Celery vs Kafka: 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, Kafka is detailed as "Distributed, fault tolerant, high throughput pub-sub messaging system". Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Celery and Kafka belong to "Message Queue" category of the tech stack.
"Task queue" is the top reason why over 84 developers like Celery, while over 95 developers mention "High-throughput" as the leading cause for choosing Kafka.
Celery and Kafka are both open source tools. Celery with 12.7K GitHub stars and 3.3K forks on GitHub appears to be more popular than Kafka with 12.5K GitHub stars and 6.7K GitHub forks.
According to the StackShare community, Kafka has a broader approval, being mentioned in 501 company stacks & 451 developers stacks; compared to Celery, which is listed in 271 company stacks and 77 developer stacks.
What is Celery?
What is Kafka?
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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.
I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).
Downsides of using Kafka are: - you have to deal with Zookeeper - you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)
The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.
I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.
For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).
I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.
So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.
I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.
Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.
We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.
Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.
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.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
Using Celery, the web service creates tasks that are executed by a background worker. Celery uses a RabbitMQ instance as a task queue.