What are the differences between threading and multiprocessing in Python?

What are the differences between threading and multiprocessing in Python?

Threading and multiprocessing are both techniques to achieve concurrency in Python, but they work differently. Threading uses threads, which are lighter and share the same memory space, while multiprocessing uses separate processes with their own memory space.

Can you explain when to use one over the other?

Sure! Use threading for I/O-bound tasks, like network operations, where waiting for external resources is common. Use multiprocessing for CPU-bound tasks, like heavy computations, where you want to utilize multiple CPU cores.

What are some examples of I/O-bound and CPU-bound tasks?

I/O-bound tasks include web scraping, file reading/writing, and database queries. CPU-bound tasks include image processing, mathematical computations, and data analysis.

Are there any downsides to using threading or multiprocessing?

Yes, threading can lead to issues like race conditions and requires careful management of shared resources. Multiprocessing has higher overhead due to process creation and inter-process communication.