MySQL Binlog FlinkCDC to ClickHouse

Introduction

In modern data processing systems, it is crucial to capture and analyze real-time data for business intelligence and decision-making. One common scenario is to extract data from MySQL binlog, transform it, and load it into ClickHouse for further analysis. In this article, we will explore how to use FlinkCDC to capture MySQL binlog changes and write them into ClickHouse using code examples.

Prerequisites

Before we begin, make sure you have the following components installed:

  • MySQL server
  • FlinkCDC
  • ClickHouse server

Architecture

Architecture

The architecture of our solution consists of three components:

  1. MySQL Server: The source database that generates binlog changes.
  2. FlinkCDC: A reliable and scalable change data capture tool for MySQL.
  3. ClickHouse Server: A fast analytical database for OLAP queries.

Implementation

Step 1: Enable Binlog in MySQL

Edit the MySQL configuration file (my.cnf or my.ini) and add the following lines under the [mysqld] section:

log_bin = mysql-binlog
binlog-format = ROW
binlog-row-image = full

Restart the MySQL server for the changes to take effect.

Step 2: Install and Configure FlinkCDC

Download and install FlinkCDC by following the official documentation. Once installed, you need to configure the MySQL source and ClickHouse sink in the application.yml file.

source:
  type: mysql
  hostname: localhost
  port: 3306
  username: root
  password: password
  database-name: mydatabase
  table-name: mytable

sink:
  type: clickhouse
  url: jdbc:clickhouse://localhost:8123/default
  username: default
  password: password
  table-name: mytable
  max-insert-rows: 10000

Step 3: Start FlinkCDC Job

To start the FlinkCDC job, run the following command:

bin/flinkcdc run -c com.alibaba.ververica.cdc.debezium.task.CdcTaskRunner \
  -p cdc-task-runner.jar \
  -a cdc-app.jar \
  --job-name myjob \
  --config application.yml

Step 4: Verify ClickHouse Data

Once the FlinkCDC job is running, it will capture the binlog changes and write them into ClickHouse. You can verify the data using the ClickHouse client:

SELECT * FROM mytable;

Conclusion

In this article, we have learned how to use FlinkCDC to capture MySQL binlog changes and write them into ClickHouse. By leveraging the power of FlinkCDC and ClickHouse, we can build real-time data pipelines for advanced analytics and reporting. This approach provides a scalable and reliable solution for capturing and processing real-time data.

Please note that the code examples provided in this article are simplified for demonstration purposes. In a real-world scenario, you may need to handle schema evolution, data type conversions, and other edge cases. It is recommended to consult the official documentation and community resources for more advanced configurations and optimizations.

Reference

  • [FlinkCDC Documentation](
  • [ClickHouse Documentation](

Appendix: Architecture Diagram

erDiagram
    MySQL }|..| FlinkCDC
    FlinkCDC }|..| ClickHouse

Architecture Diagram