How to Sync MySQL Data to Elasticsearch: 4 Practical Strategies

1. Synchronous Dual Write

Write to MySQL and simultaneously write the same data to Elasticsearch. Advantages: simple implementation. Disadvantages: tight coupling, performance impact, and limited extensibility for aggregation or personalized search.

Pros: simple implementation

Cons:

Business coupling – synchronization code embedded in product management

Performance impact – writing to two stores increases latency

Hard to extend – aggregation or personalized queries are difficult

2. Asynchronous Dual Write

Publish product data to a message queue (MQ) when a product is listed, and let a dedicated search service subscribe to these messages to sync data to Elasticsearch. This allows building a wide‑table structure in ES for efficient multi‑dimensional queries, though fallback queries (re‑checks) may still be needed.

Pros:

Decouples product service from synchronization logic

Good real‑time performance – typically seconds

Cons:

Introduces additional components and complexity

3. Scheduled Tasks

Periodically run a job to copy data from MySQL to Elasticsearch. Choosing the right frequency is critical: high frequency can cause resource spikes, while low frequency reduces freshness.

Pros: simple to implement

Cons: real‑time guarantees are weak and it can stress storage resources

4. Data Subscription (Binlog)

Use MySQL binlog‑based subscription frameworks (e.g., Canal) to capture changes. Canal‑adapter provides an ES adapter that can sync data with zero custom code, but complex aggregation still requires custom client logic and possible re‑checks.

Pros:

Minimal intrusion into business code

Better real‑time performance

Common open‑source subscription tools include Canal (Java/Go/PHP/Python/Rust), Maxwell (Java), and Python‑Mysql‑Replication (Python), each offering different client languages, high‑availability support, and message delivery mechanisms.