We work and live in a time when we rely increasingly on data to get things done. Applications, services, software, mobile devices, and other elements combine to form an intricate and far-reaching web that touches and affects most areas of our lives.

As a result, there’s an increased need to handle the information flow between these different elements. Devices and apps need to talk to each other, and there is no room for error. That’s why programmers use message brokers and similar tools to exchange information and communicate with each other.

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What’s the Difference Between a Message Broker and a Publish/Subscribe (Pub/Sub) Messaging System?

Message brokers are software modules that let applications, services, and systems communicate and exchange information. Message brokers do this by translating messages between formal messaging protocols, enabling interdependent services to directly “talk” with one another, even if they are written in different languages or running on other platforms.

Message brokers validate, route, store, and deliver messages to the designated recipients. The brokers operate as intermediaries between other applications, letting senders issue messages without knowing the consumers’ locations, whether they’re active or not, or even how many of them exist.

However, publish/Subscribe is a message distribution pattern that lets producers publish each message they want.

Data engineers and scientists refer to pub/sub as a broadcast-style distribution method, featuring a one-to-many relationship between the publisher and the consumers.

Also Read: How to Become a Data Engineer?

What is Kafka?

Kafka is an open-source distributed event streaming platform, facilitating raw throughput. Written in Java and Scala, Kafka is a pub/sub message bus geared towards streams and high-ingress data replay. Rather than relying on a message queue, Kafka appends messages to the log and leaves them there, where they remain until the consumer reads it or reaches its retention limit.

Kafka employs a “pull-based” approach, letting users request message batches from specific offsets. Users can leverage message batching for higher throughput and effective message delivery.

Although Kafka only ships with a Java client, it offers an adapter SDK, allowing programmers to build their unique system integration. There is also a growing catalog of community ecosystem projects and open-source clients.

Kafka was released in 2011, so it’s the newcomer. You can find a more detailed intro to Kafka here. You can also learn more about how to use it through this Kafka tutorial and look at the architecture of this pub/sub model here.

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What is RabbitMQ?

RabbitMQ is an open-source distributed message broker that facilitates efficient message delivery in complex routing scenarios. It’s called “distributed” because RabbitMQ typically runs as a cluster of nodes where the queues are distributed across the nodes — replicated for high availability and fault tolerance.

RabbitMQ employs a push model and prevents overwhelming users via the consumer configured prefetch limit. This model is an ideal approach for low-latency messaging. It also functions well with the RabbitMQ queue-based architecture. Think of RabbitMQ as a post office, which receives, stores, and delivers mail, whereas RabbitMQ accepts, stores, and transmits binary data messages.

RabbitMQ natively implements AMQP 0.9.1 and uses plug-ins to offer additional protocols like AMQP 1.0, HTTP, STOMP, and MQTT. RabbitMQ officially supports Elixir, Go, Java, JavaScript, .NET, PHP, Python, Ruby, Objective-C, Spring, and Swift. It also supports various dev tools and clients using community plug-ins.

What is Kafka Used For?

Kafka is best used for streaming from A to B without resorting to complex routing, but with maximum throughput. It’s also ideal for event sourcing, stream processing, and carrying out modeling changes to a system as a sequence of events. Kafka is also suitable for processing data in multi-stage pipelines.

Bottom line, use Kafka if you need a framework for storing, reading, re-reading, and analyzing streaming data. It’s ideal for routinely audited systems or that store their messages permanently. Breaking it down even further, Kafka shines with real-time processing and analyzing data.

What is RabbitMQ Used For?

Developers use RabbitMQ to process high-throughput and reliable background jobs, plus integration and intercommunication between and within applications. Programmers also use RabbitMQ to perform complex routing to consumers and integrate multiple applications and services with non-trivial routing logic.

RabbitMQ is perfect for web servers that need rapid request-response. It also shares loads between workers under high load (20K+ messages/second). RabbitMQ can also handle background jobs or long-running tasks like PDF conversion, file scanning, or image scaling.

Summing it up, use RabbitMQ with long-running tasks, reliably running background jobs, and communication/integration between and within applications.

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Understanding the Differences Between RabbitMQ vs Kafka

These messaging frameworks approach messaging from entirely different angles, and their capabilities vary wildly. For starters, this chart breaks down some of the most significant differences.

Kafka vs RabbitMQ

RabbitMQ

Kafka

Performance

4K-10K messages per second

1 million messages per second

Message Retention

Acknowledgment based

Policy-based (e.g., 30 days)

Data Type

Transactional

Operational

Consumer Mode

Smart broker/dumb consumer

Dumb broker/smart consumer

Topology

Exchange type: Direct, Fan out, Topic, Header-based

Publish/subscribe based

Payload Size

No constraints

Default 1MB limit

Usage Cases

Simple use cases

Massive data/high throughput cases

More on the top differences between Kafka vs RabbitMQ:

  • Data Flow 

    RabbitMQ uses a distinct, bounded data flow. Messages are created and sent by the producer and received by the consumer. Apache Kafka uses an unbounded data flow, with the key-value pairs continuously streaming to the assigned topic.
  • Data Usage

    RabbitMQ is best for transactional data, such as order formation and placement, and user requests. Kafka works best with operational data like process operations, auditing and logging statistics, and system activity.
  • Messaging

    RabbitMQ sends messages to users. These messages are removed from the queue once they are processed and acknowledged. Kafka is a log. It uses continuous messages, which stay in the queue until the retention time expires.
  • Design Model

    RabbitMQ employs the smart broker/dumb consumer model. The broker consistently delivers messages to consumers and keeps track of their status. Kafka uses the dumb broker/smart consumer model. Kafka doesn’t monitor the messages each user has read. Rather, it retains unread messages only, preserving all messages for a set amount of time. Consumers must monitor their position in each log.
  • Topology

    RabbitMQ uses the exchange queue topology — sending messages to an exchange where they are in turn routed to various queue bindings for the consumer’s use. Kafka employs the publish/subscribe topology, sending messages across the stream to the correct topics, and then consumed by users in the different authorized groups.

Requirements and Use Cases

In the initial stages, there was considerable difference in design between RabbitMQ and Kafka, and a difference in requirements and use cases. While RabbitMQ’s message broker design was an excellent choice for use cases having specific routing needs and pre message guarantees, Kafka’s append only log meant developers could assess the stream history and more direct stream processing. The Venn diagram of use cases fulfilled by the two technologies was quite tight. There were situations where one was evidently a better choice than the other.

However, this balance will soon be altered. RabbitMQ, besides providing its traditional queue model, will present a new data structure modeling an append-only log, with non-destructive consuming semantics. This new data structure will be an interesting addition for RabbitMQ users looking to enhance their streaming use case.  

  • Developer Experience

The developer experience of RabbitMQ and Kafka has been quite similar, with the list of clients and libraries continually rising due to the work of their respective communities. There has been a steady growth in the client library lists of both. As more languages and frameworks are getting popular, it has become easier to find a well-supported and complete library for RabbitMQ and Kafka. 

The client library implementation of Kafka streams have grown substantially, making it easier for developers to process streaming data. The implementation is used for reading data from Kafka, processing it, and writing it to another Kafka queue. Plus, ksqlDB can help developers looking to develop streaming applications leveraging their familiarity with relational databases. 

With RabbitMQ, developers can take help of Spring Cloud Data Flow for powerful streaming and batch processing. 

  • Security and Operations

Both RabbitMQ and Kafka provide built in tools for managing security and operations. Plus, both platforms offer third-party tools that enhance monitoring metrics from nodes, clusters, queues, etc. 

The emergence of Kubernetes in recent times has led to allowing infrastructure operators run both Kafka and RabbitMQ on Kubernetes.  

While RabbitMQ comes with a browser based API to manage users and queues, Kafka provides features like Transport Layer Security (TLS) encryption, and JAAS (Java Authentication and Authorization Service). Both Kafka and RabbitMQ support role-based access control (RBAC), and Simple Authentication and Security Layer (SASL) authentication. In Kafka, you can even control security policies through command line interface (CLI). 

  • Performance

It can be hard to quantify performance with so many variables involved like how the service is configured, how the code interacts with it, and the hardware. Even things like network, memory and disk speed can significantly impact service performance. Although RabbitMQ and Kafka are optimized for performance, make sure to configure your use case for maximum efficiency. 

For RabbitMQ, refer to how-to guides for maximum performance. Keep in mind things to consider while building clusters, how to benchmark and size your cluster, how to make your code interact with them for optimized performance, how to manage queue size and connections, and taking care about how end user consumes messages. 

Similarly, running Kafka in production guides cover key points on how to configure Kafka cluster, things to keep in mind for running Kafka on JVM, and more.

  • Deciding Between Kafka and RabbitMQ

Deciding between Kafka and RabbitMQ can be tricky, especially with both platforms improving every day, and the margins of advantage getting smaller. Your decision will however depend on your specific user case. 

While Kafka is best suited for big data use cases requiring the best throughput, RabbitMQ is perfect for low latency message delivery and complex routing. 

There are some common use cases for both Kafka and RabbitMQ. Both can be used as component of microservices architecture providing connection between producing and consuming apps. Another commo use case can be as message buffer, providing a temporary location for message storage while consuming apps are unavailable, or fixing spikes in producer-generated messages. 

Both Kafka and RabbitMQ technologies can handle huge amounts of messages - though in different ways – each being suitable for subtly varying use cases. 

Apache Kafka Use Cases

  • Tracking High-throughput Activity – you can use Kafka for different high volume, high throughput activity tracking like tracking website activity, ingesting data from IoT sensors, keeping tabs on shipments, monitoring patients in hospitals, etc. 
  • Stream Processing – Use Kafka to implement application logic based on streams of events. For example, for an event lasting for several minutes, you can track average value over the duration of the event or keep a running count of the types of events. 
  • Event Sourcing – Kafka supports event sourcing, wherein any changes to an app state are stored in the form of sequence of events. For example, while using Kafka for a banking app, if the account balance gets corrupted somehow, you can use the stored history of transactions to recalculate the balance. 
  • Log aggregation – Kafka can also be used to collect log files and store them in a centralized location. 

RabbitMQ Use Cases

  • Complex Routing – if you want to route messages among many consuming apps like in a microservices architecture, RabbitMQ can be your best choice. RabbitMQ consistent hash exchange can balance load processing across a distributed monitoring service.  You can also use alternate exchanges to route specific portion of events to specific services for A/B testing. 
  • Legacy Applications – another use case of RabbitMQ is to deploy it using available plugins (or building your own plugin) for connecting consumer apps to legacy apps. For example, communicate with JMS apps using Java Message Service (JMS) plug-in and JMS client library. 

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Which Should You Learn in 2022 - Kafka vs RabbitMQ?

Although this may sound like a cop-out, the answer is — it depends on what your needs are. Learn and use Apache Kafka if your operation requires any of the following use cases:

  • Event sourcing or system modeling changes as a sequence of events
  • Streaming and processing data in multiple-stage pipelines
  • Applications that need a stream history, delivered in “at least once” partitioned order
  • Streams with a throughput of at least 110K/sec events, complex routing, or “at least once” partitioned ordering

And you should learn and use RabbitMQ if any of these use cases apply to your organization:

  • Granular control over consistency/set of guarantees on a per-message basis
  • Complex routing to users/consumers
  • Applications requiring a variety of publish/subscribe, or point-to-point request/reply messaging capabilities
  • Applications that must support legacy protocols, like STOMP, MQTT, AMQP, 0-9-1

If you’re uncertain where your career will take you, you may consider learning both. This strategy boosts your skillset, enhances your flexibility in a new job environment, and increases your marketability to future prospective employers. As time and resources permit, consider gaining certification in both RabbitMQ and Kafka, and be ready for anything.

When you’re ready to take the Apache Kafka certification exam, check out these Kafka practice questions first, and then you can check Kafka interview questions for preparing for the job interview.

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