This article will explain the difference between RabbitMQ and Kafka. The article will walk through the strengths and weaknesses of each platform so that you can choose the right solution for your data streaming use case.
Before explaining the difference between RabbitMQ and Kafka and when to use each technology, it is important to understand the difference between a message broker and a distributed streaming system.
A message brokerMessage BrokerA message broker helps exchange messages across systems and applications. A messa... Read more helps exchange messages across systems and applications. A message broker is usually used to handle background jobs, long running tasks, or act as a translator between applications (including microservices). An example of a long running task is that you have to create a large PDF file programmatically by reading and formatting a lot of data from your data source. The reading and transformation of that data and writing into a file could take quite some time. A message broker in this case could help with a "fire and forget" scenario, where the requester does not have to wait for the creation of the file and that job can happen in the background.
A message broker helps broker messages by supporting different protocols. Some of the most popular protocols they support are Advanced Message Queuing protocol (AMQP), protocol for Machine to Machine (M2M) and Internet of Things (IoT) messaging (MQTT), Simple Text Oriented Messaging Protocol (STOMP).
RabbitMQ is a general-purpose message broker.
A distributed streaming systemDistributed Streaming SystemSystem that can process multiple data streams simultaneously. This allows for cre... Read more can process multiple data streams simultaneously. This allows for creating high throughput data ingestion and data processing pipelines. Streaming systems are used for large amounts of data, processing data in real-time or to analyze data over a time-period. The distributed architecture ensures a fault-tolerant publish-subscribe messaging system.
Kafka is a distributed streaming system.
Once you understand the type of use cases which each of these systems support, it is worthwhile to understand what the messaging architecture looks like. The following table will give you an understanding of the design elements - messaging, data processing, topology and redundancy of these two systems and can further guide you with the right technology selection.
In summary, Kafka is useful for high-throughput, cost-efficient streaming use cases. RabbitMQ is useful for low to medium throughput messaging with complex routing and a variety of consumers.
For the diehard RabbitMQ developers, RabbitMQ has a new data structure modeling an append-only log, with non-destructive consuming semantics. This new data structure (https://www.rabbitmq.com/streams.html) will be an interesting addition for RabbitMQ users looking to enhance their streaming use case. This is only available in RabbitMQ3.9 and above.
For message or stream processing analytics please check out this simple example on performing simple machine learning on Kafka.
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