RabbitMQ vs Apache Kafka: Comparing Message Brokers and Event Streaming Platforms
In an event-driven architecture, event routers are the components that connect event consumers to event producers. Not all implementations of event routers are the same, nor do any of them offer an all-purpose solution, so deciding which one to use depends on your use case and project's needs.
Understanding their capabilities and limitations provides key insights that empower you to confidently decide which one to use and prepare you to navigate its shortcomings.
That is why today we are going to review the differences between traditional message brokers like RabbitMQ and modern event streamers like Apache Kafka. We'll look at how these event routers work, their respective advantages and drawbacks before considering their better-fit use cases.
This post was written alongside our other event-driven architecture articles, Introduction to Synchronous and Asynchronous Processing and Understanding Event-Driven Architecture and Serverless Opportunities
Traditional Message Brokers are Persistent and Highly Centralized
Earlier versions of event-driven architectures relied on message queues to transmit messages between event producers and event routers. Some examples of common message queues include RabbitMQ and Redis.
These traditional message brokers are persistent message frameworks that fuel the flow of events in an application's event-driven architecture. With traditional message brokers, the event router is intelligent while the event consumers are more reactive than proactive. This is typically referred to as the smart broker/ dumb consumers dynamic.
RabbitMQ, a traditional message broker
When using RabbitMQ, a popular open source message broker, events can be pushed directly to queues or to exchanges of the message broker. Developers subscribe event consumers to queues and can route messages from the exchanges to queues. These event flows are the powerful routing capabilities that make RabbitMQ so famous.
These event flows guarantee persistent messages in an event-driven architecture, which makes traditional message brokers great for use cases where you need to acknowledge individual messages.
At the same time, these flows lead to highly coupled integrations between the message broker and the event consumers. This tight-coupling leads to horizontal scaling issues, which is problematic when your app's traffic increases.
Another issue with tight coupling is that you will need to create an integration between each new event consumer you add to your app. These additional integrations can result in complex routing logics that could complicate your application's architecture and future deployments.
Benefits of RabbitMQ
RabbitMQ is a traditional message broker with an established community and is celebrated for its ease of use. Additional advantages include:
- Friendly UI and management tools for monitoring,
- Mature message broker with an established and active community,
- Extensive client libraries as well as plug-ins that are both native and community supported.
Challenges with RabbitMQ
A summary of the challenges to keep in mind when considering RabbitMQ:
- Horizontal scaling and clustering does not always give you a better performance in RabbitMQ,
- Tight coupling of consumers to the message broker complicates maintenance of consumers,
- Since event messages are deleted upon successful delivery, there is no ability to replay events.
Event Streaming Brokers Changed the Game
The advent of event streaming message brokers like Apache Kafka transformed event-driven architecture and its possibilities.
Yes, both message brokers like RabbitMQ and event streaming routers like Apache Kafka receive events and pass them along to consumers, but how they do this is very different.
Apache Kafka, a modern event streaming platform
Apache Kafka is an open-source event streaming platform that keeps a recorded log of the events that pass through it and distributes this log to the services and event consumers in the application. These recorded and distributed logs are known as topics.
With Kafka, event producers emit events to topics, which store and organize the events in the order they occur. Event consumers listen to these topics and process the events accordingly.
Topics can have multiple event producers and event consumers, a feature that allows apps to scale flexibly. Moreover, topics are immutable records that exist as long as the use case requires. Unlike traditional message brokers where the messages are deleted once they are received by a consumer, event streams retain records of events, meaning events can be replayed whenever it is necessary to rebuild an app's state, to integrate a new feature, or even to correct errors.
Benefits of Apache Kafka
- Flexibility: Thanks to event replay with event streamers, you can more easily troubleshoot and integrate new features to your app by replaying previous events.
- Scale: Event streamers can collect, store, and deliver events. And they can do this at scale because of the loosely-coupled components, which are easier to isolate and scale than with previous message brokers.
- Resilience: Another benefit of event streamers and their distributed recorded logs is the ability to replay events. This possibility adds a level of flexibility and resilience to your apps. You can easily recreate your app's state in case it were to crash thanks to event replay.
Challenges with Apache Kafka
- Kafka is known for its complexity and learning curve. While there is a steep learning curve to using Apache Kafka, there are also plenty of resources both open source and commercial that facilitate its adoption.
- While horizontal scalability is great, there is a downside: complexity and overhead will increase as the number of instances grow.
Deciding which Event Router to use
As mentioned earlier, deciding which event router to use will depend on your use case and context. A simple question you can ask yourself when deciding which event router to use in your event-driven architecture:
- Is your throughput low enough and does it not need to scale with your business?
If the answer is yes, RabbitMQ could very well be a better fit for your use case. If scaling is important and you need a higher throughput than RabbitMQ can deliver, Apache Kafka may be the better choice.
Table: Use cases
| Message Queues | Event Streamers |
|---|---|
| Scenarios where per message control is necessary, | Real-time analysis and data-processing, |
| Specifically defined integrations between producers and consumers, | Activity Tracking, |
| Traditional publish-subscribe systems, | Event Sourcing, |
| Long-running asynchronous tasks. | Log aggregation. |
Serverless Event-Driven Architectures
Regardless of which event router you use to power your event-driven architecture, you can maximize the benefits of an event-driven architecture by deploying your app on a serverless platform.
In Understanding Event-Driven Architecture and Serverless Opportunities, we discussed the advantages of pairing event-driven architectures and serverless technology.
The two key takeaways:
- Serverless platforms provide autoscaling technology. Since event-driven architectures are loosely-coupled by design, it is easier than ever to scale the services during bursts of activity.
- Since IT teams are freed up to focus on more business-specific tasks and projects, businesses experience a significant reduction in both cost and complexity when combining event-driven architecture with serverless technology.
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Here are some useful resources to get you started:
- Koyeb Documentation: Learn everything you need to know about using Koyeb.
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