Data Synchronization Strategies Between MySQL and Elasticsearch

Overview

MySQL often 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 can greatly improve search performance, scalability, and user experience.

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

Synchronization Strategies

1. Synchronous Dual‑Write

When MySQL receives a write operation, the same data is immediately written to ES. This guarantees consistency and reduces read load on MySQL.

Implementation

Direct write in business code (simple but tightly coupled).

Middleware such as Kafka, Debezium, or Logstash to capture changes and forward them to ES (decouples logic, improves scalability).

Triggers or stored procedures in MySQL to invoke ES writes (less invasive but may affect MySQL performance).

Pros

Simple business logic.

Real‑time query capability.

Cons

Hard‑coded in business code.

High coupling.

Risk of data loss if one write fails.

Additional write overhead can degrade performance.

2. Asynchronous Dual‑Write

Writes to MySQL are captured and forwarded to ES asynchronously, reducing write latency and improving overall system performance.

Pros

Higher availability; backup failures do not block the primary.

Lower primary write latency.

Supports multiple downstream data sources.

Cons

Hard‑coded consumer code for new data sources.

Increased system complexity due to message middleware.

Potential eventual consistency issues.

3. Logstash Synchronization

Logstash is an open‑source data pipeline that can ingest data from MySQL, transform it, and output to ES.

Example configuration (code snippet): 存储库

4. Binlog Real‑Time Synchronization

Binlog records all data‑changing statements in MySQL. Tools like Canal or Maxwell listen to binlog events and stream changes to ES in real time.

Advantages

Real‑time data capture.

Strong consistency.

Flexibility across different targets.

Scalable and non‑intrusive.

Disadvantages

Configuration complexity.

Potential performance impact under high concurrency.

Tool dependence on MySQL binlog version.

5. Canal Data Synchronization

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

Typical workflow:

Canal connects to MySQL master using dump protocol.

Master pushes binlog; Canal parses and converts to JSON.

Canal client consumes the JSON and writes to ES.

6. Alibaba Cloud DTS (Data Transmission Service)

DTS provides real‑time data migration and synchronization between heterogeneous data sources, supporting both initial load and incremental change capture.

Key features:

High availability with active‑standby architecture.

Dynamic adaptation to source address changes.

Supports both full data initialization and continuous incremental sync.

Application Scenarios

These synchronization methods are suitable for e‑commerce platforms, analytics systems, and any scenario requiring high‑performance search while maintaining data consistency.

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