I built multiprocessing-pool-demo because understanding how Python's multiprocessing.Pool manages concurrency is key for performance. This is a ready-to-run script that makes it clear.
When you set a worker count for multiprocessing.Pool, it doesn't just fire off tasks instantly. This script logs the start and finish times for each individual task executed by the pool. By observing these timestamps, you can see when tasks begin, when they complete, and crucially, identify periods where the pool is waiting for workers to become free.
It helps visualize the scheduling behavior and the impact of the worker limit. You can observe how tasks are queued and processed within the constraints you define. This is useful if you're working with CPU-bound tasks and want to optimize their execution using multiple processes, but need to ensure you're not overwhelming your system or creating unnecessary overhead.
It's designed for developers who use Python's multiprocessing module and want a concrete example of pool behavior. You can download and run it to see these concepts in action.
