performance-optimisation

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Performance Optimisation Skill

性能优化技能

Tooling Notes

工具使用说明

This skill should only use read-only commands and avoid modifying files.

本技能仅应使用只读命令，避免修改文件。

Workflow

工作流程

Copy this checklist and use it to track your progress through the performance optimisation process:

markdown

Performance Optimisation Checklist

- [ ] Measure Baseline Performance
  - [ ] Use profiling tools to gather performance metrics.
  - [ ] Identify slow functions, database queries, and network requests.
- [ ] Identify Bottlenecks
  - [ ] Analyse profiling data to pinpoint performance issues.
  - [ ] Prioritise issues based on impact and ease of resolution.
- [ ] Implement Optimisations
  - [ ] Optimise algorithms and data structures.
  - [ ] Improve database query efficiency.
  - [ ] Reduce network latency and payload sizes.
  - [ ] Implement caching strategies where appropriate.
- [ ] Validate Improvements
  - [ ] Re-measure performance after optimisations.
  - [ ] Ensure that optimisations have led to measurable improvements.
- [ ] Document Changes
  - [ ] Update documentation to reflect performance changes.
  - [ ] Provide explanations for significant optimisations.

复制以下清单，用于跟踪性能优化过程的进度：

markdown

性能优化清单

- [ ] 测量基准性能
  - [ ] 使用性能分析工具收集性能指标。
  - [ ] 识别缓慢的函数、数据库查询和网络请求。
- [ ] 识别性能瓶颈
  - [ ] 分析性能分析数据以定位性能问题。
  - [ ] 根据影响程度和解决难度对问题进行优先级排序。
- [ ] 实施优化方案
  - [ ] 优化算法和数据结构。
  - [ ] 提升数据库查询效率。
  - [ ] 减少网络延迟和负载大小。
  - [ ] 在合适的场景下实施缓存策略。
- [ ] 验证优化效果
  - [ ] 优化后重新测量性能。
  - [ ] 确保优化措施带来了可量化的性能提升。
- [ ] 记录变更
  - [ ] 更新文档以反映性能相关变更。
  - [ ] 对重大优化措施提供说明。

Profiling Commands

性能分析命令

bash

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Node.js profiling

Node.js 性能分析

node --prof app.js node --prof-process isolate-0x*.log > processed.txt

Python profiling

Python 性能分析

python -m cProfile -o profile.out app.py snakeviz profile.out

Database query analysis (PostgreSQL example)

数据库查询分析（PostgreSQL示例）

EXPLAIN ANALYZE SELECT * FROM your_table WHERE condition;

Web performance analysis

Web性能分析

lighthouse https://yourwebsite.com --output html --output-path report.html

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lighthouse https://yourwebsite.com --output html --output-path report.html

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Common Bottlenecks and Ways to Fix Them

常见性能瓶颈及解决方法

Inefficient Algorithms: Replace with more efficient algorithms or data structures.
Database Query Performance: Optimize queries, add indexes, or denormalize data.
Network Latency: Minimize requests, use CDNs, and compress payloads.
Unnecessary Computations: Cache results of expensive operations.
Memory Leaks: Identify and fix memory leaks to improve performance over time.

低效算法：替换为更高效的算法或数据结构。
数据库查询性能：优化查询语句、添加索引或进行数据反规范化。
网络延迟：减少请求数量、使用CDN并压缩负载。
不必要的计算：缓存高开销操作的结果。
内存泄漏：识别并修复内存泄漏以提升长期性能。