How to Limit Message Consumption Rate of Kafka Consumer in SpringBoot Using Kafka Streams

Learn how to control the message consumption rate of Kafka Consumers in Spring Boot applications using Kafka Streams. Get step-by-step guidance on implementing a custom solution.
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How to Limit Message Consumption Rate of Kafka Consumer in SpringBoot

In the world of streaming data with Apache Kafka, managing the consumption rate of messages is crucial for ensuring that your application can process the data efficiently without being overwhelmed. One common scenario developers face is the need to limit the consumption rate to prevent backlog or to control system resource usage. In this post, we will explore how to limit Kafka Consumer message consumption rate to 1 message per 10 seconds in a Spring Boot application using Kafka Streams.

The Challenge: Controlling Message Consumption Rate

You may find yourself in a situation where your Kafka Consumer is ingesting messages too quickly, making it hard to process them in a timely manner. Consider the following Kafka configuration properties you might attempt to set:

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While these configurations help manage offset and message limits, they do not provide fine-grained control over the rate of message consumption. This explains why you might observe multiple messages being consumed at once, despite your settings.

The Solution: Implementing a Custom Consumption Rate Control

After exploring potential configurations without success, a simple yet effective way to control the message consumption rate is to introduce a delay in your application code. Here's how to do it:

Consume Messages in Batches: Instead of processing one message at a time, consume a small batch (e.g., 4 messages).

Introduce a Delay: After consuming the specified number of messages, make the thread sleep to pause further consumption.

Implementation Example:

Here is a basic structure for your implementation:

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Advantages of This Approach

Flexibility: You can easily adjust the number of messages consumed and the sleep duration.

Control: It provides a hands-on method to manage consumption rate according to your application's needs.

Simplicity: The method is straightforward and easily integrated into existing Kafka Streams applications.

Considerations

Error Handling: Be sure to handle exceptions that may arise during message processing or the sleep operation.

Conclusion

If you face similar challenges while working with Kafka, implementing this technique could provide the solution you need. Happy coding!