python-performance-optimization
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.
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SKILL.md
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
Files
3- SKILL.md
807aa725cc3.2 KB - references/advanced-patterns.md
dc1bca8a3710.3 KB - references/details.md
8643031fab7.2 KB
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