Deploy a Celery Worker

While processing web requests in your application, you might want to offload long-running tasks to an asynchronous background process (often called a worker). Render provides the background worker service type for this purpose.

This quickstart demonstrates using background workers with the Celery task queue.

Architecture

Our application's architecture will include the following components:

• A Flask web service that lets users create new asynchronous tasks for Celery to process
• A Celery background worker to process tasks
• A Render Key Value instance to act as the task broker between clients (Flask web service) and workers (Celery background worker)
• A Flower web service to monitor and administer Celery

Deploying to Render

We can deploy these services to Render in any of the following ways:

• Declaring services within your repository with a render.yaml file
• Manually configuring services in the Render Dashboard

Each option is described below.

Using render.yaml

1. Fork our render-examples/celery example on GitHub.

   This repo defines a render.yaml file that sets up the four services.

2. In the Render Dashboard, go your workspace's Blueprints page and click New Blueprint Instance.

3. Connect your repository fork.

4. In the deploy window, click Deploy Blueprint.

That's it! Your services will be live at their respective .onrender.com URLs as soon as the build finishes. Try out the web services using their Render URLs and play around with the small application.

Note that Flower's dashboard is not intended for public consumption. We recommend securing it by adding authentication as described in the Flower docs.

Deploying manually

1. Create a new Render Key Value instance with the settings below by following the Key Value documentation. Make a note of the instance's internal URL, which looks like redis://red-xxxxxxxxxxxxxxxxxxxx:6379.

   Maxmemory Policy	noeviction (recommended for queues/persistence)
   Plan	256mb

   We choose the noeviction maxmemory policy to ensure our instance does not delete scheduled tasks upon reaching memory capacity by preventing the creation of new tasks. Choosing allkeys-lru instead would allow the creation of new tasks by discarding the oldest potentially incomplete tasks.

   We choose the 256mb compute plan as it is the smallest compute plan with persistence, which ensures that we retain tasks when the instance restarts.

2. Fork render-examples/celery on GitHub and create a new Background Worker on Render using your fork.

   Language	Python 3
   Build Command	pip install -r requirements.txt
   Start Command	celery --app tasks worker --loglevel info --concurrency 4

   Add the following environment variable to the background worker:

   Key	Value
   CELERY_BROKER_URL	<Internal Key Value URL>, the internal URL from step 1.

3. Create a new Web Service for Flask using the same repo you created in Step 2.

   Language	Python 3
   Build Command	pip install -r requirements.txt
   Start Command	gunicorn app:app

   Add the following environment variable to the web service:

   Key	Value
   CELERY_BROKER_URL	<Internal Key Value URL>, the internal URL from step 1.

4. Once the Flask web service is live, navigate to its URL. Fill out and submit the form to create a new Celery task.

5. Create a new web service for Flower using the same repo you created in Step 2.

   Language	Python 3
   Build Command	pip install -r requirements.txt
   Start Command	celery flower --app tasks --loglevel info

   Add the following environment variable for the web service:

   Key	Value
   CELERY_BROKER_URL	<Internal Key Value URL>, the internal URL from step 1.

   Note that Flower's dashboard is not intended for public consumption. We recommend securing it by adding authentication as described in the Flower docs.

6. Once the Flower web service is live, navigate to its URL and browse the tabs. You can view information such as how many worker processes are running and how many tasks have been completed.

You can also use a Celery background worker from Django. See our Django quick start and the Django integration guide in Celery docs.

See Specifying a Python Version if you need to customize the version of Python used for your app.