Implementing Source Connectors for Change Data Capture

In this lesson, you will learn how to configure source connectors to capture real-time database changes while balancing data integrity with production database performance.

The Mechanics of Change Data Capture (CDC)

Change Data Capture (CDC) is the process of identifying and capturing changes made to a database (inserts, updates, and deletes) and streaming them into Kafka topics in real-time. Unlike polling a table for new records—which is resource-intensive and often misses deletes—CDC works by reading the database’s transaction log (e.g., MySQL binary log or PostgreSQL WAL).

Configuring for Performance and Consistency

When setting up your Kafka Connect source, you must configure it to minimize the "read load" on your production database. Instead of scanning entire tables, you point the connector to the replication stream.

To maintain consistency, ensure your connector is configured to capture the "before" and "after" images of a row. This allows your downstream consumers to accurately reconstruct the state of the data.

For example, a typical configuration block for a Debezium-based connector looks like this:

{
  "name": "inventory-connector",
  "config": {
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",
    "database.hostname": "prod-db-server",
    "database.server.name": "dbserver1",
    "table.include.list": "inventory.products",
    "snapshot.mode": "when_needed",
    "include.schema.changes": "true"
  }
}

Best Practices for Production

1. Use Replica Reads: If possible, connect your CDC tool to a database read-replica rather than the primary instance to eliminate the impact of log parsing on transaction processing.
2. Handle Snapshots Carefully: When first connecting, the tool performs an initial "snapshot" of your data. If your dataset is massive, set snapshot.mode to when_needed to prevent the connector from locking tables or saturating the network during peak traffic hours.
3. Control Topic Mapping: Map related changes to the same topic partition key (e.g., product_id) to ensure that all events for a specific record are processed in the correct order by your consumers.

By leveraging transaction log parsing, you transform your database into a dynamic, event-driven stream that feeds your Kafka cluster without burdening your operational workload.

Key Takeaway: CDC enables near real-time data streaming by tapping into transaction logs, ensuring low-latency updates while offloading processing from production queries.