &mut Dictionary, @@ -121,10 +116,7 @@ impl TagStore

{ use crate::provenance::WmcProvenance; impl TagStore { - /// Export tags as RDF-star triples enriched with proof-path explanation. - /// - /// Superset of [`Self::encode_as_rdf_star`]; also emits `prob:proofCount`, - /// `prob:hasProof`, `prob:formula`, `prob:hasSeed`, and `prob:hasNegatedSeed` triples. + /// Export tags as RDF-star triples enriched with proof-path explanation pub fn encode_as_rdf_star_with_explanation( &self, dict: &mut Dictionary, @@ -143,7 +135,6 @@ impl TagStore { for (triple, formula) in &self.tags { let derived_qt = qt_store.encode(triple.subject, triple.predicate, triple.object); - // prob:proofCount let count_lit = format!("\"{}\"^^<{}>", formula.len(), xsd_int); let count_id = dict.encode(&count_lit); result.push(Triple { @@ -152,7 +143,6 @@ impl TagStore { object: count_id, }); - // prob:formula — raw debug representation of the DNF let raw = format!("{:?}", formula); let formula_lit = format!("\"{}\"^^<{}>", raw.replace('"', "'"), xsd_str); let formula_lit_id = dict.encode(&formula_lit); @@ -162,26 +152,21 @@ impl TagStore { object: formula_lit_id, }); - // per-proof path annotations for (proof_idx, clause) in formula.iter().enumerate() { let idx_lit = format!("\"{}\"^^<{}>", proof_idx, xsd_int); let idx_id = dict.encode(&idx_lit); - // << d >> prob:hasProof "N"^^xsd:integer result.push(Triple { subject: derived_qt, predicate: has_proof_id, object: idx_id, }); - // Level-2: << << d >> prob:hasProof "N" >> prob:hasSeed << seed >> - // or prob:hasNegatedSeed for polarity=false literals let proof_annot_qt = qt_store.encode(derived_qt, has_proof_id, idx_id); for &(seed_var_id, polarity) in clause { if let Some(seed_t) = self.seed_triples.get(seed_var_id as usize) { let seed_qt = qt_store.encode(seed_t.subject, seed_t.predicate, seed_t.object); - // Polarity-aware predicate: hasSeed for positive, hasNegatedSeed for negative let pred_id = if polarity { has_seed_id } else { @@ -204,7 +189,8 @@ impl TagStore { use crate::sdd::SddProvenance; impl TagStore { - /// Export tags as RDF-star triples enriched with proof-path explanation via SDD model enumeration. + /// Export tags as RDF-star triples enriched with proof-path explanation via SDD model + /// enumeration pub fn encode_as_rdf_star_with_explanation( &self, dict: &mut Dictionary, @@ -225,10 +211,8 @@ impl TagStore { for (triple, sdd_id) in &self.tags { let derived_qt = qt_store.encode(triple.subject, triple.predicate, triple.object); - // Enumerate proof paths from the SDD let models = mgr.enumerate_models(*sdd_id); - // prob:proofCount let count_lit = format!("\"{}\"^^<{}>", models.len(), xsd_int); let count_id = dict.encode(&count_lit); result.push(Triple { @@ -237,7 +221,6 @@ impl TagStore { object: count_id, }); - // prob:formula — debug representation let raw = format!("{:?}", sdd_id); let formula_lit = format!("\"{}\"^^<{}>", raw.replace('"', "'"), xsd_str); let formula_lit_id = dict.encode(&formula_lit); @@ -247,7 +230,6 @@ impl TagStore { object: formula_lit_id, }); - // per-proof path annotations for (proof_idx, clause) in models.iter().enumerate() { let idx_lit = format!("\"{}\"^^<{}>", proof_idx, xsd_int); let idx_id = dict.encode(&idx_lit); @@ -326,11 +308,9 @@ mod tests { let t = make_triple(1, 2, 3); store.set_tag(&t, 0.5); - // max(0.5, 0.8) = 0.8 -> changed assert!(store.update_disjunction(&t, &0.8)); assert!((store.get_tag(&t) - 0.8).abs() < 1e-9); - // max(0.8, 0.6) = 0.8 -> no change (saturated) assert!(!store.update_disjunction(&t, &0.6)); assert!((store.get_tag(&t) - 0.8).abs() < 1e-9); } @@ -341,7 +321,6 @@ mod tests { let t = make_triple(1, 2, 3); store.set_tag(&t, 0.3); - // noisy-OR: 0.3 + 0.4 - 0.3*0.4 = 0.7 - 0.12 = 0.58 -> changed assert!(store.update_disjunction(&t, &0.4)); assert!((store.get_tag(&t) - 0.58).abs() < 1e-9); } @@ -352,11 +331,9 @@ mod tests { let t = make_triple(1, 2, 3); store.set_tag(&t, false); - // false ⊕ true = true -> changed assert!(store.update_disjunction(&t, &true)); assert_eq!(store.get_tag(&t), true); - // true ⊕ true = true -> no change assert!(!store.update_disjunction(&t, &true)); } @@ -374,14 +351,12 @@ mod tests { fn make_wmc_store_two_proofs() -> TagStore { use crate::provenance::{WmcClause, WmcFormula}; - // formula {{(0,T),(1,T)},{(0,T),(2,T)}} — two proof paths sharing seed 0 let p = WmcProvenance::new(); let mut store = TagStore::new(p); let clause0: WmcClause = [(0u32, true), (1u32, true)].iter().copied().collect(); let clause1: WmcClause = [(0u32, true), (2u32, true)].iter().copied().collect(); let formula: WmcFormula = [clause0, clause1].iter().cloned().collect(); store.tags.insert(make_triple(10, 20, 30), formula); - // seed_triples: id0 = (1,2,3), id1 = (4,5,6), id2 = (7,8,9) store.seed_triples = vec![ make_triple(1, 2, 3), make_triple(4, 5, 6), @@ -408,7 +383,6 @@ mod tests { "one proofCount triple per derived fact" ); - // The object should encode "2"^^xsd:integer let count_str = dict.decode(count_triples[0].object).unwrap(); assert!( count_str.contains('2'), @@ -448,7 +422,6 @@ mod tests { .iter() .filter(|t| t.predicate == has_seed_pred) .collect(); - // clause {0,1} -> 2 seeds, clause {0,2} -> 2 seeds = 4 total assert_eq!( hs_triples.len(), 4, @@ -464,7 +437,6 @@ mod tests { let clause: WmcClause = [(0u32, true), (1u32, true)].iter().copied().collect(); let formula: WmcFormula = std::iter::once(clause).collect(); store.tags.insert(make_triple(10, 20, 30), formula); - // seed_triples intentionally empty — should produce no hasSeed triples, no panic assert!(store.seed_triples.is_empty()); let mut dict = Dictionary::new(); diff --git a/shared/src/term_encoder.rs b/shared/src/term_encoder.rs index 7306cc15..be129882 100644 --- a/shared/src/term_encoder.rs +++ b/shared/src/term_encoder.rs @@ -8,86 +8,34 @@ * you can obtain one at https://mozilla.org/MPL/2.0/. */ -//! [`TermEncoder`]: reading and interning terms without naming how they are shared. -//! -//! Term ids are only meaningful against the [`Dictionary`] that minted them, so -//! anything holding its own term space — a store on disk, a window's content, a -//! second engine — has to intern through the consumer's dictionary. That used to be -//! done by hand: callers assigned `Arc>` from one component to -//! another and hoped the two stayed aligned. These are the types that let the -//! arrangement be stated rather than remembered, and no signature in the workspace -//! names the handle any more. -//! -//! # Scoped, not per-call, and that is the whole design -//! -//! [`with_terms`](TermEncoder::with_terms) lends the dictionary for the duration of a -//! closure and acquires at most once. There is deliberately no `encode(&self, &str)` -//! convenience method, because `benches/contended_dictionary.rs` measured what that -//! shape costs once more than one thread uses it: -//! -//! | threads | encode, per-call lock | decode, per-call lock | -//! |---|---:|---:| -//! | 2 | 1.6x slower than one thread | 3.3x slower | -//! | 4 | 2.2x slower | 5.4x slower | -//! | 8 | 3.9x slower | 12.3x slower | -//! -//! Per-call locking does not merely fail to scale; it is slower than a single -//! thread as soon as there are two, because each acquisition does an atomic -//! read-modify-write on one cache line and the acquisitions ping-pong between -//! cores. Reads suffer more than writes, since the operation they guard is smaller. -//! -//! A per-call method would therefore be the obvious thing to reach for and the wrong -//! one, so it is not offered. Batch the work inside one `with_terms` instead — which -//! is what the 125 call sites that hold a guard across several operations already do -//! by hand. -//! -//! # Two modes -//! -//! [`LocalTerms`] holds the dictionary outright and takes no lock, for a single -//! thread. [`SharedTerms`] wraps the `Arc>` the rest of the workspace -//! already passes around, for the case the `rsp_engine` creates when it spawns a -//! thread per window plus a coordinator. -//! -//! Only `SharedTerms` is `Sync`, which is the point rather than a limitation: a -//! field that crosses threads will not accept the local one, so the choice is -//! checked at compile time instead of being a convention. +//! [`TermEncoder`]: reading and interning terms without naming how they are shared use crate::dictionary::Dictionary; use std::cell::RefCell; use std::sync::{Arc, RwLock, RwLockReadGuard, RwLockWriteGuard}; -/// Reading and interning terms, without naming how they are stored or shared. -/// -/// Object-safe, so heterogeneous encoders can sit behind one `&dyn TermEncoder`. -/// `with_terms` returns nothing for that reason — a generic return would not be — -/// so a closure that needs a result captures it. +/// Reading and interning terms, without naming how they are stored or shared pub trait TermEncoder { - /// Lends the dictionary for the duration of `visit`, acquiring at most once. - /// - /// Do as much as possible inside one call. See the module documentation for the - /// measured cost of the alternative. + /// Lends the dictionary for the duration of `visit`, acquiring at most once fn with_terms(&self, visit: &mut dyn FnMut(&mut Dictionary)); } -/// A dictionary owned outright, with no lock, for a single thread. -/// -/// Not `Sync`, deliberately: anything that crosses a thread boundary will reject it -/// and want [`SharedTerms`]. +/// A dictionary owned outright, with no lock, for a single thread #[derive(Debug, Default)] pub struct LocalTerms(RefCell); impl LocalTerms { - /// An empty encoder. + /// An empty encoder pub fn new() -> Self { Self(RefCell::new(Dictionary::new())) } - /// Adopts an existing dictionary. + /// Adopts an existing dictionary pub fn from_dictionary(dictionary: Dictionary) -> Self { Self(RefCell::new(dictionary)) } - /// Takes the dictionary back out, to hand to something that wants it shared. + /// Takes the dictionary back out, to hand to something that wants it shared pub fn into_dictionary(self) -> Dictionary { self.0.into_inner() } @@ -99,56 +47,35 @@ impl TermEncoder for LocalTerms { } } -/// A dictionary shared across threads, behind the handle the workspace already uses. -/// -/// One term space, kept across a store, a reasoner and the engine's window threads. -/// Cloning it shares that space rather than copying it, which is how a component -/// adopts another's encoding; [`view`](Self::view) is the copy, and is a different -/// thing on purpose. -/// -/// A lock behind a newtype, so no signature has to name one: reach the terms through -/// [`with_terms`](TermEncoder::with_terms), -/// [`with_terms_read`](Self::with_terms_read), [`read`](Self::read) or -/// [`write`](Self::write). +/// A dictionary shared across threads, behind the handle the workspace already uses #[derive(Debug, Clone)] pub struct SharedTerms(Arc>); impl SharedTerms { - /// An empty encoder, shareable. + /// An empty encoder, shareable pub fn new() -> Self { Self(Arc::new(RwLock::new(Dictionary::new()))) } - /// Shares a term space that already has terms in it. - /// - /// For the callers that parse or load a dictionary first and only then decide who - /// shares it. To share a space that is *already* shared, clone the encoder: both - /// clones encode through the one space, which is the point of the type. + /// Shares a term space that already has terms in it pub fn from_dictionary(dictionary: Dictionary) -> Self { Self(Arc::new(RwLock::new(dictionary))) } - /// Whether two encoders encode through the same space, as opposed to two spaces - /// that happen to agree today. - /// - /// The property callers actually test when they ask whether two components are - /// isolated. It compares identity, not contents: two separately built spaces - /// holding identical terms are still not the same space, because the next term - /// interned into either is invisible to the other. + /// Whether two encoders encode through the same space, as opposed to two spaces that happen to + /// agree today pub fn shares_space_with(&self, other: &SharedTerms) -> bool { Arc::ptr_eq(&self.0, &other.0) } - /// Interns `text` and returns its id. - /// - /// The common case, named so a caller does not reach for the handle to do it. + /// Interns `text` and returns its id pub fn encode(&self, text: &str) -> u32 { let mut id = 0; self.with_terms(&mut |dictionary| id = dictionary.encode(text)); id } - /// Interns three terms under one lock, which is what a triple needs. + /// Interns three terms under one lock, which is what a triple needs pub fn encode_triple(&self, subject: &str, predicate: &str, object: &str) -> (u32, u32, u32) { let mut ids = (0, 0, 0); self.with_terms(&mut |dictionary| { @@ -161,7 +88,7 @@ impl SharedTerms { ids } - /// The string `id` stands for, if this term space knows it. + /// The string `id` stands for, if this term space knows it pub fn decode(&self, id: u32) -> Option { let mut text = None; self.with_terms(&mut |dictionary| { @@ -170,24 +97,7 @@ impl SharedTerms { text } - /// Borrows the space for reading, for as long as the returned value lives. - /// - /// The third way to reach the terms, beside the closure scopes and - /// [`view`](Self::view), and the one for code whose borrow does not fit a closure: - /// a decode loop in the middle of a function, a guard held across a `match`, a - /// pair of borrows compared against each other. Several lookups share one - /// acquisition, exactly as holding a lock did. - /// - /// Poisoning is absorbed rather than reported, like the scopes: a thread that - /// panicked while interning leaves the space usable, since a `Dictionary` has no - /// invariant spanning two statements. Callers that want it as an error use - /// [`try_with_terms`](Self::try_with_terms). - /// - /// # Deadlock - /// - /// Holding this across a call that writes the same space deadlocks, as any - /// reader-writer lock does. The closure scopes make the extent obvious, so prefer - /// them where the borrow does fit one. + /// Borrows the space for reading, for as long as the returned value lives #[inline] pub fn read(&self) -> TermsRef<'_> { TermsRef( @@ -197,11 +107,7 @@ impl SharedTerms { ) } - /// Borrows the space for interning, for as long as the returned value lives. - /// - /// See [`read`](Self::read). This excludes every other borrow of the space, so a - /// hot loop that merely decodes should take `read` instead: the engine's parallel - /// decode is parallel only because it does. + /// Borrows the space for interning, for as long as the returned value lives #[inline] pub fn write(&self) -> TermsMut<'_> { TermsMut( @@ -212,12 +118,7 @@ impl SharedTerms { } } -/// A shared term space, borrowed for reading. See [`SharedTerms::read`]. -/// -/// Derefs to [`Dictionary`], so it answers `decode` and the rest of the read API, and -/// nothing that interns -- `encode` takes `&mut Dictionary`, which only -/// [`TermsMut`] hands out. The borrow lasts as long as this value, so dropping it -/// early is what releases the space. +/// A shared term space, borrowed for reading #[derive(Debug)] pub struct TermsRef<'a>(RwLockReadGuard<'a, Dictionary>); @@ -229,10 +130,7 @@ impl std::ops::Deref for TermsRef<'_> { } } -/// A shared term space, borrowed for interning. See [`SharedTerms::write`]. -/// -/// Derefs mutably to [`Dictionary`], so `encode` is callable through it. Excludes -/// every other borrow for as long as it lives. +/// A shared term space, borrowed for interning #[derive(Debug)] pub struct TermsMut<'a>(RwLockWriteGuard<'a, Dictionary>); @@ -251,7 +149,7 @@ impl std::ops::DerefMut for TermsMut<'_> { } } -/// A term space whose lock a panicking thread left poisoned. +/// A term space whose lock a panicking thread left poisoned #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub struct PoisonedTerms; @@ -263,40 +161,17 @@ impl std::fmt::Display for PoisonedTerms { impl std::error::Error for PoisonedTerms {} -/// A read-only copy of a term space. -/// -/// What a caller gets when it needs to decode many ids and cannot hold the shared -/// lock across the walk -- printing a graph, serialising a result, comparing two -/// runs. It answers [`decode`](Self::decode) and nothing else. -/// -/// # Why it is not just a `Dictionary` -/// -/// It used to be, and that was a trap. An owned `Dictionary` has `encode` on it, and -/// interning into a copy is not merely ineffective: the copy's next id starts -/// wherever the shared space's was, so a term interned here can be handed a number -/// the shared space later gives to a *different* term. A fact built from such an id -/// corrupts the fact base rather than losing it. `semi_naive_parallel` does exactly -/// that and is unsound for it. -/// -/// Documentation did not stop it, so the type does: there is no way to intern -/// through this. To intern, use the shared encoder. -/// -/// # It can still be stale -/// -/// A term interned after the view is taken is missing from it, and decoding such an -/// id yields `None`. That is sound rather than wrong -- a blank, not someone else's -/// term -- and every caller is safe for one reason: the ids it decodes come from -/// facts that were already stored, so they predate the view. +/// A read-only copy of a term space #[derive(Debug, Clone)] pub struct TermView(Dictionary); impl TermView { - /// The string `id` stands for, or `None` if this view does not have it. + /// The string `id` stands for, or `None` if this view does not have it pub fn decode(&self, id: u32) -> Option<&str> { self.0.decode(id) } - /// The string `id` stands for, following quoted triples through `qt_store`. + /// The string `id` stands for, following quoted triples through `qt_store` pub fn decode_term( &self, id: u32, @@ -305,23 +180,18 @@ impl TermView { self.0.decode_term(id, qt_store) } - /// How many terms the view holds. + /// How many terms the view holds pub fn len(&self) -> usize { self.0.id_to_string.len() } - /// Whether the view holds nothing. + /// Whether the view holds nothing pub fn is_empty(&self) -> bool { self.0.id_to_string.is_empty() } } -/// Immutable access to the copy, so anything taking `&Dictionary` still works. -/// -/// This does not reopen the hazard: `Dictionary::encode` takes `&mut self`, and a -/// `Deref` only ever yields `&`. Interning through a view remains unexpressible, -/// while the read-only helpers that already take `&Dictionary` -- formatting, label -/// lookups, comparison output -- need no change. +/// Immutable access to the copy, so anything taking `&Dictionary` still works impl std::ops::Deref for TermView { type Target = Dictionary; @@ -331,22 +201,7 @@ impl std::ops::Deref for TermView { } impl SharedTerms { - /// Runs `visit` against the term space for reading only. - /// - /// # Why this exists separately from `with_terms` - /// - /// [`with_terms`](TermEncoder::with_terms) takes a *write* lock, because it hands - /// out `&mut Dictionary`. That is wrong for a hot read path in two ways at once: - /// a parallel pass would serialise on it, and nothing in the pass needs to - /// intern. The execution engine decodes a whole chunk of results under one lock, - /// once per chunk across rayon's workers, and writing there would undo the - /// parallelism. - /// - /// A copy is the other wrong answer: [`view`](Self::view) clones the dictionary, - /// which is right once per dump and ruinous once per chunk. - /// - /// So this is the third option -- a shared lock, held only for the call, with no - /// guard escaping and no copy made. + /// Runs `visit` against the term space for reading only pub fn with_terms_read(&self, visit: impl FnOnce(&Dictionary) -> R) -> R { let guard = self .0 @@ -355,18 +210,7 @@ impl SharedTerms { visit(&guard) } - /// Runs `visit` against the term space, reporting a poisoned lock instead of - /// panicking on it. - /// - /// [`with_terms`](TermEncoder::with_terms) unwraps, which is right for callers - /// that have nothing better to do than fail. These do: `datalogmtl`'s parsers - /// return `Result` and the HTTP server turns the error into a response, so a - /// thread that panicked elsewhere while holding this lock should not also take - /// down the request handling here. - /// - /// # Errors - /// - /// The lock is poisoned, meaning some thread panicked while holding it. + /// Runs `visit` against the term space, reporting a poisoned lock instead of panicking on it pub fn try_with_terms( &self, visit: impl FnOnce(&mut Dictionary) -> R, @@ -375,9 +219,7 @@ impl SharedTerms { Ok(visit(&mut guard)) } - /// A read-only copy, for decoding many ids without holding the lock. - /// - /// See [`TermView`] for when this is the right tool and what it cannot do. + /// A read-only copy, for decoding many ids without holding the lock pub fn view(&self) -> TermView { let mut copy = Dictionary::new(); self.with_terms(&mut |dictionary| copy = dictionary.clone()); @@ -405,18 +247,12 @@ mod scope_tests { fn a_read_scope_sees_what_was_interned_and_cannot_intern() { let terms = SharedTerms::new(); let id = terms.encode("urn:a"); - // `&Dictionary` is all the scope hands out, so `encode` is not callable here: - // the compiler enforces read-only rather than the comment asking for it. let seen = terms.with_terms_read(|dictionary| dictionary.decode(id).map(str::to_string)); assert_eq!(seen.as_deref(), Some("urn:a")); } #[test] fn read_scopes_do_not_exclude_each_other() { - // The property the execution engine depends on: its per-chunk decode runs - // across rayon's workers, and a write scope would serialise them. Nesting is - // the cheapest observable proof that these take a shared lock -- under a - // write lock this would deadlock rather than fail. let terms = SharedTerms::new(); let id = terms.encode("urn:nested"); let both = terms.with_terms_read(|outer| { @@ -437,7 +273,6 @@ mod scope_tests { let mut minted = 0; terms.with_terms(&mut |dictionary| minted = dictionary.encode("urn:written")); assert_eq!(terms.decode(minted).as_deref(), Some("urn:written")); - // and the read scope agrees, so the two are one space assert_eq!( terms .with_terms_read(|d| d.decode(minted).map(str::to_string)) @@ -493,18 +328,12 @@ mod tests { let held_elsewhere = shared.clone(); let mut id = 0; shared.with_terms(&mut |dict| id = dict.encode("urn:x")); - // The clone another component already holds decodes what this encoder minted. - // Cloning used to mean passing the handle; it is the same sharing either way, - // which is why the handle methods could go. assert_eq!(held_elsewhere.read().decode(id), Some("urn:x")); assert!(held_elsewhere.shares_space_with(&shared)); } #[test] fn two_separately_built_spaces_are_not_the_one_space() { - // The distinction `shares_space_with` exists to draw: equal contents today, - // and still two spaces, because the next term interned into one is invisible - // to the other. let a = SharedTerms::new(); let b = SharedTerms::new(); assert_eq!( diff --git a/shared/src/terms.rs b/shared/src/terms.rs index 9670b710..56837e78 100644 --- a/shared/src/terms.rs +++ b/shared/src/terms.rs @@ -10,89 +10,34 @@ use std::collections::HashMap; -/// One position of a triple pattern: either bound to a term or left open. -/// -/// This is the pattern-side counterpart to a [`Triple`](crate::triple::Triple)'s -/// plain `u32`: where a fact has an id in every position, a pattern may have a -/// variable instead. +/// One position of a triple pattern: either bound to a term or left open #[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)] pub enum Term { - /// A variable, held by name. Two positions carrying the same name must bind to - /// the same value — that constraint is what makes a pattern a join. + /// A variable, held by name Variable(String), - /// A fixed term, as a dictionary id. Comparable only against ids from the same - /// [`Dictionary`](crate::dictionary::Dictionary). + /// A fixed term, as a dictionary id Constant(u32), - /// RDF-star: a quoted triple pattern with potentially variable components. - /// Used in SPARQL-star WHERE clauses like `<< ?s :p ?o >>`. + /// RDF-star: a quoted triple pattern with potentially variable components QuotedTriple(Box<(Term, Term, Term)>), } -/// A triple pattern: subject, predicate and object, each bound or variable. +/// A triple pattern: subject, predicate and object, each bound or variable pub type TriplePattern = (Term, Term, Term); -/// One solution mapping, as a caller builds one. -/// -/// Kept as a map because that is how a row is *constructed* — a scan binds variables -/// as it finds them, and the names are not known until it does. [`Bindings`] stores -/// something narrower; this is the shape at its door. +/// One solution mapping, as a caller builds one pub type Binding = HashMap; -/// The value a cell holds where a row binds nothing. -/// -/// A solution mapping in SPARQL is *partial*: `OPTIONAL` and `VALUES ... UNDEF` both -/// produce sequences whose rows bind different variable sets. The map shape said so -/// by leaving a key out; a fixed-width row has to say it in the cell. -/// -/// `u32::MAX` is safe to reserve because *both* halves of the id space refuse it. -/// [`Dictionary::encode`](crate::dictionary::Dictionary::encode) asserts -/// `next_id < QUOTED_TRIPLE_ID_BIT` (`0x8000_0000`), so an ordinary term id stays in -/// the lower half. Quoted triple ids climb from that bit upwards and so *could* have -/// reached the top, which is why -/// [`QuotedTripleStore::encode`](crate::quoted_triple_store::QuotedTripleStore::encode) -/// asserts `next_qt_id < u32::MAX`. A variable can be bound to a quoted triple, so -/// without that second guard this sentinel would have been sound only by accident. -/// Both guards are pinned by a test below. -/// -/// Note that `0` would **not** have been safe: `next_id` starts at zero, so the first -/// term interned has id `0`. Conflating that with "unbound" was a real bug once. +/// The value a cell holds where a row binds nothing pub const UNBOUND: u32 = u32::MAX; -/// A solution sequence: the rows a pattern matched. -/// -/// # Schema and tuples -/// -/// One schema for the sequence, and a row of `u32` per solution, rather than a -/// `HashMap` per row. A variable name is resolved to a column once for -/// the whole sequence instead of being hashed once per row per column, and a row -/// costs one allocation of `4 × columns` bytes instead of a map with owned `String` -/// keys. Measured on a 20,000 by 20,000 hash join over 2,000 keys, the tuple shape -/// joins the same data about 4.3x faster. -/// -/// # Solution mappings are partial -/// -/// `OPTIONAL` and `VALUES ... UNDEF` produce rows binding different variable sets. -/// The map shape said so by leaving a key out; a fixed-width row says it with -/// [`UNBOUND`], which no dictionary can mint. `0` would not have worked: the first -/// term ever interned has id `0`. -/// -/// # Built from maps, read as rows -/// -/// A row is still *constructed* as a [`Binding`], because a scan does not know which -/// variables it will bind until it binds them. [`push`](Self::push) and -/// [`FromIterator`] take that shape and widen the schema as new variables appear, so -/// the sequence stays rectangular without the caller tracking it. Reading goes the -/// other way: [`iter`](Self::iter) yields a [`BindingRef`], which answers by name -/// without rebuilding a map. +/// A solution sequence: the rows a pattern matched #[derive(Debug, Clone, Default)] pub struct Bindings { - /// Variable names, in column order. Grows as rows introduce variables. schema: Vec, - /// One row per solution, each `schema.len()` wide. cells: Vec>, } -/// One solution of a [`Bindings`], answered by name without a map. +/// One solution of a [`Bindings`], answered by name without a map #[derive(Debug, Clone, Copy, PartialEq, Eq)] pub struct BindingRef<'a> { schema: &'a [String], @@ -100,7 +45,7 @@ pub struct BindingRef<'a> { } impl<'a> BindingRef<'a> { - /// The value bound to `variable`, or `None` where this solution binds nothing. + /// The value bound to `variable`, or `None` where this solution binds nothing pub fn get(&self, variable: &str) -> Option { let column = self.schema.iter().position(|name| name == variable)?; match self.cells.get(column).copied()? { @@ -109,12 +54,12 @@ impl<'a> BindingRef<'a> { } } - /// Whether this solution binds `variable`. + /// Whether this solution binds `variable` pub fn contains_key(&self, variable: &str) -> bool { self.get(variable).is_some() } - /// The variables this solution binds, in column order. + /// The variables this solution binds, in column order pub fn keys(&self) -> impl Iterator + 'a { let schema = self.schema; self.cells @@ -124,12 +69,12 @@ impl<'a> BindingRef<'a> { .map(move |(column, _)| schema[column].as_str()) } - /// The values this solution binds, in column order. + /// The values this solution binds, in column order pub fn values(&self) -> impl Iterator + 'a { self.cells.iter().copied().filter(|id| *id != UNBOUND) } - /// The variables and their values, in column order. + /// The variables and their values, in column order pub fn iter(&self) -> impl Iterator + 'a { let schema = self.schema; self.cells @@ -139,42 +84,36 @@ impl<'a> BindingRef<'a> { .map(move |(column, id)| (schema[column].as_str(), *id)) } - /// How many variables this solution binds. + /// How many variables this solution binds pub fn len(&self) -> usize { self.cells.iter().filter(|id| **id != UNBOUND).count() } - /// Whether this solution binds nothing: the unit solution mapping. + /// Whether this solution binds nothing: the unit solution mapping pub fn is_empty(&self) -> bool { self.cells.iter().all(|id| *id == UNBOUND) } - /// This solution as a map, for a caller that needs to own and mutate one. + /// This solution as a map, for a caller that needs to own and mutate one pub fn to_binding(&self) -> Binding { self.iter() .map(|(name, id)| (name.to_string(), id)) .collect() } - /// The raw cells, in the sequence's column order. + /// The raw cells, in the sequence's column order pub fn cells(&self) -> &'a [u32] { self.cells } } impl Bindings { - /// No solutions: a pattern that matched nothing. - /// - /// Distinct from [`unit`](Self::unit), and the difference is load-bearing: this - /// is failure, that is success binding nothing. + /// No solutions: a pattern that matched nothing pub fn empty() -> Self { Self::default() } - /// The unit solution sequence: one row, binding no variables. - /// - /// What the engine starts every plan from, and what a request with no pattern - /// returns. It is not the empty sequence, and a join against it is the identity. + /// The unit solution sequence: one row, binding no variables pub fn unit() -> Self { Self { schema: Vec::new(), @@ -182,14 +121,14 @@ impl Bindings { } } - /// A sequence of exactly one solution. + /// A sequence of exactly one solution pub fn single(binding: Binding) -> Self { let mut bindings = Self::empty(); bindings.push(binding); bindings } - /// An empty sequence over a known schema. + /// An empty sequence over a known schema pub fn with_schema(schema: Vec) -> Self { Self { schema, @@ -197,7 +136,7 @@ impl Bindings { } } - /// Room for `rows` solutions, allocated up front. + /// Room for `rows` solutions, allocated up front pub fn with_capacity(rows: usize) -> Self { Self { schema: Vec::new(), @@ -205,35 +144,32 @@ impl Bindings { } } - /// The variables any solution binds, in column order. + /// The variables any solution binds, in column order pub fn schema(&self) -> &[String] { &self.schema } - /// The column `variable` occupies, resolved once for the whole sequence. - /// - /// Resolve here and index rows with the result. Looking a name up per row is what - /// the map shape did and what this type exists to stop. + /// The column `variable` occupies, resolved once for the whole sequence pub fn column(&self, variable: &str) -> Option { self.schema.iter().position(|name| name == variable) } - /// How many solutions. + /// How many solutions pub fn len(&self) -> usize { self.cells.len() } - /// Whether the pattern matched nothing. + /// Whether the pattern matched nothing pub fn is_empty(&self) -> bool { self.cells.is_empty() } - /// The first solution, if any. + /// The first solution, if any pub fn first(&self) -> Option> { self.row(0) } - /// One solution, by position. + /// One solution, by position pub fn row(&self, index: usize) -> Option> { Some(BindingRef { schema: &self.schema, @@ -241,7 +177,7 @@ impl Bindings { }) } - /// The solutions, in result order. + /// The solutions, in result order pub fn iter(&self) -> impl Iterator> + '_ { self.cells.iter().map(|cells| BindingRef { schema: &self.schema, @@ -249,10 +185,7 @@ impl Bindings { }) } - /// Adds `variable` as a column if the schema lacks it, padding existing rows. - /// - /// Growing is bounded by the number of distinct variables, not by the number of - /// rows, so a sequence widens a handful of times however long it is. + /// Adds `variable` as a column if the schema lacks it, padding existing rows fn column_for(&mut self, variable: &str) -> usize { if let Some(column) = self.column(variable) { return column; @@ -264,10 +197,9 @@ impl Bindings { self.schema.len() - 1 } - /// Appends a solution, widening the schema for any variable it introduces. + /// Appends a solution, widening the schema for any variable it introduces pub fn push(&mut self, binding: Binding) { let mut names: Vec<&String> = binding.keys().collect(); - // Sorted so a new column's position does not depend on map iteration order. names.sort(); let mut row = vec![UNBOUND; self.schema.len()]; for name in names { @@ -281,11 +213,7 @@ impl Bindings { self.cells.push(row); } - /// Appends a row already laid out against this sequence's schema. - /// - /// # Panics - /// - /// In debug builds, if the row's width is not the schema's. + /// Appends a row already laid out against this sequence's schema pub fn push_cells(&mut self, row: Vec) { debug_assert_eq!( row.len(), @@ -295,16 +223,7 @@ impl Bindings { self.cells.push(row); } - /// Appends another sequence's solutions, unifying the two schemas. - /// - /// A column only one side has is unbound in the other side's rows, which is what - /// a union of branches binding different variables produces. - /// - /// This is what [`extend`](Self::extend) should be reached for instead of: that - /// takes maps, so concatenating two sequences through it rebuilds a - /// `HashMap` for every row and then takes it apart again. Measured - /// on `optimizer_hotpath`, going through maps cost `union_branches` about twice - /// its runtime. + /// Appends another sequence's solutions, unifying the two schemas pub fn append(&mut self, other: Self) { if self.schema.is_empty() && self.cells.is_empty() { *self = other; @@ -314,16 +233,9 @@ impl Bindings { return; } if self.schema == other.schema { - // The common case by far: branches of a union, or chunks of one plan's - // output, agree on their columns, so the rows move as they are. Mapping - // them cell by cell would allocate a row and index it per solution for - // no reason. self.cells.extend(other.cells); return; } - // Every missing column added in one pass, then the rows padded once. - // Calling `column_for` per column pads all the rows already present each - // time, so a branch introducing three variables walked them three times. let added: Vec<&String> = other .schema .iter() @@ -359,36 +271,36 @@ impl Bindings { } } - /// Appends every solution of `other`. + /// Appends every solution of `other` pub fn extend(&mut self, other: impl IntoIterator) { for binding in other { self.push(binding); } } - /// Keeps only the solutions satisfying `keep`. + /// Keeps only the solutions satisfying `keep` pub fn retain(&mut self, mut keep: impl FnMut(BindingRef<'_>) -> bool) { let schema = &self.schema; self.cells .retain(|cells| keep(BindingRef { schema, cells })); } - /// Discards all but the first `rows` solutions. + /// Discards all but the first `rows` solutions pub fn truncate(&mut self, rows: usize) { self.cells.truncate(rows); } - /// Removes every solution, keeping the schema. + /// Removes every solution, keeping the schema pub fn clear(&mut self) { self.cells.clear(); } - /// Removes consecutive duplicate solutions. + /// Removes consecutive duplicate solutions pub fn dedup(&mut self) { self.cells.dedup(); } - /// Orders the solutions by `compare`. + /// Orders the solutions by `compare` pub fn sort_by( &mut self, mut compare: impl FnMut(BindingRef<'_>, BindingRef<'_>) -> std::cmp::Ordering, @@ -408,19 +320,7 @@ impl Bindings { }); } - /// Binds `variable` in every solution, to one value per row in order. - /// - /// What `BIND`, `ML.PREDICT` and the function-call operators all do: compute a - /// value per solution and attach it under a new name. In the map shape each of - /// them inserted into every row's map, hashing the same name once per row; here - /// the name is resolved once and the column is filled. - /// - /// A solution with no corresponding value is left unbound, and an existing - /// column of that name is overwritten, which is what inserting into a map did. - /// - /// # Panics - /// - /// In debug builds, if `values` is longer than the sequence. + /// Binds `variable` in every solution, to one value per row in order pub fn bind_column(&mut self, variable: &str, values: impl IntoIterator) { debug_assert!( self.schema.iter().all(|name| name != variable) || self.column(variable).is_some() @@ -434,29 +334,22 @@ impl Bindings { debug_assert!(filled <= self.cells.len(), "more values than solutions"); } - /// The raw cells, one row per solution, each `schema().len()` wide. - /// - /// For code that has resolved its columns and wants to index them. The schema is - /// [`schema`](Self::schema); the two together are the whole sequence. + /// The raw cells, one row per solution, each `schema().len()` wide pub fn cells(&self) -> &[Vec] { &self.cells } - /// The cells, mutably. The schema is fixed, so rows keep their width. + /// The cells, mutably pub fn cells_mut(&mut self) -> &mut [Vec] { &mut self.cells } - /// Takes the sequence apart into its schema and its rows. + /// Takes the sequence apart into its schema and its rows pub fn into_parts(self) -> (Vec, Vec>) { (self.schema, self.cells) } - /// Builds a sequence from a schema and rows already laid out against it. - /// - /// # Panics - /// - /// In debug builds, if any row's width is not the schema's. + /// Builds a sequence from a schema and rows already laid out against it pub fn from_parts(schema: Vec, cells: Vec>) -> Self { debug_assert!( cells.iter().all(|row| row.len() == schema.len()), @@ -465,11 +358,7 @@ impl Bindings { Self { schema, cells } } - /// The solutions in parallel, which is how the engine's filters and joins read - /// a sequence. - /// - /// Yields views rather than maps, so a parallel pass over a long sequence - /// allocates nothing per row. + /// The solutions in parallel, which is how the engine's filters and joins read a sequence pub fn par_iter(&self) -> impl rayon::iter::IndexedParallelIterator> { use rayon::iter::{IntoParallelRefIterator, ParallelIterator}; let schema = &self.schema; @@ -478,16 +367,13 @@ impl Bindings { .map(move |cells| BindingRef { schema, cells }) } - /// The solutions as maps, for code not yet converted. - /// - /// Allocates a map per row, so this is a boundary with code that still wants the - /// old shape, not something to reach for in a loop. + /// The solutions as maps, for code not yet converted pub fn to_maps(&self) -> Vec { self.iter().map(|row| row.to_binding()).collect() } } -/// Compared by what the solutions bind, not by how the schema happens to be ordered. +/// Compared by what the solutions bind, not by how the schema happens to be ordered impl PartialEq for Bindings { fn eq(&self, other: &Self) -> bool { self.len() == other.len() @@ -540,8 +426,8 @@ impl<'a> IntoIterator for &'a Bindings { } } -/// Collected from a parallel iterator, which is how the engine's filters and joins -/// produce a sequence. +/// Collected from a parallel iterator, which is how the engine's filters and joins produce a +/// sequence impl rayon::iter::FromParallelIterator for Bindings { fn from_par_iter(rows: I) -> Self where @@ -562,32 +448,27 @@ impl rayon::iter::IntoParallelIterator for Bindings { } } -/// A term as it appears in source text, before prefixes are resolved and before it is -/// interned. -/// -/// Parsing produces these; binding them to a dictionary turns them into [`Term`]s. -/// Keeping the two apart is what stops a rule parsed against one vocabulary from -/// silently matching nothing against another — a failure this codebase has hit. +/// A term as it appears in source text, before prefixes are resolved and before it is interned #[derive(Debug)] pub enum UnresolvedTerm { - /// A variable, by name. + /// A variable, by name Var(String), - /// A prefixed name such as `ex:thing`, still needing its prefix expanded. + /// A prefixed name such as `ex:thing`, still needing its prefix expanded Prefixed(String), - /// A full IRI, needing only interning. + /// A full IRI, needing only interning Iri(String), } -/// A triple of [`UnresolvedTerm`]s, as parsed. +/// A triple of [`UnresolvedTerm`]s, as parsed pub type UnresolvedTriple = (UnresolvedTerm, UnresolvedTerm, UnresolvedTerm); impl Term { - /// Whether this position is a variable. + /// Whether this position is a variable pub fn is_var(&self) -> bool { matches!(self, Term::Variable(_)) } - /// Whether this position is a quoted triple. + /// Whether this position is a quoted triple pub fn is_quoted_triple(&self) -> bool { matches!(self, Term::QuotedTriple(_)) } @@ -610,8 +491,6 @@ mod bindings_tests { #[test] fn an_empty_sequence_is_not_the_unit_sequence() { - // The difference is load-bearing: one is a pattern that matched nothing, the - // other is success binding nothing, and a join against it is the identity. assert!(Bindings::empty().is_empty()); assert_eq!(Bindings::empty().len(), 0); assert!(!Bindings::unit().is_empty()); @@ -631,8 +510,6 @@ mod bindings_tests { #[test] fn pushing_a_row_widens_the_schema_for_a_new_variable() { - // A scan does not know what it will bind until it binds it, so the schema - // grows as rows arrive and the rows already present are padded. let mut bindings = Bindings::empty(); bindings.push(binding(&[("x", 1)])); bindings.push(binding(&[("x", 2), ("y", 3)])); @@ -646,8 +523,6 @@ mod bindings_tests { #[test] fn a_row_that_does_not_bind_a_variable_reads_as_absent() { - // What OPTIONAL produces. The cell holds UNBOUND, and nothing reports it as - // a term: not `get`, not `keys`, not `iter`, not `len`. let bindings = sequence(&[&[("x", 1), ("y", 2)], &[("x", 3)]]); let second = bindings.row(1).expect("second row"); @@ -661,8 +536,6 @@ mod bindings_tests { #[test] fn term_id_zero_is_a_term_and_not_unbound() { - // Dictionary::next_id starts at 0, so the first term ever interned has id 0. - // A sentinel of 0 would have made these two cases indistinguishable. let bindings = sequence(&[&[("x", 0)], &[("y", 0)]]); let x = bindings.column("x").expect("x is a column"); assert_eq!(bindings.row(0).expect("first").get("x"), Some(0)); @@ -682,8 +555,6 @@ mod bindings_tests { #[test] fn appending_sequences_with_different_schemas_unifies_them() { - // A union of branches binding different variables: a column only one side - // has is unbound in the other side's rows, never silently dropped. let mut left = sequence(&[&[("s", 1), ("v", 2)]]); left.append(sequence(&[&[("s", 3), ("d", 4)]])); @@ -711,8 +582,6 @@ mod bindings_tests { #[test] fn binding_a_column_gives_every_solution_its_own_value() { - // What BIND and ML.PREDICT do: one computed value per solution, attached - // under a new name. let mut bindings = sequence(&[&[("x", 1)], &[("x", 2)]]); bindings.bind_column("doubled", vec![10, 20]); assert_eq!(bindings.row(0).expect("first").get("doubled"), Some(10)); @@ -744,10 +613,6 @@ mod bindings_tests { #[test] fn equality_compares_what_the_solutions_bind() { - // Not the schema's order, and not the padding. Building both through `push` - // would not test this: it sorts a row's keys, so both sides come out with the - // same representation and comparing the fields directly would agree anyway. - // These are laid out deliberately differently. let left = sequence(&[&[("x", 1), ("y", 2)]]); assert_eq!(left.schema(), ["x".to_string(), "y".to_string()]); @@ -783,9 +648,6 @@ mod bindings_tests { #[test] fn neither_half_of_the_id_space_can_mint_the_unbound_sentinel() { - // UNBOUND is only sound because no id can equal it. Each guard is identified - // by its message, because these tests run in debug, where the increment past - // the end panics on its own and would make this pass with the guard deleted. use crate::dictionary::Dictionary; use crate::quoted_triple_store::{QuotedTripleStore, QUOTED_TRIPLE_ID_BIT}; @@ -834,44 +696,20 @@ mod bindings_tests { } } -/// Solution rows whose cells are terms as text, not dictionary ids. -/// -/// The unencoded sibling of [`Bindings`], and for the same reason -/// [`TermTriple`](crate::rdf_term::TermTriple) exists: an id means nothing outside -/// the dictionary that minted it, so a solution crossing out of one store carries -/// text. The RSP coordinator is where this bites — it joins results from windows -/// that need not share a dictionary. -/// -/// # Why not a map per row -/// -/// It was `Vec>`, which is the shape -/// [`Bindings`] replaced on the encoded side after measuring a 4.7x difference on -/// the join. The same two costs apply here and one is worse: a variable is hashed -/// once per row per column rather than resolved to a column once for the whole -/// sequence, and here the keys are strings rather than `u32`. -/// -/// # Partial rows -/// -/// A cell is `Option` because a solution mapping is a partial function: -/// `OPTIONAL` and `VALUES … UNDEF` produce rows that bind different variables. The -/// map shape said so by leaving a key out, and [`Bindings`] says so with a sentinel -/// id; neither trick is available for a `String`, since the empty string is a -/// perfectly good literal. +/// Solution rows whose cells are terms as text, not dictionary ids #[derive(Debug, Clone, Default, PartialEq, Eq)] pub struct TextBindings { - /// Variable names, in column order. schema: Vec, - /// One row per solution, each `schema.len()` wide. rows: Vec>>, } impl TextBindings { - /// No rows and no variables. + /// No rows and no variables pub fn new() -> Self { Self::default() } - /// An empty sequence over a known schema. + /// An empty sequence over a known schema pub fn with_schema(schema: Vec) -> Self { Self { schema, @@ -879,41 +717,38 @@ impl TextBindings { } } - /// The variables, in column order. + /// The variables, in column order pub fn schema(&self) -> &[String] { &self.schema } - /// How many solutions. + /// How many solutions pub fn len(&self) -> usize { self.rows.len() } - /// Whether there are no solutions. + /// Whether there are no solutions pub fn is_empty(&self) -> bool { self.rows.is_empty() } - /// The column `variable` occupies, if it is in the schema. + /// The column `variable` occupies, if it is in the schema pub fn column(&self, variable: &str) -> Option { self.schema.iter().position(|name| name == variable) } - /// The value of `variable` in row `row`, or `None` when unbound or absent. + /// The value of `variable` in row `row`, or `None` when unbound or absent pub fn get(&self, row: usize, variable: &str) -> Option<&str> { let column = self.column(variable)?; self.rows.get(row)?.get(column)?.as_deref() } - /// The rows, each as a slice aligned with [`schema`](Self::schema). + /// The rows, each as a slice aligned with [`schema`](Self::schema) pub fn rows(&self) -> &[Vec>] { &self.rows } - /// The bound cells of row `row`, as variable and value. - /// - /// Skips unbound cells, so a partial row yields fewer pairs than the schema is - /// wide — which is what a consumer building a solution wants. + /// The bound cells of row `row`, as variable and value pub fn pairs(&self, row: usize) -> impl Iterator + '_ { self.schema .iter() @@ -921,7 +756,7 @@ impl TextBindings { .filter_map(|(name, cell)| cell.as_deref().map(|value| (name.as_str(), value))) } - /// Adds a row from variable and value pairs, widening the schema as needed. + /// Adds a row from variable and value pairs, widening the schema as needed pub fn push_pairs(&mut self, pairs: I) where I: IntoIterator, @@ -935,8 +770,6 @@ impl TextBindings { Some(column) => column, None => { self.schema.push(variable.to_string()); - // Widen the rows already stored, so every row stays as wide as - // the schema. for existing in &mut self.rows { existing.push(None); } @@ -949,25 +782,12 @@ impl TextBindings { self.rows.push(row); } - /// Natural join: rows that agree on every shared variable, merged. - /// - /// Produces the Cartesian product when the two sides share no variable, which is - /// what the cross-window case usually is — two windows binding different - /// variables. - /// - /// An unbound cell is compatible with any value, because a solution mapping is a - /// partial function and two mappings are compatible where both are defined - /// (SPARQL 1.1 §18.5). That is why this compares rather than hashes: a hash join - /// on the shared columns cannot express "unbound matches anything", and getting - /// it wrong would silently drop or invent solutions. Shared columns are resolved - /// once from the two schemas instead of being looked up per row, which is the - /// cost the map shape paid. + /// Natural join: rows that agree on every shared variable, merged pub fn join(&self, other: &TextBindings) -> TextBindings { if self.is_empty() || other.is_empty() { return TextBindings::new(); } - // Column pairs the two sides share, resolved once. let shared: Vec<(usize, usize)> = self .schema .iter() @@ -975,7 +795,6 @@ impl TextBindings { .filter_map(|(left, name)| other.column(name).map(|right| (left, right))) .collect(); - // Output schema: this side's variables, then the ones only `other` has. let mut schema = self.schema.clone(); let carried: Vec<(usize, usize)> = other .schema @@ -994,8 +813,6 @@ impl TextBindings { let compatible = shared.iter().all(|&(left, right)| { match (&left_row[left], &right_row[right]) { (Some(left), Some(right)) => left == right, - // Unbound on either side: compatible, and the bound value - // wins in the merged row. _ => true, } }); @@ -1007,7 +824,6 @@ impl TextBindings { for &(right, target) in &carried { row[target] = right_row[right].clone(); } - // A variable both sides share takes whichever side bound it. for &(left, right) in &shared { if row[left].is_none() { row[left] = right_row[right].clone(); @@ -1019,13 +835,8 @@ impl TextBindings { TextBindings { schema, rows } } - /// Appends another sequence's rows, widening the schema to cover both. - /// - /// Concatenation, not a join: the rows stand alongside each other rather than - /// being combined. A variable only the other side has is unbound in the rows - /// already here, which is what a partial row is for. + /// Appends another sequence's rows, widening the schema to cover both pub fn extend(&mut self, other: TextBindings) { - // Where the schemas already agree, the rows can move across untouched. if self.schema == other.schema { self.rows.extend(other.rows); return; @@ -1053,7 +864,7 @@ impl TextBindings { } } - /// Builds from the map-per-row shape, for a boundary that still produces it. + /// Builds from the map-per-row shape, for a boundary that still produces it pub fn from_maps(maps: &[HashMap]) -> Self { let mut bindings = TextBindings::new(); for map in maps { @@ -1099,8 +910,6 @@ mod text_bindings_tests { #[test] fn an_unbound_cell_is_not_an_empty_string() { - // The empty string is a perfectly good literal, which is why a cell is an - // Option rather than using "" as a sentinel. let mut bindings = TextBindings::new(); bindings.push_pairs([("s", ""), ("t", "x")]); assert_eq!(bindings.get(0, "s"), Some("")); @@ -1126,7 +935,6 @@ mod text_bindings_tests { #[test] fn joining_on_no_shared_variable_is_the_cartesian_product() { - // The cross-window case: two windows binding different variables. let mut left = TextBindings::new(); left.push_pairs([("s", "a")]); left.push_pairs([("s", "b")]); @@ -1159,19 +967,14 @@ mod text_bindings_tests { #[test] fn an_unbound_shared_variable_is_compatible_with_any_value() { - // SPARQL 1.1 section 18.5: two solution mappings are compatible where both - // are defined. This is the case a hash join on the shared columns cannot - // express, and the reason this one compares. let mut left = TextBindings::new(); left.push_pairs([("s", "a"), ("x", "1")]); - left.push_pairs([("x", "2")]); // `s` unbound + left.push_pairs([("x", "2")]); let mut right = TextBindings::new(); right.push_pairs([("s", "a")]); right.push_pairs([("s", "b")]); let joined = left.join(&right); - // Row one joins only `s=a`; row two is unbound on `s` so it joins both. assert_eq!(joined.len(), 3, "got {joined:?}"); - // And the merged row takes the bound side's value. let values: Vec> = (0..joined.len()).map(|i| joined.get(i, "s")).collect(); assert_eq!(values, vec![Some("a"), Some("a"), Some("b")]); } @@ -1210,7 +1013,6 @@ mod text_bindings_tests { #[test] fn extending_realigns_a_schema_given_in_another_order() { - // The cells have to follow their names, not their positions. let mut left = TextBindings::with_schema(vec!["s".into(), "t".into()]); left.push_pairs([("s", "a"), ("t", "x")]); let mut right = TextBindings::with_schema(vec!["t".into(), "s".into()]); @@ -1230,8 +1032,6 @@ mod text_bindings_tests { let from_maps = TextBindings::from_maps(&maps); let mut from_pairs = TextBindings::new(); from_pairs.push_pairs([("s", "urn:a"), ("o", "25")]); - // Schema order follows insertion, which a HashMap does not fix, so the rows - // are compared by content. assert_eq!(row(&from_maps, 0), row(&from_pairs, 0)); } } diff --git a/shared/src/triple.rs b/shared/src/triple.rs index 5fd2a4b1..1e6f2652 100644 --- a/shared/src/triple.rs +++ b/shared/src/triple.rs @@ -10,34 +10,19 @@ use crate::terms::{Term, TriplePattern}; use serde::{Deserialize, Serialize}; -/// An RDF triple with all three positions already encoded to dictionary ids. -/// -/// The ids are only meaningful relative to the [`Dictionary`] that produced them, so -/// a `Triple` cannot be decoded — or compared against one from another store — -/// without that dictionary. Two engines exchanging triples must share one. -/// -/// An id in `subject` or `object` may be a quoted triple rather than an ordinary -/// term, which the top bit indicates; see -/// [`is_quoted_triple_id`](crate::quoted_triple_store::is_quoted_triple_id). -/// -/// `Ord` is derived, so triples sort by `(subject, predicate, object)` as integers. -/// That ordering has no relationship to the lexical order of the terms, and the -/// indexes in [`dataset_index`](crate::dataset_index) rely on it. -/// -/// [`Dictionary`]: crate::dictionary::Dictionary +/// An RDF triple with all three positions already encoded to dictionary ids #[derive(PartialEq, Debug, Clone, Eq, PartialOrd, Ord, Hash, Serialize, Deserialize)] pub struct Triple { - /// Encoded subject. + /// Encoded subject pub subject: u32, - /// Encoded predicate. + /// Encoded predicate pub predicate: u32, - /// Encoded object. + /// Encoded object pub object: u32, } impl Triple { /// Converts a triple (subject, predicate, object) (all u32 values) into a TriplePattern - /// Simply wraps each element into a constant pub fn to_pattern(&self) -> TriplePattern { ( Term::Constant(self.subject), diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..1fd4a3bf --- /dev/null +++ b/uv.lock @@ -0,0 +1,888 @@ +version = 1 +revision = 5 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.12'", + "python_full_version == '3.11.*'", + "python_full_version < '3.11'", +] + +[[package]] +name = "cloudpickle" +version = "3.1.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330, upload-time = "2025-11-03T09:25:26.604Z" } +wheels = [ + { url = 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"2026-07-02T08:40:04.659Z" }, +] From a1c9b7c73694c773fa43099ff449ad18f2198ed8 Mon Sep 17 00:00:00 2001 From: ladroid Date: Mon, 9 Nov 2026 15:01:34 +0100 Subject: [PATCH 2/2] Fixes deprecated constants and doubled module docs --- datalog/src/reasoning/rules.rs | 4 ++-- kolibrie/src/lib.rs | 2 -- kolibrie/src/ml/mod.rs | 4 ---- kolibrie/src/rsp_engine/mod.rs | 1 - ml/src/lib.rs | 3 +-- shared/src/lib.rs | 1 - 6 files changed, 3 insertions(+), 12 deletions(-) diff --git a/datalog/src/reasoning/rules.rs b/datalog/src/reasoning/rules.rs index d24e9e05..42c2262f 100644 --- a/datalog/src/reasoning/rules.rs +++ b/datalog/src/reasoning/rules.rs @@ -80,8 +80,8 @@ pub fn evaluate_filters( "<" if bound_num >= filter_num => return false, ">=" if bound_num < filter_num => return false, "<=" if bound_num > filter_num => return false, - "=" if (bound_num - filter_num).abs() > std::f64::EPSILON => return false, - "!=" if (bound_num - filter_num).abs() <= std::f64::EPSILON => return false, + "=" if (bound_num - filter_num).abs() > f64::EPSILON => return false, + "!=" if (bound_num - filter_num).abs() <= f64::EPSILON => return false, _ => {} } } diff --git a/kolibrie/src/lib.rs b/kolibrie/src/lib.rs index 7854a1ee..7d2d9789 100644 --- a/kolibrie/src/lib.rs +++ b/kolibrie/src/lib.rs @@ -12,7 +12,6 @@ //! neural extensions built on them mod aggregate; -/// The RDF store and everything hung off it pub mod database; pub(crate) mod error_handler; #[cfg(test)] @@ -23,7 +22,6 @@ pub mod ml; pub(crate) mod ml_feature_loader; pub mod ml_policy; pub mod neural_registry; -/// What a failed parse says, without naming the parser library pub mod parse_error; pub mod parser; pub mod prelude; diff --git a/kolibrie/src/ml/mod.rs b/kolibrie/src/ml/mod.rs index 2906115d..2bfb8ee1 100644 --- a/kolibrie/src/ml/mod.rs +++ b/kolibrie/src/ml/mod.rs @@ -13,10 +13,8 @@ pub mod runtime; pub(crate) mod syntax; -/// Running an `ML.PREDICT` clause through the Python backend #[cfg(feature = "ml")] pub mod execute; -/// Training a model declared by a `TRAIN` clause #[cfg(feature = "ml")] pub mod execute_train; #[cfg(feature = "ml")] @@ -24,10 +22,8 @@ pub(crate) mod predict_candle; #[cfg(feature = "ml")] pub(crate) mod predict_runtime; -/// Predicates whose facts come from a model rather than from data #[cfg(feature = "ml")] pub mod neural_relations; -/// Stubs for [`neural_relations`] when the `ml` feature is off, returning `ML_FEATURE_DISABLED` #[cfg(not(feature = "ml"))] #[path = "neural_relations_disabled.rs"] pub mod neural_relations; diff --git a/kolibrie/src/rsp_engine/mod.rs b/kolibrie/src/rsp_engine/mod.rs index 11268eb0..9f7dabc7 100644 --- a/kolibrie/src/rsp_engine/mod.rs +++ b/kolibrie/src/rsp_engine/mod.rs @@ -45,7 +45,6 @@ use shared::hybrid::{ }; use shared::triple::Triple; -/// Assembling an engine from a parsed query pub mod builder; pub use crate::rsp::simple_r2r::QueryExecutionMode; diff --git a/ml/src/lib.rs b/ml/src/lib.rs index cf93ed2d..b71578cb 100644 --- a/ml/src/lib.rs +++ b/ml/src/lib.rs @@ -34,7 +34,6 @@ fn validate_py_module_name(name: &str) -> PyResult<()> { Ok(()) } -/// A small feed-forward network, for gradients that arrive from outside it pub mod candle_model; pub use candle_model::{MlpNeuralPredicate, OutputType}; @@ -346,7 +345,7 @@ impl MLHandler { } let mut best_model = model_names[0]; - let mut best_score = std::f64::MAX; + let mut best_score = f64::MAX; for &model_name in model_names { if let Some(metrics) = self.schema_cache.get(model_name) { diff --git a/shared/src/lib.rs b/shared/src/lib.rs index 4b684665..52ead0e5 100644 --- a/shared/src/lib.rs +++ b/shared/src/lib.rs @@ -26,7 +26,6 @@ pub mod quad_source; pub mod query; /// Storage for RDF-star quoted triples, which live in a tagged id range pub mod quoted_triple_store; -/// RDF term identity: how a term is spelled so that one term is one string pub mod rdf_term; /// Rules: premises, conclusion, filters, and safety checking pub mod rule;