celery + djcelery problem with virtualenv and virtualenvwrapper

this is a tricky one - out of nowhere, production env boxes started failing at celery startup with:



ImportError: cannot import name current_app
when importing djcelery. Versions of celery were fine.
It turns out if you import celery and run
import celery.current_app you'll see the real problem, which is that the virtualenv binary is out of sync with the new python binary from a recent security update - specifically, os.urandom has been changed/removed.

if you have virtualenvwrapper, and you let $ENV=YOUR_ENVIRONEMNT_NAME
So the answer is:


deactivate (in case an env is running)
cd ~/$ENV_HOME (.virtualenvs, for me)
rm $ENV/bin/python
virtualenv $ENV


this will rebuild your python binary with the correct python post-security fix, without losing any other packages. happy hacking!

redis vs rabbit with django celery

if you're planning on putting a django celery app into heavy production use, your message queue matters. At SimpleRelevance we used RabbitMQ for months, since it's probably the most famous of the available options.
It took about 8 seconds to restart out celery workers, but that was fine. In general it was fairly reliable, and the way it routed tasks to different celery workers on different boxes often felt like magic.

However, getting results of a task back using rabbit as a result backend was a different story - it often took minutes, even hanging the box it was on. And these weren't big results either.

So for the record here, we switched to Redis. Not only is restarting about 3X faster, but as a results backend it also wins - no more hanging, and results come back as soon as they're ready. My sysops also tells me it was much easier to install and configure.
boom.

----
update!
actually it turns out redis starts to perform very badly when faced with a deep queue in our production environment. So the optimal setup for us turns out to be RabbitMQ for the queue handling, and Redis for the result backend.

django celery with remote worker nodes

I set up rabbitmq and celery. Plenty of good tutorials online about how to do that. Then I wanted a bunch of different linode boxen all running the same django project, with the following setup:

1 server running mysql and nothing else

1 server running nginx serving http requests and routing tasks to rabbitmq / celery

1 server running rabbitmq and celery and django

N boxes running django and celery

Turns out, it's easy!

• All of the above hook into the mysql server by setting the HOST and PORT settings in the django settings.
• Each slave celery box uses an environment variable to take care of any individual settings it might need, but in general each of them uses django-celery's BROKER_HOST and BROKER_PORT options to connect to the rabbitmq server.
• using fabric makes deploying code to all of them fairly simple

Believe it or not, rabbitmq effortlessly figures out who's got a free worker between all of your boxes and just does it.