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Logging documentation reorganised.

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Vinay Sajip 2010-12-19 12:56:57 +00:00
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@ -19,6 +19,8 @@ Currently, the HOWTOs are:
descriptor.rst
doanddont.rst
functional.rst
logging.rst
logging-cookbook.rst
regex.rst
sockets.rst
sorting.rst

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.. _logging-cookbook:
================
Logging Cookbook
================
:Author: Vinay Sajip <vinay_sajip at red-dove dot com>
This page contains a number of recipes related to logging, which have been found useful in the past.
.. Contents::
.. currentmodule:: logging
Using logging in multiple modules
---------------------------------
It was mentioned above that multiple calls to
``logging.getLogger('someLogger')`` return a reference to the same logger
object. This is true not only within the same module, but also across modules
as long as it is in the same Python interpreter process. It is true for
references to the same object; additionally, application code can define and
configure a parent logger in one module and create (but not configure) a child
logger in a separate module, and all logger calls to the child will pass up to
the parent. Here is a main module::
import logging
import auxiliary_module
# create logger with 'spam_application'
logger = logging.getLogger('spam_application')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler('spam.log')
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.info('creating an instance of auxiliary_module.Auxiliary')
a = auxiliary_module.Auxiliary()
logger.info('created an instance of auxiliary_module.Auxiliary')
logger.info('calling auxiliary_module.Auxiliary.do_something')
a.do_something()
logger.info('finished auxiliary_module.Auxiliary.do_something')
logger.info('calling auxiliary_module.some_function()')
auxiliary_module.some_function()
logger.info('done with auxiliary_module.some_function()')
Here is the auxiliary module::
import logging
# create logger
module_logger = logging.getLogger('spam_application.auxiliary')
class Auxiliary:
def __init__(self):
self.logger = logging.getLogger('spam_application.auxiliary.Auxiliary')
self.logger.info('creating an instance of Auxiliary')
def do_something(self):
self.logger.info('doing something')
a = 1 + 1
self.logger.info('done doing something')
def some_function():
module_logger.info('received a call to "some_function"')
The output looks like this::
2005-03-23 23:47:11,663 - spam_application - INFO -
creating an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,665 - spam_application.auxiliary.Auxiliary - INFO -
creating an instance of Auxiliary
2005-03-23 23:47:11,665 - spam_application - INFO -
created an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,668 - spam_application - INFO -
calling auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,668 - spam_application.auxiliary.Auxiliary - INFO -
doing something
2005-03-23 23:47:11,669 - spam_application.auxiliary.Auxiliary - INFO -
done doing something
2005-03-23 23:47:11,670 - spam_application - INFO -
finished auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,671 - spam_application - INFO -
calling auxiliary_module.some_function()
2005-03-23 23:47:11,672 - spam_application.auxiliary - INFO -
received a call to 'some_function'
2005-03-23 23:47:11,673 - spam_application - INFO -
done with auxiliary_module.some_function()
Multiple handlers and formatters
--------------------------------
Loggers are plain Python objects. The :func:`addHandler` method has no minimum
or maximum quota for the number of handlers you may add. Sometimes it will be
beneficial for an application to log all messages of all severities to a text
file while simultaneously logging errors or above to the console. To set this
up, simply configure the appropriate handlers. The logging calls in the
application code will remain unchanged. Here is a slight modification to the
previous simple module-based configuration example::
import logging
logger = logging.getLogger('simple_example')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler('spam.log')
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
fh.setFormatter(formatter)
# add the handlers to logger
logger.addHandler(ch)
logger.addHandler(fh)
# 'application' code
logger.debug('debug message')
logger.info('info message')
logger.warn('warn message')
logger.error('error message')
logger.critical('critical message')
Notice that the 'application' code does not care about multiple handlers. All
that changed was the addition and configuration of a new handler named *fh*.
The ability to create new handlers with higher- or lower-severity filters can be
very helpful when writing and testing an application. Instead of using many
``print`` statements for debugging, use ``logger.debug``: Unlike the print
statements, which you will have to delete or comment out later, the logger.debug
statements can remain intact in the source code and remain dormant until you
need them again. At that time, the only change that needs to happen is to
modify the severity level of the logger and/or handler to debug.
.. _multiple-destinations:
Logging to multiple destinations
--------------------------------
Let's say you want to log to console and file with different message formats and
in differing circumstances. Say you want to log messages with levels of DEBUG
and higher to file, and those messages at level INFO and higher to the console.
Let's also assume that the file should contain timestamps, but the console
messages should not. Here's how you can achieve this::
import logging
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename='/temp/myapp.log',
filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
When you run this, on the console you will see ::
root : INFO Jackdaws love my big sphinx of quartz.
myapp.area1 : INFO How quickly daft jumping zebras vex.
myapp.area2 : WARNING Jail zesty vixen who grabbed pay from quack.
myapp.area2 : ERROR The five boxing wizards jump quickly.
and in the file you will see something like ::
10-22 22:19 root INFO Jackdaws love my big sphinx of quartz.
10-22 22:19 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim.
10-22 22:19 myapp.area1 INFO How quickly daft jumping zebras vex.
10-22 22:19 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack.
10-22 22:19 myapp.area2 ERROR The five boxing wizards jump quickly.
As you can see, the DEBUG message only shows up in the file. The other messages
are sent to both destinations.
This example uses console and file handlers, but you can use any number and
combination of handlers you choose.
Configuration server example
----------------------------
Here is an example of a module using the logging configuration server::
import logging
import logging.config
import time
import os
# read initial config file
logging.config.fileConfig('logging.conf')
# create and start listener on port 9999
t = logging.config.listen(9999)
t.start()
logger = logging.getLogger('simpleExample')
try:
# loop through logging calls to see the difference
# new configurations make, until Ctrl+C is pressed
while True:
logger.debug('debug message')
logger.info('info message')
logger.warn('warn message')
logger.error('error message')
logger.critical('critical message')
time.sleep(5)
except KeyboardInterrupt:
# cleanup
logging.config.stopListening()
t.join()
And here is a script that takes a filename and sends that file to the server,
properly preceded with the binary-encoded length, as the new logging
configuration::
#!/usr/bin/env python
import socket, sys, struct
data_to_send = open(sys.argv[1], 'r').read()
HOST = 'localhost'
PORT = 9999
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
print('connecting...')
s.connect((HOST, PORT))
print('sending config...')
s.send(struct.pack('>L', len(data_to_send)))
s.send(data_to_send)
s.close()
print('complete')
Dealing with handlers that block
--------------------------------
.. currentmodule:: logging.handlers
Sometimes you have to get your logging handlers to do their work without
blocking the thread you’re logging from. This is common in Web applications,
though of course it also occurs in other scenarios.
A common culprit which demonstrates sluggish behaviour is the
:class:`SMTPHandler`: sending emails can take a long time, for a
number of reasons outside the developer’s control (for example, a poorly
performing mail or network infrastructure). But almost any network-based
handler can block: Even a :class:`SocketHandler` operation may do a
DNS query under the hood which is too slow (and this query can be deep in the
socket library code, below the Python layer, and outside your control).
One solution is to use a two-part approach. For the first part, attach only a
:class:`QueueHandler` to those loggers which are accessed from
performance-critical threads. They simply write to their queue, which can be
sized to a large enough capacity or initialized with no upper bound to their
size. The write to the queue will typically be accepted quickly, though you
will probably need to catch the :ref:`queue.Full` exception as a precaution
in your code. If you are a library developer who has performance-critical
threads in their code, be sure to document this (together with a suggestion to
attach only ``QueueHandlers`` to your loggers) for the benefit of other
developers who will use your code.
The second part of the solution is :class:`QueueListener`, which has been
designed as the counterpart to :class:`QueueHandler`. A
:class:`QueueListener` is very simple: it’s passed a queue and some handlers,
and it fires up an internal thread which listens to its queue for LogRecords
sent from ``QueueHandlers`` (or any other source of ``LogRecords``, for that
matter). The ``LogRecords`` are removed from the queue and passed to the
handlers for processing.
The advantage of having a separate :class:`QueueListener` class is that you
can use the same instance to service multiple ``QueueHandlers``. This is more
resource-friendly than, say, having threaded versions of the existing handler
classes, which would eat up one thread per handler for no particular benefit.
An example of using these two classes follows (imports omitted)::
que = queue.Queue(-1) # no limit on size
queue_handler = QueueHandler(que)
handler = logging.StreamHandler()
listener = QueueListener(que, handler)
root = logging.getLogger()
root.addHandler(queue_handler)
formatter = logging.Formatter('%(threadName)s: %(message)s')
handler.setFormatter(formatter)
listener.start()
# The log output will display the thread which generated
# the event (the main thread) rather than the internal
# thread which monitors the internal queue. This is what
# you want to happen.
root.warning('Look out!')
listener.stop()
which, when run, will produce::
MainThread: Look out!
.. _network-logging:
Sending and receiving logging events across a network
-----------------------------------------------------
Let's say you want to send logging events across a network, and handle them at
the receiving end. A simple way of doing this is attaching a
:class:`SocketHandler` instance to the root logger at the sending end::
import logging, logging.handlers
rootLogger = logging.getLogger('')
rootLogger.setLevel(logging.DEBUG)
socketHandler = logging.handlers.SocketHandler('localhost',
logging.handlers.DEFAULT_TCP_LOGGING_PORT)
# don't bother with a formatter, since a socket handler sends the event as
# an unformatted pickle
rootLogger.addHandler(socketHandler)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
At the receiving end, you can set up a receiver using the :mod:`socketserver`
module. Here is a basic working example::
import pickle
import logging
import logging.handlers
import socketserver
import struct
class LogRecordStreamHandler(socketserver.StreamRequestHandler):
"""Handler for a streaming logging request.
This basically logs the record using whatever logging policy is
configured locally.
"""
def handle(self):
"""
Handle multiple requests - each expected to be a 4-byte length,
followed by the LogRecord in pickle format. Logs the record
according to whatever policy is configured locally.
"""
while True:
chunk = self.connection.recv(4)
if len(chunk) < 4:
break
slen = struct.unpack('>L', chunk)[0]
chunk = self.connection.recv(slen)
while len(chunk) < slen:
chunk = chunk + self.connection.recv(slen - len(chunk))
obj = self.unPickle(chunk)
record = logging.makeLogRecord(obj)
self.handleLogRecord(record)
def unPickle(self, data):
return pickle.loads(data)
def handleLogRecord(self, record):
# if a name is specified, we use the named logger rather than the one
# implied by the record.
if self.server.logname is not None:
name = self.server.logname
else:
name = record.name
logger = logging.getLogger(name)
# N.B. EVERY record gets logged. This is because Logger.handle
# is normally called AFTER logger-level filtering. If you want
# to do filtering, do it at the client end to save wasting
# cycles and network bandwidth!
logger.handle(record)
class LogRecordSocketReceiver(socketserver.ThreadingTCPServer):
"""
Simple TCP socket-based logging receiver suitable for testing.
"""
allow_reuse_address = 1
def __init__(self, host='localhost',
port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
handler=LogRecordStreamHandler):
socketserver.ThreadingTCPServer.__init__(self, (host, port), handler)
self.abort = 0
self.timeout = 1
self.logname = None
def serve_until_stopped(self):
import select
abort = 0
while not abort:
rd, wr, ex = select.select([self.socket.fileno()],
[], [],
self.timeout)
if rd:
self.handle_request()
abort = self.abort
def main():
logging.basicConfig(
format='%(relativeCreated)5d %(name)-15s %(levelname)-8s %(message)s')
tcpserver = LogRecordSocketReceiver()
print('About to start TCP server...')
tcpserver.serve_until_stopped()
if __name__ == '__main__':
main()
First run the server, and then the client. On the client side, nothing is
printed on the console; on the server side, you should see something like::
About to start TCP server...
59 root INFO Jackdaws love my big sphinx of quartz.
59 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim.
69 myapp.area1 INFO How quickly daft jumping zebras vex.
69 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack.
69 myapp.area2 ERROR The five boxing wizards jump quickly.
Note that there are some security issues with pickle in some scenarios. If
these affect you, you can use an alternative serialization scheme by overriding
the :meth:`makePickle` method and implementing your alternative there, as
well as adapting the above script to use your alternative serialization.
.. _context-info:
Adding contextual information to your logging output
----------------------------------------------------
Sometimes you want logging output to contain contextual information in
addition to the parameters passed to the logging call. For example, in a
networked application, it may be desirable to log client-specific information
in the log (e.g. remote client's username, or IP address). Although you could
use the *extra* parameter to achieve this, it's not always convenient to pass
the information in this way. While it might be tempting to create
:class:`Logger` instances on a per-connection basis, this is not a good idea
because these instances are not garbage collected. While this is not a problem
in practice, when the number of :class:`Logger` instances is dependent on the
level of granularity you want to use in logging an application, it could
be hard to manage if the number of :class:`Logger` instances becomes
effectively unbounded.
Using LoggerAdapters to impart contextual information
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
An easy way in which you can pass contextual information to be output along
with logging event information is to use the :class:`LoggerAdapter` class.
This class is designed to look like a :class:`Logger`, so that you can call
:meth:`debug`, :meth:`info`, :meth:`warning`, :meth:`error`,
:meth:`exception`, :meth:`critical` and :meth:`log`. These methods have the
same signatures as their counterparts in :class:`Logger`, so you can use the
two types of instances interchangeably.
When you create an instance of :class:`LoggerAdapter`, you pass it a
:class:`Logger` instance and a dict-like object which contains your contextual
information. When you call one of the logging methods on an instance of
:class:`LoggerAdapter`, it delegates the call to the underlying instance of
:class:`Logger` passed to its constructor, and arranges to pass the contextual
information in the delegated call. Here's a snippet from the code of
:class:`LoggerAdapter`::
def debug(self, msg, *args, **kwargs):
"""
Delegate a debug call to the underlying logger, after adding
contextual information from this adapter instance.
"""
msg, kwargs = self.process(msg, kwargs)
self.logger.debug(msg, *args, **kwargs)
The :meth:`process` method of :class:`LoggerAdapter` is where the contextual
information is added to the logging output. It's passed the message and
keyword arguments of the logging call, and it passes back (potentially)
modified versions of these to use in the call to the underlying logger. The
default implementation of this method leaves the message alone, but inserts
an 'extra' key in the keyword argument whose value is the dict-like object
passed to the constructor. Of course, if you had passed an 'extra' keyword
argument in the call to the adapter, it will be silently overwritten.
The advantage of using 'extra' is that the values in the dict-like object are
merged into the :class:`LogRecord` instance's __dict__, allowing you to use
customized strings with your :class:`Formatter` instances which know about
the keys of the dict-like object. If you need a different method, e.g. if you
want to prepend or append the contextual information to the message string,
you just need to subclass :class:`LoggerAdapter` and override :meth:`process`
to do what you need. Here's an example script which uses this class, which
also illustrates what dict-like behaviour is needed from an arbitrary
'dict-like' object for use in the constructor::
import logging
class ConnInfo:
"""
An example class which shows how an arbitrary class can be used as
the 'extra' context information repository passed to a LoggerAdapter.
"""
def __getitem__(self, name):
"""
To allow this instance to look like a dict.
"""
from random import choice
if name == 'ip':
result = choice(['127.0.0.1', '192.168.0.1'])
elif name == 'user':
result = choice(['jim', 'fred', 'sheila'])
else:
result = self.__dict__.get(name, '?')
return result
def __iter__(self):
"""
To allow iteration over keys, which will be merged into
the LogRecord dict before formatting and output.
"""
keys = ['ip', 'user']
keys.extend(self.__dict__.keys())
return keys.__iter__()
if __name__ == '__main__':
from random import choice
levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
a1 = logging.LoggerAdapter(logging.getLogger('a.b.c'),
{ 'ip' : '123.231.231.123', 'user' : 'sheila' })
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s')
a1.debug('A debug message')
a1.info('An info message with %s', 'some parameters')
a2 = logging.LoggerAdapter(logging.getLogger('d.e.f'), ConnInfo())
for x in range(10):
lvl = choice(levels)
lvlname = logging.getLevelName(lvl)
a2.log(lvl, 'A message at %s level with %d %s', lvlname, 2, 'parameters')
When this script is run, the output should look something like this::
2008-01-18 14:49:54,023 a.b.c DEBUG IP: 123.231.231.123 User: sheila A debug message
2008-01-18 14:49:54,023 a.b.c INFO IP: 123.231.231.123 User: sheila An info message with some parameters
2008-01-18 14:49:54,023 d.e.f CRITICAL IP: 192.168.0.1 User: jim A message at CRITICAL level with 2 parameters
2008-01-18 14:49:54,033 d.e.f INFO IP: 192.168.0.1 User: jim A message at INFO level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters
2008-01-18 14:49:54,033 d.e.f ERROR IP: 127.0.0.1 User: fred A message at ERROR level with 2 parameters
2008-01-18 14:49:54,033 d.e.f ERROR IP: 127.0.0.1 User: sheila A message at ERROR level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: jim A message at WARNING level with 2 parameters
2008-01-18 14:49:54,033 d.e.f INFO IP: 192.168.0.1 User: fred A message at INFO level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 127.0.0.1 User: jim A message at WARNING level with 2 parameters
.. _filters-contextual:
Using Filters to impart contextual information
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
You can also add contextual information to log output using a user-defined
:class:`Filter`. ``Filter`` instances are allowed to modify the ``LogRecords``
passed to them, including adding additional attributes which can then be output
using a suitable format string, or if needed a custom :class:`Formatter`.
For example in a web application, the request being processed (or at least,
the interesting parts of it) can be stored in a threadlocal
(:class:`threading.local`) variable, and then accessed from a ``Filter`` to
add, say, information from the request - say, the remote IP address and remote
user's username - to the ``LogRecord``, using the attribute names 'ip' and
'user' as in the ``LoggerAdapter`` example above. In that case, the same format
string can be used to get similar output to that shown above. Here's an example
script::
import logging
from random import choice
class ContextFilter(logging.Filter):
"""
This is a filter which injects contextual information into the log.
Rather than use actual contextual information, we just use random
data in this demo.
"""
USERS = ['jim', 'fred', 'sheila']
IPS = ['123.231.231.123', '127.0.0.1', '192.168.0.1']
def filter(self, record):
record.ip = choice(ContextFilter.IPS)
record.user = choice(ContextFilter.USERS)
return True
if __name__ == '__main__':
levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
a1 = logging.LoggerAdapter(logging.getLogger('a.b.c'),
{ 'ip' : '123.231.231.123', 'user' : 'sheila' })
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s')
a1 = logging.getLogger('a.b.c')
a2 = logging.getLogger('d.e.f')
f = ContextFilter()
a1.addFilter(f)
a2.addFilter(f)
a1.debug('A debug message')
a1.info('An info message with %s', 'some parameters')
for x in range(10):
lvl = choice(levels)
lvlname = logging.getLevelName(lvl)
a2.log(lvl, 'A message at %s level with %d %s', lvlname, 2, 'parameters')
which, when run, produces something like::
2010-09-06 22:38:15,292 a.b.c DEBUG IP: 123.231.231.123 User: fred A debug message
2010-09-06 22:38:15,300 a.b.c INFO IP: 192.168.0.1 User: sheila An info message with some parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1 User: sheila A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR IP: 127.0.0.1 User: jim A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG IP: 127.0.0.1 User: sheila A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR IP: 123.231.231.123 User: fred A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 192.168.0.1 User: jim A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1 User: sheila A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG IP: 192.168.0.1 User: jim A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f ERROR IP: 127.0.0.1 User: sheila A message at ERROR level with 2 parameters
2010-09-06 22:38:15,301 d.e.f DEBUG IP: 123.231.231.123 User: fred A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f INFO IP: 123.231.231.123 User: fred A message at INFO level with 2 parameters
.. _multiple-processes:
Logging to a single file from multiple processes
------------------------------------------------
Although logging is thread-safe, and logging to a single file from multiple
threads in a single process *is* supported, logging to a single file from
*multiple processes* is *not* supported, because there is no standard way to
serialize access to a single file across multiple processes in Python. If you
need to log to a single file from multiple processes, one way of doing this is
to have all the processes log to a :class:`SocketHandler`, and have a separate
process which implements a socket server which reads from the socket and logs
to file. (If you prefer, you can dedicate one thread in one of the existing
processes to perform this function.) The following section documents this
approach in more detail and includes a working socket receiver which can be
used as a starting point for you to adapt in your own applications.
If you are using a recent version of Python which includes the
:mod:`multiprocessing` module, you could write your own handler which uses the
:class:`Lock` class from this module to serialize access to the file from
your processes. The existing :class:`FileHandler` and subclasses do not make
use of :mod:`multiprocessing` at present, though they may do so in the future.
Note that at present, the :mod:`multiprocessing` module does not provide
working lock functionality on all platforms (see
http://bugs.python.org/issue3770).
.. currentmodule:: logging.handlers
Alternatively, you can use a ``Queue`` and a :class:`QueueHandler` to send
all logging events to one of the processes in your multi-process application.
The following example script demonstrates how you can do this; in the example
a separate listener process listens for events sent by other processes and logs
them according to its own logging configuration. Although the example only
demonstrates one way of doing it (for example, you may want to use a listener
thread rather than a separate listener process - the implementation would be
analogous) it does allow for completely different logging configurations for
the listener and the other processes in your application, and can be used as
the basis for code meeting your own specific requirements::
# You'll need these imports in your own code
import logging
import logging.handlers
import multiprocessing
# Next two import lines for this demo only
from random import choice, random
import time
#
# Because you'll want to define the logging configurations for listener and workers, the
# listener and worker process functions take a configurer parameter which is a callable
# for configuring logging for that process. These functions are also passed the queue,
# which they use for communication.
#
# In practice, you can configure the listener however you want, but note that in this
# simple example, the listener does not apply level or filter logic to received records.
# In practice, you would probably want to do ths 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('/tmp/mptest.log', 'a', 300, 10)
f = logging.Formatter('%(asctime)s %(processName)-10s %(name)s %(levelname)-8s %(message)s')
h.setFormatter(f)
root.addHandler(h)
# This is the listener process top-level loop: wait for logging events
# (LogRecords)on the queue and handle them, quit when you get a None for a
# LogRecord.
def listener_process(queue, configurer):
configurer()
while True:
try:
record = queue.get()
if record is None: # We send this as a sentinel to tell the listener to quit.
break
logger = logging.getLogger(record.name)
logger.handle(record) # No level or filter logic applied - just do it!
except (KeyboardInterrupt, SystemExit):
raise
except:
import sys, traceback
print >> sys.stderr, 'Whoops! Problem:'
traceback.print_exc(file=sys.stderr)
# Arrays used for random selections in this demo
LEVELS = [logging.DEBUG, logging.INFO, logging.WARNING,
logging.ERROR, logging.CRITICAL]
LOGGERS = ['a.b.c', 'd.e.f']
MESSAGES = [
'Random message #1',
'Random message #2',
'Random message #3',
]
# The worker configuration is done at the start of the worker process run.
# Note that on Windows you can't rely on fork semantics, so each process
# will run the logging configuration code when it starts.
def worker_configurer(queue):
h = logging.handlers.QueueHandler(queue) # Just the one handler needed
root = logging.getLogger()
root.addHandler(h)
root.setLevel(logging.DEBUG) # send all messages, for demo; no other level or filter logic applied.
# This is the worker process top-level loop, which just logs ten events with
# random intervening delays before terminating.
# The print messages are just so you know it's doing something!
def worker_process(queue, configurer):
configurer(queue)
name = multiprocessing.current_process().name
print('Worker started: %s' % name)
for i in range(10):
time.sleep(random())
logger = logging.getLogger(choice(LOGGERS))
level = choice(LEVELS)
message = choice(MESSAGES)
logger.log(level, message)
print('Worker finished: %s' % name)
# Here's where the demo gets orchestrated. Create the queue, create and start
# the listener, create ten workers and start them, wait for them to finish,
# then send a None to the queue to tell the listener to finish.
def main():
queue = multiprocessing.Queue(-1)
listener = multiprocessing.Process(target=listener_process,
args=(queue, listener_configurer))
listener.start()
workers = []
for i in range(10):
worker = multiprocessing.Process(target=worker_process,
args=(queue, worker_configurer))
workers.append(worker)
worker.start()
for w in workers:
w.join()
queue.put_nowait(None)
listener.join()
if __name__ == '__main__':
main()
Using file rotation
-------------------
.. sectionauthor:: Doug Hellmann, Vinay Sajip (changes)
.. (see <http://blog.doughellmann.com/2007/05/pymotw-logging.html>)
Sometimes you want to let a log file grow to a certain size, then open a new
file and log to that. You may want to keep a certain number of these files, and
when that many files have been created, rotate the files so that the number of
files and the size of the files both remin bounded. For this usage pattern, the
logging package provides a :class:`RotatingFileHandler`::
import glob
import logging
import logging.handlers
LOG_FILENAME = 'logging_rotatingfile_example.out'
# Set up a specific logger with our desired output level
my_logger = logging.getLogger('MyLogger')
my_logger.setLevel(logging.DEBUG)
# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(
LOG_FILENAME, maxBytes=20, backupCount=5)
my_logger.addHandler(handler)
# Log some messages
for i in range(20):
my_logger.debug('i = %d' % i)
# See what files are created
logfiles = glob.glob('%s*' % LOG_FILENAME)
for filename in logfiles:
print(filename)
The result should be 6 separate files, each with part of the log history for the
application::
logging_rotatingfile_example.out
logging_rotatingfile_example.out.1
logging_rotatingfile_example.out.2
logging_rotatingfile_example.out.3
logging_rotatingfile_example.out.4
logging_rotatingfile_example.out.5
The most current file is always :file:`logging_rotatingfile_example.out`,
and each time it reaches the size limit it is renamed with the suffix
``.1``. Each of the existing backup files is renamed to increment the suffix
(``.1`` becomes ``.2``, etc.) and the ``.6`` file is erased.
Obviously this example sets the log length much much too small as an extreme
example. You would want to set *maxBytes* to an appropriate value.
.. _zeromq-handlers:
Subclassing QueueHandler
------------------------
You can use a :class:`QueueHandler` subclass to send messages to other kinds
of queues, for example a ZeroMQ 'publish' socket. In the example below,the
socket is created separately and passed to the handler (as its 'queue')::
import zmq # using pyzmq, the Python binding for ZeroMQ
import json # for serializing records portably
ctx = zmq.Context()
sock = zmq.Socket(ctx, zmq.PUB) # or zmq.PUSH, or other suitable value
sock.bind('tcp://*:5556') # or wherever
class ZeroMQSocketHandler(QueueHandler):
def enqueue(self, record):
data = json.dumps(record.__dict__)
self.queue.send(data)
handler = ZeroMQSocketHandler(sock)
Of course there are other ways of organizing this, for example passing in the
data needed by the handler to create the socket::
class ZeroMQSocketHandler(QueueHandler):
def __init__(self, uri, socktype=zmq.PUB, ctx=None):
self.ctx = ctx or zmq.Context()
socket = zmq.Socket(self.ctx, socktype)
socket.bind(uri)
QueueHandler.__init__(self, socket)
def enqueue(self, record):
data = json.dumps(record.__dict__)
self.queue.send(data)
def close(self):
self.queue.close()
Subclassing QueueListener
-------------------------
You can also subclass :class:`QueueListener` to get messages from other kinds
of queues, for example a ZeroMQ 'subscribe' socket. Here's an example::
class ZeroMQSocketListener(QueueListener):
def __init__(self, uri, *handlers, **kwargs):
self.ctx = kwargs.get('ctx') or zmq.Context()
socket = zmq.Socket(self.ctx, zmq.SUB)
socket.setsockopt(zmq.SUBSCRIBE, '') # subscribe to everything
socket.connect(uri)
def dequeue(self):
msg = self.queue.recv()
return logging.makeLogRecord(json.loads(msg))

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optparse.rst
getopt.rst
logging.rst
logging.config.rst
logging.handlers.rst
getpass.rst
curses.rst
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:mod:`logging.config` --- Logging configuration
===============================================
.. module:: logging.config
:synopsis: Configuration of the logging module.
.. moduleauthor:: Vinay Sajip <vinay_sajip@red-dove.com>
.. sectionauthor:: Vinay Sajip <vinay_sajip@red-dove.com>
.. _logging-config-api:
Configuration functions
^^^^^^^^^^^^^^^^^^^^^^^
The following functions configure the logging module. They are located in the
:mod:`logging.config` module. Their use is optional --- you can configure the
logging module using these functions or by making calls to the main API (defined
in :mod:`logging` itself) and defining handlers which are declared either in
:mod:`logging` or :mod:`logging.handlers`.
.. function:: dictConfig(config)
Takes the logging configuration from a dictionary. The contents of
this dictionary are described in :ref:`logging-config-dictschema`
below.
If an error is encountered during configuration, this function will
raise a :exc:`ValueError`, :exc:`TypeError`, :exc:`AttributeError`
or :exc:`ImportError` with a suitably descriptive message. The
following is a (possibly incomplete) list of conditions which will
raise an error:
* A ``level`` which is not a string or which is a string not
corresponding to an actual logging level.
* A ``propagate`` value which is not a boolean.
* An id which does not have a corresponding destination.
* A non-existent handler id found during an incremental call.
* An invalid logger name.
* Inability to resolve to an internal or external object.
Parsing is performed by the :class:`DictConfigurator` class, whose
constructor is passed the dictionary used for configuration, and
has a :meth:`configure` method. The :mod:`logging.config` module
has a callable attribute :attr:`dictConfigClass`
which is initially set to :class:`DictConfigurator`.
You can replace the value of :attr:`dictConfigClass` with a
suitable implementation of your own.
:func:`dictConfig` calls :attr:`dictConfigClass` passing
the specified dictionary, and then calls the :meth:`configure` method on
the returned object to put the configuration into effect::
def dictConfig(config):
dictConfigClass(config).configure()
For example, a subclass of :class:`DictConfigurator` could call
``DictConfigurator.__init__()`` in its own :meth:`__init__()`, then
set up custom prefixes which would be usable in the subsequent
:meth:`configure` call. :attr:`dictConfigClass` would be bound to
this new subclass, and then :func:`dictConfig` could be called exactly as
in the default, uncustomized state.
.. function:: fileConfig(fname[, defaults])
Reads the logging configuration from a :mod:`configparser`\-format file named
*fname*. This function can be called several times from an application,
allowing an end user to select from various pre-canned
configurations (if the developer provides a mechanism to present the choices
and load the chosen configuration). Defaults to be passed to the ConfigParser
can be specified in the *defaults* argument.
.. function:: listen(port=DEFAULT_LOGGING_CONFIG_PORT)
Starts up a socket server on the specified port, and listens for new
configurations. If no port is specified, the module's default
:const:`DEFAULT_LOGGING_CONFIG_PORT` is used. Logging configurations will be
sent as a file suitable for processing by :func:`fileConfig`. Returns a
:class:`Thread` instance on which you can call :meth:`start` to start the
server, and which you can :meth:`join` when appropriate. To stop the server,
call :func:`stopListening`.
To send a configuration to the socket, read in the configuration file and
send it to the socket as a string of bytes preceded by a four-byte length
string packed in binary using ``struct.pack('>L', n)``.
.. function:: stopListening()
Stops the listening server which was created with a call to :func:`listen`.
This is typically called before calling :meth:`join` on the return value from
:func:`listen`.
.. _logging-config-dictschema:
Configuration dictionary schema
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Describing a logging configuration requires listing the various
objects to create and the connections between them; for example, you
may create a handler named 'console' and then say that the logger
named 'startup' will send its messages to the 'console' handler.
These objects aren't limited to those provided by the :mod:`logging`
module because you might write your own formatter or handler class.
The parameters to these classes may also need to include external
objects such as ``sys.stderr``. The syntax for describing these
objects and connections is defined in :ref:`logging-config-dict-connections`
below.
Dictionary Schema Details
"""""""""""""""""""""""""
The dictionary passed to :func:`dictConfig` must contain the following
keys:
* *version* - to be set to an integer value representing the schema
version. The only valid value at present is 1, but having this key
allows the schema to evolve while still preserving backwards
compatibility.
All other keys are optional, but if present they will be interpreted
as described below. In all cases below where a 'configuring dict' is
mentioned, it will be checked for the special ``'()'`` key to see if a
custom instantiation is required. If so, the mechanism described in
:ref:`logging-config-dict-userdef` below is used to create an instance;
otherwise, the context is used to determine what to instantiate.
* *formatters* - the corresponding value will be a dict in which each
key is a formatter id and each value is a dict describing how to
configure the corresponding Formatter instance.
The configuring dict is searched for keys ``format`` and ``datefmt``
(with defaults of ``None``) and these are used to construct a
:class:`logging.Formatter` instance.
* *filters* - the corresponding value will be a dict in which each key
is a filter id and each value is a dict describing how to configure
the corresponding Filter instance.
The configuring dict is searched for the key ``name`` (defaulting to the
empty string) and this is used to construct a :class:`logging.Filter`
instance.
* *handlers* - the corresponding value will be a dict in which each
key is a handler id and each value is a dict describing how to
configure the corresponding Handler instance.
The configuring dict is searched for the following keys:
* ``class`` (mandatory). This is the fully qualified name of the
handler class.
* ``level`` (optional). The level of the handler.
* ``formatter`` (optional). The id of the formatter for this
handler.
* ``filters`` (optional). A list of ids of the filters for this
handler.
All *other* keys are passed through as keyword arguments to the
handler's constructor. For example, given the snippet::
handlers:
console:
class : logging.StreamHandler
formatter: brief
level : INFO
filters: [allow_foo]
stream : ext://sys.stdout
file:
class : logging.handlers.RotatingFileHandler
formatter: precise
filename: logconfig.log
maxBytes: 1024
backupCount: 3
the handler with id ``console`` is instantiated as a
:class:`logging.StreamHandler`, using ``sys.stdout`` as the underlying
stream. The handler with id ``file`` is instantiated as a
:class:`logging.handlers.RotatingFileHandler` with the keyword arguments
``filename='logconfig.log', maxBytes=1024, backupCount=3``.
* *loggers* - the corresponding value will be a dict in which each key
is a logger name and each value is a dict describing how to
configure the corresponding Logger instance.
The configuring dict is searched for the following keys:
* ``level`` (optional). The level of the logger.
* ``propagate`` (optional). The propagation setting of the logger.
* ``filters`` (optional). A list of ids of the filters for this
logger.
* ``handlers`` (optional). A list of ids of the handlers for this
logger.
The specified loggers will be configured according to the level,
propagation, filters and handlers specified.
* *root* - this will be the configuration for the root logger.
Processing of the configuration will be as for any logger, except
that the ``propagate`` setting will not be applicable.
* *incremental* - whether the configuration is to be interpreted as
incremental to the existing configuration. This value defaults to
``False``, which means that the specified configuration replaces the
existing configuration with the same semantics as used by the
existing :func:`fileConfig` API.
If the specified value is ``True``, the configuration is processed
as described in the section on :ref:`logging-config-dict-incremental`.
* *disable_existing_loggers* - whether any existing loggers are to be
disabled. This setting mirrors the parameter of the same name in
:func:`fileConfig`. If absent, this parameter defaults to ``True``.
This value is ignored if *incremental* is ``True``.
.. _logging-config-dict-incremental:
Incremental Configuration
"""""""""""""""""""""""""
It is difficult to provide complete flexibility for incremental
configuration. For example, because objects such as filters
and formatters are anonymous, once a configuration is set up, it is
not possible to refer to such anonymous objects when augmenting a
configuration.
Furthermore, there is not a compelling case for arbitrarily altering
the object graph of loggers, handlers, filters, formatters at
run-time, once a configuration is set up; the verbosity of loggers and
handlers can be controlled just by setting levels (and, in the case of
loggers, propagation flags). Changing the object graph arbitrarily in
a safe way is problematic in a multi-threaded environment; while not
impossible, the benefits are not worth the complexity it adds to the
implementation.
Thus, when the ``incremental`` key of a configuration dict is present
and is ``True``, the system will completely ignore any ``formatters`` and
``filters`` entries, and process only the ``level``
settings in the ``handlers`` entries, and the ``level`` and
``propagate`` settings in the ``loggers`` and ``root`` entries.
Using a value in the configuration dict lets configurations to be sent
over the wire as pickled dicts to a socket listener. Thus, the logging
verbosity of a long-running application can be altered over time with
no need to stop and restart the application.
.. _logging-config-dict-connections:
Object connections
""""""""""""""""""
The schema describes a set of logging objects - loggers,
handlers, formatters, filters - which are connected to each other in
an object graph. Thus, the schema needs to represent connections
between the objects. For example, say that, once configured, a
particular logger has attached to it a particular handler. For the
purposes of this discussion, we can say that the logger represents the
source, and the handler the destination, of a connection between the
two. Of course in the configured objects this is represented by the
logger holding a reference to the handler. In the configuration dict,
this is done by giving each destination object an id which identifies
it unambiguously, and then using the id in the source object's
configuration to indicate that a connection exists between the source
and the destination object with that id.
So, for example, consider the following YAML snippet::
formatters:
brief:
# configuration for formatter with id 'brief' goes here
precise:
# configuration for formatter with id 'precise' goes here
handlers:
h1: #This is an id
# configuration of handler with id 'h1' goes here
formatter: brief
h2: #This is another id
# configuration of handler with id 'h2' goes here
formatter: precise
loggers:
foo.bar.baz:
# other configuration for logger 'foo.bar.baz'
handlers: [h1, h2]
(Note: YAML used here because it's a little more readable than the
equivalent Python source form for the dictionary.)
The ids for loggers are the logger names which would be used
programmatically to obtain a reference to those loggers, e.g.
``foo.bar.baz``. The ids for Formatters and Filters can be any string
value (such as ``brief``, ``precise`` above) and they are transient,
in that they are only meaningful for processing the configuration
dictionary and used to determine connections between objects, and are
not persisted anywhere when the configuration call is complete.
The above snippet indicates that logger named ``foo.bar.baz`` should
have two handlers attached to it, which are described by the handler
ids ``h1`` and ``h2``. The formatter for ``h1`` is that described by id
``brief``, and the formatter for ``h2`` is that described by id
``precise``.
.. _logging-config-dict-userdef:
User-defined objects
""""""""""""""""""""
The schema supports user-defined objects for handlers, filters and
formatters. (Loggers do not need to have different types for
different instances, so there is no support in this configuration
schema for user-defined logger classes.)
Objects to be configured are described by dictionaries
which detail their configuration. In some places, the logging system
will be able to infer from the context how an object is to be
instantiated, but when a user-defined object is to be instantiated,
the system will not know how to do this. In order to provide complete
flexibility for user-defined object instantiation, the user needs
to provide a 'factory' - a callable which is called with a
configuration dictionary and which returns the instantiated object.
This is signalled by an absolute import path to the factory being
made available under the special key ``'()'``. Here's a concrete
example::
formatters:
brief:
format: '%(message)s'
default:
format: '%(asctime)s %(levelname)-8s %(name)-15s %(message)s'
datefmt: '%Y-%m-%d %H:%M:%S'
custom:
(): my.package.customFormatterFactory
bar: baz
spam: 99.9
answer: 42
The above YAML snippet defines three formatters. The first, with id
``brief``, is a standard :class:`logging.Formatter` instance with the
specified format string. The second, with id ``default``, has a
longer format and also defines the time format explicitly, and will
result in a :class:`logging.Formatter` initialized with those two format
strings. Shown in Python source form, the ``brief`` and ``default``
formatters have configuration sub-dictionaries::
{
'format' : '%(message)s'
}
and::
{
'format' : '%(asctime)s %(levelname)-8s %(name)-15s %(message)s',
'datefmt' : '%Y-%m-%d %H:%M:%S'
}
respectively, and as these dictionaries do not contain the special key
``'()'``, the instantiation is inferred from the context: as a result,
standard :class:`logging.Formatter` instances are created. The
configuration sub-dictionary for the third formatter, with id
``custom``, is::
{
'()' : 'my.package.customFormatterFactory',
'bar' : 'baz',
'spam' : 99.9,
'answer' : 42
}
and this contains the special key ``'()'``, which means that
user-defined instantiation is wanted. In this case, the specified
factory callable will be used. If it is an actual callable it will be
used directly - otherwise, if you specify a string (as in the example)
the actual callable will be located using normal import mechanisms.
The callable will be called with the **remaining** items in the
configuration sub-dictionary as keyword arguments. In the above
example, the formatter with id ``custom`` will be assumed to be
returned by the call::
my.package.customFormatterFactory(bar='baz', spam=99.9, answer=42)
The key ``'()'`` has been used as the special key because it is not a
valid keyword parameter name, and so will not clash with the names of
the keyword arguments used in the call. The ``'()'`` also serves as a
mnemonic that the corresponding value is a callable.
.. _logging-config-dict-externalobj:
Access to external objects
""""""""""""""""""""""""""
There are times where a configuration needs to refer to objects
external to the configuration, for example ``sys.stderr``. If the
configuration dict is constructed using Python code, this is
straightforward, but a problem arises when the configuration is
provided via a text file (e.g. JSON, YAML). In a text file, there is
no standard way to distinguish ``sys.stderr`` from the literal string
``'sys.stderr'``. To facilitate this distinction, the configuration
system looks for certain special prefixes in string values and
treat them specially. For example, if the literal string
``'ext://sys.stderr'`` is provided as a value in the configuration,
then the ``ext://`` will be stripped off and the remainder of the
value processed using normal import mechanisms.
The handling of such prefixes is done in a way analogous to protocol
handling: there is a generic mechanism to look for prefixes which
match the regular expression ``^(?P<prefix>[a-z]+)://(?P<suffix>.*)$``
whereby, if the ``prefix`` is recognised, the ``suffix`` is processed
in a prefix-dependent manner and the result of the processing replaces
the string value. If the prefix is not recognised, then the string
value will be left as-is.
.. _logging-config-dict-internalobj:
Access to internal objects
""""""""""""""""""""""""""
As well as external objects, there is sometimes also a need to refer
to objects in the configuration. This will be done implicitly by the
configuration system for things that it knows about. For example, the
string value ``'DEBUG'`` for a ``level`` in a logger or handler will
automatically be converted to the value ``logging.DEBUG``, and the
``handlers``, ``filters`` and ``formatter`` entries will take an
object id and resolve to the appropriate destination object.
However, a more generic mechanism is needed for user-defined
objects which are not known to the :mod:`logging` module. For
example, consider :class:`logging.handlers.MemoryHandler`, which takes
a ``target`` argument which is another handler to delegate to. Since
the system already knows about this class, then in the configuration,
the given ``target`` just needs to be the object id of the relevant
target handler, and the system will resolve to the handler from the
id. If, however, a user defines a ``my.package.MyHandler`` which has
an ``alternate`` handler, the configuration system would not know that
the ``alternate`` referred to a handler. To cater for this, a generic
resolution system allows the user to specify::
handlers:
file:
# configuration of file handler goes here
custom:
(): my.package.MyHandler
alternate: cfg://handlers.file
The literal string ``'cfg://handlers.file'`` will be resolved in an
analogous way to strings with the ``ext://`` prefix, but looking
in the configuration itself rather than the import namespace. The
mechanism allows access by dot or by index, in a similar way to
that provided by ``str.format``. Thus, given the following snippet::
handlers:
email:
class: logging.handlers.SMTPHandler
mailhost: localhost
fromaddr: my_app@domain.tld
toaddrs:
- support_team@domain.tld
- dev_team@domain.tld
subject: Houston, we have a problem.
in the configuration, the string ``'cfg://handlers'`` would resolve to
the dict with key ``handlers``, the string ``'cfg://handlers.email``
would resolve to the dict with key ``email`` in the ``handlers`` dict,
and so on. The string ``'cfg://handlers.email.toaddrs[1]`` would
resolve to ``'dev_team.domain.tld'`` and the string
``'cfg://handlers.email.toaddrs[0]'`` would resolve to the value
``'support_team@domain.tld'``. The ``subject`` value could be accessed
using either ``'cfg://handlers.email.subject'`` or, equivalently,
``'cfg://handlers.email[subject]'``. The latter form only needs to be
used if the key contains spaces or non-alphanumeric characters. If an
index value consists only of decimal digits, access will be attempted
using the corresponding integer value, falling back to the string
value if needed.
Given a string ``cfg://handlers.myhandler.mykey.123``, this will
resolve to ``config_dict['handlers']['myhandler']['mykey']['123']``.
If the string is specified as ``cfg://handlers.myhandler.mykey[123]``,
the system will attempt to retrieve the value from
``config_dict['handlers']['myhandler']['mykey'][123]``, and fall back
to ``config_dict['handlers']['myhandler']['mykey']['123']`` if that
fails.
.. _logging-config-fileformat:
Configuration file format
^^^^^^^^^^^^^^^^^^^^^^^^^
The configuration file format understood by :func:`fileConfig` is based on
:mod:`configparser` functionality. The file must contain sections called
``[loggers]``, ``[handlers]`` and ``[formatters]`` which identify by name the
entities of each type which are defined in the file. For each such entity, there
is a separate section which identifies how that entity is configured. Thus, for
a logger named ``log01`` in the ``[loggers]`` section, the relevant
configuration details are held in a section ``[logger_log01]``. Similarly, a
handler called ``hand01`` in the ``[handlers]`` section will have its
configuration held in a section called ``[handler_hand01]``, while a formatter
called ``form01`` in the ``[formatters]`` section will have its configuration
specified in a section called ``[formatter_form01]``. The root logger
configuration must be specified in a section called ``[logger_root]``.
Examples of these sections in the file are given below. ::
[loggers]
keys=root,log02,log03,log04,log05,log06,log07
[handlers]
keys=hand01,hand02,hand03,hand04,hand05,hand06,hand07,hand08,hand09
[formatters]
keys=form01,form02,form03,form04,form05,form06,form07,form08,form09
The root logger must specify a level and a list of handlers. An example of a
root logger section is given below. ::
[logger_root]
level=NOTSET
handlers=hand01
The ``level`` entry can be one of ``DEBUG, INFO, WARNING, ERROR, CRITICAL`` or
``NOTSET``. For the root logger only, ``NOTSET`` means that all messages will be
logged. Level values are :func:`eval`\ uated in the context of the ``logging``
package's namespace.
The ``handlers`` entry is a comma-separated list of handler names, which must
appear in the ``[handlers]`` section. These names must appear in the
``[handlers]`` section and have corresponding sections in the configuration
file.
For loggers other than the root logger, some additional information is required.
This is illustrated by the following example. ::
[logger_parser]
level=DEBUG
handlers=hand01
propagate=1
qualname=compiler.parser
The ``level`` and ``handlers`` entries are interpreted as for the root logger,
except that if a non-root logger's level is specified as ``NOTSET``, the system
consults loggers higher up the hierarchy to determine the effective level of the
logger. The ``propagate`` entry is set to 1 to indicate that messages must
propagate to handlers higher up the logger hierarchy from this logger, or 0 to
indicate that messages are **not** propagated to handlers up the hierarchy. The
``qualname`` entry is the hierarchical channel name of the logger, that is to
say the name used by the application to get the logger.
Sections which specify handler configuration are exemplified by the following.
::
[handler_hand01]
class=StreamHandler
level=NOTSET
formatter=form01
args=(sys.stdout,)
The ``class`` entry indicates the handler's class (as determined by :func:`eval`
in the ``logging`` package's namespace). The ``level`` is interpreted as for
loggers, and ``NOTSET`` is taken to mean 'log everything'.
The ``formatter`` entry indicates the key name of the formatter for this
handler. If blank, a default formatter (``logging._defaultFormatter``) is used.
If a name is specified, it must appear in the ``[formatters]`` section and have
a corresponding section in the configuration file.
The ``args`` entry, when :func:`eval`\ uated in the context of the ``logging``
package's namespace, is the list of arguments to the constructor for the handler
class. Refer to the constructors for the relevant handlers, or to the examples
below, to see how typical entries are constructed. ::
[handler_hand02]
class=FileHandler
level=DEBUG
formatter=form02
args=('python.log', 'w')
[handler_hand03]
class=handlers.SocketHandler
level=INFO
formatter=form03
args=('localhost', handlers.DEFAULT_TCP_LOGGING_PORT)
[handler_hand04]
class=handlers.DatagramHandler
level=WARN
formatter=form04
args=('localhost', handlers.DEFAULT_UDP_LOGGING_PORT)
[handler_hand05]
class=handlers.SysLogHandler
level=ERROR
formatter=form05
args=(('localhost', handlers.SYSLOG_UDP_PORT), handlers.SysLogHandler.LOG_USER)
[handler_hand06]
class=handlers.NTEventLogHandler
level=CRITICAL
formatter=form06
args=('Python Application', '', 'Application')
[handler_hand07]
class=handlers.SMTPHandler
level=WARN
formatter=form07
args=('localhost', 'from@abc', ['user1@abc', 'user2@xyz'], 'Logger Subject')
[handler_hand08]
class=handlers.MemoryHandler
level=NOTSET
formatter=form08
target=
args=(10, ERROR)
[handler_hand09]
class=handlers.HTTPHandler
level=NOTSET
formatter=form09
args=('localhost:9022', '/log', 'GET')
Sections which specify formatter configuration are typified by the following. ::
[formatter_form01]
format=F1 %(asctime)s %(levelname)s %(message)s
datefmt=
class=logging.Formatter
The ``format`` entry is the overall format string, and the ``datefmt`` entry is
the :func:`strftime`\ -compatible date/time format string. If empty, the
package substitutes ISO8601 format date/times, which is almost equivalent to
specifying the date format string ``'%Y-%m-%d %H:%M:%S'``. The ISO8601 format
also specifies milliseconds, which are appended to the result of using the above
format string, with a comma separator. An example time in ISO8601 format is
``2003-01-23 00:29:50,411``.
The ``class`` entry is optional. It indicates the name of the formatter's class
(as a dotted module and class name.) This option is useful for instantiating a
:class:`Formatter` subclass. Subclasses of :class:`Formatter` can present
exception tracebacks in an expanded or condensed format.
.. seealso::
Module :mod:`logging`
API reference for the logging module.
Module :mod:`logging.handlers`
Useful handlers included with the logging module.

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:mod:`logging.handlers` --- Logging handlers
============================================
.. module:: logging.handlers
:synopsis: Handlers for the logging module.
.. moduleauthor:: Vinay Sajip <vinay_sajip@red-dove.com>
.. sectionauthor:: Vinay Sajip <vinay_sajip@red-dove.com>
The following useful handlers are provided in the package.
.. currentmodule:: logging
.. _stream-handler:
StreamHandler
^^^^^^^^^^^^^
The :class:`StreamHandler` class, located in the core :mod:`logging` package,
sends logging output to streams such as *sys.stdout*, *sys.stderr* or any
file-like object (or, more precisely, any object which supports :meth:`write`
and :meth:`flush` methods).
.. class:: StreamHandler(stream=None)
Returns a new instance of the :class:`StreamHandler` class. If *stream* is
specified, the instance will use it for logging output; otherwise, *sys.stderr*
will be used.
.. method:: emit(record)
If a formatter is specified, it is used to format the record. The record
is then written to the stream with a trailing newline. If exception
information is present, it is formatted using
:func:`traceback.print_exception` and appended to the stream.
.. method:: flush()
Flushes the stream by calling its :meth:`flush` method. Note that the
:meth:`close` method is inherited from :class:`Handler` and so does
no output, so an explicit :meth:`flush` call may be needed at times.
.. versionchanged:: 3.2
The ``StreamHandler`` class now has a ``terminator`` attribute, default
value ``'\n'``, which is used as the terminator when writing a formatted
record to a stream. If you don't want this newline termination, you can
set the handler instance's ``terminator`` attribute to the empty string.
.. _file-handler:
FileHandler
^^^^^^^^^^^
The :class:`FileHandler` class, located in the core :mod:`logging` package,
sends logging output to a disk file. It inherits the output functionality from
:class:`StreamHandler`.
.. class:: FileHandler(filename, mode='a', encoding=None, delay=False)
Returns a new instance of the :class:`FileHandler` class. The specified file is
opened and used as the stream for logging. If *mode* is not specified,
:const:`'a'` is used. If *encoding* is not *None*, it is used to open the file
with that encoding. If *delay* is true, then file opening is deferred until the
first call to :meth:`emit`. By default, the file grows indefinitely.
.. method:: close()
Closes the file.
.. method:: emit(record)
Outputs the record to the file.
.. _null-handler:
NullHandler
^^^^^^^^^^^
.. versionadded:: 3.1
The :class:`NullHandler` class, located in the core :mod:`logging` package,
does not do any formatting or output. It is essentially a 'no-op' handler
for use by library developers.
.. class:: NullHandler()
Returns a new instance of the :class:`NullHandler` class.
.. method:: emit(record)
This method does nothing.
.. method:: handle(record)
This method does nothing.
.. method:: createLock()
This method returns ``None`` for the lock, since there is no
underlying I/O to which access needs to be serialized.
See :ref:`library-config` for more information on how to use
:class:`NullHandler`.
.. _watched-file-handler:
WatchedFileHandler
^^^^^^^^^^^^^^^^^^
.. currentmodule:: logging.handlers
The :class:`WatchedFileHandler` class, located in the :mod:`logging.handlers`
module, is a :class:`FileHandler` which watches the file it is logging to. If
the file changes, it is closed and reopened using the file name.
A file change can happen because of usage of programs such as *newsyslog* and
*logrotate* which perform log file rotation. This handler, intended for use
under Unix/Linux, watches the file to see if it has changed since the last emit.
(A file is deemed to have changed if its device or inode have changed.) If the
file has changed, the old file stream is closed, and the file opened to get a
new stream.
This handler is not appropriate for use under Windows, because under Windows
open log files cannot be moved or renamed - logging opens the files with
exclusive locks - and so there is no need for such a handler. Furthermore,
*ST_INO* is not supported under Windows; :func:`stat` always returns zero for
this value.
.. class:: WatchedFileHandler(filename[,mode[, encoding[, delay]]])
Returns a new instance of the :class:`WatchedFileHandler` class. The specified
file is opened and used as the stream for logging. If *mode* is not specified,
:const:`'a'` is used. If *encoding* is not *None*, it is used to open the file
with that encoding. If *delay* is true, then file opening is deferred until the
first call to :meth:`emit`. By default, the file grows indefinitely.
.. method:: emit(record)
Outputs the record to the file, but first checks to see if the file has
changed. If it has, the existing stream is flushed and closed and the
file opened again, before outputting the record to the file.
.. _rotating-file-handler:
RotatingFileHandler
^^^^^^^^^^^^^^^^^^^
The :class:`RotatingFileHandler` class, located in the :mod:`logging.handlers`
module, supports rotation of disk log files.
.. class:: RotatingFileHandler(filename, mode='a', maxBytes=0, backupCount=0, encoding=None, delay=0)
Returns a new instance of the :class:`RotatingFileHandler` class. The specified
file is opened and used as the stream for logging. If *mode* is not specified,
``'a'`` is used. If *encoding* is not *None*, it is used to open the file
with that encoding. If *delay* is true, then file opening is deferred until the
first call to :meth:`emit`. By default, the file grows indefinitely.
You can use the *maxBytes* and *backupCount* values to allow the file to
:dfn:`rollover` at a predetermined size. When the size is about to be exceeded,
the file is closed and a new file is silently opened for output. Rollover occurs
whenever the current log file is nearly *maxBytes* in length; if *maxBytes* is
zero, rollover never occurs. If *backupCount* is non-zero, the system will save
old log files by appending the extensions '.1', '.2' etc., to the filename. For
example, with a *backupCount* of 5 and a base file name of :file:`app.log`, you
would get :file:`app.log`, :file:`app.log.1`, :file:`app.log.2`, up to
:file:`app.log.5`. The file being written to is always :file:`app.log`. When
this file is filled, it is closed and renamed to :file:`app.log.1`, and if files
:file:`app.log.1`, :file:`app.log.2`, etc. exist, then they are renamed to
:file:`app.log.2`, :file:`app.log.3` etc. respectively.
.. method:: doRollover()
Does a rollover, as described above.
.. method:: emit(record)
Outputs the record to the file, catering for rollover as described
previously.
.. _timed-rotating-file-handler:
TimedRotatingFileHandler
^^^^^^^^^^^^^^^^^^^^^^^^
The :class:`TimedRotatingFileHandler` class, located in the
:mod:`logging.handlers` module, supports rotation of disk log files at certain
timed intervals.
.. class:: TimedRotatingFileHandler(filename, when='h', interval=1, backupCount=0, encoding=None, delay=False, utc=False)
Returns a new instance of the :class:`TimedRotatingFileHandler` class. The
specified file is opened and used as the stream for logging. On rotating it also
sets the filename suffix. Rotating happens based on the product of *when* and
*interval*.
You can use the *when* to specify the type of *interval*. The list of possible
values is below. Note that they are not case sensitive.
+----------------+-----------------------+
| Value | Type of interval |
+================+=======================+
| ``'S'`` | Seconds |
+----------------+-----------------------+
| ``'M'`` | Minutes |
+----------------+-----------------------+
| ``'H'`` | Hours |
+----------------+-----------------------+
| ``'D'`` | Days |
+----------------+-----------------------+
| ``'W'`` | Week day (0=Monday) |
+----------------+-----------------------+
| ``'midnight'`` | Roll over at midnight |
+----------------+-----------------------+
The system will save old log files by appending extensions to the filename.
The extensions are date-and-time based, using the strftime format
``%Y-%m-%d_%H-%M-%S`` or a leading portion thereof, depending on the
rollover interval.
When computing the next rollover time for the first time (when the handler
is created), the last modification time of an existing log file, or else
the current time, is used to compute when the next rotation will occur.
If the *utc* argument is true, times in UTC will be used; otherwise
local time is used.
If *backupCount* is nonzero, at most *backupCount* files
will be kept, and if more would be created when rollover occurs, the oldest
one is deleted. The deletion logic uses the interval to determine which
files to delete, so changing the interval may leave old files lying around.
If *delay* is true, then file opening is deferred until the first call to
:meth:`emit`.
.. method:: doRollover()
Does a rollover, as described above.
.. method:: emit(record)
Outputs the record to the file, catering for rollover as described above.
.. _socket-handler:
SocketHandler
^^^^^^^^^^^^^
The :class:`SocketHandler` class, located in the :mod:`logging.handlers` module,
sends logging output to a network socket. The base class uses a TCP socket.
.. class:: SocketHandler(host, port)
Returns a new instance of the :class:`SocketHandler` class intended to
communicate with a remote machine whose address is given by *host* and *port*.
.. method:: close()
Closes the socket.
.. method:: emit()
Pickles the record's attribute dictionary and writes it to the socket in
binary format. If there is an error with the socket, silently drops the
packet. If the connection was previously lost, re-establishes the
connection. To unpickle the record at the receiving end into a
:class:`LogRecord`, use the :func:`makeLogRecord` function.
.. method:: handleError()
Handles an error which has occurred during :meth:`emit`. The most likely
cause is a lost connection. Closes the socket so that we can retry on the
next event.
.. method:: makeSocket()
This is a factory method which allows subclasses to define the precise
type of socket they want. The default implementation creates a TCP socket
(:const:`socket.SOCK_STREAM`).
.. method:: makePickle(record)
Pickles the record's attribute dictionary in binary format with a length
prefix, and returns it ready for transmission across the socket.
Note that pickles aren't completely secure. If you are concerned about
security, you may want to override this method to implement a more secure
mechanism. For example, you can sign pickles using HMAC and then verify
them on the receiving end, or alternatively you can disable unpickling of
global objects on the receiving end.
.. method:: send(packet)
Send a pickled string *packet* to the socket. This function allows for
partial sends which can happen when the network is busy.
.. _datagram-handler:
DatagramHandler
^^^^^^^^^^^^^^^
The :class:`DatagramHandler` class, located in the :mod:`logging.handlers`
module, inherits from :class:`SocketHandler` to support sending logging messages
over UDP sockets.
.. class:: DatagramHandler(host, port)
Returns a new instance of the :class:`DatagramHandler` class intended to
communicate with a remote machine whose address is given by *host* and *port*.
.. method:: emit()
Pickles the record's attribute dictionary and writes it to the socket in
binary format. If there is an error with the socket, silently drops the
packet. To unpickle the record at the receiving end into a
:class:`LogRecord`, use the :func:`makeLogRecord` function.
.. method:: makeSocket()
The factory method of :class:`SocketHandler` is here overridden to create
a UDP socket (:const:`socket.SOCK_DGRAM`).
.. method:: send(s)
Send a pickled string to a socket.
.. _syslog-handler:
SysLogHandler
^^^^^^^^^^^^^
The :class:`SysLogHandler` class, located in the :mod:`logging.handlers` module,
supports sending logging messages to a remote or local Unix syslog.
.. class:: SysLogHandler(address=('localhost', SYSLOG_UDP_PORT), facility=LOG_USER, socktype=socket.SOCK_DGRAM)
Returns a new instance of the :class:`SysLogHandler` class intended to
communicate with a remote Unix machine whose address is given by *address* in
the form of a ``(host, port)`` tuple. If *address* is not specified,
``('localhost', 514)`` is used. The address is used to open a socket. An
alternative to providing a ``(host, port)`` tuple is providing an address as a
string, for example '/dev/log'. In this case, a Unix domain socket is used to
send the message to the syslog. If *facility* is not specified,
:const:`LOG_USER` is used. The type of socket opened depends on the
*socktype* argument, which defaults to :const:`socket.SOCK_DGRAM` and thus
opens a UDP socket. To open a TCP socket (for use with the newer syslog
daemons such as rsyslog), specify a value of :const:`socket.SOCK_STREAM`.
Note that if your server is not listening on UDP port 514,
:class:`SysLogHandler` may appear not to work. In that case, check what
address you should be using for a domain socket - it's system dependent.
For example, on Linux it's usually '/dev/log' but on OS/X it's
'/var/run/syslog'. You'll need to check your platform and use the
appropriate address (you may need to do this check at runtime if your
application needs to run on several platforms). On Windows, you pretty
much have to use the UDP option.
.. versionchanged:: 3.2
*socktype* was added.
.. method:: close()
Closes the socket to the remote host.
.. method:: emit(record)
The record is formatted, and then sent to the syslog server. If exception
information is present, it is *not* sent to the server.
.. method:: encodePriority(facility, priority)
Encodes the facility and priority into an integer. You can pass in strings
or integers - if strings are passed, internal mapping dictionaries are
used to convert them to integers.
The symbolic ``LOG_`` values are defined in :class:`SysLogHandler` and
mirror the values defined in the ``sys/syslog.h`` header file.
**Priorities**
+--------------------------+---------------+
| Name (string) | Symbolic value|
+==========================+===============+
| ``alert`` | LOG_ALERT |
+--------------------------+---------------+
| ``crit`` or ``critical`` | LOG_CRIT |
+--------------------------+---------------+
| ``debug`` | LOG_DEBUG |
+--------------------------+---------------+
| ``emerg`` or ``panic`` | LOG_EMERG |
+--------------------------+---------------+
| ``err`` or ``error`` | LOG_ERR |
+--------------------------+---------------+
| ``info`` | LOG_INFO |
+--------------------------+---------------+
| ``notice`` | LOG_NOTICE |
+--------------------------+---------------+
| ``warn`` or ``warning`` | LOG_WARNING |
+--------------------------+---------------+
**Facilities**
+---------------+---------------+
| Name (string) | Symbolic value|
+===============+===============+
| ``auth`` | LOG_AUTH |
+---------------+---------------+
| ``authpriv`` | LOG_AUTHPRIV |
+---------------+---------------+
| ``cron`` | LOG_CRON |
+---------------+---------------+
| ``daemon`` | LOG_DAEMON |
+---------------+---------------+
| ``ftp`` | LOG_FTP |
+---------------+---------------+
| ``kern`` | LOG_KERN |
+---------------+---------------+
| ``lpr`` | LOG_LPR |
+---------------+---------------+
| ``mail`` | LOG_MAIL |
+---------------+---------------+
| ``news`` | LOG_NEWS |
+---------------+---------------+
| ``syslog`` | LOG_SYSLOG |
+---------------+---------------+
| ``user`` | LOG_USER |
+---------------+---------------+
| ``uucp`` | LOG_UUCP |
+---------------+---------------+
| ``local0`` | LOG_LOCAL0 |
+---------------+---------------+
| ``local1`` | LOG_LOCAL1 |
+---------------+---------------+
| ``local2`` | LOG_LOCAL2 |
+---------------+---------------+
| ``local3`` | LOG_LOCAL3 |
+---------------+---------------+
| ``local4`` | LOG_LOCAL4 |
+---------------+---------------+
| ``local5`` | LOG_LOCAL5 |
+---------------+---------------+
| ``local6`` | LOG_LOCAL6 |
+---------------+---------------+
| ``local7`` | LOG_LOCAL7 |
+---------------+---------------+
.. method:: mapPriority(levelname)
Maps a logging level name to a syslog priority name.
You may need to override this if you are using custom levels, or
if the default algorithm is not suitable for your needs. The
default algorithm maps ``DEBUG``, ``INFO``, ``WARNING``, ``ERROR`` and
``CRITICAL`` to the equivalent syslog names, and all other level
names to 'warning'.
.. _nt-eventlog-handler:
NTEventLogHandler
^^^^^^^^^^^^^^^^^
The :class:`NTEventLogHandler` class, located in the :mod:`logging.handlers`
module, supports sending logging messages to a local Windows NT, Windows 2000 or
Windows XP event log. Before you can use it, you need Mark Hammond's Win32
extensions for Python installed.
.. class:: NTEventLogHandler(appname, dllname=None, logtype='Application')
Returns a new instance of the :class:`NTEventLogHandler` class. The *appname* is
used to define the application name as it appears in the event log. An
appropriate registry entry is created using this name. The *dllname* should give
the fully qualified pathname of a .dll or .exe which contains message
definitions to hold in the log (if not specified, ``'win32service.pyd'`` is used
- this is installed with the Win32 extensions and contains some basic
placeholder message definitions. Note that use of these placeholders will make
your event logs big, as the entire message source is held in the log. If you
want slimmer logs, you have to pass in the name of your own .dll or .exe which
contains the message definitions you want to use in the event log). The
*logtype* is one of ``'Application'``, ``'System'`` or ``'Security'``, and
defaults to ``'Application'``.
.. method:: close()
At this point, you can remove the application name from the registry as a
source of event log entries. However, if you do this, you will not be able
to see the events as you intended in the Event Log Viewer - it needs to be
able to access the registry to get the .dll name. The current version does
not do this.
.. method:: emit(record)
Determines the message ID, event category and event type, and then logs
the message in the NT event log.
.. method:: getEventCategory(record)
Returns the event category for the record. Override this if you want to
specify your own categories. This version returns 0.
.. method:: getEventType(record)
Returns the event type for the record. Override this if you want to
specify your own types. This version does a mapping using the handler's
typemap attribute, which is set up in :meth:`__init__` to a dictionary
which contains mappings for :const:`DEBUG`, :const:`INFO`,
:const:`WARNING`, :const:`ERROR` and :const:`CRITICAL`. If you are using
your own levels, you will either need to override this method or place a
suitable dictionary in the handler's *typemap* attribute.
.. method:: getMessageID(record)
Returns the message ID for the record. If you are using your own messages,
you could do this by having the *msg* passed to the logger being an ID
rather than a format string. Then, in here, you could use a dictionary
lookup to get the message ID. This version returns 1, which is the base
message ID in :file:`win32service.pyd`.
.. _smtp-handler:
SMTPHandler
^^^^^^^^^^^
The :class:`SMTPHandler` class, located in the :mod:`logging.handlers` module,
supports sending logging messages to an email address via SMTP.
.. class:: SMTPHandler(mailhost, fromaddr, toaddrs, subject, credentials=None)
Returns a new instance of the :class:`SMTPHandler` class. The instance is
initialized with the from and to addresses and subject line of the email. The
*toaddrs* should be a list of strings. To specify a non-standard SMTP port, use
the (host, port) tuple format for the *mailhost* argument. If you use a string,
the standard SMTP port is used. If your SMTP server requires authentication, you
can specify a (username, password) tuple for the *credentials* argument.
.. method:: emit(record)
Formats the record and sends it to the specified addressees.
.. method:: getSubject(record)
If you want to specify a subject line which is record-dependent, override
this method.
.. _memory-handler:
MemoryHandler
^^^^^^^^^^^^^
The :class:`MemoryHandler` class, located in the :mod:`logging.handlers` module,
supports buffering of logging records in memory, periodically flushing them to a
:dfn:`target` handler. Flushing occurs whenever the buffer is full, or when an
event of a certain severity or greater is seen.
:class:`MemoryHandler` is a subclass of the more general
:class:`BufferingHandler`, which is an abstract class. This buffers logging
records in memory. Whenever each record is added to the buffer, a check is made
by calling :meth:`shouldFlush` to see if the buffer should be flushed. If it
should, then :meth:`flush` is expected to do the needful.
.. class:: BufferingHandler(capacity)
Initializes the handler with a buffer of the specified capacity.
.. method:: emit(record)
Appends the record to the buffer. If :meth:`shouldFlush` returns true,
calls :meth:`flush` to process the buffer.
.. method:: flush()
You can override this to implement custom flushing behavior. This version
just zaps the buffer to empty.
.. method:: shouldFlush(record)
Returns true if the buffer is up to capacity. This method can be
overridden to implement custom flushing strategies.
.. class:: MemoryHandler(capacity, flushLevel=ERROR, target=None)
Returns a new instance of the :class:`MemoryHandler` class. The instance is
initialized with a buffer size of *capacity*. If *flushLevel* is not specified,
:const:`ERROR` is used. If no *target* is specified, the target will need to be
set using :meth:`setTarget` before this handler does anything useful.
.. method:: close()
Calls :meth:`flush`, sets the target to :const:`None` and clears the
buffer.
.. method:: flush()
For a :class:`MemoryHandler`, flushing means just sending the buffered
records to the target, if there is one. The buffer is also cleared when
this happens. Override if you want different behavior.
.. method:: setTarget(target)
Sets the target handler for this handler.
.. method:: shouldFlush(record)
Checks for buffer full or a record at the *flushLevel* or higher.
.. _http-handler:
HTTPHandler
^^^^^^^^^^^
The :class:`HTTPHandler` class, located in the :mod:`logging.handlers` module,
supports sending logging messages to a Web server, using either ``GET`` or
``POST`` semantics.
.. class:: HTTPHandler(host, url, method='GET', secure=False, credentials=None)
Returns a new instance of the :class:`HTTPHandler` class. The *host* can be
of the form ``host:port``, should you need to use a specific port number.
If no *method* is specified, ``GET`` is used. If *secure* is True, an HTTPS
connection will be used. If *credentials* is specified, it should be a
2-tuple consisting of userid and password, which will be placed in an HTTP
'Authorization' header using Basic authentication. If you specify
credentials, you should also specify secure=True so that your userid and
password are not passed in cleartext across the wire.
.. method:: emit(record)
Sends the record to the Web server as a percent-encoded dictionary.
.. _queue-handler:
QueueHandler
^^^^^^^^^^^^
.. versionadded:: 3.2
The :class:`QueueHandler` class, located in the :mod:`logging.handlers` module,
supports sending logging messages to a queue, such as those implemented in the
:mod:`queue` or :mod:`multiprocessing` modules.
Along with the :class:`QueueListener` class, :class:`QueueHandler` can be used
to let handlers do their work on a separate thread from the one which does the
logging. This is important in Web applications and also other service
applications where threads servicing clients need to respond as quickly as
possible, while any potentially slow operations (such as sending an email via
:class:`SMTPHandler`) are done on a separate thread.
.. class:: QueueHandler(queue)
Returns a new instance of the :class:`QueueHandler` class. The instance is
initialized with the queue to send messages to. The queue can be any queue-
like object; it's used as-is by the :meth:`enqueue` method, which needs
to know how to send messages to it.
.. method:: emit(record)
Enqueues the result of preparing the LogRecord.
.. method:: prepare(record)
Prepares a record for queuing. The object returned by this
method is enqueued.
The base implementation formats the record to merge the message
and arguments, and removes unpickleable items from the record
in-place.
You might want to override this method if you want to convert
the record to a dict or JSON string, or send a modified copy
of the record while leaving the original intact.
.. method:: enqueue(record)
Enqueues the record on the queue using ``put_nowait()``; you may
want to override this if you want to use blocking behaviour, or a
timeout, or a customised queue implementation.
.. queue-listener:
QueueListener
^^^^^^^^^^^^^
.. versionadded:: 3.2
The :class:`QueueListener` class, located in the :mod:`logging.handlers`
module, supports receiving logging messages from a queue, such as those
implemented in the :mod:`queue` or :mod:`multiprocessing` modules. The
messages are received from a queue in an internal thread and passed, on
the same thread, to one or more handlers for processing. While
:class:`QueueListener` is not itself a handler, it is documented here
because it works hand-in-hand with :class:`QueueHandler`.
Along with the :class:`QueueHandler` class, :class:`QueueListener` can be used
to let handlers do their work on a separate thread from the one which does the
logging. This is important in Web applications and also other service
applications where threads servicing clients need to respond as quickly as
possible, while any potentially slow operations (such as sending an email via
:class:`SMTPHandler`) are done on a separate thread.
.. class:: QueueListener(queue, *handlers)
Returns a new instance of the :class:`QueueListener` class. The instance is
initialized with the queue to send messages to and a list of handlers which
will handle entries placed on the queue. The queue can be any queue-
like object; it's passed as-is to the :meth:`dequeue` method, which needs
to know how to get messages from it.
.. method:: dequeue(block)
Dequeues a record and return it, optionally blocking.
The base implementation uses ``get()``. You may want to override this
method if you want to use timeouts or work with custom queue
implementations.
.. method:: prepare(record)
Prepare a record for handling.
This implementation just returns the passed-in record. You may want to
override this method if you need to do any custom marshalling or
manipulation of the record before passing it to the handlers.
.. method:: handle(record)
Handle a record.
This just loops through the handlers offering them the record
to handle. The actual object passed to the handlers is that which
is returned from :meth:`prepare`.
.. method:: start()
Starts the listener.
This starts up a background thread to monitor the queue for
LogRecords to process.
.. method:: stop()
Stops the listener.
This asks the thread to terminate, and then waits for it to do so.
Note that if you don't call this before your application exits, there
may be some records still left on the queue, which won't be processed.
.. seealso::
Module :mod:`logging`
API reference for the logging module.
Module :mod:`logging.config`
Configuration API for the logging module.

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