AgentSkillsCN

performance

Splitrail性能优化指南。在优化解析效率、降低内存占用、提升吞吐量时，此指南将为您提供切实可行的优化建议。

SKILL.md
--- frontmatter
name: performance
description: 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)