What methods are used to implement parallel data processing in systems?

Parallel data processing in Python systems is usually implemented using the following methods:

1. threading module — allows creating threads for parallel code execution. However, due to GIL (Global Interpreter Lock) in CPython, threads do not provide true parallelism for computations, but are suitable for I/O operations.

2. multiprocessing module — creates separate processes that bypass GIL limitations and enable parallel computation on multiple CPU cores.

3. Asynchronous programming with asyncio — allows writing non-blocking code for parallel I/O processing, but not for CPU-intensive tasks.

4. Libraries for parallel computing — such as concurrent.futures (a wrapper over threads and processes), joblib (for parallel loop execution), dask (for distributed processing of large data).

Example of using multiprocessing:

from multiprocessing import Pool

def square(x):
    return x * x

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