Deadlock with logging multiprocess/multithread pyt

Question

I am facing the problem with collecting logs from the following script. Once I set up the SLEEP_TIME to too "small" value, the LoggingThread threads somehow block the logging module. The script freeze on logging request in the action function. If the SLEEP_TIME is about 0.1 the script collect all log messages as I expect.

I tried to follow this answer but it does not solve my problem.

import multiprocessing
import threading
import logging
import time

SLEEP_TIME = 0.000001

logger = logging.getLogger()

ch = logging.StreamHandler()
ch.setFormatter(logging.Formatter('%(asctime)s %(levelname)s %(funcName)s(): %(message)s'))
ch.setLevel(logging.DEBUG)

logger.setLevel(logging.DEBUG)
logger.addHandler(ch)


class LoggingThread(threading.Thread):

    def __init__(self):
        threading.Thread.__init__(self)

    def run(self):
        while True:
            logger.debug('LoggingThread: {}'.format(self))
            time.sleep(SLEEP_TIME)


def action(i):
    logger.debug('action: {}'.format(i))


def do_parallel_job():

    processes = multiprocessing.cpu_count()
    pool = multiprocessing.Pool(processes=processes)
    for i in range(20):
        pool.apply_async(action, args=(i,))
    pool.close()
    pool.join()



if __name__ == '__main__':

    logger.debug('START')

    #
    # multithread part
    #
    for _ in range(10):
        lt = LoggingThread()
        lt.setDaemon(True)
        lt.start()

    #
    # multiprocess part
    #
    do_parallel_job()

    logger.debug('FINISH')

How to use logging module in multiprocess and multithread scripts?

Answer 1

This is probably bug 6721.

The problem is common in any situation where you have locks, threads and forks. If thread 1 had a lock while thread 2 calls fork, in the forked process, there will only be thread 2 and the lock will be held forever. In your case, that is logging.StreamHandler.lock.

A fix can be found here for the logging module. Note that you need to take care of any other locks, too.