What are the best practices for using concurrent futures in Python?

What are the best practices for using concurrent futures in Python?

When using concurrent futures in Python, it's important to manage resources effectively. Here are some best practices:

1. Use ThreadPoolExecutor for I/O-bound tasks and ProcessPoolExecutor for CPU-bound tasks.
2. Limit the number of workers to avoid overwhelming the system.
3. Handle exceptions properly to avoid silent failures.
4. Use futures to track the progress of tasks.

Can you explain how to handle exceptions in concurrent futures?

Certainly! You can handle exceptions in concurrent futures by using the result() method of a Future object. If the task raises an exception, calling result() will raise that exception. You can wrap it in a try-except block to manage it gracefully.

What is the difference between ThreadPoolExecutor and ProcessPoolExecutor?

ThreadPoolExecutor is designed for I/O-bound tasks and uses threads, which are lighter and share memory space. ProcessPoolExecutor, on the other hand, is meant for CPU-bound tasks and uses separate processes, which can take advantage of multiple CPU cores but have higher overhead due to inter-process communication.

How can I monitor the progress of tasks in concurrent futures?

You can monitor the progress of tasks by using the as_completed() function, which yields futures as they complete. This allows you to check the status of each task and retrieve results or handle exceptions as they finish.