python-async-patterns

Async/await in Python lets you write non-blocking concurrent code. Use `async def` to define coroutines, `await` to pause execution until an operation completes, and `asyncio.gather()` to run multiple coroutines concurrently. Proper await usage prevents deadlocks and ensures event loop correctness.

Common anti-patterns include forgetting to await coroutines, blocking the event loop with synchronous calls, improper error handling in async code, and mismanaging resources without async context managers. Avoiding these ensures reliable, performant async implementations in FastAPI, Django, and aiohttp.

FastAPI and Django both support async views and handlers. Define endpoints as `async def`, use `await` for I/O operations like database queries or HTTP calls via aiohttp, and manage concurrency with semaphores or gather to control resource usage and prevent bottlenecks.

Use `asyncio.timeout()` or `asyncio.wait_for()` to enforce timeouts on async operations. Wrap coroutines in try-except blocks, handle cancellation properly, and use async context managers to guarantee cleanup. This prevents hanging requests and resource leaks.

Async context managers (`async with`) are essential for managing resources in concurrent code—database connections, HTTP sessions with aiohttp, and locks. They guarantee setup and teardown occur correctly even when coroutines are cancelled, maintaining safety across concurrent tasks.

Use `asyncio.gather()` to run multiple coroutines simultaneously and collect results, or `asyncio.Semaphore` to limit concurrent tasks. For finer control, use `asyncio.create_task()` to schedule coroutines. Choose based on whether you need all results or want to process them as they complete.