Async Programming
asyncio Fundamentals
Understanding Python async/await and the event loop
Interview: High demand — FastAPI, async web, real-time systems
Python's asyncio enables concurrent I/O operations without threads. It uses cooperative multitasking — coroutines voluntarily yield control at await points, allowing the event loop to run other tasks.
When to Use Async
- I/O-bound tasks: HTTP requests, database queries, file operations
- Many concurrent connections: Web servers, chat applications, websockets
- NOT for CPU-bound work: Use multiprocessing instead
Async vs Threading
Threading uses OS-level context switching (preemptive). Asyncio uses cooperative multitasking (you control when to yield). Async has lower overhead and avoids race conditions, but requires all I/O to be async-compatible.
Use Cases
FastAPI and async web frameworks
Concurrent HTTP API calls (fan-out pattern)
WebSocket servers for real-time applications
Async database drivers (asyncpg, motor for MongoDB)
Common Mistakes
Calling blocking code (requests.get) inside an async function — use aiohttp instead
Forgetting await on coroutine calls — returns a coroutine object, not the result
Using asyncio for CPU-bound work — use multiprocessing instead
Not handling exceptions in gathered tasks — one failure cancels all