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 containing LogRecord attributes ; however, you can also write custom formatters to implement specific formatting behavior.

Filters can be installed on loggers or on handlers; multiple filters can be used in a chain to perform multiple filtering actions.

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.

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.

A filter is used to provide additional control over which log records are passed from logger to handler.

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.

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.

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.

Once a logger has determined that a message needs to be processed, it is passed to a Handler.

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.

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.

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:

A logger is the entry point into the logging system. Each logger is a named bucket to which messages can be written for processing.

Django uses Python’s builtin 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.

There are two other logging calls available:

The logger instance contains an entry method for each of the default log levels:

This propagation can be controlled on a per-logger basis. If you don’t want a particular logger to propagate to its parents, you can turn off this behavior.

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 logger defined in the project namespace will catch all logging messages issued on the project.interesting and project.interesting.stuff loggers.

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.

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:

The call to 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.

And that’s it! Every time the bad_mojo condition is activated, an error log record will be written.

Once you have configured your loggers, handlers, filters and formatters, you need to place logging calls into your code. Using the logging framework works like this:

Configuring logging¶

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 you can use the logging output.

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.

In order to configure logging, you use 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.

By default, the LOGGING setting is merged with Django’s default logging configuration using the following scheme.

If the disable_existing_loggers key in the LOGGING dictConfig is set to True (which is the dictConfig default if the key is missing) then all loggers from the default configuration will be disabled. Disabled loggers are not the same as removed; the logger will still exist, but will silently discard anything logged to it, not even propagating entries to a parent logger. Thus you should be very careful using 'disable_existing_loggers': True ; it’s probably not what you want. Instead, you can set disable_existing_loggers to False and redefine some or all of the default loggers; or you can set LOGGING_CONFIG to None and handle logging config yourself.

Logging is configured as part of the general Django setup() function. Therefore, you can be certain that loggers are always ready for use in your project code.

Examples¶ 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 are several examples. To begin, here’s a small configuration that will allow you to output all log messages to the console: settings.py ¶ import os LOGGING = { 'version' : 1 , 'disable_existing_loggers' : False , 'handlers' : { 'console' : { 'class' : 'logging.StreamHandler' , }, }, 'root' : { 'handlers' : [ 'console' ], 'level' : 'WARNING' , }, } This configures the parent root logger to send messages with the WARNING level and higher to the console handler. By adjusting the level to INFO or DEBUG you can display more messages. This may be useful during development. Next we can add more fine-grained logging. Here’s an example of how to make the logging system print more messages from just the django named logger: settings.py ¶ import os LOGGING = { 'version' : 1 , 'disable_existing_loggers' : False , 'handlers' : { 'console' : { 'class' : 'logging.StreamHandler' , }, }, 'root' : { 'handlers' : [ 'console' ], 'level' : 'WARNING' , }, 'loggers' : { 'django' : { 'handlers' : [ 'console' ], 'level' : os . getenv ( 'DJANGO_LOG_LEVEL' , 'INFO' ), 'propagate' : False , }, }, } By default, this config sends messages from the django logger of level INFO or higher to the console. This is the same level as Django’s default logging config, except that the default config only displays log records when DEBUG=True . Django does not log many such INFO level messages. With this config, however, you can also set the environment variable DJANGO_LOG_LEVEL=DEBUG to see all of Django’s debug logging which is very verbose as it includes all database queries. You don’t have to log to the console. Here’s a configuration which writes all logging from the django named logger to a local file: settings.py ¶ LOGGING = { 'version' : 1 , 'disable_existing_loggers' : False , 'handlers' : { 'file' : { 'level' : 'DEBUG' , 'class' : 'logging.FileHandler' , 'filename' : '/path/to/django/debug.log' , }, }, 'loggers' : { 'django' : { 'handlers' : [ 'file' ], 'level' : 'DEBUG' , 'propagate' : True , }, }, } If you use this example, be sure to change the 'filename' path to a location that’s writable by the user that’s running the Django application. Finally, here’s an example of a fairly complex logging setup: settings.py ¶ LOGGING = { 'version' : 1 , 'disable_existing_loggers' : False , 'formatters' : { 'verbose' : { 'format' : '{levelname} {asctime} {module} {process:d} {thread:d} {message}' , 'style' : '{' , }, 'simple' : { 'format' : '{levelname} {message}' , 'style' : '{' , }, }, 'filters' : { 'special' : { '()' : 'project.logging.SpecialFilter' , 'foo' : 'bar' , }, 'require_debug_true' : { '()' : 'django.utils.log.RequireDebugTrue' , }, }, 'handlers' : { 'console' : { 'level' : 'INFO' , 'filters' : [ 'require_debug_true' ], 'class' : 'logging.StreamHandler' , 'formatter' : 'simple' }, 'mail_admins' : { 'level' : 'ERROR' , 'class' : 'django.utils.log.AdminEmailHandler' , 'filters' : [ 'special' ] } }, 'loggers' : { 'django' : { 'handlers' : [ 'console' ], 'propagate' : True , }, '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.

Defines two formatters: simple , that 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 Formatter Objects. verbose , that outputs the log level name, the log message, plus the time, process, thread and module that generate the log message.

Defines two filters: project.logging.SpecialFilter , using the alias special . If this filter required additional arguments, 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 SpecialFilter . django.utils.log.RequireDebugTrue , which passes on records when DEBUG is True .

Defines two handlers: console , a StreamHandler , which prints any INFO (or higher) message to sys.stderr . This handler uses the simple output format. mail_admins , an AdminEmailHandler , which emails any ERROR (or higher) message to the site ADMINS . This handler uses the special filter.

Configures three loggers: django , which passes all messages to the console 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.



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 LOGGING_CONFIG setting defines the callable that will be used to configure Django’s loggers. By default, it points at Python’s logging.config.dictConfig() function. However, if you want to use a different configuration process, you can use any other callable that takes a single argument. The contents of LOGGING will be provided as the value of that argument when logging is configured.