In what cases is it recommended to use multi-process data processing?

Multithreaded data processing in Python is recommended in the following cases:

• CPU-bound tasks: when data processing requires intensive computations, and the CPU is the bottleneck. In such cases, multiprocessing allows utilizing multiple CPU cores, bypassing the GIL (Global Interpreter Lock).

• Parallel processing of independent data: if data can be divided into independent parts that are processed in parallel without the need for constant synchronization.

• Processing large volumes of data: when sequential processing takes too much time, and parallelism can significantly speed up execution.

• Process isolation: if you need to avoid issues with shared memory or state, multiprocessing provides a separate address space.

Example of using the multiprocessing module for parallel processing of a list of numbers:

from multiprocessing import Pool

def square(x):
    return x * x

if __name__ == '__main__':
    data = [1, 2, 3, 4, 5]
    with Pool(4) as p:
        results = p.map(square, data)
    print(results)  # [1, 4, 9, 16, 25]

If the task is IO-bound (e.g., network requests), it is better to use multithreading or asynchronous programming.