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. For links to tutorial and reference
information, please see Other resources.


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.warning('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='/tmp/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.

Note that the above choice of log filename "/tmp/myapp.log" implies
use of a standard location for temporary files on POSIX systems. On
Windows, you may need to choose a different directory name for the log
- just ensure that the directory exists and that you have the
permissions to create and update files in it.


Custom handling of levels
=========================

Sometimes, you might want to do something slightly different from the
standard handling of levels in handlers, where all levels above a
threshold get processed by a handler. To do this, you need to use
filters. Let's look at a scenario where you want to arrange things as
follows:

* Send messages of severity "INFO" and "WARNING" to "sys.stdout"

* Send messages of severity "ERROR" and above to "sys.stderr"

* Send messages of severity "DEBUG" and above to file "app.log"

Suppose you configure logging with the following JSON:

   {
       "version": 1,
       "disable_existing_loggers": false,
       "formatters": {
           "simple": {
               "format": "%(levelname)-8s - %(message)s"
           }
       },
       "handlers": {
           "stdout": {
               "class": "logging.StreamHandler",
               "level": "INFO",
               "formatter": "simple",
               "stream": "ext://sys.stdout"
           },
           "stderr": {
               "class": "logging.StreamHandler",
               "level": "ERROR",
               "formatter": "simple",
               "stream": "ext://sys.stderr"
           },
           "file": {
               "class": "logging.FileHandler",
               "formatter": "simple",
               "filename": "app.log",
               "mode": "w"
           }
       },
       "root": {
           "level": "DEBUG",
           "handlers": [
               "stderr",
               "stdout",
               "file"
           ]
       }
   }

This configuration does *almost* what we want, except that
"sys.stdout" would show messages of severity "ERROR" and above as well
as "INFO" and "WARNING" messages. To prevent this, we can set up a
filter which excludes those messages and add it to the relevant
handler. This can be configured by adding a "filters" section parallel
to "formatters" and "handlers":

   {
       "filters": {
           "warnings_and_below": {
               "()" : "__main__.filter_maker",
               "level": "WARNING"
           }
       }
   }

and changing the section on the "stdout" handler to add it:

   {
       "stdout": {
           "class": "logging.StreamHandler",
           "level": "INFO",
           "formatter": "simple",
           "stream": "ext://sys.stdout",
           "filters": ["warnings_and_below"]
       }
   }

A filter is just a function, so we can define the "filter_maker" (a
factory function) as follows:

   def filter_maker(level):
       level = getattr(logging, level)

       def filter(record):
           return record.levelno <= level

       return filter

This converts the string argument passed in to a numeric level, and
returns a function which only returns "True" if the level of the
passed in record is at or below the specified level. Note that in this
example I have defined the "filter_maker" in a test script "main.py"
that I run from the command line, so its module will be "__main__" -
hence the "__main__.filter_maker" in the filter configuration. You
will need to change that if you define it in a different module.

With the filter added, we can run "main.py", which in full is:

   import json
   import logging
   import logging.config

   CONFIG = '''
   {
       "version": 1,
       "disable_existing_loggers": false,
       "formatters": {
           "simple": {
               "format": "%(levelname)-8s - %(message)s"
           }
       },
       "filters": {
           "warnings_and_below": {
               "()" : "__main__.filter_maker",
               "level": "WARNING"
           }
       },
       "handlers": {
           "stdout": {
               "class": "logging.StreamHandler",
               "level": "INFO",
               "formatter": "simple",
               "stream": "ext://sys.stdout",
               "filters": ["warnings_and_below"]
           },
           "stderr": {
               "class": "logging.StreamHandler",
               "level": "ERROR",
               "formatter": "simple",
               "stream": "ext://sys.stderr"
           },
           "file": {
               "class": "logging.FileHandler",
               "formatter": "simple",
               "filename": "app.log",
               "mode": "w"
           }
       },
       "root": {
           "level": "DEBUG",
           "handlers": [
               "stderr",
               "stdout",
               "file"
           ]
       }
   }
   '''

   def filter_maker(level):
       level = getattr(logging, level)

       def filter(record):
           return record.levelno <= level

       return filter

   logging.config.dictConfig(json.loads(CONFIG))
   logging.debug('A DEBUG message')
   logging.info('An INFO message')
   logging.warning('A WARNING message')
   logging.error('An ERROR message')
   logging.critical('A CRITICAL message')

And after running it like this:

   python main.py 2>stderr.log >stdout.log

We can see the results are as expected:

   $ more *.log
   ::::::::::::::
   app.log
   ::::::::::::::
   DEBUG    - A DEBUG message
   INFO     - An INFO message
   WARNING  - A WARNING message
   ERROR    - An ERROR message
   CRITICAL - A CRITICAL message
   ::::::::::::::
   stderr.log
   ::::::::::::::
   ERROR    - An ERROR message
   CRITICAL - A CRITICAL message
   ::::::::::::::
   stdout.log
   ::::::::::::::
   INFO     - An INFO message
   WARNING  - A WARNING message


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.warning('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!

Note:

  Although the earlier discussion wasn't specifically talking about
  async code, but rather about slow logging handlers, it should be
  noted that when logging from async code, network and even file
  handlers could lead to problems (blocking the event loop) because
  some logging is done from "asyncio" internals. It might be best, if
  any async code is used in an application, to use the above approach
  for logging, so that any blocking code runs only in the
  "QueueListener" thread.

Changed in version 3.5: Prior to Python 3.5, the "QueueListener"
always passed every message received from the queue to every handler
it was initialized with. (This was because it was assumed that level
filtering was all done on the other side, where the queue is filled.)
From 3.5 onwards, this behaviour can be changed by passing a keyword
argument "respect_handler_level=True" to the listener's constructor.
When this is done, the listener compares the level of each message
with the handler's level, and only passes a message to a handler if
it's appropriate to do so.


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 = True

       def __init__(self, host='localhost',
                    port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
                    handler=LogRecordStreamHandler):
           socketserver.ThreadingTCPServer.__init__(self, (host, port), handler)
           self.abort = 0
           self.timeout = 1
           self.logname = None

       def serve_until_stopped(self):
           import select
           abort = 0
           while not abort:
               rd, wr, ex = select.select([self.socket.fileno()],
                                          [], [],
                                          self.timeout)
               if rd:
                   self.handle_request()
               abort = self.abort

   def main():
       logging.basicConfig(
           format='%(relativeCreated)5d %(name)-15s %(levelname)-8s %(message)s')
       tcpserver = LogRecordSocketReceiver()
       print('About to start TCP server...')
       tcpserver.serve_until_stopped()

   if __name__ == '__main__':
       main()

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

   About to start TCP server...
      59 root            INFO     Jackdaws love my big sphinx of quartz.
      59 myapp.area1     DEBUG    Quick zephyrs blow, vexing daft Jim.
      69 myapp.area1     INFO     How quickly daft jumping zebras vex.
      69 myapp.area2     WARNING  Jail zesty vixen who grabbed pay from quack.
      69 myapp.area2     ERROR    The five boxing wizards jump quickly.

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


Running a logging socket listener in production
-----------------------------------------------

To run a logging listener in production, you may need to use a
process-management tool such as Supervisor. Here is a Gist which
provides the bare-bones files to run the above functionality using
Supervisor. It consists of the following files:

+---------------------------+------------------------------------------------------+
| File                      | Purpose                                              |
|===========================|======================================================|
| "prepare.sh"              | A Bash script to prepare the environment for testing |
+---------------------------+------------------------------------------------------+
| "supervisor.conf"         | The Supervisor configuration file, which has entries |
|                           | for the listener and a multi-process web application |
+---------------------------+------------------------------------------------------+
| "ensure_app.sh"           | A Bash script to ensure that Supervisor is running   |
|                           | with the above configuration                         |
+---------------------------+------------------------------------------------------+
| "log_listener.py"         | The socket listener program which receives log       |
|                           | events and records them to a file                    |
+---------------------------+------------------------------------------------------+
| "main.py"                 | A simple web application which performs logging via  |
|                           | a socket connected to the listener                   |
+---------------------------+------------------------------------------------------+
| "webapp.json"             | A JSON configuration file for the web application    |
+---------------------------+------------------------------------------------------+
| "client.py"               | A Python script to exercise the web application      |
+---------------------------+------------------------------------------------------+

The web application uses Gunicorn, which is a popular web application
server that starts multiple worker processes to handle requests. This
example setup shows how the workers can write to the same log file
without conflicting with one another --- they all go through the
socket listener.

To test these files, do the following in a POSIX environment:

1. Download the Gist as a ZIP archive using the Download ZIP button.

2. Unzip the above files from the archive into a scratch directory.

3. In the scratch directory, run "bash prepare.sh" to get things
   ready. This creates a "run" subdirectory to contain Supervisor-
   related and log files, and a "venv" subdirectory to contain a
   virtual environment into which "bottle", "gunicorn" and
   "supervisor" are installed.

4. Run "bash ensure_app.sh" to ensure that Supervisor is running with
   the above configuration.

5. Run "venv/bin/python client.py" to exercise the web application,
   which will lead to records being written to the log.

6. Inspect the log files in the "run" subdirectory. You should see the
   most recent log lines in files matching the pattern "app.log*".
   They won't be in any particular order, since they have been handled
   concurrently by different worker processes in a non-deterministic
   way.

7. You can shut down the listener and the web application by running
   "venv/bin/supervisorctl -c supervisor.conf shutdown".

You may need to tweak the configuration files in the unlikely event
that the configured ports clash with something else in your test
environment.


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


Use of "contextvars"
====================

Since Python 3.7, the "contextvars" module has provided context-local
storage which works for both "threading" and "asyncio" processing
needs. This type of storage may thus be generally preferable to
thread-locals. The following example shows how, in a multi-threaded
environment, logs can populated with contextual information such as,
for example, request attributes handled by web applications.

For the purposes of illustration, say that you have different web
applications, each independent of the other but running in the same
Python process and using a library common to them. How can each of
these applications have their own log, where all logging messages from
the library (and other request processing code) are directed to the
appropriate application's log file, while including in the log
additional contextual information such as client IP, HTTP request
method and client username?

Let's assume that the library can be simulated by the following code:

   # webapplib.py
   import logging
   import time

   logger = logging.getLogger(__name__)

   def useful():
       # Just a representative event logged from the library
       logger.debug('Hello from webapplib!')
       # Just sleep for a bit so other threads get to run
       time.sleep(0.01)

We can simulate the multiple web applications by means of two simple
classes, "Request" and "WebApp". These simulate how real threaded web
applications work - each request is handled by a thread:

   # main.py
   import argparse
   from contextvars import ContextVar
   import logging
   import os
   from random import choice
   import threading
   import webapplib

   logger = logging.getLogger(__name__)
   root = logging.getLogger()
   root.setLevel(logging.DEBUG)

   class Request:
       """
       A simple dummy request class which just holds dummy HTTP request method,
       client IP address and client username
       """
       def __init__(self, method, ip, user):
           self.method = method
           self.ip = ip
           self.user = user

   # A dummy set of requests which will be used in the simulation - we'll just pick
   # from this list randomly. Note that all GET requests are from 192.168.2.XXX
   # addresses, whereas POST requests are from 192.16.3.XXX addresses. Three users
   # are represented in the sample requests.

   REQUESTS = [
       Request('GET', '192.168.2.20', 'jim'),
       Request('POST', '192.168.3.20', 'fred'),
       Request('GET', '192.168.2.21', 'sheila'),
       Request('POST', '192.168.3.21', 'jim'),
       Request('GET', '192.168.2.22', 'fred'),
       Request('POST', '192.168.3.22', 'sheila'),
   ]

   # Note that the format string includes references to request context information
   # such as HTTP method, client IP and username

   formatter = logging.Formatter('%(threadName)-11s %(appName)s %(name)-9s %(user)-6s %(ip)s %(method)-4s %(message)s')

   # Create our context variables. These will be filled at the start of request
   # processing, and used in the logging that happens during that processing

   ctx_request = ContextVar('request')
   ctx_appname = ContextVar('appname')

   class InjectingFilter(logging.Filter):
       """
       A filter which injects context-specific information into logs and ensures
       that only information for a specific webapp is included in its log
       """
       def __init__(self, app):
           self.app = app

       def filter(self, record):
           request = ctx_request.get()
           record.method = request.method
           record.ip = request.ip
           record.user = request.user
           record.appName = appName = ctx_appname.get()
           return appName == self.app.name

   class WebApp:
       """
       A dummy web application class which has its own handler and filter for a
       webapp-specific log.
       """
       def __init__(self, name):
           self.name = name
           handler = logging.FileHandler(name + '.log', 'w')
           f = InjectingFilter(self)
           handler.setFormatter(formatter)
           handler.addFilter(f)
           root.addHandler(handler)
           self.num_requests = 0

       def process_request(self, request):
           """
           This is the dummy method for processing a request. It's called on a
           different thread for every request. We store the context information into
           the context vars before doing anything else.
           """
           ctx_request.set(request)
           ctx_appname.set(self.name)
           self.num_requests += 1
           logger.debug('Request processing started')
           webapplib.useful()
           logger.debug('Request processing finished')

   def main():
       fn = os.path.splitext(os.path.basename(__file__))[0]
       adhf = argparse.ArgumentDefaultsHelpFormatter
       ap = argparse.ArgumentParser(formatter_class=adhf, prog=fn,
                                    description='Simulate a couple of web '
                                                'applications handling some '
                                                'requests, showing how request '
                                                'context can be used to '
                                                'populate logs')
       aa = ap.add_argument
       aa('--count', '-c', type=int, default=100, help='How many requests to simulate')
       options = ap.parse_args()

       # Create the dummy webapps and put them in a list which we can use to select
       # from randomly
       app1 = WebApp('app1')
       app2 = WebApp('app2')
       apps = [app1, app2]
       threads = []
       # Add a common handler which will capture all events
       handler = logging.FileHandler('app.log', 'w')
       handler.setFormatter(formatter)
       root.addHandler(handler)

       # Generate calls to process requests
       for i in range(options.count):
           try:
               # Pick an app at random and a request for it to process
               app = choice(apps)
               request = choice(REQUESTS)
               # Process the request in its own thread
               t = threading.Thread(target=app.process_request, args=(request,))
               threads.append(t)
               t.start()
           except KeyboardInterrupt:
               break

       # Wait for the threads to terminate
       for t in threads:
           t.join()

       for app in apps:
           print('%s processed %s requests' % (app.name, app.num_requests))

   if __name__ == '__main__':
       main()

If you run the above, you should find that roughly half the requests
go into "app1.log" and the rest into "app2.log", and the all the
requests are logged to "app.log". Each webapp-specific log will
contain only log entries for only that webapp, and the request
information will be displayed consistently in the log (i.e. the
information in each dummy request will always appear together in a log
line). This is illustrated by the following shell output:

   ~/logging-contextual-webapp$ python main.py
   app1 processed 51 requests
   app2 processed 49 requests
   ~/logging-contextual-webapp$ wc -l *.log
     153 app1.log
     147 app2.log
     300 app.log
     600 total
   ~/logging-contextual-webapp$ head -3 app1.log
   Thread-3 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
   Thread-3 (process_request) app1 webapplib jim    192.168.3.21 POST Hello from webapplib!
   Thread-5 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
   ~/logging-contextual-webapp$ head -3 app2.log
   Thread-1 (process_request) app2 __main__  sheila 192.168.2.21 GET  Request processing started
   Thread-1 (process_request) app2 webapplib sheila 192.168.2.21 GET  Hello from webapplib!
   Thread-2 (process_request) app2 __main__  jim    192.168.2.20 GET  Request processing started
   ~/logging-contextual-webapp$ head app.log
   Thread-1 (process_request) app2 __main__  sheila 192.168.2.21 GET  Request processing started
   Thread-1 (process_request) app2 webapplib sheila 192.168.2.21 GET  Hello from webapplib!
   Thread-2 (process_request) app2 __main__  jim    192.168.2.20 GET  Request processing started
   Thread-3 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
   Thread-2 (process_request) app2 webapplib jim    192.168.2.20 GET  Hello from webapplib!
   Thread-3 (process_request) app1 webapplib jim    192.168.3.21 POST Hello from webapplib!
   Thread-4 (process_request) app2 __main__  fred   192.168.2.22 GET  Request processing started
   Thread-5 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
   Thread-4 (process_request) app2 webapplib fred   192.168.2.22 GET  Hello from webapplib!
   Thread-6 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
   ~/logging-contextual-webapp$ grep app1 app1.log | wc -l
   153
   ~/logging-contextual-webapp$ grep app2 app2.log | wc -l
   147
   ~/logging-contextual-webapp$ grep app1 app.log | wc -l
   153
   ~/logging-contextual-webapp$ grep app2 app.log | wc -l
   147


Imparting contextual information in handlers
============================================

Each "Handler" has its own chain of filters. If you want to add
contextual information to a "LogRecord" without leaking it to other
handlers, you can use a filter that returns a new "LogRecord" instead
of modifying it in-place, as shown in the following script:

   import copy
   import logging

   def filter(record: logging.LogRecord):
       record = copy.copy(record)
       record.user = 'jim'
       return record

   if __name__ == '__main__':
       logger = logging.getLogger()
       logger.setLevel(logging.INFO)
       handler = logging.StreamHandler()
       formatter = logging.Formatter('%(message)s from %(user)-8s')
       handler.setFormatter(formatter)
       handler.addFilter(filter)
       logger.addHandler(handler)

       logger.info('A log message')


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.

You could also write your own handler which uses the "Lock" class from
the "multiprocessing" 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).

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 Exception:
               import sys, traceback
               print('Whoops! Problem:', file=sys.stderr)
               traceback.print_exc(file=sys.stderr)

   # Arrays used for random selections in this demo

   LEVELS = [logging.DEBUG, logging.INFO, logging.WARNING,
             logging.ERROR, logging.CRITICAL]

   LOGGERS = ['a.b.c', 'd.e.f']

   MESSAGES = [
       'Random message #1',
       'Random message #2',
       'Random message #3',
   ]

   # The worker configuration is done at the start of the worker process run.
   # Note that on Windows you can't rely on fork semantics, so each process
   # will run the logging configuration code when it starts.
   def worker_configurer(queue):
       h = logging.handlers.QueueHandler(queue)  # Just the one handler needed
       root = logging.getLogger()
       root.addHandler(h)
       # send all messages, for demo; no other level or filter logic applied.
       root.setLevel(logging.DEBUG)

   # This is the worker process top-level loop, which just logs ten events with
   # random intervening delays before terminating.
   # The print messages are just so you know it's doing something!
   def worker_process(queue, configurer):
       configurer(queue)
       name = multiprocessing.current_process().name
       print('Worker started: %s' % name)
       for i in range(10):
           time.sleep(random())
           logger = logging.getLogger(choice(LOGGERS))
           level = choice(LEVELS)
           message = choice(MESSAGES)
           logger.log(level, message)
       print('Worker finished: %s' % name)

   # Here's where the demo gets orchestrated. Create the queue, create and start
   # the listener, create ten workers and start them, wait for them to finish,
   # then send a None to the queue to tell the listener to finish.
   def main():
       queue = multiprocessing.Queue(-1)
       listener = multiprocessing.Process(target=listener_process,
                                          args=(queue, listener_configurer))
       listener.start()
       workers = []
       for i in range(10):
           worker = multiprocessing.Process(target=worker_process,
                                            args=(queue, worker_configurer))
           workers.append(worker)
           worker.start()
       for w in workers:
           w.join()
       queue.put_nowait(None)
       listener.join()

   if __name__ == '__main__':
       main()

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 concurrent.futures.ProcessPoolExecutor
--------------------------------------------

If you want to use "concurrent.futures.ProcessPoolExecutor" to start
your worker processes, you need to create the queue slightly
differently. Instead of

   queue = multiprocessing.Queue(-1)

you should use

   queue = multiprocessing.Manager().Queue(-1)  # also works with the examples above

and you can then replace the worker creation from this:

   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()

to this (remembering to first import "concurrent.futures"):

   with concurrent.futures.ProcessPoolExecutor(max_workers=10) as executor:
       for i in range(10):
           executor.submit(worker_process, queue, worker_configurer)


Deploying Web applications using Gunicorn and uWSGI
---------------------------------------------------

When deploying Web applications using Gunicorn or uWSGI (or similar),
multiple worker processes are created to handle client requests. In
such environments, avoid creating file-based handlers directly in your
web application. Instead, use a "SocketHandler" to log from the web
application to a listener in a separate process. This can be set up
using a process management tool such as Supervisor - see Running a
logging socket listener in production for more details.


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.


Use of alternative formatting styles
====================================

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

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

   >>> import logging
   >>> root = logging.getLogger()
   >>> root.setLevel(logging.DEBUG)
   >>> handler = logging.StreamHandler()
   >>> bf = logging.Formatter('{asctime} {name} {levelname:8s} {message}',
   ...                        style='{')
   >>> handler.setFormatter(bf)
   >>> root.addHandler(handler)
   >>> logger = logging.getLogger('foo.bar')
   >>> logger.debug('This is a DEBUG message')
   2010-10-28 15:11:55,341 foo.bar DEBUG    This is a DEBUG message
   >>> logger.critical('This is a CRITICAL message')
   2010-10-28 15:12:11,526 foo.bar CRITICAL This is a CRITICAL message
   >>> df = logging.Formatter('$asctime $name ${levelname} $message',
   ...                        style='$')
   >>> handler.setFormatter(df)
   >>> logger.debug('This is a DEBUG message')
   2010-10-28 15:13:06,924 foo.bar DEBUG This is a DEBUG message
   >>> logger.critical('This is a CRITICAL message')
   2010-10-28 15:13:11,494 foo.bar CRITICAL This is a CRITICAL message
   >>>

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

   >>> logger.error('This is an%s %s %s', 'other,', 'ERROR,', 'message')
   2010-10-28 15:19:29,833 foo.bar ERROR This is another, ERROR, message
   >>>

Logging calls ("logger.debug()", "logger.info()" etc.) only take
positional parameters for the actual logging message itself, with
keyword parameters used only for determining options for how to handle
the actual logging call (e.g. the "exc_info" keyword parameter to
indicate that traceback information should be logged, or the "extra"
keyword parameter to indicate additional contextual information to be
added to the log). So you cannot directly make logging calls using
"str.format()" or "string.Template" syntax, because internally the
logging package uses %-formatting to merge the format string and the
variable arguments. There would be no changing this while preserving
backward compatibility, since all logging calls which are out there in
existing code will be using %-format strings.

There is, however, a way that you can use {}- and $- formatting to
construct your individual log messages. Recall that for a message you
can use an arbitrary object as a message format string, and that the
logging package will call "str()" on that object to get the actual
format string. Consider the following two classes:

   class BraceMessage:
       def __init__(self, fmt, /, *args, **kwargs):
           self.fmt = fmt
           self.args = args
           self.kwargs = kwargs

       def __str__(self):
           return self.fmt.format(*self.args, **self.kwargs)

   class DollarMessage:
       def __init__(self, fmt, /, **kwargs):
           self.fmt = fmt
           self.kwargs = kwargs

       def __str__(self):
           from string import Template
           return Template(self.fmt).substitute(**self.kwargs)

Either of these can be used in place of a format string, to allow {}-
or $-formatting to be used to build the actual "message" part which
appears in the formatted log output in place of "%(message)s" or
"{message}" or "$message". It's a little unwieldy to use the class
names whenever you want to log something, but it's quite palatable if
you use an alias such as __ (double underscore --- not to be confused
with _, the single underscore used as a synonym/alias for
"gettext.gettext()" or its brethren).

The above classes are not included in Python, though they're easy
enough to copy and paste into your own code. They can be used as
follows (assuming that they're declared in a module called
"wherever"):

   >>> from wherever import BraceMessage as __
   >>> print(__('Message with {0} {name}', 2, name='placeholders'))
   Message with 2 placeholders
   >>> class Point: pass
   ...
   >>> p = Point()
   >>> p.x = 0.5
   >>> p.y = 0.5
   >>> print(__('Message with coordinates: ({point.x:.2f}, {point.y:.2f})',
   ...       point=p))
   Message with coordinates: (0.50, 0.50)
   >>> from wherever import DollarMessage as __
   >>> print(__('Message with $num $what', num=2, what='placeholders'))
   Message with 2 placeholders
   >>>

While the above examples use "print()" to show how the formatting
works, you would of course use "logger.debug()" or similar to actually
log using this approach.

One thing to note is that you pay no significant performance penalty
with this approach: the actual formatting happens not when you make
the logging call, but when (and if) the logged message is actually
about to be output to a log by a handler. So the only slightly unusual
thing which might trip you up is that the parentheses go around the
format string and the arguments, not just the format string. That's
because the __ notation is just syntax sugar for a constructor call to
one of the "*XXX*Message" classes.

If you prefer, you can use a "LoggerAdapter" to achieve a similar
effect to the above, as in the following example:

   import logging

   class Message:
       def __init__(self, fmt, args):
           self.fmt = fmt
           self.args = args

       def __str__(self):
           return self.fmt.format(*self.args)

   class StyleAdapter(logging.LoggerAdapter):
       def __init__(self, logger, extra=None):
           super().__init__(logger, extra or {})

       def log(self, level, msg, /, *args, **kwargs):
           if self.isEnabledFor(level):
               msg, kwargs = self.process(msg, kwargs)
               self.logger._log(level, Message(msg, args), (), **kwargs)

   logger = StyleAdapter(logging.getLogger(__name__))

   def main():
       logger.debug('Hello, {}', 'world!')

   if __name__ == '__main__':
       logging.basicConfig(level=logging.DEBUG)
       main()

The above script should log the message "Hello, world!" when run with
Python 3.2 or later.


Customizing "LogRecord"
=======================

Every logging event is represented by a "LogRecord" instance. When an
event is logged and not filtered out by a logger's level, a
"LogRecord" is created, populated with information about the event and
then passed to the handlers for that logger (and its ancestors, up to
and including the logger where further propagation up the hierarchy is
disabled). Before Python 3.2, there were only two places where this
creation was done:

* "Logger.makeRecord()", which is called in the normal process of
  logging an event. This invoked "LogRecord" directly to create an
  instance.

* "makeLogRecord()", which is called with a dictionary containing
  attributes to be added to the LogRecord. This is typically invoked
  when a suitable dictionary has been received over the network (e.g.
  in pickle form via a "SocketHandler", or in JSON form via an
  "HTTPHandler").

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

* Create your own "Logger" subclass, which overrides
  "Logger.makeRecord()", and set it using "setLoggerClass()" before
  any loggers that you care about are instantiated.

* Add a "Filter" to a logger or handler, which does the necessary
  special manipulation you need when its "filter()" method is called.

The first approach would be a little unwieldy in the scenario where
(say) several different libraries wanted to do different things. Each
would attempt to set its own "Logger" subclass, and the one which did
this last would win.

The second approach works reasonably well for many cases, but does not
allow you to e.g. use a specialized subclass of "LogRecord". Library
developers can set a suitable filter on their loggers, but they would
have to remember to do this every time they introduced a new logger
(which they would do simply by adding new packages or modules and
doing

   logger = logging.getLogger(__name__)

at module level). It's probably one too many things to think about.
Developers could also add the filter to a "NullHandler" attached to
their top-level logger, but this would not be invoked if an
application developer attached a handler to a lower-level library
logger --- so output from that handler would not reflect the
intentions of the library developer.

In Python 3.2 and later, "LogRecord" creation is done through a
factory, which you can specify. The factory is just a callable you can
set with "setLogRecordFactory()", and interrogate with
"getLogRecordFactory()". The factory is invoked with the same
signature as the "LogRecord" constructor, as "LogRecord" is the
default setting for the factory.

This approach allows a custom factory to control all aspects of
LogRecord creation. For example, you could return a subclass, or just
add some additional attributes to the record once created, using a
pattern similar to this:

   old_factory = logging.getLogRecordFactory()

   def record_factory(*args, **kwargs):
       record = old_factory(*args, **kwargs)
       record.custom_attribute = 0xdecafbad
       return record

   logging.setLogRecordFactory(record_factory)

This pattern allows different libraries to chain factories together,
and as long as they don't overwrite each other's attributes or
unintentionally overwrite the attributes provided as standard, there
should be no surprises. However, it should be borne in mind that each
link in the chain adds run-time overhead to all logging operations,
and the technique should only be used when the use of a "Filter" does
not provide the desired result.


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

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

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

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

   class ZeroMQSocketHandler(QueueHandler):
       def enqueue(self, record):
           self.queue.send_json(record.__dict__)


   handler = ZeroMQSocketHandler(sock)

Of course there are other ways of organizing this, for example passing
in the data needed by the handler to create the socket:

   class ZeroMQSocketHandler(QueueHandler):
       def __init__(self, uri, socktype=zmq.PUB, ctx=None):
           self.ctx = ctx or zmq.Context()
           socket = zmq.Socket(self.ctx, socktype)
           socket.bind(uri)
           super().__init__(socket)

       def enqueue(self, record):
           self.queue.send_json(record.__dict__)

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


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

You can also subclass "QueueListener" to get messages from other kinds
of queues, for example a ZeroMQ 'subscribe' socket. Here's an example:

   class ZeroMQSocketListener(QueueListener):
       def __init__(self, uri, /, *handlers, **kwargs):
           self.ctx = kwargs.get('ctx') or zmq.Context()
           socket = zmq.Socket(self.ctx, zmq.SUB)
           socket.setsockopt_string(zmq.SUBSCRIBE, '')  # subscribe to everything
           socket.connect(uri)
           super().__init__(socket, *handlers, **kwargs)

       def dequeue(self):
           msg = self.queue.recv_json()
           return logging.makeLogRecord(msg)

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


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

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

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

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


Using a rotator and namer to customize log rotation processing
==============================================================

An example of how you can define a namer and rotator is given in the
following runnable script, which shows gzip compression of the log
file:

   import gzip
   import logging
   import logging.handlers
   import os
   import shutil

   def namer(name):
       return name + ".gz"

   def rotator(source, dest):
       with open(source, 'rb') as f_in:
           with gzip.open(dest, 'wb') as f_out:
               shutil.copyfileobj(f_in, f_out)
       os.remove(source)


   rh = logging.handlers.RotatingFileHandler('rotated.log', maxBytes=128, backupCount=5)
   rh.rotator = rotator
   rh.namer = namer

   root = logging.getLogger()
   root.setLevel(logging.INFO)
   root.addHandler(rh)
   f = logging.Formatter('%(asctime)s %(message)s')
   rh.setFormatter(f)
   for i in range(1000):
       root.info(f'Message no. {i + 1}')

After running this, you will see six new files, five of which are
compressed:

   $ ls rotated.log*
   rotated.log       rotated.log.2.gz  rotated.log.4.gz
   rotated.log.1.gz  rotated.log.3.gz  rotated.log.5.gz
   $ zcat rotated.log.1.gz
   2023-01-20 02:28:17,767 Message no. 996
   2023-01-20 02:28:17,767 Message no. 997
   2023-01-20 02:28:17,767 Message no. 998


A more elaborate multiprocessing example
========================================

The following working example shows how logging can be used with
multiprocessing using configuration files. The configurations are
fairly simple, but serve to illustrate how more complex ones could be
implemented in a real multiprocessing scenario.

In the example, the main process spawns a listener process and some
worker processes. Each of the main process, the listener and the
workers have three separate configurations (the workers all share the
same configuration). We can see logging in the main process, how the
workers log to a QueueHandler and how the listener implements a
QueueListener and a more complex logging configuration, and arranges
to dispatch events received via the queue to the handlers specified in
the configuration. Note that these configurations are purely
illustrative, but you should be able to adapt this example to your own
scenario.

Here's the script - the docstrings and the comments hopefully explain
how it works:

   import logging
   import logging.config
   import logging.handlers
   from multiprocessing import Process, Queue, Event, current_process
   import os
   import random
   import time

   class MyHandler:
       """
       A simple handler for logging events. It runs in the listener process and
       dispatches events to loggers based on the name in the received record,
       which then get dispatched, by the logging system, to the handlers
       configured for those loggers.
       """

       def handle(self, record):
           if record.name == "root":
               logger = logging.getLogger()
           else:
               logger = logging.getLogger(record.name)

           if logger.isEnabledFor(record.levelno):
               # The process name is transformed just to show that it's the listener
               # doing the logging to files and console
               record.processName = '%s (for %s)' % (current_process().name, record.processName)
               logger.handle(record)

   def listener_process(q, stop_event, config):
       """
       This could be done in the main process, but is just done in a separate
       process for illustrative purposes.

       This initialises logging according to the specified configuration,
       starts the listener and waits for the main process to signal completion
       via the event. The listener is then stopped, and the process exits.
       """
       logging.config.dictConfig(config)
       listener = logging.handlers.QueueListener(q, MyHandler())
       listener.start()
       if os.name == 'posix':
           # On POSIX, the setup logger will have been configured in the
           # parent process, but should have been disabled following the
           # dictConfig call.
           # On Windows, since fork isn't used, the setup logger won't
           # exist in the child, so it would be created and the message
           # would appear - hence the "if posix" clause.
           logger = logging.getLogger('setup')
           logger.critical('Should not appear, because of disabled logger ...')
       stop_event.wait()
       listener.stop()

   def worker_process(config):
       """
       A number of these are spawned for the purpose of illustration. In
       practice, they could be a heterogeneous bunch of processes rather than
       ones which are identical to each other.

       This initialises logging according to the specified configuration,
       and logs a hundred messages with random levels to randomly selected
       loggers.

       A small sleep is added to allow other processes a chance to run. This
       is not strictly needed, but it mixes the output from the different
       processes a bit more than if it's left out.
       """
       logging.config.dictConfig(config)
       levels = [logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
                 logging.CRITICAL]
       loggers = ['foo', 'foo.bar', 'foo.bar.baz',
                  'spam', 'spam.ham', 'spam.ham.eggs']
       if os.name == 'posix':
           # On POSIX, the setup logger will have been configured in the
           # parent process, but should have been disabled following the
           # dictConfig call.
           # On Windows, since fork isn't used, the setup logger won't
           # exist in the child, so it would be created and the message
           # would appear - hence the "if posix" clause.
           logger = logging.getLogger('setup')
           logger.critical('Should not appear, because of disabled logger ...')
       for i in range(100):
           lvl = random.choice(levels)
           logger = logging.getLogger(random.choice(loggers))
           logger.log(lvl, 'Message no. %d', i)
           time.sleep(0.01)

   def main():
       q = Queue()
       # The main process gets a simple configuration which prints to the console.
       config_initial = {
           'version': 1,
           'handlers': {
               'console': {
                   'class': 'logging.StreamHandler',
                   'level': 'INFO'
               }
           },
           'root': {
               'handlers': ['console'],
               'level': 'DEBUG'
           }
       }
       # The worker process configuration is just a QueueHandler attached to the
       # root logger, which allows all messages to be sent to the queue.
       # We disable existing loggers to disable the "setup" logger used in the
       # parent process. This is needed on POSIX because the logger will
       # be there in the child following a fork().
       config_worker = {
           'version': 1,
           'disable_existing_loggers': True,
           'handlers': {
               'queue': {
                   'class': 'logging.handlers.QueueHandler',
                   'queue': q
               }
           },
           'root': {
               'handlers': ['queue'],
               'level': 'DEBUG'
           }
       }
       # The listener process configuration shows that the full flexibility of
       # logging configuration is available to dispatch events to handlers however
       # you want.
       # We disable existing loggers to disable the "setup" logger used in the
       # parent process. This is needed on POSIX because the logger will
       # be there in the child following a fork().
       config_listener = {
           'version': 1,
           'disable_existing_loggers': True,
           'formatters': {
               'detailed': {
                   'class': 'logging.Formatter',
                   'format': '%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
               },
               'simple': {
                   'class': 'logging.Formatter',
                   'format': '%(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
               }
           },
           'handlers': {
               'console': {
                   'class': 'logging.StreamHandler',
                   'formatter': 'simple',
                   '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',
                   'formatter': 'detailed',
                   'level': 'ERROR'
               }
           },
           'loggers': {
               'foo': {
                   'handlers': ['foofile']
               }
           },
           'root': {
               'handlers': ['console', 'file', 'errors'],
               'level': 'DEBUG'
           }
       }
       # Log some initial events, just to show that logging in the parent works
       # normally.
       logging.config.dictConfig(config_initial)
       logger = logging.getLogger('setup')
       logger.info('About to create workers ...')
       workers = []
       for i in range(5):
           wp = Process(target=worker_process, name='worker %d' % (i + 1),
                        args=(config_worker,))
           workers.append(wp)
           wp.start()
           logger.info('Started worker: %s', wp.name)
       logger.info('About to create listener ...')
       stop_event = Event()
       lp = Process(target=listener_process, name='listener',
                    args=(q, stop_event, config_listener))
       lp.start()
       logger.info('Started listener')
       # We now hang around for the workers to finish their work.
       for wp in workers:
           wp.join()
       # Workers all done, listening can now stop.
       # Logging in the parent still works normally.
       logger.info('Telling listener to stop ...')
       stop_event.set()
       lp.join()
       logger.info('All done.')

   if __name__ == '__main__':
       main()


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

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

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

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

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

      'ASCII section\ufeffUnicode section'

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

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

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

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


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

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

   import json
   import logging

   class StructuredMessage:
       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:

   import json
   import logging


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

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

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

   _ = StructuredMessage   # optional, to improve readability

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

   if __name__ == '__main__':
       main()

When the above script is run, it prints:

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

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


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

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

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

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

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

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

   import logging, logging.config, os, shutil

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

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

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

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

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

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

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

   '()': owned_file_handler,

you could use e.g.:

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

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

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

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


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

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

Logging calls ("debug()", "info()" etc.) only take positional
parameters for the actual logging message itself, with keyword
parameters used only for determining options for how to handle the
logging call (e.g. the "exc_info" keyword parameter to indicate that
traceback information should be logged, or the "extra" keyword
parameter to indicate additional contextual information to be added to
the log). So you cannot directly make logging calls using
"str.format()" or "string.Template" syntax, because internally the
logging package uses %-formatting to merge the format string and the
variable arguments. There would be no changing this while preserving
backward compatibility, since all logging calls which are out there in
existing code will be using %-format strings.

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

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


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

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

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


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

There is another, perhaps simpler way that you can use {}- and $-
formatting to construct your individual log messages. You may recall
(from Using arbitrary objects as messages) that when logging you can
use an arbitrary object as a message format string, and that the
logging package will call "str()" on that object to get the actual
format string. Consider the following two classes:

   class BraceMessage:
       def __init__(self, fmt, /, *args, **kwargs):
           self.fmt = fmt
           self.args = args
           self.kwargs = kwargs

       def __str__(self):
           return self.fmt.format(*self.args, **self.kwargs)

   class DollarMessage:
       def __init__(self, fmt, /, **kwargs):
           self.fmt = fmt
           self.kwargs = kwargs

       def __str__(self):
           from string import Template
           return Template(self.fmt).substitute(**self.kwargs)

Either of these can be used in place of a format string, to allow {}-
or $-formatting to be used to build the actual "message" part which
appears in the formatted log output in place of “%(message)s” or
“{message}” or “$message”. If you find it a little unwieldy to use the
class names whenever you want to log something, you can make it more
palatable if you use an alias such as "M" or "_" for the message (or
perhaps "__", if you are using "_" for localization).

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

   >>> __ = BraceMessage
   >>> print(__('Message with {0} {1}', 2, 'placeholders'))
   Message with 2 placeholders
   >>> class Point: pass
   ...
   >>> p = Point()
   >>> p.x = 0.5
   >>> p.y = 0.5
   >>> print(__('Message with coordinates: ({point.x:.2f}, {point.y:.2f})', point=p))
   Message with coordinates: (0.50, 0.50)

Secondly, formatting with "string.Template":

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

One thing to note is that you pay no significant performance penalty
with this approach: the actual formatting happens not when you make
the logging call, but when (and if) the logged message is actually
about to be output to a log by a handler. So the only slightly unusual
thing which might trip you up is that the parentheses go around the
format string and the arguments, not just the format string. That’s
because the __ notation is just syntax sugar for a constructor call to
one of the "*XXX*Message" classes shown above.


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

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

   import logging
   import logging.config
   import sys

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

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

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

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

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

   changed: hello

which shows that the filter is working as configured.

A couple of extra points to note:

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

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


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

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

   import logging

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

       def format(self, record):
           s = super().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 (number of
records buffered). 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):
       ...


Sending logging messages to email, with buffering
=================================================

To illustrate how you can send log messages via email, so that a set
number of messages are sent per email, you can subclass
"BufferingHandler". In the following  example, which you can adapt to
suit your specific needs, a simple test harness is provided which
allows you to run the script with command line arguments specifying
what you typically need to send things via SMTP. (Run the downloaded
script with the "-h" argument to see the required and optional
arguments.)

   import logging
   import logging.handlers
   import smtplib

   class BufferingSMTPHandler(logging.handlers.BufferingHandler):
       def __init__(self, mailhost, port, username, password, fromaddr, toaddrs,
                    subject, capacity):
           logging.handlers.BufferingHandler.__init__(self, capacity)
           self.mailhost = mailhost
           self.mailport = port
           self.username = username
           self.password = password
           self.fromaddr = fromaddr
           if isinstance(toaddrs, str):
               toaddrs = [toaddrs]
           self.toaddrs = toaddrs
           self.subject = subject
           self.setFormatter(logging.Formatter("%(asctime)s %(levelname)-5s %(message)s"))

       def flush(self):
           if len(self.buffer) > 0:
               try:
                   smtp = smtplib.SMTP(self.mailhost, self.mailport)
                   smtp.starttls()
                   smtp.login(self.username, self.password)
                   msg = "From: %s\r\nTo: %s\r\nSubject: %s\r\n\r\n" % (self.fromaddr, ','.join(self.toaddrs), self.subject)
                   for record in self.buffer:
                       s = self.format(record)
                       msg = msg + s + "\r\n"
                   smtp.sendmail(self.fromaddr, self.toaddrs, msg)
                   smtp.quit()
               except Exception:
                   if logging.raiseExceptions:
                       raise
               self.buffer = []

   if __name__ == '__main__':
       import argparse

       ap = argparse.ArgumentParser()
       aa = ap.add_argument
       aa('host', metavar='HOST', help='SMTP server')
       aa('--port', '-p', type=int, default=587, help='SMTP port')
       aa('user', metavar='USER', help='SMTP username')
       aa('password', metavar='PASSWORD', help='SMTP password')
       aa('to', metavar='TO', help='Addressee for emails')
       aa('sender', metavar='SENDER', help='Sender email address')
       aa('--subject', '-s',
          default='Test Logging email from Python logging module (buffering)',
          help='Subject of email')
       options = ap.parse_args()
       logger = logging.getLogger()
       logger.setLevel(logging.DEBUG)
       h = BufferingSMTPHandler(options.host, options.port, options.user,
                                options.password, options.sender,
                                options.to, options.subject, 10)
       logger.addHandler(h)
       for i in range(102):
           logger.info("Info index = %d", i)
       h.flush()
       h.close()

If you run this script and your SMTP server is correctly set up, you
should find that it sends eleven emails to the addressee you specify.
The first ten emails will each have ten log messages, and the eleventh
will have two messages. That makes up 102 messages as specified in the
script.


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:
       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.


A CLI application starter template
==================================

Here's an example which shows how you can:

* Use a logging level based on command-line arguments

* Dispatch to multiple subcommands in separate files, all logging at
  the same level in a consistent way

* Make use of simple, minimal configuration

Suppose we have a command-line application whose job is to stop, start
or restart some services. This could be organised for the purposes of
illustration as a file "app.py" that is the main script for the
application, with individual commands implemented in "start.py",
"stop.py" and "restart.py". Suppose further that we want to control
the verbosity of the application via a command-line argument,
defaulting to "logging.INFO". Here's one way that "app.py" could be
written:

   import argparse
   import importlib
   import logging
   import os
   import sys

   def main(args=None):
       scriptname = os.path.basename(__file__)
       parser = argparse.ArgumentParser(scriptname)
       levels = ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL')
       parser.add_argument('--log-level', default='INFO', choices=levels)
       subparsers = parser.add_subparsers(dest='command',
                                          help='Available commands:')
       start_cmd = subparsers.add_parser('start', help='Start a service')
       start_cmd.add_argument('name', metavar='NAME',
                              help='Name of service to start')
       stop_cmd = subparsers.add_parser('stop',
                                        help='Stop one or more services')
       stop_cmd.add_argument('names', metavar='NAME', nargs='+',
                             help='Name of service to stop')
       restart_cmd = subparsers.add_parser('restart',
                                           help='Restart one or more services')
       restart_cmd.add_argument('names', metavar='NAME', nargs='+',
                                help='Name of service to restart')
       options = parser.parse_args()
       # the code to dispatch commands could all be in this file. For the purposes
       # of illustration only, we implement each command in a separate module.
       try:
           mod = importlib.import_module(options.command)
           cmd = getattr(mod, 'command')
       except (ImportError, AttributeError):
           print('Unable to find the code for command \'%s\'' % options.command)
           return 1
       # Could get fancy here and load configuration from file or dictionary
       logging.basicConfig(level=options.log_level,
                           format='%(levelname)s %(name)s %(message)s')
       cmd(options)

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

And the "start", "stop" and "restart" commands can be implemented in
separate modules, like so for starting:

   # start.py
   import logging

   logger = logging.getLogger(__name__)

   def command(options):
       logger.debug('About to start %s', options.name)
       # actually do the command processing here ...
       logger.info('Started the \'%s\' service.', options.name)

and thus for stopping:

   # stop.py
   import logging

   logger = logging.getLogger(__name__)

   def command(options):
       n = len(options.names)
       if n == 1:
           plural = ''
           services = '\'%s\'' % options.names[0]
       else:
           plural = 's'
           services = ', '.join('\'%s\'' % name for name in options.names)
           i = services.rfind(', ')
           services = services[:i] + ' and ' + services[i + 2:]
       logger.debug('About to stop %s', services)
       # actually do the command processing here ...
       logger.info('Stopped the %s service%s.', services, plural)

and similarly for restarting:

   # restart.py
   import logging

   logger = logging.getLogger(__name__)

   def command(options):
       n = len(options.names)
       if n == 1:
           plural = ''
           services = '\'%s\'' % options.names[0]
       else:
           plural = 's'
           services = ', '.join('\'%s\'' % name for name in options.names)
           i = services.rfind(', ')
           services = services[:i] + ' and ' + services[i + 2:]
       logger.debug('About to restart %s', services)
       # actually do the command processing here ...
       logger.info('Restarted the %s service%s.', services, plural)

If we run this application with the default log level, we get output
like this:

   $ python app.py start foo
   INFO start Started the 'foo' service.

   $ python app.py stop foo bar
   INFO stop Stopped the 'foo' and 'bar' services.

   $ python app.py restart foo bar baz
   INFO restart Restarted the 'foo', 'bar' and 'baz' services.

The first word is the logging level, and the second word is the module
or package name of the place where the event was logged.

If we change the logging level, then we can change the information
sent to the log. For example, if we want more information:

   $ python app.py --log-level DEBUG start foo
   DEBUG start About to start foo
   INFO start Started the 'foo' service.

   $ python app.py --log-level DEBUG stop foo bar
   DEBUG stop About to stop 'foo' and 'bar'
   INFO stop Stopped the 'foo' and 'bar' services.

   $ python app.py --log-level DEBUG restart foo bar baz
   DEBUG restart About to restart 'foo', 'bar' and 'baz'
   INFO restart Restarted the 'foo', 'bar' and 'baz' services.

And if we want less:

   $ python app.py --log-level WARNING start foo
   $ python app.py --log-level WARNING stop foo bar
   $ python app.py --log-level WARNING restart foo bar baz

In this case, the commands don't print anything to the console, since
nothing at "WARNING" level or above is logged by them.


A Qt GUI for logging
====================

A question that comes up from time to time is about how to log to a
GUI application. The Qt framework is a popular cross-platform UI
framework with Python bindings using PySide2 or PyQt5 libraries.

The following example shows how to log to a Qt GUI. This introduces a
simple "QtHandler" class which takes a callable, which should be a
slot in the main thread that does GUI updates. A worker thread is also
created to show how you can log to the GUI from both the UI itself
(via a button for manual logging) as well as a worker thread doing
work in the background (here, just logging messages at random levels
with random short delays in between).

The worker thread is implemented using Qt's "QThread" class rather
than the "threading" module, as there are circumstances where one has
to use "QThread", which offers better integration with other "Qt"
components.

The code should work with recent releases of either "PySide2" or
"PyQt5". You should be able to adapt the approach to earlier versions
of Qt. Please refer to the comments in the code snippet for more
detailed information.

   import datetime
   import logging
   import random
   import sys
   import time

   # Deal with minor differences between PySide2 and PyQt5
   try:
       from PySide2 import QtCore, QtGui, QtWidgets
       Signal = QtCore.Signal
       Slot = QtCore.Slot
   except ImportError:
       from PyQt5 import QtCore, QtGui, QtWidgets
       Signal = QtCore.pyqtSignal
       Slot = QtCore.pyqtSlot


   logger = logging.getLogger(__name__)


   #
   # Signals need to be contained in a QObject or subclass in order to be correctly
   # initialized.
   #
   class Signaller(QtCore.QObject):
       signal = Signal(str, logging.LogRecord)

   #
   # Output to a Qt GUI is only supposed to happen on the main thread. So, this
   # handler is designed to take a slot function which is set up to run in the main
   # thread. In this example, the function takes a string argument which is a
   # formatted log message, and the log record which generated it. The formatted
   # string is just a convenience - you could format a string for output any way
   # you like in the slot function itself.
   #
   # You specify the slot function to do whatever GUI updates you want. The handler
   # doesn't know or care about specific UI elements.
   #
   class QtHandler(logging.Handler):
       def __init__(self, slotfunc, *args, **kwargs):
           super().__init__(*args, **kwargs)
           self.signaller = Signaller()
           self.signaller.signal.connect(slotfunc)

       def emit(self, record):
           s = self.format(record)
           self.signaller.signal.emit(s, record)

   #
   # This example uses QThreads, which means that the threads at the Python level
   # are named something like "Dummy-1". The function below gets the Qt name of the
   # current thread.
   #
   def ctname():
       return QtCore.QThread.currentThread().objectName()


   #
   # Used to generate random levels for logging.
   #
   LEVELS = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
             logging.CRITICAL)

   #
   # This worker class represents work that is done in a thread separate to the
   # main thread. The way the thread is kicked off to do work is via a button press
   # that connects to a slot in the worker.
   #
   # Because the default threadName value in the LogRecord isn't much use, we add
   # a qThreadName which contains the QThread name as computed above, and pass that
   # value in an "extra" dictionary which is used to update the LogRecord with the
   # QThread name.
   #
   # This example worker just outputs messages sequentially, interspersed with
   # random delays of the order of a few seconds.
   #
   class Worker(QtCore.QObject):
       @Slot()
       def start(self):
           extra = {'qThreadName': ctname() }
           logger.debug('Started work', extra=extra)
           i = 1
           # Let the thread run until interrupted. This allows reasonably clean
           # thread termination.
           while not QtCore.QThread.currentThread().isInterruptionRequested():
               delay = 0.5 + random.random() * 2
               time.sleep(delay)
               level = random.choice(LEVELS)
               logger.log(level, 'Message after delay of %3.1f: %d', delay, i, extra=extra)
               i += 1

   #
   # Implement a simple UI for this cookbook example. This contains:
   #
   # * A read-only text edit window which holds formatted log messages
   # * A button to start work and log stuff in a separate thread
   # * A button to log something from the main thread
   # * A button to clear the log window
   #
   class Window(QtWidgets.QWidget):

       COLORS = {
           logging.DEBUG: 'black',
           logging.INFO: 'blue',
           logging.WARNING: 'orange',
           logging.ERROR: 'red',
           logging.CRITICAL: 'purple',
       }

       def __init__(self, app):
           super().__init__()
           self.app = app
           self.textedit = te = QtWidgets.QPlainTextEdit(self)
           # Set whatever the default monospace font is for the platform
           f = QtGui.QFont('nosuchfont')
           f.setStyleHint(f.Monospace)
           te.setFont(f)
           te.setReadOnly(True)
           PB = QtWidgets.QPushButton
           self.work_button = PB('Start background work', self)
           self.log_button = PB('Log a message at a random level', self)
           self.clear_button = PB('Clear log window', self)
           self.handler = h = QtHandler(self.update_status)
           # Remember to use qThreadName rather than threadName in the format string.
           fs = '%(asctime)s %(qThreadName)-12s %(levelname)-8s %(message)s'
           formatter = logging.Formatter(fs)
           h.setFormatter(formatter)
           logger.addHandler(h)
           # Set up to terminate the QThread when we exit
           app.aboutToQuit.connect(self.force_quit)

           # Lay out all the widgets
           layout = QtWidgets.QVBoxLayout(self)
           layout.addWidget(te)
           layout.addWidget(self.work_button)
           layout.addWidget(self.log_button)
           layout.addWidget(self.clear_button)
           self.setFixedSize(900, 400)

           # Connect the non-worker slots and signals
           self.log_button.clicked.connect(self.manual_update)
           self.clear_button.clicked.connect(self.clear_display)

           # Start a new worker thread and connect the slots for the worker
           self.start_thread()
           self.work_button.clicked.connect(self.worker.start)
           # Once started, the button should be disabled
           self.work_button.clicked.connect(lambda : self.work_button.setEnabled(False))

       def start_thread(self):
           self.worker = Worker()
           self.worker_thread = QtCore.QThread()
           self.worker.setObjectName('Worker')
           self.worker_thread.setObjectName('WorkerThread')  # for qThreadName
           self.worker.moveToThread(self.worker_thread)
           # This will start an event loop in the worker thread
           self.worker_thread.start()

       def kill_thread(self):
           # Just tell the worker to stop, then tell it to quit and wait for that
           # to happen
           self.worker_thread.requestInterruption()
           if self.worker_thread.isRunning():
               self.worker_thread.quit()
               self.worker_thread.wait()
           else:
               print('worker has already exited.')

       def force_quit(self):
           # For use when the window is closed
           if self.worker_thread.isRunning():
               self.kill_thread()

       # The functions below update the UI and run in the main thread because
       # that's where the slots are set up

       @Slot(str, logging.LogRecord)
       def update_status(self, status, record):
           color = self.COLORS.get(record.levelno, 'black')
           s = '<pre><font color="%s">%s</font></pre>' % (color, status)
           self.textedit.appendHtml(s)

       @Slot()
       def manual_update(self):
           # This function uses the formatted message passed in, but also uses
           # information from the record to format the message in an appropriate
           # color according to its severity (level).
           level = random.choice(LEVELS)
           extra = {'qThreadName': ctname() }
           logger.log(level, 'Manually logged!', extra=extra)

       @Slot()
       def clear_display(self):
           self.textedit.clear()


   def main():
       QtCore.QThread.currentThread().setObjectName('MainThread')
       logging.getLogger().setLevel(logging.DEBUG)
       app = QtWidgets.QApplication(sys.argv)
       example = Window(app)
       example.show()
       sys.exit(app.exec_())

   if __name__=='__main__':
       main()


Logging to syslog with RFC5424 support
======================================

Although **RFC 5424** dates from 2009, most syslog servers are
configured by default to use the older **RFC 3164**, which hails from
2001. When "logging" was added to Python in 2003, it supported the
earlier (and only existing) protocol at the time. Since RFC5424 came
out, as there has not been widespread deployment of it in syslog
servers, the "SysLogHandler" functionality has not been updated.

RFC 5424 contains some useful features such as support for structured
data, and if you need to be able to log to a syslog server with
support for it, you can do so with a subclassed handler which looks
something like this:

   import datetime
   import logging.handlers
   import re
   import socket
   import time

   class SysLogHandler5424(logging.handlers.SysLogHandler):

       tz_offset = re.compile(r'([+-]\d{2})(\d{2})$')
       escaped = re.compile(r'([\]"\\])')

       def __init__(self, *args, **kwargs):
           self.msgid = kwargs.pop('msgid', None)
           self.appname = kwargs.pop('appname', None)
           super().__init__(*args, **kwargs)

       def format(self, record):
           version = 1
           asctime = datetime.datetime.fromtimestamp(record.created).isoformat()
           m = self.tz_offset.match(time.strftime('%z'))
           has_offset = False
           if m and time.timezone:
               hrs, mins = m.groups()
               if int(hrs) or int(mins):
                   has_offset = True
           if not has_offset:
               asctime += 'Z'
           else:
               asctime += f'{hrs}:{mins}'
           try:
               hostname = socket.gethostname()
           except Exception:
               hostname = '-'
           appname = self.appname or '-'
           procid = record.process
           msgid = '-'
           msg = super().format(record)
           sdata = '-'
           if hasattr(record, 'structured_data'):
               sd = record.structured_data
               # This should be a dict where the keys are SD-ID and the value is a
               # dict mapping PARAM-NAME to PARAM-VALUE (refer to the RFC for what these
               # mean)
               # There's no error checking here - it's purely for illustration, and you
               # can adapt this code for use in production environments
               parts = []

               def replacer(m):
                   g = m.groups()
                   return '\\' + g[0]

               for sdid, dv in sd.items():
                   part = f'[{sdid}'
                   for k, v in dv.items():
                       s = str(v)
                       s = self.escaped.sub(replacer, s)
                       part += f' {k}="{s}"'
                   part += ']'
                   parts.append(part)
               sdata = ''.join(parts)
           return f'{version} {asctime} {hostname} {appname} {procid} {msgid} {sdata} {msg}'

You'll need to be familiar with RFC 5424 to fully understand the above
code, and it may be that you have slightly different needs (e.g. for
how you pass structural data to the log). Nevertheless, the above
should be adaptable to your speciric needs. With the above handler,
you'd pass structured data using something like this:

   sd = {
       'foo@12345': {'bar': 'baz', 'baz': 'bozz', 'fizz': r'buzz'},
       'foo@54321': {'rab': 'baz', 'zab': 'bozz', 'zzif': r'buzz'}
   }
   extra = {'structured_data': sd}
   i = 1
   logger.debug('Message %d', i, extra=extra)


How to treat a logger like an output stream
===========================================

Sometimes, you need to interface to a third-party API which expects a
file-like object to write to, but you want to direct the API's output
to a logger. You can do this using a class which wraps a logger with a
file-like API. Here's a short script illustrating such a class:

   import logging

   class LoggerWriter:
       def __init__(self, logger, level):
           self.logger = logger
           self.level = level

       def write(self, message):
           if message != '\n':  # avoid printing bare newlines, if you like
               self.logger.log(self.level, message)

       def flush(self):
           # doesn't actually do anything, but might be expected of a file-like
           # object - so optional depending on your situation
           pass

       def close(self):
           # doesn't actually do anything, but might be expected of a file-like
           # object - so optional depending on your situation. You might want
           # to set a flag so that later calls to write raise an exception
           pass

   def main():
       logging.basicConfig(level=logging.DEBUG)
       logger = logging.getLogger('demo')
       info_fp = LoggerWriter(logger, logging.INFO)
       debug_fp = LoggerWriter(logger, logging.DEBUG)
       print('An INFO message', file=info_fp)
       print('A DEBUG message', file=debug_fp)

   if __name__ == "__main__":
       main()

When this script is run, it prints

   INFO:demo:An INFO message
   DEBUG:demo:A DEBUG message

You could also use "LoggerWriter" to redirect "sys.stdout" and
"sys.stderr" by doing something like this:

   import sys

   sys.stdout = LoggerWriter(logger, logging.INFO)
   sys.stderr = LoggerWriter(logger, logging.WARNING)

You should do this *after* configuring logging for your needs. In the
above example, the "basicConfig()" call does this (using the
"sys.stderr" value *before* it is overwritten by a "LoggerWriter"
instance). Then, you'd get this kind of result:

   >>> print('Foo')
   INFO:demo:Foo
   >>> print('Bar', file=sys.stderr)
   WARNING:demo:Bar
   >>>

Of course, the examples above show output according to the format used
by "basicConfig()", but you can use a different formatter when you
configure logging.

Note that with the above scheme, you are somewhat at the mercy of
buffering and the sequence of write calls which you are intercepting.
For example, with the definition of "LoggerWriter" above, if you have
the snippet

   sys.stderr = LoggerWriter(logger, logging.WARNING)
   1 / 0

then running the script results in

   WARNING:demo:Traceback (most recent call last):

   WARNING:demo:  File "/home/runner/cookbook-loggerwriter/test.py", line 53, in <module>

   WARNING:demo:
   WARNING:demo:main()
   WARNING:demo:  File "/home/runner/cookbook-loggerwriter/test.py", line 49, in main

   WARNING:demo:
   WARNING:demo:1 / 0
   WARNING:demo:ZeroDivisionError
   WARNING:demo::
   WARNING:demo:division by zero

As you can see, this output isn't ideal. That's because the underlying
code which writes to "sys.stderr" makes multiple writes, each of which
results in a separate logged line (for example, the last three lines
above). To get around this problem, you need to buffer things and only
output log lines when newlines are seen. Let's use a slghtly better
implementation of "LoggerWriter":

   class BufferingLoggerWriter(LoggerWriter):
       def __init__(self, logger, level):
           super().__init__(logger, level)
           self.buffer = ''

       def write(self, message):
           if '\n' not in message:
               self.buffer += message
           else:
               parts = message.split('\n')
               if self.buffer:
                   s = self.buffer + parts.pop(0)
                   self.logger.log(self.level, s)
               self.buffer = parts.pop()
               for part in parts:
                   self.logger.log(self.level, part)

This just buffers up stuff until a newline is seen, and then logs
complete lines. With this approach, you get better output:

   WARNING:demo:Traceback (most recent call last):
   WARNING:demo:  File "/home/runner/cookbook-loggerwriter/main.py", line 55, in <module>
   WARNING:demo:    main()
   WARNING:demo:  File "/home/runner/cookbook-loggerwriter/main.py", line 52, in main
   WARNING:demo:    1/0
   WARNING:demo:ZeroDivisionError: division by zero


Patterns to avoid
=================

Although the preceding sections have described ways of doing things
you might need to do or deal with, it is worth mentioning some usage
patterns which are *unhelpful*, and which should therefore be avoided
in most cases. The following sections are in no particular order.


Opening the same log file multiple times
----------------------------------------

On Windows, you will generally not be able to open the same file
multiple times as this will lead to a "file is in use by another
process" error. However, on POSIX platforms you'll not get any errors
if you open the same file multiple times. This could be done
accidentally, for example by:

* Adding a file handler more than once which references the same file
  (e.g. by a copy/paste/forget-to-change error).

* Opening two files that look different, as they have different names,
  but are the same because one is a symbolic link to the other.

* Forking a process, following which both parent and child have a
  reference to the same file. This might be through use of the
  "multiprocessing" module, for example.

Opening a file multiple times might *appear* to work most of the time,
but can lead to a number of problems in practice:

* Logging output can be garbled because multiple threads or processes
  try to write to the same file. Although logging guards against
  concurrent use of the same handler instance by multiple threads,
  there is no such protection if concurrent writes are attempted by
  two different threads using two different handler instances which
  happen to point to the same file.

* An attempt to delete a file (e.g. during file rotation) silently
  fails, because there is another reference pointing to it. This can
  lead to confusion and wasted debugging time - log entries end up in
  unexpected places, or are lost altogether. Or a file that was
  supposed to be moved remains in place, and grows in size
  unexpectedly despite size-based rotation being supposedly in place.

Use the techniques outlined in Logging to a single file from multiple
processes to circumvent such issues.


Using loggers as attributes in a class or passing them as parameters
--------------------------------------------------------------------

While there might be unusual cases where you'll need to do this, in
general there is no point because loggers are singletons. Code can
always access a given logger instance by name using
"logging.getLogger(name)", so passing instances around and holding
them as instance attributes is pointless. Note that in other languages
such as Java and C#, loggers are often static class attributes.
However, this pattern doesn't make sense in Python, where the module
(and not the class) is the unit of software decomposition.


Adding handlers other than "NullHandler" to a logger in a library
-----------------------------------------------------------------

Configuring logging by adding handlers, formatters and filters is the
responsibility of the application developer, not the library
developer. If you are maintaining a library, ensure that you don't add
handlers to any of your loggers other than a "NullHandler" instance.


Creating a lot of loggers
-------------------------

Loggers are singletons that are never freed during a script execution,
and so creating lots of loggers will use up memory which can't then be
freed. Rather than create a logger per e.g. file processed or network
connection made, use the existing mechanisms for passing contextual
information into your logs and restrict the loggers created to those
describing areas within your application (generally modules, but
occasionally slightly more fine-grained than that).


Other resources
===============

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.

  Basic Tutorial

  Advanced Tutorial
