Part 1, Chapter 1

This chapter looks at the basic building blocks of Celery and producer/consumer-based task queues in general.


By the end of this chapter, you will be able to:

  1. Explain why you may want to use a task queue like Celery
  2. Describe the basic producer/consumer model and how it relates to Celery

Why Celery?

Celery is an open source, asynchronous task queue that's often coupled with Python-based web frameworks like FastAPI, Django, or Flask to manage background work outside the typical request/response cycle. In other words, you can return an HTTP response back immediately and run the process as a background task, instead of forcing the user to wait for the task finish.

Potential use cases:

  1. You've developed a messaging app that provides "@ mention" functionality where a user can reference another user via @<user_name> in a comment. The mentioned user then receives an email notification. This is probably fine to handle synchronously for a single mention, but if one user mentions ten users in a single comment, you'll need to send ten different emails. Since you'll probably have to talk to an external service you could run into network issues. Regardless, this is a task that you'll want to run in the background.
  2. If your messaging app allows a user to upload a profile image, you'll probably want to use a background process to generate a thumbnail.

As you build out your web app, you should try to ensure that the response time of a particular view is lower than 500ms. Application Performance Monitoring tools like New Relic or Scout can be used to help surface potential issues and isolate longer process that could be moved to a background process managed by Celery.

Celery vs FastAPI's BackgroundTasks

It's worth noting that you can leverage FastAPI's BackgroundTasks class, which comes directly from Starlette, to run tasks in the background.

For example:

from fastapi import BackgroundTasks

def send_email(email, message):

async def ping(background_tasks: BackgroundTasks):
    background_tasks.add_task(send_email, "[email protected]", "Hi!")
    return {"message": "pong!"}

So, when should you use Celery instead of BackgroundTasks?

  1. CPU intensive tasks: Celery should be used for tasks that perform heavy background computations since BackgroundTasks runs in the same event loop that serves your app's requests.
  2. Task queue: If you require a task queue to manage the tasks and workers, you should use Celery. Often you'll want to retrieve the status of a job and then perform some action based on the status -- i.e., send an error email, kick off a different background task, or retry the task. Celery manages all this for you.

Celery vs RQ vs Huey

RQ (Redis Queue) and Huey are other open source, Python-based task queues that are often compared to Celery. While the core logic of Celery, RQ, and Huey are very much the same in that they all use the producer/consumer model, they differ in that:

  1. Both RQ and Huey are much simpler to use and easier to learn than Celery. However, both lack some features and can only be used with Redis.
  2. Celery is quite a bit more complex and harder to implement and learn, but it's much more flexible and has many more features. It supports Redis along with a number of other backends.

For more, review this Stack Overflow answer.

Message broker and Result backend

Let's start with some terminology:

  • Message broker is an intermediary program used as the transport for producing or consuming tasks.
  • Result backend is used to store the result of a Celery task.

The Celery client is the producer which adds a new task to the queue via the message broker. Celery workers then consume new tasks from the queue, again, via the message broker. Once processed, results are then stored in the result backend.

In terms of tools, RabbitMQ is arguably the better choice for a message broker since it supports AMQP (Advanced Message Queuing Protocol) while Redis is fine as your result backend.

For simplicity, we'll use Redis for both our message broker and backend for the first part of this course. We'll then switch to RabbitMQ for our message broker in the deployment chapter. Feel free to use a different message broker and/or result backend based on your specific requirements.

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