HLD: Database CDC with Kafka for Real-Time Events

What is Change Data Capture (CDC)?

Change Data Capture (CDC) is a design pattern that captures changes made to a database and streams those changes to other systems. This allows multiple services to react to database updates without the need for constant polling.

How CDC Works with Kafka

1. Database: The source of data changes.
2. CDC Connector: A tool that captures changes from the database's transaction logs.
3. Kafka: A distributed messaging system that publishes the change events.
4. Consumers: Various downstream services that process these events independently.

Key Operations

• INSERT: New records added to the database become events.
• UPDATE: Changes to existing records are captured as events.
• DELETE: Removal of records triggers delete events.

Benefits of Using Kafka for CDC

• Scalability: Kafka can handle a large number of events efficiently.
• Decoupling: Consumers can operate independently, allowing for flexible architecture.
• Real-time Processing: Events are available immediately after they occur.

Real-World Example

Consider a customer management system where a customer updates their address. This change is captured by the CDC connector, sent to Kafka, and then distributed to various services like analytics, search, and caching systems.

Important Considerations

• Duplicate Events: Design consumers to handle duplicates gracefully.
• Event Ordering: Ensure events are processed in the correct order.
• Schema Evolution: Be prepared for changes in the data structure over time.
• Delivery Guarantees: Understand that Kafka does not guarantee zero latency or exactly-once processing without proper configuration.

Memory Trick

• CDC = CAPTURE
• KAFKA = DISTRIBUTE
• CONSUMERS = PROCESS