
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.


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 ``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.


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

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
``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 ``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 ``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 ``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 ``QueueListener``, which has been
designed as the counterpart to ``QueueHandler``.  A ``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 ``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!


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
``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
``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 ``makePickle()`` method and
implementing your alternative there, as well as adapting the above
script to use your alternative serialization.


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 ``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
``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 ``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 ``LoggerAdapter``
class. This class is designed to look like a ``Logger``, so that you
can call ``debug()``, ``info()``, ``warning()``, ``error()``,
``exception()``, ``critical()`` and ``log()``. These methods have the
same signatures as their counterparts in ``Logger``, so you can use
the two types of instances interchangeably.

When you create an instance of ``LoggerAdapter``, you pass it a
``Logger`` instance and a dict-like object which contains your
contextual information. When you call one of the logging methods on an
instance of ``LoggerAdapter``, it delegates the call to the underlying
instance of ``Logger`` passed to its constructor, and arranges to pass
the contextual information in the delegated call. Here's a snippet
from the code of ``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 ``process()`` method of ``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 ``LogRecord`` instance's __dict__, allowing
you to use customized strings with your ``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
``LoggerAdapter`` and override ``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


Using Filters to impart contextual information
----------------------------------------------

You can also add contextual information to log output using a user-
defined ``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 ``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
(``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


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

Alternatively, you can use a ``Queue`` and a ``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 >> sys.stderr, 'Whoops! Problem:'
               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
===================

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
``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 ``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 much too small as an
extreme example.  You would want to set *maxBytes* to an appropriate
value.


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

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

   import zmq # using pyzmq, the Python binding for ZeroMQ
   import json # for serializing records portably

   ctx = zmq.Context()
   sock = zmq.Socket(ctx, zmq.PUB) # or zmq.PUSH, or other suitable value
   sock.bind('tcp://*:5556') # or wherever

   class ZeroMQSocketHandler(QueueHandler):
       def enqueue(self, record):
           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 ``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))

See also:

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

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

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

   *A basic logging tutorial*

   *A more advanced logging tutorial*
