When choosing a messaging system, the decision usually boils down to RabbitMQ vs. Apache Kafka. Whether you are implementing a message broker for your microservices, processing data streams in real-time, or designing a pub-sub architecture, RabbitMQ and Kafka will most likely be your two leading choices.
The platforms offer overlapping features but have different architectures and messaging approaches. Depending on your use case, one may be a better fit than the other.
The following article will compare RabbitMQ and Kafka in different departments, including architecture, features, messaging, performance, scalability, security, and monitoring.
RabbitMQ is a message broker that supports different messaging protocols, including AMQP, STOMP, MQTT, and RabbitMQ streams. It also allows you to build a messaging system over HTTP and WebSockets.
RabbitMQ offers a robust and configurable messaging mechanism. You can make a message queue durable, which ensures that it will retain data even if a broker is restarted. You can make it exclusive, which binds the queue to a connection and deletes it when the connection dies. Several other configurations, including queue TTL (time to live), length limit, and consumer priorities, allow you to implement different messaging use cases.
Other key features include reliable delivery, message acknowledgment, multiple exchange types, distributed deployment, native monitoring via a dashboard and CLI, and queue replication.
RabbitMQ is an ideal fit for any use case that requires reliable, flexible, and secure messaging between different entities, e.g., asynchronous message processing, pub-sub systems, and inter-process communication between applications.
At its core, Kafka is an event streaming platform that can be used to store, transfer, and process high-volume, event-driven data. It offers built-in stream processing with features like transformations, joins, filters, and more.
Kafka is designed to store and provide access to large volumes of data with little overhead. A broker represents the primary element of Kafka’s storage layer. Kafka partitions and distributes data across brokers, which may exist across different nodes.
Kafka’s key features include out-of-the-box integration with hundreds of data sources, guaranteed ordering, zero message loss, cross-cluster data mirroring, and configurable data replication.
Kafka is an ideal choice for use cases that require collection, storage, and processing of event messages, e.g., log aggregation, event-driven applications, real-time data analytics, real-time transaction processing, data processing pipelines, and pub-sub systems.
In the following sections, we will compare Kafka and RabbitMQ in different areas of significance.
In the RabbitMQ world, publishers/producers are applications that publish messages to an exchange. The exchange is responsible for routing these messages to different queues based on a concept known as bindings. A binding represents the relationship between a queue and an exchange. For a queue to receive messages from an exchange, it must be explicitly bound to it.
Consumers are entities that consume data from a queue in one of two ways. They can either subscribe to a queue, in which case the messages are automatically delivered to them (push-based approach), or they can pull data from a queue whenever needed (pull-based approach).
RabbitMQ supports both synchronous and asynchronous communication. Implementing a Remote Procedure Call (RPC) pattern is also possible. RPC allows you to implement an asynchronous request-response model in which publishers expect a response from the consumer but aren’t blocked on it.
The Streams data structure can be used to store data for real-time or later processing reliably. Streams are a good fit when many consumers want to consume data from the same queue or when large amounts of data may need to be queued.
In the Kafka architecture, a producer is an entity that writes event messages. These messages are categorized as topics. Topics are divided into partitions, which may exist on different Kafka brokers. A broker is a standalone server that stores data on the file system.
By dividing topics into a configurable number of partitions, Kafka achieves high levels of reliability and scalability. A Kafka producer connects to a broker to publish event messages. Producers can choose to write data to a specific partition or across different partitions. Kafka assumes the responsibility of ensuring that the order and integrity of messages are preserved.
Consumers connect to brokers to pull data from different topics. Kafka consumers have the flexibility to choose between batch or real-time processing of event messages. Unlike RabbitMQ, Kafka doesn’t offer a push-based approach in which messages are directly delivered to consumers.
RabbitMQ offers several features that allow users to cater to a wide range of use cases:
Some of Apache Kafka’s most useful features include:
Under most circumstances, Kafka delivers better throughput than RabbitMQ. RabbitMQ can process tens of thousands of messages per second whereas Kafka can be scaled to handle millions.
RabbitMQ offers several distributed deployment options, which contribute to its high availability and reliability. The Federation plugin helps distribute messages across different RabbitMQ instances without the need for clustering.
RabbitMQ clustering is a great way to group together nodes and scale up. A RabbitMQ cluster can be created via a configuration file, Kubernetes discovery, DNS-based discovery, and etcd-based discovery.
One clear advantage of Kafka over RabbitMQ is that it offers high throughput while storing large-scale data. Conversely, RabbitMQ queues are the fastest when they are empty because they aren’t designed to retain large volumes of data indefinitely.
You can scale up a Kafka cluster by adding new brokers or nodes. Kafka also offers the ability to spread clusters across different availability zones and connect clusters spread across different geographic zones.
A message in RabbitMQ can contain several attributes, including content type, content encoding, delivery mode, routing key, publisher application ID, message publishing timestamp, expiration period, priority, and more.
RabbitMQ has a flexible messaging model. For instance, users can choose from the following exchange types to cater to different use cases:
To ensure reliable delivery, RabbitMQ only removes a message from a queue after the consumer has acknowledged its reception. If a message fails to be routed, RabbitMQ may return it to the publisher. The publisher can choose how to react in cases of failure. If message processing fails at the consumer end, the consumer can notify RabbitMQ and ask to scrap or requeue it.
A typical event message in Kafka consists of a key, a value, a timestamp, metadata, headers, partition and offset, and compression type. Compared to RabbitMQ, Kafka offers limited support for defining customized routing strategies.
You can use key hashing to ensure that messages with the same key always end up in the same topic-partition. If you don’t specify a key, Kafka uses the round-robin technique to evenly distribute keys across partitions. Another option is to implement dynamic routing using Kafka streams to route event messages to topics. But as far as built-in routing support goes, there is little to none.
To allow Kafka to track processed messages, consumers must periodically commit offsets, known as consumer offsets. As this may be a manual process, it’s prone to user errors. If an incorrect offset is committed, the integrity of the entire system can be compromised. Conversely, RabbitMQ automatically tracks consumed and acknowledged messages.
RabbitMQ offers several security controls and configurations that can be used to protect an instance from unauthorized access. It ships with three SASL authentication mechanisms: PLAIN, AMQPLAIN, and RABBIT-CR-DEMO. Additional mechanisms can be enabled via plugins.
Authorization governs which users can access which resources, present inside which virtual host, and perform which operations. Allowed operations are configure, write, and read. Authorization can also be applied at the topic level.
RabbitMQ also provides built-in TLS support. TLS can be used to encrypt client connections and inter-node connections and perform peer verification. RabbitMQ doesn’t offer encryption at rest.
Read/write operations by clients on brokers can be authorized. It’s also possible to integrate with a third-party authorization module for authorization. Compared to RabbitMQ, Kafka offers slightly less flexibility with regard to authorization.
Data transferred between brokers as well as between a broker and its clients, can also be encrypted using SSL. Kafka doesn’t offer encryption at rest either.
RabbitMQ offers several ways to manage and monitor nodes and clusters. The HTTP API can be used to programmatically retrieve various performance metrics related to clusters, producers, consumers, connections, queues, and more. Several monitoring systems, including Prometheus, can integrate with the API and display metrics in real-time.
The user-friendly web-based UI offers several features to administrators related to connections, exchanges, queues, channels, and more. They can add or delete queues or exchanges, monitor message rate, send and receive messages, tweak policies and runtime settings, purge queues, and force-close connections with clients.
A command line tool, rabbitmqadmin, can also be used to perform some administrative tasks, like listing exchanges, queues, or users, getting an overview of the instance’s health, publishing and getting messages, purging queues, and force-closing client connections.
Kafka exposes key performance metrics via JMX. Jolokia, an HTTP-JMX bridge, can be used to fetch these metrics for aggregation and analysis. Jolokia is not a part of the Kafka core but can be loaded and enabled natively. JMX exposes metrics related to nodes, producers, consumers, connect and streams.
Unlike RabbitMQ, Kafka doesn’t contain built-in tools for management and monitoring. However, there are several third-party tools, both open-source and commercial, that can be used for these purposes.
RabbitMQ is officially supported on all major operating systems, including Linux, Windows, Windows Server, and macOS. Client libraries exist for several programming languages and frameworks, including Java, Spring, C++, .NET, Ruby, Python, and PHP.
Numerous modules, adapters, and plugins are supported by the community and the RabbitMQ team. RabbitMQTools, for example, is a PowerShell module to manage RabbitMQ, Celery is a distributed task queue for Python and Django, and amqp-client is a TypeScript-based client for NodeJS.
The RabbitMQ Cluster Kubernetes Operator can be used to automatically provision and manage RabbitMQ pods running in a Kubernetes cluster.
Even though Kafka is optimized for Linux-based systems, it can run on any operating system that supports the Java Virtual Machine (JVM). Client libraries exist for Java, Scala, Python, Go, C/C++, Node.js, .NET, and more.
Several built-in and community plugins are available, including connectors for file streams, S3, IBM MQ, HDFS, Elasticsearch, ActiveMQ, JDBC, and more. Even though Kafka doesn’t offer any built-in support for Kubernetes, it’s possible to run Kafka clusters inside Kubernetes.
Apache Kafka and RabbitMQ are both stable, fault-tolerant, and feature-rich platforms. However, there are use cases where one may be a better fit than the other.
Apache Kafka and RabbitMQ are two great choices for building messaging infrastructures. Each has several strengths and a few weaknesses. In this article, we explored how the two platforms fare against each other in different departments. We hope that it helps you choose the right platform for your business.
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