日志操作手册

作者

Vinay Sajip <vinay_sajip at red-dove dot com>

本页包含了许多日志记录相关的概念，这些概念在过去一直被认为很有用。

在多个模块中使用日志

无论对 logging.getLogger('someLogger') 进行多少次调用，都会返回同一个 logger 对象的引用。不仅在同一个模块内如此，只要是在同一个 Python 解释器进程中，跨模块调用也是一样。同样是引用同一个对象，应用程序也可以在一个模块中定义和配置一个父 logger，而在另一个单独的模块中创建（但不配置）子 logger，对于子 logger 的所有调用都会传给父 logger。以下是主模块：

import logging
import auxiliary_module
# create logger with 'spam_application'
logger = logging.getLogger('spam_application')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler('spam.log')
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.info('creating an instance of auxiliary_module.Auxiliary')
a = auxiliary_module.Auxiliary()
logger.info('created an instance of auxiliary_module.Auxiliary')
logger.info('calling auxiliary_module.Auxiliary.do_something')
a.do_something()
logger.info('finished auxiliary_module.Auxiliary.do_something')
logger.info('calling auxiliary_module.some_function()')
auxiliary_module.some_function()
logger.info('done with auxiliary_module.some_function()')

以下是辅助模块：

import logging
# create logger
module_logger = logging.getLogger('spam_application.auxiliary')
class Auxiliary:
    def __init__(self):
        self.logger = logging.getLogger('spam_application.auxiliary.Auxiliary')
        self.logger.info('creating an instance of Auxiliary')
    def do_something(self):
        self.logger.info('doing something')
        a = 1 + 1
        self.logger.info('done doing something')
def some_function():
    module_logger.info('received a call to "some_function"')

输出结果会像这样:

2005-03-23 23:47:11,663 - spam_application - INFO -
   creating an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,665 - spam_application.auxiliary.Auxiliary - INFO -
   creating an instance of Auxiliary
2005-03-23 23:47:11,665 - spam_application - INFO -
   created an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,668 - spam_application - INFO -
   calling auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,668 - spam_application.auxiliary.Auxiliary - INFO -
   doing something
2005-03-23 23:47:11,669 - spam_application.auxiliary.Auxiliary - INFO -
   done doing something
2005-03-23 23:47:11,670 - spam_application - INFO -
   finished auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,671 - spam_application - INFO -
   calling auxiliary_module.some_function()
2005-03-23 23:47:11,672 - spam_application.auxiliary - INFO -
   received a call to 'some_function'
2005-03-23 23:47:11,673 - spam_application - INFO -
   done with auxiliary_module.some_function()

在多个线程中记录日志

多线程记录日志并不需要特殊处理，以下示例演示了在主线程（起始线程）和其他线程中记录日志的过程：

import logging
import threading
import time
def worker(arg):
    while not arg['stop']:
        logging.debug('Hi from myfunc')
        time.sleep(0.5)
def main():
    logging.basicConfig(level=logging.DEBUG, format='%(relativeCreated)6d %(threadName)s %(message)s')
    info = {'stop': False}
    thread = threading.Thread(target=worker, args=(info,))
    thread.start()
    while True:
        try:
            logging.debug('Hello from main')
            time.sleep(0.75)
        except KeyboardInterrupt:
            info['stop'] = True
            break
    thread.join()
if __name__ == '__main__':
    main()

运行结果会像如下这样:

   0 Thread-1 Hi from myfunc
   3 MainThread Hello from main
 505 Thread-1 Hi from myfunc
 755 MainThread Hello from main
1007 Thread-1 Hi from myfunc
1507 MainThread Hello from main
1508 Thread-1 Hi from myfunc
2010 Thread-1 Hi from myfunc
2258 MainThread Hello from main
2512 Thread-1 Hi from myfunc
3009 MainThread Hello from main
3013 Thread-1 Hi from myfunc
3515 Thread-1 Hi from myfunc
3761 MainThread Hello from main
4017 Thread-1 Hi from myfunc
4513 MainThread Hello from main
4518 Thread-1 Hi from myfunc

以上如期显示了不同线程的日志是交替输出的。当然更多的线程也会如此。

多个 handler 和多种 formatter

日志是个普通的 Python 对象。 addHandler() 方法可加入不限数量的日志 handler。有时候，应用程序需把严重错误信息记入文本文件，而将一般错误或其他级别的信息输出到控制台。若要进行这样的设定，只需多配置几个日志 handler 即可，应用程序的日志调用代码可以保持不变。下面对之前的分模块日志示例略做修改：

import logging
logger = logging.getLogger('simple_example')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler('spam.log')
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
fh.setFormatter(formatter)
# add the handlers to logger
logger.addHandler(ch)
logger.addHandler(fh)
# 'application' code
logger.debug('debug message')
logger.info('info message')
logger.warning('warn message')
logger.error('error message')
logger.critical('critical message')

需要注意的是，“应用程序”内的代码并不关心是否存在多个日志 handler。示例中所做的改变，只是新加入并配置了一个名为 fh 的 handler。

在编写和测试应用程序时，若能创建日志 handler 对不同严重级别的日志信息进行过滤，这将十分有用。调试时无需用多条 print 语句，而是采用 logger.debug ：print 语句以后还得注释或删掉，而 logger.debug 语句可以原样留在源码中保持静默。当需要再次调试时，只要改变日志对象或 handler 的严重级别即可。

在多个地方记录日志

假定要根据不同的情况将日志以不同的格式写入控制台和文件。比如把 DEBUG 以上级别的日志信息写于文件，并且把 INFO 以上的日志信息输出到控制台。再假设日志文件需要包含时间戳，控制台信息则不需要。以下演示了做法：

import logging
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
                    datefmt='%m-%d %H:%M',
                    filename='/temp/myapp.log',
                    filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')

当运行后，你会看到控制台如下所示

root        : INFO     Jackdaws love my big sphinx of quartz.
myapp.area1 : INFO     How quickly daft jumping zebras vex.
myapp.area2 : WARNING  Jail zesty vixen who grabbed pay from quack.
myapp.area2 : ERROR    The five boxing wizards jump quickly.

而日志文件将如下所示：

10-22 22:19 root         INFO     Jackdaws love my big sphinx of quartz.
10-22 22:19 myapp.area1  DEBUG    Quick zephyrs blow, vexing daft Jim.
10-22 22:19 myapp.area1  INFO     How quickly daft jumping zebras vex.
10-22 22:19 myapp.area2  WARNING  Jail zesty vixen who grabbed pay from quack.
10-22 22:19 myapp.area2  ERROR    The five boxing wizards jump quickly.

如您所见，DEBUG 级别的日志信息只出现在了文件中，而其他信息则两个地方都会输出。

上述示例只用到了控制台和文件 handler，当然还可以自由组合任意数量的日志 handler。

日志配置服务器示例

以下是一个用到了日志配置服务器的模块示例：

import logging
import logging.config
import time
import os
# read initial config file
logging.config.fileConfig('logging.conf')
# create and start listener on port 9999
t = logging.config.listen(9999)
t.start()
logger = logging.getLogger('simpleExample')
try:
    # loop through logging calls to see the difference
    # new configurations make, until Ctrl+C is pressed
    while True:
        logger.debug('debug message')
        logger.info('info message')
        logger.warning('warn message')
        logger.error('error message')
        logger.critical('critical message')
        time.sleep(5)
except KeyboardInterrupt:
    # cleanup
    logging.config.stopListening()
    t.join()

以下脚本将接受文件名作为参数，然后将此文件发送到服务器，前面加上文件的二进制编码长度，做为新的日志配置：

#!/usr/bin/env python
import socket, sys, struct
with open(sys.argv[1], 'rb') as f:
    data_to_send = f.read()
HOST = 'localhost'
PORT = 9999
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
print('connecting...')
s.connect((HOST, PORT))
print('sending config...')
s.send(struct.pack('>L', len(data_to_send)))
s.send(data_to_send)
s.close()
print('complete')

处理日志 handler 的阻塞

有时需让日志 handler 不要阻塞当前的线程。这在 Web 应用程序中比较常见，当然在其他场景中也会发生。

有一种原因往往会让程序表现迟钝，这就是 SMTPHandler：由于很多因素是开发人员无法控制的（例如邮件或网络基础设施的性能不佳），发送电子邮件可能需要很长时间。不过几乎所有网络 handler 都可能会发生阻塞：即使是 SocketHandler 操作也可能在后台执行 DNS 查询，而这种查询实在太慢了（并且 DNS 查询还可能在很底层的套接字库代码中，位于 Python 层之下，超出了可控范围）。

有一种解决方案是分成两部分实现。第一部分，针对那些对性能有要求的关键线程，只为日志对象连接一个 QueueHandler。日志对象只需简单地写入队列即可，可为队列设置足够大的容量，或者可以在初始化时不设置容量上限。尽管为以防万一，可能需要在代码中捕获 queue.Full 异常,不过队列写入操作通常会很快得以处理。如果要开发库代码，包含性能要求较高的线程，为了让使用该库的开发人员受益，请务必在开发文档中进行标明（包括建议仅连接 QueueHandlers ）。

解决方案的另一部分就是 QueueListener，它被设计为 QueueHandler 的对应部分。QueueListener 非常简单：传入一个队列和一些 handler，并启动一个内部线程，用于侦听 QueueHandlers``（或其他 ``LogRecords 源）发送的 LogRecord 队列。LogRecords 会从队列中移除并传给 handler 处理。

QueueListener 作为单独的类，好处就是可以用同一个实例为多个 QueueHandlers 服务。这比把现有 handler 类线程化更加资源友好，后者会每个 handler 会占用一个线程，却没有特别的好处。

以下是这两个类的运用示例（省略了 import 语句）：

que = queue.Queue(-1)  # no limit on size
queue_handler = QueueHandler(que)
handler = logging.StreamHandler()
listener = QueueListener(que, handler)
root = logging.getLogger()
root.addHandler(queue_handler)
formatter = logging.Formatter('%(threadName)s: %(message)s')
handler.setFormatter(formatter)
listener.start()
# The log output will display the thread which generated
# the event (the main thread) rather than the internal
# thread which monitors the internal queue. This is what
# you want to happen.
root.warning('Look out!')
listener.stop()

在运行后会产生:

MainThread: Look out!

在 3.5 版更改: 在 Python 3.5 之前，QueueListener 总会把由队列接收到的每条信息都传递给已初始化的每个处理程序。（因为这里假定级别过滤操作已在写入队列时完成了。）从 3.5 版开始，可以修改这种处理方式，只要将关键字参数 respect_handler_level=True 传给侦听器的构造函数即可。这样侦听器将会把每条信息的级别与 handler 的级别进行比较，只在适配时才会将信息传给 handler 。

通过网络收发日志事件

假定现在要通过网络发送日志事件，并在接收端进行处理。有一种简单的方案，就是在发送端的根日志对象连接一个 SocketHandler 实例：

import logging, logging.handlers
rootLogger = logging.getLogger('')
rootLogger.setLevel(logging.DEBUG)
socketHandler = logging.handlers.SocketHandler('localhost',
                    logging.handlers.DEFAULT_TCP_LOGGING_PORT)
# don't bother with a formatter, since a socket handler sends the event as
# an unformatted pickle
rootLogger.addHandler(socketHandler)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')

在接收端，可以用 socketserver 模块设置一个接收器。简要示例如下：

import pickle
import logging
import logging.handlers
import socketserver
import struct
class LogRecordStreamHandler(socketserver.StreamRequestHandler):
    """Handler for a streaming logging request.
    This basically logs the record using whatever logging policy is
    configured locally.
    """
    def handle(self):
        """
        Handle multiple requests - each expected to be a 4-byte length,
        followed by the LogRecord in pickle format. Logs the record
        according to whatever policy is configured locally.
        """
        while True:
            chunk = self.connection.recv(4)
            if len(chunk) < 4:
                break
            slen = struct.unpack('>L', chunk)[0]
            chunk = self.connection.recv(slen)
            while len(chunk) < slen:
                chunk = chunk + self.connection.recv(slen - len(chunk))
            obj = self.unPickle(chunk)
            record = logging.makeLogRecord(obj)
            self.handleLogRecord(record)
    def unPickle(self, data):
        return pickle.loads(data)
    def handleLogRecord(self, record):
        # if a name is specified, we use the named logger rather than the one
        # implied by the record.
        if self.server.logname is not None:
            name = self.server.logname
        else:
            name = record.name
        logger = logging.getLogger(name)
        # N.B. EVERY record gets logged. This is because Logger.handle
        # is normally called AFTER logger-level filtering. If you want
        # to do filtering, do it at the client end to save wasting
        # cycles and network bandwidth!
        logger.handle(record)
class LogRecordSocketReceiver(socketserver.ThreadingTCPServer):
    """
    Simple TCP socket-based logging receiver suitable for testing.
    """
    allow_reuse_address = True
    def __init__(self, host='localhost',
                 port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
                 handler=LogRecordStreamHandler):
        socketserver.ThreadingTCPServer.__init__(self, (host, port), handler)
        self.abort = 0
        self.timeout = 1
        self.logname = None
    def serve_until_stopped(self):
        import select
        abort = 0
        while not abort:
            rd, wr, ex = select.select([self.socket.fileno()],
                                       [], [],
                                       self.timeout)
            if rd:
                self.handle_request()
            abort = self.abort
def main():
    logging.basicConfig(
        format='%(relativeCreated)5d %(name)-15s %(levelname)-8s %(message)s')
    tcpserver = LogRecordSocketReceiver()
    print('About to start TCP server...')
    tcpserver.serve_until_stopped()
if __name__ == '__main__':
    main()

先运行服务端，再运行客户端。客户端控制台不会显示什么信息；在服务端应该会看到如下内容：

About to start TCP server...
   59 root            INFO     Jackdaws love my big sphinx of quartz.
   59 myapp.area1     DEBUG    Quick zephyrs blow, vexing daft Jim.
   69 myapp.area1     INFO     How quickly daft jumping zebras vex.
   69 myapp.area2     WARNING  Jail zesty vixen who grabbed pay from quack.
   69 myapp.area2     ERROR    The five boxing wizards jump quickly.

请注意，某些时候 pickle 会存在一些安全问题。若有问题可换用自己的序列化方案，只要覆盖 makePickle() 方法即可，并调整上述脚本以采用自己的序列化方案。

在自己的输出日志中添加上下文信息

有时，除了调用日志对象时传入的参数之外，还希望日志输出中能包含上下文信息。 比如在网络应用程序中，可能需要在日志中记录某客户端专属的信息（如远程客户端的用户名或 IP 地址）。 这虽然可以用 extra 参数实现，但传递起来并不总是很方便。 虽然为每个网络连接都创建 Logger 实例貌似不错，但并不是个好主意，因为这些实例不会被垃圾回收。 虽然在实践中不是问题，但当 Logger 实例的数量取决于应用程序要采用的日志粒度时，如果 Logger 实例的数量实际上是无限的，则有可能难以管理。

利用 LoggerAdapter 传递上下文信息

要传递上下文信息和日志事件信息，有一种简单方案是利用 LoggerAdapter 类。这个类设计得类似 Logger，所以可以直接调用 debug()、info()、 warning()、 error()、exception()、 critical() 和 log()。这些方法的签名与 Logger 对应的方法相同，所以这两类实例可以交换使用。

当你创建一个 LoggerAdapter 的实例时，你会传入一个 Logger 的实例和一个包含了上下文信息的字典对象。当你调用一个 LoggerAdapter 实例的方法时，它会把调用委托给内部的 Logger 的实例，并为其整理相关的上下文信息。这是 LoggerAdapter 的一个代码片段:

def debug(self, msg, /, *args, **kwargs):
    """
    Delegate a debug call to the underlying logger, after adding
    contextual information from this adapter instance.
    """
    msg, kwargs = self.process(msg, kwargs)
    self.logger.debug(msg, *args, **kwargs)

LoggerAdapter 的 process() 方法是将上下文信息添加到日志的输出中。 它传入日志消息和日志调用的关键字参数，并传回（隐式的）这些修改后的内容去调用底层的日志记录器。此方法的默认参数只是一个消息字段，但留有一个 ‘extra’ 的字段作为关键字参数传给构造器。当然，如果你在调用适配器时传入了一个 ‘extra’ 字段的参数，它会被静默覆盖。

使用 ‘extra’ 的优点是这些键值对会被传入 LogRecord 实例的 __dict__ 中，让你通过 Formatter 的实例直接使用定制的字符串，实例能找到这个字典类对象的键。 如果你需要一个其他的方法，比如说，想要在消息字符串前后增加上下文信息，你只需要创建一个 LoggerAdapter 的子类，并覆盖它的 process() 方法来做你想做的事情，以下是一个简单的示例:

class CustomAdapter(logging.LoggerAdapter):
    """
    This example adapter expects the passed in dict-like object to have a
    'connid' key, whose value in brackets is prepended to the log message.
    """
    def process(self, msg, kwargs):
        return '[%s] %s' % (self.extra['connid'], msg), kwargs

你可以这样使用:

logger = logging.getLogger(__name__)
adapter = CustomAdapter(logger, {'connid': some_conn_id})

然后，你记录在适配器中的任何事件消息前将添加``some_conn_id``的值。

使用除字典之外的其它对象传递上下文信息

你不需要将一个实际的字典传递给 LoggerAdapter-你可以传入一个实现了``__getitem__`` 和``__iter__``的类的实例，这样它就像是一个字典。这对于你想动态生成值（而字典中的值往往是常量）将很有帮助。

使用过滤器传递上下文信息

你也可以使用一个用户定义的类 Filter 在日志输出中添加上下文信息。Filter 的实例是被允许修改传入的 LogRecords，包括添加其他的属性，然后可以使用合适的格式化字符串输出，或者可以使用一个自定义的类 Formatter。

例如，在一个web应用程序中，正在处理的请求（或者至少是请求的一部分），可以存储在一个线程本地 (threading.local) 变量中，然后从``Filter`` 中去访问。请求中的信息，如IP地址和用户名将被存储在``LogRecord``中，使用上例 LoggerAdapter 中的 ‘ip’ 和 ‘user’ 属性名。在这种情况下，可以使用相同的格式化字符串来得到上例中类似的输出结果。这是一段示例代码:

import logging
from random import choice
class ContextFilter(logging.Filter):
    """
    This is a filter which injects contextual information into the log.
    Rather than use actual contextual information, we just use random
    data in this demo.
    """
    USERS = ['jim', 'fred', 'sheila']
    IPS = ['123.231.231.123', '127.0.0.1', '192.168.0.1']
    def filter(self, record):
        record.ip = choice(ContextFilter.IPS)
        record.user = choice(ContextFilter.USERS)
        return True
if __name__ == '__main__':
    levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
    logging.basicConfig(level=logging.DEBUG,
                        format='%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s')
    a1 = logging.getLogger('a.b.c')
    a2 = logging.getLogger('d.e.f')
    f = ContextFilter()
    a1.addFilter(f)
    a2.addFilter(f)
    a1.debug('A debug message')
    a1.info('An info message with %s', 'some parameters')
    for x in range(10):
        lvl = choice(levels)
        lvlname = logging.getLevelName(lvl)
        a2.log(lvl, 'A message at %s level with %d %s', lvlname, 2, 'parameters')

在运行时，产生如下内容:

2010-09-06 22:38:15,292 a.b.c DEBUG    IP: 123.231.231.123 User: fred     A debug message
2010-09-06 22:38:15,300 a.b.c INFO     IP: 192.168.0.1     User: sheila   An info message with some parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1       User: sheila   A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR    IP: 127.0.0.1       User: jim      A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG    IP: 127.0.0.1       User: sheila   A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR    IP: 123.231.231.123 User: fred     A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 192.168.0.1     User: jim      A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1       User: sheila   A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG    IP: 192.168.0.1     User: jim      A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f ERROR    IP: 127.0.0.1       User: sheila   A message at ERROR level with 2 parameters
2010-09-06 22:38:15,301 d.e.f DEBUG    IP: 123.231.231.123 User: fred     A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f INFO     IP: 123.231.231.123 User: fred     A message at INFO level with 2 parameters

从多个进程记录至单个文件

尽管 logging 是线程安全的，将单个进程中的多个线程日志记录至单个文件也 是 受支持的，但将 多个进程 中的日志记录至单个文件则 不是 受支持的，因为在 Python 中并没有在多个进程中实现对单个文件访问的序列化的标准方案。 如果你需要将多个进程中的日志记录至单个文件，有一个方案是让所有进程都将日志记录至一个 SocketHandler，然后用一个实现了套接字服务器的单独进程一边从套接字中读取一边将日志记录至文件。 （如果愿意的话，你可以在一个现有进程中专门开一个线程来执行此项功能。） 这一部分 文档对此方式有更详细的介绍，并包含一个可用的套接字接收器，你自己的应用可以在此基础上进行适配。

你也可以编写你自己的处理程序，让其使用 multiprocessing 模块中的 Lock 类来顺序访问你的多个进程中的文件。 现有的 FileHandler 及其子类目前并不使用 multiprocessing，尽管它们将来可能会这样做。 请注意在目前，multiprocessing 模块并未在所有平台上都提供可用的锁功能 (参见 https://bugs.python.org/issue3770)。

或者，你也可以使用 Queue 和 QueueHandler 将所有的日志事件发送至你的多进程应用的一个进程中。 以下示例脚本演示了如何执行此操作。 在示例中，一个单独的监听进程负责监听其他进程的日志事件，并根据自己的配置记录。 尽管示例只演示了这种方法（例如你可能希望使用单独的监听线程而非监听进程 —— 它们的实现是类似的），但你也可以在应用程序的监听进程和其他进程使用不同的配置，它可以作为满足你特定需求的一个基础:

# You'll need these imports in your own code
import logging
import logging.handlers
import multiprocessing
# Next two import lines for this demo only
from random import choice, random
import time
#
# Because you'll want to define the logging configurations for listener and workers, the
# listener and worker process functions take a configurer parameter which is a callable
# for configuring logging for that process. These functions are also passed the queue,
# which they use for communication.
#
# In practice, you can configure the listener however you want, but note that in this
# simple example, the listener does not apply level or filter logic to received records.
# In practice, you would probably want to do this logic in the worker processes, to avoid
# sending events which would be filtered out between processes.
#
# The size of the rotated files is made small so you can see the results easily.
def listener_configurer():
    root = logging.getLogger()
    h = logging.handlers.RotatingFileHandler('mptest.log', 'a', 300, 10)
    f = logging.Formatter('%(asctime)s %(processName)-10s %(name)s %(levelname)-8s %(message)s')
    h.setFormatter(f)
    root.addHandler(h)
# This is the listener process top-level loop: wait for logging events
# (LogRecords)on the queue and handle them, quit when you get a None for a
# LogRecord.
def listener_process(queue, configurer):
    configurer()
    while True:
        try:
            record = queue.get()
            if record is None:  # We send this as a sentinel to tell the listener to quit.
                break
            logger = logging.getLogger(record.name)
            logger.handle(record)  # No level or filter logic applied - just do it!
        except Exception:
            import sys, traceback
            print('Whoops! Problem:', file=sys.stderr)
            traceback.print_exc(file=sys.stderr)
# Arrays used for random selections in this demo
LEVELS = [logging.DEBUG, logging.INFO, logging.WARNING,
          logging.ERROR, logging.CRITICAL]
LOGGERS = ['a.b.c', 'd.e.f']
MESSAGES = [
    'Random message #1',
    'Random message #2',
    'Random message #3',
]
# The worker configuration is done at the start of the worker process run.
# Note that on Windows you can't rely on fork semantics, so each process
# will run the logging configuration code when it starts.
def worker_configurer(queue):
    h = logging.handlers.QueueHandler(queue)  # Just the one handler needed
    root = logging.getLogger()
    root.addHandler(h)
    # send all messages, for demo; no other level or filter logic applied.
    root.setLevel(logging.DEBUG)
# This is the worker process top-level loop, which just logs ten events with
# random intervening delays before terminating.
# The print messages are just so you know it's doing something!
def worker_process(queue, configurer):
    configurer(queue)
    name = multiprocessing.current_process().name
    print('Worker started: %s' % name)
    for i in range(10):
        time.sleep(random())
        logger = logging.getLogger(choice(LOGGERS))
        level = choice(LEVELS)
        message = choice(MESSAGES)
        logger.log(level, message)
    print('Worker finished: %s' % name)
# Here's where the demo gets orchestrated. Create the queue, create and start
# the listener, create ten workers and start them, wait for them to finish,
# then send a None to the queue to tell the listener to finish.
def main():
    queue = multiprocessing.Queue(-1)
    listener = multiprocessing.Process(target=listener_process,
                                       args=(queue, listener_configurer))
    listener.start()
    workers = []
    for i in range(10):
        worker = multiprocessing.Process(target=worker_process,
                                         args=(queue, worker_configurer))
        workers.append(worker)
        worker.start()
    for w in workers:
        w.join()
    queue.put_nowait(None)
    listener.join()
if __name__ == '__main__':
    main()

上面脚本的一个变种，仍然在主进程中记录日志，但使用一个单独的线程:

import logging
import logging.config
import logging.handlers
from multiprocessing import Process, Queue
import random
import threading
import time
def logger_thread(q):
    while True:
        record = q.get()
        if record is None:
            break
        logger = logging.getLogger(record.name)
        logger.handle(record)
def worker_process(q):
    qh = logging.handlers.QueueHandler(q)
    root = logging.getLogger()
    root.setLevel(logging.DEBUG)
    root.addHandler(qh)
    levels = [logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
              logging.CRITICAL]
    loggers = ['foo', 'foo.bar', 'foo.bar.baz',
               'spam', 'spam.ham', 'spam.ham.eggs']
    for i in range(100):
        lvl = random.choice(levels)
        logger = logging.getLogger(random.choice(loggers))
        logger.log(lvl, 'Message no. %d', i)
if __name__ == '__main__':
    q = Queue()
    d = {
        'version': 1,
        'formatters': {
            'detailed': {
                'class': 'logging.Formatter',
                'format': '%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
            }
        },
        'handlers': {
            'console': {
                'class': 'logging.StreamHandler',
                'level': 'INFO',
            },
            'file': {
                'class': 'logging.FileHandler',
                'filename': 'mplog.log',
                'mode': 'w',
                'formatter': 'detailed',
            },
            'foofile': {
                'class': 'logging.FileHandler',
                'filename': 'mplog-foo.log',
                'mode': 'w',
                'formatter': 'detailed',
            },
            'errors': {
                'class': 'logging.FileHandler',
                'filename': 'mplog-errors.log',
                'mode': 'w',
                'level': 'ERROR',
                'formatter': 'detailed',
            },
        },
        'loggers': {
            'foo': {
                'handlers': ['foofile']
            }
        },
        'root': {
            'level': 'DEBUG',
            'handlers': ['console', 'file', 'errors']
        },
    }
    workers = []
    for i in range(5):
        wp = Process(target=worker_process, name='worker %d' % (i + 1), args=(q,))
        workers.append(wp)
        wp.start()
    logging.config.dictConfig(d)
    lp = threading.Thread(target=logger_thread, args=(q,))
    lp.start()
    # At this point, the main process could do some useful work of its own
    # Once it's done that, it can wait for the workers to terminate...
    for wp in workers:
        wp.join()
    # And now tell the logging thread to finish up, too
    q.put(None)
    lp.join()

这段变种的代码展示了如何使用特定的日志记录配置 - 例如``foo``记录器使用了特殊的处理程序，将 foo 子系统中所有的事件记录至一个文件 mplog-foo.log。在主进程（即使是在工作进程中产生的日志事件）的日志记录机制中将直接使用恰当的配置。

Using concurrent.futures.ProcessPoolExecutor

If you want to use concurrent.futures.ProcessPoolExecutor to start your worker processes, you need to create the queue slightly differently. Instead of

queue = multiprocessing.Queue(-1)

you should use

queue = multiprocessing.Manager().Queue(-1)  # also works with the examples above

and you can then replace the worker creation from this:

workers = []
for i in range(10):
    worker = multiprocessing.Process(target=worker_process,
                                     args=(queue, worker_configurer))
    workers.append(worker)
    worker.start()
for w in workers:
    w.join()

to this (remembering to first import concurrent.futures):

with concurrent.futures.ProcessPoolExecutor(max_workers=10) as executor:
    for i in range(10):
        executor.submit(worker_process, queue, worker_configurer)

轮换日志文件

有时，你希望当日志文件不断记录增长至一定大小时，打开一个新的文件接着记录。 你可能希望只保留一定数量的日志文件，当不断的创建文件到达该数量时，又覆盖掉最开始的文件形成循环。 对于这种使用场景，日志包提供了 RotatingFileHandler:

import glob
import logging
import logging.handlers
LOG_FILENAME = 'logging_rotatingfile_example.out'
# Set up a specific logger with our desired output level
my_logger = logging.getLogger('MyLogger')
my_logger.setLevel(logging.DEBUG)
# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(
              LOG_FILENAME, maxBytes=20, backupCount=5)
my_logger.addHandler(handler)
# Log some messages
for i in range(20):
    my_logger.debug('i = %d' % i)
# See what files are created
logfiles = glob.glob('%s*' % LOG_FILENAME)
for filename in logfiles:
    print(filename)

结果应该是6个单独的文件，每个文件都包含了应用程序的部分历史日志:

logging_rotatingfile_example.out
logging_rotatingfile_example.out.1
logging_rotatingfile_example.out.2
logging_rotatingfile_example.out.3
logging_rotatingfile_example.out.4
logging_rotatingfile_example.out.5

最新的文件始终是:file:logging_rotatingfile_example.out，每次到达大小限制时，都会使用后缀``.1``重命名。每个现有的备份文件都会被重命名并增加其后缀（例如``.1`` 变为``.2``），而``.6``文件会被删除掉。

显然，这个例子将日志长度设置得太小，这是一个极端的例子。 你可能希望将 maxBytes 设置为一个合适的值。

使用其他日志格式化方式

当日志模块被添加至 Python 标准库时，只有一种格式化消息内容的方法即 %-formatting。 在那之后，Python 又增加了两种格式化方法: string.Template (在 Python 2.4 中新增) 和 str.format() (在 Python 2.6 中新增)。

日志（从 3.2 开始）为这两种格式化方式提供了更多支持。Formatter 类可以添加一个额外的可选关键字参数 style。它的默认值是 '%'，其他的值 '{' 和 '$' 也支持，对应了其他两种格式化样式。其保持了向后兼容（如您所愿），但通过显示指定样式参数，你可以指定格式化字符串的方式是使用 str.format() 或 string.Template。 这里是一个控制台会话的示例，展示了这些方式：

>>> import logging
>>> root = logging.getLogger()
>>> root.setLevel(logging.DEBUG)
>>> handler = logging.StreamHandler()
>>> bf = logging.Formatter('{asctime} {name} {levelname:8s} {message}',
...                        style='{')
>>> handler.setFormatter(bf)
>>> root.addHandler(handler)
>>> logger = logging.getLogger('foo.bar')
>>> logger.debug('This is a DEBUG message')
2010-10-28 15:11:55,341 foo.bar DEBUG    This is a DEBUG message
>>> logger.critical('This is a CRITICAL message')
2010-10-28 15:12:11,526 foo.bar CRITICAL This is a CRITICAL message
>>> df = logging.Formatter('$asctime $name ${levelname} $message',
...                        style='$')
>>> handler.setFormatter(df)
>>> logger.debug('This is a DEBUG message')
2010-10-28 15:13:06,924 foo.bar DEBUG This is a DEBUG message
>>> logger.critical('This is a CRITICAL message')
2010-10-28 15:13:11,494 foo.bar CRITICAL This is a CRITICAL message
>>>

请注意最终输出到日志的消息格式完全独立于单条日志消息的构造方式。 它仍然可以使用 %-formatting，如下所示:

>>> logger.error('This is an%s %s %s', 'other,', 'ERROR,', 'message')
2010-10-28 15:19:29,833 foo.bar ERROR This is another, ERROR, message
>>>

Logging calls (logger.debug(), logger.info() etc.) only take positional parameters for the actual logging message itself, with keyword parameters used only for determining options for how to handle the actual logging call (e.g. the exc_info keyword parameter to indicate that traceback information should be logged, or the extra keyword parameter to indicate additional contextual information to be added to the log). So you cannot directly make logging calls using str.format() or string.Template syntax, because internally the logging package uses %-formatting to merge the format string and the variable arguments. There would be no changing this while preserving backward compatibility, since all logging calls which are out there in existing code will be using %-format strings.

There is, however, a way that you can use {}- and $- formatting to construct your individual log messages. Recall that for a message you can use an arbitrary object as a message format string, and that the logging package will call str() on that object to get the actual format string. Consider the following two classes:

class BraceMessage:
    def __init__(self, fmt, /, *args, **kwargs):
        self.fmt = fmt
        self.args = args
        self.kwargs = kwargs
    def __str__(self):
        return self.fmt.format(*self.args, **self.kwargs)
class DollarMessage:
    def __init__(self, fmt, /, **kwargs):
        self.fmt = fmt
        self.kwargs = kwargs
    def __str__(self):
        from string import Template
        return Template(self.fmt).substitute(**self.kwargs)

Either of these can be used in place of a format string, to allow {}- or $-formatting to be used to build the actual “message” part which appears in the formatted log output in place of “%(message)s” or “{message}” or “$message”. It’s a little unwieldy to use the class names whenever you want to log something, but it’s quite palatable if you use an alias such as __ (double underscore —- not to be confused with _, the single underscore used as a synonym/alias for gettext.gettext() or its brethren).

The above classes are not included in Python, though they’re easy enough to copy and paste into your own code. They can be used as follows (assuming that they’re declared in a module called wherever):

>>> from wherever import BraceMessage as __
>>> print(__('Message with {0} {name}', 2, name='placeholders'))
Message with 2 placeholders
>>> class Point: pass
...
>>> p = Point()
>>> p.x = 0.5
>>> p.y = 0.5
>>> print(__('Message with coordinates: ({point.x:.2f}, {point.y:.2f})',
...       point=p))
Message with coordinates: (0.50, 0.50)
>>> from wherever import DollarMessage as __
>>> print(__('Message with $num $what', num=2, what='placeholders'))
Message with 2 placeholders
>>>

While the above examples use print() to show how the formatting works, you would of course use logger.debug() or similar to actually log using this approach.

One thing to note is that you pay no significant performance penalty with this approach: the actual formatting happens not when you make the logging call, but when (and if) the logged message is actually about to be output to a log by a handler. So the only slightly unusual thing which might trip you up is that the parentheses go around the format string and the arguments, not just the format string. That’s because the __ notation is just syntax sugar for a constructor call to one of the XXXMessage classes.

If you prefer, you can use a LoggerAdapter to achieve a similar effect to the above, as in the following example:

import logging
class Message:
    def __init__(self, fmt, args):
        self.fmt = fmt
        self.args = args
    def __str__(self):
        return self.fmt.format(*self.args)
class StyleAdapter(logging.LoggerAdapter):
    def __init__(self, logger, extra=None):
        super().__init__(logger, extra or {})
    def log(self, level, msg, /, *args, **kwargs):
        if self.isEnabledFor(level):
            msg, kwargs = self.process(msg, kwargs)
            self.logger._log(level, Message(msg, args), (), **kwargs)
logger = StyleAdapter(logging.getLogger(__name__))
def main():
    logger.debug('Hello, {}', 'world!')
if __name__ == '__main__':
    logging.basicConfig(level=logging.DEBUG)
    main()

The above script should log the message Hello, world! when run with Python 3.2 or later.

Customizing LogRecord

Every logging event is represented by a LogRecord instance. When an event is logged and not filtered out by a logger’s level, a LogRecord is created, populated with information about the event and then passed to the handlers for that logger (and its ancestors, up to and including the logger where further propagation up the hierarchy is disabled). Before Python 3.2, there were only two places where this creation was done:

• Logger.makeRecord(), which is called in the normal process of logging an event. This invoked LogRecord directly to create an instance.

• makeLogRecord(), which is called with a dictionary containing attributes to be added to the LogRecord. This is typically invoked when a suitable dictionary has been received over the network (e.g. in pickle form via a SocketHandler, or in JSON form via an HTTPHandler).

This has usually meant that if you need to do anything special with a LogRecord, you’ve had to do one of the following.

• Create your own Logger subclass, which overrides Logger.makeRecord(), and set it using setLoggerClass() before any loggers that you care about are instantiated.

• Add a Filter to a logger or handler, which does the necessary special manipulation you need when its filter() method is called.

The first approach would be a little unwieldy in the scenario where (say) several different libraries wanted to do different things. Each would attempt to set its own Logger subclass, and the one which did this last would win.

The second approach works reasonably well for many cases, but does not allow you to e.g. use a specialized subclass of LogRecord. Library developers can set a suitable filter on their loggers, but they would have to remember to do this every time they introduced a new logger (which they would do simply by adding new packages or modules and doing

logger = logging.getLogger(__name__)

at module level). It’s probably one too many things to think about. Developers could also add the filter to a NullHandler attached to their top-level logger, but this would not be invoked if an application developer attached a handler to a lower-level library logger —- so output from that handler would not reflect the intentions of the library developer.

In Python 3.2 and later, LogRecord creation is done through a factory, which you can specify. The factory is just a callable you can set with setLogRecordFactory(), and interrogate with getLogRecordFactory(). The factory is invoked with the same signature as the LogRecord constructor, as LogRecord is the default setting for the factory.

This approach allows a custom factory to control all aspects of LogRecord creation. For example, you could return a subclass, or just add some additional attributes to the record once created, using a pattern similar to this:

old_factory = logging.getLogRecordFactory()
def record_factory(*args, **kwargs):
    record = old_factory(*args, **kwargs)
    record.custom_attribute = 0xdecafbad
    return record
logging.setLogRecordFactory(record_factory)

This pattern allows different libraries to chain factories together, and as long as they don’t overwrite each other’s attributes or unintentionally overwrite the attributes provided as standard, there should be no surprises. However, it should be borne in mind that each link in the chain adds run-time overhead to all logging operations, and the technique should only be used when the use of a Filter does not provide the desired result.

Subclassing QueueHandler - a ZeroMQ example

You can use a QueueHandler subclass to send messages to other kinds of queues, for example a ZeroMQ ‘publish’ socket. In the example below,the socket is created separately and passed to the handler (as its ‘queue’):

import zmq   # using pyzmq, the Python binding for ZeroMQ
import json  # for serializing records portably
ctx = zmq.Context()
sock = zmq.Socket(ctx, zmq.PUB)  # or zmq.PUSH, or other suitable value
sock.bind('tcp://*:5556')        # or wherever
class ZeroMQSocketHandler(QueueHandler):
    def enqueue(self, record):
        self.queue.send_json(record.__dict__)
handler = ZeroMQSocketHandler(sock)

Of course there are other ways of organizing this, for example passing in the data needed by the handler to create the socket:

class ZeroMQSocketHandler(QueueHandler):
    def __init__(self, uri, socktype=zmq.PUB, ctx=None):
        self.ctx = ctx or zmq.Context()
        socket = zmq.Socket(self.ctx, socktype)
        socket.bind(uri)
        super().__init__(socket)
    def enqueue(self, record):
        self.queue.send_json(record.__dict__)
    def close(self):
        self.queue.close()

Subclassing QueueListener - a ZeroMQ example

You can also subclass QueueListener to get messages from other kinds of queues, for example a ZeroMQ ‘subscribe’ socket. Here’s an example:

class ZeroMQSocketListener(QueueListener):
    def __init__(self, uri, /, *handlers, **kwargs):
        self.ctx = kwargs.get('ctx') or zmq.Context()
        socket = zmq.Socket(self.ctx, zmq.SUB)
        socket.setsockopt_string(zmq.SUBSCRIBE, '')  # subscribe to everything
        socket.connect(uri)
        super().__init__(socket, *handlers, **kwargs)
    def dequeue(self):
        msg = self.queue.recv_json()
        return logging.makeLogRecord(msg)

参见

模块 logging

日志记录模块的 API 参考。

模块 logging.config

日志记录模块的配置 API 。

模块 logging.handlers

日志记录模块附带的有用处理程序。

A basic logging tutorial

A more advanced logging tutorial

An example dictionary-based configuration

Below is an example of a logging configuration dictionary - it’s taken from the documentation on the Django project. This dictionary is passed to dictConfig() to put the configuration into effect:

LOGGING = {
    'version': 1,
    'disable_existing_loggers': True,
    'formatters': {
        'verbose': {
            'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'
        },
        'simple': {
            'format': '%(levelname)s %(message)s'
        },
    },
    'filters': {
        'special': {
            '()': 'project.logging.SpecialFilter',
            'foo': 'bar',
        }
    },
    'handlers': {
        'null': {
            'level':'DEBUG',
            'class':'django.utils.log.NullHandler',
        },
        'console':{
            'level':'DEBUG',
            'class':'logging.StreamHandler',
            'formatter': 'simple'
        },
        'mail_admins': {
            'level': 'ERROR',
            'class': 'django.utils.log.AdminEmailHandler',
            'filters': ['special']
        }
    },
    'loggers': {
        'django': {
            'handlers':['null'],
            'propagate': True,
            'level':'INFO',
        },
        'django.request': {
            'handlers': ['mail_admins'],
            'level': 'ERROR',
            'propagate': False,
        },
        'myproject.custom': {
            'handlers': ['console', 'mail_admins'],
            'level': 'INFO',
            'filters': ['special']
        }
    }
}

For more information about this configuration, you can see the relevant section of the Django documentation.

Using a rotator and namer to customize log rotation processing

An example of how you can define a namer and rotator is given in the following snippet, which shows zlib-based compression of the log file:

def namer(name):
    return name + ".gz"
def rotator(source, dest):
    with open(source, "rb") as sf:
        data = sf.read()
        compressed = zlib.compress(data, 9)
        with open(dest, "wb") as df:
            df.write(compressed)
    os.remove(source)
rh = logging.handlers.RotatingFileHandler(...)
rh.rotator = rotator
rh.namer = namer

These are not “true” .gz files, as they are bare compressed data, with no “container” such as you’d find in an actual gzip file. This snippet is just for illustration purposes.

A more elaborate multiprocessing example

The following working example shows how logging can be used with multiprocessing using configuration files. The configurations are fairly simple, but serve to illustrate how more complex ones could be implemented in a real multiprocessing scenario.

In the example, the main process spawns a listener process and some worker processes. Each of the main process, the listener and the workers have three separate configurations (the workers all share the same configuration). We can see logging in the main process, how the workers log to a QueueHandler and how the listener implements a QueueListener and a more complex logging configuration, and arranges to dispatch events received via the queue to the handlers specified in the configuration. Note that these configurations are purely illustrative, but you should be able to adapt this example to your own scenario.

Here’s the script - the docstrings and the comments hopefully explain how it works:

import logging
import logging.config
import logging.handlers
from multiprocessing import Process, Queue, Event, current_process
import os
import random
import time
class MyHandler:
    """
    A simple handler for logging events. It runs in the listener process and
    dispatches events to loggers based on the name in the received record,
    which then get dispatched, by the logging system, to the handlers
    configured for those loggers.
    """
    def handle(self, record):
        if record.name == "root":
            logger = logging.getLogger()
        else:
            logger = logging.getLogger(record.name)
        if logger.isEnabledFor(record.levelno):
            # The process name is transformed just to show that it's the listener
            # doing the logging to files and console
            record.processName = '%s (for %s)' % (current_process().name, record.processName)
            logger.handle(record)
def listener_process(q, stop_event, config):
    """
    This could be done in the main process, but is just done in a separate
    process for illustrative purposes.
    This initialises logging according to the specified configuration,
    starts the listener and waits for the main process to signal completion
    via the event. The listener is then stopped, and the process exits.
    """
    logging.config.dictConfig(config)
    listener = logging.handlers.QueueListener(q, MyHandler())
    listener.start()
    if os.name == 'posix':
        # On POSIX, the setup logger will have been configured in the
        # parent process, but should have been disabled following the
        # dictConfig call.
        # On Windows, since fork isn't used, the setup logger won't
        # exist in the child, so it would be created and the message
        # would appear - hence the "if posix" clause.
        logger = logging.getLogger('setup')
        logger.critical('Should not appear, because of disabled logger ...')
    stop_event.wait()
    listener.stop()
def worker_process(config):
    """
    A number of these are spawned for the purpose of illustration. In
    practice, they could be a heterogeneous bunch of processes rather than
    ones which are identical to each other.
    This initialises logging according to the specified configuration,
    and logs a hundred messages with random levels to randomly selected
    loggers.
    A small sleep is added to allow other processes a chance to run. This
    is not strictly needed, but it mixes the output from the different
    processes a bit more than if it's left out.
    """
    logging.config.dictConfig(config)
    levels = [logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
              logging.CRITICAL]
    loggers = ['foo', 'foo.bar', 'foo.bar.baz',
               'spam', 'spam.ham', 'spam.ham.eggs']
    if os.name == 'posix':
        # On POSIX, the setup logger will have been configured in the
        # parent process, but should have been disabled following the
        # dictConfig call.
        # On Windows, since fork isn't used, the setup logger won't
        # exist in the child, so it would be created and the message
        # would appear - hence the "if posix" clause.
        logger = logging.getLogger('setup')
        logger.critical('Should not appear, because of disabled logger ...')
    for i in range(100):
        lvl = random.choice(levels)
        logger = logging.getLogger(random.choice(loggers))
        logger.log(lvl, 'Message no. %d', i)
        time.sleep(0.01)
def main():
    q = Queue()
    # The main process gets a simple configuration which prints to the console.
    config_initial = {
        'version': 1,
        'handlers': {
            'console': {
                'class': 'logging.StreamHandler',
                'level': 'INFO'
            }
        },
        'root': {
            'handlers': ['console'],
            'level': 'DEBUG'
        }
    }
    # The worker process configuration is just a QueueHandler attached to the
    # root logger, which allows all messages to be sent to the queue.
    # We disable existing loggers to disable the "setup" logger used in the
    # parent process. This is needed on POSIX because the logger will
    # be there in the child following a fork().
    config_worker = {
        'version': 1,
        'disable_existing_loggers': True,
        'handlers': {
            'queue': {
                'class': 'logging.handlers.QueueHandler',
                'queue': q
            }
        },
        'root': {
            'handlers': ['queue'],
            'level': 'DEBUG'
        }
    }
    # The listener process configuration shows that the full flexibility of
    # logging configuration is available to dispatch events to handlers however
    # you want.
    # We disable existing loggers to disable the "setup" logger used in the
    # parent process. This is needed on POSIX because the logger will
    # be there in the child following a fork().
    config_listener = {
        'version': 1,
        'disable_existing_loggers': True,
        'formatters': {
            'detailed': {
                'class': 'logging.Formatter',
                'format': '%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
            },
            'simple': {
                'class': 'logging.Formatter',
                'format': '%(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
            }
        },
        'handlers': {
            'console': {
                'class': 'logging.StreamHandler',
                'formatter': 'simple',
                'level': 'INFO'
            },
            'file': {
                'class': 'logging.FileHandler',
                'filename': 'mplog.log',
                'mode': 'w',
                'formatter': 'detailed'
            },
            'foofile': {
                'class': 'logging.FileHandler',
                'filename': 'mplog-foo.log',
                'mode': 'w',
                'formatter': 'detailed'
            },
            'errors': {
                'class': 'logging.FileHandler',
                'filename': 'mplog-errors.log',
                'mode': 'w',
                'formatter': 'detailed',
                'level': 'ERROR'
            }
        },
        'loggers': {
            'foo': {
                'handlers': ['foofile']
            }
        },
        'root': {
            'handlers': ['console', 'file', 'errors'],
            'level': 'DEBUG'
        }
    }
    # Log some initial events, just to show that logging in the parent works
    # normally.
    logging.config.dictConfig(config_initial)
    logger = logging.getLogger('setup')
    logger.info('About to create workers ...')
    workers = []
    for i in range(5):
        wp = Process(target=worker_process, name='worker %d' % (i + 1),
                     args=(config_worker,))
        workers.append(wp)
        wp.start()
        logger.info('Started worker: %s', wp.name)
    logger.info('About to create listener ...')
    stop_event = Event()
    lp = Process(target=listener_process, name='listener',
                 args=(q, stop_event, config_listener))
    lp.start()
    logger.info('Started listener')
    # We now hang around for the workers to finish their work.
    for wp in workers:
        wp.join()
    # Workers all done, listening can now stop.
    # Logging in the parent still works normally.
    logger.info('Telling listener to stop ...')
    stop_event.set()
    lp.join()
    logger.info('All done.')
if __name__ == '__main__':
    main()