What are the best practices for using thread pools in Python?

What are the best practices for using thread pools in Python?

When using thread pools in Python, consider the following best practices: 1. Use the concurrent.futures.ThreadPoolExecutor for easier management. 2. Limit the number of threads to avoid context switching overhead. 3. Use submit() for tasks that return results and map() for iterable inputs. 4. Handle exceptions properly to avoid silent failures.

How do I handle exceptions in a thread pool?

You can handle exceptions in a thread pool by using the Future object returned by submit(). Check if the future has an exception using future.exception(). If it does, you can handle it accordingly.

What is the difference between ThreadPoolExecutor and ProcessPoolExecutor?

ThreadPoolExecutor uses threads and is suitable for I/O-bound tasks, while ProcessPoolExecutor uses separate processes and is better for CPU-bound tasks due to Python's Global Interpreter Lock (GIL).

Can I use thread pools for network requests?

Yes, thread pools are great for network requests, especially when you have many I/O-bound tasks. They can help manage multiple simultaneous connections efficiently.