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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What tools integrate with Celery?
What tools integrate with Kafka?
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.