Introduction

In the process of building a data warehouse,data synchronizationis a critical step.

Apache SeaTunnel, as a high-performance*distributed data integration tool*, is widely used for synchronizing MySQL data to OLAP databases like Doris.

However, optimizing this synchronization process to*improve efficiency and reduce resource consumption*is a challenge that every data engineer faces.

In this article, we will explore optimization strategies for*syncing MySQL to Doris with Apache SeaTunnel*, using real configuration files as examples.

Environment Optimization

Parallelism Settings

Parallelism is a key factor affecting synchronization performance.

In our real-time data lake project, we tested different parallelism settings:

env {
  parallelism = 4  \# Full load configuration
}

env {
  parallelism = 8  \# CDC mode configuration
}

Optimization Recommendations:

• Full Load Mode:Adjust parallelism based on table size and server resources. Larger tables may require higher parallelism.
• CDC Mode:Consider the load on the source database to prevent excessive parallelism from causing stress.
• Different tables can have different parallelism settings— for example, an orders table can use higher parallelism, while a configuration table may use a lower setting.

JVM Parameter Optimization

Proper*JVM parameters*can enhance the stability and performance of SeaTunnel:

execution.jvm-options = "-Xms4g -Xmx8g -XX:+UseG1GC -XX:MaxGCPauseMillis=100"

Optimization Recommendations:

• Adjust heap size based on*available memory;ensure max heap memory does not exceed 70% of physical memory*.
• Use the*G1 garbage collectorforbetter large memory management*.
• Set a reasonable*GC pause time*to balance throughput and latency.

Checkpoint Configuration

Checkpoint settings affect task*fault tolerance and recovery*capabilities:

checkpoint.interval = 10000  \# CDC mode
checkpoint.interval = 30000  \# Full load mode

Optimization Recommendations:

• CDC Mode:Use shorter checkpoint intervals (e.g.,10 seconds) to ensure real-time data and quick recovery.
• Full Load Mode:Longer checkpoint intervals*reduce checkpoint overhead*.
• Configure a*local checkpoint storage pathforfaster recovery*:

execution.checkpoint.data-uri = "file:///opt/seatunnel/checkpoints"

Source-side Optimization

Read Rate Limiting

To prevent excessive load on the*source MySQL database*, rate limiting should be applied:

read\_limit.bytes\_per_second = 10000000  \# Limit read rate to ~10MB/s
read\_limit.rows\_per_second = 1000       \# Limit read rate to 1000 rows/sec

Optimization Recommendations:

• Adjust rate limits based on*source database load capacity*.
• Loosen restrictions*during off-peak hours, tighten themduring peak periods*.
• Apply*stricter limits*for critical business tables.

Partitioned Parallel Reads

Using an*effective partition strategyimproves efficiency duringfull synchronization*:

query = "select id, ... from gmall.order_info"
partition_column = "id"
partition_num = 4

Optimization Recommendations:

• Choose*evenly distributed columns(e.g., auto-incrementid) aspartition keys*.
• Set the number of partitions*based on table size and parallelism, usuallyequal to or slightly higher than parallelism*.
• For*large tables, usecustom partition SQLto ensurebalanced data distribution*across partitions.

Connection Pool Optimization

Optimizing connection pool settings can enhance*data source reading efficiency*.

Optimization Recommendations:

• Setmax_sizeto*1.5-2 times the parallelism level*.
• Maintain appropriatemin_idleconnections to*reduce creation overhead*.
• Adjustmax_idle_msbased on workload to*prevent frequent connection creation and destruction*.

CDC-Specific Configurations

For*CDC mode*, additional optimization parameters are needed:

snapshot.mode = "initial"
snapshot.fetch.size = 10000
chunk.size.rows = 8096

Optimization Recommendations:

• Use**initial**mode for the first sync, and**latest**mode for incremental sync.
• Adjust**snapshot.fetch.size**to balance*memory usageandnetwork overhead*.
• Increase**chunk.size.rows**for*large tables*to enhance parallel efficiency.

Transformation Optimization

SQL Transformation Optimization

Efficient SQL transformations can*reduce processing overhead*:

transform {
  Sql {
    query = """
      select 
        id, 
        date(create_time) as k1,  # Ensure k1 is DATE type
        ...other fields...
      from mysql_seatunnel
    """
  }
}

Optimization Recommendations:

• Select*only necessary fieldstoreduce data transmission*.
• Perform*data type conversions at the sourcetolighten the load on Doris*.
• Use appropriate functions for*date-time fields*to ensure compatibility with the target table.
• For*complex transformations, considermultiple transformation steps*for better maintainability.

Destination-side Optimization

Write Mode Configuration

A well-configured*write modecan improveDoris ingestion performance*:

sink.properties {
  format = "json"
  read\_json\_by_line = "true"
  max\_filter\_ratio = "1.0"
  merge_type = "MERGE"
  delete_enable = "true"
}

Optimization Recommendations:

• Use**JSON**formatfor*simpler processing*.
• Adjust**max_filter_ratio**based on*data quality needs*.
• Enable**MERGE**mode and**delete_enable**for CDC scenarios.

Conclusion

Optimizing*MySQL to Doris synchronization with Apache SeaTunnelrequires acomprehensive approachinvolvingparallelism settings, JVM tuning, checkpoint optimization, read/write rate limits, and transformation strategies*.

By applying the best practices outlined in this article, you can*enhance synchronization performance, reduce resource consumption, and ensure data reliabilityin yourdata warehouse*. 🚀