Using Kafka to stream Change Data Capture data between databases

In today's fast-paced business environment, the ability to capture and process data in near real-time is crucial for staying competitive. 

Change Data Capture (CDC) is a technique that allows businesses to capture and replicate data changes from one database to another, enabling real-time analytics, data integration, and synchronization. 

Apache Kafka, a popular distributed event streaming platform, offers a powerful solution for streaming CDC data between databases. By leveraging Kafka's distributed architecture and robust messaging system, businesses can ensure reliable and efficient data replication. In this comprehensive guide, we will explore the concept of CDC, the benefits of using Kafka for streaming CDC data, and step-by-step instructions on setting up CDC using Kafka. 

Whether you are a data engineer, developer, or business analyst, this guide will provide you with the knowledge and tools to implement CDC and unlock the full potential of your data.

1. Introduction to Change Data Capture

Change Data Capture (CDC) is a technique used to capture and replicate data changes from one database to another in real-time or near real-time. It enables businesses to track and react to data changes as they happen, facilitating real-time analytics, data integration, and synchronization.

CDC works by capturing the changes made to the source database and transforming them into a format that can be consumed by the target database or downstream systems. These changes typically include INSERT, UPDATE, and DELETE operations on tables. CDC can be implemented using various approaches, including trigger-based, log-based, and query-based methods. 

In trigger-based CDC, database triggers are used to capture and record data changes in real time. Whenever a change is made to a table, the trigger captures the change and triggers an action, such as inserting the change into a separate CDC table. 

Log-based CDC, on the other hand, leverages the transaction log or redo log of the source database to capture data changes. The transaction log records all modifications made to the database, including INSERTs, UPDATEs, and DELETEs. By analyzing the log, CDC processes can identify and extract the changes and replicate them to the target database or downstream systems.

Query-based CDC involves periodically querying the source database for changes since the last synchronization. The queries are based on timestamp or incremental column values that indicate the last synchronized data. This approach is less resource-intensive on the source database but may introduce latency in capturing changes.

2. The Power of Apache Kafka

In real-time data pipelines and streaming applications, Apache Kafka is widely used as a distributed event streaming platform.  It is designed to handle high-throughput, fault-tolerant, and scalable data streaming. At its core, Kafka uses a distributed commit log architecture, where data is organized into topics and stored in a distributed manner across a cluster of servers called brokers. Producers write data to Kafka topics, and consumers read data from topics, enabling real-time data processing and streaming.

Kafka's key features make it an ideal choice for streaming CDC data between databases:

• Scalability and Fault Tolerance: Kafka is built to handle high-throughput data streams and can scale horizontally to accommodate growing data volumes. It provides fault tolerance by replicating data across multiple brokers, ensuring data availability even in the face of failures.
• Low Latency and High Throughput: Kafka's distributed commit log design enables low-latency data ingestion and processing. It can handle millions of events per second, making it suitable for streaming real-time data.
• Exactly-Once Semantics: Kafka guarantees that each message sent to a topic is delivered to consumers exactly once, ensuring data integrity and consistency.
• Connectivity and Extensibility: Kafka supports various connectors, including source and sink connectors for integrating with different databases and systems. This allows seamless integration with existing data infrastructure and enables easy data replication and synchronization.

3. Benefits of Streaming CDC Data with Kafka

Streaming CDC data using Kafka offers several benefits for businesses:

• Real-time Data Integration: By capturing and replicating data changes in real-time, Kafka enables real-time data integration and synchronization across databases and systems. This allows businesses to have up-to-date, consistent data across their entire data ecosystem.
• Efficient Data Replication: Kafka's distributed architecture and messaging system ensure efficient and reliable data replication. Changes captured by CDC processes are published to Kafka topics, and consumers can consume these changes at their own pace, ensuring data consistency and minimizing data loss.
• Scalability and Fault Tolerance: Kafka's scalability and fault tolerance make it suitable for handling large-scale data replication and synchronization. It can handle high-throughput data streams and can scale horizontally to accommodate growing data volumes.
• Flexibility and Extensibility: Kafka's ecosystem includes a wide range of connectors and tools that allow seamless integration with various databases, systems, and frameworks. This enables businesses to extend the functionality of their data pipelines and integrate with existing data infrastructure.
• Real-time Analytics and Insights: Streaming CDC data with Kafka enables real-time analytics and insights. Businesses can analyze data changes as they happen, enabling proactive decision-making and faster time-to-insights.

4. Getting Started with Kafka CDC

To get started with streaming CDC data using Kafka, you need to set up the necessary infrastructure and configure the required components. This section will guide you through the steps of setting up the database sources, installing Kafka and associated services, creating Kafka topics, and configuring Kafka connectors.

Setting Up the Database Sources

Before you can start streaming CDC data, you need to set up the source and target databases. In this example, we will use MySQL as the source database and Postgres as the target database. However, the principles discussed here can be applied to other databases as well.

• Install and configure MySQL on your system. Create a database and table that will act as the data producer for Kafka. For example, you can create a "customers" table with columns such as "id", "name", and "email".
• Install and configure Postgres on your system. Create a corresponding table in Postgres that will act as the data consumer for Kafka. Ensure that the table schema matches the schema of the source table in MySQL.

Installing Kafka and Associated Services

To install Kafka and associated services, we will use Docker and Docker Compose. Docker allows you to run Kafka and other required services in isolated containers, making it easy to manage and deploy.

• Install Docker on your system following the official Docker documentation for your operating system.
• Create a new directory for your Kafka project and navigate to it in your terminal.
• Create a docker-compose.yml file in the project directory and add the following configuration:

 1 version: '3'
 2 services:
 3   zookeeper:
 4     image: confluentinc/cp-zookeeper:latest
 5     ports:
 6       - "2181:2181"
 7     environment:
 8       ZOOKEEPER_CLIENT_PORT: 2181
 9       ZOOKEEPER_TICK_TIME: 2000
10 
11   kafka:
12     image: confluentinc/cp-kafka:latest
13     ports:
14       - "9092:9092"
15     environment:
16       KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
17       KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
18       KAFKA_CREATE_TOPICS: "cdc_data:1:1"
19 
20   kafka-connect:
21     image: confluentinc/cp-kafka-connect:latest
22     ports:
23       - "8083:8083"
24     environment:
25       CONNECT_BOOTSTRAP_SERVERS: kafka:9092
26       CONNECT_REST_PORT: 8083
27       CONNECT_GROUP_ID: kafka-connect-cdc
28       CONNECT_PLUGIN_PATH: /usr/share/java
29       CONNECT_CONFIG_STORAGE_TOPIC: cdc_data
30       CONNECT_OFFSET_STORAGE_TOPIC: cdc_data
31       CONNECT_STATUS_STORAGE_TOPIC: cdc_data
32       CONNECT_KEY_CONVERTER: org.apache.kafka.connect.storage.StringConverter
33       CONNECT_VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverter
34       CONNECT_VALUE_CONVERTER_SCHEMAS_ENABLE: "false"
35 

This configuration sets up ZooKeeper, Kafka, and Kafka Connect services using the official Confluent Platform Docker images. It also creates a Kafka topic named "cdc_data" with one partition and one replica.

• Open a terminal and navigate to the project directory. Run the following command to start the Kafka services:

 1 docker-compose up -d

This command will start the ZooKeeper, Kafka, and Kafka Connect services in the background.

Creating Kafka Topics

Once the Kafka services are up and running, you can create Kafka topics to store the CDC data. In this example, we will create a topic named "cdc_data" using the Kafka command-line tool.

• Open a terminal and navigate to the directory where Kafka is installed.
• Run the following command to create the "cdc_data" topic:

 1 bin/kafka-topics.sh --create --topic 
 2 cdc_data --bootstrap-server 
 3 localhost:9092 --partitions 1 --replication-factor 1

This command creates a topic named "cdc_data" with one partition and one replica.

Configuring Kafka Connectors

To stream CDC data between the MySQL source database and the Postgres target database, we will configure Kafka connectors. Kafka Connectors are plugins that enable Kafka to integrate with external systems. In this example, we will use the Debezium MySQL Connector for capturing CDC data from MySQL and the JDBC Sink Connector for sending the CDC data to Postgres.

• Open a terminal and navigate to the directory where Kafka is installed.
• Run the following command to install the Debezium MySQL Connector:

 1 bin/connect-cli.sh install --component-dir ./connectors debezium/debezium-connector-mysql:1.6.1

This command installs the Debezium MySQL Connector in the "connectors" directory.

• Create a configuration file named "mysql-source.properties" for the MySQL source connector and add the following configuration:

 1 name=mysql-source-connector
 2 connector.class=io.debezium.connector.mysql.MySqlConnector
 3 database.hostname=localhost
 4 database.port=3306
 5 database.user=root
 6 database.password=your_password
 7 database.server.id=1
 8 database.server.name=mysql-server
 9 database.whitelist=my_database
10 table.whitelist=my_database.customers
11 database.history.kafka.bootstrap.servers=localhost:9092
12 database.history.kafka.topic=cdc_data
13 

This configuration specifies the connection details for the MySQL database, the tables to capture changes from, and the Kafka topic to store the CDC data.

• Create a configuration file named "postgres-sink.properties" for the Postgres sink connector and add the following configuration:

 1 name=postgres-sink-connector
 2 connector.class=io.confluent.connect.jdbc.JdbcSinkConnector
 3 tasks.max=1
 4 topics=cdc_data
 5 connection.url=jdbc:postgresql://localhost:5432/postgres
 6 connection.user=postgres
 7 connection.password=your_password
 8 auto.create=true

This configuration specifies the connection details for the Postgres database, the Kafka topic to consume data from, and the target table where the CDC data will be inserted.

• Run the following command to start the MySQL source connector:

 1 bin/connect-cli.sh load mysql-source-connector --config-file mysql-source.properties --config 
 2 connector.class=io.debezium.connector.mysql.MySqlConnector

This command starts the MySQL source connector and begins capturing CDC data from the MySQL database.

• Run the following command to start the Postgres sink connector:

 1 bin/connect-cli.sh load postgres-sink-connector --config-file postgres-sink.properties --config 
 2 connector.class=io.confluent.connect.jdbc.JdbcSinkConnector

This command starts the Postgres sink connector and begins consuming CDC data from the Kafka topic and inserting it into the Postgres database.

Congratulations! You have successfully set up Kafka CDC and configured the necessary components to stream CDC data between MySQL and Postgres databases.

5. Streaming CDC Data with Kafka

Once you have set up Kafka CDC, you can start streaming CDC data between databases. There are two main approaches to streaming CDC data: the push approach and the pull approach.

Push Approach

In the push approach, the source database takes the responsibility of capturing and sending the CDC data to the target systems. The source database implements the logic and processes to identify and capture data changes, and then pushes those changes to the target systems.

To implement the push approach, a messaging system is typically used between the source and target systems to ensure that changes are not lost. The source database captures the changes and sends them as events to the messaging system, which then delivers the events to the target systems.

The advantage of the push approach is that target systems receive the latest data in near real-time. However, if the target systems are unreachable or offline, the changed data may be lost. To mitigate this, the messaging system can store the changes until they are committed to their final destinations.

Pull Approach

In the pull approach, the source database logs the data changes in a column on each table, and it is the responsibility of the target systems to continuously poll the source database and retrieve the changes. In this approach, the source database's role is lighter compared to the push approach.

The target systems regularly query the source database for changes since the last synchronization. The queries are based on timestamps or incremental column values that indicate the last synchronized data. The target systems then take the necessary actions on the retrieved changes.

To ensure that changes are not lost when the target systems are unavailable, a messaging system is typically used between the source and target systems. The source database logs the changes to the messaging system, which stores them until the target systems can consume them.

The pull approach may introduce a lag in capturing changes since the changes are batched between the pull requests. However, it reduces the load on the source database and allows the target systems to control the pace of consuming the changes.

When choosing between the push and pull approaches, consider the requirements of your use case, the availability of the target systems, and the desired latency of data replication.

6. Implementing CDC with Kafka Streams

Kafka Streams is a powerful Java library provided by Apache Kafka for building real-time streaming applications. It enables developers to process and transform data streams using a high-level DSL or the Kafka Streams Processor API.

To implement CDC with Kafka Streams, you can leverage the capabilities of Kafka Connect and the Debezium MySQL Connector. The Debezium MySQL Connector captures CDC data from the MySQL database and publishes it to Kafka topics. Kafka Streams can then consume these topics, perform data transformations, and write the transformed data to the target database.

In this section, we will walk through the steps of setting up the MySQL database as a data producer, creating Kafka topics for CDC data, setting up the MySQL CDC source connector, and streaming CDC data to the Postgres database.

Setting Up MySQL Database as a Data Producer

Before we can start streaming CDC data with Kafka Streams, we need to set up the MySQL database as a data producer. We will create a database table that will act as the source of CDC data.

• Install and configure MySQL on your system if you haven't already done so.
• Create a database and a table that will act as the source of CDC data. For example, you can create a "customers" table with columns such as "id", "name", and "email".
• Insert some sample data into the "customers" table.

Creating Kafka Topics for CDC Data

Next, we need to create Kafka topics to store the CDC data captured from the MySQL database. Kafka topics are the channels through which data is published and consumed in Kafka.

• Open a terminal and navigate to the directory where Kafka is installed.
• Run the following command to create the Kafka topic:

 1 bin/kafka-topics.sh --create --topic 
 2 cdc_data --bootstrap-server 
 3 localhost:9092 --partitions 1 --replication-factor 1

This command creates a topic named "cdc_data" with one partition and one replica.

Setting Up MySQL CDC Source Connector

To capture CDC data from the MySQL database, we will use the Debezium MySQL Connector. This connector captures data changes from the MySQL binary log and publishes them to Kafka topics.

• Open a terminal and navigate to the directory where Kafka is installed.
• Run the following command to install the Debezium MySQL Connector:

 1 bin/connect-cli.sh install --component-dir ./connectors debezium/debezium-connector-mysql:1.6.1

This command installs the Debezium MySQL Connector in the "connectors" directory.

• Create a configuration file named "mysql-source.properties" for the MySQL CDC source connector and add the following configuration:

 1 name=mysql-source-connector
 2 connector.class=io.debezium.connector.mysql.MySqlConnector
 3 database.hostname=localhost
 4 database.port=3306
 5 database.user=root
 6 database.password=your_password
 7 database.server.id=1
 8 database.server.name=mysql-server
 9 database.whitelist=my_database
10 table.whitelist=my_database.customers
11 database.history.kafka.bootstrap.servers=localhost:9092
12 database.history.kafka.topic=cdc_data

This configuration specifies the connection details for the MySQL database, the tables to capture changes from, and the Kafka topic to store the CDC data.

• Run the following command to start the MySQL CDC source connector:

 1 bin/connect-cli.sh load mysql-source-connector --config-file mysql-source.properties --config
 2  connector.class=io.debezium.connector.mysql.MySqlConnector

This command starts the MySQL CDC source connector and begins capturing CDC data from the MySQL database.

Streaming CDC Data to Postgres Database

Now that we have set up the MySQL CDC source connector, we can start streaming CDC data to the Postgres database. We will use Kafka Streams to consume the CDC data from the Kafka topic, perform data transformations, and write the transformed data to the Postgres database.

• Create a new Java project in your preferred IDE and add the necessary Kafka Streams dependencies. You will need the following dependencies:

 1 org.apache.kafka:kafka-clients:2.8.0
 2 org.apache.kafka:kafka-streams:2.8.0
 3 io.debezium:debezium-connector-mysql:1.6.1
 4 org.postgresql:postgresql:42.2.23

• Create a new Java class named "CDCDataProcessor" and add the following code:

 1 import org.apache.kafka.streams.KafkaStreams;
 2 import org.apache.kafka.streams.StreamsBuilder;
 3 import org.apache.kafka.streams.StreamsConfig;
 4 import org.apache.kafka.streams.kstream.KStream;
 5 import org.apache.kafka.streams.kstream.Produced;
 6 
 7 import java.util.Properties;
 8 
 9 public class CDCDataProcessor {
10 
11     public static void main(String[] args) {
12         Properties props = new Properties();
13         props.put(StreamsConfig.APPLICATION_ID_CONFIG, "cdc-data-processor");
14         props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
15 
16         StreamsBuilder builder = new StreamsBuilder();
17 
18         KStream cdcData = builder.stream("cdc_data");
19         
20         // Perform data transformations
21         
22         cdcData.to("postgres_sink_topic", Produced.with(StreamsSerdes.String(), StreamsSerdes.String()));
23 
24         KafkaStreams streams = new KafkaStreams(builder.build(), props);
25         streams.start();
26 
27         Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
28     }
29 }
30 

This code sets up a Kafka Streams application that consumes CDC data from the "cdc_data" topic, performs data transformations, and writes the transformed data to the "postgres_sink_topic". Replace the transformation logic with your own business logic as needed.

• Build and run the Java application. Kafka Streams will start consuming CDC data from the Kafka topic and apply the specified transformations. The transformed data will be written to the "postgres_sink_topic".

Congratulations! You have successfully implemented CDC with Kafka Streams. The MySQL CDC source connector captures CDC data from the MySQL database, Kafka Streams consumes and transforms the data, and the transformed data is written to the Postgres database.

7. Best Practices for Kafka CDC

When implementing CDC with Kafka, it is important to follow best practices to ensure data consistency, handle data loss and recovery, monitor and troubleshoot CDC processes, and scale Kafka CDC for large-scale deployments. Here are some best practices to consider:

Ensuring Data Consistency

• Use transactional guarantees: When capturing CDC data from the source database, ensure that the changes are captured within a single transaction. This ensures atomicity and consistency of the captured changes.
• Handle schema changes: When the schema of the source database changes, ensure that the CDC processes can handle and adapt to these changes. Use schema evolution techniques, such as Avro schemas or schema registries, to ensure compatibility between the source and target schemas.
• Implement data validation: Perform data validation and integrity checks on the captured CDC data to ensure its quality. Use data validation techniques such as checksums, hashing, or data profiling to identify and handle data anomalies.

Handling Data Loss and Recovery

• Use a durable messaging system: To ensure that CDC data is not lost in case of failures, use a durable messaging system such as Apache Kafka. Kafka's distributed commit log architecture ensures that data changes are reliably stored and replicated across multiple brokers.
• Implement fault tolerance: Configure Kafka connectors and consumer applications to be fault-tolerant. Use features such as Kafka's replication factor and data replication techniques to ensure that CDC data is replicated across multiple brokers and can be recovered in case of failures.
• Set up data recovery mechanisms: Implement mechanisms to recover lost or corrupted CDC data. This can include setting up backup and restore procedures, implementing data replay mechanisms, or using time-based snapshots to recover the state of the data.

Monitoring and Troubleshooting CDC

• Monitor CDC processes: Set up monitoring and alerting systems to monitor the health and performance of your CDC processes. Monitor key metrics such as data throughput, latency, and error rates to ensure that the CDC processes are running smoothly.
• Implement logging and tracing: Use logging and tracing mechanisms to capture detailed information about the CDC processes. This can help in troubleshooting and diagnosing issues, identifying performance bottlenecks, and tracking the flow of data through the CDC pipeline.
• Implement error handling and retry mechanisms: Handle errors and failures gracefully in your CDC processes. Implement mechanisms to handle transient errors, retry failed operations, and log and track errors for further analysis.

Scaling Kafka CDC for Large-scale Deployments

• Scale Kafka brokers: When deploying Kafka CDC in a large-scale environment, scale the number of Kafka brokers to handle the increased data volume. This can be achieved by adding more broker nodes to the Kafka cluster and distributing the data across multiple brokers.
• Use Kafka Connect distributed mode: When deploying Kafka Connect for CDC, use the distributed mode to scale the number of worker nodes. This allows for parallel processing and load balancing across multiple worker nodes, improving the overall throughput and efficiency of the CDC processes.
• Optimize Kafka settings: Fine-tune the configurations of Kafka, such as message batch size, number of partitions, and replication factor, to optimize its performance for your specific use case and data volume.
• Implement partitioning strategies: Use partitioning strategies to distribute CDC data across multiple Kafka topics and partitions. This ensures better load balancing, improves data parallelism, and allows for more efficient consumption by downstream applications.
• Plan for future growth: When designing your Kafka CDC architecture, consider the future growth of your data and infrastructure. Ensure that the architecture is scalable and flexible enough to accommodate increased data volumes, new data sources, and evolving business requirements.

Real-world Use Cases of Kafka CDC

• Real-time Analytics and Reporting: Kafka CDC can be used to capture and replicate real-time data changes from operational databases to analytical databases. This enables businesses to generate real-time reports, dashboards, and insights, providing timely and actionable intelligence.
• Data Synchronization between Microservices: In a microservices architecture, Kafka CDC can be used to synchronize data between different microservices. By streaming CDC data through Kafka, microservices can consume and react to data changes in real-time, ensuring data consistency across the system.
• Database Migration and Replication: Kafka CDC can be used to migrate data from one database to another or to replicate data across multiple database instances. This is useful in scenarios such as disaster recovery, data archiving, or load balancing.
• Event-driven Architecture and Pub/Sub Systems: Kafka CDC can be used to build event-driven architectures where applications react to data changes as they happen. By streaming CDC data as events through Kafka, applications can subscribe to specific topics and consume relevant data changes in real-time.

Conclusion

Change Data Capture (CDC) is a vital technique for businesses to stay agile and responsive in a fast-paced data-driven world. Kafka, with its robust architecture, offers a comprehensive solution for streaming CDC data between databases. By adopting Kafka CDC, businesses can unlock numerous benefits such as real-time data integration, efficient replication, scalability, and real-time analytics. It's crucial, however, to ensure that best practices are followed, from ensuring data consistency and handling data loss to effectively scaling for large-scale deployments. With a good understanding of the principles, advantages, and use cases of Kafka CDC, businesses are well-positioned to harness the full potential of their data, drive innovation, and maintain a competitive edge.