Data Synchronization Strategies Between MySQL and Elasticsearch

Overview

In many projects MySQL serves as the core business database, but as data volume and query complexity grow, relying solely on MySQL for efficient retrieval becomes increasingly difficult. Introducing Elasticsearch (ES) as a dedicated query engine improves search performance, flexibility, and scalability, while read‑write separation alleviates pressure on MySQL.

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

Synchronization Schemes

1. Synchronous Dual‑Write

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

Advantages Simple business logic High real‑time query capability

Disadvantages Hard‑coded write logic in every MySQL write path Strong coupling between business code and ES Risk of data loss if one write fails Performance degradation due to extra ES writes

2. Asynchronous Dual‑Write

Writes to MySQL are captured and forwarded to ES asynchronously, reducing write latency and improving overall performance, though consistency may be temporarily compromised.

Advantages Higher system availability Reduced primary‑database write latency Support for multiple downstream data sources

Disadvantages Hard‑coded 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, collecting data from multiple sources, transforming it, and sending it to a 存储库 . It can bridge MySQL and ES without modifying application code.

4. Binlog Real‑Time Synchronization

Binlog records all data‑changing SQL statements. Tools such as Canal or Maxwell listen to Binlog events and replicate changes to ES in real time, offering high timeliness, consistency, flexibility, and scalability, though setup can be complex and may impact performance under high concurrency.

5. Canal Data Synchronization

Canal, an open‑source Alibaba project, parses MySQL binlog by masquerading as a slave, converting events to JSON and forwarding them to ES via RESTful APIs, achieving millisecond‑level latency without impacting the source database.

6. Alibaba Cloud DTS (Data Transmission Service)

DTS provides a managed, highly available data transmission solution that supports real‑time synchronization, initial data load, and incremental changes between heterogeneous data sources, including MySQL and ES, with serverless scaling options.

Promotional Content

The remainder of the article promotes various AI‑related products and services, including a paid DeepSeek practice collection, ChatGPT account giveaways, a knowledge‑sharing community, and discount offers for a subscription‑based AI platform.