SeaTunnel: An Open‑Source Ultra‑Scale Data Integration Platform – Design Goals, Architecture, and Future Roadmap

SeaTunnel aims to provide a simple‑to‑use, distributed, and extensible data integration platform that can handle ultra‑large data volumes with high throughput and low latency, addressing challenges such as diverse data sources, fragmented offline/real‑time sync management, varied enterprise tech stacks, and strict consistency requirements.

The platform currently supports more than 50 connectors (20+ sources, 20+ sinks, and dozens of transforms) that work for both batch and streaming jobs. It runs on multiple engines—including Flink, Spark, and its own SeaTunnel Engine—to fit existing enterprise ecosystems while offering high throughput, low latency, and exactly‑once processing guarantees.

The overall architecture consists of data sources, target sinks, a data‑processing engine, a connector translation layer, and a Table API that abstracts connector usage for both web‑based and programmatic job creation.

Execution flow: users define jobs via SQL or API, which generate connectors; the job is submitted to the chosen engine, and data flows from SourceReader through SourceCoordinator to SinkWriter, with coordinators ensuring state storage and two‑phase commits for consistency.

Connector Translation decouples connectors from engines, enabling a single connector implementation to run on different engines. The Source API unifies offline and real‑time processing, supports parallel reads, dynamic split discovery, and coordinated reads for CDC scenarios.

The Sink API provides exactly‑once semantics through write, state storage, distributed transactions, per‑task committers, and aggregated commits, adapting to both Flink and Spark execution models.

GlobalCommit can run in three modes: driver‑side commit with worker writers, worker‑side commit (for Flink ≤ 1.11), or per‑task commit (supported by all Flink versions, not Spark).

SeaTunnel also offers Table & Catalog APIs for source management, metadata retrieval, data‑type definition, and connector creation, simplifying job configuration and visualization.

Future plans include doubling the number of connectors to over 80 (V2 versions for Spark and Flink), releasing a SeaTunnel Web module for visual job management and scheduling, and launching a dedicated SeaTunnel Engine with shared JDBC resources, finer‑grained fault tolerance, and performance improvements.

Q&A highlights: SeaTunnel supports Flink checkpointing for real‑time sync, and DolphinScheduler can schedule SeaTunnel tasks via the upcoming web UI.

For more details, see the accompanying diagrams and images in the original article.