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As a result, Spring Cloud Stream doesn’t allow to send a message to the Kafka topic.

If a new schema is not compatible with the previous version, a schema registry rejects it.

The following diagram illustrates our architecture. If there is no result, it submits the data to the server, which replies with versioning information. Before sending a message to Kafka the producer application tries to load schema definition from a remote server. The consumer Consumer-A is expecting events compatible with the v1 of schema, while the second subscriber is expecting events compatible with the v2 of schema. The integration with Kafka is built on top of Spring Cloud Stream. One of them is sending events to the Kafka topic, while two others are receiving them. Event-driven architecture with Spring Cloud and schema registry Therefore our choice fell on the Confluent schema registry. On the other hand, it is not possible to easily integrate it with Spring Cloud Stream. Currently, it doesn’t allow verifying compatibility between different versions. Although it can be easily integrated with Spring Cloud Stream, we won’t use it. Spring Cloud has its own implementation of a schema registry server. We may choose between several available products. It also stores a versioned history of all schemas and provides schema compatibility checks. It provides a RESTful interface for storing and retrieving schemas in different formats like JSON, Protobuf, or Avro. If a schema is evolving, the existing microservices are still required to work.

It is often necessary for all those microservices to agree on a contract that is based on a schema.

In a typical microservices architecture, we may have many producers and many subscribers. We sent that message to a Kafka topic, but we don’t have many subscribers is receiving such events. What is this? And why we may use it in our event-driven architecture? Let’s imagine we change the message on the producer side, by adding or removing some fields. Ok, but before we start, let’s say some words about schema registry. Spring Cloud Stream provides a handy mechanism for integration with Kafka and schema registry. We will use Apache Avro to serialize and deserialize events exchanged between our applications. In this article, you will learn how to use Confluent Schema Registry with Spring Cloud Stream and Kafka in a microservices architecture.