What are the differences between multithreading and multiprocessing in Python?

What are the differences between multithreading and multiprocessing in Python?

Multithreading involves multiple threads within a single process, while multiprocessing uses multiple processes. Multithreading is more memory efficient but can lead to issues with thread safety, whereas multiprocessing can handle CPU-bound tasks better by utilizing multiple cores.

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

Sure! Use multithreading for I/O-bound tasks like web scraping, where waiting for responses is common. Use multiprocessing for CPU-bound tasks like data processing, where you want to leverage multiple CPU cores.

What are the performance implications of each?

Multithreading can lead to context switching overhead, which may slow down performance if not managed well. Multiprocessing avoids this by running separate memory spaces, but it has higher memory usage and inter-process communication overhead.

Are there any libraries in Python that help with these?

Yes! For multithreading, you can use the threading module, and for multiprocessing, the multiprocessing module is available. Additionally, libraries like concurrent.futures provide a higher-level interface for both.