What are the best practices for multithreading and multiprocessing in Python?

What are the best practices for multithreading and multiprocessing in Python?

When using multithreading, ensure that your threads are lightweight and avoid blocking operations. For multiprocessing, use the multiprocessing module to create separate processes, which can bypass the Global Interpreter Lock (GIL). Always manage shared resources carefully to avoid race conditions.

Can you explain the difference between multithreading and multiprocessing?

Sure! Multithreading involves multiple threads within a single process, sharing the same memory space, while multiprocessing uses multiple processes, each with its own memory space. This means multiprocessing can take full advantage of multiple CPU cores.

What are some common pitfalls to avoid?

Common pitfalls include not handling exceptions properly, failing to join threads or processes, and neglecting to manage shared data, which can lead to deadlocks or race conditions.

How can I improve performance in my Python application?

To improve performance, profile your application to identify bottlenecks, use multiprocessing for CPU-bound tasks, and consider using asynchronous programming for I/O-bound tasks.