What are the limitations of process pools in Python?
Process pools in Python can be limited by the number of available CPU cores, memory usage, and inter-process communication overhead.
How does the number of CPU cores affect process pools?
The number of CPU cores determines how many processes can run concurrently. If you exceed this number, processes may be queued.
What happens if I have too many processes in the pool?
Having too many processes can lead to increased context switching, higher memory usage, and can slow down overall performance.
Are there any alternatives to process pools?
Yes, alternatives include thread pools, asynchronous programming, or using libraries like Dask for parallel computing.
Can I adjust the size of the process pool?
Yes, you can specify the number of processes when creating a process pool using the Pool
class in the multiprocessing
module.
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