MySQL to Elasticsearch Data Synchronization Strategies: Sync Write, Async Write, Logstash, Binlog, Canal, and DTS

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 improve search performance, flexibility, and scalability.

Effective synchronization between MySQL and ES is essential to ensure data consistency, real‑time updates, and system stability.

Synchronization Options

Use tools such as Logstash, Kafka Connect, Debezium for real‑time capture and transfer.

Employ scheduled tasks (Cron) combined with batch imports for periodic sync.

1. Synchronous Double Write

Writes are performed to both MySQL and ES simultaneously during a transaction, guaranteeing immediate consistency and reducing read load on MySQL.

Implementation :

Direct Sync : Application code writes to MySQL and ES together (simple but tightly coupled).

Middleware : Use message queues (Kafka) or CDC tools (Debezium, Logstash) to capture changes and forward them to ES (decouples logic, improves scalability).

Triggers/Procedures : MySQL triggers invoke ES writes (less invasive to business code but adds load to MySQL).

Pros : Simple business logic, high real‑time query capability.

Cons : Hard‑coded, high coupling, risk of double‑write failures, potential performance degradation.

2. Asynchronous Double Write

Changes are written to MySQL first, then asynchronously propagated to ES via a message queue, reducing write latency and improving system availability.

Pros : Higher system availability, lower primary write latency, easy to add more downstream data sources.

Cons : Requires additional middleware, lower real‑time guarantee, potential data consistency gaps during propagation.

3. Logstash Synchronization

Logstash acts as a server‑side data pipeline, ingesting data from MySQL and outputting to ES without modifying application code.

Pros : Non‑intrusive, no hard‑coding, no performance impact on existing services.

Cons : Limited timeliness (batch polling), adds load on the database, cannot handle delete operations automatically, requires matching IDs between MySQL and ES.

4. Binlog Real‑Time Sync

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

Pros : Real‑time capture, strong consistency, supports multiple target systems, no code intrusion.

Cons : Configuration complexity, potential performance impact under high concurrency, dependency on Binlog availability.

5. Canal Data Sync

Canal pretends to be a MySQL slave, subscribes to the master’s Binlog, parses it into JSON, and forwards changes to ES via TCP or MQ.

Typical workflow: Canal server requests dump → MySQL master streams Binlog → Canal parses to JSON → Canal client pushes to ES.

6. Alibaba Cloud DTS (Data Transmission Service)

DTS provides real‑time data flow between heterogeneous data sources, supporting full‑load initialization and incremental synchronization.

Features : High availability, dynamic source address adaptation, supports both OLTP and OLAP scenarios.

Application Scenarios

Synchronous double write suits high‑consistency, query‑intensive use cases such as e‑commerce product search. Asynchronous double write fits scenarios where slight latency is acceptable but performance is critical, e.g., syncing non‑critical analytics data. Logstash, Binlog, Canal, and DTS are chosen based on real‑time requirements, operational complexity, and infrastructure constraints.

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