I am looking into 'asyncio' for a new web scraping tool. Is asynchronous programming better for CPU-intensive calculations, or should I stick to multi-processing for those types of software development tasks?
3 answers
Asyncio is designed for IO-bound tasks, like web scraping or database queries, where the program spends most of its time waiting for a response. It allows a single thread to handle thousands of concurrent connections efficiently. However, for CPU-bound tasks like heavy mathematical calculations or image processing, asyncio won't help because of the Global Interpreter Lock (GIL). For those, you must use the Multiprocessing module to utilize multiple CPU cores. In modern software development, we often combine both: async for network calls and multiprocessing for data crunching.
How do you manage the complexity of "callback hell" or nested await statements when your asynchronous software architecture starts growing in size?
For web scraping specifically, libraries like Playwright or httpx are built for async and perform much better than the older, synchronous requests library.
I agree with Amy. Httpx is a fantastic drop-in replacement that supports both sync and async, making it a very versatile tool for software development.
Using TaskGroups and properly structured Coroutines helps, Justin. Python 3.11 introduced better TaskGroups which make managing multiple concurrent tasks much cleaner. In software development, keeping your async functions small and focused is the best way to prevent the code from becoming unreadable or unmanageable.