backend-python-performance

Profile your Python backend using cProfile to identify time-consuming functions, then memory-profiler and line-profiler for detailed analysis. Run cProfile on your script to generate call statistics, examine the results with pstats, and focus optimization efforts on the highest-impact functions.

Yes, profile FastAPI endpoints by wrapping them with cProfile or using py-spy for production profiling. Identify which functions consume the most CPU time, then apply optimization patterns like caching with lru_cache, generators, or list comprehensions to reduce latency.

Use memory-profiler to track memory consumption line-by-line and identify where memory usage spikes. Combine it with cProfile call graphs to pinpoint functions holding onto memory, then refactor to release resources or optimize data structures.

Use py-spy for production profiling without code changes, then apply optimization patterns: replace loops with list comprehensions, use generators for large datasets, and cache expensive function calls with lru_cache. Benchmark improvements with pytest-benchmark.

Most tools like cProfile, memory-profiler, and line-profiler run without code changes—invoke them from the command line or import decorators. py-spy attaches to running processes, making it ideal for production. Some tools offer optional decorators for targeted profiling.

Use pytest-benchmark to measure function execution time before and after optimization. Write benchmark tests, run them repeatedly to get stable results, and compare output metrics. This validates that optimization patterns like caching or generators actually improve throughput.