athola Python Performance Optimization
Profiling and optimization patterns for Python code.
Table of Contents
Quick Start
# Basic timing
import timeit
time = timeit.timeit("sum(range(1000000))", number=100)
print(f"Average: {time / 100:.6f}s")
Verification: Run the command with --help flag to verify availability.
When To Use
- Identifying performance bottlenecks
- Reducing application latency
- Optimizing CPU-intensive operations
- Reducing memory consumption
- Profiling production applications
- Improving database query performance
When NOT To Use
- Async concurrency - use python-async instead
- CPU/GPU system monitoring - use conservation:cpu-gpu-performance
- Async concurrency - use python-async instead
- CPU/GPU system monitoring - use conservation:cpu-gpu-performance
Modules
This skill is organized into focused modules for progressive loading:
profiling-tools
CPU profiling with cProfile, line profiling, memory profiling, and production profiling with py-spy. Essential for identifying where your code spends time and memory.
optimization-patterns
Eleven proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, database operations, and loop transformations (what works in Python vs the compiler).
memory-management
Memory optimization techniques including leak tracking with tracemalloc and weak references for caches. Depends on profiling-tools.
benchmarking-tools
Benchmarking tools including custom decorators and pytest-benchmark for verifying performance improvements.
best-practices
Best practices, common pitfalls, and exit criteria for performance optimization work. Synthesizes guidance from profiling-tools and optimization-patterns.
Exit Criteria
- Profiled code to identify bottlenecks
- Applied appropriate optimization patterns
- Verified improvements with benchmarks
- Memory usage acceptable
- No performance regressions