How to Integrate Celery with Django

How to Integrate Celery with Django: A Step-by-Step Guide for Asynchronous Task Processing


Celery is a powerful, distributed task queue system that integrates seamlessly with Django. It allows you to run time-consuming tasks asynchronously, improving your application's performance and responsiveness. By offloading heavy computations, long-running processes, or I/O-bound tasks to Celery, you can ensure that your Django application remains fast and responsive, even when dealing with complex operations.

In this tutorial, we'll walk you through the process of integrating Celery with your Django project. We'll cover installation, configuration, and usage, providing you with a solid foundation for implementing asynchronous task processing in your Django applications.

Before we dive into the steps, it's important to note that Celery uses a message broker to facilitate communication between your Django application and Celery workers. In this tutorial, we'll use Redis as our message broker due to its simplicity and performance. However, Celery supports other brokers like RabbitMQ as well.

Now, let's get started with the integration process!

1. Install Celery

In this step, we'll install Celery with Redis support. Redis will serve as our message broker, allowing efficient communication between our Django application and Celery workers.

1.1 Install Celery with Redis support

Open your terminal and run the following command to install Celery with Redis support:

pip install celery[redis]

This command installs Celery along with the necessary dependencies for Redis integration.

2. Configure Celery

Now that we have Celery installed, we need to configure it to work with our Django project. We'll create a Celery configuration file and modify our Django settings to include Celery-specific configurations.

2.1 Create Celery configuration file

We'll create a file in your main application directory (where is located). This file will contain the basic configuration for Celery.

Create and edit the file mysaas/

import os

from celery import Celery

# Set the default Django settings module for the 'celery' program
# This will depend on the name of your application - see or to find out
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mysaas.settings')

app = Celery('mysaas')

# Pull all Celery settings from the Django by using the CELERY_ prefix
# This will allow you to configure Celery using the Django file.
# - namespace='CELERY' means all celery-related configuration keys
#   should have a `CELERY_` prefix.
app.config_from_object('django.conf:settings', namespace='CELERY')

# Load task modules from all registered Django apps.

This configuration sets up Celery to use your Django settings and automatically discover tasks in your Django apps.

2.2 Configure Celery broker settings

Next, we'll configure the Celery broker settings in your Django file. We'll set the BROKER_URL to our Redis host and configure serialization to use Pickle instead of JSON for better performance.

Edit mysaas/ and add the following configurations:

CELERY_BROKER_URL = "redis://localhost:6379/0"
CELERY_CACHE_BACKEND = "django-cache"
CELERY_ACCEPT_CONTENT = ["application/json", "application/x-python-serialize"]

These settings configure Celery to use Redis as the message broker, Django's database as the result backend, and Pickle for serialization.

3. Run Celery

With Celery configured, we now need to run the Celery worker process. This process will be responsible for executing the tasks we define.

3.1 Start the Celery worker

Open a new terminal window and run the following command to start the Celery worker:

celery -A mysaas worker -l info

This command starts a Celery worker for your Django project (mysaas) with the log level set to "info". The worker will now be ready to process tasks.

4. Create Celery Tasks

Now that we have Celery up and running, let's create a simple task to demonstrate how to define and use Celery tasks in your Django project.

4.1 Define a Celery task

Create a new file in one of your Django app directories. For this example, we'll create it in myapp/

from celery import shared_task

def add(a, b):
    result = a + b
    return result

This simple task adds two numbers together. The @shared_task decorator allows the task to be used by any part of your Django project.

5. Calling Celery Tasks

With our task defined, we can now call it asynchronously from our Django views or any other part of our application.

5.1 Call a Celery task

Here's an example of how to call the task we just created:

from myapp.tasks import add

result = add.delay(3, 5)
if result.ready():
    print('Task result: ', result.get(timeout=10))

In this example, we use the delay() method to call our task asynchronously. This means the main thread will continue to run while the task is being processed in the background by the Celery worker.

The ready() method checks if the task has completed, and get() retrieves the result. Note that generally, you won't want to depend on the result of the task immediately, as this can negate the benefits of asynchronous processing. However, the get() method is useful for cases where you do need to wait for the result.


Congratulations! You've successfully integrated Celery with your Django project. You now have the power to run time-consuming tasks asynchronously, greatly improving the performance and responsiveness of your Django application.

Remember, this tutorial covered the basics of Celery integration. Celery offers many more advanced features, such as periodic tasks, task prioritization, and error handling. As you become more comfortable with Celery, explore these features to fully leverage the power of asynchronous task processing in your Django projects.

By using Celery, you can build more scalable and efficient Django applications that can handle complex, time-consuming operations without compromising user experience. Happy coding!