BE-494: HashQL: Loop-breaker selection for recursive inlining

[indietyp]

🌟 What is the purpose of this PR?

This PR implements a three-color depth-first search algorithm for directed graphs and integrates it with a loop-breaker selection system for the MIR inliner. The three-color DFS enables cycle detection and postorder traversal, while the loop-breaker system allows the inliner to handle mutually recursive functions by strategically selecting which functions to avoid inlining within strongly connected components (SCCs).

🔗 Related links

• Related to MIR optimization and inlining strategies for recursive function groups

🔍 What does this change?

• Adds a new three-color depth-first search implementation in libs/@local/hashql/core/src/graph/algorithms/color/mod.rs with support for cycle detection and visitor callbacks
• Implements a loop-breaker selection algorithm in libs/@local/hashql/mir/src/pass/transform/inline/loop_breaker.rs that uses scoring heuristics to choose which functions in recursive SCCs should not be inlined
• Extends the inline pass to process SCCs with loop-breaker awareness, allowing inlining of non-breaker functions while avoiding infinite recursion
• Adds comprehensive test coverage for both the three-color DFS algorithm and loop-breaker selection
• Updates the call graph analysis to support querying callers of a function
• Adds iterator support for SCC members with both immutable and mutable access
• Changes unary NOT operator representation from ! to ~ in MIR output formatting
• Extends inline configuration with InlineLoopBreakerConfig for tuning breaker selection heuristics

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

• does not modify any publishable blocks or libraries, or modifications do not need publishing

📜 Does this require a change to the docs?

The changes in this PR:

• are internal and do not require a docs change

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

• do not affect the execution graph

🛡 What tests cover this?

• Comprehensive unit tests for three-color DFS including cycle detection, postorder traversal, edge filtering, and state accumulation
• Loop-breaker selection tests covering mutual recursion, cost-based selection, multi-cycle SCCs, and ordering verification
• Integration tests showing the complete inlining behavior with loop breakers
• Existing MIR transformation tests updated to reflect the new unary operator formatting

❓ How to test this?

1. Run the test suite to verify all three-color DFS and loop-breaker functionality
2. Test with mutually recursive MIR functions to confirm proper breaker selection and inlining behavior
3. Verify that the inliner no longer gets stuck in infinite loops when processing recursive function groups

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[indietyp]

• BE-537: Remove old filter API backend and wire up HashQL in the API #8827
• BE-513: HashQL: Rework dynamic aggregate size estimation #8697
• BE-512: HashQL: Allow cross-backend transitions for SwitchInt terminators #8696
• BE-524: HashQL: Remove island schedule module #8694
• BE-525: HashQL: Remove implicit type-widening from Integer operations, and make comparison size aware #8693
• BE-515: HashQL: Remove OFFSET 0 materialization fence from generated query #8650
• BE-514: HashQL: implement iterative adjustment passes in placement solver #8635
• BE-500, BE-501: HashQL: Unify mixed parameter resolution in data dependency analysis #8607
• BE-494: HashQL: Loop-breaker selection for recursive inlining #8600 👈 (View in Graphite)
• BE-482: HashQL: Remove logical not from MIR and fix postgres boolean lowering #8595
• BE-474: HashQL: Take into account terminator target eligibility in execution backend placement #8587
• BE-455: HashQL: Introduce evaluation orchestrator #8586
• BE-523: HashQL: Suspendable interpreter with structured values and input system #8675
• BE-522: HashQL: Expand stdlib type system with temporal types #8674
• BE-306: HashQL: PostgreSQL translation #8526
• BE-457: HashQL: MIR execution pipeline extensions for postgres compilation #8525
• BE-456: HashQL: Entity type model enrichment and graph store query extensions #8524
• BE-429: HashQL: Add island dependency graph with data requirement resolution #8501
• BE-428: HashQL: Simplify traversal tracking to path recording #8498
• BE-434: HashQL: Refactor entity storage path resolution with centralized schema mapping #8496
• BE-427: Add hashql-macros crate with procedural macros for ID types and symbols #8495
• main

This stack of pull requests is managed by Graphite. Learn more about stacking.

[codecov]

Codecov Report

❌ Patch coverage is 83.33333% with 44 lines in your changes missing coverage. Please review.
✅ Project coverage is 59.09%. Comparing base (a8da2f8) to head (cd68cdf).
⚠️ Report is 7 commits behind head on main.

Files with missing lines	Patch %	Lines
...cal/hashql/core/src/graph/algorithms/tarjan/mod.rs	0.00%	22 Missing ⚠️
...shql/mir/src/pass/transform/inline/loop_breaker.rs	90.00%	9 Missing and 4 partials ⚠️
...ocal/hashql/core/src/graph/algorithms/color/mod.rs	93.24%	5 Missing ⚠️
...@local/hashql/mir/src/pass/transform/inline/mod.rs	89.65%	1 Missing and 2 partials ⚠️
...local/hashql/mir/src/pass/transform/inline/find.rs	0.00%	0 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #8600      +/-   ##
==========================================
+ Coverage   59.04%   59.09%   +0.04%     
==========================================
  Files        1341     1343       +2     
  Lines      129387   129632     +245     
  Branches     5837     5858      +21     
==========================================
+ Hits        76398    76601     +203     
- Misses      52088    52124      +36     
- Partials      901      907       +6     

Flag	Coverage Δ
apps.hash-ai-worker-ts	1.41% <ø> (ø)
apps.hash-api	0.00% <ø> (ø)
local.hash-backend-utils	2.81% <ø> (ø)
local.hash-graph-sdk	9.63% <ø> (ø)
local.hash-isomorphic-utils	0.00% <ø> (ø)
rust.hash-graph-api	2.52% <ø> (ø)
rust.hashql-ast	87.23% <ø> (ø)
rust.hashql-compiletest	28.26% <ø> (ø)
rust.hashql-core	79.28% <71.87%> (-0.04%)	⬇️
rust.hashql-eval	75.69% <ø> (ø)
rust.hashql-hir	89.06% <ø> (ø)
rust.hashql-mir	86.77% <89.88%> (+0.02%)	⬆️
rust.hashql-syntax-jexpr	94.06% <ø> (ø)

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[codspeed-hq]

Merging this PR will not alter performance

✅ 80 untouched benchmarks

---

_{Comparing bm/be-494-hashql-scc-loop-breaker-inlining (cd68cdf) with main (798008c)¹}

Footnotes

1. No successful run was found on main (15428b2) during the generation of this report, so 798008c was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

[vercel]

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[cursor]

PR Summary

Medium Risk
Changes MIR inlining behavior for mutually recursive functions and SCC processing order; incorrect breaker selection could affect codegen size or stability, though heavily tested.

Overview
Adds three-color iterative DFS (TriColorDepthFirstSearch) to hashql_core for back-edge cycle detection, postorder, optional edge filtering, and multi-root traversal via run_from.

Wires that into a new loop-breaker pass for the MIR inline transform: for each non-trivial call-graph SCC, members are scored (cost, callers, leaf/unique-site, inline directives), breakers are chosen greedily until the non-breaker subgraph is acyclic, and SCC member order is rewritten (callees-first postorder, then breakers). The inline pass now skips calls to breakers inside the same SCC (and self-calls) instead of blocking all intra-SCC inlining; aggressive filter inlining drops the old per-caller “already inlined this SCC” matrix in favor of the breaker bitset. CallGraph::callers, Members::iter / iter_mut, and InlineLoopBreakerConfig support the new flow, with unit and insta tests.

^{Reviewed by Cursor Bugbot for commit cd68cdf. Bugbot is set up for automated code reviews on this repo. Configure here.}

[augmentcode]

🤖 Augment PR Summary

Summary: Adds loop-breaker-aware inlining for mutually recursive HashQL MIR functions, enabling safe inlining within recursive SCCs without infinite expansion.

Changes:

• Introduces an iterative three-color DFS utility (with cycle detection and postorder callbacks) in hashql_core::graph::algorithms::color.
• Adds loop-breaker selection (loop_breaker.rs) that scores SCC members and greedily picks a feedback-vertex set to break recursion.
• Reorders SCC members so non-breakers are processed in postorder of the breaker-removed DAG, followed by breakers, improving within-SCC optimization ordering.
• Extends the MIR inliner to inline within SCCs except for self-calls and calls targeting loop breakers.
• Enhances call graph analysis with a callers(def) iterator and SCC member iteration helpers.
• Updates MIR formatting/tests to render unary NOT as ~ (instead of !) and refreshes snapshots.
• Adds substantial unit/integration tests covering breaker selection, multi-breaker SCCs, ordering, and aggressive filter behavior.

Technical Notes: Breaker selection uses repeated cycle checks over the remaining subgraph (filtered via DFS edge ignoring) to ensure the non-breaker remainder is acyclic before allowing within-SCC inlining.

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[augmentcode]

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[github-actions]

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2002	$$26.8 \mathrm{ms} \pm 190 \mathrm{μs}\left({\color{lightgreen}-6.312 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.54 \mathrm{ms} \pm 17.6 \mathrm{μs}\left({\color{gray}-2.396 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.2 \mathrm{ms} \pm 76.0 \mathrm{μs}\left({\color{lightgreen}-13.466 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$43.0 \mathrm{ms} \pm 337 \mathrm{μs}\left({\color{gray}-2.356 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.7 \mathrm{ms} \pm 105 \mathrm{μs}\left({\color{gray}-2.562 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.3 \mathrm{ms} \pm 177 \mathrm{μs}\left({\color{lightgreen}-7.081 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$27.8 \mathrm{ms} \pm 202 \mathrm{μs}\left({\color{lightgreen}-6.585 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.85 \mathrm{ms} \pm 21.1 \mathrm{μs}\left({\color{gray}-2.580 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.1 \mathrm{ms} \pm 77.7 \mathrm{μs}\left({\color{lightgreen}-14.333 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.76 \mathrm{ms} \pm 21.2 \mathrm{μs}\left({\color{gray}-3.820 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.96 \mathrm{ms} \pm 12.6 \mathrm{μs}\left({\color{gray}-4.144 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.34 \mathrm{ms} \pm 17.8 \mathrm{μs}\left({\color{gray}-2.282 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.15 \mathrm{ms} \pm 29.7 \mathrm{μs}\left({\color{gray}-4.292 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.54 \mathrm{ms} \pm 20.5 \mathrm{μs}\left({\color{gray}-2.052 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.13 \mathrm{ms} \pm 22.2 \mathrm{μs}\left({\color{gray}-2.558 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.42 \mathrm{ms} \pm 24.3 \mathrm{μs}\left({\color{gray}-3.285 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.43 \mathrm{ms} \pm 16.8 \mathrm{μs}\left({\color{gray}-3.566 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.11 \mathrm{ms} \pm 25.4 \mathrm{μs}\left({\color{gray}-2.146 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.62 \mathrm{ms} \pm 16.4 \mathrm{μs}\left({\color{lightgreen}-6.117 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.53 \mathrm{ms} \pm 13.4 \mathrm{μs}\left({\color{gray}-3.707 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.58 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{lightgreen}-6.911 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.85 \mathrm{ms} \pm 15.9 \mathrm{μs}\left({\color{lightgreen}-5.960 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.65 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{lightgreen}-5.911 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.90 \mathrm{ms} \pm 21.7 \mathrm{μs}\left({\color{gray}-4.950 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.11 \mathrm{ms} \pm 33.7 \mathrm{μs}\left({\color{gray}0.257 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.83 \mathrm{ms} \pm 22.3 \mathrm{μs}\left({\color{gray}-0.872 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$3.11 \mathrm{ms} \pm 25.8 \mathrm{μs}\left({\color{gray}0.355 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.46 \mathrm{ms} \pm 19.7 \mathrm{μs}\left({\color{gray}-2.487 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.99 \mathrm{ms} \pm 14.3 \mathrm{μs}\left({\color{gray}-2.722 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.30 \mathrm{ms} \pm 22.1 \mathrm{μs}\left({\color{gray}-1.639 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.39 \mathrm{ms} \pm 20.0 \mathrm{μs}\left({\color{gray}-2.894 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.95 \mathrm{ms} \pm 12.7 \mathrm{μs}\left({\color{gray}-4.207 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.32 \mathrm{ms} \pm 16.7 \mathrm{μs}\left({\color{gray}-4.303 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$54.9 \mathrm{ms} \pm 269 \mathrm{μs}\left({\color{lightgreen}-5.794 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$47.0 \mathrm{ms} \pm 276 \mathrm{μs}\left({\color{gray}-1.599 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$52.9 \mathrm{ms} \pm 400 \mathrm{μs}\left({\color{gray}2.10 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$46.9 \mathrm{ms} \pm 266 \mathrm{μs}\left({\color{gray}-2.750 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$64.2 \mathrm{ms} \pm 522 \mathrm{μs}\left({\color{gray}-1.077 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$61.6 \mathrm{ms} \pm 296 \mathrm{μs}\left({\color{gray}-3.742 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$56.2 \mathrm{ms} \pm 266 \mathrm{μs}\left({\color{gray}-2.728 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$108 \mathrm{ms} \pm 714 \mathrm{μs}\left({\color{gray}2.69 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$46.5 \mathrm{ms} \pm 211 \mathrm{μs}\left({\color{gray}-3.597 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$309 \mathrm{ms} \pm 946 \mathrm{μs}\left({\color{gray}2.51 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$19.9 \mathrm{ms} \pm 166 \mathrm{μs}\left({\color{gray}-0.798 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$20.4 \mathrm{ms} \pm 136 \mathrm{μs}\left({\color{gray}-1.552 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$20.3 \mathrm{ms} \pm 119 \mathrm{μs}\left({\color{lightgreen}-5.864 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$19.6 \mathrm{ms} \pm 107 \mathrm{μs}\left({\color{gray}-3.088 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$25.3 \mathrm{ms} \pm 153 \mathrm{μs}\left({\color{lightgreen}-5.069 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$19.3 \mathrm{ms} \pm 205 \mathrm{μs}\left({\color{lightgreen}-5.017 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$19.3 \mathrm{ms} \pm 104 \mathrm{μs}\left({\color{gray}-4.376 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$19.9 \mathrm{ms} \pm 112 \mathrm{μs}\left({\color{gray}-3.353 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$20.9 \mathrm{ms} \pm 109 \mathrm{μs}\left({\color{gray}-0.930 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$27.7 \mathrm{ms} \pm 237 \mathrm{μs}\left({\color{gray}-2.201 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity

Function	Value	Mean	Flame graphs
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/block/v/1	$$34.2 \mathrm{ms} \pm 297 \mathrm{μs}\left({\color{lightgreen}-7.505 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$35.9 \mathrm{ms} \pm 360 \mathrm{μs}\left({\color{gray}-3.997 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$33.9 \mathrm{ms} \pm 280 \mathrm{μs}\left({\color{lightgreen}-10.853 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$34.5 \mathrm{ms} \pm 332 \mathrm{μs}\left({\color{lightgreen}-5.696 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$34.4 \mathrm{ms} \pm 301 \mathrm{μs}\left({\color{lightgreen}-13.121 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$34.7 \mathrm{ms} \pm 335 \mathrm{μs}\left({\color{lightgreen}-7.594 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$34.6 \mathrm{ms} \pm 337 \mathrm{μs}\left({\color{lightgreen}-10.311 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$34.7 \mathrm{ms} \pm 331 \mathrm{μs}\left({\color{lightgreen}-10.035 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$33.9 \mathrm{ms} \pm 284 \mathrm{μs}\left({\color{lightgreen}-11.549 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.77 \mathrm{ms} \pm 47.6 \mathrm{μs}\left({\color{lightgreen}-6.781 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$94.5 \mathrm{ms} \pm 488 \mathrm{μs}\left({\color{lightgreen}-11.199 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$153 \mathrm{ms} \pm 635 \mathrm{μs}\left({\color{lightgreen}-5.247 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$101 \mathrm{ms} \pm 588 \mathrm{μs}\left({\color{lightgreen}-9.231 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$114 \mathrm{ms} \pm 846 \mathrm{μs}\left({\color{lightgreen}-8.989 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 602 \mathrm{μs}\left({\color{lightgreen}-8.696 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$128 \mathrm{ms} \pm 534 \mathrm{μs}\left({\color{lightgreen}-7.531 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$105 \mathrm{ms} \pm 581 \mathrm{μs}\left({\color{lightgreen}-5.486 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$132 \mathrm{ms} \pm 576 \mathrm{μs}\left({\color{lightgreen}-8.299 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$112 \mathrm{ms} \pm 640 \mathrm{μs}\left({\color{lightgreen}-5.595 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 542 \mathrm{μs}\left({\color{lightgreen}-8.693 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$121 \mathrm{ms} \pm 619 \mathrm{μs}\left({\color{lightgreen}-6.407 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$120 \mathrm{ms} \pm 506 \mathrm{μs}\left({\color{lightgreen}-7.951 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$132 \mathrm{ms} \pm 630 \mathrm{μs}\left({\color{lightgreen}-13.056 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$145 \mathrm{ms} \pm 594 \mathrm{μs}\left({\color{gray}0.775 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$40.3 \mathrm{ms} \pm 236 \mathrm{μs}\left({\color{lightgreen}-6.199 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$568 \mathrm{ms} \pm 1.11 \mathrm{ms}\left({\color{gray}1.07 \mathrm{\%}}\right) $$	Flame Graph