How to Use the Apache Kafka Schema Registry

Apache Kafka is a distributed streaming platform that has become an industry standard for processing and analyzing large volumes of data in real time. However, managing the data schema evolution in Kafka can be challenging without a proper system. This is where Apache Kafka schema registry comes in. This article covers everything you need to know about using the Apache Kafka schema registry.

What Is Apache Kafka Schema Registry?

Apache Kafka schema registry is a central repository that stores the schema for the data that is sent through Kafka. It provides a way to manage the schema evolution and ensures compatibility between producers and consumers. Using Schema Registry, you can ensure that the data is always produced and consumed with the correct schema, preventing errors and data loss.

The schema registry stores and manages the metadata and schema information of Kafka messages which are serialized in different data formats such as Avro, JSON, or Protobuf. It is a central authority to track the evolution of data schemas that are used in the messages that are exchanged between producers and consumers in the Kafka ecosystem.

How to Set Up the Schema Registry

Setting up the Apache Kafka schema registry is a straightforward process. First, you need to have the Kafka installed and running on your system. Next, you can download the schema registry from the Confluent website or the Maven repository. Once you have the Schema Registry package, you can start the server by running the following command:

$ bin/schema-registry-start etc/schema-registry/schema-registry.properties

This starts the Schema Registry server on the default port of 8081.

Registering the Schemas

To use the schema registry, register your schemas with the server. This is done using the REST API which is provided by schema registry. The API supports the CRUD operations for schemas and provides compatibility checks between different versions of the same schema.

To register a new schema, you can use the following command:

$ curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \ --data '{"schema": "{"type": "string"}"}' \ http://localhost:8081/subjects/my_topic/versions

This registers a new schema for the “my_topic” topic. Using the API, you can also retrieve a schema by its ID or version.

Schema Evolution

One of the main benefits of using a schema registry is managing the schema evolution. As your data evolves, you may need to update your schema to reflect these changes. Schema registry provides several features to help you manage the schema evolution, including:

Backward Compatibility

Backward compatibility ensures that the older consumers can still consume the data that is produced by the newer producers. This means adding new fields to your schema without breaking compatibility with the existing consumers.

Forward Compatibility

Forward compatibility ensures that the newer consumers can still consume the data that is produced by the older producers. This means removing fields from your schema without breaking compatibility with the existing producers.

Full Compatibility

Full compatibility ensures that the older and newer consumers and producers can interoperate without issues. This means that you can change your schema without breaking compatibility with any existing consumers or producers.

Using the Schema Registry with Kafka

Once you register your schema with schema registry, you can use it with Kafka. To use the schema registry with Kafka, specify the “value.serializer” and “value.deserializer” properties in your producer and consumer configurations.

For example, to produce the data using the Avro serializer, you can use the following configuration:

props.put("value.serializer", "io.confluent.kafka.serializers.KafkaAvroSerializer"); props.put("schema.registry.url", "http://localhost:8081");

This ensures that the data is produced using the correct schema from schema registry.

Conclusion

Apache Kafka schema registry is an essential tool to manage the schema evolution in Kafka. Using the schema registry, you can ensure that your data is always produced and consumed with the correct schema, preventing errors and data loss.