When Should I Use Python's ThreadPool Instead of multiprocessing.Pool?

Threading Pools in Python: An Alternative to the multiprocessing Pool

The multiprocessing module's Pool class provides a convenient way to parallelize code using worker processes. However, for certain use cases, it may be desirable to leverage threads instead of processes. This article explores an alternative thread-based Pool interface available within the multiprocessing module.

Problem Statement:

A user seeks a Python library that offers a "Pool" class for threading worker processes, similar to the multiprocessing module's Pool class. This would allow for easy parallelization of tasks similar to the following example using the multiprocessing Pool:

def long_running_func(p):
    c_func_no_gil(p)

p = multiprocessing.Pool(4)
xs = p.map(long_running_func, range(100))

However, the user desires to avoid the overhead associated with creating new processes.

Solution:

The multiprocessing module includes a thread-based Pool interface that is worth exploring. This little-known interface can be imported using the ThreadPool class from the multiprocessing.pool module:

from multiprocessing.pool import ThreadPool

Behind the scenes, this ThreadPool class utilizes a dummy Process class that wraps a Python thread. This dummy Process class is implemented in the multiprocessing.dummy module, which offers a complete multiprocessing interface based on threads.

Example Usage:

To use the ThreadPool, instantiate a ThreadPool object with the desired number of worker threads. Then, invoke the map method to parallelize a function across the worker threads.

# Create a ThreadPool with 4 worker threads
pool = ThreadPool(4)

# Parallelize the `long_running_func` on 100 inputs
results = pool.map(long_running_func, range(100))

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