Change Data Capture (CDC) is a critical technique in modern data architectures, enabling real-time synchronization between databases and downstream systems. Debezium, an open-source CDC platform, excels at capturing row-level changes from databases like MySQL, PostgreSQL, MongoDB, and more. When combined with Docker, you gain a flexible, containerized environment that simplifies deployment, scaling, and management of CDC pipelines.

In this article, we deep dive into running real-time CDC pipelines using Debezium inside Docker containers. This approach is ideal for intermediate and advanced users aiming to build scalable, fault-tolerant data streaming solutions.

Why Use Debezium with Docker for CDC?

Deploying Debezium connectors inside Docker containers offers multiple benefits for CDC pipelines:

  • Portability: Containers encapsulate all dependencies, ensuring consistent environments across development, testing, and production.
  • Scalability: Easily scale CDC connectors horizontally by launching multiple container instances.
  • Isolation: Separate Debezium connectors from other infrastructure components, avoiding version conflicts.
  • Simplified Orchestration: Integrate with container orchestrators like Kubernetes for high availability and automated recovery.

These advantages make Docker an essential tool for managing CDC pipelines in modern data ecosystems.

Setting Up Debezium with Docker: Components Overview

A typical CDC pipeline with Debezium in Docker involves several key components:

  • Kafka: Acts as the message broker for change events.
  • Kafka Connect: The runtime framework where Debezium connectors run.
  • Zookeeper: Required by Kafka for cluster management.
  • Debezium Connectors: Specific connectors for each source database.
  • Source Database: The database to capture changes from.

All these components can run as individual Docker containers, orchestrated via docker-compose or Kubernetes manifests.

Creating a Docker Compose File for Debezium CDC Pipeline

To get started quickly, a docker-compose.yml file defines the services:

version: '3.7'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:latest
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181

  kafka:
    image: confluentinc/cp-kafka:latest
    depends_on:
      - zookeeper
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1

  connect:
    image: debezium/connect:latest
    depends_on:
      - kafka
    ports:
      - 8083:8083
    environment:
      BOOTSTRAP_SERVERS: kafka:9092
      GROUP_ID: 1
      CONFIG_STORAGE_TOPIC: my_connect_configs
      OFFSET_STORAGE_TOPIC: my_connect_offsets
      STATUS_STORAGE_TOPIC: my_connect_statuses
      KEY_CONVERTER_SCHEMAS_ENABLE: "false"
      VALUE_CONVERTER_SCHEMAS_ENABLE: "false"
      KEY_CONVERTER: org.apache.kafka.connect.json.JsonConverter
      VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverter
      PLUGIN_PATH: /kafka/connect/debezium-connector-mysql

  mysql:
    image: mysql:5.7
    environment:
      MYSQL_ROOT_PASSWORD: debezium
      MYSQL_USER: debezium
      MYSQL_PASSWORD: dbz
      MYSQL_DATABASE: inventory
    ports:
      - 3306:3306

This setup launches a full CDC environment: Zookeeper, Kafka, Kafka Connect with Debezium MySQL connector, and a sample MySQL database.

Configuring Debezium Connectors via REST API

Once the containers are up, register a Debezium MySQL connector by sending a POST request to Kafka Connect’s REST API:

{
  "name": "inventory-connector",
  "config": {
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",
    "database.hostname": "mysql",
    "database.port": "3306",
    "database.user": "debezium",
    "database.password": "dbz",
    "database.server.id": "184054",
    "database.server.name": "dbserver1",
    "database.include.list": "inventory",
    "database.history.kafka.bootstrap.servers": "kafka:9092",
    "database.history.kafka.topic": "schema-changes.inventory"
  }
}

Use curl or tools like Postman to submit this configuration to http://localhost:8083/connectors.

Best Practices for Running Debezium in Containers

  • Resource Allocation: Assign appropriate CPU and memory limits to avoid bottlenecks, especially under heavy change workloads.
  • Persistent Storage: Use Docker volumes for Kafka and Zookeeper data to ensure durability.
  • Network Configuration: Ensure Docker networks allow proper communication between all services.
  • Security: Secure credentials and sensitive configurations using Docker secrets or environment variable management tools.
  • Monitoring: Integrate monitoring tools (Prometheus, Grafana) by exposing metrics endpoints from Kafka Connect.

Scaling and Orchestration Considerations

For production-grade pipelines, consider deploying Debezium connectors in orchestration platforms such as Kubernetes. Benefits include:

  • Automatic Failover: Self-healing capabilities keep CDC pipelines running.
  • Load Balancing: Distribute connector workloads across multiple pods.
  • Rolling Updates: Deploy new Debezium versions with zero downtime.
  • Configuration Management: Utilize ConfigMaps and Secrets for secure and flexible configuration.

Troubleshooting Common Issues

  • Connector Fails to Start: Verify database connectivity and credentials.
  • Missing Debezium Plugins: Confirm that connector plugins are correctly mounted or included in the Docker image.
  • Kafka Topic Errors: Check Kafka broker logs for replication or topic creation issues.
  • High Latency: Profile resource usage and optimize Kafka Connect thread pools.

Conclusion

Leveraging Debezium and Docker together empowers developers and data engineers to build real-time, scalable CDC pipelines with ease. Containerization enhances portability, scalability, and operational simplicity while Debezium’s robust connectors ensure accurate and consistent data capture. Whether for development or production environments, running Debezium in Docker containers is a best practice for modern data streaming architectures.

Harness this powerful combination to accelerate your data integration workflows and unlock real-time analytics and event-driven applications at scale.