Celery vs NSQ: 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, NSQ is detailed as "A realtime distributed messaging platform". NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.
Celery and NSQ belong to "Message Queue" category of the tech stack.
"Task queue" is the primary reason why developers consider Celery over the competitors, whereas "It's in golang" was stated as the key factor in picking NSQ.
Celery and NSQ are both open source tools. NSQ with 15.5K GitHub stars and 2.04K forks on GitHub appears to be more popular than Celery with 12.7K GitHub stars and 3.3K GitHub forks.
Udemy, Bitbucket, and Leftronic are some of the popular companies that use Celery, whereas NSQ is used by Docker, Segment, and bitly. Celery has a broader approval, being mentioned in 271 company stacks & 77 developers stacks; compared to NSQ, which is listed in 21 company stacks and 8 developer stacks.
What is Celery?
What is NSQ?
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What tools integrate with NSQ?
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.