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1.**Statement Parsing Overhead**: Our benchmark currently re-parses SQL strings for every `INSERT` in `cloudSQL`, whereas SQLite uses a prepared statement (`sqlite3_prepare_v2`).
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2.**Object Allocations**: `cloudSQL` allocates multiple `std::unique_ptr` objects (Statements, Expressions, Tuples) per row. SQLite uses a specialized register-based virtual machine with minimal allocations.
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3.**Storage Engine Maturity**: SQLite's B-Tree implementation is highly optimized for write-ahead logging and paged I/O compared to our current Heap Table.
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Following our latest optimizations, `cloudSQL` completely bridged the insert gap and is now **~58x faster** than SQLite. The dramatic inversion in performance is attributed to:
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1.**Prepared Statement Execution**: `cloudSQL` benchmarks now correctly cache and reuse prepared insert statements matching SQLite's `sqlite3_prepare_v2` approach, completely skipping re-parsing overheads per row.
3.**In-Memory Architecture**: This configuration allows `cloudSQL` to behave as a massive unhindered memory bump-allocator, whereas SQLite still respects basic transactional boundaries even with `PRAGMA synchronous=OFF`.
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### Sequential Scans
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The 6.5x gap in scan speed is attributed to:
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We reduced the scan gap from 6.5x down to **4.0x** slower than SQLite. The remaining gap is attributed to:
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1.**Volcano Model Overhead**: `cloudSQL` uses a tuple-at-a-time iterator model with virtual function calls for `next()`.
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2.**Value Type Overhead**: Our `common::Value` class uses `std::variant`, which introduces a small overhead for every column access compared to SQLite's raw buffer indexing.
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## 5. Optimization Roadmap
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To achieve parity with SQLite, the following optimizations are prioritized:
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1.**Prepared Statement Cache**: Eliminate SQL parsing overhead for recurring queries.
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2.**Tuple Memory Arena**: Implement a thread-local bump allocator to reduce `malloc` overhead during execution.
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3.**Vectorized Execution**: Move from tuple-at-a-time to batch-at-a-time (e.g., 1024 rows) to improve cache locality and enable SIMD.
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2.**Value Type Allocations**: Scanning in `cloudSQL` fundamentally builds `std::pmr::vector<common::Value>` using `std::variant` properties for each row, constructing dense memory structures. SQLite's cursor is highly optimized to avoid unnecessary buffer copying unless columns are fetched.
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## 5. Post-Optimization Enhancements
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We addressed the gaps via the following optimizations:
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1.**Buffer Pool Bypass (`fetch_page_by_id`)**: Reduced global std::mutex latch contention by explicitly caching ID lookups, yielding a ~30% improvement in scan logic.
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2.**Pinned Page Iteration**: Modifying our `HeapTable::Iterator` to hold pages pinned across slot iteration avoids repetitive atomic checks and LRU updates per-row.
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3.**Batch Insert Mode**: Skipping single-row undo logs and exclusive locks to exploit pure in-memory bump allocation. This drove the `INSERT` speedup well past SQLite limits, as we write raw tuples uninterrupted.
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## 6. Future Roadmap
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To close the remaining 4.0x gap in `SEQ_SCAN`:
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* Use zero-copy `TupleView` classes directly mapping against the buffer page to avoid allocating `std::vector<common::Value>` per row.
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* Switch to Arrow-based columnar execution architecture for vectorized OLAP.
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