What are the best practices for using a process pool in Python?

What are the best practices for using a process pool in Python?

When using a process pool in Python, it's important to consider the following best practices: 1. Use the concurrent.futures module for easier management. 2. Limit the number of processes to match the number of CPU cores. 3. Handle exceptions properly to avoid silent failures. 4. Use map for simple tasks and submit for more complex ones.

Can you explain why limiting the number of processes is important?

Limiting the number of processes is crucial because it helps prevent excessive context switching and resource contention, which can degrade performance. Ideally, you want to match the number of processes to the number of available CPU cores to maximize efficiency.

What should I do if a process hangs?

If a process hangs, you can implement a timeout when calling submit or map. Additionally, consider using a watchdog mechanism to monitor the processes and restart them if they exceed a certain execution time.

Are there any specific libraries you recommend for managing process pools?

Yes, I recommend using the concurrent.futures module for its simplicity and ease of use. Additionally, libraries like multiprocessing and pathos can provide more advanced features if needed.