How can I improve performance issues with threading in Python?

How can I improve performance issues with threading in Python?

To improve performance with threading in Python, consider using the concurrent.futures module for better thread management.

What are some common pitfalls when using threading?

Common pitfalls include race conditions, deadlocks, and the Global Interpreter Lock (GIL) affecting performance.

How can I avoid race conditions?

You can avoid race conditions by using locks, semaphores, or other synchronization mechanisms to control access to shared resources.

Is there a better alternative to threading for parallelism in Python?

Yes, you might consider using the multiprocessing module, which allows for true parallelism by using separate memory spaces.