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FTS Engine (src/barabadb/fts/engine.nim): - Fix bm25Score doing O(n) linear scan per document - Cache IDF per token instead of recomputing for each doc - Use entry.termFreq directly instead of searching postings again - Result: FTS search +438% (249 -> 1360 queries/s) HNSW Vector Engine (src/barabadb/vector/engine.nim): - Optimize distance functions with float32 + 4x loop unrolling - Rewrite searchLayer: swap+pop instead of O(n) del, track worst-nearest instead of sorting nearest on every iteration - Result: HNSW insert +117% (245 -> 543 ops/s), search 2.2x faster Benchmarks: - Add real PostgreSQL comparison script (benchmarks/pg_bench.py) - Add report generator (benchmarks/generate_report.py) - Fix compare.nim cpuTime() bug (was dividing by 1M incorrectly) - Add nimble tasks: bench_pg, bench_report Docs: - Update README.md and docs/en/performance.md with real measured numbers - Add benchmarks/REAL_COMPARISON.md Version bump: 1.1.7 -> 1.1.8
39 lines
1.5 KiB
Markdown
39 lines
1.5 KiB
Markdown
# BaraDB vs PostgreSQL — Real Benchmark Results
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Generated from actual execution on:
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- **CPU:** AMD Ryzen 9 5900X
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- **PostgreSQL:** 15.18 (local)
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- **BaraDB:** git `42043f3`
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## Methodology
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- PostgreSQL: single-row INSERT/SELECT via psycopg2 (client-server overhead included)
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- BaraDB: in-process Nim code (no network overhead)
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- Same dataset sizes for both systems
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## Results
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| Test | PostgreSQL | BaraDB | Speedup |
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|------|-----------|--------|---------|
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| KV Write (100K) | 16.82K/s (5.946s) | 32.23K/s (3.103s) | 1.9x (BaraDB) |
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| KV Read (100K) | 15.08K/s (6.630s) | 3.95M/s (25.3ms) | 261.9x (BaraDB) |
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| BTree Insert (100K) | 17.66K/s (5.664s) | 2.52M/s (39.7ms) | 142.8x (BaraDB) |
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| BTree Get (100K) | 14.50K/s (6.899s) | 2.34M/s (42.7ms) | 161.4x (BaraDB) |
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| BTree Scan (1K ranges) | 2.39K/s (419.2ms) | 11.03M/s (1.0ms) | 4623.3x (BaraDB) |
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| FTS Index (10K docs) | 17.98K/s (556.3ms) | 119.99K/s (83.3ms) | 6.7x (BaraDB) |
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| FTS Search (1K queries) | 784.12/s (1.275s) | 1.36K/s (734.0ms) | 1.7x (BaraDB) |
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## Summary
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- **Total PostgreSQL time:** 27.389s
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- **Total BaraDB time:** 4.029s
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- **Overall speedup:** BaraDB is **6.8x faster**
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## Notes
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- PostgreSQL includes network round-trip and SQL parsing overhead per operation.
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- BaraDB runs in-process with zero serialization/network cost.
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- For embedded/single-node use cases, BaraDB shows significant advantage.
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- BaraDB now outperforms PostgreSQL on all tested metrics including FTS search after optimizations.
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- PostgreSQL excels at durability, replication, and complex ACID transactions.
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