Message Queue vs. Apache Kafka
Developed by a team at LinkedIn, Kafka is non-open source middleware intended as an alternative to a messaging queue, but is it the right solution for your company? Let's dive into the differences between Kafka and a message queue, and the pros and cons of each that will help you make a decision for your microservices or serverless environment.
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4 key takeaways from this article:
- Message Queues ensure delivery and scaling, while Kafka focuses on high-throughput and low-latency.
- Kafka suits high data volumes and streaming, and Message Queues excel in decoupling services and workloads.
- Kafka's log-based storage ensures persistence; Message Queues rely on acknowledgements for delivery.
- Choice between both depends on application requirements, data volume, and processing needs.
What Is a Message Queue?
Many providers offer a message queuing service, or MQ, which helps connect distributed systems and applications together while enabling asynchronous communication. With a message queue, a producer/publisher can add messages to the queue without having to wait for a response. The number of messages isn't limited. Meanwhile, consumers/recipients can retrieve and process messages from the queue whenever they're available to do so, but each message can only have a single consumer.
When implemented correctly, message queues can improve process efficiency and help developers optimize resources while reducing errors, downtime, and wait times. Popular message queues include AWS SQS, ActiveMQ, Azure Queue, and IronMQ.
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What Is Apache Kafka?
In short, Kafka is a message queuing system with a couple of twists. It offers low-latency message processing just like a great message queue, along with high availability and fault tolerance, but it brings additional possibilities that simple queuing can't offer.
Kafka is able to support publish-subscribe ("pub-sub") patterns while also being able to scale out across multiple servers and replay messages. As such, Kafka is a popular event streaming platform with replication, fanout, retention, and routing flexibility. These things matter when you're interested in reactive programming over imperative programming.
Imperative Programming vs. Reactive Programming
To understand the differences between a message queue and Kafka, and the use cases for Kafka, you must first understand imperative and reactive programming.
- Imperative programming is what everyone starts with. An event happens and your code is notified of it. For instance, if a user clicks on a button where you handle the code's event, you decide what your system should do in response. You might send an email, call a service, or complete a combination of actions in response. The main thing is that, in imperative programming, the event is coupled with the actions that result from it.
- Reactive programming provides more flexibility, allowing you to respond to events often in the form of data streams. Reactive programming enables more loosely coupled code that can be easily extended. So, an event can lead to multiple effects across domains, and the effect in one domain will not impact the effect in another.
Pros and Cons of a Message Queue
To better understand what Kafka can do for you, it's important to first understand the benefits and limitations of a basic message queue.
In its simplest form, a message queue allows subscribers to pull a message from the end of the queue for processing. A subscriber can pull a single message or a batch of messages at once. Queues typically allow for some transaction, to ensure the message's desired action was successfully executed, and then the message is removed from the queue entirely.
In this sense, message queues are very similar to imperative programming in that once something happens, the queue decides that a certain action should occur downstream in the system. You can scale a message queue by having multiple consumers, but this doesn't necessarily enable you to trigger independent actions from the same event.
In other words, the messages in a message queue are more like commands, suited for imperative programming, while Kafka manages events that are suited for reactive programming.
Pros and Cons of Apache Kafka
Unlike a message queue, Kafka enables you to publish messages (or events) to Kafka topics. These messages will not be removed when a consumer retrieves them, making them persistent messages. Kafka lets you replay messages to allow for reactive programming, but more crucially, Kafka lets multiple consumers process different logic based on a single message. This makes Kafka a message broker or a streaming platform.
With Kafka, you can still scale your system to enable parallel processing, but you can also add different kinds of consumers that execute various types of logic when a single event occurs. This allows you to adopt a reactive programming approach with a publish-subscribe pattern. Stream processing means high throughput and different functionality compared to a message queue.
Kafka makes reactive programming possible because it retains messages and uses consumer groups, which identify themselves to Kafka when they retrieve a message. Kafka then records the messages and which consumer group retrieved them, so they aren't served to the same consumer twice. Plenty more configuration options allow you to use Kafka in whatever way is best for you, with near limitless customization.
How Iron Can Help
Are you seeking a message queue solution? Kafka certainly has its use cases, but the complexity of reactive programming isn't necessary for most systems. If you are looking for a message queue that offers both point-to-point processing and the option to use a publish-subscribe pattern, all while ensuring persistence, availability, and scalability, look no further than IronMQ.
IronMQ is a lightning-fast messaging system that outpaces SQS, RabbitMQ, and countless competitors. With Iron, your development team can optimize processes and realize new possibilities thanks to an intuitive dashboard, built-in metrics, Rest-based APIs, and more. Start your 14-day free trial today.
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