Asynchronous Tasks With Django and Celery
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Integrating Celery with your Django application allows you to offload time-consuming tasks, ensuring smooth user experiences. Celery is a distributed task queue that processes tasks asynchronously, preventing delays in your web app’s response time. By using Celery with Django, you can efficiently manage tasks like sending emails, processing images, and analyzing data without slowing down your application.
Celery works by leveraging a message broker like Redis to communicate between your Django app and Celery workers. This setup enables you to handle tasks outside the main execution thread, improving your app’s performance.
By the end of this tutorial, you’ll understand that:
- Celery is a distributed task queue that handles tasks outside the main Django app flow.
- Python’s Celery excels at offloading work and scheduling tasks independently.
- Using Celery in Django helps maintain app responsiveness during time-intensive tasks.
- Configuring Celery in Django involves setting up a message broker and defining tasks.
- A Celery worker performs tasks asynchronously, freeing up the main app.
- Running a task in Celery requires calling the task with
.delay()or.apply_async(). - Celery is not a message queue but uses a message broker like Redis for communication.
You’re in the right place if you’ve never used Celery in a Django app before, or if you’ve peeked into Celery’s documentation but couldn’t find your way around. You’ll learn how to configure Celery in Django to handle tasks asynchronously, ensuring your application remains responsive and efficient.
To focus this tutorial on the essentials, you’ll integrate Celery into an existing Django app. Go ahead and download the code for that app so that you can follow along:
Get Your Code: Click here to download free the sample code you’ll use to integrate Celery into your Django app.
Python Celery Basics
Celery is a distributed task queue that can collect, record, schedule, and perform tasks outside of your main program.
Note: Celery dropped support for Windows in version 4, so while you may still be able to get it to work on Windows, you’re better off using a different task queue, such as huey or Dramatiq, instead.
In this tutorial, you’ll focus on using Celery on UNIX systems, so if you’re trying to set up a distributed task queue on Windows, then this might not be the right tutorial for you.
To receive tasks from your program and send results to a back end, Celery requires a message broker for communication. Redis and RabbitMQ are two message brokers that developers often use together with Celery.
In this tutorial, you’ll use Redis as the message broker. To challenge yourself, you can stray from the instructions and use RabbitMQ as a message broker instead.
If you want to keep track of the results of your task runs, then you also need to set up a results back end database.
Note: Connecting Celery to a results back end is optional. Once you instruct Celery to run a task, it’ll do its duty whether you keep track of the task result or not.
However, keeping a record of all task results is often helpful, especially if you’re distributing tasks to multiple queues. To persist information about task results, you need a database back end.
You can use many different databases to keep track of Celery task results. In this tutorial, you’ll work with Redis both as a message broker and as a results back end. By using Redis, you limit the dependencies that you need to install because it can take on both roles.
You won’t do any work with the recorded task results in the scope of this tutorial. However, as a next step, you could inspect the results with the Redis command-line interface (CLI) or pull information into a dedicated page in your Django project.
Why Use Celery?
There are two main reasons why most developers want to start using Celery:
- Offloading work from your app to distributed processes that can run independently of your app
- Scheduling task execution at a specific time, sometimes as recurring events
Celery is an excellent choice for both of these use cases. It defines itself as “a task queue with focus on real-time processing, while also supporting task scheduling” (Source).
Even though both of these functionalities are part of Celery, they’re often addressed separately:
- Celery workers are worker processes that run tasks independently from one another and outside the context of your main service.
- Celery beat is a scheduler that orchestrates when to run tasks. You can use it to schedule periodic tasks as well.
Celery workers are the backbone of Celery. Even if you aim to schedule recurring tasks using Celery beat, a Celery worker will pick up your instructions and handle them at the scheduled time. What Celery beat adds to the mix is a time-based scheduler for Celery workers.
In this tutorial, you’ll learn how to integrate Celery with Django to perform operations asynchronously from the main execution thread of your app using Celery workers.
You won’t tackle task scheduling with Celery beat in this tutorial, but once you understand the basics of Celery tasks, you’ll be well equipped to set up periodic tasks with Celery beat.
Read the full article at https://realpython.com/asynchronous-tasks-with-django-and-celery/ »
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December 08, 2024 at 07:30PM
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