concurrency-async

To choose the right Python concurrency model, match your workload type to the appropriate library: use asyncio for I/O-bound tasks, threading for simple parallel I/O, and multiprocessing for CPU-bound tasks. The selection depends on library support and specific task characteristics.

The difference between asyncio TaskGroup and gather lies in error handling and structure. TaskGroup provides built-in error handling for managing Python async tasks, while gather is used for concurrent execution. Guidelines recommend choosing based on your specific rate limiting and error management needs.

You should use multiprocessing instead of asyncio in Python when your workload is CPU-bound. Multiprocessing bypasses the Global Interpreter Lock to execute parallel tasks, whereas asyncio is designed primarily for I/O-bound operations and concurrent network requests.

Yes, you can bridge synchronous Python code to async using threading. Threading patterns allow you to integrate blocking synchronous libraries into an asyncio event loop, ensuring the asynchronous application maintains performance without freezing the main event loop.

Common Python concurrency pitfalls include choosing the wrong model for your workload type and improper error handling in asyncio. Avoiding these anti-patterns ensures resources are used efficiently and performance gains are realized without common concurrency mistakes.