MySQL CDC realizes data change capture by reading binlog, and the selection needs to consider tools, configuration and deployment. 1. In principle, binlog should be set as ROW mode and enable relevant permissions; 2. The tool can be selected as Debezium, Canal, Maxwell or self-developed programs, and decisions should be made based on the message queue foundation, technology stack and latency tolerance; 3. The deployment needs to ensure coordination between MySQL configuration, CDC agent, message middleware and consumers, and pay attention to network, breakpoint continuous transmission, performance and security; 4. Log cleaning, offset inconsistency, event accumulation, and DDL support should be handled during operation and maintenance, and periodically monitored to prevent risks.

MySQL Change Data Capture (CDC) is a technology used to track data changes in databases in real time, and is widely used in data synchronization, ETL processes, data replication and other scenarios. If you are considering introducing a MySQL CDC solution into your project, the key is to select the right technology stack and configure it reasonably.

1. Understand how MySQL CDC works

MySQL CDC mainly captures data addition, deletion and modification operations by reading binary logs (binlogs). These logs record all changes to the database structure or data and are the core source for implementing CDC.

• binlog format requirements : It must be set to ROW mode to obtain specific row-level change information.
• Server configuration : Make sure MySQL has binlog enabled and remains for long enough to avoid data loss due to log cleaning.
• Permission Settings : Users executing CDC need to have REPLICATION SLAVE and REPLICATION CLIENT permissions.

Once you understand these basic mechanisms, you can choose the right CDC tool or framework based on your business needs.

2. Comparison and selection of common MySQL CDC tools

There are currently several mainstream MySQL CDC implementation methods on the market:

• Debezium
  The open source CDC tool based on Kafka supports a variety of databases and can publish change events to Kafka. Suitable for systems that require high reliability and scalability.

  

• Canal / Alibaba Canal
  Alibaba's open source MySQL database incremental log analysis tool is often used in real-time computing scenarios of big data. For example, the combination of Flink Canal is very popular.

• Maxwell
  A lightweight CDC tool that outputs data change events in JSON format and can be written directly to Kafka, Kinesis, or other message middleware.

• Self-developed scripts or programs
  If your needs are simple, you can also use binlog parser written in Python or Java, but the maintenance cost is high.

Factors to consider when choosing include: whether there is a message queue infrastructure such as Kafka, team technology stack familiarity, data latency tolerance, etc.

3. Typical deployment structure and considerations

A typical CDC architecture usually includes the following components:

• MySQL Source : enable binlog and configure permissions;
• CDC Agent : Run Debezium, Canal and other services, connect to MySQL and listen to binlog;
• Message Broker (such as Kafka): receives and caches change events;
• Downstream consumers : such as ETL handlers, data warehouse import tasks, etc.

The following points should be paid attention to when deploying:

• Network connectivity : Ensure that the CDC agent can access the MySQL server;
• Breakpoint continuous transmission mechanism : Most tools support continuing consumption from the last location, but you need to confirm the offset storage method (such as Zookeeper, Kafka comes with offset, etc.);
• Performance Impact Evaluation : binlog parsing itself will not have much impact on MySQL performance, but it may lead to a backlog if downstream processing is slow;
• Security policy : Limit CDC user permissions to prevent sensitive data leakage.

For example, when using Debezium, it is recommended to deploy it in a Kafka Connect cluster for easy management and monitoring.

4. Daily operation and maintenance and FAQ troubleshooting

During actual operation, you may encounter the following problems:

• binlog file is cleaned : MySQL cleansing old logs will cause the CDC to fail to restore location, and the retention period can be extended by adjusting the expire_logs_days parameter;
• offset is inconsistent : In some cases, the offset recorded by the CDC agent is inconsistent with the actual binlog position, and manual intervention is required;
• Event accumulation : If the downstream consumption speed cannot keep up with the production speed, it will lead to a large number of backlogs in Kafka, and consumer logic should be optimized or concurrency should be increased;
• DDL support issues : Some tools have limited support for table structure changes, so you need to confirm whether it meets your business needs.

For these issues, it is recommended to regularly check logs and monitoring indicators to warn of potential risks in advance.

Basically that's it. Although the implementation of MySQL CDC seems complicated, as long as you understand the working mechanism of binlog and choose the right tools, the whole process is actually not difficult. However, you still need to pay attention to some details, such as permission configuration and log retention policies, which are easily overlooked.

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