I recently wrote a couple Celery tasks that are purely IO bound. So instead of using the default multiprocessing execution pool, I used the Eventlet execution pool. With just a small change in Celery settings, I was off to the races.

Wrong! After some amount of time, it just sits at 100% CPU and no longer processes tasks. Unfortunately, Celery calls monkey_patch a little late when coupled with Django. Django does some magic of its own to various components so monkey_patch needs to be called before Django does any initialization. After a little digging, I found I can just set an environment variable to prevent Celery from doing the monkey patching and at the same time use it to signal manage.py to call monkey_patch before the initialization my Django app.

Just add this to the top of your manage.py:

if os.environ.get('EVENTLET_NOPATCH'):
    import eventlet
    import eventlet.debug

    eventlet.monkey_patch()
    eventlet.debug.hub_prevent_multiple_readers(False)

    eventlet_timeout = os.environ.get('EVENTLET_TIMEOUT')
    if eventlet_timeout:
        eventlet.debug.hub_blocking_detection(True, float(eventlet_timeout))

Now when starting celeryd just add the environment variable EVENTLET_NOPATCH='yes' to your manage.py command

EVENTLET_NOPATCH='yes' python ./manage.py celeryd -c 1000 -Q eventlet_tasks -P eventlet


Also, if you’re worker seems to be running a little slow, you can now add EVENTLET_TIMEOUT='1.0' to cause Eventlet to print a stacktrace of the blocking thread to stderr. hub_blocking_detection takes a float in number of seconds to set the alarm for.

EVENTLET_TIMEOUT='0.1' EVENTLET_NOPATCH='yes' python ./manage.py celeryd -c 10 -Q eventlet_tasks -P eventlet


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