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)