Python programmers will often use print() in their code as a quick and\nconvenient debugging tool. Using the logging framework is only a little more\neffort than that, but it’s much more elegant and flexible. As well as being\nuseful for debugging, logging can also provide you with more - and better\nstructured - information about the state and health of your application.
Django uses and extends Python’s builtin logging module to perform\nsystem logging. This module is discussed in detail in Python’s own\ndocumentation; this section provides a quick overview.
A Python logging configuration consists of four parts:
\n\nA logger is the entry point into the logging system. Each logger is a named\nbucket to which messages can be written for processing.
\nA logger is configured to have a log level. This log level describes\nthe severity of the messages that the logger will handle. Python\ndefines the following log levels:
\nDEBUG: Low level system information for debugging purposes
INFO: General system information
WARNING: Information describing a minor problem that has\noccurred.
ERROR: Information describing a major problem that has\noccurred.
CRITICAL: Information describing a critical problem that has\noccurred.
Each message that is written to the logger is a Log Record. Each log\nrecord also has a log level indicating the severity of that specific\nmessage. A log record can also contain useful metadata that describes\nthe event that is being logged. This can include details such as a\nstack trace or an error code.
\nWhen a message is given to the logger, the log level of the message is\ncompared to the log level of the logger. If the log level of the\nmessage meets or exceeds the log level of the logger itself, the\nmessage will undergo further processing. If it doesn’t, the message\nwill be ignored.
\nOnce a logger has determined that a message needs to be processed,\nit is passed to a Handler.
\nThe handler is the engine that determines what happens to each message\nin a logger. It describes a particular logging behavior, such as\nwriting a message to the screen, to a file, or to a network socket.
\nLike loggers, handlers also have a log level. If the log level of a\nlog record doesn’t meet or exceed the level of the handler, the\nhandler will ignore the message.
\nA logger can have multiple handlers, and each handler can have a\ndifferent log level. In this way, it is possible to provide different\nforms of notification depending on the importance of a message. For\nexample, you could install one handler that forwards ERROR and\nCRITICAL messages to a paging service, while a second handler\nlogs all messages (including ERROR and CRITICAL messages) to a\nfile for later analysis.
A filter is used to provide additional control over which log records\nare passed from logger to handler.
\nBy default, any log message that meets log level requirements will be\nhandled. However, by installing a filter, you can place additional\ncriteria on the logging process. For example, you could install a\nfilter that only allows ERROR messages from a particular source to\nbe emitted.
Filters can also be used to modify the logging record prior to being\nemitted. For example, you could write a filter that downgrades\nERROR log records to WARNING records if a particular set of\ncriteria are met.
Filters can be installed on loggers or on handlers; multiple filters\ncan be used in a chain to perform multiple filtering actions.
\nUltimately, a log record needs to be rendered as text. Formatters\ndescribe the exact format of that text. A formatter usually consists\nof a Python formatting string containing\nLogRecord attributes; however,\nyou can also write custom formatters to implement specific formatting behavior.
\nThe logging system handles potentially sensitive information. For example, the\nlog record may contain information about a web request or a stack trace, while\nsome of the data you collect in your own loggers may also have security\nimplications. You need to be sure you know:
\nwhat information is collected
where it will subsequently be stored
how it will be transferred
who might have access to it.
To help control the collection of sensitive information, you can explicitly\ndesignate certain sensitive information to be filtered out of error reports –\nread more about how to filter error reports.
\nAdminEmailHandlerLink to this heading#The built-in AdminEmailHandler deserves a mention in\nthe context of security. If its include_html option is enabled, the email\nmessage it sends will contain a full traceback, with names and values of local\nvariables at each level of the stack, plus the values of your Django settings\n(in other words, the same level of detail that is exposed in a web page when\nDEBUG is True).
It’s generally not considered a good idea to send such potentially sensitive\ninformation over email. Consider instead using one of the many third-party\nservices to which detailed logs can be sent to get the best of multiple worlds\n– the rich information of full tracebacks, clear management of who is notified\nand has access to the information, and so on.
\nPython’s logging library provides several techniques to configure\nlogging, ranging from a programmatic interface to configuration files.\nBy default, Django uses the dictConfig format.
\nIn order to configure logging, you use LOGGING to define a\ndictionary of logging settings. These settings describe the loggers,\nhandlers, filters and formatters that you want in your logging setup,\nand the log levels and other properties that you want those components\nto have.
By default, the LOGGING setting is merged with Django’s\ndefault logging configuration using the\nfollowing scheme.
If the disable_existing_loggers key in the LOGGING dictConfig is\nset to True (which is the dictConfig default if the key is missing)\nthen all loggers from the default configuration will be disabled. Disabled\nloggers are not the same as removed; the logger will still exist, but will\nsilently discard anything logged to it, not even propagating entries to a\nparent logger. Thus you should be very careful using\n'disable_existing_loggers': True; it’s probably not what you want. Instead,\nyou can set disable_existing_loggers to False and redefine some or all\nof the default loggers; or you can set LOGGING_CONFIG to None\nand handle logging config yourself.
Logging is configured as part of the general Django setup() function.\nTherefore, you can be certain that loggers are always ready for use in your\nproject code.
The full documentation for dictConfig format\nis the best source of information about logging configuration dictionaries.\nHowever, to give you a taste of what is possible, here are several examples.
\nTo begin, here’s a small configuration that will allow you to output all log\nmessages to the console:
\nsettings.pyimport os\n\nLOGGING = {\n "version": 1,\n "disable_existing_loggers": False,\n "handlers": {\n "console": {\n "class": "logging.StreamHandler",\n },\n },\n "root": {\n "handlers": ["console"],\n "level": "WARNING",\n },\n}\nThis configures the parent root logger to send messages with the\nWARNING level and higher to the console handler. By adjusting the level to\nINFO or DEBUG you can display more messages. This may be useful during\ndevelopment.
Next we can add more fine-grained logging. Here’s an example of how to make the\nlogging system print more messages from just the django named\nlogger:
\nsettings.pyimport os\n\nLOGGING = {\n "version": 1,\n "disable_existing_loggers": False,\n "handlers": {\n "console": {\n "class": "logging.StreamHandler",\n },\n },\n "root": {\n "handlers": ["console"],\n "level": "WARNING",\n },\n "loggers": {\n "django": {\n "handlers": ["console"],\n "level": os.getenv("DJANGO_LOG_LEVEL", "INFO"),\n "propagate": False,\n },\n },\n}\nBy default, this config sends messages from the django logger of level\nINFO or higher to the console. This is the same level as Django’s default\nlogging config, except that the default config only displays log records when\nDEBUG=True. Django does not log many such INFO level messages. With\nthis config, however, you can also set the environment variable\nDJANGO_LOG_LEVEL=DEBUG to see all of Django’s debug logging which is very\nverbose as it includes all database queries.
You don’t have to log to the console. Here’s a configuration which writes all\nlogging from the django named logger to a local file:
\nsettings.pyLOGGING = {\n "version": 1,\n "disable_existing_loggers": False,\n "handlers": {\n "file": {\n "level": "DEBUG",\n "class": "logging.FileHandler",\n "filename": "/path/to/django/debug.log",\n },\n },\n "loggers": {\n "django": {\n "handlers": ["file"],\n "level": "DEBUG",\n "propagate": True,\n },\n },\n}\nIf you use this example, be sure to change the 'filename' path to a\nlocation that’s writable by the user that’s running the Django application.
Finally, here’s an example of a fairly complex logging setup:
\nsettings.pyLOGGING = {\n "version": 1,\n "disable_existing_loggers": False,\n "formatters": {\n "verbose": {\n "format": "{levelname} {asctime} {module} {process:d} {thread:d} {message}",\n "style": "{",\n },\n "simple": {\n "format": "{levelname} {message}",\n "style": "{",\n },\n },\n "filters": {\n "special": {\n "()": "project.logging.SpecialFilter",\n "foo": "bar",\n },\n "require_debug_true": {\n "()": "django.utils.log.RequireDebugTrue",\n },\n },\n "handlers": {\n "console": {\n "level": "INFO",\n "filters": ["require_debug_true"],\n "class": "logging.StreamHandler",\n "formatter": "simple",\n },\n "mail_admins": {\n "level": "ERROR",\n "class": "django.utils.log.AdminEmailHandler",\n "filters": ["special"],\n },\n },\n "loggers": {\n "django": {\n "handlers": ["console"],\n "propagate": True,\n },\n "django.request": {\n "handlers": ["mail_admins"],\n "level": "ERROR",\n "propagate": False,\n },\n "myproject.custom": {\n "handlers": ["console", "mail_admins"],\n "level": "INFO",\n "filters": ["special"],\n },\n },\n}\nThis logging configuration does the following things:
\nIdentifies the configuration as being in ‘dictConfig version 1’\nformat. At present, this is the only dictConfig format version.
Defines two formatters:
\nsimple, that outputs the log level name (e.g., DEBUG) and the log\nmessage.
The format string is a normal Python formatting string\ndescribing the details that are to be output on each logging\nline. The full list of detail that can be output can be\nfound in Formatter Objects.
verbose, that outputs the log level name, the log\nmessage, plus the time, process, thread and module that\ngenerate the log message.
Defines two filters:
\nproject.logging.SpecialFilter, using the alias special. If this\nfilter required additional arguments, they can be provided as additional\nkeys in the filter configuration dictionary. In this case, the argument\nfoo will be given a value of bar when instantiating\nSpecialFilter.
django.utils.log.RequireDebugTrue, which passes on records when\nDEBUG is True.
Defines two handlers:
\nconsole, a StreamHandler, which prints any INFO\n(or higher) message to sys.stderr. This handler uses the simple\noutput format.
mail_admins, an AdminEmailHandler, which\nemails any ERROR (or higher) message to the site ADMINS.\nThis handler uses the special filter.
Configures three loggers:
\ndjango, which passes all messages to the console handler.
django.request, which passes all ERROR messages to\nthe mail_admins handler. In addition, this logger is\nmarked to not propagate messages. This means that log\nmessages written to django.request will not be handled\nby the django logger.
myproject.custom, which passes all messages at INFO\nor higher that also pass the special filter to two\nhandlers – the console, and mail_admins. This\nmeans that all INFO level messages (or higher) will be\nprinted to the console; ERROR and CRITICAL\nmessages will also be output via email.
If you don’t want to use Python’s dictConfig format to configure your\nlogger, you can specify your own configuration scheme.
\nThe LOGGING_CONFIG setting defines the callable that will\nbe used to configure Django’s loggers. By default, it points at\nPython’s logging.config.dictConfig() function. However, if you want to\nuse a different configuration process, you can use any other callable\nthat takes a single argument. The contents of LOGGING will\nbe provided as the value of that argument when logging is configured.
If you don’t want to configure logging at all (or you want to manually\nconfigure logging using your own approach), you can set\nLOGGING_CONFIG to None. This will disable the\nconfiguration process for Django’s default logging.
Setting LOGGING_CONFIG to None only means that the automatic\nconfiguration process is disabled, not logging itself. If you disable the\nconfiguration process, Django will still make logging calls, falling back to\nwhatever default logging behavior is defined.
Here’s an example that disables Django’s logging configuration and then\nmanually configures logging:
\nsettings.pyLOGGING_CONFIG = None\n\nimport logging.config\n\nlogging.config.dictConfig(...)\nNote that the default configuration process only calls\nLOGGING_CONFIG once settings are fully-loaded. In contrast, manually\nconfiguring the logging in your settings file will load your logging config\nimmediately. As such, your logging config must appear after any settings on\nwhich it depends.