RabbitMQ vs Kafka vs ActiveMQ: What are the differences?
RabbitMQ, Kafka, and ActiveMQ are all messaging technologies used to provide asynchronous communication and decouple processes (detaching the sender and receiver of a message). They are called message queues, message brokers, or messaging tools. RabbitMQ, Kafka, and ActiveMQ all serve the same basic purpose, but can go about their jobs differently. Kafka is a high-throughput distributed messaging system. RabbitMQ is an AMQP based reliable message broker. ActiveMQ and Kafka are both Apache products, and both written in Java; RabbitMQ is written in Erlang.
What is ActiveMQ?
What is Kafka?
Want advice about which of these to choose?Ask the StackShare community!
What are the cons of using ActiveMQ?
Why do developers choose RabbitMQ vs Kafka vs ActiveMQ?
- Users of RabbitMQ say it’s fast, easy to configure, and intuitive.
- Fans of Kafka cite its scalability, high performance, and high-throughput abilities.
- ActiveMQ users call it efficient and easy to use, and celebrate its open source roots.
What tools integrate with ActiveMQ?
What tools integrate with Kafka?
Beamery runs a #microservices architecture in the backend on top of Google Cloud with Kubernetes There are a 100+ different microservice split between Node.js and Go . Data flows between the microservices over REST and gRPC and passes through Kafka RabbitMQ as a message bus. Beamery stores data in MongoDB with near-realtime replication to Elasticsearch . In addition, Beamery uses Redis for various memory-optimized tasks.
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.
I developed one of the largest queue based medical results delivery systems in the world, 18,000+ queues and still growing over a decade later all using MQSeries, later called Websphere MQ. When I left that company I started using RabbitMQ after doing some research on free offerings.. it works brilliantly and is incredibly flexible from small scale single instance use to large scale multi-server - multi-site architectures.
If you can think in queues then RabbitMQ should be a viable solution for integrating disparate systems.
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.
The poster child for scalable messaging systems, RabbitMQ has been used in countless large scale systems as the messaging backbone of any large cluster, and has proven itself time and again in many production settings.
Rabbit acts as our coordinator for all actions that happen during game time. All worker containers connect to rabbit in order to receive game events and emit their own events when applicable.
Used as central Message Broker; off-loading tasks to be executed asynchronous, used as communication tool between different microservices, used as tool to handle peaks in incoming data, etc.
RabbitMQ is the enterprise message bus for our platform, providing infrastructure for managing our ETL queues, real-time event notifications for applications, and audit logging.
RabbitMQ is an all purpose queuing service for our stack. We use it for user facing jobs as well as keeping track of behind the scenes jobs.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
Remote broker and local client for incoming data feeds. Local broker for republishing data feeds to other systems.