=======
Logging
=======
.. versionadded:: 1.3
.. module:: django.utils.log
:synopsis: Logging tools for Django applications
A quick logging primer
======================
Django uses Python's builtin :mod:`logging` module to perform system logging.
The usage of this module is discussed in detail in Python's own documentation.
However, if you've never used Python's logging framework (or even if you have),
here's a quick primer.
The cast of players
-------------------
A Python logging configuration consists of four parts:
* :ref:`topic-logging-parts-loggers`
* :ref:`topic-logging-parts-handlers`
* :ref:`topic-logging-parts-filters`
* :ref:`topic-logging-parts-formatters`
.. _topic-logging-parts-loggers:
Loggers
~~~~~~~
A logger is the entry point into the logging system. Each logger is
a named bucket to which messages can be written for processing.
A logger is configured to have a *log level*. This log level describes
the severity of the messages that the logger will handle. Python
defines the following log levels:
* ``DEBUG``: Low level system information for debugging purposes
* ``INFO``: General system information
* ``WARNING``: Information describing a minor problem that has
occurred.
* ``ERROR``: Information describing a major problem that has
occurred.
* ``CRITICAL``: Information describing a critical problem that has
occurred.
Each message that is written to the logger is a *Log Record*. Each log
record also has a *log level* indicating the severity of that specific
message. A log record can also contain useful metadata that describes
the event that is being logged. This can include details such as a
stack trace or an error code.
When a message is given to the logger, the log level of the message is
compared to the log level of the logger. If the log level of the
message meets or exceeds the log level of the logger itself, the
message will undergo further processing. If it doesn't, the message
will be ignored.
Once a logger has determined that a message needs to be processed,
it is passed to a *Handler*.
.. _topic-logging-parts-handlers:
Handlers
~~~~~~~~
The handler is the engine that determines what happens to each message
in a logger. It describes a particular logging behavior, such as
writing a message to the screen, to a file, or to a network socket.
Like loggers, handlers also have a log level. If the log level of a
log record doesn't meet or exceed the level of the handler, the
handler will ignore the message.
A logger can have multiple handlers, and each handler can have a
different log level. In this way, it is possible to provide different
forms of notification depending on the importance of a message. For
example, you could install one handler that forwards ``ERROR`` and
``CRITICAL`` messages to a paging service, while a second handler
logs all messages (including ``ERROR`` and ``CRITICAL`` messages) to a
file for later analysis.
.. _topic-logging-parts-filters:
Filters
~~~~~~~
A filter is used to provide additional control over which log records
are passed from logger to handler.
By default, any log message that meets log level requirements will be
handled. However, by installing a filter, you can place additional
criteria on the logging process. For example, you could install a
filter that only allows ``ERROR`` messages from a particular source to
be emitted.
Filters can also be used to modify the logging record prior to being
emitted. For example, you could write a filter that downgrades
``ERROR`` log records to ``WARNING`` records if a particular set of
criteria are met.
Filters can be installed on loggers or on handlers; multiple filters
can be used in a chain to perform multiple filtering actions.
.. _topic-logging-parts-formatters:
Formatters
~~~~~~~~~~
Ultimately, a log record needs to be rendered as text. Formatters
describe the exact format of that text. A formatter usually consists
of a Python formatting string; however, you can also write custom
formatters to implement specific formatting behavior.
Using logging
=============
Once you have configured your loggers, handlers, filters and
formatters, you need to place logging calls into your code. Using the
logging framework is very simple. Here's an example::
# import the logging library
import logging
# Get an instance of a logger
logger = logging.getLogger(__name__)
def my_view(request, arg1, arg):
...
if bad_mojo:
# Log an error message
logger.error('Something went wrong!')
And that's it! Every time the ``bad_mojo`` condition is activated, an
error log record will be written.
Naming loggers
--------------
The call to :meth:`logging.getLogger()` obtains (creating, if
necessary) an instance of a logger. The logger instance is identified
by a name. This name is used to identify the logger for configuration
purposes.
By convention, the logger name is usually ``__name__``, the name of
the python module that contains the logger. This allows you to filter
and handle logging calls on a per-module basis. However, if you have
some other way of organizing your logging messages, you can provide
any dot-separated name to identify your logger::
# Get an instance of a specific named logger
logger = logging.getLogger('project.interesting.stuff')
The dotted paths of logger names define a hierarchy. The
``project.interesting`` logger is considered to be a parent of the
``project.interesting.stuff`` logger; the ``project`` logger
is a parent of the ``project.interesting`` logger.
Why is the hierarchy important? Well, because loggers can be set to
*propagate* their logging calls to their parents. In this way, you can
define a single set of handlers at the root of a logger tree, and
capture all logging calls in the subtree of loggers. A logging handler
defined in the ``project`` namespace will catch all logging messages
issued on the ``project.interesting`` and
``project.interesting.stuff`` loggers.
This propagation can be controlled on a per-logger basis. If
you don't want a particular logger to propagate to it's parents, you
can turn off this behavior.
Making logging calls
--------------------
The logger instance contains an entry method for each of the default
log levels:
* ``logger.critical()``
* ``logger.error()``
* ``logger.warning()``
* ``logger.info()``
* ``logger.debug()``
There are two other logging calls available:
* ``logger.log()``: Manually emits a logging message with a
specific log level.
* ``logger.exception()``: Creates an ``ERROR`` level logging
message wrapping the current exception stack frame.
Configuring logging
===================
Of course, it isn't enough to just put logging calls into your code.
You also need to configure the loggers, handlers, filters and
formatters to ensure that logging output is output in a useful way.
Python's logging library provides several techniques to configure
logging, ranging from a programmatic interface to configuration files.
By default, Django uses the `dictConfig format`_.
.. note::
``logging.dictConfig`` is a builtin library in Python 2.7. In
order to make this library available for users of earlier Python
versions, Django includes a copy as part of ``django.utils.log``.
If you have Python 2.7, the system native library will be used; if
you have Python 2.6 or earlier, Django's copy will be used.
In order to configure logging, you use :setting:`LOGGING` to define a
dictionary of logging settings. These settings describes the loggers,
handlers, filters and formatters that you want in your logging setup,
and the log levels and other properties that you want those components
to have.
Logging is configured as soon as settings have been loaded
(either manually using :func:`~django.conf.settings.configure` or when at least
one setting is accessed). Since the loading of settings is one of the first
things that Django does, you can be certain that loggers are always ready for
use in your project code.
.. _dictConfig format: http://docs.python.org/library/logging.config.html#configuration-dictionary-schema
.. _a third-party library: http://bitbucket.org/vinay.sajip/dictconfig
An example
----------
The full documentation for `dictConfig format`_ is the best source of
information about logging configuration dictionaries. However, to give
you a taste of what is possible, here is an example of a fairly
complex logging setup, configured using :meth:`logging.dictConfig`::
LOGGING = {
'version': 1,
'disable_existing_loggers': True,
'formatters': {
'verbose': {
'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'
},
'simple': {
'format': '%(levelname)s %(message)s'
},
},
'filters': {
'special': {
'()': 'project.logging.SpecialFilter',
'foo': 'bar',
}
},
'handlers': {
'null': {
'level':'DEBUG',
'class':'django.utils.log.NullHandler',
},
'console':{
'level':'DEBUG',
'class':'logging.StreamHandler',
'formatter': 'simple'
},
'mail_admins': {
'level': 'ERROR',
'class': 'django.utils.log.AdminEmailHandler',
'filters': ['special']
}
},
'loggers': {
'django': {
'handlers':['null'],
'propagate': True,
'level':'INFO',
},
'django.request': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
},
'myproject.custom': {
'handlers': ['console', 'mail_admins'],
'level': 'INFO',
'filters': ['special']
}
}
}
This logging configuration does the following things:
* Identifies the configuration as being in 'dictConfig version 1'
format. At present, this is the only dictConfig format version.
* Disables all existing logging configurations.
* Defines two formatters:
* ``simple``, that just outputs the log level name (e.g.,
``DEBUG``) and the log message.
The `format` string is a normal Python formatting string
describing the details that are to be output on each logging
line. The full list of detail that can be output can be
found in the `formatter documentation`_.
* ``verbose``, that outputs the log level name, the log
message, plus the time, process, thread and module that
generate the log message.
* Defines one filter -- :class:`project.logging.SpecialFilter`,
using the alias ``special``. If this filter required additional
arguments at time of construction, they can be provided as
additional keys in the filter configuration dictionary. In this
case, the argument ``foo`` will be given a value of ``bar`` when
instantiating the :class:`SpecialFilter`.
* Defines three handlers:
* ``null``, a NullHandler, which will pass any ``DEBUG`` (or
higher) message to ``/dev/null``.
* ``console``, a StreamHandler, which will print any ``DEBUG``
(or higher) message to stderr. This handler uses the `simple` output
format.
* ``mail_admins``, an AdminEmailHandler, which will email any
``ERROR`` (or higher) message to the site admins. This handler uses
the ``special`` filter.
* Configures three loggers:
* ``django``, which passes all messages at ``INFO`` or higher
to the ``null`` handler.
* ``django.request``, which passes all ``ERROR`` messages to
the ``mail_admins`` handler. In addition, this logger is
marked to *not* propagate messages. This means that log
messages written to ``django.request`` will not be handled
by the ``django`` logger.
* ``myproject.custom``, which passes all messages at ``INFO``
or higher that also pass the ``special`` filter to two
handlers -- the ``console``, and ``mail_admins``. This
means that all ``INFO`` level messages (or higher) will be
printed to the console; ``ERROR`` and ``CRITICAL``
messages will also be output via email.
.. admonition:: Custom handlers and circular imports
If your ``settings.py`` specifies a custom handler class and the file
defining that class also imports ``settings.py`` a circular import will
occur.
For example, if ``settings.py`` contains the following config for
:setting:`LOGGING`::
LOGGING = {
'version': 1,
'handlers': {
'custom_handler': {
'level': 'INFO',
'class': 'myproject.logconfig.MyHandler',
}
}
}
and ``myproject/logconfig.py`` has the following line before the
``MyHandler`` definition::
from django.conf import settings
then the ``dictconfig`` module will raise an exception like the following::
ValueError: Unable to configure handler 'custom_handler':
Unable to configure handler 'custom_handler':
'module' object has no attribute 'logconfig'
.. _formatter documentation: http://docs.python.org/library/logging.html#formatter-objects
Custom logging configuration
----------------------------
If you don't want to use Python's dictConfig format to configure your
logger, you can specify your own configuration scheme.
The :setting:`LOGGING_CONFIG` setting defines the callable that will
be used to configure Django's loggers. By default, it points at
Python's :meth:`logging.dictConfig()` method. However, if you want to
use a different configuration process, you can use any other callable
that takes a single argument. The contents of :setting:`LOGGING` will
be provided as the value of that argument when logging is configured.
Disabling logging configuration
-------------------------------
If you don't want to configure logging at all (or you want to manually
configure logging using your own approach), you can set
:setting:`LOGGING_CONFIG` to ``None``. This will disable the
configuration process.
.. note::
Setting :setting:`LOGGING_CONFIG` to ``None`` only means that the
configuration process is disabled, not logging itself. If you
disable the configuration process, Django will still make logging
calls, falling back to whatever default logging behavior is
defined.
Django's logging extensions
===========================
Django provides a number of utilities to handle the unique
requirements of logging in Web server environment.
Loggers
-------
Django provides three built-in loggers.
``django``
~~~~~~~~~~
``django`` is the catch-all logger. No messages are posted directly to
this logger.
``django.request``
~~~~~~~~~~~~~~~~~~
Log messages related to the handling of requests. 5XX responses are
raised as ``ERROR`` messages; 4XX responses are raised as ``WARNING``
messages.
Messages to this logger have the following extra context:
* ``status_code``: The HTTP response code associated with the
request.
* ``request``: The request object that generated the logging
message.
``django.db.backends``
~~~~~~~~~~~~~~~~~~~~~~
Messages relating to the interaction of code with the database.
For example, every SQL statement executed by a request is logged
at the ``DEBUG`` level to this logger.
Messages to this logger have the following extra context:
* ``duration``: The time taken to execute the SQL statement.
* ``sql``: The SQL statement that was executed.
* ``params``: The parameters that were used in the SQL call.
For performance reasons, SQL logging is only enabled when
``settings.DEBUG`` is set to ``True``, regardless of the logging
level or handlers that are installed.
Handlers
--------
Django provides one log handler in addition to those provided by the
Python logging module.
.. class:: AdminEmailHandler([include_html=False])
This handler sends an email to the site admins for each log
message it receives.
If the log record contains a ``request`` attribute, the full details
of the request will be included in the email.
If the log record contains stack trace information, that stack
trace will be included in the email.
The ``include_html`` argument of ``AdminEmailHandler`` is used to
control whether the traceback email includes an HTML attachment
containing the full content of the debug Web page that would have been
produced if :setting:`DEBUG` were ``True``. To set this value in your
configuration, include it in the handler definition for
``django.utils.log.AdminEmailHandler``, like this::
'handlers': {
'mail_admins': {
'level': 'ERROR',
'class': 'django.utils.log.AdminEmailHandler',
'include_html': True,
}
},
Note that this HTML version of the email contains a full traceback,
with names and values of local variables at each level of the stack, plus
the values of your Django settings. This information is potentially very
sensitive, and you may not want to send it over email. Consider using
something such as `django-sentry`_ to get the best of both worlds -- the
rich information of full tracebacks plus the security of *not* sending the
information over email. You may also explicitly designate certain
sensitive information to be filtered out of error reports -- learn more on
:ref:`Filtering error reports<filtering-error-reports>`.
.. _django-sentry: http://pypi.python.org/pypi/django-sentry
Filters
-------
Django provides two log filters in addition to those provided by the Python
logging module.
.. class:: CallbackFilter(callback)
.. versionadded:: 1.4
This filter accepts a callback function (which should accept a single
argument, the record to be logged), and calls it for each record that passes
through the filter. Handling of that record will not proceed if the callback
returns False.
For instance, to filter out :class:`~django.http.UnreadablePostError`
(raised when a user cancels an upload) from the admin emails, you would
create a filter function::
from django.http import UnreadablePostError
def skip_unreadable_post(record):
if record.exc_info:
exc_type, exc_value = record.exc_info[:2]
if isinstance(exc_value, UnreadablePostError):
return False
return True
and then add it to your logging config::
'filters': {
'skip_unreadable_posts': {
'()': 'django.utils.log.CallbackFilter',
'callback': skip_unreadable_post,
}
},
'handlers': {
'mail_admins': {
'level': 'ERROR',
'filters': ['skip_unreadable_posts'],
'class': 'django.utils.log.AdminEmailHandler'
}
},
.. class:: RequireDebugFalse()
.. versionadded:: 1.4
This filter will only pass on records when settings.DEBUG is False.
This filter is used as follows in the default :setting:`LOGGING`
configuration to ensure that the :class:`AdminEmailHandler` only sends error
emails to admins when :setting:`DEBUG` is `False`::
'filters': {
'require_debug_false': {
'()': 'django.utils.log.RequireDebugFalse',
}
},
'handlers': {
'mail_admins': {
'level': 'ERROR',
'filters': ['require_debug_false'],
'class': 'django.utils.log.AdminEmailHandler'
}
},
