Django's logging configuration facilities, which arrived in version 1.3, have greatly eased (and standardized) the process of configuring logging for Django projects. When building complex and interactive web applications at Caktus, we've found that detailed (and properly configured!) logs are key to successful and efficient debugging. Another step in that process—which can be particularly useful in environments where you have multiple web servers—is setting up a centralized logging server to receive all your logs and make them available through an easily accessible web interface. There are a number useful tools to do this, but one we've found that works quite well is Graylog2. Installing and configuring Graylog2 is outside the scope of this post, but there are plenty of tutorials on how to do so accessible through your search engine of choice.

Once you have it setup, getting logs flowing to Graylog2 from Django is relatively straightforward. First, grab a copy of the graypy package from PyPI and add it to your requirements file:

pip install -U graypy

Next, add the following configuration inside the LOGGING['handlers'] dictionary in your settings.py , where graylog2.example.com is the hostname of your Graylog2 server:

LOGGING = { # ... 'handlers' : { # ... 'gelf' : { 'class' : 'graypy.GELFHandler' , 'host' : 'graylog2.example.com' , 'port' : 12201 , }, }, }

You'll most likely want to tell your project's top-level logger to send logs to the new gelf handler as well, like so:

LOGGING = { # ... 'loggers' : { # ... 'projectname' : { # mail_admins will only accept ERROR and higher 'handlers' : [ 'mail_admins' , 'gelf' ], 'level' : 'DEBUG' , }, }, }

With this configuration in place, log messages with a severity of DEBUG or greater that are sent to the projectname logger should begin flowing to Graylog2. You can easily test this by opening Django's python manage.py shell , grabbing the logger manually, and sending a log message:

import logging logger = logging . getLogger ( 'projectname' ) logger . debug ( 'testing message to graylog2' )

You should see the message show up in Graylog2 almost immediately.

Now, this is all well and good, but if you want to use your Graylog2 server for multiple projects, you'll quickly find that all the log messages are interspersed and it can be difficult to tell what messages are coming from what projects. To address this issue, Graylog2 supports the concept of "streams," that is, filters that you can setup (which work only on incoming messages, not existing messages) to show messages that match only certain criteria. A simple solution here could be to filter on the hostname of the originating web servers, but this may not scale well in environments like Amazon Web Services' EC2 where you're often adding or removing web servers. As a better alternative, you can add metadata to log messages at the Python level prior to sending them to Graylog2 that will help you more easily identify the messages for different projects.

To do this, you need to use a feature of Python logging filters. While filters are most commonly used to filter out certain types of messages from being emitted altogether (as discussed in the Django documentation), they can also be used to modify the log records in transit and impart contextual metadata to be transmitted with the original message. To add this to our logging configuration, first create the following filter class in a Python module accessible from your project:

class StaticFieldFilter ( logging . Filter ): """ Python logging filter that adds the given static contextual information in the ``fields`` dictionary to all logging records. """ def __init__ ( self , fields ): self . static_fields = fields def filter ( self , record ): for k , v in self . static_fields . items (): setattr ( record , k , v ) return True

Next, we need to load this filter in our logging configuration and tell the gelf logger to pass records through it:

LOGGING = { # ... 'filters' : { # ... 'static_fields' : { '()' : 'projectname.core.logfilters.StaticFieldFilter' , 'fields' : { 'project' : 'projectname' , # CHANGEME 'environment' : 'staging' , # can be overridden in local_settings.py }, }, }, 'handlers' : { # ... 'gelf' : { 'class' : 'graypy.GELFHandler' , 'host' : 'graylog2.example.com' , 'port' : 12201 , 'filters' : [ 'static_fields' ], }, # ... }, }

The configuration under filters instantiates the StaticFieldFilter class and passes in the static fields that we want to attach to all of our log records. In this case, two fields are attached, a 'project' field with value 'projectname' and an 'environment' field with value 'staging' . The configuration for the gelf logger is the same, with the addition of the static_fields filter on the last line.

With these two items in place, you should be able to create streams via the Graylog2 web interface to trap and display records that match the combination of project and environment names that you're looking for.

Lastly, as an optional addition to this logging configuration, it may be desirable to filter out Django request objects from being sent to Graylog2. The request is added to log messages created by Django's exception handler and may contain sensitive information or in some cases may not be capable of being pickled (which is necessary to encode and send it with the log message). You can remove them from log messages with the following filter:

class RequestFilter ( logging . Filter ): """ Python logging filter that removes the (non-pickable) Django ``request`` object from the logging record. """ def filter ( self , record ): if hasattr ( record , 'request' ): del record . request return True

and this corresponding filter configuration:

LOGGING = { # ... 'filters' : { # ... 'django_exc' : { '()' : 'projectname.core.logfilters.RequestFilter' , }, }, 'handlers' : { # ... 'gelf' : { 'class' : 'graypy.GELFHandler' , 'host' : 'graylog2.example.com' , 'port' : 12201 , 'filters' : [ 'static_fields' , 'django_exc' ], }, # ... }, }

With this configuration in place, you can have log messages flowing to Graylog2 from any number of project and server environment combinations, limited only by the resources of the log server itself.