cpython/Doc/howto/logging-cookbook.rst

.. _logging-cookbook:

================
Logging Cookbook
================

:Author: Vinay Sajip <vinay_sajip at red-dove dot com>

This page contains a number of recipes related to logging, which have been found
useful in the past.

.. currentmodule:: logging

Using logging in multiple modules
---------------------------------

Multiple calls to ``logging.getLogger('someLogger')`` return a reference to the
same logger object.  This is true not only within the same module, but also
across modules as long as it is in the same Python interpreter process.  It is
true for references to the same object; additionally, application code can
define and configure a parent logger in one module and create (but not
configure) a child logger in a separate module, and all logger calls to the
child will pass up to the parent.  Here is a main module::

    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()')

Here is the auxiliary module::

    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"')

The output looks like this::

    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()

Multiple handlers and formatters
--------------------------------

Loggers are plain Python objects.  The :meth:`~Logger.addHandler` method has no
minimum or maximum quota for the number of handlers you may add.  Sometimes it
will be beneficial for an application to log all messages of all severities to a
text file while simultaneously logging errors or above to the console.  To set
this up, simply configure the appropriate handlers.  The logging calls in the
application code will remain unchanged.  Here is a slight modification to the
previous simple module-based configuration example::

    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')

Notice that the 'application' code does not care about multiple handlers.  All
that changed was the addition and configuration of a new handler named *fh*.

The ability to create new handlers with higher- or lower-severity filters can be
very helpful when writing and testing an application.  Instead of using many
``print`` statements for debugging, use ``logger.debug``: Unlike the print
statements, which you will have to delete or comment out later, the logger.debug
statements can remain intact in the source code and remain dormant until you
need them again.  At that time, the only change that needs to happen is to
modify the severity level of the logger and/or handler to debug.

.. _multiple-destinations:

Logging to multiple destinations
--------------------------------

Let's say you want to log to console and file with different message formats and
in differing circumstances. Say you want to log messages with levels of DEBUG
and higher to file, and those messages at level INFO and higher to the console.
Let's also assume that the file should contain timestamps, but the console
messages should not. Here's how you can achieve this::

   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.')

When you run this, on the console you will see ::

   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.

and in the file you will see something like ::

   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.

As you can see, the DEBUG message only shows up in the file. The other messages
are sent to both destinations.

This example uses console and file handlers, but you can use any number and
combination of handlers you choose.


Configuration server example
----------------------------

Here is an example of a module using the logging configuration server::

    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()

And here is a script that takes a filename and sends that file to the server,
properly preceded with the binary-encoded length, as the new logging
configuration::

    #!/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')


Dealing with handlers that block
--------------------------------

.. currentmodule:: logging.handlers

Sometimes you have to get your logging handlers to do their work without
blocking the thread you're logging from. This is common in Web applications,
though of course it also occurs in other scenarios.

A common culprit which demonstrates sluggish behaviour is the
:class:`SMTPHandler`: sending emails can take a long time, for a
number of reasons outside the developer's control (for example, a poorly
performing mail or network infrastructure). But almost any network-based
handler can block: Even a :class:`SocketHandler` operation may do a
DNS query under the hood which is too slow (and this query can be deep in the
socket library code, below the Python layer, and outside your control).

One solution is to use a two-part approach. For the first part, attach only a
:class:`QueueHandler` to those loggers which are accessed from
performance-critical threads. They simply write to their queue, which can be
sized to a large enough capacity or initialized with no upper bound to their
size. The write to the queue will typically be accepted quickly, though you
will probably need to catch the :exc:`queue.Full` exception as a precaution
in your code. If you are a library developer who has performance-critical
threads in their code, be sure to document this (together with a suggestion to
attach only ``QueueHandlers`` to your loggers) for the benefit of other
developers who will use your code.

The second part of the solution is :class:`QueueListener`, which has been
designed as the counterpart to :class:`QueueHandler`.  A
:class:`QueueListener` is very simple: it's passed a queue and some handlers,
and it fires up an internal thread which listens to its queue for LogRecords
sent from ``QueueHandlers`` (or any other source of ``LogRecords``, for that
matter). The ``LogRecords`` are removed from the queue and passed to the
handlers for processing.

The advantage of having a separate :class:`QueueListener` class is that you
can use the same instance to service multiple ``QueueHandlers``. This is more
resource-friendly than, say, having threaded versions of the existing handler
classes, which would eat up one thread per handler for no particular benefit.

An example of using these two classes follows (imports omitted)::

    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()

which, when run, will produce::

    MainThread: Look out!


.. _network-logging:

Sending and receiving logging events across a network
-----------------------------------------------------

Let's say you want to send logging events across a network, and handle them at
the receiving end. A simple way of doing this is attaching a
:class:`SocketHandler` instance to the root logger at the sending end::

   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.')

At the receiving end, you can set up a receiver using the :mod:`socketserver`
module. Here is a basic working example::

   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()

First run the server, and then the client. On the client side, nothing is
printed on the console; on the server side, you should see something like::

   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.

Note that there are some security issues with pickle in some scenarios. If
these affect you, you can use an alternative serialization scheme by overriding
the :meth:`~handlers.SocketHandler.makePickle` method and implementing your
alternative there, as well as adapting the above script to use your alternative
serialization.


.. _context-info:

Adding contextual information to your logging output
----------------------------------------------------

Sometimes you want logging output to contain contextual information in
addition to the parameters passed to the logging call. For example, in a
networked application, it may be desirable to log client-specific information
in the log (e.g. remote client's username, or IP address). Although you could
use the *extra* parameter to achieve this, it's not always convenient to pass
the information in this way. While it might be tempting to create
:class:`Logger` instances on a per-connection basis, this is not a good idea
because these instances are not garbage collected. While this is not a problem
in practice, when the number of :class:`Logger` instances is dependent on the
level of granularity you want to use in logging an application, it could
be hard to manage if the number of :class:`Logger` instances becomes
effectively unbounded.


Using LoggerAdapters to impart contextual information
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

An easy way in which you can pass contextual information to be output along
with logging event information is to use the :class:`LoggerAdapter` class.
This class is designed to look like a :class:`Logger`, so that you can call
:meth:`debug`, :meth:`info`, :meth:`warning`, :meth:`error`,
:meth:`exception`, :meth:`critical` and :meth:`log`. These methods have the
same signatures as their counterparts in :class:`Logger`, so you can use the
two types of instances interchangeably.

When you create an instance of :class:`LoggerAdapter`, you pass it a
:class:`Logger` instance and a dict-like object which contains your contextual
information. When you call one of the logging methods on an instance of
:class:`LoggerAdapter`, it delegates the call to the underlying instance of
:class:`Logger` passed to its constructor, and arranges to pass the contextual
information in the delegated call. Here's a snippet from the code of
:class:`LoggerAdapter`::

    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)

The :meth:`~LoggerAdapter.process` method of :class:`LoggerAdapter` is where the
contextual information is added to the logging output. It's passed the message
and keyword arguments of the logging call, and it passes back (potentially)
modified versions of these to use in the call to the underlying logger. The
default implementation of this method leaves the message alone, but inserts
an 'extra' key in the keyword argument whose value is the dict-like object
passed to the constructor. Of course, if you had passed an 'extra' keyword
argument in the call to the adapter, it will be silently overwritten.

The advantage of using 'extra' is that the values in the dict-like object are
merged into the :class:`LogRecord` instance's __dict__, allowing you to use
customized strings with your :class:`Formatter` instances which know about
the keys of the dict-like object. If you need a different method, e.g. if you
want to prepend or append the contextual information to the message string,
you just need to subclass :class:`LoggerAdapter` and override
:meth:`~LoggerAdapter.process` to do what you need. Here is a simple example::

    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

which you can use like this::

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

Then any events that you log to the adapter will have the value of
``some_conn_id`` prepended to the log messages.

Using objects other than dicts to pass contextual information
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You don't need to pass an actual dict to a :class:`LoggerAdapter` - you could
pass an instance of a class which implements ``__getitem__`` and ``__iter__`` so
that it looks like a dict to logging. This would be useful if you want to
generate values dynamically (whereas the values in a dict would be constant).


.. _filters-contextual:

Using Filters to impart contextual information
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

You can also add contextual information to log output using a user-defined
:class:`Filter`. ``Filter`` instances are allowed to modify the ``LogRecords``
passed to them, including adding additional attributes which can then be output
using a suitable format string, or if needed a custom :class:`Formatter`.

For example in a web application, the request being processed (or at least,
the interesting parts of it) can be stored in a threadlocal
(:class:`threading.local`) variable, and then accessed from a ``Filter`` to
add, say, information from the request - say, the remote IP address and remote
user's username - to the ``LogRecord``, using the attribute names 'ip' and
'user' as in the ``LoggerAdapter`` example above. In that case, the same format
string can be used to get similar output to that shown above. Here's an example
script::

    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')

which, when run, produces something like::

    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


.. _multiple-processes:

Logging to a single file from multiple processes
------------------------------------------------

Although logging is thread-safe, and logging to a single file from multiple
threads in a single process *is* supported, logging to a single file from
*multiple processes* is *not* supported, because there is no standard way to
serialize access to a single file across multiple processes in Python. If you
need to log to a single file from multiple processes, one way of doing this is
to have all the processes log to a :class:`~handlers.SocketHandler`, and have a
separate process which implements a socket server which reads from the socket
and logs to file. (If you prefer, you can dedicate one thread in one of the
existing processes to perform this function.)
:ref:`This section <network-logging>` documents this approach in more detail and
includes a working socket receiver which can be used as a starting point for you
to adapt in your own applications.

If you are using a recent version of Python which includes the
:mod:`multiprocessing` module, you could write your own handler which uses the
:class:`~multiprocessing.Lock` class from this module to serialize access to the
file from your processes. The existing :class:`FileHandler` and subclasses do
not make use of :mod:`multiprocessing` at present, though they may do so in the
future. Note that at present, the :mod:`multiprocessing` module does not provide
working lock functionality on all platforms (see
https://bugs.python.org/issue3770).

.. currentmodule:: logging.handlers

Alternatively, you can use a ``Queue`` and a :class:`QueueHandler` to send
all logging events to one of the processes in your multi-process application.
The following example script demonstrates how you can do this; in the example
a separate listener process listens for events sent by other processes and logs
them according to its own logging configuration. Although the example only
demonstrates one way of doing it (for example, you may want to use a listener
thread rather than a separate listener process -- the implementation would be
analogous) it does allow for completely different logging configurations for
the listener and the other processes in your application, and can be used as
the basis for code meeting your own specific requirements::

    # 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)
        root.setLevel(logging.DEBUG) # send all messages, for demo; no other level or filter logic applied.

    # 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()

A variant of the above script keeps the logging in the main process, in a
separate thread::

    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()

This variant shows how you can e.g. apply configuration for particular loggers
- e.g. the ``foo`` logger has a special handler which stores all events in the
``foo`` subsystem in a file ``mplog-foo.log``. This will be used by the logging
machinery in the main process (even though the logging events are generated in
the worker processes) to direct the messages to the appropriate destinations.

Using file rotation
-------------------

.. sectionauthor:: Doug Hellmann, Vinay Sajip (changes)
.. (see <http://blog.doughellmann.com/2007/05/pymotw-logging.html>)

Sometimes you want to let a log file grow to a certain size, then open a new
file and log to that. You may want to keep a certain number of these files, and
when that many files have been created, rotate the files so that the number of
files and the size of the files both remain bounded. For this usage pattern, the
logging package provides a :class:`~handlers.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)

The result should be 6 separate files, each with part of the log history for the
application::

   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

The most current file is always :file:`logging_rotatingfile_example.out`,
and each time it reaches the size limit it is renamed with the suffix
``.1``. Each of the existing backup files is renamed to increment the suffix
(``.1`` becomes ``.2``, etc.)  and the ``.6`` file is erased.

Obviously this example sets the log length much too small as an extreme
example.  You would want to set *maxBytes* to an appropriate value.

.. _format-styles:

Use of alternative formatting styles
------------------------------------

When logging was added to the Python standard library, the only way of
formatting messages with variable content was to use the %-formatting
method. Since then, Python has gained two new formatting approaches:
:class:`string.Template` (added in Python 2.4) and :meth:`str.format`
(added in Python 2.6).

Logging (as of 3.2) provides improved support for these two additional
formatting styles. The :class:`Formatter` class been enhanced to take an
additional, optional keyword parameter named ``style``. This defaults to
``'%'``, but other possible values are ``'{'`` and ``'$'``, which correspond
to the other two formatting styles. Backwards compatibility is maintained by
default (as you would expect), but by explicitly specifying a style parameter,
you get the ability to specify format strings which work with
:meth:`str.format` or :class:`string.Template`. Here's an example console
session to show the possibilities:

.. code-block:: pycon

    >>> 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
    >>>

Note that the formatting of logging messages for final output to logs is
completely independent of how an individual logging message is constructed.
That can still use %-formatting, as shown here::

    >>> 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 :meth:`str.format` or
:class:`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 :func:`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``):

.. code-block:: pycon

    >>> 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 :class:`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.


.. currentmodule:: logging

.. _custom-logrecord:

Customizing ``LogRecord``
-------------------------

Every logging event is represented by a :class:`LogRecord` instance.
When an event is logged and not filtered out by a logger's level, a
:class:`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:

* :meth:`Logger.makeRecord`, which is called in the normal process of
  logging an event. This invoked :class:`LogRecord` directly to create an
  instance.
* :func:`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 :class:`~handlers.SocketHandler`, or in JSON form via an
  :class:`~handlers.HTTPHandler`).

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

* Create your own :class:`Logger` subclass, which overrides
  :meth:`Logger.makeRecord`, and set it using :func:`~logging.setLoggerClass`
  before any loggers that you care about are instantiated.
* Add a :class:`Filter` to a logger or handler, which does the
  necessary special manipulation you need when its
  :meth:`~Filter.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 :class:`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 :class:`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 :class:`~logging.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, :class:`~logging.LogRecord` creation is done through a
factory, which you can specify. The factory is just a callable you can set with
:func:`~logging.setLogRecordFactory`, and interrogate with
:func:`~logging.getLogRecordFactory`. The factory is invoked with the same
signature as the :class:`~logging.LogRecord` constructor, as :class:`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 :class:`Filter` does not provide the desired result.


.. _zeromq-handlers:

Subclassing QueueHandler - a ZeroMQ example
-------------------------------------------

You can use a :class:`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):
            data = json.dumps(record.__dict__)
            self.queue.send(data)

    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)
            QueueHandler.__init__(self, socket)

        def enqueue(self, record):
            data = json.dumps(record.__dict__)
            self.queue.send(data)

        def close(self):
            self.queue.close()


Subclassing QueueListener - a ZeroMQ example
--------------------------------------------

You can also subclass :class:`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(zmq.SUBSCRIBE, '') # subscribe to everything
            socket.connect(uri)

        def dequeue(self):
            msg = self.queue.recv()
            return logging.makeLogRecord(json.loads(msg))


.. seealso::

   Module :mod:`logging`
      API reference for the logging module.

   Module :mod:`logging.config`
      Configuration API for the logging module.

   Module :mod:`logging.handlers`
      Useful handlers included with the logging module.

   :ref:`A basic logging tutorial <logging-basic-tutorial>`

   :ref:`A more advanced logging tutorial <logging-advanced-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 <https://docs.djangoproject.com/en/1.3/topics/logging/#configuring-logging>`_.
This dictionary is passed to :func:`~config.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 <https://docs.djangoproject.com/en/1.6/topics/logging/#configuring-logging>`_
of the Django documentation.

.. _cookbook-rotator-namer:

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 heterogenous 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 <http://tools.ietf.org/html/rfc5424>`_ 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 <http://tools.ietf.org/html/rfc5424#section-6>`_.)

In Python 3.1, code was added to
:class:`~logging.handlers.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:

#. Attach a :class:`~logging.Formatter` instance to your
   :class:`~logging.handlers.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'``.

#. 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).

#. 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 cirumstances 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.


.. _custom-handlers:

.. currentmodule:: logging.config

Customizing handlers with :func:`dictConfig`
--------------------------------------------

There are times when you want to customize logging handlers in particular ways,
and if you use :func:`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 :func:`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 :func:`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 :func:`shutil.chown`
makes an appearance. This approach should work with any Python version that
supports :func:`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.
:func:`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 :func:`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 :func:`os.chmod`.

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


.. currentmodule:: logging

.. _formatting-styles:

Using particular formatting styles throughout your application
--------------------------------------------------------------

In Python 3.2, the :class:`~logging.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 :meth:`str.format` and :class:`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 (:meth:`~Logger.debug`, :meth:`~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 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 :meth:`str.format` or :class:`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 :class:`~logging.Formatter` changes mentioned
above, the logging package gained the ability to allow users to set their own
:class:`LogRecord` subclasses, using the :func:`setLogRecordFactory` function.
You can use this to set your own subclass of :class:`LogRecord`, which does the
Right Thing by overriding the :meth:`~LogRecord.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 :func:`setLogRecordFactory` and
:class:`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
:ref:`arbitrary-object-messages`) that when logging you can use an arbitrary
object as a message format string, and that the logging package will call
:func:`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
:meth:`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 :class:`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.


.. _filters-dictconfig:

.. currentmodule:: logging.config

Configuring filters with :func:`dictConfig`
-------------------------------------------

You *can* configure filters using :func:`~logging.config.dictConfig`, though it
might not be obvious at first glance how to do it (hence this recipe). Since
:class:`~logging.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 :class:`~logging.Filter`
subclass with an overridden :meth:`~logging.Filter.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
:class:`~logging.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 :ref:`logging-config-dict-externalobj`. 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 :ref:`logging-config-dict-userdef` for more
  information on how logging supports using user-defined objects in its
  configuration, and see the other cookbook recipe :ref:`custom-handlers` above.