async-python-patterns

Asyncio enables non-blocking concurrent requests by using coroutines and tasks. Define async functions for each request, gather them with asyncio.gather(), and execute multiple requests simultaneously without blocking, dramatically reducing total runtime for I/O-bound web scraping workloads.

Coroutines are functions defined with async/await syntax. Tasks wrap coroutines for concurrent execution on the event loop. Futures represent a result that will be available later. Tasks are Futures with scheduling; coroutines must be wrapped in tasks to run concurrently.

Use asyncio.wait_for() to set timeouts on coroutines and asyncio.CancelledError to handle cancellation. Async context managers allow safe cleanup when operations timeout or are cancelled, preventing resource leaks in long-running applications.

Yes. FastAPI and aiohttp both integrate directly with asyncio. Define endpoints as async functions, use async context managers for database and HTTP connections, and leverage the event loop for handling concurrent requests efficiently in production APIs.

Async context managers (async with) manage resources like database connections in async code. Async iterators (async for) allow iterating over asynchronous data streams. Both enable clean, non-blocking resource handling and data consumption in async applications.

Asyncio is preferable for I/O-bound tasks because it avoids threading overhead and GIL contention through cooperative multitasking on a single thread. It scales better for thousands of concurrent operations and simplifies error handling compared to threading.