Seatunnel
SeaTunnel is a multimodal, high-performance, distributed, massive data integration tool.
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What is Seatunnel?
Apache SeaTunnel is a multimodal, high-performance, distributed data integration tool designed for synchronizing massive amounts of data daily. It supports integrating data between Transaction DB, Cloud DB, SaaS, and Binlog sources using SQL-like code, making it accessible for users familiar with SQL queries.
The tool offers hundreds of connectors covering mainstream databases, distributed file systems, message queues, and SaaS services. Key features include batch and realtime integration support, visual job management and monitoring, Zeta engine (no need for Spark/Flink dependencies), distributed snapshot algorithm for data consistency, and support for full/incremental/CDC synchronization scenarios.
SeaTunnel is built for data engineers, data integration teams, and organizations handling trillion-scale data synchronization. It's trusted by nearly 100 companies in production environments for its efficiency, stability, and ability to handle tens of billions of data records daily with high throughput and low latency.
The platform provides both coding and visual development methods, data quality monitoring to prevent data loss or duplication, and modular plug-in architecture for easy extension. It supports standalone and cluster deployment with decentralized Master/Worker design for flexible resource management.
Seatunnel pricing
Pricing model: Freemium
Apache SeaTunnel is completely free and open source under the Apache 2.0 License, allowing commercial use. There are no paid tiers or subscription plans. The software can be downloaded freely from the official website and GitHub repository.
Seatunnel pros
- Supports over 100 connectors covering databases, file systems, message queues, and SaaS
- Zeta Engine eliminates dependency on Spark or Flink for deployment
- Handles trillion-scale data synchronization with high performance
- 40-80% faster than DataX under same test scenarios
- Supports both batch and streaming integration in one ecosystem
- Real-time monitoring during data synchronization
- Distributed snapshot algorithm ensures data consistency
- JDBC multiplexing reduces resource consumption and JDBC connections
- Low-code and no-code visual job management capabilities
- Supports standalone and cluster deployment options
- CDC (Change Data Capture) and full database synchronization support
- Declarative SQL-like configuration for data pipelines
- Modular plug-in architecture for easy custom connector development
- Minimizes computing resources with 50%+ reduction vs alternatives
- High throughput up to nearly 20000 rows per second faster than third-party platforms
Seatunnel cons
- Limited information available for production deployment scenarios
- Requires manual definition of partition columns and where clauses for some sources
- No automatic schema evolution support
- Complex source and primitive settings can be cumbersome for plugin implementations
- Heavy dependency on Debezium for PostgreSQL CDC
- Configuration can be intricate for advanced use cases
- OOM issues possible when consuming Kafka without proper rate limiting
- Learning curve for custom plugin development
Frequently asked questions about Seatunnel
Why should I install a computing engine like Spark or Flink?
SeaTunnel now uses computing engines such as Spark and Flink to complete resource scheduling and node communication, so developers can focus on the ease of use of data synchronization and development of high-performance components. However, this is only temporary as SeaTunnel's Zeta Engine is the default and doesn't require these dependencies.
How do I declare a variable in SeaTunnel configuration?
Since v1.2.4, SeaTunnel supports variable substitution in configuration using the format ${variable_name}. This is often used for timing or non-timing offline processing to replace variables like time and date. Use the parameter -i or --variable followed by key=value when starting SeaTunnel to specify variable values.
Is SeaTunnel supported in Azkaban, Oozie, DolphinScheduler?
Yes, SeaTunnel is supported in Azkaban, Oozie, and DolphinScheduler for workflow scheduling and orchestration.
How can I solve OOM when SeaTunnel consumes Kafka?
OOM is usually caused by not having a rate limit for consumption. Configure spark.streaming.kafka.maxRatePerPartition to limit the consumption speed. The size is positively correlated with spark.executor.cores spark.executor.instances, so you can increase maxRatePerPartition while increasing resources to speed up consumption.
Where can I place self-written plugins or third-party jars?
Place the Jar package under the specified structure of the plugins directory: cd SeaTunnel, mkdir -p plugins/my_plugins/lib, cp third-part.jar plugins/my_plugins/lib. The my_plugins directory can be any string name you choose.
When developing custom plugins, do I need to understand SeaTunnel code?
No, plugins developed by developers are independent from the SeaTunnel project and don't need to include SeaTunnel code. Plugins can be written using Java, Scala, Maven, sbt, Gradle, or whatever framework you prefer. They don't need to be integrated into the SeaTunnel project.
What should I do if OOM appears when running SeaTunnel in Spark local[] mode?
If running in local mode, modify the start-seatunnel.sh startup script and add the parameter --driver-memory 4g after spark-submit. Local mode is generally not used in production environments, so this parameter usually doesn't need to be set when running on Yarn.