如果在日志模块中的锁、线程和fork的组合中出现死锁，则在错误报告6721中报告(另见相关SO问题)。

有一个小的解决方案张贴在这里。

但是，这只会修复日志记录中任何潜在的死锁。这并不能解决问题，事情可能会变得混乱。请参阅此处提供的其他答案。

解决这个问题的唯一方法是非侵入性的:

Spawn each worker process such that its log goes to a different file descriptor (to disk or to pipe.) Ideally, all log entries should be timestamped. Your controller process can then do one of the following: If using disk files: Coalesce the log files at the end of the run, sorted by timestamp If using pipes (recommended): Coalesce log entries on-the-fly from all pipes, into a central log file. (E.g., Periodically select from the pipes' file descriptors, perform merge-sort on the available log entries, and flush to centralized log. Repeat.)

如何将所有日志记录委托给另一个进程，从队列中读取所有日志条目?

LOG_QUEUE = multiprocessing.JoinableQueue()

class CentralLogger(multiprocessing.Process):
    def __init__(self, queue):
        multiprocessing.Process.__init__(self)
        self.queue = queue
        self.log = logger.getLogger('some_config')
        self.log.info("Started Central Logging process")

    def run(self):
        while True:
            log_level, message = self.queue.get()
            if log_level is None:
                self.log.info("Shutting down Central Logging process")
                break
            else:
                self.log.log(log_level, message)

central_logger_process = CentralLogger(LOG_QUEUE)
central_logger_process.start()

只需通过任何多进程机制甚至继承共享LOG_QUEUE，就可以很好地工作!

对于可能需要这个的人，我为multiprocessing_logging包写了一个装饰器，它将当前进程名添加到日志中，这样就可以清楚地看到谁记录了什么。

它还运行install_mp_handler()，因此在创建池之前运行它是没有用的。

这让我可以看到哪个工作人员创建了哪些日志消息。

下面是蓝图和示例:

import sys
import logging
from functools import wraps
import multiprocessing
import multiprocessing_logging

# Setup basic console logger as 'logger'
logger = logging.getLogger()
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(logging.Formatter(u'%(asctime)s :: %(levelname)s :: %(message)s'))
logger.setLevel(logging.DEBUG)
logger.addHandler(console_handler)


# Create a decorator for functions that are called via multiprocessing pools
def logs_mp_process_names(fn):
    class MultiProcessLogFilter(logging.Filter):
        def filter(self, record):
            try:
                process_name = multiprocessing.current_process().name
            except BaseException:
                process_name = __name__
            record.msg = f'{process_name} :: {record.msg}'
            return True

    multiprocessing_logging.install_mp_handler()
    f = MultiProcessLogFilter()

    # Wraps is needed here so apply / apply_async know the function name
    @wraps(fn)
    def wrapper(*args, **kwargs):
        logger.removeFilter(f)
        logger.addFilter(f)
        return fn(*args, **kwargs)

    return wrapper


# Create a test function and decorate it
@logs_mp_process_names
def test(argument):
    logger.info(f'test function called via: {argument}')


# You can also redefine undecored functions
def undecorated_function():
    logger.info('I am not decorated')


@logs_mp_process_names
def redecorated(*args, **kwargs):
    return undecorated_function(*args, **kwargs)


# Enjoy
if __name__ == '__main__':
    with multiprocessing.Pool() as mp_pool:
        # Also works with apply_async
        mp_pool.apply(test, ('mp pool',))
        mp_pool.apply(redecorated)
        logger.info('some main logs')
        test('main program')

最简单的想法是:

获取当前进程的文件名和进程id。 设置一个[WatchedFileHandler][1]。这里将详细讨论此处理程序的原因，但简而言之，其他日志处理程序存在某些更糟糕的竞争条件。这个有最短的竞态条件窗口。 选择日志保存路径，例如“/var/log/…”

只需将日志记录器的实例发布到某个地方。这样，其他模块和客户端就可以使用您的API来获取记录器，而不必导入multiprocessing。