How to configure and use logging¶
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.