中文描述

本技能提供全栈开发相关的最佳实践和模式指导。

name: python-performance-optimization description: Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

Python Performance Optimization

Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.

When to Use This Skill

Identifying performance bottlenecks in Python applications
Reducing application latency and response times
Optimizing CPU-intensive operations
Reducing memory consumption and memory leaks
Improving database query performance
Optimizing I/O operations
Speeding up data processing pipelines
Implementing high-performance algorithms
Profiling production applications

Core Concepts

1. Profiling Types

CPU Profiling: Identify time-consuming functions
Memory Profiling: Track memory allocation and leaks
Line Profiling: Profile at line-by-line granularity
Call Graph: Visualize function call relationships

2. Performance Metrics

Execution Time: How long operations take
Memory Usage: Peak and average memory consumption
CPU Utilization: Processor usage patterns
I/O Wait: Time spent on I/O operations

3. Optimization Strategies

Algorithmic: Better algorithms and data structures
Implementation: More efficient code patterns
Parallelization: Multi-threading/processing
Caching: Avoid redundant computation
Native Extensions: C/Rust for critical paths

Quick Start

Basic Timing

import time

def measure_time():
    """Simple timing measurement."""
    start = time.time()

    # Your code here
    result = sum(range(1000000))

    elapsed = time.time() - start
    print(f"Execution time: {elapsed:.4f} seconds")
    return result

# Better: use timeit for accurate measurements
import timeit

execution_time = timeit.timeit(
    "sum(range(1000000))",
    number=100
)
print(f"Average time: {execution_time/100:.6f} seconds")

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Profile before optimizing - Measure to find real bottlenecks
Focus on hot paths - Optimize code that runs most frequently
Use appropriate data structures - Dict for lookups, set for membership
Avoid premature optimization - Clarity first, then optimize
Use built-in functions - They're implemented in C
Cache expensive computations - Use lru_cache
Batch I/O operations - Reduce system calls
Use generators for large datasets
Consider NumPy for numerical operations
Profile production code - Use py-spy for live systems

Common Pitfalls

Optimizing without profiling
Using global variables unnecessarily
Not using appropriate data structures
Creating unnecessary copies of data
Not using connection pooling for databases
Ignoring algorithmic complexity
Over-optimizing rare code paths
Not considering memory usage

能力边界

✅ 擅长处理

全栈开发相关的最佳实践
代码架构设计指导
性能优化建议

⚠️ 需要素材

具体的项目上下文
技术栈信息

❌ 超出范围

非技术领域的业务决策
具体的代码实现（仅提供指导）

工作流程

Step 1: 需求分析

理解项目需求和技术约束

Step 2: 架构设计

设计系统架构和组件划分

Step 3: 实现指导

提供具体的实现建议和代码模式

Step 4: 代码审查

审查代码质量和最佳实践遵循情况

Step 5: 优化建议

提供性能优化和改进建议

Gotchas

1. 过度设计

避免在简单场景中使用复杂的设计模式

2. 性能忽视

不要忽视性能优化，特别是在生产环境中

3. 安全考虑

始终考虑安全性和数据保护

4. 测试覆盖

确保有足够的测试覆盖，特别是边界情况

5. 文档维护

保持文档与代码同步更新