Kafka VS RabbitMQ | Difference between RabbitMQ & Kafka

It is very frequent question that, what are the differences between RabbitMQ and Kafka. So, in this article “Kafka VS RabbitMQ”, we will learn the complete feature wise comparison of Apache Kafka vs RabbitMQ.

Moreover, we will throw light on the best scenarios for when to use Kafka as well as RabbitMQ. Also, we will see a brief intro of Apache Kafka and RabbitMQ. Along with this, we will also go through the requirement and use cases for both Kafka and RabbitMQ.

So let’s start Kafka Vs RabbitMQ.

Introduction to Kafka & RabbitMQ

a. What is Apache Kafka?

Apache Kafka is a leading performer. We can say Kafka outplays RabbitMQ as well as all other message brokers. Moreover, Kafka scales nicely up to 100,000 msg/sec even on a single server, as we add more hardware.

The output of Kafka’s design:

• To a topic, messages published are distributed into partitions.
• In partition, messages are represented as a log stream.
• The consumer is responsible for moving through this stream.
• Moreover, messages from each partition are processed in-order only.

Basically, in order to work with “fast” Kafka Consumers, Kafka is optimized. Although, it can also work with “slow” consumers, still, dealing with some critical situations is somehow difficult because there are some consequences with the partition-centric design of it.

However, there is one major limitation, that is each partition can have only one logical consumer in the consumer group. Hence, that implies while working with “slow” messages single issue (slow processing) blocks all other messages submitted to this partition after that message.

Although, we can resolve these issues with different strategies, like to run another Kafka consumer group and synchronize it with existing consumer in order to avoid duplicated processing of messages.

b. What is RabbitMQ?

RabbitMQ supports a huge number of development platforms, with ease of use and maturity. Basically, it scales nicely about 20,000 msg/sec on a single server. Although, as we add more servers, it also scales well. The output of RabbitMQ design:

• Messages published to queues (through exchange points).
• Multiple consumers can connect to a queue.
• Message broker distribute messages across all available consumers.
• Also, we can redeliver the message if the consumer fails.
• Delivery order guaranteed for queues with a single consumer (this is not possible when the queue has multiple consumers).

Hence, if overall throughput is enough for our requirements, this message broker can work well for “fast” consumers.

However, in work with “slow” consumers, we have the additional benefit. For suppose, if some consumer is stuck with some very slow processing, other messages will stick in the queue as well unless we have other consumers connected.

That says by adding more consumers to the queue we are bypassing processing limitations. So, we just need to run another process and RabbitMQ will take care of the rest.

Apache Kafka vs RabbitMQ

Let’s start complete feature wise comparison between Kafka vs RabbitMQ.

Kafka VS RabbitMQ

a. What is it?

• Apache Kafka

In simple words, for high-ingress data streams and replay, Apache Kafka is a message bus optimized.

• RabbitMQ

Whereas, RabbitMQ is a solid, mature, general-purpose message broker which supports various standardized protocols like AMQP

b. Origins

• Apache Kafka

It is developed in Scala. Moreover, it started out at LinkedIn as a way to connect different internal systems. Afterwards, Apache Software Foundation adopted Kafka within the ecosystem of products. Moreover, in event-driven architecture, it is useful.

• RabbitMQ

Originally, it was developed to implement AMQP, an open wire protocol for messaging with powerful routing features.

However, just because  Java already has messaging standards like JMS, hence, for non-Java applications it is not helpful since that need distributed messaging which is severely limiting to any integration scenario, either microservice or monolithic.

So, cross-language flexibility became real for open source message brokers, with the advent of AMQP.

c. Primary use

• Apache Kafka

Apache Kafka, build applications that process and re-process streamed data on disk.

• RabbitMQ

It process high-throughput and reliable background jobs, communication and integration within, and between applications.

d. License

• Apache Kafka

Apache Kafka is an open Source through Apache License 2.0.

• RabbitMQ

Whereas RabbitMQ is also open Source through Mozilla Public License.

e. Written in

• Apache Kafka

Apache Kafka is Written in Scala (JVM).

• RabbitMQ

And, RabbitMQ is written in Erlang.

f. Client Libraries

• Apache Kafka

There are many client libraries including Ruby, Python, Node.js, and Java.

• RabbitMQ

RabbitMQ includes many mature libraries, like Ruby, Python, Node.js, Clojure, Go, Java and C.

g. Developer Experience

• Apache Kafka

It has made strides in this area, and while it only ships a Java client, there is a growing catalogue of community open source clients, ecosystem projects, and well as an adapter SDK allowing you to build your own system integration. Here, much of the configuration is performs by .properties files.

• RabbitMQ

Its client libraries are mature and well documented and officially supports following: Java, Spring, .NET, PHP, Python, Ruby, JavaScript, Go, Elixir, Objective-C, Swift- with many other clients and dev tools via community plugins.

h. Support for High Availability (HA)

• Apache Kafka

Yes, Apache Kafka supports for high availability.

• RabbitMQ

Similarly, it also supports high availability.

i. Federated Queues

• Apache Kafka

No, Apache Kafka does not support federated queues.

• RabbitMQ

Whereas, it supports federated queues. This feature offers a way of balancing the load of a single queue across nodes or clusters.

j. Complex Routing Scenarios

• Apache Kafka

Apache Kafka is capable to perform Complex routing scenarios.

• RabbitMQ

Whereas, it is not capable of performing Complex routing scenarios.

k. Hosted Solution & Enterprise Support

• Apache Kafka

It is available from CloudKarafka. In the whole world, CloudKarafka is first with a free hosted Apache Kafka as Service plan.

• RabbitMQ

It is available from CloudAMQP. CloudAMQP is operating and providing support to the largest fleet of RabbitMQ clusters in the world.

Requirements and Use Cases

Below, we are discussing requirements and use cases of Kafka and RabbitMQ

• Apache Kafka

Here, we are listing some best scenarios for Kafka, such as:

1. Through Kafka, stream from A to B without complex routing, with maximal throughput (100k/sec+), delivered in partitioned order at least once.
2. While our application requires access to stream history, delivered in partitioned order at least once. Hence, we can say it is a durable message store and also on demand, clients can get a “replay” of the event stream, as it is not possible in more traditional message brokers where once a message has been delivered, it is removed from the queue.
3. In order to perform Stream Processing.
4. Also, for Event Sourcing.

• RabbitMQ

Here, we are listing best scenarios for RabbitMQ, such as:

1. In order to work with any combination of existing protocols such as AMQP 0-9-1, STOMP, MQTT, AMQP 1.0.
2. While we require a finer-grained consistency control/guarantees on a per-message basis (dead letter queues, etc.) although, we can say for transactions, Kafka has recently added better support.
3. Also, our application requires variety from point to point, request/reply, and publish/subscribe messaging.
4. Moreover, for Complex routing to consumers, integrate multiple services/apps with non-trivial routing logic.

So, this was all about Apache Kafka vs RabbitMQ. Hope you like our explanation.

Conclusion

Hence, in this article Kafka vs RabbitMQ, we have seen Kafka’s design, 100k/sec performance is often a key driver for people choosing Apache Kafka.

Although, above comparison will resolve many of your doubt regarding Apache Kafka VS RabbitMQ. Still, if any doubt occurs regarding Kafka vs RabbitMQ, feel free to ask in the comment section.