⚡ Bolt: Optimize AdaptiveMemory performance - #12
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- Implement persistent SQLite connection reuse in AdaptiveMemory to eliminate connect/close overhead. - Add thread safety with threading.RLock for the shared connection. - Enable WAL journal mode and normal synchronous mode for better performance. - Results in ~14x speedup (0.29ms vs 4.1ms per op) in benchmarks. - Refactor all db methods to use the protected persistent connection.
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💡 What: Optimized
AdaptiveMemoryby replacing per-operation SQLite connections with a persistent connection managed by a reentrant lock (threading.RLock). Enabled Write-Ahead Logging (WAL) and setsynchronous=NORMALfor the database.🎯 Why: The original implementation opened and closed a new SQLite connection for every memory operation (add, get, update), creating significant overhead in high-frequency paths. This optimization reduces latency and improves throughput for memory operations.
📊 Impact: Benchmarks show a ~14x speedup for mixed read/write operations (avg iteration time reduced from ~4.1ms to ~0.29ms).
🔬 Measurement: Verified with a custom benchmark script
tests/benchmark_memory_lock.py(deleted before submission) and existing teststests/test_core.pyandtests/test_features.pypass.PR created automatically by Jules for task 11585012180690215672 started by @Rohith-Shimori