In this case, using decorator is the right way to ensure you’ll have everything in place. If the tasks you write will serve in reusable apps that can’t depend on the project itself, then app instance can’t be imported directly. No matter if a task is registered or shared, Celery is able to discover it. # We can have either registered send_import_summary(): Well done so far - now it’s time to move to tasks.py in your app module. However, there’s no rush in updating it as this well-adapted format is still supported. There is new lowercase settings standard introduced in the latest versions of Celery Beat. # We're going to have our tasks rolling soon, so that will be handy CELERY_BEAT_SCHEDULE = # If time zones are active (USE_TZ = True) define your local CELERY_TIMEZONE = 'Europe/Warsaw' The most simple config will look like this: CELERY_BROKER_URL = 'redis://localhost:6379' With basics taken care of, let’s move on to settings.py and set up the Celery Beat’s specifics. The other way of configuration, besides using ‘config_from_object’, is assigning config values directly within the app - check out the documentation for further details. # Celery will apply all configuration keys with defined namespace app.config_from_object('nf:settings', namespace='CELERY') # Set default Django settings os.tdefault('DJANGO_SETTINGS_MODULE', 'ttings') Additionally, each app _init_.py should point to default config: default_app_config = 'my_'Īn exception is _init_.py in your project folder, which stores Celery configuration: from _future_ import absolute_import, unicode_literalsĪt this point in the configuration, the next step is to move on to celery.py: from _future_ import absolute_import, unicode_literals Remember to have all apps.py files setup. In this guide I’ll work with the standard Django structure: proj How to use Celery Beat?Ĭelery config may be tricky at times even for top software developers. At the later stage, you’ll also use benefits of django_celery_beat=1.1.1. In the next step, you need to ensure that either your virtual environment or container are equipped with packages: celery=4.20 and redis=2.10.6. With Redis installed you just call ‘redis-server’ to run it. Redis has a reputation of being far easier to install and administrate, what makes my choice simple. An alternative for Redis is RabbitMQ - you can read more about it in the official Celery documentation. My choice is Redis 4.0.10, which also is deployed in a container. To get Celery on wheels, you’ll need a broker to send and receive messages. Be aware that Celery is a project with minimal funding and doesn’t support Microsoft Windows. Its latest version (4.2) still supports Python 2.7, but since the new ones won’t, i t’s recommended to use Python 3 if you want to work with Celery.įor the purpose of this article, I ’m running Django 2.0.6 from Python 3.6.5 image in Docker container. Let’s get to work! How to start working with Celery?įirst of all, you’ll need to have Celery running. In this guide, you will find out how it can help you manage even the most tedious of tasks. What is Celery Beat? It combines Celery, a well-known task delegation tool, with a nifty scheduler called Beat.
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