Logging Cookbook
****************

Author:
   原文作者 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()


Logging from multiple threads
=============================

Logging from multiple threads requires no special effort. The
following example shows logging from the main (initIal) thread and
another thread:

   import logging
   import threading
   import time

   def worker(arg):
       while not arg['stop']:
           logging.debug('Hi from myfunc')
           time.sleep(0.5)

   def main():
       logging.basicConfig(level=logging.DEBUG, format='%(relativeCreated)6d %(threadName)s %(message)s')
       info = {'stop': False}
       thread = threading.Thread(target=worker, args=(info,))
       thread.start()
       while True:
           try:
               logging.debug('Hello from main')
               time.sleep(0.75)
           except KeyboardInterrupt:
               info['stop'] = True
               break
       thread.join()

   if __name__ == '__main__':
       main()

When run, the script should print something like the following:

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

This shows the logging output interspersed as one might expect. This
approach works for more threads than shown here, of course.


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')


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 is
a simple example:

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

which you can use like this:

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

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


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

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


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.) This 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
https://bugs.python.org/issue3770).


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


An example dictionary-based configuration
=========================================

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

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

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


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

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

In Python 2.6 and 2.7, code was added to "SysLogHandler" to insert a
BOM into the message, but unfortunately, it was implemented
incorrectly, with the BOM appearing at the beginning of the message
and hence not allowing any pure-ASCII component to appear before it.

As this behaviour is broken, the incorrect BOM insertion code is being
removed from Python 2.7.4 and later. However, it is not being
replaced, and if you want to produce RFC 5424-compliant messages which
include a BOM, an optional pure-ASCII sequence before it and arbitrary
Unicode after it, encoded using UTF-8, then you need to do the
following:

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

      u'ASCII section\ufeffUnicode section'

   The Unicode code point "u'\ufeff'", when encoded using UTF-8, will
   be encoded as a UTF-8 BOM – the byte-string "'\xef\xbb\xbf'".

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

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

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


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

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

   import json
   import logging

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

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

   _ = StructuredMessage   # optional, to improve readability

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

If the above script is run, it prints:

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

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

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

   from __future__ import unicode_literals

   import json
   import logging

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

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

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

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

   _ = StructuredMessage   # optional, to improve readability

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

   if __name__ == '__main__':
       main()

When the above script is run, it prints:

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

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


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

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

   def owned_file_handler(filename, mode='a', encoding=None, owner=None):
       if owner:
           import os, pwd, grp
           # convert user and group names to uid and gid
           uid = pwd.getpwnam(owner[0]).pw_uid
           gid = grp.getgrnam(owner[1]).gr_gid
           owner = (uid, gid)
           if not os.path.exists(filename):
               open(filename, 'a').close()
           os.chown(filename, *owner)
       return logging.FileHandler(filename, mode, encoding)

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

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

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

   import logging, logging.config, os, shutil

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

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

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

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

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

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

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

   '()': owned_file_handler,

you could use e.g.:

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

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

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

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


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

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

   import logging
   import logging.config
   import sys

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

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

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

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

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

   changed: hello

which shows that the filter is working as configured.

A couple of extra points to note:

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

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


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

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

   import logging

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

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

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

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

   if __name__ == '__main__':
       main()

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

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

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


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

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

   import logging
   import subprocess
   import sys

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

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

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

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

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

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


Buffering logging messages and outputting them conditionally
============================================================

There might be situations where you want to log messages in a
temporary area and only output them if a certain condition occurs. For
example, you may want to start logging debug events in a function, and
if the function completes without errors, you don’t want to clutter
the log with the collected debug information, but if there is an
error, you want all the debug information to be output as well as the
error.

Here is an example which shows how you could do this using a decorator
for your functions where you want logging to behave this way. It makes
use of the "logging.handlers.MemoryHandler", which allows buffering of
logged events until some condition occurs, at which point the buffered
events are "flushed" - passed to another handler (the "target"
handler) for processing. By default, the "MemoryHandler" flushed when
its buffer gets filled up or an event whose level is greater than or
equal to a specified threshold is seen. You can use this recipe with a
more specialised subclass of "MemoryHandler" if you want custom
flushing behavior.

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

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

Here’s the script:

   import logging
   from logging.handlers import MemoryHandler
   import sys

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

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

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

       return decorator

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

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

   decorated_foo = log_if_errors(logger)(foo)

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

When this script is run, the following output should be observed:

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

As you can see, actual logging output only occurs when an event is
logged whose severity is ERROR or greater, but in that case, any
previous events at lower severities are also logged.

You can of course use the conventional means of decoration:

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


Formatting times using UTC (GMT) via configuration
==================================================

Sometimes you want to format times using UTC, which can be done using
a class such as *UTCFormatter*, shown below:

   import logging
   import time

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

and you can then use the "UTCFormatter" in your code instead of
"Formatter". If you want to do that via configuration, you can use the
"dictConfig()" API with an approach illustrated by the following
complete example:

   import logging
   import logging.config
   import time

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

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

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

When this script is run, it should print something like:

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

showing how the time is formatted both as local time and UTC, one for
each handler.


Using a context manager for selective logging
=============================================

There are times when it would be useful to temporarily change the
logging configuration and revert it back after doing something. For
this, a context manager is the most obvious way of saving and
restoring the logging context. Here is a simple example of such a
context manager, which allows you to optionally change the logging
level and add a logging handler purely in the scope of the context
manager:

   import logging
   import sys

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

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

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

If you specify a level value, the logger’s level is set to that value
in the scope of the with block covered by the context manager. If you
specify a handler, it is added to the logger on entry to the block and
removed on exit from the block. You can also ask the manager to close
the handler for you on block exit - you could do this if you don’t
need the handler any more.

To illustrate how it works, we can add the following block of code to
the above:

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

We initially set the logger’s level to "INFO", so message #1 appears
and message #2 doesn’t. We then change the level to "DEBUG"
temporarily in the following "with" block, and so message #3 appears.
After the block exits, the logger’s level is restored to "INFO" and so
message #4 doesn’t appear. In the next "with" block, we set the level
to "DEBUG" again but also add a handler writing to "sys.stdout". Thus,
message #5 appears twice on the console (once via "stderr" and once
via "stdout"). After the "with" statement’s completion, the status is
as it was before so message #6 appears (like message #1) whereas
message #7 doesn’t (just like message #2).

If we run the resulting script, the result is as follows:

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

If we run it again, but pipe "stderr" to "/dev/null", we see the
following, which is the only message written to "stdout":

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

Once again, but piping "stdout" to "/dev/null", we get:

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

In this case, the message #5 printed to "stdout" doesn’t appear, as
expected.

Of course, the approach described here can be generalised, for example
to attach logging filters temporarily. Note that the above code works
in Python 2 as well as Python 3.
