
``logging`` --- Logging facility for Python
*******************************************

New in version 2.3.

This module defines functions and classes which implement a flexible
error logging system for applications.

Logging is performed by calling methods on instances of the ``Logger``
class (hereafter called *loggers*). Each instance has a name, and they
are conceptually arranged in a namespace hierarchy using dots
(periods) as separators. For example, a logger named "scan" is the
parent of loggers "scan.text", "scan.html" and "scan.pdf". Logger
names can be anything you want, and indicate the area of an
application in which a logged message originates.

Logged messages also have levels of importance associated with them.
The default levels provided are ``DEBUG``, ``INFO``, ``WARNING``,
``ERROR`` and ``CRITICAL``. As a convenience, you indicate the
importance of a logged message by calling an appropriate method of
``Logger``. The methods are ``debug()``, ``info()``, ``warning()``,
``error()`` and ``critical()``, which mirror the default levels. You
are not constrained to use these levels: you can specify your own and
use a more general ``Logger`` method, ``log()``, which takes an
explicit level argument.


Logging tutorial
================

The key benefit of having the logging API provided by a standard
library module is that all Python modules can participate in logging,
so your application log can include messages from third-party modules.

It is, of course, possible to log messages with different verbosity
levels or to different destinations.  Support for writing log messages
to files, HTTP GET/POST locations, email via SMTP, generic sockets, or
OS-specific logging mechanisms are all supported by the standard
module.  You can also create your own log destination class if you
have special requirements not met by any of the built-in classes.


Simple examples
---------------

Most applications are probably going to want to log to a file, so
let's start with that case. Using the ``basicConfig()`` function, we
can set up the default handler so that debug messages are written to a
file (in the example, we assume that you have the appropriate
permissions to create a file called *example.log* in the current
directory):

   import logging
   LOG_FILENAME = 'example.log'
   logging.basicConfig(filename=LOG_FILENAME,level=logging.DEBUG)

   logging.debug('This message should go to the log file')

And now if we open the file and look at what we have, we should find
the log message:

   DEBUG:root:This message should go to the log file

If you run the script repeatedly, the additional log messages are
appended to the file.  To create a new file each time, you can pass a
*filemode* argument to ``basicConfig()`` with a value of ``'w'``.
Rather than managing the file size yourself, though, it is simpler to
use a ``RotatingFileHandler``:

   import glob
   import logging
   import logging.handlers

   LOG_FILENAME = 'logging_rotatingfile_example.out'

   # Set up a specific logger with our desired output level
   my_logger = logging.getLogger('MyLogger')
   my_logger.setLevel(logging.DEBUG)

   # Add the log message handler to the logger
   handler = logging.handlers.RotatingFileHandler(
                 LOG_FILENAME, maxBytes=20, backupCount=5)

   my_logger.addHandler(handler)

   # Log some messages
   for i in range(20):
       my_logger.debug('i = %d' % i)

   # See what files are created
   logfiles = glob.glob('%s*' % LOG_FILENAME)

   for filename in logfiles:
       print filename

The result should be 6 separate files, each with part of the log
history for the application:

   logging_rotatingfile_example.out
   logging_rotatingfile_example.out.1
   logging_rotatingfile_example.out.2
   logging_rotatingfile_example.out.3
   logging_rotatingfile_example.out.4
   logging_rotatingfile_example.out.5

The most current file is always ``logging_rotatingfile_example.out``,
and each time it reaches the size limit it is renamed with the suffix
``.1``. Each of the existing backup files is renamed to increment the
suffix (``.1`` becomes ``.2``, etc.)  and the ``.6`` file is erased.

Obviously this example sets the log length much much too small as an
extreme example.  You would want to set *maxBytes* to an appropriate
value.

Another useful feature of the logging API is the ability to produce
different messages at different log levels.  This allows you to
instrument your code with debug messages, for example, but turning the
log level down so that those debug messages are not written for your
production system.  The default levels are ``NOTSET``, ``DEBUG``,
``INFO``, ``WARNING``, ``ERROR`` and ``CRITICAL``.

The logger, handler, and log message call each specify a level.  The
log message is only emitted if the handler and logger are configured
to emit messages of that level or lower.  For example, if a message is
``CRITICAL``, and the logger is set to ``ERROR``, the message is
emitted.  If a message is a ``WARNING``, and the logger is set to
produce only ``ERROR``s, the message is not emitted:

   import logging
   import sys

   LEVELS = {'debug': logging.DEBUG,
             'info': logging.INFO,
             'warning': logging.WARNING,
             'error': logging.ERROR,
             'critical': logging.CRITICAL}

   if len(sys.argv) > 1:
       level_name = sys.argv[1]
       level = LEVELS.get(level_name, logging.NOTSET)
       logging.basicConfig(level=level)

   logging.debug('This is a debug message')
   logging.info('This is an info message')
   logging.warning('This is a warning message')
   logging.error('This is an error message')
   logging.critical('This is a critical error message')

Run the script with an argument like 'debug' or 'warning' to see which
messages show up at different levels:

   $ python logging_level_example.py debug
   DEBUG:root:This is a debug message
   INFO:root:This is an info message
   WARNING:root:This is a warning message
   ERROR:root:This is an error message
   CRITICAL:root:This is a critical error message

   $ python logging_level_example.py info
   INFO:root:This is an info message
   WARNING:root:This is a warning message
   ERROR:root:This is an error message
   CRITICAL:root:This is a critical error message

You will notice that these log messages all have ``root`` embedded in
them.  The logging module supports a hierarchy of loggers with
different names.  An easy way to tell where a specific log message
comes from is to use a separate logger object for each of your
modules.  Each new logger "inherits" the configuration of its parent,
and log messages sent to a logger include the name of that logger.
Optionally, each logger can be configured differently, so that
messages from different modules are handled in different ways.  Let's
look at a simple example of how to log from different modules so it is
easy to trace the source of the message:

   import logging

   logging.basicConfig(level=logging.WARNING)

   logger1 = logging.getLogger('package1.module1')
   logger2 = logging.getLogger('package2.module2')

   logger1.warning('This message comes from one module')
   logger2.warning('And this message comes from another module')

And the output:

   $ python logging_modules_example.py
   WARNING:package1.module1:This message comes from one module
   WARNING:package2.module2:And this message comes from another module

There are many more options for configuring logging, including
different log message formatting options, having messages delivered to
multiple destinations, and changing the configuration of a long-
running application on the fly using a socket interface.  All of these
options are covered in depth in the library module documentation.


Loggers
-------

The logging library takes a modular approach and offers the several
categories of components: loggers, handlers, filters, and formatters.
Loggers expose the interface that application code directly uses.
Handlers send the log records to the appropriate destination. Filters
provide a finer grained facility for determining which log records to
send on to a handler.  Formatters specify the layout of the resultant
log record.

``Logger`` objects have a threefold job.  First, they expose several
methods to application code so that applications can log messages at
runtime. Second, logger objects determine which log messages to act
upon based upon severity (the default filtering facility) or filter
objects.  Third, logger objects pass along relevant log messages to
all interested log handlers.

The most widely used methods on logger objects fall into two
categories: configuration and message sending.

* ``Logger.setLevel()`` specifies the lowest-severity log message a
  logger will handle, where debug is the lowest built-in severity
  level and critical is the highest built-in severity.  For example,
  if the severity level is info, the logger will handle only info,
  warning, error, and critical messages and will ignore debug
  messages.

* ``Logger.addFilter()`` and ``Logger.removeFilter()`` add and remove
  filter objects from the logger object.  This tutorial does not
  address filters.

With the logger object configured, the following methods create log
messages:

* ``Logger.debug()``, ``Logger.info()``, ``Logger.warning()``,
  ``Logger.error()``, and ``Logger.critical()`` all create log records
  with a message and a level that corresponds to their respective
  method names. The message is actually a format string, which may
  contain the standard string substitution syntax of ``%s``, ``%d``,
  ``%f``, and so on.  The rest of their arguments is a list of objects
  that correspond with the substitution fields in the message.  With
  regard to ``**kwargs``, the logging methods care only about a
  keyword of ``exc_info`` and use it to determine whether to log
  exception information.

* ``Logger.exception()`` creates a log message similar to
  ``Logger.error()``.  The difference is that ``Logger.exception()``
  dumps a stack trace along with it.  Call this method only from an
  exception handler.

* ``Logger.log()`` takes a log level as an explicit argument.  This is
  a little more verbose for logging messages than using the log level
  convenience methods listed above, but this is how to log at custom
  log levels.

``getLogger()`` returns a reference to a logger instance with the
specified name if it is provided, or ``root`` if not.  The names are
period-separated hierarchical structures.  Multiple calls to
``getLogger()`` with the same name will return a reference to the same
logger object.  Loggers that are further down in the hierarchical list
are children of loggers higher up in the list. For example, given a
logger with a name of ``foo``, loggers with names of ``foo.bar``,
``foo.bar.baz``, and ``foo.bam`` are all descendants of ``foo``. Child
loggers propagate messages up to the handlers associated with their
ancestor loggers.  Because of this, it is unnecessary to define and
configure handlers for all the loggers an application uses. It is
sufficient to configure handlers for a top-level logger and create
child loggers as needed.


Handlers
--------

``Handler`` objects are responsible for dispatching the appropriate
log messages (based on the log messages' severity) to the handler's
specified destination.  Logger objects can add zero or more handler
objects to themselves with an ``addHandler()`` method.  As an example
scenario, an application may want to send all log messages to a log
file, all log messages of error or higher to stdout, and all messages
of critical to an email address.  This scenario requires three
individual handlers where each handler is responsible for sending
messages of a specific severity to a specific location.

The standard library includes quite a few handler types; this tutorial
uses only ``StreamHandler`` and ``FileHandler`` in its examples.

There are very few methods in a handler for application developers to
concern themselves with.  The only handler methods that seem relevant
for application developers who are using the built-in handler objects
(that is, not creating custom handlers) are the following
configuration methods:

* The ``Handler.setLevel()`` method, just as in logger objects,
  specifies the lowest severity that will be dispatched to the
  appropriate destination.  Why are there two ``setLevel()`` methods?
  The level set in the logger determines which severity of messages it
  will pass to its handlers.  The level set in each handler determines
  which messages that handler will send on.

* ``setFormatter()`` selects a Formatter object for this handler to
  use.

* ``addFilter()`` and ``removeFilter()`` respectively configure and
  deconfigure filter objects on handlers.

Application code should not directly instantiate and use instances of
``Handler``.  Instead, the ``Handler`` class is a base class that
defines the interface that all handlers should have and establishes
some default behavior that child classes can use (or override).


Formatters
----------

Formatter objects configure the final order, structure, and contents
of the log message.  Unlike the base ``logging.Handler`` class,
application code may instantiate formatter classes, although you could
likely subclass the formatter if your application needs special
behavior.  The constructor takes two optional arguments: a message
format string and a date format string.  If there is no message format
string, the default is to use the raw message.  If there is no date
format string, the default date format is:

   %Y-%m-%d %H:%M:%S

with the milliseconds tacked on at the end.

The message format string uses ``%(<dictionary key>)s`` styled string
substitution; the possible keys are documented in *Formatter Objects*.

The following message format string will log the time in a human-
readable format, the severity of the message, and the contents of the
message, in that order:

   "%(asctime)s - %(levelname)s - %(message)s"

Formatters use a user-configurable function to convert the creation
time of a record to a tuple. By default, ``time.localtime()`` is used;
to change this for a particular formatter instance, set the
``converter`` attribute of the instance to a function with the same
signature as ``time.localtime()`` or ``time.gmtime()``. To change it
for all formatters, for example if you want all logging times to be
shown in GMT, set the ``converter`` attribute in the Formatter class
(to ``time.gmtime`` for GMT display).


Configuring Logging
-------------------

Programmers can configure logging in three ways:

1. Creating loggers, handlers, and formatters explicitly using Python
   code that calls the configuration methods listed above.

2. Creating a logging config file and reading it using the
   ``fileConfig()`` function.

3. Creating a dictionary of configuration information and passing it
   to the ``dictConfig()`` function.

The following example configures a very simple logger, a console
handler, and a simple formatter using Python code:

   import logging

   # create logger
   logger = logging.getLogger("simple_example")
   logger.setLevel(logging.DEBUG)

   # create console handler and set level to debug
   ch = logging.StreamHandler()
   ch.setLevel(logging.DEBUG)

   # create formatter
   formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")

   # add formatter to ch
   ch.setFormatter(formatter)

   # add ch to logger
   logger.addHandler(ch)

   # "application" code
   logger.debug("debug message")
   logger.info("info message")
   logger.warn("warn message")
   logger.error("error message")
   logger.critical("critical message")

Running this module from the command line produces the following
output:

   $ python simple_logging_module.py
   2005-03-19 15:10:26,618 - simple_example - DEBUG - debug message
   2005-03-19 15:10:26,620 - simple_example - INFO - info message
   2005-03-19 15:10:26,695 - simple_example - WARNING - warn message
   2005-03-19 15:10:26,697 - simple_example - ERROR - error message
   2005-03-19 15:10:26,773 - simple_example - CRITICAL - critical message

The following Python module creates a logger, handler, and formatter
nearly identical to those in the example listed above, with the only
difference being the names of the objects:

   import logging
   import logging.config

   logging.config.fileConfig("logging.conf")

   # create logger
   logger = logging.getLogger("simpleExample")

   # "application" code
   logger.debug("debug message")
   logger.info("info message")
   logger.warn("warn message")
   logger.error("error message")
   logger.critical("critical message")

Here is the logging.conf file:

   [loggers]
   keys=root,simpleExample

   [handlers]
   keys=consoleHandler

   [formatters]
   keys=simpleFormatter

   [logger_root]
   level=DEBUG
   handlers=consoleHandler

   [logger_simpleExample]
   level=DEBUG
   handlers=consoleHandler
   qualname=simpleExample
   propagate=0

   [handler_consoleHandler]
   class=StreamHandler
   level=DEBUG
   formatter=simpleFormatter
   args=(sys.stdout,)

   [formatter_simpleFormatter]
   format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
   datefmt=

The output is nearly identical to that of the non-config-file-based
example:

   $ python simple_logging_config.py
   2005-03-19 15:38:55,977 - simpleExample - DEBUG - debug message
   2005-03-19 15:38:55,979 - simpleExample - INFO - info message
   2005-03-19 15:38:56,054 - simpleExample - WARNING - warn message
   2005-03-19 15:38:56,055 - simpleExample - ERROR - error message
   2005-03-19 15:38:56,130 - simpleExample - CRITICAL - critical message

You can see that the config file approach has a few advantages over
the Python code approach, mainly separation of configuration and code
and the ability of noncoders to easily modify the logging properties.

Note that the class names referenced in config files need to be either
relative to the logging module, or absolute values which can be
resolved using normal import mechanisms. Thus, you could use either
``handlers.WatchedFileHandler`` (relative to the logging module) or
``mypackage.mymodule.MyHandler`` (for a class defined in package
``mypackage`` and module ``mymodule``, where ``mypackage`` is
available on the Python import path).

Changed in version 2.7.

In Python 2.7, a new means of configuring logging has been introduced,
using dictionaries to hold configuration information. This provides a
superset of the functionality of the config-file-based approach
outlined above, and is the recommended configuration method for new
applications and deployments. Because a Python dictionary is used to
hold configuration information, and since you can populate that
dictionary using different means, you have more options for
configuration. For example, you can use a configuration file in JSON
format, or, if you have access to YAML processing functionality, a
file in YAML format, to populate the configuration dictionary. Or, of
course, you can construct the dictionary in Python code, receive it in
pickled form over a socket, or use whatever approach makes sense for
your application.

Here's an example of the same configuration as above, in YAML format
for the new dictionary-based approach:

   version: 1
   formatters:
     simple:
       format: format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
   handlers:
     console:
       class: logging.StreamHandler
       level: DEBUG
       formatter: simple
       stream: ext://sys.stdout
   loggers:
     simpleExample:
       level: DEBUG
       handlers: [console]
       propagate: no
   root:
       level: DEBUG
       handlers: [console]

For more information about logging using a dictionary, see
*Configuration functions*.


Configuring Logging for a Library
---------------------------------

When developing a library which uses logging, some consideration needs
to be given to its configuration. If the using application does not
use logging, and library code makes logging calls, then a one-off
message "No handlers could be found for logger X.Y.Z" is printed to
the console. This message is intended to catch mistakes in logging
configuration, but will confuse an application developer who is not
aware of logging by the library.

In addition to documenting how a library uses logging, a good way to
configure library logging so that it does not cause a spurious message
is to add a handler which does nothing. This avoids the message being
printed, since a handler will be found: it just doesn't produce any
output. If the library user configures logging for application use,
presumably that configuration will add some handlers, and if levels
are suitably configured then logging calls made in library code will
send output to those handlers, as normal.

A do-nothing handler can be simply defined as follows:

   import logging

   class NullHandler(logging.Handler):
       def emit(self, record):
           pass

An instance of this handler should be added to the top-level logger of
the logging namespace used by the library. If all logging by a library
*foo* is done using loggers with names matching "foo.x.y", then the
code:

   import logging

   h = NullHandler()
   logging.getLogger("foo").addHandler(h)

should have the desired effect. If an organisation produces a number
of libraries, then the logger name specified can be "orgname.foo"
rather than just "foo".

**PLEASE NOTE:** It is strongly advised that you *do not add any
handlers other than* ``NullHandler`` *to your library's loggers*. This
is because the configuration of handlers is the prerogative of the
application developer who uses your library. The application developer
knows their target audience and what handlers are most appropriate for
their application: if you add handlers "under the hood", you might
well interfere with their ability to carry out unit tests and deliver
logs which suit their requirements.

New in version 2.7.

The ``NullHandler`` class was not present in previous versions, but is
now included, so that it need not be defined in library code.


Logging Levels
==============

The numeric values of logging levels are given in the following table.
These are primarily of interest if you want to define your own levels,
and need them to have specific values relative to the predefined
levels. If you define a level with the same numeric value, it
overwrites the predefined value; the predefined name is lost.

+----------------+-----------------+
| Level          | Numeric value   |
+================+=================+
| ``CRITICAL``   | 50              |
+----------------+-----------------+
| ``ERROR``      | 40              |
+----------------+-----------------+
| ``WARNING``    | 30              |
+----------------+-----------------+
| ``INFO``       | 20              |
+----------------+-----------------+
| ``DEBUG``      | 10              |
+----------------+-----------------+
| ``NOTSET``     | 0               |
+----------------+-----------------+

Levels can also be associated with loggers, being set either by the
developer or through loading a saved logging configuration. When a
logging method is called on a logger, the logger compares its own
level with the level associated with the method call. If the logger's
level is higher than the method call's, no logging message is actually
generated. This is the basic mechanism controlling the verbosity of
logging output.

Logging messages are encoded as instances of the ``LogRecord`` class.
When a logger decides to actually log an event, a ``LogRecord``
instance is created from the logging message.

Logging messages are subjected to a dispatch mechanism through the use
of *handlers*, which are instances of subclasses of the ``Handler``
class. Handlers are responsible for ensuring that a logged message (in
the form of a ``LogRecord``) ends up in a particular location (or set
of locations) which is useful for the target audience for that message
(such as end users, support desk staff, system administrators,
developers). Handlers are passed ``LogRecord`` instances intended for
particular destinations. Each logger can have zero, one or more
handlers associated with it (via the ``addHandler()`` method of
``Logger``). In addition to any handlers directly associated with a
logger, *all handlers associated with all ancestors of the logger* are
called to dispatch the message (unless the *propagate* flag for a
logger is set to a false value, at which point the passing to ancestor
handlers stops).

Just as for loggers, handlers can have levels associated with them. A
handler's level acts as a filter in the same way as a logger's level
does. If a handler decides to actually dispatch an event, the
``emit()`` method is used to send the message to its destination. Most
user-defined subclasses of ``Handler`` will need to override this
``emit()``.


Custom Levels
-------------

Defining your own levels is possible, but should not be necessary, as
the existing levels have been chosen on the basis of practical
experience. However, if you are convinced that you need custom levels,
great care should be exercised when doing this, and it is possibly *a
very bad idea to define custom levels if you are developing a
library*. That's because if multiple library authors all define their
own custom levels, there is a chance that the logging output from such
multiple libraries used together will be difficult for the using
developer to control and/or interpret, because a given numeric value
might mean different things for different libraries.


Useful Handlers
===============

In addition to the base ``Handler`` class, many useful subclasses are
provided:

1. *StreamHandler* instances send error messages to streams (file-like
   objects).

2. *FileHandler* instances send error messages to disk files.

3. ``BaseRotatingHandler`` is the base class for handlers that rotate
   log files at a certain point. It is not meant to be  instantiated
   directly. Instead, use *RotatingFileHandler* or
   *TimedRotatingFileHandler*.

4. *RotatingFileHandler* instances send error messages to disk files,
   with support for maximum log file sizes and log file rotation.

5. *TimedRotatingFileHandler* instances send error messages to disk
   files, rotating the log file at certain timed intervals.

6. *SocketHandler* instances send error messages to TCP/IP sockets.

7. *DatagramHandler* instances send error messages to UDP sockets.

8. *SMTPHandler* instances send error messages to a designated email
   address.

9. *SysLogHandler* instances send error messages to a Unix syslog
   daemon, possibly on a remote machine.

10. *NTEventLogHandler* instances send error messages to a Windows
    NT/2000/XP event log.

11. *MemoryHandler* instances send error messages to a buffer in
    memory, which is flushed whenever specific criteria are met.

12. *HTTPHandler* instances send error messages to an HTTP server
    using either ``GET`` or ``POST`` semantics.

13. *WatchedFileHandler* instances watch the file they are logging to.
    If the file changes, it is closed and reopened using the file
    name. This handler is only useful on Unix-like systems; Windows
    does not support the underlying mechanism used.

14. *NullHandler* instances do nothing with error messages. They are
    used by library developers who want to use logging, but want to
    avoid the "No handlers could be found for logger XXX" message
    which can be displayed if the library user has not configured
    logging. See *Configuring Logging for a Library* for more
    information.

New in version 2.7.

The ``NullHandler`` class was not present in previous versions.

The ``NullHandler``, ``StreamHandler`` and ``FileHandler`` classes are
defined in the core logging package. The other handlers are defined in
a sub- module, ``logging.handlers``. (There is also another sub-
module, ``logging.config``, for configuration functionality.)

Logged messages are formatted for presentation through instances of
the ``Formatter`` class. They are initialized with a format string
suitable for use with the % operator and a dictionary.

For formatting multiple messages in a batch, instances of
``BufferingFormatter`` can be used. In addition to the format string
(which is applied to each message in the batch), there is provision
for header and trailer format strings.

When filtering based on logger level and/or handler level is not
enough, instances of ``Filter`` can be added to both ``Logger`` and
``Handler`` instances (through their ``addFilter()`` method). Before
deciding to process a message further, both loggers and handlers
consult all their filters for permission. If any filter returns a
false value, the message is not processed further.

The basic ``Filter`` functionality allows filtering by specific logger
name. If this feature is used, messages sent to the named logger and
its children are allowed through the filter, and all others dropped.


Module-Level Functions
======================

In addition to the classes described above, there are a number of
module- level functions.

logging.getLogger([name])

   Return a logger with the specified name or, if no name is
   specified, return a logger which is the root logger of the
   hierarchy. If specified, the name is typically a dot-separated
   hierarchical name like *"a"*, *"a.b"* or *"a.b.c.d"*. Choice of
   these names is entirely up to the developer who is using logging.

   All calls to this function with a given name return the same logger
   instance. This means that logger instances never need to be passed
   between different parts of an application.

logging.getLoggerClass()

   Return either the standard ``Logger`` class, or the last class
   passed to ``setLoggerClass()``. This function may be called from
   within a new class definition, to ensure that installing a
   customised ``Logger`` class will not undo customisations already
   applied by other code. For example:

      class MyLogger(logging.getLoggerClass()):
          # ... override behaviour here

logging.debug(msg[, *args[, **kwargs]])

   Logs a message with level ``DEBUG`` on the root logger. The *msg*
   is the message format string, and the *args* are the arguments
   which are merged into *msg* using the string formatting operator.
   (Note that this means that you can use keywords in the format
   string, together with a single dictionary argument.)

   There are two keyword arguments in *kwargs* which are inspected:
   *exc_info* which, if it does not evaluate as false, causes
   exception information to be added to the logging message. If an
   exception tuple (in the format returned by ``sys.exc_info()``) is
   provided, it is used; otherwise, ``sys.exc_info()`` is called to
   get the exception information.

   The other optional keyword argument is *extra* which can be used to
   pass a dictionary which is used to populate the __dict__ of the
   LogRecord created for the logging event with user-defined
   attributes. These custom attributes can then be used as you like.
   For example, they could be incorporated into logged messages. For
   example:

      FORMAT = "%(asctime)-15s %(clientip)s %(user)-8s %(message)s"
      logging.basicConfig(format=FORMAT)
      d = {'clientip': '192.168.0.1', 'user': 'fbloggs'}
      logging.warning("Protocol problem: %s", "connection reset", extra=d)

   would print something like:

      2006-02-08 22:20:02,165 192.168.0.1 fbloggs  Protocol problem: connection reset

   The keys in the dictionary passed in *extra* should not clash with
   the keys used by the logging system. (See the ``Formatter``
   documentation for more information on which keys are used by the
   logging system.)

   If you choose to use these attributes in logged messages, you need
   to exercise some care. In the above example, for instance, the
   ``Formatter`` has been set up with a format string which expects
   'clientip' and 'user' in the attribute dictionary of the LogRecord.
   If these are missing, the message will not be logged because a
   string formatting exception will occur. So in this case, you always
   need to pass the *extra* dictionary with these keys.

   While this might be annoying, this feature is intended for use in
   specialized circumstances, such as multi-threaded servers where the
   same code executes in many contexts, and interesting conditions
   which arise are dependent on this context (such as remote client IP
   address and authenticated user name, in the above example). In such
   circumstances, it is likely that specialized ``Formatter``s would
   be used with particular ``Handler``s.

   Changed in version 2.5: *extra* was added.

logging.info(msg[, *args[, **kwargs]])

   Logs a message with level ``INFO`` on the root logger. The
   arguments are interpreted as for ``debug()``.

logging.warning(msg[, *args[, **kwargs]])

   Logs a message with level ``WARNING`` on the root logger. The
   arguments are interpreted as for ``debug()``.

logging.error(msg[, *args[, **kwargs]])

   Logs a message with level ``ERROR`` on the root logger. The
   arguments are interpreted as for ``debug()``.

logging.critical(msg[, *args[, **kwargs]])

   Logs a message with level ``CRITICAL`` on the root logger. The
   arguments are interpreted as for ``debug()``.

logging.exception(msg[, *args])

   Logs a message with level ``ERROR`` on the root logger. The
   arguments are interpreted as for ``debug()``. Exception info is
   added to the logging message. This function should only be called
   from an exception handler.

logging.log(level, msg[, *args[, **kwargs]])

   Logs a message with level *level* on the root logger. The other
   arguments are interpreted as for ``debug()``.

   PLEASE NOTE: The above module-level functions which delegate to the
   root logger should *not* be used in threads, in versions of Python
   earlier than 2.7.1 and 3.2, unless at least one handler has been
   added to the root logger *before* the threads are started. These
   convenience functions call ``basicConfig()`` to ensure that at
   least one handler is available; in earlier versions of Python, this
   can (under rare circumstances) lead to handlers being added
   multiple times to the root logger, which can in turn lead to
   multiple messages for the same event.

logging.disable(lvl)

   Provides an overriding level *lvl* for all loggers which takes
   precedence over the logger's own level. When the need arises to
   temporarily throttle logging output down across the whole
   application, this function can be useful. Its effect is to disable
   all logging calls of severity *lvl* and below, so that if you call
   it with a value of INFO, then all INFO and DEBUG events would be
   discarded, whereas those of severity WARNING and above would be
   processed according to the logger's effective level.

logging.addLevelName(lvl, levelName)

   Associates level *lvl* with text *levelName* in an internal
   dictionary, which is used to map numeric levels to a textual
   representation, for example when a ``Formatter`` formats a message.
   This function can also be used to define your own levels. The only
   constraints are that all levels used must be registered using this
   function, levels should be positive integers and they should
   increase in increasing order of severity.

   NOTE: If you are thinking of defining your own levels, please see
   the section on *Custom Levels*.

logging.getLevelName(lvl)

   Returns the textual representation of logging level *lvl*. If the
   level is one of the predefined levels ``CRITICAL``, ``ERROR``,
   ``WARNING``, ``INFO`` or ``DEBUG`` then you get the corresponding
   string. If you have associated levels with names using
   ``addLevelName()`` then the name you have associated with *lvl* is
   returned. If a numeric value corresponding to one of the defined
   levels is passed in, the corresponding string representation is
   returned. Otherwise, the string "Level %s" % lvl is returned.

logging.makeLogRecord(attrdict)

   Creates and returns a new ``LogRecord`` instance whose attributes
   are defined by *attrdict*. This function is useful for taking a
   pickled ``LogRecord`` attribute dictionary, sent over a socket, and
   reconstituting it as a ``LogRecord`` instance at the receiving end.

logging.basicConfig([**kwargs])

   Does basic configuration for the logging system by creating a
   ``StreamHandler`` with a default ``Formatter`` and adding it to the
   root logger. The functions ``debug()``, ``info()``, ``warning()``,
   ``error()`` and ``critical()`` will call ``basicConfig()``
   automatically if no handlers are defined for the root logger.

   This function does nothing if the root logger already has handlers
   configured for it.

   Changed in version 2.4: Formerly, ``basicConfig()`` did not take
   any keyword arguments.

   PLEASE NOTE: This function should be called from the main thread
   before other threads are started. In versions of Python prior to
   2.7.1 and 3.2, if this function is called from multiple threads, it
   is possible (in rare circumstances) that a handler will be added to
   the root logger more than once, leading to unexpected results such
   as messages being duplicated in the log.

   The following keyword arguments are supported.

   +----------------+-----------------------------------------------+
   | Format         | Description                                   |
   +================+===============================================+
   | ``filename``   | Specifies that a FileHandler be created,      |
   |                | using the specified filename, rather than a   |
   |                | StreamHandler.                                |
   +----------------+-----------------------------------------------+
   | ``filemode``   | Specifies the mode to open the file, if       |
   |                | filename is specified (if filemode is         |
   |                | unspecified, it defaults to 'a').             |
   +----------------+-----------------------------------------------+
   | ``format``     | Use the specified format string for the       |
   |                | handler.                                      |
   +----------------+-----------------------------------------------+
   | ``datefmt``    | Use the specified date/time format.           |
   +----------------+-----------------------------------------------+
   | ``level``      | Set the root logger level to the specified    |
   |                | level.                                        |
   +----------------+-----------------------------------------------+
   | ``stream``     | Use the specified stream to initialize the    |
   |                | StreamHandler. Note that this argument is     |
   |                | incompatible with 'filename' - if both are    |
   |                | present, 'stream' is ignored.                 |
   +----------------+-----------------------------------------------+

logging.shutdown()

   Informs the logging system to perform an orderly shutdown by
   flushing and closing all handlers. This should be called at
   application exit and no further use of the logging system should be
   made after this call.

logging.setLoggerClass(klass)

   Tells the logging system to use the class *klass* when
   instantiating a logger. The class should define ``__init__()`` such
   that only a name argument is required, and the ``__init__()``
   should call ``Logger.__init__()``. This function is typically
   called before any loggers are instantiated by applications which
   need to use custom logger behavior.

See also:

   **PEP 282** - A Logging System
      The proposal which described this feature for inclusion in the
      Python standard library.

   Original Python logging package
      This is the original source for the ``logging`` package.  The
      version of the package available from this site is suitable for
      use with Python 1.5.2, 2.1.x and 2.2.x, which do not include the
      ``logging`` package in the standard library.


Logger Objects
==============

Loggers have the following attributes and methods. Note that Loggers
are never instantiated directly, but always through the module-level
function ``logging.getLogger(name)``.

Logger.propagate

   If this evaluates to false, logging messages are not passed by this
   logger or by its child loggers to the handlers of higher level
   (ancestor) loggers. The constructor sets this attribute to 1.

Logger.setLevel(lvl)

   Sets the threshold for this logger to *lvl*. Logging messages which
   are less severe than *lvl* will be ignored. When a logger is
   created, the level is set to ``NOTSET`` (which causes all messages
   to be processed when the logger is the root logger, or delegation
   to the parent when the logger is a non-root logger). Note that the
   root logger is created with level ``WARNING``.

   The term "delegation to the parent" means that if a logger has a
   level of NOTSET, its chain of ancestor loggers is traversed until
   either an ancestor with a level other than NOTSET is found, or the
   root is reached.

   If an ancestor is found with a level other than NOTSET, then that
   ancestor's level is treated as the effective level of the logger
   where the ancestor search began, and is used to determine how a
   logging event is handled.

   If the root is reached, and it has a level of NOTSET, then all
   messages will be processed. Otherwise, the root's level will be
   used as the effective level.

Logger.isEnabledFor(lvl)

   Indicates if a message of severity *lvl* would be processed by this
   logger. This method checks first the module-level level set by
   ``logging.disable(lvl)`` and then the logger's effective level as
   determined by ``getEffectiveLevel()``.

Logger.getEffectiveLevel()

   Indicates the effective level for this logger. If a value other
   than ``NOTSET`` has been set using ``setLevel()``, it is returned.
   Otherwise, the hierarchy is traversed towards the root until a
   value other than ``NOTSET`` is found, and that value is returned.

Logger.getChild(suffix)

   Returns a logger which is a descendant to this logger, as
   determined by the suffix. Thus,
   ``logging.getLogger('abc').getChild('def.ghi')`` would return the
   same logger as would be returned by
   ``logging.getLogger('abc.def.ghi')``. This is a convenience method,
   useful when the parent logger is named using e.g. ``__name__``
   rather than a literal string.

   New in version 2.7.

Logger.debug(msg[, *args[, **kwargs]])

   Logs a message with level ``DEBUG`` on this logger. The *msg* is
   the message format string, and the *args* are the arguments which
   are merged into *msg* using the string formatting operator. (Note
   that this means that you can use keywords in the format string,
   together with a single dictionary argument.)

   There are two keyword arguments in *kwargs* which are inspected:
   *exc_info* which, if it does not evaluate as false, causes
   exception information to be added to the logging message. If an
   exception tuple (in the format returned by ``sys.exc_info()``) is
   provided, it is used; otherwise, ``sys.exc_info()`` is called to
   get the exception information.

   The other optional keyword argument is *extra* which can be used to
   pass a dictionary which is used to populate the __dict__ of the
   LogRecord created for the logging event with user-defined
   attributes. These custom attributes can then be used as you like.
   For example, they could be incorporated into logged messages. For
   example:

      FORMAT = "%(asctime)-15s %(clientip)s %(user)-8s %(message)s"
      logging.basicConfig(format=FORMAT)
      d = { 'clientip' : '192.168.0.1', 'user' : 'fbloggs' }
      logger = logging.getLogger("tcpserver")
      logger.warning("Protocol problem: %s", "connection reset", extra=d)

   would print something like

      2006-02-08 22:20:02,165 192.168.0.1 fbloggs  Protocol problem: connection reset

   The keys in the dictionary passed in *extra* should not clash with
   the keys used by the logging system. (See the ``Formatter``
   documentation for more information on which keys are used by the
   logging system.)

   If you choose to use these attributes in logged messages, you need
   to exercise some care. In the above example, for instance, the
   ``Formatter`` has been set up with a format string which expects
   'clientip' and 'user' in the attribute dictionary of the LogRecord.
   If these are missing, the message will not be logged because a
   string formatting exception will occur. So in this case, you always
   need to pass the *extra* dictionary with these keys.

   While this might be annoying, this feature is intended for use in
   specialized circumstances, such as multi-threaded servers where the
   same code executes in many contexts, and interesting conditions
   which arise are dependent on this context (such as remote client IP
   address and authenticated user name, in the above example). In such
   circumstances, it is likely that specialized ``Formatter``s would
   be used with particular ``Handler``s.

   Changed in version 2.5: *extra* was added.

Logger.info(msg[, *args[, **kwargs]])

   Logs a message with level ``INFO`` on this logger. The arguments
   are interpreted as for ``debug()``.

Logger.warning(msg[, *args[, **kwargs]])

   Logs a message with level ``WARNING`` on this logger. The arguments
   are interpreted as for ``debug()``.

Logger.error(msg[, *args[, **kwargs]])

   Logs a message with level ``ERROR`` on this logger. The arguments
   are interpreted as for ``debug()``.

Logger.critical(msg[, *args[, **kwargs]])

   Logs a message with level ``CRITICAL`` on this logger. The
   arguments are interpreted as for ``debug()``.

Logger.log(lvl, msg[, *args[, **kwargs]])

   Logs a message with integer level *lvl* on this logger. The other
   arguments are interpreted as for ``debug()``.

Logger.exception(msg[, *args])

   Logs a message with level ``ERROR`` on this logger. The arguments
   are interpreted as for ``debug()``. Exception info is added to the
   logging message. This method should only be called from an
   exception handler.

Logger.addFilter(filt)

   Adds the specified filter *filt* to this logger.

Logger.removeFilter(filt)

   Removes the specified filter *filt* from this logger.

Logger.filter(record)

   Applies this logger's filters to the record and returns a true
   value if the record is to be processed.

Logger.addHandler(hdlr)

   Adds the specified handler *hdlr* to this logger.

Logger.removeHandler(hdlr)

   Removes the specified handler *hdlr* from this logger.

Logger.findCaller()

   Finds the caller's source filename and line number. Returns the
   filename, line number and function name as a 3-element tuple.

   Changed in version 2.4: The function name was added. In earlier
   versions, the filename and line number were returned as a 2-element
   tuple..

Logger.handle(record)

   Handles a record by passing it to all handlers associated with this
   logger and its ancestors (until a false value of *propagate* is
   found). This method is used for unpickled records received from a
   socket, as well as those created locally. Logger-level filtering is
   applied using ``filter()``.

Logger.makeRecord(name, lvl, fn, lno, msg, args, exc_info[, func, extra])

   This is a factory method which can be overridden in subclasses to
   create specialized ``LogRecord`` instances.

   Changed in version 2.5: *func* and *extra* were added.


Basic example
=============

Changed in version 2.4: formerly ``basicConfig()`` did not take any
keyword arguments.

The ``logging`` package provides a lot of flexibility, and its
configuration can appear daunting.  This section demonstrates that
simple use of the logging package is possible.

The simplest example shows logging to the console:

   import logging

   logging.debug('A debug message')
   logging.info('Some information')
   logging.warning('A shot across the bows')

If you run the above script, you'll see this:

   WARNING:root:A shot across the bows

Because no particular logger was specified, the system used the root
logger. The debug and info messages didn't appear because by default,
the root logger is configured to only handle messages with a severity
of WARNING or above. The message format is also a configuration
default, as is the output destination of the messages -
``sys.stderr``. The severity level, the message format and destination
can be easily changed, as shown in the example below:

   import logging

   logging.basicConfig(level=logging.DEBUG,
                       format='%(asctime)s %(levelname)s %(message)s',
                       filename='myapp.log',
                       filemode='w')
   logging.debug('A debug message')
   logging.info('Some information')
   logging.warning('A shot across the bows')

The ``basicConfig()`` method is used to change the configuration
defaults, which results in output (written to ``myapp.log``) which
should look something like the following:

   2004-07-02 13:00:08,743 DEBUG A debug message
   2004-07-02 13:00:08,743 INFO Some information
   2004-07-02 13:00:08,743 WARNING A shot across the bows

This time, all messages with a severity of DEBUG or above were
handled, and the format of the messages was also changed, and output
went to the specified file rather than the console.

Formatting uses standard Python string formatting - see section
*String Formatting Operations*. The format string takes the following
common specifiers. For a complete list of specifiers, consult the
``Formatter`` documentation.

+---------------------+-------------------------------------------------+
| Format              | Description                                     |
+=====================+=================================================+
| ``%(name)s``        | Name of the logger (logging channel).           |
+---------------------+-------------------------------------------------+
| ``%(levelname)s``   | Text logging level for the message              |
|                     | (``'DEBUG'``, ``'INFO'``, ``'WARNING'``,        |
|                     | ``'ERROR'``, ``'CRITICAL'``).                   |
+---------------------+-------------------------------------------------+
| ``%(asctime)s``     | Human-readable time when the ``LogRecord`` was  |
|                     | created.  By default this is of the form        |
|                     | "2003-07-08 16:49:45,896" (the numbers after    |
|                     | the comma are millisecond portion of the time). |
+---------------------+-------------------------------------------------+
| ``%(message)s``     | The logged message.                             |
+---------------------+-------------------------------------------------+

To change the date/time format, you can pass an additional keyword
parameter, *datefmt*, as in the following:

   import logging

   logging.basicConfig(level=logging.DEBUG,
                       format='%(asctime)s %(levelname)-8s %(message)s',
                       datefmt='%a, %d %b %Y %H:%M:%S',
                       filename='/temp/myapp.log',
                       filemode='w')
   logging.debug('A debug message')
   logging.info('Some information')
   logging.warning('A shot across the bows')

which would result in output like

   Fri, 02 Jul 2004 13:06:18 DEBUG    A debug message
   Fri, 02 Jul 2004 13:06:18 INFO     Some information
   Fri, 02 Jul 2004 13:06:18 WARNING  A shot across the bows

The date format string follows the requirements of ``strftime()`` -
see the documentation for the ``time`` module.

If, instead of sending logging output to the console or a file, you'd
rather use a file-like object which you have created separately, you
can pass it to ``basicConfig()`` using the *stream* keyword argument.
Note that if both *stream* and *filename* keyword arguments are
passed, the *stream* argument is ignored.

Of course, you can put variable information in your output. To do
this, simply have the message be a format string and pass in
additional arguments containing the variable information, as in the
following example:

   import logging

   logging.basicConfig(level=logging.DEBUG,
                       format='%(asctime)s %(levelname)-8s %(message)s',
                       datefmt='%a, %d %b %Y %H:%M:%S',
                       filename='/temp/myapp.log',
                       filemode='w')
   logging.error('Pack my box with %d dozen %s', 5, 'liquor jugs')

which would result in

   Wed, 21 Jul 2004 15:35:16 ERROR    Pack my box with 5 dozen liquor jugs


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.


Exceptions raised during logging
================================

The logging package is designed to swallow exceptions which occur
while logging in production. This is so that errors which occur while
handling logging events - such as logging misconfiguration, network or
other similar errors - do not cause the application using logging to
terminate prematurely.

``SystemExit`` and ``KeyboardInterrupt`` exceptions are never
swallowed. Other exceptions which occur during the ``emit()`` method
of a ``Handler`` subclass are passed to its ``handleError()`` method.

The default implementation of ``handleError()`` in ``Handler`` checks
to see if a module-level variable, ``raiseExceptions``, is set. If
set, a traceback is printed to ``sys.stderr``. If not set, the
exception is swallowed.

**Note:** The default value of ``raiseExceptions`` is ``True``. This
is because during development, you typically want to be notified of
any exceptions that occur. It's advised that you set
``raiseExceptions`` to ``False`` for production usage.


Adding contextual information to your logging output
====================================================

Sometimes you want logging output to contain contextual information in
addition to the parameters passed to the logging call. For example, in
a networked application, it may be desirable to log client-specific
information in the log (e.g. remote client's username, or IP address).
Although you could use the *extra* parameter to achieve this, it's not
always convenient to pass the information in this way. While it might
be tempting to create ``Logger`` instances on a per-connection basis,
this is not a good idea because these instances are not garbage
collected. While this is not a problem in practice, when the number of
``Logger`` instances is dependent on the level of granularity you want
to use in logging an application, it could be hard to manage if the
number of ``Logger`` instances becomes effectively unbounded.


Using LoggerAdapters to impart contextual information
-----------------------------------------------------

An easy way in which you can pass contextual information to be output
along with logging event information is to use the ``LoggerAdapter``
class. This class is designed to look like a ``Logger``, so that you
can call ``debug()``, ``info()``, ``warning()``, ``error()``,
``exception()``, ``critical()`` and ``log()``. These methods have the
same signatures as their counterparts in ``Logger``, so you can use
the two types of instances interchangeably.

When you create an instance of ``LoggerAdapter``, you pass it a
``Logger`` instance and a dict-like object which contains your
contextual information. When you call one of the logging methods on an
instance of ``LoggerAdapter``, it delegates the call to the underlying
instance of ``Logger`` passed to its constructor, and arranges to pass
the contextual information in the delegated call. Here's a snippet
from the code of ``LoggerAdapter``:

   def debug(self, msg, *args, **kwargs):
       """
       Delegate a debug call to the underlying logger, after adding
       contextual information from this adapter instance.
       """
       msg, kwargs = self.process(msg, kwargs)
       self.logger.debug(msg, *args, **kwargs)

The ``process()`` method of ``LoggerAdapter`` is where the contextual
information is added to the logging output. It's passed the message
and keyword arguments of the logging call, and it passes back
(potentially) modified versions of these to use in the call to the
underlying logger. The default implementation of this method leaves
the message alone, but inserts an "extra" key in the keyword argument
whose value is the dict-like object passed to the constructor. Of
course, if you had passed an "extra" keyword argument in the call to
the adapter, it will be silently overwritten.

The advantage of using "extra" is that the values in the dict-like
object are merged into the ``LogRecord`` instance's __dict__, allowing
you to use customized strings with your ``Formatter`` instances which
know about the keys of the dict-like object. If you need a different
method, e.g. if you want to prepend or append the contextual
information to the message string, you just need to subclass
``LoggerAdapter`` and override ``process()`` to do what you need.
Here's an example script which uses this class, which also illustrates
what dict-like behaviour is needed from an arbitrary "dict-like"
object for use in the constructor:

   import logging

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

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

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

   if __name__ == "__main__":
       from random import choice
       levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
       a1 = logging.LoggerAdapter(logging.getLogger("a.b.c"),
                                  { "ip" : "123.231.231.123", "user" : "sheila" })
       logging.basicConfig(level=logging.DEBUG,
                           format="%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s")
       a1.debug("A debug message")
       a1.info("An info message with %s", "some parameters")
       a2 = logging.LoggerAdapter(logging.getLogger("d.e.f"), ConnInfo())
       for x in range(10):
           lvl = choice(levels)
           lvlname = logging.getLevelName(lvl)
           a2.log(lvl, "A message at %s level with %d %s", lvlname, 2, "parameters")

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

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

New in version 2.6.

The ``LoggerAdapter`` class was not present in previous versions.


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)
      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


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, the best way of doing this is to have all the
processes log to a ``SocketHandler``, and have a separate process
which implements a socket server which reads from the socket and logs
to file. (If you prefer, you can dedicate one thread in one of the
existing processes to perform this function.) The following section
documents this approach in more detail and includes a working socket
receiver which can be used as a starting point for you to adapt in
your own applications.

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


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 cPickle
   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 1:
               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 cPickle.loads(data)

       def handleLogRecord(self, record):
           # if a name is specified, we use the named logger rather than the one
           # implied by the record.
           if self.server.logname is not None:
               name = self.server.logname
           else:
               name = record.name
           logger = logging.getLogger(name)
           # N.B. EVERY record gets logged. This is because Logger.handle
           # is normally called AFTER logger-level filtering. If you want
           # to do filtering, do it at the client end to save wasting
           # cycles and network bandwidth!
           logger.handle(record)

   class LogRecordSocketReceiver(SocketServer.ThreadingTCPServer):
       """simple TCP socket-based logging receiver suitable for testing.
       """

       allow_reuse_address = 1

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

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

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

   if __name__ == "__main__":
       main()

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

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

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


Using arbitrary objects as messages
===================================

In the preceding sections and examples, it has been assumed that the
message passed when logging the event is a string. However, this is
not the only possibility. You can pass an arbitrary object as a
message, and its ``__str__()`` method will be called when the logging
system needs to convert it to a string representation. In fact, if you
want to, you can avoid computing a string representation altogether -
for example, the ``SocketHandler`` emits an event by pickling it and
sending it over the wire.


Optimization
============

Formatting of message arguments is deferred until it cannot be
avoided. However, computing the arguments passed to the logging method
can also be expensive, and you may want to avoid doing it if the
logger will just throw away your event. To decide what to do, you can
call the ``isEnabledFor()`` method which takes a level argument and
returns true if the event would be created by the Logger for that
level of call. You can write code like this:

   if logger.isEnabledFor(logging.DEBUG):
       logger.debug("Message with %s, %s", expensive_func1(),
                                           expensive_func2())

so that if the logger's threshold is set above ``DEBUG``, the calls to
``expensive_func1()`` and ``expensive_func2()`` are never made.

There are other optimizations which can be made for specific
applications which need more precise control over what logging
information is collected. Here's a list of things you can do to avoid
processing during logging which you don't need:

+-------------------------------------------------+------------------------------------------+
| What you don't want to collect                  | How to avoid collecting it               |
+=================================================+==========================================+
| Information about where calls were made from.   | Set ``logging._srcfile`` to ``None``.    |
+-------------------------------------------------+------------------------------------------+
| Threading information.                          | Set ``logging.logThreads`` to ``0``.     |
+-------------------------------------------------+------------------------------------------+
| Process information.                            | Set ``logging.logProcesses`` to ``0``.   |
+-------------------------------------------------+------------------------------------------+

Also note that the core logging module only includes the basic
handlers. If you don't import ``logging.handlers`` and
``logging.config``, they won't take up any memory.


Handler Objects
===============

Handlers have the following attributes and methods. Note that
``Handler`` is never instantiated directly; this class acts as a base
for more useful subclasses. However, the ``__init__()`` method in
subclasses needs to call ``Handler.__init__()``.

Handler.__init__(level=NOTSET)

   Initializes the ``Handler`` instance by setting its level, setting
   the list of filters to the empty list and creating a lock (using
   ``createLock()``) for serializing access to an I/O mechanism.

Handler.createLock()

   Initializes a thread lock which can be used to serialize access to
   underlying I/O functionality which may not be threadsafe.

Handler.acquire()

   Acquires the thread lock created with ``createLock()``.

Handler.release()

   Releases the thread lock acquired with ``acquire()``.

Handler.setLevel(lvl)

   Sets the threshold for this handler to *lvl*. Logging messages
   which are less severe than *lvl* will be ignored. When a handler is
   created, the level is set to ``NOTSET`` (which causes all messages
   to be processed).

Handler.setFormatter(form)

   Sets the ``Formatter`` for this handler to *form*.

Handler.addFilter(filt)

   Adds the specified filter *filt* to this handler.

Handler.removeFilter(filt)

   Removes the specified filter *filt* from this handler.

Handler.filter(record)

   Applies this handler's filters to the record and returns a true
   value if the record is to be processed.

Handler.flush()

   Ensure all logging output has been flushed. This version does
   nothing and is intended to be implemented by subclasses.

Handler.close()

   Tidy up any resources used by the handler. This version does no
   output but removes the handler from an internal list of handlers
   which is closed when ``shutdown()`` is called. Subclasses should
   ensure that this gets called from overridden ``close()`` methods.

Handler.handle(record)

   Conditionally emits the specified logging record, depending on
   filters which may have been added to the handler. Wraps the actual
   emission of the record with acquisition/release of the I/O thread
   lock.

Handler.handleError(record)

   This method should be called from handlers when an exception is
   encountered during an ``emit()`` call. By default it does nothing,
   which means that exceptions get silently ignored. This is what is
   mostly wanted for a logging system - most users will not care about
   errors in the logging system, they are more interested in
   application errors. You could, however, replace this with a custom
   handler if you wish. The specified record is the one which was
   being processed when the exception occurred.

Handler.format(record)

   Do formatting for a record - if a formatter is set, use it.
   Otherwise, use the default formatter for the module.

Handler.emit(record)

   Do whatever it takes to actually log the specified logging record.
   This version is intended to be implemented by subclasses and so
   raises a ``NotImplementedError``.


StreamHandler
-------------

The ``StreamHandler`` class, located in the core ``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
``write()`` and ``flush()`` methods).

class class logging.StreamHandler([stream])

   Returns a new instance of the ``StreamHandler`` class. If *stream*
   is specified, the instance will use it for logging output;
   otherwise, *sys.stderr* will be used.

   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 ``traceback.print_exception()`` and appended to the
      stream.

   flush()

      Flushes the stream by calling its ``flush()`` method. Note that
      the ``close()`` method is inherited from ``Handler`` and so does
      no output, so an explicit ``flush()`` call may be needed at
      times.


FileHandler
-----------

The ``FileHandler`` class, located in the core ``logging`` package,
sends logging output to a disk file.  It inherits the output
functionality from ``StreamHandler``.

class class logging.FileHandler(filename[, mode[, encoding[, delay]]])

   Returns a new instance of the ``FileHandler`` 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 ``emit()``. By
   default, the file grows indefinitely.

   Changed in version 2.6: *delay* was added.

   close()

      Closes the file.

   emit(record)

      Outputs the record to the file.


NullHandler
-----------

New in version 2.7.

The ``NullHandler`` class, located in the core ``logging`` package,
does not do any formatting or output. It is essentially a "no-op"
handler for use by library developers.

class class logging.NullHandler

   Returns a new instance of the ``NullHandler`` class.

   emit(record)

      This method does nothing.

   handle(record)

      This method does nothing.

   createLock()

      This method returns *None* for the lock, since there is no
      underlying I/O to which access needs to be serialized.

See *Configuring Logging for a Library* for more information on how to
use ``NullHandler``.


WatchedFileHandler
------------------

New in version 2.6.

The ``WatchedFileHandler`` class, located in the ``logging.handlers``
module, is a ``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;
``stat()`` always returns zero for this value.

class class logging.handlers.WatchedFileHandler(filename[, mode[, encoding[, delay]]])

   Returns a new instance of the ``WatchedFileHandler`` 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
   ``emit()``.  By default, the file grows indefinitely.

   Changed in version 2.6: *delay* was added.

   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.


RotatingFileHandler
-------------------

The ``RotatingFileHandler`` class, located in the ``logging.handlers``
module, supports rotation of disk log files.

class class logging.handlers.RotatingFileHandler(filename[, mode[, maxBytes[, backupCount[, encoding[, delay]]]]])

   Returns a new instance of the ``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
   ``emit()``.  By default, the file grows indefinitely.

   You can use the *maxBytes* and *backupCount* values to allow the
   file to *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
   ``app.log``, you would get ``app.log``, ``app.log.1``,
   ``app.log.2``, up to ``app.log.5``. The file being written to is
   always ``app.log``.  When this file is filled, it is closed and
   renamed to ``app.log.1``, and if files ``app.log.1``,
   ``app.log.2``, etc.  exist, then they are renamed to ``app.log.2``,
   ``app.log.3`` etc.  respectively.

   Changed in version 2.6: *delay* was added.

   doRollover()

      Does a rollover, as described above.

   emit(record)

      Outputs the record to the file, catering for rollover as
      described previously.


TimedRotatingFileHandler
------------------------

The ``TimedRotatingFileHandler`` class, located in the
``logging.handlers`` module, supports rotation of disk log files at
certain timed intervals.

class class logging.handlers.TimedRotatingFileHandler(filename[, when[, interval[, backupCount[, encoding[, delay[, utc]]]]]])

   Returns a new instance of the ``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 ``emit()``.

   Changed in version 2.6: *delay* was added.

   doRollover()

      Does a rollover, as described above.

   emit(record)

      Outputs the record to the file, catering for rollover as
      described above.


SocketHandler
-------------

The ``SocketHandler`` class, located in the ``logging.handlers``
module, sends logging output to a network socket. The base class uses
a TCP socket.

class class logging.handlers.SocketHandler(host, port)

   Returns a new instance of the ``SocketHandler`` class intended to
   communicate with a remote machine whose address is given by *host*
   and *port*.

   close()

      Closes the socket.

   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 ``LogRecord``, use the
      ``makeLogRecord()`` function.

   handleError()

      Handles an error which has occurred during ``emit()``. The most
      likely cause is a lost connection. Closes the socket so that we
      can retry on the next event.

   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 (``socket.SOCK_STREAM``).

   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.

   send(packet)

      Send a pickled string *packet* to the socket. This function
      allows for partial sends which can happen when the network is
      busy.


DatagramHandler
---------------

The ``DatagramHandler`` class, located in the ``logging.handlers``
module, inherits from ``SocketHandler`` to support sending logging
messages over UDP sockets.

class class logging.handlers.DatagramHandler(host, port)

   Returns a new instance of the ``DatagramHandler`` class intended to
   communicate with a remote machine whose address is given by *host*
   and *port*.

   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 ``LogRecord``, use the ``makeLogRecord()``
      function.

   makeSocket()

      The factory method of ``SocketHandler`` is here overridden to
      create a UDP socket (``socket.SOCK_DGRAM``).

   send(s)

      Send a pickled string to a socket.


SysLogHandler
-------------

The ``SysLogHandler`` class, located in the ``logging.handlers``
module, supports sending logging messages to a remote or local Unix
syslog.

class class logging.handlers.SysLogHandler([address[, facility[, socktype]]])

   Returns a new instance of the ``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, ``LOG_USER`` is used.
   The type of socket opened depends on the *socktype* argument, which
   defaults to ``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 ``socket.SOCK_STREAM``.

   Changed in version 2.7: *socktype* was added.

   close()

      Closes the socket to the remote host.

   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.

   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 ``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      |
      +-----------------+-----------------+

   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".


NTEventLogHandler
-----------------

The ``NTEventLogHandler`` class, located in the ``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 class logging.handlers.NTEventLogHandler(appname[, dllname[, logtype]])

   Returns a new instance of the ``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'``.

   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.

   emit(record)

      Determines the message ID, event category and event type, and
      then logs the message in the NT event log.

   getEventCategory(record)

      Returns the event category for the record. Override this if you
      want to specify your own categories. This version returns 0.

   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 ``__init__()``
      to a dictionary which contains mappings for ``DEBUG``, ``INFO``,
      ``WARNING``, ``ERROR`` and ``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.

   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
      ``win32service.pyd``.


SMTPHandler
-----------

The ``SMTPHandler`` class, located in the ``logging.handlers`` module,
supports sending logging messages to an email address via SMTP.

class class logging.handlers.SMTPHandler(mailhost, fromaddr, toaddrs, subject[, credentials])

   Returns a new instance of the ``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.

   Changed in version 2.6: *credentials* was added.

   emit(record)

      Formats the record and sends it to the specified addressees.

   getSubject(record)

      If you want to specify a subject line which is record-dependent,
      override this method.


MemoryHandler
-------------

The ``MemoryHandler`` class, located in the ``logging.handlers``
module, supports buffering of logging records in memory, periodically
flushing them to a *target* handler. Flushing occurs whenever the
buffer is full, or when an event of a certain severity or greater is
seen.

``MemoryHandler`` is a subclass of the more general
``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 ``shouldFlush()`` to see if the buffer should
be flushed.  If it should, then ``flush()`` is expected to do the
needful.

class class logging.handlers.BufferingHandler(capacity)

   Initializes the handler with a buffer of the specified capacity.

   emit(record)

      Appends the record to the buffer. If ``shouldFlush()`` returns
      true, calls ``flush()`` to process the buffer.

   flush()

      You can override this to implement custom flushing behavior.
      This version just zaps the buffer to empty.

   shouldFlush(record)

      Returns true if the buffer is up to capacity. This method can be
      overridden to implement custom flushing strategies.

class class logging.handlers.MemoryHandler(capacity[, flushLevel[, target]])

   Returns a new instance of the ``MemoryHandler`` class. The instance
   is initialized with a buffer size of *capacity*. If *flushLevel* is
   not specified, ``ERROR`` is used. If no *target* is specified, the
   target will need to be set using ``setTarget()`` before this
   handler does anything useful.

   close()

      Calls ``flush()``, sets the target to ``None`` and clears the
      buffer.

   flush()

      For a ``MemoryHandler``, flushing means just sending the
      buffered records to the target, if there is one. Override if you
      want different behavior.

   setTarget(target)

      Sets the target handler for this handler.

   shouldFlush(record)

      Checks for buffer full or a record at the *flushLevel* or
      higher.


HTTPHandler
-----------

The ``HTTPHandler`` class, located in the ``logging.handlers`` module,
supports sending logging messages to a Web server, using either
``GET`` or ``POST`` semantics.

class class logging.handlers.HTTPHandler(host, url[, method])

   Returns a new instance of the ``HTTPHandler`` class. The instance
   is initialized with a host address, url and HTTP method. 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.

   emit(record)

      Sends the record to the Web server as a percent-encoded
      dictionary.


Formatter Objects
=================

``Formatter``s have the following attributes and methods. They are
responsible for converting a ``LogRecord`` to (usually) a string which
can be interpreted by either a human or an external system. The base
``Formatter`` allows a formatting string to be specified. If none is
supplied, the default value of ``'%(message)s'`` is used.

A Formatter can be initialized with a format string which makes use of
knowledge of the ``LogRecord`` attributes - such as the default value
mentioned above making use of the fact that the user's message and
arguments are pre-formatted into a ``LogRecord``'s *message*
attribute.  This format string contains standard Python %-style
mapping keys. See section *String Formatting Operations* for more
information on string formatting.

Currently, the useful mapping keys in a ``LogRecord`` are:

+---------------------------+-------------------------------------------------+
| Format                    | Description                                     |
+===========================+=================================================+
| ``%(name)s``              | Name of the logger (logging channel).           |
+---------------------------+-------------------------------------------------+
| ``%(levelno)s``           | Numeric logging level for the message           |
|                           | (``DEBUG``, ``INFO``, ``WARNING``, ``ERROR``,   |
|                           | ``CRITICAL``).                                  |
+---------------------------+-------------------------------------------------+
| ``%(levelname)s``         | Text logging level for the message              |
|                           | (``'DEBUG'``, ``'INFO'``, ``'WARNING'``,        |
|                           | ``'ERROR'``, ``'CRITICAL'``).                   |
+---------------------------+-------------------------------------------------+
| ``%(pathname)s``          | Full pathname of the source file where the      |
|                           | logging call was issued (if available).         |
+---------------------------+-------------------------------------------------+
| ``%(filename)s``          | Filename portion of pathname.                   |
+---------------------------+-------------------------------------------------+
| ``%(module)s``            | Module (name portion of filename).              |
+---------------------------+-------------------------------------------------+
| ``%(funcName)s``          | Name of function containing the logging call.   |
+---------------------------+-------------------------------------------------+
| ``%(lineno)d``            | Source line number where the logging call was   |
|                           | issued (if available).                          |
+---------------------------+-------------------------------------------------+
| ``%(created)f``           | Time when the ``LogRecord`` was created (as     |
|                           | returned by ``time.time()``).                   |
+---------------------------+-------------------------------------------------+
| ``%(relativeCreated)d``   | Time in milliseconds when the LogRecord was     |
|                           | created, relative to the time the logging       |
|                           | module was loaded.                              |
+---------------------------+-------------------------------------------------+
| ``%(asctime)s``           | Human-readable time when the ``LogRecord`` was  |
|                           | created.  By default this is of the form        |
|                           | "2003-07-08 16:49:45,896" (the numbers after    |
|                           | the comma are millisecond portion of the time). |
+---------------------------+-------------------------------------------------+
| ``%(msecs)d``             | Millisecond portion of the time when the        |
|                           | ``LogRecord`` was created.                      |
+---------------------------+-------------------------------------------------+
| ``%(thread)d``            | Thread ID (if available).                       |
+---------------------------+-------------------------------------------------+
| ``%(threadName)s``        | Thread name (if available).                     |
+---------------------------+-------------------------------------------------+
| ``%(process)d``           | Process ID (if available).                      |
+---------------------------+-------------------------------------------------+
| ``%(processName)s``       | Process name (if available).                    |
+---------------------------+-------------------------------------------------+
| ``%(message)s``           | The logged message, computed as ``msg % args``. |
+---------------------------+-------------------------------------------------+

Changed in version 2.5: *funcName* was added.

Changed in version 2.6: *processName* was added.

class class logging.Formatter([fmt[, datefmt]])

   Returns a new instance of the ``Formatter`` class. The instance is
   initialized with a format string for the message as a whole, as
   well as a format string for the date/time portion of a message. If
   no *fmt* is specified, ``'%(message)s'`` is used. If no *datefmt*
   is specified, the ISO8601 date format is used.

   format(record)

      The record's attribute dictionary is used as the operand to a
      string formatting operation. Returns the resulting string.
      Before formatting the dictionary, a couple of preparatory steps
      are carried out. The *message* attribute of the record is
      computed using *msg* % *args*. If the formatting string contains
      ``'(asctime)'``, ``formatTime()`` is called to format the event
      time. If there is exception information, it is formatted using
      ``formatException()`` and appended to the message. Note that the
      formatted exception information is cached in attribute
      *exc_text*. This is useful because the exception information can
      be pickled and sent across the wire, but you should be careful
      if you have more than one ``Formatter`` subclass which
      customizes the formatting of exception information. In this
      case, you will have to clear the cached value after a formatter
      has done its formatting, so that the next formatter to handle
      the event doesn't use the cached value but recalculates it
      afresh.

   formatTime(record[, datefmt])

      This method should be called from ``format()`` by a formatter
      which wants to make use of a formatted time. This method can be
      overridden in formatters to provide for any specific
      requirement, but the basic behavior is as follows: if *datefmt*
      (a string) is specified, it is used with ``time.strftime()`` to
      format the creation time of the record. Otherwise, the ISO8601
      format is used.  The resulting string is returned.

   formatException(exc_info)

      Formats the specified exception information (a standard
      exception tuple as returned by ``sys.exc_info()``) as a string.
      This default implementation just uses
      ``traceback.print_exception()``. The resulting string is
      returned.


Filter Objects
==============

``Filter``s can be used by ``Handler``s and ``Logger``s for more
sophisticated filtering than is provided by levels. The base filter
class only allows events which are below a certain point in the logger
hierarchy. For example, a filter initialized with "A.B" will allow
events logged by loggers "A.B", "A.B.C", "A.B.C.D", "A.B.D" etc. but
not "A.BB", "B.A.B" etc. If initialized with the empty string, all
events are passed.

class class logging.Filter([name])

   Returns an instance of the ``Filter`` class. If *name* is
   specified, it names a logger which, together with its children,
   will have its events allowed through the filter. If *name* is the
   empty string, allows every event.

   filter(record)

      Is the specified record to be logged? Returns zero for no,
      nonzero for yes. If deemed appropriate, the record may be
      modified in-place by this method.

Note that filters attached to handlers are consulted whenever an event
is emitted by the handler, whereas filters attached to loggers are
consulted whenever an event is logged to the handler (using
``debug()``, ``info()``, etc.) This means that events which have been
generated by descendant loggers will not be filtered by a logger's
filter setting, unless the filter has also been applied to those
descendant loggers.

You don't actually need to subclass ``Filter``: you can pass any
instance which has a ``filter`` method with the same semantics.


Other uses for filters
----------------------

Although filters are used primarily to filter records based on more
sophisticated criteria than levels, they get to see every record which
is processed by the handler or logger they're attached to: this can be
useful if you want to do things like counting how many records were
processed by a particular logger or handler, or adding, changing or
removing attributes in the LogRecord being processed. Obviously
changing the LogRecord needs to be done with some care, but it does
allow the injection of contextual information into logs (see *Using
Filters to impart contextual information*).


LogRecord Objects
=================

``LogRecord`` instances are created automatically by the ``Logger``
every time something is logged, and can be created manually via
``makeLogRecord()`` (for example, from a pickled event received over
the wire).

class class logging.LogRecord(name, lvl, pathname, lineno, msg, args, exc_info[, func=None])

   Contains all the information pertinent to the event being logged.

   The primary information is passed in ``msg`` and ``args``, which
   are combined using ``msg % args`` to create the ``message`` field
   of the record.

   args

      Tuple of arguments to be used in formatting ``msg``.

   exc_info

      Exception tuple (à la *sys.exc_info*) or *None* if no exception
      information is available.

   func

      Name of the function of origin (i.e. in which the logging call
      was made).

   lineno

      Line number in the source file of origin.

   lvl

      Numeric logging level.

   message

      Bound to the result of ``getMessage()`` when
      ``Formatter.format(record)`` is invoked.

   msg

      User-supplied *format string* or arbitrary object (see *Using
      arbitrary objects as messages*) used in ``getMessage()``.

   name

      Name of the logger that emitted the record.

   pathname

      Absolute pathname of the source file of origin.

   getMessage()

      Returns the message for this ``LogRecord`` instance after
      merging any user-supplied arguments with the message. If the
      user-supplied message argument to the logging call is not a
      string, ``str()`` is called on it to convert it to a string.
      This allows use of user-defined classes as messages, whose
      ``__str__`` method can return the actual format string to be
      used.

   Changed in version 2.5: *func* was added.


LoggerAdapter Objects
=====================

New in version 2.6.

``LoggerAdapter`` instances are used to conveniently pass contextual
information into logging calls. For a usage example , see the section
on adding contextual information to your logging output.

class class logging.LoggerAdapter(logger, extra)

   Returns an instance of ``LoggerAdapter`` initialized with an
   underlying ``Logger`` instance and a dict-like object.

   process(msg, kwargs)

      Modifies the message and/or keyword arguments passed to a
      logging call in order to insert contextual information. This
      implementation takes the object passed as *extra* to the
      constructor and adds it to *kwargs* using key 'extra'. The
      return value is a (*msg*, *kwargs*) tuple which has the
      (possibly modified) versions of the arguments passed in.

In addition to the above, ``LoggerAdapter`` supports all the logging
methods of ``Logger``, i.e. ``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.

Changed in version 2.7.

The ``isEnabledFor()`` method was added to ``LoggerAdapter``. This
method delegates to the underlying logger.


Thread Safety
=============

The logging module is intended to be thread-safe without any special
work needing to be done by its clients. It achieves this though using
threading locks; there is one lock to serialize access to the module's
shared data, and each handler also creates a lock to serialize access
to its underlying I/O.

If you are implementing asynchronous signal handlers using the
``signal`` module, you may not be able to use logging from within such
handlers. This is because lock implementations in the ``threading``
module are not always re-entrant, and so cannot be invoked from such
signal handlers.


Integration with the warnings module
====================================

The ``captureWarnings()`` function can be used to integrate
``logging`` with the ``warnings`` module.

logging.captureWarnings(capture)

   This function is used to turn the capture of warnings by logging on
   and off.

   If *capture* is ``True``, warnings issued by the ``warnings``
   module will be redirected to the logging system. Specifically, a
   warning will be formatted using ``warnings.formatwarning()`` and
   the resulting string logged to a logger named "py.warnings" with a
   severity of ``WARNING``.

   If *capture* is ``False``, the redirection of warnings to the
   logging system will stop, and warnings will be redirected to their
   original destinations (i.e. those in effect before
   ``captureWarnings(True)`` was called).


Configuration
=============


Configuration functions
-----------------------

The following functions configure the logging module. They are located
in the ``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 ``logging`` itself) and defining handlers
which are declared either in ``logging`` or ``logging.handlers``.

logging.dictConfig(config)

   Takes the logging configuration from a dictionary.  The contents of
   this dictionary are described in *Configuration dictionary schema*
   below.

   If an error is encountered during configuration, this function will
   raise a ``ValueError``, ``TypeError``, ``AttributeError`` or
   ``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 ``DictConfigurator`` class, whose
   constructor is passed the dictionary used for configuration, and
   has a ``configure()`` method.  The ``logging.config`` module has a
   callable attribute ``dictConfigClass`` which is initially set to
   ``DictConfigurator``. You can replace the value of
   ``dictConfigClass`` with a suitable implementation of your own.

   ``dictConfig()`` calls ``dictConfigClass`` passing the specified
   dictionary, and then calls the ``configure()`` method on the
   returned object to put the configuration into effect:

      def dictConfig(config):
          dictConfigClass(config).configure()

   For example, a subclass of ``DictConfigurator`` could call
   ``DictConfigurator.__init__()`` in its own ``__init__()``, then set
   up custom prefixes which would be usable in the subsequent
   ``configure()`` call. ``dictConfigClass`` would be bound to this
   new subclass, and then ``dictConfig()`` could be called exactly as
   in the default, uncustomized state.

logging.fileConfig(fname[, defaults])

   Reads the logging configuration from a ``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.

logging.listen([port])

   Starts up a socket server on the specified port, and listens for
   new configurations. If no port is specified, the module's default
   ``DEFAULT_LOGGING_CONFIG_PORT`` is used. Logging configurations
   will be sent as a file suitable for processing by ``fileConfig()``.
   Returns a ``Thread`` instance on which you can call ``start()`` to
   start the server, and which you can ``join()`` when appropriate. To
   stop the server, call ``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)``.

logging.stopListening()

   Stops the listening server which was created with a call to
   ``listen()``. This is typically called before calling ``join()`` on
   the return value from ``listen()``.


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 ``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 *Object connections* below.


Dictionary Schema Details
~~~~~~~~~~~~~~~~~~~~~~~~~

The dictionary passed to ``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
*User-defined objects* 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
  ``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 ``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
  ``logging.StreamHandler``, using ``sys.stdout`` as the underlying
  stream.  The handler with id ``file`` is instantiated as a
  ``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 ``fileConfig()`` API.

  If the specified value is ``True``, the configuration is processed
  as described in the section on *Incremental Configuration*.

* *disable_existing_loggers* - whether any existing loggers are to be
  disabled. This setting mirrors the parameter of the same name in
  ``fileConfig()``. If absent, this parameter defaults to ``True``.
  This value is ignored if *incremental* is ``True``.


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.


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``.


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 ``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 ``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 ``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.


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.


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 ``logging`` module.  For example, consider
``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.


Configuration file format
-------------------------

The configuration file format understood by ``fileConfig()`` is based
on ``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 ``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
``eval()`` in the ``logging`` package's namespace). The ``level`` is
interpreted as for loggers, and ``NOTSET`` is taken to mean "log
everything".

Changed in version 2.6: Added support for resolving the handler's
class as a dotted module and class name.

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 ``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 ``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 ``Formatter`` subclass.  Subclasses of
``Formatter`` can present exception tracebacks in an expanded or
condensed format.


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"


More examples
=============


Multiple handlers and formatters
--------------------------------

Loggers are plain Python objects.  The ``addHandler()`` method has no
minimum or maximum quota for the number of handlers you may add.
Sometimes it will be beneficial for an application to log all messages
of all severities to a text file while simultaneously logging errors
or above to the console.  To set this up, simply configure the
appropriate handlers.  The logging calls in the application code will
remain unchanged.  Here is a slight modification to the previous
simple module-based configuration example:

   import logging

   logger = logging.getLogger("simple_example")
   logger.setLevel(logging.DEBUG)
   # create file handler which logs even debug messages
   fh = logging.FileHandler("spam.log")
   fh.setLevel(logging.DEBUG)
   # create console handler with a higher log level
   ch = logging.StreamHandler()
   ch.setLevel(logging.ERROR)
   # create formatter and add it to the handlers
   formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
   ch.setFormatter(formatter)
   fh.setFormatter(formatter)
   # add the handlers to logger
   logger.addHandler(ch)
   logger.addHandler(fh)

   # "application" code
   logger.debug("debug message")
   logger.info("info message")
   logger.warn("warn message")
   logger.error("error message")
   logger.critical("critical message")

Notice that the "application" code does not care about multiple
handlers.  All that changed was the addition and configuration of a
new handler named *fh*.

The ability to create new handlers with higher- or lower-severity
filters can be very helpful when writing and testing an application.
Instead of using many ``print`` statements for debugging, use
``logger.debug``: Unlike the print statements, which you will have to
delete or comment out later, the logger.debug statements can remain
intact in the source code and remain dormant until you need them
again.  At that time, the only change that needs to happen is to
modify the severity level of the logger and/or handler to debug.


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()
