Python: Writing to a single file with queue while using multiprocessing Pool

Multiprocessing pools implement a queue for you. Just use a pool method that returns the worker return value to the caller. imap works well:

import multiprocessing 
import re

def mp_worker(filename):
    with open(filename) as f:
        text = f.read()
    m = re.findall("x+", text)
    count = len(max(m, key=len))
    return filename, count

def mp_handler():
    p = multiprocessing.Pool(32)
    with open('infilenamess.txt') as f:
        filenames = [line for line in (l.strip() for l in f) if line]
    with open('results.txt', 'w') as f:
        for result in p.imap(mp_worker, filenames):
            # (filename, count) tuples from worker
            f.write('%s: %d\n' % result)

if __name__=='__main__':
    mp_handler()

I took the accepted answer and simplified it for my own understanding of how this works. I am posting it here in case it helps someone else.

import multiprocessing

def mp_worker(number):
    number += 1
    return number

def mp_handler():
    p = multiprocessing.Pool(32)
    numbers = list(range(1000))
    with open('results.txt', 'w') as f:
        for result in p.imap(mp_worker, numbers):
            f.write('%d\n' % result)

if __name__=='__main__':
    mp_handler()

Here's my approach using a multiprocessing Manager object. The nice thing about this approach is that when processing drops out of the manager with block in the run_multi() function, the filewriter queue is automatically closed making code very easy to read and you have no hassle trying to stop listening on the queue.

from functools import partial
from multiprocessing import Manager, Pool, Queue
from random import randint
import time

def run_multi():
    input = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    with Manager() as manager:
        pool = Pool()  # By default pool will size depending on cores available
        message_queue = manager.Queue()  # Queue for sending messages to file writer listener
        pool.apply_async(file_writer, (message_queue, ))  # Start file listener ahead of doing the work
        pool.map(partial(worker, message_queue=message_queue), input)  # Partial function allows us to use map to divide workload

def worker(input: int, message_queue: Queue):
    message_queue.put(input * 10)
    time.sleep(randint(1, 5))  # Simulate hard work

def file_writer(message_queue: Queue):
    with open("demo.txt", "a") as report:
        while True:
            report.write(f"Value is: {message_queue.get()}\n")

if __name__ == "__main__":
    run_multi()