Handling Schema Evolution in Debezium Kafka Connectors: A Step-by-Step Guide

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Introduction:

Schema evolution is a common challenge when working with streaming data pipelines, particularly in environments where databases undergo frequent schema changes. In this blog post, we’ll explore a scenario where a PostgreSQL database table schema evolves, causing deserialization errors in a Debezium Kafka source connector. We’ll walk through the problem statement and provide a step-by-step solution to address the issue.

Problem Statement:

Imagine you have set up a Debezium Kafka source connector to capture changes from a PostgreSQL database table. Initially, the table has two columns: “name” and “address”. However, due to evolving requirements, a new column “email” is added to the table. After this schema change, the Debezium connector starts encountering deserialization errors when processing messages, leading to data inconsistency and potential downstream issues.

Solution:

1. Identify the Issue:

• Monitor the logs of the Debezium Kafka source connector and Kafka Connect to pinpoint the exact nature of the deserialization errors.
• Verify that the errors are indeed caused by schema evolution, particularly the addition of the “email” column to the PostgreSQL table.

2. Adjust Schema Registry Compatibility:

• Access the Schema Registry endpoint and check the current compatibility level. By default, it might be set to “BACKWARD” or “FULL”.

curl -X GET http://localhost:8081/config

##{"compatibilityLevel":"BACKWARD"}

• Use the following command to temporarily disable schema compatibility checks:

curl -X PUT -H "Content-Type: application/vnd.schemaregistry.v1+json" --data '{"compatibility": "NONE"}' http://localhost:8081/config

• Verify that the compatibility level is set to “NONE” by querying the Schema Registry configuration:

curl -X GET http://localhost:8081/config

3. Restart the Connector:

• After adjusting the compatibility level, restart the Debezium Kafka source connector to apply the changes:

curl -i -X POST "http://localhost:8083/connectors/source_connector_test/restart?includeTasks=true"

4. Validate and Monitor:

• Monitor the logs of the Debezium connector and Kafka Connect to ensure that the deserialization errors have been resolved.
• Validate that the data captured from PostgreSQL is correctly serialized and sent to Kafka without any further issues.
• Keep an eye on the performance of the connector and consider implementing additional monitoring to detect any potential regressions or issues in the future.

5. Considerations for the Future:

• While setting the compatibility level to “NONE” can provide a quick fix, it’s essential to revisit this setting and define a robust schema evolution strategy.
• Explore options for handling schema changes more gracefully in the future, such as specifying compatibility rules or versioning schemas.

Conclusion:

By following the step-by-step solution outlined above, you can effectively address deserialization errors caused by schema evolution in Debezium Kafka source connectors. It’s crucial to monitor the performance of your connectors and establish a proactive approach to handling schema changes as your data pipelines evolve over time.

Happy Coding :)