MySQL to Elasticsearch Data Synchronization Strategies and Solutions

Overview

In many projects MySQL serves as the core business database, but as data volume and query complexity grow, relying solely on MySQL for fast retrieval becomes a bottleneck. Introducing Elasticsearch (ES) as a dedicated query engine improves search performance, scalability, and flexibility.

Ensuring reliable data synchronization between MySQL and ES is essential for real‑time consistency and system stability.

Synchronization Approaches

1. Synchronous Write (双写)

When a write operation occurs on MySQL, the same data is immediately written to ES, guaranteeing consistency but increasing code complexity and risk of double‑write failures.

Advantages Simple business logic High real‑time query capability

Disadvantages Hard‑coded in business code High coupling Risk of data loss if one write fails Performance degradation due to extra ES write

2. Asynchronous Write (异步双写)

Writes to MySQL are propagated to ES asynchronously, reducing write latency and improving overall performance, but may cause temporary data inconsistency.

Advantages Higher system availability Reduced primary DB write latency Supports multiple downstream data sources

Disadvantages Requires additional consumer code for new data sources Increased system complexity due to message middleware Potential delay in data visibility Eventual consistency challenges

3. Logstash Synchronization

Logstash acts as a server‑side data pipeline that collects, transforms, and forwards data to a target repository.

In the MySQL‑ES scenario, Logstash can capture changes and push them to ES without modifying application code.

存储库

4. Binlog Real‑time Synchronization

Binlog records all data‑changing SQL statements in MySQL. Tools such as Canal or Maxwell listen to binlog events, parse them, and replicate changes to ES in real time.

Advantages Real‑time data capture Strong consistency Flexibility across different targets Scalable and extensible No code intrusion

Disadvantages Complex configuration and maintenance Potential performance impact on high‑traffic databases Dependency on binlog availability and version compatibility

5. Canal Data Synchronization

Canal, an open‑source project from Alibaba, pretends to be a MySQL slave to subscribe to binlog events, converting them to JSON and forwarding them to ES via RESTful APIs.

Canal server requests dump protocol from MySQL master.

Master pushes binlog; Canal parses binary data to JSON.

Canal client receives data via TCP or MQ and writes to ES.

6. Alibaba Cloud DTS (Data Transmission Service)

DTS provides real‑time data flow between heterogeneous data sources, supporting full‑load and incremental synchronization. It offers high availability, dynamic source address adaptation, and serverless scaling.