Understanding Why is My Spring Cloud Stream Config Creating Multiple Kafka Consumers?

Explore the reasons behind the creation of multiple Kafka consumers in your Spring Cloud Stream setup and learn how to manage them effectively.
---
This video is based on the question https://stackoverflow.com/q/67084583/ asked by the user 'pChip' ( https://stackoverflow.com/u/1748306/ ) and on the answer https://stackoverflow.com/a/67094493/ provided by the user 'Gary Russell' ( https://stackoverflow.com/u/1240763/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Why is my spring cloud stream config creating multiple kafka consumers

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding Why is My Spring Cloud Stream Config Creating Multiple Kafka Consumers?

When working with Spring Cloud Stream, especially in deployments with Kafka, it can sometimes be puzzling to see multiple consumer instances being created. For example, you might find that even with a simple consumer setup, many consumers are generated during application startup. Understanding why this happens and how to manage these consumers is crucial for optimizing your application.

The Problem

A developer noticed that their Spring Cloud Stream consumer setup against a local Kafka broker created three consumers at runtime. However, when checking the local broker, only one consumer belonging to the group was actively listening. This raised concerns about what was happening behind the scenes and whether or not these extra consumers could be controlled.

Here's a summary of the application's configuration:

[[See Video to Reveal this Text or Code Snippet]]

And the accompanying consumer class:

[[See Video to Reveal this Text or Code Snippet]]

Despite this seemingly straightforward configuration, the developer observed three consumers being created upon running the application.

The Explanation

There are a few reasons behind the creation of these multiple Kafka consumer instances:

1. Temporary Consumer for Partitions

Upon startup, Spring Cloud Stream creates a temporary consumer to gather information about the Kafka topic's partitions. This is an expected behavior; it's necessary for the application to understand the structure of the topic it is going to consume messages from. This temporary consumer won't actively process messages from the topic but ensures that all necessary details about topic partitions are available.

2. Actual Consumer

The second consumer created is indeed the actual consumer of the message stream. This instance is the one that subscribes to the specified topic (test-topic) and processes incoming messages.

3. Consumer for Metrics Monitoring

If the Spring Actuator or Micrometer is included in your classpath, an additional consumer is spawned, known as KafkaBinderMetrics. This consumer’s purpose is to monitor the consumer lag, providing valuable metrics about message consumption. Notably, this consumer does not consume any messages from the Kafka topic; instead, it exists solely for monitoring performance metrics.

Summary of Consumer Instances

Temporary Consumer: Created at startup to gather partition information.

Actual Consumer: Subscribes and processes messages from the Kafka topic.

Metrics Consumer: Monitors lag without consuming messages.

Conclusion

Understanding why multiple Kafka consumers appear in a Spring Cloud Stream application can clarify any confusion about their functionalities. The presence of a temporary consumer for partition information and a metrics consumer alongside the actual consumer is a design choice aimed at providing better messaging capabilities and insights.

If you find multiple consumers in your application, remember that this is normal behavior, especially when monitoring tools are in use. You can analyze the Kafka consumer group behavior from your local broker, but typically only the actual consumer will be consuming messages while the others serve their intended purposes.

Hopefully, this article has shed some light on the inner workings of Spring Cloud Stream and Kafka consumers, as well as how to navigate and manage them effectively in your applications.

Видео Understanding Why is My Spring Cloud Stream Config Creating Multiple Kafka Consumers? канала vlogize