Debezium

Debezium is a popular open-source distributed platform for Change Data Capture (CDC). It's designed to monitor specific source databases, capture their row-level changes (inserts, updates, deletes) in near real-time, and produce streams of corresponding change events. These event streams are typically published to a messaging platform, most commonly Apache Kafka, making the database changes available for consumption by downstream applications, microservices, or stream processing systems like RisingWave.

Debezium acts as a source connector in a broader data pipeline, transforming low-level database transaction log entries into structured, consumable event records.

The Problem: Getting Changes Out of Databases Reliably

As discussed under CDC, accessing database changes efficiently and reliably is crucial for many real-time use cases. Debezium specifically addresses this by providing a dedicated, robust, and scalable platform focusing primarily on log-based CDC. It aims to:

• Provide a unified way to capture changes from various databases (PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, Db2, etc.).
• Minimize the performance impact on the source database by reading transaction logs asynchronously.
• Produce detailed and consistently structured change event records.
• Integrate seamlessly with Kafka as the event streaming backbone.
• Handle complexities like schema changes, high availability, and fault tolerance.

How Debezium Works

Debezium typically runs as a distributed system using the Kafka Connect framework, although standalone modes exist:

1. Connector Deployment: A specific Debezium connector (e.g., 'debezium-connector-postgres') is configured and deployed to a Kafka Connect cluster. The configuration specifies the source database connection details, the tables to monitor, the target Kafka topics, and other parameters.
2. Initial Snapshot (Optional): Upon starting for the first time for a monitored table, the connector usually performs an initial consistent snapshot of the table's existing data, producing a series of 'read' ('r') operation events to Kafka. This ensures downstream consumers have a complete view of the data.
3. Log Reading: After the snapshot (or if configured to skip it), the connector connects to the source database and starts reading its transaction log (e.g., PostgreSQL WAL using logical decoding, MySQL Binlog) from the point where the snapshot finished or from a configured starting point.
4. Event Generation: The connector parses the low-level log entries, filtering for changes to the monitored tables. For each relevant row-level change (INSERT, UPDATE, DELETE), it generates a structured change event record.
5. Standard Event Format: Debezium events typically follow a standard structure (often serialized as JSON or Avro) containing:
   • 'before': The state of the row before the change (for UPDATE, DELETE).
   • 'after': The state of the row after the change (for INSERT, UPDATE).
   • 'source': Metadata about the origin (database type, name, table, timestamp, transaction ID, log position - LSN/GTID).
   • 'op': The type of operation ('c' for create/insert, 'u' for update, 'd' for delete, 'r' for read/snapshot).
   • 'ts_ms': Timestamp of when the event was processed by Debezium.
6. Publishing to Kafka: The connector publishes these change events to the configured Kafka topics, often using the table name as part of the topic name. Events related to the same database row typically share the same Kafka message key (based on the table's primary key), ensuring they land in the same Kafka partition for ordered processing.
7. Offset Management: The Debezium connector tracks the transaction log position it has processed successfully. This position is periodically committed (often back to Kafka itself via Kafka Connect's offset management) so that if the connector restarts after a failure, it can resume reading from where it left off, ensuring At-Least-Once Semantics.

Key Benefits

• Log-Based CDC: Low impact on source databases, captures all changes accurately.
• Wide Database Support: Connectors available for many popular relational and NoSQL databases.
• Standardized Event Format: Provides consistent, detailed change events regardless of the source database type.
• Kafka Integration: Leverages Kafka's scalability, durability, and ecosystem via Kafka Connect.
• Resilience: Handles schema changes in source tables and manages fault tolerance through Kafka Connect's distributed nature and offset tracking.
• Open Source: Active community and development.

Common Use Cases

Debezium is used in scenarios requiring real-time capture of database changes, including:

• Feeding data into real-time analytics and stream processing systems.
• Populating search indexes (e.g., Elasticsearch).
• Invalidating caches.
• Synchronizing data between microservices.
• Creating audit logs.
• Replicating data to data warehouses or data lakes.

Debezium and RisingWave

Debezium is a very common way to stream data from operational databases into RisingWave, typically via Kafka:

1. Setup: Configure Debezium to monitor the source database and publish change events (usually in Avro or JSON format) to Kafka topics. A Schema Registry is often used with Avro.
2. RisingWave Source: Define a 'CREATE SOURCE' statement in RisingWave targeting the Kafka topic(s) populated by Debezium. Specify the correct format ('AVRO' or 'JSON') and potentially the Schema Registry details. RisingWave has built-in understanding of the Debezium event structure.
3. Processing: RisingWave ingests the 'before' and 'after' images from the Debezium events, allowing it to process inserts, updates, and deletes correctly and maintain real-time Materialized Views that reflect the state of the source database tables.

This Debezium -> Kafka -> RisingWave pattern provides a robust, decoupled, and scalable way to perform real-time analytics on operational data. Alternatively, RisingWave also offers direct CDC connectors for some databases (like PostgreSQL and MySQL) which bypass the need for Debezium and Kafka in simpler setups.

Related Glossary Terms

• Change Data Capture (CDC)
• Apache Kafka
• Kafka Connect (Framework)
• Event Streaming Platform (ESP)
• Transaction Log (Concept)
• Logical Decoding (PostgreSQL Concept)
• Binlog (MySQL Concept)
• Source / Sink
• RisingWave Source
• Avro / JSON (Formats)
• Schema Registry
• At-Least-Once Semantics