Task Management#


Task management using Celery is available in Arches if you have a message broker like RabbitMQ or Redis. The Celery documentation provides information on broker installation.


Once you have your broker available, you will need to configure your settings. You will find this in your project’s settings.py file. Each setting that begins with the CELERY prefix will be used as a celery config, so you can configure celery by adding the configs you need

CELERY_BROKER_URL = 'amqp://guest:guest@localhost'
CELERY_RESULT_BACKEND = 'django-db'  # Use 'django-cache' if you want to use your cache as your backend

The settings you are likely to want to modify right away are the CELERY_BROKER_URL and the CELERY_RESULT_BACKEND. Your CELERY_BROKER_URL should point to your broker’s service URL. If you are using RabbitMQ, you will probably want to create a new user and password and replace ‘guest:guest’ with the new users credentials.

Your CELERY_RESULT_BACKEND is set to the Django ORM by default. If your task will be run frequently, you may want to consider a more performant option. If you’ve configured a cache for django, you could use that, or you could use another backend option that Celery supports.

Adding Tasks to Your Project#

To add additional tasks you your project, all you need to do is add a tasks.py file to your project. This could be placed in your project’s root directory (next to manage.py) or any sub-directory. However, you will likely want to put it - at least to start in the directory below root (next to urls.py).

Here’s an example of a very simple task in tasks.py:

from __future__ import absolute_import, unicode_literals
from celery import shared_task

def add(x, y):
    return x + y

To call your task, just import tasks into the module that will run it.

Running Celery#

For your tasks to run both your broker service and a Celery worker need to be running. For production you will likely want to run a worker as service. One way to do this is to use supervisord. For more information see: Setting up Supervisord for Celery

However for development, you probably want to run your worker in a terminal. To do so just cd into your project’s root directory with your virtual environment activated and run:

python manage.py celery start

Users of Apple Silicon Macs may encounter billiard.exceptions.WorkerLostError following a warning emitted by the OS explaining it is “crashing instead” rather than unsafely calling fork(). Until this issue is resolved by celery, launching like so will silence the errors:

OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES python manage.py celery start

Note that in the event of celery errors which do not clearly indicate what is breaking or preventing your tasks from succeeding, we recommend running the following command instead for the purpose of debugging and isolating any unhandled exceptions:

celery -A [app_name] worker --loglevel=warning

Once the worker is running you should be able check your database and see you task result output with the following query:

select * from django_celery_results_taskresult;

If you want to monitor your tasks with a realtime console, you can use Flower.