What are common mistakes when using parallelism in Python?

What are common mistakes when using parallelism in Python?

Some common mistakes include not managing shared state properly, using too many processes, and not handling exceptions.

Can you explain why managing shared state is important?

Managing shared state is crucial because it prevents race conditions and ensures data consistency across processes.

What happens if I use too many processes?

Using too many processes can lead to overhead, increased context switching, and can actually slow down your program.

How can I handle exceptions in parallel tasks?

You can handle exceptions by using try-except blocks within your parallel tasks and ensuring proper logging.

Are there any tools or libraries that can help with parallelism in Python?

Yes, libraries like multiprocessing, concurrent.futures, and joblib are great for managing parallel tasks in Python.