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Transitive Compatibility Types in Schema Registry for Apache Kafka
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Transitive Compatibility Types in Schema Registry for Apache Kafka

Tags: application content management
DATE POSTED:January 8, 2025
Hacker Noon - Medium
Original article

Apache Kafka provides schema management capabilities through the Schema Registry, including various levels of compatibility. In this article, we will discuss three types of transitive compatibility: BACKWARDTRANSITIVE, FORWARDTRANSITIVE, and FULL_TRANSITIVE. Additionally, we will analyze their pros, cons, and provide code examples.

BACKWARD_TRANSITIVE Description

Backward transitive compatibility means that a new version of the schema must be compatible with all previous versions. This is useful in systems with a long data lifecycle.

Code Example curl -X PUT \ -H "Content-Type: application/vnd.schemaregistry.v1+json" \ --data '{"compatibility": "BACKWARD_TRANSITIVE"}' \ http://localhost:8081/config/ Java Code Example import io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient; import io.confluent.kafka.schemaregistry.client.SchemaRegistryClient; import io.confluent.kafka.schemaregistry.rest.exceptions.RestClientException; import java.io.IOException; public class BackwardTransitiveExample { public static void main(String[] args) throws IOException, RestClientException { String schemaRegistryUrl = "http://localhost:8081"; SchemaRegistryClient client = new CachedSchemaRegistryClient(schemaRegistryUrl, 10); String subject = ""; String compatibility = "BACKWARD_TRANSITIVE"; // Set compatibility type client.updateCompatibility(subject, compatibility); // Check current compatibility type String currentCompatibility = client.getCompatibility(subject); System.out.println("Current compatibility level: " + currentCompatibility); } } Pros
  • ✅ Ensures compatibility with historical data.
  • ✅ Useful for long-term data storage.
Cons
  • ❌ May restrict adding new fields or changing the structure.
FORWARD_TRANSITIVE Description

Forward transitive compatibility requires that a new version of the schema be compatible with all future versions. This is useful when consumers are updated asynchronously with producers.

Code Example curl -X PUT \ -H "Content-Type: application/vnd.schemaregistry.v1+json" \ --data '{"compatibility": "FORWARD_TRANSITIVE"}' \ http://localhost:8081/config/ Java Code Example import io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient; import io.confluent.kafka.schemaregistry.client.SchemaRegistryClient; import io.confluent.kafka.schemaregistry.rest.exceptions.RestClientException; import java.io.IOException; public class ForwardTransitiveExample { public static void main(String[] args) throws IOException, RestClientException { String schemaRegistryUrl = "http://localhost:8081"; SchemaRegistryClient client = new CachedSchemaRegistryClient(schemaRegistryUrl, 10); String subject = ""; String compatibility = "FORWARD_TRANSITIVE"; // Set compatibility type client.updateCompatibility(subject, compatibility); // Check current compatibility type String currentCompatibility = client.getCompatibility(subject); System.out.println("Current compatibility level: " + currentCompatibility); } } Pros
  • ✅ Ensures stability for old consumers with new data.

\

  • ✅ Convenient for systems with asynchronous updates.
Cons
  • ❌ Does not guarantee compatibility with historical data.
FULL_TRANSITIVE Description

Full transitive compatibility combines the requirements of backward and forward compatibility. A new schema version must be compatible with both previous and future versions.

Code Example curl -X PUT \ -H "Content-Type: application/vnd.schemaregistry.v1+json" \ --data '{"compatibility": "FULL_TRANSITIVE"}' \ http://localhost:8081/config/ Java Code Example import io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient; import io.confluent.kafka.schemaregistry.client.SchemaRegistryClient; import io.confluent.kafka.schemaregistry.rest.exceptions.RestClientException; import java.io.IOException; public class FullTransitiveExample { public static void main(String[] args) throws IOException, RestClientException { String schemaRegistryUrl = "http://localhost:8081"; SchemaRegistryClient client = new CachedSchemaRegistryClient(schemaRegistryUrl, 10); String subject = ""; String compatibility = "FULL_TRANSITIVE"; // Set compatibility type client.updateCompatibility(subject, compatibility); // Check current compatibility type String currentCompatibility = client.getCompatibility(subject); System.out.println("Current compatibility level: " + currentCompatibility); } } Pros
  • ✅ Maximum flexibility and resilience.
  • ✅ Guarantees compatibility with all schema versions.
Cons
  • ❌ The strictest compatibility type.
  • ❌ Requires careful planning for schema changes.
Comparison Table

| Compatibility Type | Compatibility with Past Versions | Compatibility with Future Versions | Ease of Use | Applicability | |----|----|----|----|----| | BACKWARDTRANSITIVE | ✅ | ❌ | Medium | Long-term data storage | | FORWARDTRANSITIVE | ❌ | ✅ | Medium | Asynchronous updates | | FULL_TRANSITIVE | ✅ | ✅ | Low | Maximum resilience |

\ These compatibility types allow you to configure the system to meet your stability and flexibility requirements. The correct choice depends on your system architecture and use cases.

Hacker Noon - Medium
Original article
Tags: application content management
Newswire > Transitive Compatibility Types in Schema Registry for Apache Kafka

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