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 :func:`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 = 1

       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:`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:`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:`process`
to do what you need. Here's an example script which uses this class, which
also illustrates what dict-like behaviour is needed from an arbitrary
'dict-like' object for use in the constructor::

   import logging

   class ConnInfo:
       """
       An example class which shows how an arbitrary class can be used as
       the 'extra' context information repository passed to a LoggerAdapter.
       """

       def __getitem__(self, name):
           """
           To allow this instance to look like a dict.
           """
           from random import choice
           if name == 'ip':
               result = choice(['127.0.0.1', '192.168.0.1'])
           elif name == 'user':
               result = choice(['jim', 'fred', 'sheila'])
           else:
               result = self.__dict__.get(name, '?')
           return result

       def __iter__(self):
           """
           To allow iteration over keys, which will be merged into
           the LogRecord dict before formatting and output.
           """
           keys = ['ip', 'user']
           keys.extend(self.__dict__.keys())
           return keys.__iter__()

   if __name__ == '__main__':
       from random import choice
       levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
       a1 = logging.LoggerAdapter(logging.getLogger('a.b.c'),
                                  { 'ip' : '123.231.231.123', 'user' : 'sheila' })
       logging.basicConfig(level=logging.DEBUG,
                           format='%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s')
       a1.debug('A debug message')
       a1.info('An info message with %s', 'some parameters')
       a2 = logging.LoggerAdapter(logging.getLogger('d.e.f'), ConnInfo())
       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')

When this script is run, the output should look something like this::

   2008-01-18 14:49:54,023 a.b.c DEBUG    IP: 123.231.231.123 User: sheila   A debug message
   2008-01-18 14:49:54,023 a.b.c INFO     IP: 123.231.231.123 User: sheila   An info message with some parameters
   2008-01-18 14:49:54,023 d.e.f CRITICAL IP: 192.168.0.1     User: jim      A message at CRITICAL level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f INFO     IP: 192.168.0.1     User: jim      A message at INFO level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f WARNING  IP: 192.168.0.1     User: sheila   A message at WARNING level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f ERROR    IP: 127.0.0.1       User: fred     A message at ERROR level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f ERROR    IP: 127.0.0.1       User: sheila   A message at ERROR level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f WARNING  IP: 192.168.0.1     User: sheila   A message at WARNING level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f WARNING  IP: 192.168.0.1     User: jim      A message at WARNING level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f INFO     IP: 192.168.0.1     User: fred     A message at INFO level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f WARNING  IP: 192.168.0.1     User: sheila   A message at WARNING level with 2 parameters
   2008-01-18 14:49:54,033 d.e.f WARNING  IP: 127.0.0.1       User: jim      A message at WARNING level with 2 parameters


.. _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:`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:`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
http://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 (KeyboardInterrupt, SystemExit):
                raise
            except:
                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:`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(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".
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.


.. currentmodule:: logging

.. _custom-logrecord:

Customising ``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:`~logging.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/1.3/topics/logging/#configuring-logging>`_
of the Django documentation.