Celery vs Resque: What are the differences?
Celery: 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; Resque: A Redis-backed Ruby library for creating background jobs, placing them on multiple queues, and processing them later. Background jobs can be any Ruby class or module that responds to perform. Your existing classes can easily be converted to background jobs or you can create new classes specifically to do work. Or, you can do both.
Celery can be classified as a tool in the "Message Queue" category, while Resque is grouped under "Background Processing".
"Task queue" is the primary reason why developers consider Celery over the competitors, whereas "Free" was stated as the key factor in picking Resque.
Celery and Resque are both open source tools. Celery with 12.7K GitHub stars and 3.3K forks on GitHub appears to be more popular than Resque with 8.53K GitHub stars and 1.58K GitHub forks.
Sentry, Ansible, and OpenLabel are some of the popular companies that use Celery, whereas Resque is used by MAK IT, Stitched, and Youboox. Celery has a broader approval, being mentioned in 271 company stacks & 77 developers stacks; compared to Resque, which is listed in 34 company stacks and 8 developer stacks.
What is Celery?
What is Resque?
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As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.
Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.
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.
Using Celery, the web service creates tasks that are executed by a background worker. Celery uses a RabbitMQ instance as a task queue.