What methods did you use for organizing parallel data processing?

To organize parallel data processing in Python, I used several approaches:

1. threading module — for thread-level parallelism, useful for I/O-bound tasks.

2. multiprocessing module — to run multiple processes, which bypasses the GIL and efficiently utilizes multiple CPU cores, especially for CPU-bound tasks.

3. concurrent.futures library — provides a convenient interface for running tasks in threads (ThreadPoolExecutor) or processes (ProcessPoolExecutor).

4. Asynchronous programming (asyncio) — for efficient handling of a large number of I/O operations without creating many threads.

Example using concurrent.futures for parallel processing of a list of numbers:

from concurrent.futures import ThreadPoolExecutor

def process_item(item):
    return item * item

items = [1, 2, 3, 4, 5]

with ThreadPoolExecutor(max_workers=3) as executor:
    results = list(executor.map(process_item, items))

print(results)  # [1, 4, 9, 16, 25]