How to configure and use logging

See also

• Django logging reference
• Django logging overview

Django provides a working default logging configuration that is readily extended.

Make a basic logging call

To send a log message from within your code, you place a logging call into it.

Don’t be tempted to use logging calls in settings.py.

The way that Django logging is configured as part of the setup() function means that logging calls placed in settings.py may not work as expected, because logging will not be set up at that point. To explore logging, use a view function as suggested in the example below.

First, import the Python logging library, and then obtain a logger instance with logging.getLogger(). Provide the getLogger() method with a name to identify it and the records it emits. A good option is to use __name__ (see Use logger namespacing below for more on this) which will provide the name of the current Python module as a dotted path:

import logging
logger = logging.getLogger(__name__)

It’s a good convention to perform this declaration at module level.

And then in a function, for example in a view, send a record to the logger:

def some_view(request):
    ...
    if some_risky_state:
        logger.warning("Platform is running at risk")

When this code is executed, a LogRecord containing that message will be sent to the logger. If you’re using Django’s default logging configuration, the message will appear in the console.

The WARNING level used in the example above is one of several logging severity levels: DEBUG, INFO, WARNING, ERROR, CRITICAL. So, another example might be:

logger.critical("Payment system is not responding")

Important

Records with a level lower than WARNING will not appear in the console by default. Changing this behavior requires additional configuration.

Customize logging configuration

Although Django’s logging configuration works out of the box, you can control exactly how your logs are sent to various destinations - to log files, external services, email and so on - with some additional configuration.

You can configure:

• logger mappings, to determine which records are sent to which handlers
• handlers, to determine what they do with the records they receive
• filters, to provide additional control over the transfer of records, and even modify records in-place
• formatters, to convert LogRecord objects to a string or other form for consumption by human beings or another system

There are various ways of configuring logging. In Django, the LOGGING setting is most commonly used. The setting uses the dictConfig format, and extends the default logging configuration.

See Configuring logging for an explanation of how your custom settings are merged with Django’s defaults.

See the Python logging documentation for details of other ways of configuring logging. For the sake of simplicity, this documentation will only consider configuration via the LOGGING setting.

Basic logging configuration

When configuring logging, it makes sense to

Create a LOGGING dictionary

In your settings.py:

LOGGING = {
    "version": 1,  # the dictConfig format version
    "disable_existing_loggers": False,  # retain the default loggers
}

It nearly always makes sense to retain and extend the default logging configuration by setting disable_existing_loggers to False.

Configure a handler

This example configures a single handler named file, that uses Python’s FileHandler to save logs of level DEBUG and higher to the file general.log (at the project root):

LOGGING = {
    # ...
    "handlers": {
        "file": {
            "class": "logging.FileHandler",
            "filename": "general.log",
        },
    },
}

Different handler classes take different configuration options. For more information on available handler classes, see the AdminEmailHandler provided by Django and the various handler classes provided by Python.

Logging levels can also be set on the handlers (by default, they accept log messages of all levels). Using the example above, adding:

{
    "class": "logging.FileHandler",
    "filename": "general.log",
    "level": "DEBUG",
}

would define a handler configuration that only accepts records of level DEBUG and higher.

Configure a logger mapping

To send records to this handler, configure a logger mapping to use it for example:

LOGGING = {
    # ...
    "loggers": {
        "": {
            "level": "DEBUG",
            "handlers": ["file"],
        },
    },
}

The mapping’s name determines which log records it will process. This configuration ('') is unnamed. That means that it will process records from all loggers (see Use logger namespacing below on how to use the mapping name to determine the loggers for which it will process records).

It will forward messages of levels DEBUG and higher to the handler named file.

Note that a logger can forward messages to multiple handlers, so the relation between loggers and handlers is many-to-many.

If you execute:

logger.debug("Attempting to connect to API")

in your code, you will find that message in the file general.log in the root of the project.

Configure a formatter

By default, the final log output contains the message part of each log record. Use a formatter if you want to include additional data. First name and define your formatters - this example defines formatters named verbose and simple:

LOGGING = {
    # ...
    "formatters": {
        "verbose": {
            "format": "{name} {levelname} {asctime} {module} {process:d} {thread:d} {message}",
            "style": "{",
        },
        "simple": {
            "format": "{levelname} {message}",
            "style": "{",
        },
    },
}

The style keyword allows you to specify { for str.format() or $ for string.Template formatting; the default is $.

See LogRecord attributes for the LogRecord attributes you can include.

To apply a formatter to a handler, add a formatter entry to the handler’s dictionary referring to the formatter by name, for example:

"handlers": {
    "file": {
        "class": "logging.FileHandler",
        "filename": "general.log",
        "formatter": "verbose",
    },
}

Use logger namespacing

The unnamed logging configuration '' captures logs from any Python application. A named logging configuration will capture logs only from loggers with matching names.

The namespace of a logger instance is defined using getLogger(). For example in views.py of my_app:

logger = logging.getLogger(__name__)

will create a logger in the my_app.views namespace. __name__ allows you to organize log messages according to their provenance within your project’s applications automatically. It also ensures that you will not experience name collisions.

A logger mapping named my_app.views will capture records from this logger:

LOGGING = {
    # ...
    "loggers": {
        "my_app.views": {...},
    },
}

A logger mapping named my_app will be more permissive, capturing records from loggers anywhere within the my_app namespace (including my_app.views, my_app.utils, and so on):

LOGGING = {
    # ...
    "loggers": {
        "my_app": {...},
    },
}

You can also define logger namespacing explicitly:

logger = logging.getLogger("project.payment")

and set up logger mappings accordingly.

Using logger hierarchies and propagation

Logger naming is hierarchical. my_app is the parent of my_app.views, which is the parent of my_app.views.private. Unless specified otherwise, logger mappings will propagate the records they process to their parents - a record from a logger in the my_app.views.private namespace will be handled by a mapping for both my_app and my_app.views.

To manage this behavior, set the propagation key on the mappings you define:

LOGGING = {
    # ...
    "loggers": {
        "my_app": {
            # ...
        },
        "my_app.views": {
            # ...
        },
        "my_app.views.private": {
            # ...
            "propagate": False,
        },
    },
}

propagate defaults to True. In this example, the logs from my_app.views.private will not be handled by the parent, but logs from my_app.views will.

Configure responsive logging

Logging is most useful when it contains as much information as possible, but not information that you don’t need - and how much you need depends upon what you’re doing. When you’re debugging, you need a level of information that would be excessive and unhelpful if you had to deal with it in production.

You can configure logging to provide you with the level of detail you need, when you need it. Rather than manually change configuration to achieve this, a better way is to apply configuration automatically according to the environment.

For example, you could set an environment variable DJANGO_LOG_LEVEL appropriately in your development and staging environments, and make use of it in a logger mapping thus:

"level": os.getenv("DJANGO_LOG_LEVEL", "WARNING")

- so that unless the environment specifies a lower log level, this configuration will only forward records of severity WARNING and above to its handler.

Other options in the configuration (such as the level or formatter option of handlers) can be similarly managed.