Change Data Capture (CDC) is a set of software design patterns used to detect and capture changes made to data in a database. Instead of querying the database at regular intervals (polling) to detect changes, CDC allows for real-time data monitoring and synchronization by capturing changes as they happen.
The usefulness of CDC
- Real-Time Data Integration: CDC enables the immediate detection and replication of data changes, allowing for real-time data integration across systems.
- Data Synchronization: It ensures data consistency across different databases and systems by continuously monitoring and replicating changes.
- Reduced Latency: Since CDC captures changes as they happen, it significantly reduces the latency compared to traditional batch processing or polling mechanisms.
- Audit Trail: CDC provides a complete audit trail of all changes, which is useful for compliance and tracking historical changes.
- Efficient Resource Usage: By only processing changed data, CDC reduces the overhead and resource consumption associated with full data processing.
How CDC works
CDC can be implemented using several methods, depending on the database and use case.
- Database Triggers: These are database-specific procedures that execute automatically in response to certain events (e.g., insert, update, delete) on a table.
- Transaction Logs: CDC tools read the database’s transaction log to capture changes. This method is highly efficient as it does not interfere with the database’s normal operations.
- Timestamp Columns: Tables are designed with timestamp columns that record the last modification time, allowing CDC systems to identify changes by comparing timestamps.
- Polling with Optimization: While not true CDC, optimized polling checks for changes in a more efficient manner by using indexed columns or other mechanisms to reduce overhead.
Benefits of CDC
- Reduced Overhead: Polling requires frequent querying of the database, which can be resource-intensive. CDC, by contrast, captures changes as they happen, leading to less database load.
- Timeliness: CDC captures and propagates changes in real-time, whereas polling introduces a delay based on the polling interval.
- Scalability: CDC can handle large volumes of changes more efficiently than polling, which can struggle with scalability as the frequency and size of data changes increase.
- Deletion Capture: CDC tracks all types of database operations, including deletions, ensuring that applications or systems accurately reflect the current state of the database. Unlike polling, which requires soft deletes or other deletion markers to capture deletions, CDC can capture deletions directly.
Downsides of CDC
- Complexity: Implementing CDC can be more complex than polling, requiring deeper integration with the database and careful handling of transaction logs or triggers.
- Database-Specific Implementations: Each database system may have its own way of implementing CDC, which can lead to vendor lock-in and difficulties in migrating to different databases.
- Resource Usage: While CDC reduces query overhead, reading transaction logs or maintaining triggers can still consume resources and may impact performance if not managed properly.
Tools for implementing CDC
- Debezium: An open-source CDC tool that supports various databases like MySQL, PostgreSQL, MongoDB, and more. It reads database transaction logs and streams changes to other systems.
- AWS Database Migration Service (DMS): A cloud-based service that supports CDC for various databases, enabling data replication and migration with minimal downtime.
- Apache Kafka Connect: With connectors like the Debezium Connector, Kafka Connect provides a scalable and reliable way to implement CDC and stream changes to Kafka topics.
- Oracle GoldenGate: A comprehensive CDC solution for Oracle databases that captures and replicates changes in real-time.
- Microsoft SQL Server Change Data Capture: A built-in feature of SQL Server that captures changes in user tables and makes them available for downstream processing.
- Google Cloud Datastream: A serverless change data capture and replication service that allows for real-time data integration and synchronization across various databases and services.
- MaxWell for MySQL: An open-source CDC tool for MySQL databases that reads MySQL binlogs and writes row updates as JSON to Kafka, Kinesis, or other streaming platforms.
In summary, CDC is a powerful approach for real-time data monitoring and synchronization. It offers significant advantages over polling, including reduced latency and resource usage, but comes with complexities and implementation-specific challenges. Various tools and platforms provide support for CDC, making it accessible for different database environments.