Celery and Rabbit

Kitsune uses Celery to enable offline task processing for long-running jobs like sending email notifications and re-rendering the Knowledge Base.

Though Celery supports multiple message backends, we use, and recommend that you use, RabbitMQ. RabbitMQ is an AMQP message broker written in Erlang.

When is Celery Appropriate

You can use Celery to do any processing that doesn’t need to happen in the current request-response cycle. Examples are generating thumbnails, sending out notification emails, updating content that isn’t about to be displayed to the user, and others.

Ask yourself the question: “Is the user going to need this data on the page I’m about to send them?” If not, using a Celery task may be a good choice.



RabbitMQ should be installed via your favorite package manager. It can be installed from source but has a number of Erlang dependencies.


RabbitMQ takes very little configuration.

# Start the server.
sudo rabbitmq-server -detached

# Set up the permissions.
rabbitmqctl add_user kitsune kitsune
rabbitmqctl add_vhost kitsune
rabbitmqctl set_permissions -p kitsune kitsune ".*" ".*" ".*"

That should do it. You may need to use sudo for rabbitmqctl. It depends on the OS and how Rabbit was installed.



Celery (and Django-Celery) is part of our dependencies. You shouldn’t need to do any manual installation.

Configuring and Running

We set some reasonable defaults for Celery in settings.py. These can be overriden either in settings_local.py or via the command line when running manage.py celeryd.

In settings_local.py you should set at least this, if you want to use Celery:


This defaults to True, which causes all task processing to be done online. This lets you run Kitsune even if you don’t have Rabbit or want to deal with running workers all the time.

You can also configure the log level or concurrency. Here are the defaults:


Then to start the Celery workers, you just need to run:

./manage.py celeryd

This will start Celery with the default number of worker threads and the default logging level. You can change those with:

./manage.py celeryd --log-level=DEBUG -c 10

This would start Celery with 10 worker threads and a log level of DEBUG.