Скачать или смотреть Understanding How the default kafkaListenerContainerFactory Works in Spring Kafka

Explore the mechanics of the `kafkaListenerContainerFactory` in Spring Kafka, clarifying how different topics can use distinct message schemas without separate factory instances.
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Understanding How the default kafkaListenerContainerFactory Works in Spring Kafka

When working with Apache Kafka in Java applications, particularly those utilizing Spring Kafka, you may encounter the kafkaListenerContainerFactory. This factory plays a pivotal role in how messages from various topics are consumed. However, a common point of confusion arises when you have multiple Kafka topics with different serialized message formats. In this post, we'll dive into how kafkaListenerContainerFactory functions within Spring Kafka and address the question of consuming messages from multiple topics with different schema types.

The Problem

In your Kafka consumer application, you have set up two topics to read messages from — let's call them topic1 and topic2. You've defined a custom KafkaListenerContainerFactory for topic1, using a specific POJO class (Pojo1) that conforms to a given Avro schema. Here's the key snippet of the factory configuration:

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You also have a listener method for topic1:

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However, you are puzzled about how your application seems to be functioning with topic2, which uses another POJO (Pojo2), also built from its own Avro schema, even though you only defined a listener container factory for topic1.

The Solution

The behavior you're observing can largely be attributed to a mechanism known as type erasure and the default behavior of Spring Kafka.

Type Erasure

Type erasure is a concept from Java Generics where the generic types used at compile time are not retained at runtime. Here’s what this means for your Kafka application:

At compile time, your listener is expecting a message of type Pojo1 when consuming from topic1.

At runtime, as long as your deserializer is capable of producing the correct object type (in this case, Pojo2 for messages from topic2), the Kafka listener can still process the messages, despite the mismatched generics.

Cleaner Approach

To simplify interactions, it’s advisable to define your factory with a more general type:

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Using Object as the type can alleviate confusion, especially with Avro or Json deserializers that can output various types.

Default Factory Behavior

In Spring Kafka, if you don’t explicitly specify a container factory in your listener, it defaults to using one named kafkaListenerContainerFactory. For your topic2, if you are indeed processing messages successfully with a distinct schema, this is likely due to the following:

The KafkaListener automatically uses the default factory (unless otherwise specified).

Because both your topics share the same Kafka cluster and the deserializer can convert Avro messages into the required POJO format, the consumers for both topics can function without separate configurations.

Specifying Non-Standard Factories

If you need to use a non-standard factory for your listeners, you should specify the factory name directly in the @ KafkaListener annotation:

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This requires you to define another factory named factoryForTopic2 which utilizes the appropriate deserializer for Pojo2.

Conclusion

In summary, the kafkaListenerContainerFactory in Spring Kafka is a powerful feature that allows you to consume messages from different Kafka topics, even when they contain messages in various formats. By understanding type erasure and how Spring defaults to certain factory instances, you can effectively manage and configure your listeners for diverse data schemas.

If you ever feel uncertain, consider using a more general approach to your factory or explicitly