async-python-patterns

Run non-blocking I/O tasks concurrently in Python using asyncio by implementing coroutines and executing them with asyncio.gather(). This pattern allows parallel network requests and database operations without blocking the event loop.

Handle producer-consumer queues in async Python by utilizing asyncio primitives like Queue. This pattern coordinates background tasks safely, ensuring concurrent producers and consumers can pass data without race conditions.

Use semaphores in async Python applications to limit concurrency during high-volume operations like web scraping. Semaphores restrict the number of simultaneous active coroutines, preventing resource exhaustion and optimizing performance.

Build WebSocket servers using async context managers in Python to manage connection lifecycles cleanly. This approach handles real-time application states like chat systems efficiently within the asyncio event loop.

An async Python event loop blocks during database operations when synchronous drivers execute instead of non-blocking I/O. Resolve this by adopting async database libraries and avoiding blocking calls within coroutines.

Develop APIs with FastAPI or aiohttp using async context managers to manage resources like database sessions. They ensure proper setup and cleanup of non-blocking I/O connections during concurrent request handling.