Kafka vs RabbitMQ - A Head-to-Head Comparison for 2024

Kafka vs RabbitMQ - A side-by-side comparison of the performance and architectural differences between the two popular open-source messaging systems.

Kafka vs RabbitMQ - A Head-to-Head Comparison for 2024
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As a big data architect or a big data developer, when working with Microservices-based systems, you might often end up in a dilemma whether to use Apache Kafka or RabbitMQ for messaging. Rabbit MQ vs. Kafka - Which one is a better message broker? You might find some articles across the web that conclude that Apache Kafka is better than RabbitMQ and few others that mention RabbitMQ to be more reliable than Kafka. However, if you're here to choose between Kafka vs. RabbitMQ, we would like to tell you this might not be the right question to ask because each of these big data tools excels with its architectural features, and one can make a decision as to which is the best based on the business use case. So, if you want to find the answer to the question - Should I use RabbitMQ vs. Kafka, then we suggest you get an in-depth understanding of the two messaging systems before you decide on a message broker for your next big data project. This article will discuss Apache Kafka and RabbitMQ messaging systems briefly to help you understand their architecture and then look at the trade-offs each of these big data tools has to offer. 


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Kafka vs. RabbitMQ - An Overview

But first, let's understand the need for message brokers like Kafka and RabbitMQ. Big data engineers or developers face challenges with successful data exchange, particularly when they have to make applications interact with each other. Message brokers solve this problem of data exchange by making it reliable and simple using various protocols for messaging that show how a message has to be transmitted and consumed at the receiver.

Apache Kafka and RabbitMQ are messaging systems used in distributed computing to handle big data streams– read, write, processing, etc. They act as the message brokers between applications/services endpoints. Applications exchanging messages on the two ends can be written in a different programming language and don't have to conform to a specific message format. Since protocol methods (messages) sent are not guaranteed to reach the peer or be successfully processed by it, both publishers and consumers need a mechanism for delivery and processing confirmation. 

What is RabbitMQ?

With over 10K+ users, RabbitMQ is one of the most widely deployed message brokers that help applications and services exchange information with each other without maintaining homogeneous exchange protocols. Like in most protocol methods, messages sent do not guarantee that they've been delivered and processed, so RabbitMQ adopts message delivery acknowledgment and implements smart broker design in its architecture. A smart broker is one that provides messages to consumers by handling the processing at its side. The consumer needn't worry about asking for data. Consumers fundamentally act as dummy recipients of the information. Data is promptly deleted from RabbitMQ as soon as consumers have finished processing it. 

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What is Kafka?

Kafka is a commit-log/message-processing implementation that stresses data storage and retrieval more, with scalability and data redundancy. It implements an intelligent client/subscriber policy where the consumer is solely responsible for requesting and retrieving data from the broker. Messages can be processed in batches or individually from the broker and can be re-requested multiple times for processing after that.

Kafka vs. RabbitMQ -What's the difference?

Compare? What parameters… wait, isn't comparison the killer of joy?

kafka va rabbitmq

Why Kafka vs RabbitMQ ?

Apache Kafka and RabbitMQ are equally excellent and veracious when put against in comparison as messaging systems. RabbitMQ vs. Kafka discussion isn't about which is better among the two but which messaging system is ideal for a given business use case.  

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Apache Kafka Architecture

Kafka is a distributed publish-subscribe message delivery and logging system that follows a publisher/subscriber model with message persistence capability. Producers push event streams to the brokers, and consumers pull the data from brokers. Events streams are segregated by Topics that tag messages with their type/kind.

rabbitmq vs kafka

Topics -These are logical demarcations that specify the kinds of data coming in from the producer. It distinguishes data on semantics. For example, order data and credit card data from a retail website are two separate Topics. 

Partitions-  Containers that hold subsets of data from a particular topic. Ideally, there are multiple partitions inside a topic. And messages are pushed cyclically among all the partitions. By default, it's in a Round-Robin fashion, but the algorithm is mutable. 

Message -These are the building blocks of partitions. A message contains a key, value, and offset field. Messages need to have topics assigned to them for the broker to insert them in partitions that belong to the topic. Offset is a sequential integer number maintained by Kafka for each message. It is the basis on which messages are ordered inside a partition. Among partitions belonging to a topic, the offset may not be ordered. But the offsets will be ordered for messages inside any particular partition.

Brokers -These are servers that store topics and their partitions inside them. A broker may have partitions from multiple topics, and a big data system that implements Kafka architecture will have many such brokers. A partition is replicated among numerous brokers constrained by the Replication factor. A replication factor of 3 ( default ) would mean each partition is stored in 3 separate brokers. Among these brokers, one, in particular, will be made the leader, and others will be deemed as followers. The leader interfaces in data transactions - adding and removing messages - while followers sync in with the leader.

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Flow Of Messages in Kafka

Producers send in data in the form of messages to the Kafka Cluster. Messages have a Topic ID data field in them, which is used by Kafka to forward the message to the leader broker for that topic. As the data is written onto the partition in the topic, the Zookeeper saves the Offset number in a unique topic called 'offsets.' Finally, when the consumer pull request arrives, it contains an offset of the last message read a topic. Zookeeper uses this number to send data starting from the offset. It can send messages in a batch that the consumer can set.

How is fail-safe achieved in Kafka?

Here comes the role of ZooKeeper. Zookeeper maintains state logs of Kafka brokers by sending heartbeats (asking, are you okay, are you?) to the brokers. If a leader broker fails or malfunctions accidentally, Zookeeper elects a new leader among the alive brokers. This fail-safe model comes directly from the world of Big-Data Distributed systems architecture like Hadoop.

Message Replay/Retention in Kafka

 Most of the big data use cases deal with messages being consumed as they are produced. When a message is processed successfully, consumers move on to processing new messages. However, under certain circumstances, it might be necessary to replay older messages. An example of this could be a scenario where a bug in the consumer would require it to be deployed on a new version. In such circumstances, replaying of a few or all of the messages would be required. Kafka messages are durable and persistent, meaning they have a retention period before they are removed from the queue, making replaying messages easier.

Order and Offset in Kafka

Kafka doesn't provide Priority Queues, unlike RabbitMQ. It maintains the order by keeping an offset number for each message, ideally assigned by Zookeeper. The offset is a unique sequential number. The messages of a topic inside a queue are ordered by offset.

Pull Design (Smart Consumer) in Kafka

Kafka follows a pull design where consumers ask the broker for a data batch starting from a particular offset. The consumer has to keep track of the offset and do the logical operations on its end. That is why this design is also called dumb broker, the intelligent consumer. 

RabbitMQ Architecture 

RabbitMQ is a message broker with producer/consumer design and complex routing rules with a message delivery confirmation feature. RabbitMQ will know precisely when data fails to reach a consumer. It also lends us ways to handle delivery failure scenarios.

kafka vs rabbitmq performance

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Messages have a header and body. The content goes in the body, whereas the header contains routing-related details. Messages from the producer are sent to exchange, which then forwards the message to appropriate queues by checking the binding rules. The way the messages are sent to the correct queues is managed with routing keys and binding.

There are four types of exchanges -

  • Direct -> Messages are sent to every queue which has the same routing key.

  • Fanout -> Messages are delivered to all the queues that the exchange is connected to for broadcasting. 

  • Topic -> This uses routing key as well as wildcard character – topic to select the queues that will receive the message.

  • Headers -> Match the header in the message. Either one among all headers should match (ANY), or all the headers in the message should match (ALL); all is the default state. Headers come in arguments in messages which can contain key-value pairs.

Message deletion from the queue happens via two rules—automatic and Explicit deletion. 

  • Automatic deletion is when the message is deleted right after the consumer has read/pulled the message.

  • Explicit deletion is when a consumer sends back an acknowledgment saying it has received the message. This can happen in three ways; right after the consumer receives the message, the consumer stores the message in persistent storage, after the consumer processes, and then stores the message in storage.

You can set priority for messages, and essentially, RabbitMQ queues can act as a priority queue as well. RabbitMQ follows a push design where data is pushed to the consumer from the broker side automatically. Make sure to set the pre-fetch limit, which tells the broker how many messages or what size it should push to the consumer without overwhelming it. 

Kafka vs. RabbitMQ - A Head-to-Head Comparison

Now, Let's delve into the complex differences between Kafka and RabbitMQ and begin the journey to the underworld of comparisons.

Kafka vs. RabbitMQ - Architectural Differences

Kafka employs a publisher/subscriber model where events are stored inside partitions as topics. These partitions reside within the broker. And multiple brokers come together to form a cluster of brokers. A consumer can read the data and process it using the offset number. The offset number itself is written inside the partitions. The data stays in the partition for a specific time, referred to as the retention period. After which, it is deleted. Consumers can also form their clusters, and those are identified by consumer group ID. Consumers know exactly which partition to pool data from. That's the idea behind Kafka's architecture. Recurrent retrieval of data. Copies of the same topics are replicated in multiple brokers to avoid failure. The flow of events in Kafka is as follows.

Consumer -> broker -> partition -> Consumer 

RabbitMQ's architecture, unlike Kafka, has a routing mechanism/design in place. A major difference between Kafka and RabbitMQ architecture is that messages in RabbitMQ aren't supposed to persist for long, though they may be. Finally, queues are an implementation choice for the sequential ordering of messages inside the broker. These three features mainly distinguish RabbitMQ from Kafka's architecture. As will be evident eventually in our discussion, these design choices give RabbitMQ different use cases even though Kafka and RabbitMQ are both – to say it loosely - message brokers. The message flow in RabbitMQ happens as follows.

Consumer-> Exchange -> binding rules -> queue -> producer 

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Kafka vs RabbitMQ - Performance 

High throughput is of prime concern for most big data projects.  Apache Kafka employs sequential disk I/O for enhanced performance for implementing queues compared to message brokers in RabbitMQ. RabbitMQ queues are faster only when they’re empty, unlike Kafka that can retain lots of data with minimal overhead. Kafka is capable of processing millions of messages in a second. Though RabbitMQ can also process millions of messages in a second, it would require more resources to do so. 

Kafka vs. RabbitMQ -Source language 

Kafka, written in Java and Scala, was first released in 2011 and is an open-source technology, while RabbitMQ was built in Erlang in 2007

Kafka vs. RabbitMQ - Push/Pull - Smart/Dumb

Kafka employs a pull mechanism where clients/consumers can pull data from the broker in batches. The client/consumer is smart and maintains the tab on offset – last pulled message counter. Kafka uses offset to order the data elements in its partitions. RabbitMQ uses a Push design where the consumer is dumb and doesn't care about message retrieval. The broker takes care of the message delivery to the consumer. Also, after processing the consumer's data, it sends back an acknowledgment to ensure that messages are guaranteed to be delivered to the consumer. If a Negative-Acknowledgement (NACK) message is returned, message delivery is reattempted by putting it back in the queue like a new message would have been.

Kafka vs. RabbitMQ - Scalability, and Redundancy

In Kafka, partitions play the crucial role of providing scalability and redundancy. A partition in Kafka is replicated across many brokers. If one broker fails, the same partition can be served to the consumer from other brokers. If all partitions for a topic were to be kept in a single broker, the throughput of serving would be directly dependent on that particular broker/server and might suffer, which could be a major bottleneck. Distributing the partitions among brokers can increase throughput/speed manifolds. This distribution is done using a key. If a key is not provided, Kafka uses the Round Robin method on all servers/brokers. Likewise, many consumers in a group can read data at the same time. Thus again increasing throughput at the subscriber endpoint.

RabbitMQ uses a queue to replicate messages. In a Round-Robin fashion, messages are distributed between the queues to increase throughput and balance load without overwhelming a specific queue. Multiple consumers can read messages from multiple queues at the same time. 

RabbitMQ is designed to scale vertically by adding more power, unlike Kafka that is designed to scale horizontally by adding more machines.

Recommended Reading: 

Kafka vs. RabbitMQ - Message Deletion 

Messages in Kafka are stored based on the retention period and are deleted once the retention period is over. In RabbitMQ, the consumer has to send a positive ACK (acknowledgment) message to get deleted from the queue. ACK message arrives from the consumer end after any of these three scenarios. The message is received successfully, messages are stored on the consumer, or the message is processed and stored on the consumer. If, instead, a NACK (negative acknowledgment) is received, the message is put back in the queue. 

Kafka vs. RabbitMQ - Message Consumption

In Kafka, consumers read messages from the broker and keep offset to track the current position of the counter inside the queue. After reading a message, the consumer increments its offset, and thus the counter is updated for subsequent retrieval.

RabbitMQ's smart broker does the job of message delivery instead of the consumer. Generally, messages are fetched in batch transactions – several messages are read together at once. A limit can be set on the number of messages that can be taken up for batch fetching not to overwhelm consumers.

Kafka vs. RabbitMQ -Message Priority 

RabbitMQ provides the capability to assign priority to messages being sent in by the consumer. In such cases, a priority queue is maintained, and the message is enqueued accordingly. There is no way to set priorities for messages in Kafka, and it's the same for all messages.

Kafka vs. RabbitMQ - Sequential Ordering

Kafka segregates messages using topics. An offset is an integer number maintained for each partition by Zookeeper. So consumers that want to read from a particular topic will have to use offset to get the messages. Updating offset needs to happen after the Smart Consumer consumes every message. RabbitMQ by design uses a queue inside the broker in its implementation. So naturally, the order is maintained inside the queue. 

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Kafka vs. RabbitMQ - Libraries and Language Support 

RabbitMQ supports Elixir, Go, Java, JavaScript, Ruby, C, Swift, Spring, .Net, Python and PHP, while Kafka supports Ruby, Python, Java, and Node.js

Kafka vs. RabbitMQ - Protocols Supported  

RabbitMQ supports MQTT, AMQP, STOMP, HTTP protocols.

AMQP – >It is a default implementation in RabbitMQ

MQTT –> used for light scenarios. Binary exchange.

STOMP –> text-based data exchange 

HTTP -> our very popular internet protocol

While Kafka supports Binary protocol over TCP.

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Kafka vs. RabbitMQ in a Nutshell

Tables are easy, and the chairs are nice. Let's recap quickly -

 

Kafka

RabbitMQ

Language is written in

Java and Scala

Erlang

Year released

2011

2007

Protocols 

Binary messages, int8, int16

AMQP by default

Popular for

For message replay and message streams

Message broker for communication b/w applications

Message retention/deletion

Messages are deleted once the retention period is over.

Right after consumers receives the message or finishes processing and saving the data message is deleted

Message replay

Yes, possible under the retention period

No, since messages are deleted off the queue promptly after delivery

Delivery ACK

Yes, 

 

Routing Available

Doesn’t have routing algorithms/rules. Topics define the necessary segregation 

Comes with many complex routing rules. Exchange and binding is used to push messages to appropriate queues

Priority 

No in-built priority feature.

Priority queues are implemented in place

License

Open Source: Apache Licence

Open source: Mozilla public license

Design 

Dumb broker, Smart consumer

Smart broker, dumb consumer

Architecture 

Producer->exchange->binding rules->queue->consumer

Producer->topic wise->broker->partition->consumer

General Use case 

Operational – process operation, logging.

Transactional – e.g. order formation.

Easy to remember 

Kafka is used for Logging ( since its capability of message retention)

RabbitMQ is ideally used as a message broker, that helps two different services/applications to communicate.

Consumer Reading data

In a group of many consumers asking to read the same topic, only one will be given permission.

Same

Filtering message 

All the messages in the subscribed topic are sent to the consumer.

Message-headers and topic-exchange allow the consumer to be selective in receiving specific messages only

Message format

Key-value pair, timestamp

Header and body

Authentication

Kafka supports Oauth2, Standard Authentication, and Kerberos.

RabbitMQ supports Standard Authentication and Oauth2.

Maintaining sequential Order 

Kafka maintains offset to keep the order of arrival of messages intact.

RabbitMQ implicitly uses Queue that follows the FIFO property and thus keeps proper order of messages.

Libraries supported

Python, JAVA, Ruby, Node.JS

Spring, Swift. Python, PHP, .NET, C, Ruby

RabbitMQ vs Kafka - Who is the Winner?

Now you see the conundrum. Like everything good in life, even this comparison doesn't come in black and white. There are so many big data use cases and features in both Kafka and RabbitMQ that picking one over the other is being ignorant and reductive. The question of the 'best' is simply absurd. More importantly, it's always best to check the requirements for the use case, previous deployment, and expectations before choosing between Kafka and RabbitMQ. Each of these excels at its own features so choose the one according to your organizational needs, project, and business requirements.

Though a simple affirmation to keep in mind is that their major differences help discern our expectations.

  1. Kafka's message retention makes it incline towards a message logger, or a huge and fast data streaming server. Since the onus is on the consumer to retry for a message after a failure, Kafka doesn't mind if messages are delivered successfully or not. This relieves it of extra implementation and focus is put on data replaying and querying.

  2. RabbitMQ's lack of retention of messages and guarantee of acknowledgment messages from consumers makes it a better fit for being an application mediator, a robust message broker.

And those are the major use cases for these services. And thus we have reached the end of this long discussion over Franz Kafka and the white Rabbit that could talk in 'The Wizards of Oz'. Both equally compelling and interesting ☺ Check out some interesting Kafka Projects to get hands-on experience working with messaging systems.

 

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