How do Python multiprocessing and threading compare in performance?

How do Python multiprocessing and threading compare in performance?

Python multiprocessing allows for parallel execution on multiple CPU cores, while threading is limited by the Global Interpreter Lock (GIL). Generally, multiprocessing is better for CPU-bound tasks, while threading can be more efficient for I/O-bound tasks.

Can you give me an example of when to use each?

Sure! Use multiprocessing for tasks like image processing or data analysis that require heavy computation. Use threading for tasks like web scraping or network requests where waiting for I/O is common.

What are the downsides of using multiprocessing?

Multiprocessing can have higher memory overhead due to separate memory spaces for each process. It also involves more complex inter-process communication.

Is there a way to combine both approaches?

Yes, you can use multiprocessing for CPU-bound tasks and threading for I/O-bound tasks within the same application to optimize performance.