现在我在框架中有一个中心模块,它使用Python 2.6 multiprocessing模块生成多个进程。因为它使用多处理,所以有一个模块级的多处理感知日志,log = multiprocessing.get_logger()。根据文档,这个日志记录器(EDIT)没有进程共享锁,所以你不会在sys. exe中弄乱东西。Stderr(或任何文件句柄),让多个进程同时写入它。

我现在遇到的问题是框架中的其他模块不支持多处理。在我看来,我需要让这个中心模块上的所有依赖都使用多处理感知日志。这在框架内很烦人,更不用说对框架的所有客户端了。还有我想不到的选择吗?


当前回答

然而,另一种选择可能是日志包中各种非基于文件的日志处理程序:

套接字处理程序 数据报处理程序 系统日志处理程序

(和其他人)

通过这种方式,您可以轻松地在某个地方创建一个日志守护进程,以便安全地对其进行写入并正确地处理结果。(例如,一个简单的套接字服务器,它只是解pickle消息并将其发送到自己的旋转文件处理程序。)

SyslogHandler也会为您处理这个问题。当然,您可以使用自己的syslog实例,而不是系统实例。

其他回答

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。

其他线程的变体,它将日志记录和队列线程分开。

"""sample code for logging in subprocesses using multiprocessing

* Little handler magic - The main process uses loggers and handlers as normal.
* Only a simple handler is needed in the subprocess that feeds the queue.
* Original logger name from subprocess is preserved when logged in main
  process.
* As in the other implementations, a thread reads the queue and calls the
  handlers. Except in this implementation, the thread is defined outside of a
  handler, which makes the logger definitions simpler.
* Works with multiple handlers.  If the logger in the main process defines
  multiple handlers, they will all be fed records generated by the
  subprocesses loggers.

tested with Python 2.5 and 2.6 on Linux and Windows

"""

import os
import sys
import time
import traceback
import multiprocessing, threading, logging, sys

DEFAULT_LEVEL = logging.DEBUG

formatter = logging.Formatter("%(levelname)s: %(asctime)s - %(name)s - %(process)s - %(message)s")

class SubProcessLogHandler(logging.Handler):
    """handler used by subprocesses

    It simply puts items on a Queue for the main process to log.

    """

    def __init__(self, queue):
        logging.Handler.__init__(self)
        self.queue = queue

    def emit(self, record):
        self.queue.put(record)

class LogQueueReader(threading.Thread):
    """thread to write subprocesses log records to main process log

    This thread reads the records written by subprocesses and writes them to
    the handlers defined in the main process's handlers.

    """

    def __init__(self, queue):
        threading.Thread.__init__(self)
        self.queue = queue
        self.daemon = True

    def run(self):
        """read from the queue and write to the log handlers

        The logging documentation says logging is thread safe, so there
        shouldn't be contention between normal logging (from the main
        process) and this thread.

        Note that we're using the name of the original logger.

        """
        # Thanks Mike for the error checking code.
        while True:
            try:
                record = self.queue.get()
                # get the logger for this record
                logger = logging.getLogger(record.name)
                logger.callHandlers(record)
            except (KeyboardInterrupt, SystemExit):
                raise
            except EOFError:
                break
            except:
                traceback.print_exc(file=sys.stderr)

class LoggingProcess(multiprocessing.Process):

    def __init__(self, queue):
        multiprocessing.Process.__init__(self)
        self.queue = queue

    def _setupLogger(self):
        # create the logger to use.
        logger = logging.getLogger('test.subprocess')
        # The only handler desired is the SubProcessLogHandler.  If any others
        # exist, remove them. In this case, on Unix and Linux the StreamHandler
        # will be inherited.

        for handler in logger.handlers:
            # just a check for my sanity
            assert not isinstance(handler, SubProcessLogHandler)
            logger.removeHandler(handler)
        # add the handler
        handler = SubProcessLogHandler(self.queue)
        handler.setFormatter(formatter)
        logger.addHandler(handler)

        # On Windows, the level will not be inherited.  Also, we could just
        # set the level to log everything here and filter it in the main
        # process handlers.  For now, just set it from the global default.
        logger.setLevel(DEFAULT_LEVEL)
        self.logger = logger

    def run(self):
        self._setupLogger()
        logger = self.logger
        # and here goes the logging
        p = multiprocessing.current_process()
        logger.info('hello from process %s with pid %s' % (p.name, p.pid))


if __name__ == '__main__':
    # queue used by the subprocess loggers
    queue = multiprocessing.Queue()
    # Just a normal logger
    logger = logging.getLogger('test')
    handler = logging.StreamHandler()
    handler.setFormatter(formatter)
    logger.addHandler(handler)
    logger.setLevel(DEFAULT_LEVEL)
    logger.info('hello from the main process')
    # This thread will read from the subprocesses and write to the main log's
    # handlers.
    log_queue_reader = LogQueueReader(queue)
    log_queue_reader.start()
    # create the processes.
    for i in range(10):
        p = LoggingProcess(queue)
        p.start()
    # The way I read the multiprocessing warning about Queue, joining a
    # process before it has finished feeding the Queue can cause a deadlock.
    # Also, Queue.empty() is not realiable, so just make sure all processes
    # are finished.
    # active_children joins subprocesses when they're finished.
    while multiprocessing.active_children():
        time.sleep(.1)

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.)

我有一个解决方案,类似于ironhacker的,除了我使用日志。在我的一些代码中,我发现我需要在将异常传递回队列之前格式化它,因为回溯是不能pickle的:

class QueueHandler(logging.Handler):
    def __init__(self, queue):
        logging.Handler.__init__(self)
        self.queue = queue
    def emit(self, record):
        if record.exc_info:
            # can't pass exc_info across processes so just format now
            record.exc_text = self.formatException(record.exc_info)
            record.exc_info = None
        self.queue.put(record)
    def formatException(self, ei):
        sio = cStringIO.StringIO()
        traceback.print_exception(ei[0], ei[1], ei[2], None, sio)
        s = sio.getvalue()
        sio.close()
        if s[-1] == "\n":
            s = s[:-1]
        return s

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