日志操作手册
************

作者:
   原文作者 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。


在多个模块中记录日志
====================

多次调用``logging.getLogger('someLogger')``会返回对同一个日志记录器对
象的引用。不仅在同一个模块中是这样的，而且在不同模块之间，只要是在同一
个Python解释器进程中，也是如此。 这就是对同一个对象的多个引用；此外，
应用程序代码也可以在一个模块中定义和配置父日志记录器，在单独的模块中创
建（但不配置）一个子日志记录器，并且对子日志记录器的所有调用都将传递给
父日志记录器。 这里是一个主要模块:

   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()')

这里是辅助模块:

   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
====================

在多个线程中记录日志并不需要特殊的处理，以下示例展示了如何在主（初始）
线程和另一个线程中记录日志:

   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()

脚本会运行输出类似下面的内容:

      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

这表明不同线程的日志像期望的那样穿插输出，当然更多的线程也会像这样输出
。


多个日志处理器以及多种格式化器
==============================

日志记录器是普通的Python对象。"addHandler()" 方法对可以添加的日志处理
器的数量没有限制。有时候，应用程序需要将所有严重性的所有消息记录到一个
文本文件，而将错误或更高等级的消息输出到控制台。要进行这样的设定，只需
配置适当的日志处理器即可。在应用程序代码中，记录日志的调用将保持不变。
以下是对之前基于模块的简单配置示例的略微修改:

   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.warn('warn message')
   logger.error('error message')
   logger.critical('critical message')

需要注意的是，'应用程序' 代码并不关心是否有多个日志处理器。所有的改变
的只是添加和配置了一个新的名为*fh*的日志处理器。

在编写和测试应用程序时，能够创建带有更高或更低消息等级的过滤器的日志处
理器是非常有用的。为了避免过多地使用 "print" 语句去调试，请使用
"logger.debug" ：它不像 "print" 语句需要你不得不在调试结束后注释或删除
掉，logger.debug 语句可以在源代码中保持不变，在你再一次需要它之前保持
无效。那时，唯一需要改变的是修改日志记录器和/或日志处理器的消息等级，
以进行调试。


在多个地方记录日志
==================

假设有这样一种情况，你需要将日志按不同的格式和不同的情况存储在控制台和
文件中。比如说想把日志等级为DEBUG或更高的消息记录于文件中，而把那些等
级为INFO或更高的消息输出在控制台。而且记录在文件中的消息格式需要包含时
间戳，打印在控制台的不需要。以下示例展示了如何做到:

   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.

而在文件中会看到像这样

   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.

正如你所看到的，DEBUG级别的消息只展示在文件中，而其他消息两个地方都会
输出。

这个示例只演示了在控制台和文件中去记录日志，但你也可以自由组合任意数量
的日志处理器。


日志服务器配置示例
==================

以下是在一个模块中使用日志服务器配置的示例:

   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.warn('warn message')
           logger.error('error message')
           logger.critical('critical message')
           time.sleep(5)
   except KeyboardInterrupt:
       # cleanup
       logging.config.stopListening()
       t.join()

然后如下的脚本，它接收文件名做为命令行参数，并将该文件以二进制编码的方
式传给服务器，做为新的日志服务器配置:

   #!/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')


处理日志处理器的阻塞
====================

有时候需要让日志处理程序在不阻塞当前正在记录线程的情况下完成工作。 这
在Web应用程序中很常见，当然也会在其他场景中出现。

一个常见的缓慢行为是 "SMTPHandler": 由于开发者无法控制的多种原因（例如
，性能不佳的邮件或网络基础架构），发送电子邮件可能需要很长时间。 其实
几乎所有基于网络的处理程序都可能造成阻塞：即便是 "SocketHandler" 也可
能在底层进行 DNS 查询，这太慢了（这个查询会深入至套接字代码，位于
Python 层之下，这是不受开发者控制的）。

一种解决方案是分成两部分去处理。第一部分，针对那些对性能有要求的关键线
程的日志记录附加一个 "QueueHandler"。 日志记录器只需简单写入队列，该队
列可以设置一个足够大的容量甚至不设置容量上限。通常写入队列是一个快速的
操作，即使可能需要在代码中去捕获例如 "queue.Full" 等异常。 如果你是一
名处理关键线程的开发者，请务必记录这些信息 (包括建议只为日志处理器附加
"QueueHandlers") 以便于其他开发者使用你的代码。

解决方案的另一部分是 "QueueListener"，它被设计用来作为 "QueueHandler"
的对应。 "QueueListener" 非常简单：向其传入一个队列和一些处理句柄，它
会启动一个内部线程来监听从 "QueueHandlers" (或任何其他可用的
"LogRecords" 源) 发送过来的 LogRecords 队列。 "LogRecords" 会从队列中
被移除，并被传递给句柄进行处理。

使用一个单独的类 "QueueListener" 优点是可以使用同一个实例去服务于多个
``QueueHandlers``。这样会更节省资源，否则每个处理程序都占用一个线程没
有任何益处。

以下是使用了这样两个类的示例(省略了导入语句):

   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!

3.5 版更變: 在Python 3.5之前，"QueueListener" 总是把从队列中接收的每个
消息都传给它初始化的日志处理程序。(这是因为它会假设过滤级别总是在队列
的另一侧去设置的。) 从Python 3.5开始，可以通过在监听器构造函数中添加一
个参数``respect_handler_level=True``改变这种情况。当这样设置时，监听器
会比较每条消息的等级和日志处理器中设置的等级，只把需要传递的消息传给对
应的日志处理器。


通过网络发送和接收日志
======================

如果你想在网络上发送日志，并在接收端处理它们。一个简单的方式是通过附加
一个 "SocketHandler" 的实例在发送端的根日志处理器中:

   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，你可以使用 "socketserver" 模块设置一个接收器。这里是一个基础
示例:

   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()

首先运行服务端，然后是客户端。在客户端，没有什么内容会打印在控制台中；
在服务端，你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，在某些情况下序列化会存在一些安全。如果这影响到你，那么你可以通
过覆盖 "makePickle()" 方法，使用自己的实现来解决，并调整上述脚本也使用
覆盖后的序列化方法。


在日志记录中添加上下文信息
==========================

有时，除了传递给日志记录器调用的参数外，我们还希望日志记录中包含上下文
信息。例如，有一个网络应用，可能需要记录一些特殊的客户端信息在日志中（
比如客户端的用户名、IP地址等）。虽然你可以通过设置额外的参数去达到这个
目的，但这种方式不一定方便。或者你可能想到在每个连接的基础上创建一个
"Logger" 的实例，但这些实例是不会被垃圾回收的，这在练习中也许不是问题
，但当 "Logger" 的实例数量取决于你应用程序中想记录的细致程度时，如果
"Logger" 的实例数量不受限制的话，将会变得难以管理。


使用日志适配器传递上下文信息
----------------------------

一个传递上下文信息和日志事件信息的简单办法是使用类 "LoggerAdapter"。
这个类设计的像 "Logger"，所以可以直接调用 "debug()"、"info()"、
"warning()"、 "error()"、"exception()"、 "critical()" 和 "log()"。 这
些方法在对应的 "Logger" 中使用相同的签名，所以可以交替使用两种类型的实
例。

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。

如果你使用的是包含了 "multiprocessing" 模块的较新版本的 Python，你也可
以使用 "Lock" 来编写自己的处理程序让其从多个进程中按顺序记录至文件。
现有的 "FileHandler" 和它的子类目前没有使用 "multiprocessing"，尽管将
来可能会这样做。 请注意目前 "multiprocessing" 模块并非在所有平台上提供
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。


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 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(object):
       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(StyleAdapter, self).__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)

也參考:

  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):
           logger = logging.getLogger(record.name)
           # 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,
           'formatters': {
               'detailed': {
                   'class': 'logging.Formatter',
                   'format': '%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
               }
           },
           'handlers': {
               'console': {
                   'class': 'logging.StreamHandler',
                   'level': 'INFO',
               },
           },
           'root': {
               'level': 'DEBUG',
               'handlers': ['console']
           },
       }
       # 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': {
               'level': 'DEBUG',
               'handlers': ['queue']
           },
       }
       # 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',
                   'level': 'INFO',
                   'formatter': 'simple',
               },
               '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']
           },
       }
       # 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()


Inserting a BOM into messages sent to a SysLogHandler
=====================================================

**RFC 5424** requires that a Unicode message be sent to a syslog
daemon as a set of bytes which have the following structure: an
optional pure-ASCII component, followed by a UTF-8 Byte Order Mark
(BOM), followed by Unicode encoded using UTF-8. (See the **relevant
section of the specification**.)

In Python 3.1, code was added to "SysLogHandler" to insert a BOM into
the message, but unfortunately, it was implemented incorrectly, with
the BOM appearing at the beginning of the message and hence not
allowing any pure-ASCII component to appear before it.

As this behaviour is broken, the incorrect BOM insertion code is being
removed from Python 3.2.4 and later. However, it is not being
replaced, and if you want to produce **RFC 5424**-compliant messages
which include a BOM, an optional pure-ASCII sequence before it and
arbitrary Unicode after it, encoded using UTF-8, then you need to do
the following:

1. Attach a "Formatter" instance to your "SysLogHandler" instance,
   with a format string such as:

      'ASCII section\ufeffUnicode section'

   The Unicode code point U+FEFF, when encoded using UTF-8, will be
   encoded as a UTF-8 BOM -- the byte-string "b'\xef\xbb\xbf'".

2. Replace the ASCII section with whatever placeholders you like,
   but make sure that the data that appears in there after
   substitution is always ASCII (that way, it will remain unchanged
   after UTF-8 encoding).

3. Replace the Unicode section with whatever placeholders you like;
   if the data which appears there after substitution contains
   characters outside the ASCII range, that's fine -- it will be
   encoded using UTF-8.

The formatted message *will* be encoded using UTF-8 encoding by
"SysLogHandler". If you follow the above rules, you should be able to
produce **RFC 5424**-compliant messages. If you don't, logging may not
complain, but your messages will not be RFC 5424-compliant, and your
syslog daemon may complain.


Implementing structured logging
===============================

Although most logging messages are intended for reading by humans, and
thus not readily machine-parseable, there might be circumstances where
you want to output messages in a structured format which *is* capable
of being parsed by a program (without needing complex regular
expressions to parse the log message). This is straightforward to
achieve using the logging package. There are a number of ways in which
this could be achieved, but the following is a simple approach which
uses JSON to serialise the event in a machine-parseable manner:

   import json
   import logging

   class StructuredMessage(object):
       def __init__(self, message, **kwargs):
           self.message = message
           self.kwargs = kwargs

       def __str__(self):
           return '%s >>> %s' % (self.message, json.dumps(self.kwargs))

   _ = StructuredMessage   # optional, to improve readability

   logging.basicConfig(level=logging.INFO, format='%(message)s')
   logging.info(_('message 1', foo='bar', bar='baz', num=123, fnum=123.456))

If the above script is run, it prints:

   message 1 >>> {"fnum": 123.456, "num": 123, "bar": "baz", "foo": "bar"}

Note that the order of items might be different according to the
version of Python used.

If you need more specialised processing, you can use a custom JSON
encoder, as in the following complete example:

   from __future__ import unicode_literals

   import json
   import logging

   # This next bit is to ensure the script runs unchanged on 2.x and 3.x
   try:
       unicode
   except NameError:
       unicode = str

   class Encoder(json.JSONEncoder):
       def default(self, o):
           if isinstance(o, set):
               return tuple(o)
           elif isinstance(o, unicode):
               return o.encode('unicode_escape').decode('ascii')
           return super(Encoder, self).default(o)

   class StructuredMessage(object):
       def __init__(self, message, **kwargs):
           self.message = message
           self.kwargs = kwargs

       def __str__(self):
           s = Encoder().encode(self.kwargs)
           return '%s >>> %s' % (self.message, s)

   _ = StructuredMessage   # optional, to improve readability

   def main():
       logging.basicConfig(level=logging.INFO, format='%(message)s')
       logging.info(_('message 1', set_value={1, 2, 3}, snowman='\u2603'))

   if __name__ == '__main__':
       main()

When the above script is run, it prints:

   message 1 >>> {"snowman": "\u2603", "set_value": [1, 2, 3]}

Note that the order of items might be different according to the
version of Python used.


Customizing handlers with "dictConfig()"
========================================

There are times when you want to customize logging handlers in
particular ways, and if you use "dictConfig()" you may be able to do
this without subclassing. As an example, consider that you may want to
set the ownership of a log file. On POSIX, this is easily done using
"shutil.chown()", but the file handlers in the stdlib don't offer
built-in support. You can customize handler creation using a plain
function such as:

   def owned_file_handler(filename, mode='a', encoding=None, owner=None):
       if owner:
           if not os.path.exists(filename):
               open(filename, 'a').close()
           shutil.chown(filename, *owner)
       return logging.FileHandler(filename, mode, encoding)

You can then specify, in a logging configuration passed to
"dictConfig()", that a logging handler be created by calling this
function:

   LOGGING = {
       'version': 1,
       'disable_existing_loggers': False,
       'formatters': {
           'default': {
               'format': '%(asctime)s %(levelname)s %(name)s %(message)s'
           },
       },
       'handlers': {
           'file':{
               # The values below are popped from this dictionary and
               # used to create the handler, set the handler's level and
               # its formatter.
               '()': owned_file_handler,
               'level':'DEBUG',
               'formatter': 'default',
               # The values below are passed to the handler creator callable
               # as keyword arguments.
               'owner': ['pulse', 'pulse'],
               'filename': 'chowntest.log',
               'mode': 'w',
               'encoding': 'utf-8',
           },
       },
       'root': {
           'handlers': ['file'],
           'level': 'DEBUG',
       },
   }

In this example I am setting the ownership using the "pulse" user and
group, just for the purposes of illustration. Putting it together into
a working script, "chowntest.py":

   import logging, logging.config, os, shutil

   def owned_file_handler(filename, mode='a', encoding=None, owner=None):
       if owner:
           if not os.path.exists(filename):
               open(filename, 'a').close()
           shutil.chown(filename, *owner)
       return logging.FileHandler(filename, mode, encoding)

   LOGGING = {
       'version': 1,
       'disable_existing_loggers': False,
       'formatters': {
           'default': {
               'format': '%(asctime)s %(levelname)s %(name)s %(message)s'
           },
       },
       'handlers': {
           'file':{
               # The values below are popped from this dictionary and
               # used to create the handler, set the handler's level and
               # its formatter.
               '()': owned_file_handler,
               'level':'DEBUG',
               'formatter': 'default',
               # The values below are passed to the handler creator callable
               # as keyword arguments.
               'owner': ['pulse', 'pulse'],
               'filename': 'chowntest.log',
               'mode': 'w',
               'encoding': 'utf-8',
           },
       },
       'root': {
           'handlers': ['file'],
           'level': 'DEBUG',
       },
   }

   logging.config.dictConfig(LOGGING)
   logger = logging.getLogger('mylogger')
   logger.debug('A debug message')

To run this, you will probably need to run as "root":

   $ sudo python3.3 chowntest.py
   $ cat chowntest.log
   2013-11-05 09:34:51,128 DEBUG mylogger A debug message
   $ ls -l chowntest.log
   -rw-r--r-- 1 pulse pulse 55 2013-11-05 09:34 chowntest.log

Note that this example uses Python 3.3 because that's where
"shutil.chown()" makes an appearance. This approach should work with
any Python version that supports "dictConfig()" - namely, Python 2.7,
3.2 or later. With pre-3.3 versions, you would need to implement the
actual ownership change using e.g. "os.chown()".

In practice, the handler-creating function may be in a utility module
somewhere in your project. Instead of the line in the configuration:

   '()': owned_file_handler,

you could use e.g.:

   '()': 'ext://project.util.owned_file_handler',

where "project.util" can be replaced with the actual name of the
package where the function resides. In the above working script, using
"'ext://__main__.owned_file_handler'" should work. Here, the actual
callable is resolved by "dictConfig()" from the "ext://"
specification.

This example hopefully also points the way to how you could implement
other types of file change - e.g. setting specific POSIX permission
bits - in the same way, using "os.chmod()".

Of course, the approach could also be extended to types of handler
other than a "FileHandler" - for example, one of the rotating file
handlers, or a different type of handler altogether.


Using particular formatting styles throughout your application
==============================================================

In Python 3.2, the "Formatter" gained a "style" keyword parameter
which, while defaulting to "%" for backward compatibility, allowed the
specification of "{" or "$" to support the formatting approaches
supported by "str.format()" and "string.Template". Note that this
governs the formatting of logging messages for final output to logs,
and is completely orthogonal to how an individual logging message is
constructed.

Logging calls ("debug()", "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
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 no changing this while preserving
backward compatibility, since all logging calls which are out there in
existing code will be using %-format strings.

There have been suggestions to associate format styles with specific
loggers, but that approach also runs into backward compatibility
problems because any existing code could be using a given logger name
and using %-formatting.

For logging to work interoperably between any third-party libraries
and your code, decisions about formatting need to be made at the level
of the individual logging call. This opens up a couple of ways in
which alternative formatting styles can be accommodated.


Using LogRecord factories
-------------------------

In Python 3.2, along with the "Formatter" changes mentioned above, the
logging package gained the ability to allow users to set their own
"LogRecord" subclasses, using the "setLogRecordFactory()" function.
You can use this to set your own subclass of "LogRecord", which does
the Right Thing by overriding the "getMessage()" method. The base
class implementation of this method is where the "msg % args"
formatting happens, and where you can substitute your alternate
formatting; however, you should be careful to support all formatting
styles and allow %-formatting as the default, to ensure
interoperability with other code. Care should also be taken to call
"str(self.msg)", just as the base implementation does.

Refer to the reference documentation on "setLogRecordFactory()" and
"LogRecord" for more information.


Using custom message objects
----------------------------

There is another, perhaps simpler way that you can use {}- and $-
formatting to construct your individual log messages. You may recall
(from 使用任意对象作为消息) that when logging 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(object):
       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(object):
       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”. If you find it a little unwieldy to use the
class names whenever you want to log something, you can make it more
palatable if you use an alias such as "M" or "_" for the message (or
perhaps "__", if you are using "_" for localization).

Examples of this approach are given below. Firstly, formatting with
"str.format()":

   >>> __ = BraceMessage
   >>> print(__('Message with {0} {1}', 2, '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)

Secondly, formatting with "string.Template":

   >>> __ = DollarMessage
   >>> print(__('Message with $num $what', num=2, what='placeholders'))
   Message with 2 placeholders
   >>>

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 shown above.


Configuring filters with "dictConfig()"
=======================================

You *can* configure filters using "dictConfig()", though it might not
be obvious at first glance how to do it (hence this recipe). Since
"Filter" is the only filter class included in the standard library,
and it is unlikely to cater to many requirements (it's only there as a
base class), you will typically need to define your own "Filter"
subclass with an overridden "filter()" method. To do this, specify the
"()" key in the configuration dictionary for the filter, specifying a
callable which will be used to create the filter (a class is the most
obvious, but you can provide any callable which returns a "Filter"
instance). Here is a complete example:

   import logging
   import logging.config
   import sys

   class MyFilter(logging.Filter):
       def __init__(self, param=None):
           self.param = param

       def filter(self, record):
           if self.param is None:
               allow = True
           else:
               allow = self.param not in record.msg
           if allow:
               record.msg = 'changed: ' + record.msg
           return allow

   LOGGING = {
       'version': 1,
       'filters': {
           'myfilter': {
               '()': MyFilter,
               'param': 'noshow',
           }
       },
       'handlers': {
           'console': {
               'class': 'logging.StreamHandler',
               'filters': ['myfilter']
           }
       },
       'root': {
           'level': 'DEBUG',
           'handlers': ['console']
       },
   }

   if __name__ == '__main__':
       logging.config.dictConfig(LOGGING)
       logging.debug('hello')
       logging.debug('hello - noshow')

This example shows how you can pass configuration data to the callable
which constructs the instance, in the form of keyword parameters. When
run, the above script will print:

   changed: hello

which shows that the filter is working as configured.

A couple of extra points to note:

* If you can't refer to the callable directly in the configuration
  (e.g. if it lives in a different module, and you can't import it
  directly where the configuration dictionary is), you can use the
  form "ext://..." as described in Access to external objects. For
  example, you could have used the text "'ext://__main__.MyFilter'"
  instead of "MyFilter" in the above example.

* As well as for filters, this technique can also be used to
  configure custom handlers and formatters. See User-defined objects
  for more information on how logging supports using user-defined
  objects in its configuration, and see the other cookbook recipe
  Customizing handlers with dictConfig() above.


Customized exception formatting
===============================

There might be times when you want to do customized exception
formatting - for argument's sake, let's say you want exactly one line
per logged event, even when exception information is present. You can
do this with a custom formatter class, as shown in the following
example:

   import logging

   class OneLineExceptionFormatter(logging.Formatter):
       def formatException(self, exc_info):
           """
           Format an exception so that it prints on a single line.
           """
           result = super(OneLineExceptionFormatter, self).formatException(exc_info)
           return repr(result)  # or format into one line however you want to

       def format(self, record):
           s = super(OneLineExceptionFormatter, self).format(record)
           if record.exc_text:
               s = s.replace('\n', '') + '|'
           return s

   def configure_logging():
       fh = logging.FileHandler('output.txt', 'w')
       f = OneLineExceptionFormatter('%(asctime)s|%(levelname)s|%(message)s|',
                                     '%d/%m/%Y %H:%M:%S')
       fh.setFormatter(f)
       root = logging.getLogger()
       root.setLevel(logging.DEBUG)
       root.addHandler(fh)

   def main():
       configure_logging()
       logging.info('Sample message')
       try:
           x = 1 / 0
       except ZeroDivisionError as e:
           logging.exception('ZeroDivisionError: %s', e)

   if __name__ == '__main__':
       main()

When run, this produces a file with exactly two lines:

   28/01/2015 07:21:23|INFO|Sample message|
   28/01/2015 07:21:23|ERROR|ZeroDivisionError: integer division or modulo by zero|'Traceback (most recent call last):\n  File "logtest7.py", line 30, in main\n    x = 1 / 0\nZeroDivisionError: integer division or modulo by zero'|

While the above treatment is simplistic, it points the way to how
exception information can be formatted to your liking. The "traceback"
module may be helpful for more specialized needs.


Speaking logging messages
=========================

There might be situations when it is desirable to have logging
messages rendered in an audible rather than a visible format. This is
easy to do if you have text-to-speech (TTS) functionality available in
your system, even if it doesn't have a Python binding. Most TTS
systems have a command line program you can run, and this can be
invoked from a handler using "subprocess". It's assumed here that TTS
command line programs won't expect to interact with users or take a
long time to complete, and that the frequency of logged messages will
be not so high as to swamp the user with messages, and that it's
acceptable to have the messages spoken one at a time rather than
concurrently, The example implementation below waits for one message
to be spoken before the next is processed, and this might cause other
handlers to be kept waiting. Here is a short example showing the
approach, which assumes that the "espeak" TTS package is available:

   import logging
   import subprocess
   import sys

   class TTSHandler(logging.Handler):
       def emit(self, record):
           msg = self.format(record)
           # Speak slowly in a female English voice
           cmd = ['espeak', '-s150', '-ven+f3', msg]
           p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
                                stderr=subprocess.STDOUT)
           # wait for the program to finish
           p.communicate()

   def configure_logging():
       h = TTSHandler()
       root = logging.getLogger()
       root.addHandler(h)
       # the default formatter just returns the message
       root.setLevel(logging.DEBUG)

   def main():
       logging.info('Hello')
       logging.debug('Goodbye')

   if __name__ == '__main__':
       configure_logging()
       sys.exit(main())

When run, this script should say "Hello" and then "Goodbye" in a
female voice.

The above approach can, of course, be adapted to other TTS systems and
even other systems altogether which can process messages via external
programs run from a command line.


缓冲日志消息并有条件地输出它们
==============================

在某些情况下，你可能希望在临时区域中记录日志消息，并且只在发生某种特定
的情况下才输出它们。 例如，你可能希望起始在函数中记录调试事件，如果函
数执行完成且没有错误，你不希望输出收集的调试信息以避免造成日志混乱，但
如果出现错误，那么你希望所有调试以及错误消息被输出。

下面是一个示例，展示如何在你的日志记录函数上使用装饰器以实现这一功能。
该示例使用 "logging.handlers.MemoryHandler" ，它允许缓冲已记录的事件直
到某些条件发生，缓冲的事件才会被刷新（"flushed"） - 传递给另一个处理程
序（ "target" handler）进行处理。 默认情况下， "MemoryHandler" 在其缓
冲区被填满时被刷新，或者看到一个级别大于或等于指定阈值的事件。 如果想
要自定义刷新行为，你可以通过更专业的 "MemoryHandler" 子类来使用这个秘
诀。

The example script has a simple function, "foo", which just cycles
through all the logging levels, writing to "sys.stderr" to say what
level it's about to log at, and then actually logging a message at
that level. You can pass a parameter to "foo" which, if true, will log
at ERROR and CRITICAL levels - otherwise, it only logs at DEBUG, INFO
and WARNING levels.

The script just arranges to decorate "foo" with a decorator which will
do the conditional logging that's required. The decorator takes a
logger as a parameter and attaches a memory handler for the duration
of the call to the decorated function. The decorator can be
additionally parameterised using a target handler, a level at which
flushing should occur, and a capacity for the buffer. These default to
a "StreamHandler" which writes to "sys.stderr", "logging.ERROR" and
"100" respectively.

Here's the script:

   import logging
   from logging.handlers import MemoryHandler
   import sys

   logger = logging.getLogger(__name__)
   logger.addHandler(logging.NullHandler())

   def log_if_errors(logger, target_handler=None, flush_level=None, capacity=None):
       if target_handler is None:
           target_handler = logging.StreamHandler()
       if flush_level is None:
           flush_level = logging.ERROR
       if capacity is None:
           capacity = 100
       handler = MemoryHandler(capacity, flushLevel=flush_level, target=target_handler)

       def decorator(fn):
           def wrapper(*args, **kwargs):
               logger.addHandler(handler)
               try:
                   return fn(*args, **kwargs)
               except Exception:
                   logger.exception('call failed')
                   raise
               finally:
                   super(MemoryHandler, handler).flush()
                   logger.removeHandler(handler)
           return wrapper

       return decorator

   def write_line(s):
       sys.stderr.write('%s\n' % s)

   def foo(fail=False):
       write_line('about to log at DEBUG ...')
       logger.debug('Actually logged at DEBUG')
       write_line('about to log at INFO ...')
       logger.info('Actually logged at INFO')
       write_line('about to log at WARNING ...')
       logger.warning('Actually logged at WARNING')
       if fail:
           write_line('about to log at ERROR ...')
           logger.error('Actually logged at ERROR')
           write_line('about to log at CRITICAL ...')
           logger.critical('Actually logged at CRITICAL')
       return fail

   decorated_foo = log_if_errors(logger)(foo)

   if __name__ == '__main__':
       logger.setLevel(logging.DEBUG)
       write_line('Calling undecorated foo with False')
       assert not foo(False)
       write_line('Calling undecorated foo with True')
       assert foo(True)
       write_line('Calling decorated foo with False')
       assert not decorated_foo(False)
       write_line('Calling decorated foo with True')
       assert decorated_foo(True)

运行此脚本时，应看到以下输出：

   Calling undecorated foo with False
   about to log at DEBUG ...
   about to log at INFO ...
   about to log at WARNING ...
   Calling undecorated foo with True
   about to log at DEBUG ...
   about to log at INFO ...
   about to log at WARNING ...
   about to log at ERROR ...
   about to log at CRITICAL ...
   Calling decorated foo with False
   about to log at DEBUG ...
   about to log at INFO ...
   about to log at WARNING ...
   Calling decorated foo with True
   about to log at DEBUG ...
   about to log at INFO ...
   about to log at WARNING ...
   about to log at ERROR ...
   Actually logged at DEBUG
   Actually logged at INFO
   Actually logged at WARNING
   Actually logged at ERROR
   about to log at CRITICAL ...
   Actually logged at CRITICAL

如你所见，实际日志记录输出仅在消息等级为ERROR或更高的事件时发生，但在
这种情况下，任何之前较低消息等级的事件还会被记录。

你当然可以使用传统的装饰方法:

   @log_if_errors(logger)
   def foo(fail=False):
       ...


通过配置使用UTC (GMT) 格式化时间
================================

有时候，你希望使用UTC来格式化时间，这可以通过使用一个类来实现，例如
`UTCFormatter`，如下所示：

   import logging
   import time

   class UTCFormatter(logging.Formatter):
       converter = time.gmtime

然后你可以在你的代码中使用 "UTCFormatter"，而不是 "Formatter"。 如果你
想通过配置来实现这一功能，你可以使用 "dictConfig()" API 来完成，该方法
在以下完整示例中展示:

   import logging
   import logging.config
   import time

   class UTCFormatter(logging.Formatter):
       converter = time.gmtime

   LOGGING = {
       'version': 1,
       'disable_existing_loggers': False,
       'formatters': {
           'utc': {
               '()': UTCFormatter,
               'format': '%(asctime)s %(message)s',
           },
           'local': {
               'format': '%(asctime)s %(message)s',
           }
       },
       'handlers': {
           'console1': {
               'class': 'logging.StreamHandler',
               'formatter': 'utc',
           },
           'console2': {
               'class': 'logging.StreamHandler',
               'formatter': 'local',
           },
       },
       'root': {
           'handlers': ['console1', 'console2'],
      }
   }

   if __name__ == '__main__':
       logging.config.dictConfig(LOGGING)
       logging.warning('The local time is %s', time.asctime())

脚本会运行输出类似下面的内容:

   2015-10-17 12:53:29,501 The local time is Sat Oct 17 13:53:29 2015
   2015-10-17 13:53:29,501 The local time is Sat Oct 17 13:53:29 2015

展示了如何将时间格式化为本地时间和UTC两种形式，其中每种形式对应一个日
志处理器 。


使用上下文管理器的可选的日志记录
================================

有时候，我们需要暂时更改日志配置，并在执行某些操作后将其还原。为此，上
下文管理器是实现保存和恢复日志上下文的最明显的方式。这是一个关于上下文
管理器的简单例子，它允许你在上下文管理器的作用域内更改日志记录等级以及
增加日志处理器：

   import logging
   import sys

   class LoggingContext(object):
       def __init__(self, logger, level=None, handler=None, close=True):
           self.logger = logger
           self.level = level
           self.handler = handler
           self.close = close

       def __enter__(self):
           if self.level is not None:
               self.old_level = self.logger.level
               self.logger.setLevel(self.level)
           if self.handler:
               self.logger.addHandler(self.handler)

       def __exit__(self, et, ev, tb):
           if self.level is not None:
               self.logger.setLevel(self.old_level)
           if self.handler:
               self.logger.removeHandler(self.handler)
           if self.handler and self.close:
               self.handler.close()
           # implicit return of None => don't swallow exceptions

如果指定上下文管理器的日志记录等级属性，则在上下文管理器的with语句所涵
盖的代码中，日志记录器的记录等级将临时设置为上下文管理器所配置的日志记
录等级。 如果指定上下文管理的日志处理器属性，则该句柄在进入上下文管理
器的上下文时添加到记录器中，并在退出时被删除。 如果你再也不需要该日志
处理器时，你可以让上下文管理器在退出上下文管理器的上下文时关闭它。

为了说明它是如何工作的，我们可以在上面添加以下代码块:

   if __name__ == '__main__':
       logger = logging.getLogger('foo')
       logger.addHandler(logging.StreamHandler())
       logger.setLevel(logging.INFO)
       logger.info('1. This should appear just once on stderr.')
       logger.debug('2. This should not appear.')
       with LoggingContext(logger, level=logging.DEBUG):
           logger.debug('3. This should appear once on stderr.')
       logger.debug('4. This should not appear.')
       h = logging.StreamHandler(sys.stdout)
       with LoggingContext(logger, level=logging.DEBUG, handler=h, close=True):
           logger.debug('5. This should appear twice - once on stderr and once on stdout.')
       logger.info('6. This should appear just once on stderr.')
       logger.debug('7. This should not appear.')

我们最初设置日志记录器的消息等级为``INFO``，因此消息#1出现，消息#2没有
出现。在接下来的``with``代码块中我们暂时将消息等级变更为``DEBUG``，从
而消息#3出现。在这一代码块退出后，日志记录器的消息等级恢复为``INFO``，
从而消息#4没有出现。在下一个``with``代码块中，我们再一次将设置消息等级
设置为``DEBUG``，同时添加一个将消息写入``sys.stdout``的日志处理器。因
此，消息#5在控制台出现两次（分别通过``stderr``和``stdout``）。在
``with``语句完成后，状态与之前一样，因此消息#6出现（类似消息#1），而消
息#7没有出现（类似消息#2）。

如果我们运行生成的脚本，结果如下：

   $ python logctx.py
   1. This should appear just once on stderr.
   3. This should appear once on stderr.
   5. This should appear twice - once on stderr and once on stdout.
   5. This should appear twice - once on stderr and once on stdout.
   6. This should appear just once on stderr.

我们将``stderr``标准错误重定向到``/dev/null``，我再次运行生成的脚步，
唯一被写入``stdout``标准输出的消息，即我们所能看见的消息，如下：

   $ python logctx.py 2>/dev/null
   5. This should appear twice - once on stderr and once on stdout.

再一次，将``stdout``标准输出重定向到``/dev/null``，我获得如下结果：

   $ python logctx.py >/dev/null
   1. This should appear just once on stderr.
   3. This should appear once on stderr.
   5. This should appear twice - once on stderr and once on stdout.
   6. This should appear just once on stderr.

在这种情况下，与预期一致，打印到``stdout``标准输出的消息＃5不会出现。

当然，这里描述的方法可以被推广，例如临时附加日志记录过滤器。 请注意，
上面的代码适用于Python 2以及Python 3。
