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.