Python with Async Code

Non-blocking I/O in asyncio uses async/await syntax to run coroutines concurrently without freezing the event loop. Define async functions, use await on I/O operations, and run them with asyncio.run() or gather() to coordinate multiple tasks simultaneously.

Asyncio concurrency patterns include gather() for parallel coroutines, wait() for conditional task completion, and asyncio.to_thread() for CPU-bound work. Use gather() for independent tasks, wait() for complex dependencies, and to_thread() when blocking operations cannot be avoided.

Test async code by marking test functions with @pytest.mark.asyncio, using await inside tests, and ensuring proper teardown of event loops. Pytest-asyncio handles loop creation and fixture injection automatically for clean async testing.

Yes, asyncio is well-suited for web services and data pipelines requiring non-blocking I/O. It enables handling multiple concurrent requests or stream processing without dedicated threads, improving responsiveness and resource efficiency in event-driven applications.

gather() runs coroutines concurrently and returns results in order, failing if any task raises an exception. wait() offers fine-grained control by returning pending and done task sets, allowing conditional handling based on task completion state.

Avoid asyncio for purely CPU-bound tasks without I/O; use multiprocessing instead. Asyncio excels with I/O-bound workloads. For CPU work, wrap it with asyncio.to_thread() or delegate to process pools to prevent blocking the event loop.