Celery for Twisted: manage Celery tasks from twisted using the Deferred API
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.gitignore Updated gitignore Dec 19, 2016



Celery for Twisted: manage Celery tasks from twisted using the Deferred API

PyPI version


Celery is an outstanding choice for dispatching short-lived, computationally-expensive tasks to a distributed backend system. Note the emphasis; Celery is ill-suited for tasks tasks that require updating some in-memory representation with out-of-process data. If you want a specific process to read data from standard input, for instance, good luck...

Twisted can be though of as having the opposite problem. Twisted is very good at maintaining and updating in-memory representations over extended periods of time, but fails miserably at performing expensive computations. Twisted notably has no built-in constructs for managing distributed task queues.

As its name suggests, txCelery elegantly couples these two frameworks together, and in so doing allows them to compliment each other. Developers can now create long-running processes whose expensive subroutines can be farmed out to a distributed computational cluster.

And best of all, txCelery fully leverages Twisted's Deferred API, so there's no need to drink yet another framework's Koolaid.


Note: These instructions assume you have a working installation of Celery.

The recommended way of installing txCelery is through pip. PyPI will contain the latest stable version of txCelery.


First, install pip. On Debian/Ubuntu systems, this is achieved with the sudo apt-get install python-pip command.

Next, let's install the latest stable version of txCelery:

  • pip install txCelery --user to install for your user
  • sudo pip install txCelery to install system-wide


The latest development files can be obtained by cloning the github repo, checking out the dev branch, and running python setup.py develop --user. It is strongly recommended that you do not use the development version in production.


txCelery's API is so simple it brings tears to our eyes. There are exactly one and a half constructs. Yes, one and one half.

The "one" construct: wrapping a Celery task

In order to use a Celery task with Twisted, you must wrap your Celery task with a DeferrableTask-class decorator. In your tasks.py (or wherever you keep your Celery tasks):

from celery import Celery
from txcelery.defer import DeferrableTask

app = Celery('tasks', backend='amqp', broker='amqp://guest@localhost//')

def my_task(*args, **kw):
	# do something

There's just one thing to bear in mind: contrary to the Celery documentation's insistance that @app.task be the top-most decorator in your function definition, DeferrableTask expects to wrap a celery task and will throw a TypeError if it receives anything else.

Once you've wrapped your task with the DeferrableTask-class decorator, you'll find all the usual task methods like delay, apply_async, subtask, chain, etc. The difference is that those which used to return a celery.result.AsyncResult will now return a twisted.internet.defer.Deferred instance when they are called (ok, actually a subclass of Deferred, but more on that in a second).

The "one half": a (Deferred) rose by any other name...

So what of this subclass of Twisted's Deferred? It can be thought of as a Deferred that also gives transparent access to all the attributes and methods of it's associated AsyncResult instance. It can be thought of in those terms because that's exactly what it is, and that's why this part of the API only constitutes half of a thing to learn.

Our subclass is called DeferredTask, it lives in txcelery.defer and as far as Twisted is concerned it's just a plain old Deferred. DeferredTasks can be chained, passed to maybeDeferred, joined via gatherResults and DeferredList, etc.

DeferredTask monitors the state of the task and fires with a callback if the task succeeds, or with an errback if the task fails. If the task is revoked, DeferredTask fires with an errback containing a twisted.defer.CancelledError as it's Failure value.

In summary

  1. Wrap a task with a DeferrableTask
  2. Call task methods and obtain a DeferredTask instance in lieu of an AsyncResult
  3. Use DeferredTask as if it were a regular Deferred or a regular AsyncResult

And that's really all there is to it.