由于我们可以将多进程日志记录表示为多个发布者和一个订阅者(侦听器)，因此使用ZeroMQ实现PUB-SUB消息传递确实是一种选择。

此外，PyZMQ模块(ZMQ的Python绑定)实现了PUBHandler，这是通过ZMQ发布日志消息的对象。酒吧的套接字。

在web上有一个解决方案，使用PyZMQ和PUBHandler从分布式应用程序集中记录日志，可以很容易地在本地使用多个发布进程。

formatters = {
    logging.DEBUG: logging.Formatter("[%(name)s] %(message)s"),
    logging.INFO: logging.Formatter("[%(name)s] %(message)s"),
    logging.WARN: logging.Formatter("[%(name)s] %(message)s"),
    logging.ERROR: logging.Formatter("[%(name)s] %(message)s"),
    logging.CRITICAL: logging.Formatter("[%(name)s] %(message)s")
}

# This one will be used by publishing processes
class PUBLogger:
    def __init__(self, host, port=config.PUBSUB_LOGGER_PORT):
        self._logger = logging.getLogger(__name__)
        self._logger.setLevel(logging.DEBUG)
        self.ctx = zmq.Context()
        self.pub = self.ctx.socket(zmq.PUB)
        self.pub.connect('tcp://{0}:{1}'.format(socket.gethostbyname(host), port))
        self._handler = PUBHandler(self.pub)
        self._handler.formatters = formatters
        self._logger.addHandler(self._handler)

    @property
    def logger(self):
        return self._logger

# This one will be used by listener process
class SUBLogger:
    def __init__(self, ip, output_dir="", port=config.PUBSUB_LOGGER_PORT):
        self.output_dir = output_dir
        self._logger = logging.getLogger()
        self._logger.setLevel(logging.DEBUG)

        self.ctx = zmq.Context()
        self._sub = self.ctx.socket(zmq.SUB)
        self._sub.bind('tcp://*:{1}'.format(ip, port))
        self._sub.setsockopt(zmq.SUBSCRIBE, "")

        handler = handlers.RotatingFileHandler(os.path.join(output_dir, "client_debug.log"), "w", 100 * 1024 * 1024, 10)
        handler.setLevel(logging.DEBUG)
        formatter = logging.Formatter("%(asctime)s;%(levelname)s - %(message)s")
        handler.setFormatter(formatter)
        self._logger.addHandler(handler)

  @property
  def sub(self):
      return self._sub

  @property
  def logger(self):
      return self._logger

#  And that's the way we actually run things:

# Listener process will forever listen on SUB socket for incoming messages
def run_sub_logger(ip, event):
    sub_logger = SUBLogger(ip)
    while not event.is_set():
        try:
            topic, message = sub_logger.sub.recv_multipart(flags=zmq.NOBLOCK)
            log_msg = getattr(logging, topic.lower())
            log_msg(message)
        except zmq.ZMQError as zmq_error:
            if zmq_error.errno == zmq.EAGAIN:
                pass


# Publisher processes loggers should be initialized as follows:

class Publisher:
    def __init__(self, stop_event, proc_id):
        self.stop_event = stop_event
        self.proc_id = proc_id
        self._logger = pub_logger.PUBLogger('127.0.0.1').logger

     def run(self):
         self._logger.info("{0} - Sending message".format(proc_id))

def run_worker(event, proc_id):
    worker = Publisher(event, proc_id)
    worker.run()

# Starting subscriber process so we won't loose publisher's messages
sub_logger_process = Process(target=run_sub_logger,
                                 args=('127.0.0.1'), stop_event,))
sub_logger_process.start()

#Starting publisher processes
for i in range(MAX_WORKERS_PER_CLIENT):
    processes.append(Process(target=run_worker,
                                 args=(stop_event, i,)))
for p in processes:
    p.start()

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.)

有一个很棒的套餐

包: https://pypi.python.org/pypi/multiprocessing-logging/

代码: https://github.com/jruere/multiprocessing-logging

安装:

pip install multiprocessing-logging

然后添加:

import multiprocessing_logging

# This enables logs inside process
multiprocessing_logging.install_mp_handler()

通过使用处理程序，当前所有解决方案都与日志记录配置过于耦合。我的解决方案具有以下架构和功能:

您可以使用任何想要的日志记录配置 日志记录在守护进程线程中完成 使用上下文管理器安全关闭守护进程 与日志线程的通信是由多进程完成的。队列 在子进程中，日志记录。Logger(和已经定义的实例)被打补丁以将所有记录发送到队列 新:在发送到队列之前格式化回溯和消息，以防止pickle错误

带有使用示例和输出的代码可以在以下Gist中找到:https://gist.github.com/schlamar/7003737

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。

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/…”