Celery is a popular open-source task queue/job queue that allows you to execute tasks asynchronously in the background. It is written in Python and utilizes distributed message passing to manage the execution of tasks. With celery, you can schedule tasks such as sending emails, processing images, and more.

RabbitMQ, on the other hand, is a message broker that facilitates communication between different components of a system. It is based on the Advanced Message Queuing Protocol (AMQP) and allows for the exchange of messages between a web application, a database, and more. When using Celery with RabbitMQ, the Celery worker process runs in the background and listens for tasks on a queue. When a task is received, the worker performs the task and sends a message back to the sender with the result. This allows for a separation of concerns between the sender and the worker and enables tasks to be performed asynchronously.

To use Celery with RabbitMQ, you need to install both libraries and configure them to work together. This can be done by setting the CELERY_BROKER_URL and CELERY_RESULT_BACKEND settings in your Celery configuration to point to your RabbitMQ instance.

In summary, Celery and RabbitMQ are powerful tools that can be used together to manage and process background tasks in a distributed system. They provide a flexible and robust solution for performing tasks asynchronously and can be used in a variety of different use cases.

Here’s an example of how to use Celery with RabbitMQ to perform a task asynchronously:

from celery import Celery
app = Celery('tasks', broker='pyamqp://guest@localhost//')
def add(x, y):
return x + y
result = add.apply_async(args=[4, 4])

In this example, we first import Celery and create a new Celery app, specifying the broker as pyamqp://guest@localhost// which means we are using rabbitmq as our message broker and it is running on localhost. Then, we define a simple task add which takes two numbers as input and returns their sum. After that, we use the apply_async method to add the task to the queue and pass the arguments to it. Finally, we use the get() method on the AsyncResult object returned by apply_async to retrieve the result of the task.
You need to start the celery worker in a different terminal using the celery -A tasks worker –loglevel=info command and run the above script.
You can also check the status of tasks and other details in the rabbitmq management console by visiting http://localhost:15672/ in your browser.