What is CDC and it's use cases
Change Data Capture (CDC) is a mechanism to identify, capture, and track changes (inserts, updates, and deletes) in a data source (typically a database) in near real-time.
CDC can be implemented using various tools and frameworks like Debezium, Kafka Connect, AWS DMS, and built-in database features like MySQL binary logs or PostgreSQL WAL (Write-Ahead Logging).
How CDC Works
Capture Changes: CDC captures changes to the source database (e.g., when new rows are inserted, updated, or deleted).
Transform (Optional): Changes may be transformed (e.g., reformatting, filtering) before they are sent downstream.
Deliver Changes: The changes are delivered to a target system, which could be another database, a data lake, a messaging system (e.g., Kafka), or an analytics platform.
CDC often operates by:
Listening to Logs: Tapping into transaction logs (e.g., MySQL binlog, PostgreSQL WAL).
Polling Tables: Periodically querying database tables to detect changes (less efficient).
Triggers: Using database triggers to capture changes (adds overhead).
Uses of CDC
CDC is a foundational technology for modern systems where real-time or near-real-time data replication and synchronization are critical. Here are the primary use cases:
1. Data Replication
Use Case: Synchronizing a primary database with a replica (or replicas) for high availability, failover, or disaster recovery.
Example: Keeping a read replica in sync with the main database.
2. Real-Time Analytics
Use Case: Sending real-time data to a data warehouse, analytics system, or stream-processing application for business intelligence or monitoring.
Example: Analyzing customer behavior in near real-time by feeding changes to a tool like Apache Flink or Snowflake.
3. Event-Driven Architectures
Use Case: Capturing database changes as events and propagating them via messaging systems like Apache Kafka for downstream processing.
Example: Triggering a workflow when a new order is placed or a user updates their profile.
4. Cache Invalidation
Use Case: Keeping distributed caches (e.g., Redis, Memcached) synchronized with the source database.
Example: Invalidating or updating cached entries when the corresponding data in the database changes.
5. Data Migration
Use Case: Migrating data from one database to another with minimal downtime by keeping the target database updated with the source's ongoing changes.
Example: Moving from on-premise databases to cloud-managed databases (e.g., MySQL to Amazon Aurora).
6. Microservices Data Sharing
Use Case: Sharing state changes between services in a microservices architecture.
Example: A customer service updating an email address triggers a notification service via CDC.
7. Building Audit Trails
Use Case: Keeping a historical log of all changes made to a database for compliance, debugging, or audit purposes.
Example: Tracking every update to customer account details.
8. Data Lake Updates
Use Case: Updating a data lake with incremental changes instead of performing bulk imports.
Example: Keeping an S3-based data lake synchronized with transactional updates from a relational database.
9. Backup and Restore
Use Case: Capturing ongoing database changes to keep a backup system in sync with minimal latency.
Example: Creating consistent backups without locking tables for long periods.
10. Master-Data Management
Use Case: Synchronizing changes to a master record across multiple systems.
Example: Ensuring a central "Customer Record" is consistent across CRM, billing, and support platforms.
Key Benefits of CDC
Low Latency: Enables near real-time data updates across systems.
Event-Driven: Transforms database changes into events for stream processing.
Minimized Impact: Reduces the load on the source database by reading logs instead of querying.
Scalable and Reliable: Facilitates horizontal scaling and decoupling of systems.
Limitations of CDC
Log Parsing Complexity: Requires support for transaction logs, which might not be available in all databases.
Performance Overhead: Improper configurations or excessive data change rates may impact database performance.
Schema Evolution Challenges: Changes to the source database schema need to be handled gracefully downstream.
Event Ordering: Ensuring the correct order of events, especially in distributed systems, can be complex.
Common CDC Tools
Debezium: Open-source CDC tool built on Kafka Connect, supporting databases like MySQL, PostgreSQL, MongoDB, and Oracle.
AWS DMS (Database Migration Service): Helps migrate databases with built-in CDC capabilities.
Oracle GoldenGate: A proprietary tool for Oracle database CDC and replication.
Kafka Connect: Provides connectors for CDC, like the Debezium connector.
StreamSets: Supports CDC pipelines for real-time data processing.
CDC in Real-World Systems
E-Commerce: Real-time stock updates or order status changes reflected across inventory, analytics, and user-facing apps.
Streaming Platforms: Synchronizing metadata and comments in real time for video or media applications.
Banking: Reflecting account balance changes across multiple services instantly.
Social Media: Propagating user activity (e.g., likes, shares, comments) for analytics and feeds.
CDC is a critical enabler for real-time data-driven systems, empowering modern architectures like microservices, event-driven applications, and distributed analytics.
2. How CDC Works
CDC can be implemented using various methods:
2.1. Log-Based CDC
Mechanism: Reads database transaction logs (e.g., MySQL binlog, PostgreSQL WAL, or Oracle redo logs).
Advantages:
High performance: No need for frequent table scans.
Low latency: Changes are captured as they happen.
Reliable: Captures every change in order and ensures transactional consistency.
Disadvantages:
Requires access to database logs (which might need administrative privileges).
Handling schema changes can be complex.
2.2. Query-Based CDC
Mechanism: Periodically queries the database tables to identify changes (e.g., comparing timestamps or incremental IDs).
Advantages:
Simpler setup: Works without access to transaction logs.
Database-agnostic: Works on databases without log-based CDC support.
Disadvantages:
Inefficient: High overhead on the database, especially for large tables.
Limited granularity: May miss changes between polling intervals.
2.3. Trigger-Based CDC
Mechanism: Uses database triggers to record changes in a separate table or directly push changes to downstream systems.
Advantages:
Precise: Captures all changes immediately.
Supports downstream delivery: Changes can be pushed directly to messaging systems or applications.
Disadvantages:
High overhead: Triggers can slow down database operations.
Complex management: Changes in triggers need careful handling during schema evolution.
2.4. Application-Based CDC
Mechanism: Change detection is built into the application logic.
Advantages:
Highly customizable: Tailored to application needs.
Disadvantages:
Developer effort: Requires changes to the application code.
Not database-agnostic: Tightly coupled to application logic.
3. Key Architectural Components
A typical CDC pipeline includes the following stages:
3.1. Change Detection
Monitors and captures changes at the source database.
Examples:
Log-based detection (e.g., Debezium).
Trigger-based detection.
3.2. Event Transformation
Transforms raw database changes into structured events.
Examples:
Mapping database operations (INSERT, UPDATE, DELETE) into events.
Adding metadata like timestamps, table names, or operation type.
3.3. Event Propagation
Sends the transformed events to downstream systems.
Examples:
Stream-processing systems like Kafka, Apache Flink, or Apache Pulsar.
Data warehouses like Snowflake or Google BigQuery.
3.4. Downstream Processing
Consumes CDC events for various purposes:
Data replication.
Real-time analytics.
Triggering workflows.
4. CDC with Debezium
Debezium is a widely used CDC tool built on Kafka Connect. It supports log-based CDC for databases like MySQL, PostgreSQL, MongoDB, and others.
Features of Debezium:
Transactional Consistency: Ensures events are delivered in the correct order with ACID properties.
Scalability: Can handle high-volume databases with minimal performance overhead.
Schema Evolution: Supports schema changes like adding columns.
How Debezium Works:
Connector: Connects to the database and monitors its transaction log.
Kafka Integration: Streams CDC events to Kafka topics for consumption.
Consumers: Downstream services consume these events for replication, analytics, or other use cases.
5. Use Cases in Detail
5.1. Real-Time Analytics
Scenario: An e-commerce company wants to track real-time sales trends.
Solution: Use CDC to stream order data from a transactional database to a real-time analytics system like Apache Flink or Snowflake.
Benefits: Enables dashboards to display up-to-the-minute sales trends.
5.2. Microservices Synchronization
Scenario: A customer microservice updates a user’s address, and this change needs to propagate to billing and shipping microservices.
Solution: CDC captures the change in the customer database and sends an event to Kafka, which other microservices consume.
Benefits: Ensures eventual consistency across microservices.
5.3. Cache Invalidation
Scenario: A web application caches product details in Redis, but the database is updated with new product prices.
Solution: CDC captures the database update and sends an event to invalidate the relevant cache entry.
Benefits: Keeps cache data fresh without complex custom logic.
5.4. Data Migration
Scenario: Migrating data from an on-premises database to a cloud database.
Solution: CDC keeps the target database updated with incremental changes while the migration is in progress.
Benefits: Minimal downtime during migration.
6. Challenges in CDC
6.1. Handling High Data Volume
Problem: Large-scale systems generate high volumes of database changes.
Solution:
Use partitioning strategies (e.g., topic partitioning in Kafka).
Filter unnecessary data at the CDC source.
6.2. Ensuring Event Ordering
Problem: Distributed systems can cause events to arrive out of order.
Solution:
Use unique transaction IDs to reconstruct order.
Leverage tools like Kafka, which provide ordering guarantees within partitions.
6.3. Schema Evolution
Problem: Database schema changes (e.g., adding/dropping columns) can break downstream systems.
Solution:
CDC tools like Debezium automatically detect schema changes.
Downstream systems must be designed to handle schema evolution gracefully.
6.4. Latency
Problem: Delays in propagating changes can impact real-time use cases.
Solution:
Optimize CDC pipelines for low latency.
Monitor pipeline health and address bottlenecks.
7. CDC in Modern System Design
CDC is a backbone for systems requiring data synchronization and event-driven architectures. Here's where it fits:
7.1. Event-Driven Architectures
CDC turns database changes into events, decoupling services in microservices architectures.
7.2. Data Lakes
CDC enables real-time data ingestion into lakes like AWS S3 or Azure Data Lake for machine learning or analytics.
7.3. Multi-Region Systems
Synchronizes data across geographically distributed systems with low latency.
Conclusion
CDC is a cornerstone technology for modern distributed systems, enabling real-time data pipelines, event-driven systems, and scalable architectures. Tools like Debezium, Kafka, and AWS DMS have made CDC more accessible and efficient. When designing a system with CDC, consider factors like performance, schema evolution, event ordering, and scalability to ensure robustness and reliability.
source :- wikipedia