How does Python handle concurrency and performance?

How does Python handle concurrency and performance?

Python offers several concurrency models, including threading, multiprocessing, and async I/O. Each has its own performance characteristics.

Can you explain the differences between threading and multiprocessing?

Threading is suitable for I/O-bound tasks, while multiprocessing is better for CPU-bound tasks due to Python's Global Interpreter Lock (GIL).

What about async I/O? When should I use it?

Async I/O is ideal for handling many I/O-bound tasks concurrently, like web scraping or network requests, without blocking.

Which method is the fastest for a CPU-bound task?

For CPU-bound tasks, multiprocessing is generally the fastest as it bypasses the GIL by using separate memory spaces.