RabbitMQ vs Kafka

What’s the Difference Between Kafka and RabbitMQ?

Kafka and RabbitMQ are message queue systems you can use in stream processing. A data stream is high-volume, continuous, incremental data that requires high-speed processing. For example, it could be sensor data about the environment that you must continuously collect and process to observe real-time changes in temperature or air pressure. RabbitMQ is a distributed message broker that collects streaming data from multiple sources to route it to different destinations for processing. Apache Kafka is a streaming platform for building real-time data pipelines and streaming applications. Kafka provides a highly scalable, fault-tolerant, and durable messaging system with more capabilities than RabbitMQ.

Architectural differences: Kafka vs. RabbitMQ

RabbitMQ and Apache Kafka allow producers to send messages to consumers. Producers are applications that publish information, while consumers are applications that subscribe to and process information.

Producers and consumers interact differently in RabbitMQ and Kafka. In RabbitMQ, the producer sends and monitors if the message reaches the intended consumer. On the other hand, Kafka producers publish messages to the queue regardless of whether consumers have retrieved them.

You can think of RabbitMQ as a post office that receives mail and delivers it to the intended recipients. Meanwhile, Kafka is similar to a library, which organizes messages on shelves with different genres that producers publish. Then, consumers read the messages from the respective shelves and remember what they have read.

RabbitMQ architectural approach

A RabbitMQ broker allows for low latency and complex message distributions with the following components:

• An exchange receives messages from the producer and determines where they should be routed to

• A queue is storage that receives messages from an exchange and sends them to consumers

• A binding is a path that connects an exchange and a broker

In RabbitMQ, a routing key is a message attribute that is used to route messages from an exchange to a specific queue. When a producer sends a message to an exchange, it includes a routing key as part of the message. The exchange then uses this routing key to determine which queue the message should be delivered to.

Kafka architectural approach

A Kafka cluster provides high-throughput stream event processing with a more complex architecture. These are some key Kafka components:

• A Kafka broker is a Kafka server that allows producers to stream data to consumers. The Kafka broker contains topics and their respective partitions.

• A topic is data storage that groups similar data in a Kafka broker.

• A partition is smaller data storage within a topic that consumers subscribe to.

• ZooKeeper is special software that manages the Kafka clusters and partitions to provide fault-tolerant streaming. ZooKeeper has recently been replaced with the Apache Kafka Raft (KRaft) protocol.

Producers in Kafka assign a message key for each message. Then, the Kafka broker stores the message in the leading partition of that specific topic. KRaft protocol uses consensus algorithms to determine the leading partition.

How do Kafka and RabbitMQ handle messaging differently?

RabbitMQ and Apache Kafka move data from producers to consumers in different ways. RabbitMQ is a general-purpose message broker that prioritizes end-to-end message delivery. Kafka is a distributed event streaming platform that supports the real-time exchange of continuous big data.

RabbitMQ and Kafka are designed for different use cases, which is why they handle messaging differently. Next, we discuss some specific differences.

Message consumption

In RabbitMQ, the broker ensures that consumers receive the message. The consumer application takes a passive role and waits for the RabbitMQ broker to push the message into the queue. For example, a banking application might wait for SMS alerts from the central transaction processing software.

Kafka consumers, however, are more proactive in reading and tracking information. As messages are added to physical log files, Kafka consumers keep track of the last message they've read and update their offset tracker accordingly. An offset tracker is a counter that increments after reading a message. With Kafka, the producer is not aware of message retrieval by consumers.

Message priority

RabbitMQ brokers allow producer software to escalate certain messages by using the priority queue. Instead of sending messages with the first in, first out order, the broker processes higher priority messages ahead of normal messages. For example, a retail application might queue sales transactions every hour. However, if the system administrator issues a priority backup database message, the broker sends it immediately.

Unlike RabbitMQ, Apache Kafka doesn't support priority queues. It treats all messages as equal when distributing them to their respective partitions.

Message ordering

RabbitMQ sends and queues messages in a specific order. Unless a higher priority message is queued into the system, consumers receive messages in the order they were sent.

Meanwhile, Kafka uses topics and partitions to queue messages. When a producer sends a message, it goes into a specific topic and partition. Because Kafka does not support direct producer-consumer exchanges, the consumer pulls messages from the partition in a different order.

Message deletion

A RabbitMQ broker routes the message to the destination queue. Once read, the consumer sends an acknowledgement (ACK) reply to the broker, which then deletes the message from the queue.

Unlike RabbitMQ, Apache Kafka appends the message to a log file, which remains until its retention period expires. That way, consumers can reprocess streamed data at any time within the stipulated period.

Other key differences: Kafka vs. RabbitMQ

RabbitMQ provides complex message routing with simple architecture, while Kafka offers a durable message broker system that allows applications to process data in stream history.

Next, we share more differences between both message brokers.

Performance

Both RabbitMQ and Kafka offer high-performance message transmission for their intended use cases. However, Kafka outperforms RabbitMQ in message transmission capacity.

Kafka can send millions of messages per second as it uses sequential disk I/O to enable a high-throughput message exchange. Sequential disk I/O is a storage system that stores and accesses data from adjacent memory space, and it's faster than random disk access.

RabbitMQ can also send millions of messages per second, but it requires multiple brokers to do so. Typically, RabbitMQ's performance averages thousands of messages per second and might slow down if RabbitMQ's queues are congested.

Security

RabbitMQ and Kafka allow applications to exchange messages securely but with different technologies.

RabbitMQ comes with administrative tools to manage user permissions and broker security.

Meanwhile, the Apache Kafka architecture provides secure event streams with TLS and Java Authentication and Authorization Service (JAAS). TLS is an encryption technology that prevents unintended eavesdropping on messages, and JAAS controls which application has access to the broker system.

Programming language and protocols

Kafka and RabbitMQ both support various languages, frameworks, and protocols that developers are familiar with.

You can code in Java and Ruby when building client applications for Kafka and RabbitMQ. Additionally, Kafka supports Python and Node.js, while RabbitMQ supports JavaScript, Go, C, Swift, Spring, Elixir, PHP, and .NET.

Kafka uses the binary protocol over TCP to stream messages across real-time data pipelines, while RabbitMQ supports Advanced Message Queuing Protocol (AMQP) by default. RabbitMQ also supports legacy protocols like Simple Text Orientated Messaging Protocol (STOMP) and MQTT to route messages.

What are the similarities between Kafka and RabbitMQ?

Applications need reliable message brokers to exchange data on the cloud. Both RabbitMQ and Kafka provide scalable and fault-tolerant platforms to meet growing traffic demands and high availability.

Next, we discuss some key similarities between RabbitMQ and Kafka.

Scalability

RabbitMQ can expand its message-handling capacity both horizontally and vertically. You can allocate more compute resources to RabbitMQ's server to increase message exchange efficiency. In some cases, developers use a message distribution technique called RabbitMQ consistent hash exchange to balance load processing across multiple brokers.

Likewise, Kafka architecture allows adding more partitions to a specific topic to distribute the message load evenly.

Fault tolerance

Both Kafka and RabbitMQ are robust message-queuing architectures resilient to system failure.

You can group multiple RabbitMQ brokers into clusters and deploy them on different servers. RabbitMQ also replicates queued messages across distributed nodes. This allows the system to recover from failure affecting any server.

Like RabbitMQ, Apache Kafka shares similar recoverability and redundancy by hosting Kafka clusters on different servers. Each cluster consists of replicas of log files that you can recover in case of failure.

Ease of use

Both message queue systems have strong community support and libraries that make it simple to send, read, and process messages. This makes developing client applications easier for developers on both systems.

For example, you can use Kafka Streams (a client library) to build messaging systems on Kafka and Spring Cloud Data Flow to build event-driven microservices with RabbitMQ.

When to use Kafka vs. RabbitMQ

It's important to understand that RabbitMQ and Kafka are not competing message brokers. Both were designed to support data exchange in different use cases where one is more suitable than the other.

Next, we discuss some use cases to consider for RabbitMQ and Kafka.

Event stream replays

Kafka is suitable for applications that need to reanalyze the received data. You can process streaming data multiple times within the retention period or collect log files for analysis.

Log aggregation with RabbitMQ is more challenging, as messages are deleted once consumed. A workaround is to replay the stored messages from the producers.

Real-time data processing

Kafka streams messages with very low latency and is suitable to analyze streaming data in real time. For example, you can use Kafka as a distributed monitoring service to raise alerts for online transaction processing in real time.

Complex routing architecture

RabbitMQ provides flexibility for clients with vague requirements or complex routing scenarios. For example, you can set up RabbitMQ to route data to different applications with different bindings and exchanges.

Effective message delivery

RabbitMQ applies the push model, which means the producer knows whether the client application consumed the message. It suits applications that must adhere to specific sequences and delivery guarantees when exchanging and analyzing data.

Language and protocol support

Developers use RabbitMQ for clients' applications that require backward compatibility with legacy protocols such as MQTT and STOMP. RabbitMQ also supports a broader range of programming languages compared to Kafka.

Does Kafka use RabbitMQ?

Kafka does not use RabbitMQ. It's an independent message broker that distributes real-time event streams without using RabbitMQ. Both are separate data exchange systems that work independently of each other.

However, some developers route messages from the RabbitMQ network into Kafka. They do so because it takes more effort to deconstruct existing RabbitMQ data pipelines and rebuild them with Kafka.

Summary of differences: Kafka vs. RabbitMQ

	RabbitMQ	Kafka
Architecture	RabbitMQ’s architecture is designed for complex message routing. It uses the push model. Producers send messages to consumers with different rules.	Kafka uses partition-based design for real-time, high-throughput stream processing. It uses the pull model. Producers publish messages to topics and partitions that consumers subscribe to.
Message handling	RabbitMQ brokers monitor message consumption. It deletes messages after they’re consumed. It supports message priorities.	Consumers keep track of message retrieval with an offset tracker. Kafka retains messages according to the retention policy. There’s no message priority.
Performance	RabbitMQ has low latency. It sends thousands of messages per second.	Kafka has real-time transmission of up to millions of messages per second.
Programming language and protocol	RabbitMQ supports a broad range of languages and legacy protocols.	Kafka has limited choices of programming languages. It uses binary protocol over TCP for data transmission.