Celery vs Django Channels: What are the differences?
Developers describe Celery as "Distributed task queue". Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. On the other hand, Django Channels is detailed as "It extends Django's abilities beyond HTTP - to handle WebSockets, chat protocols, IoT protocols". It does this by taking the core of Django and adding a fully asynchronous layer underneath, running Django itself in a synchronous mode but handling connections and sockets asynchronously, and giving you the choice to write in either style.
Celery can be classified as a tool in the "Message Queue" category, while Django Channels is grouped under "Frameworks (Full Stack)".
Celery and Django Channels are both open source tools. It seems that Celery with 13.1K GitHub stars and 3.36K forks on GitHub has more adoption than Django Channels with 3.94K GitHub stars and 542 GitHub forks.
According to the StackShare community, Celery has a broader approval, being mentioned in 355 company stacks & 455 developers stacks; compared to Django Channels, which is listed in 10 company stacks and 6 developer stacks.
What is Celery?
What is Django Channels?
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What tools integrate with Django Channels?
All of our background jobs (e.g., image resizing, file uploading, email and SMS sending) are done through Celery (using Redis as its broker). Celery's scheduling and retrying features are especially useful for error-prone tasks, such as email and SMS sending.
For orchestrating the creation of the correct number of instances, managing errors and retries, and finally managing the deallocation of resources we use RabbitMQ in conjunction with the Celery Project framework, along with a self-developed workflow engine.
We maintain a fork of Celery 3 that adds HTTPS support for Redis brokers. The Winning Model currently uses Celery 3 because Celery 4 dropped support for Windows.
We plan on migrating to Celery 4 once Azure ASE supports Linux apps
We used celery, in combination with RabbitMQ and celery-beat, to run periodic tasks, as well as some user-initiated long-running tasks on the server.