Change Data Capture (CDC) has become essential for real-time data integration and streaming analytics. Debezium is an open-source CDC platform that enables reliable streaming of database changes into systems like Apache Kafka. When paired with MySQL, Debezium provides a seamless way to capture insert, update, and delete events with minimal latency. This guide targets intermediate and advanced users who want to configure Debezium for MySQL with a focus on performance, reliability, and scalability.

Prerequisites and Environment Setup

Before diving into configuration, ensure you have the following in place:

  • A running MySQL server (version 5.7+ recommended) with binary logging enabled.
  • Apache Kafka cluster accessible to the Debezium connector.
  • Docker or local environment to run Debezium connectors.
  • Kafka Connect framework installed and configured.

Verify that MySQL binary logging is enabled with the following settings in your my.cnf:

[mysqld]
server-id=223344
log_bin=mysql-bin
binlog_format=row
binlog_row_image=full
expire_logs_days=10

The row-based binary logging (binlog_format=row) is required for Debezium to capture data changes accurately.

Step 1 Defining MySQL User for Debezium

Create a dedicated MySQL user with replication privileges to allow Debezium to read the binlog:

CREATE USER 'debezium'@'%' IDENTIFIED BY 'dbz_password';
GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'debezium'@'%';
FLUSH PRIVILEGES;

This user must have sufficient privileges to read the binlog and metadata for all databases you intend to capture.

Step 2 Configuring Debezium MySQL Connector Properties

The Debezium MySQL connector requires a JSON configuration or properties file defining connection details and behavior:

{
  "name": "mysql-connector",
  "connector.class": "io.debezium.connector.mysql.MySqlConnector",
  "tasks.max": "1",
  "database.hostname": "mysql-host",
  "database.port": "3306",
  "database.user": "debezium",
  "database.password": "dbz_password",
  "database.server.id": "184054",
  "database.server.name": "dbserver1",
  "database.include.list": "your_database",
  "database.history.kafka.bootstrap.servers": "kafka:9092",
  "database.history.kafka.topic": "schema-changes.mysql",
  "include.schema.changes": "true",
  "heartbeat.interval.ms": "10000",
  "max.batch.size": "2048",
  "snapshot.mode": "initial"
}

Key parameters explained:

  • database.server.id: A unique numeric ID used for MySQL replication. Ensure it does not conflict with other replicas.
  • database.server.name: Logical name for the MySQL server; prefixes all topic names.
  • database.include.list: Comma-separated list of databases to capture.
  • database.history.kafka.topic: Topic where schema changes are stored to maintain CDC consistency.
  • snapshot.mode: Controls when and how initial snapshotting happens. initial takes a snapshot on startup.

Step 3 Deploying the Connector to Kafka Connect

If you are using Kafka Connect REST API, POST your connector configuration:

curl -X POST -H "Content-Type: application/json" --data @mysql-connector.json http://localhost:8083/connectors

Check the connector status to ensure it’s running without errors:

curl http://localhost:8083/connectors/mysql-connector/status

Logs provide detailed insight if the connector fails to start or encounters replication issues.

Step 4 Handling Schema Evolution and Data Types

Debezium automatically captures schema changes; however, complex MySQL data types require attention:

  • Enum and Set types are converted to strings.
  • JSON columns are emitted as strings and require downstream parsing.
  • Spatial data types are not natively supported and may require custom converters.

To optimize schema handling, configure the connector with:

"decimal.handling.mode": "precise",
"include.schema.changes": "true"

This ensures decimals are represented precisely in Kafka events and schema changes are propagated timely.

Step 5 Optimizing Performance and Reliability

For production workloads, consider these advanced configurations:

  • Snapshot mode tuning: Switch to schema_only if you want to avoid initial snapshots and start CDC from the current binlog position.
  • Heartbeat interval: Set heartbeat.interval.ms to detect connector downtime quickly.
  • Max batch size: Larger batches improve throughput but increase latency.
  • Error handling: Configure errors.tolerance=all to skip problematic records and avoid connector failure.
  • Offset storage: Use Kafka or external storage for offsets to ensure connector recovery after restarts.

Monitoring and Troubleshooting

Monitor the Kafka Connect logs and Kafka topics:

  • Use Kafka consumer tools to inspect CDC events.
  • Check connector status via REST API.
  • Monitor MySQL server performance to prevent replication lag.
  • Use Debezium’s metrics exposed via JMX to track throughput and error rates.

Common issues include:

  • Incorrect MySQL privileges causing replication errors.
  • Binlog format misconfiguration.
  • Server ID conflicts in replication.
  • Network latency affecting Kafka communication.

Conclusion

Configuring Debezium for MySQL involves careful setup of MySQL replication, connector properties, and Kafka Connect deployment. By following this step-by-step guide, intermediate and advanced users can implement robust Change Data Capture pipelines that power real-time analytics, event-driven architectures, and data synchronization. With proper tuning and monitoring, Debezium becomes a powerful tool to unlock MySQL data streams efficiently.

Harness the power of Debezium CDC to build scalable, reactive systems and enable cutting-edge data integrations for your enterprise.