How to Profile and Fix Performance Issues in Asyncio Apps: A Practical Guide for Developers
How to Profile and Fix Performance Issues in Asyncio Apps: A Practical Guide for Developers Asyncio apps are powerful: they let you handle thousands of concurrent connections on a single process. But when latency, p95/p99 spikes, or memory growth appear, debugging feels like chasing ghosts — tasks are suspended and resumed across await points, the event loop is invisible, and traditional profilers can mislead. This guide gives you a production-safe, step-by-step playbook to profile async Python , use low-overhead tools like py-spy , combine sampling with tracing, capture coroutine stacks, and apply practical fixes. You'll get checklists, copy-paste commands, code snippets, and real case studies so you can quickly diagnose async performance and prevent regressions. Why async performance problems are different: common causes and symptoms Event loop fundamentals that matter for performance In asyncio, a single event loop schedules coroutines and callbacks. If any piece of co...