performance
Performance optimization guidelines for Splitrail. Use when optimizing parsing, reducing memory usage, or improving throughput.
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- Piebald-AI/splitrail
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- 2026年1月1日 00:15
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- performance
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- Performance optimization guidelines for Splitrail. Use when optimizing parsing, reducing memory usage, or improving throughput.
# Performance Considerations
## Techniques Used
- **Parallel analyzer loading** - `futures::join_all()` for concurrent stats loading
- **Parallel file parsing** - `rayon` for parallel iteration over files
- **Fast JSON parsing** - `simd_json` exclusively for all JSON operations (note: `rmcp` crate re-exports `serde_json` for MCP server types)
- **Fast directory walking** - `jwalk` for parallel directory traversal
- **Lazy message loading** - TUI loads messages on-demand for session view
See existing analyzers in `src/analyzers/` for usage patterns.
## Guidelines
1. Prefer parallel processing for I/O-bound operations
2. Use `parking_lot` locks over `std::sync` for better performance
3. Avoid loading all messages into memory when not needed
4. Use `BTreeMap` for date-ordered data (sorted iteration)
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