How to Choose Apache SeaTunnel Zeta, Flink, or Spark?

This article provides a deep dive into the three execution engines supported by Apache SeaTunnel: Zeta (SeaTunnel Engine), Flink, and Spark.

We analyze them from multiple dimensions — architecture design, core capabilities, strengths and weaknesses, and practical usage — to help you choose the most suitable engine based on your business requirements.

1. Engine Overview

SeaTunnel adopts an API–engine decoupled architecture, meaning the same data integration logic (Config) can run seamlessly on different execution engines.

• 
  Zeta Engine: A next-generation engine built by the SeaTunnel community specifically for data integration, focusing on high performance and low latency.


• 
  Flink Engine: Leverages Flink’s powerful stream processing capabilities, ideal for teams with existing Flink clusters.


• 
  Spark Engine: Built on Spark’s strong batch processing ecosystem, suitable for large-scale offline ETL scenarios.

2. Zeta Engine — The Core Recommendation

Zeta is the community’s default and recommended engine.

It was designed to address the heavy resource usage and operational complexity of Flink and Spark in simple data synchronization scenarios.

2.1 Core Architecture

Zeta uses a decentralized or Master–Worker architecture (depending on deployment mode), consisting of:

• 
  

  Coordinator (Master)

  
  
  
  
  
  • Job parsing: converts Logical DAG into Physical DAG
  
  
  • Resource scheduling: manages slots and assigns tasks to workers
  
  
  • Checkpoint coordination: triggers and coordinates distributed snapshots based on the Chandy–Lamport algorithm
  
  
  
  


• 
  

  Worker (Slave)

  
  
  
  
  
  • Task execution: runs Source, Transform, and Sink tasks
  
  
  • Data transport: handles inter-node data transfer
  
  
  
  


• 
  

  ResourceManager

  
  
  
  
  
  • Supports Standalone, YARN, and Kubernetes deployments
  
  
  
  

2.2 Key Features

1. Pipeline-level Fault Tolerance
   
   Unlike Flink’s global job restart, Zeta can restart only the failed pipeline (e.g., failure of table A does not affect table B).




2. Incremental Checkpointing
   
   Supports high-frequency checkpoints with minimal performance overhead while reducing data loss.




3. Dynamic Scaling
   
   Workers can be added or removed at runtime without restarting jobs.




4. Schema Evolution
   
   Native support for DDL changes (e.g., adding columns), critical for CDC scenarios.

2.3 Usage Guide

Zeta is bundled with SeaTunnel and works out of the box.

Local mode (development/testing):

./bin/seatunnel.sh --config ./config/your_job.conf -e local

Cluster mode (production):

./bin/seatunnel-cluster.sh -d

./bin/seatunnel.sh --config ./config/your_job.conf -e cluster

3. Flink Engine

SeaTunnel adapts its internal Source/Sink API to Flink’s SourceFunction / SinkFunction (or the new Source/Sink API) via a translation layer.

3.1 Architecture

• Config is translated into a Flink JobGraph on the client side


• The job runs as a standard Flink application


• State is managed by Flink’s checkpoint mechanism (RocksDB / FsStateBackend)

3.2 Pros & Cons

• 
  Pros: Mature ecosystem, strong operational tooling, suitable for complex streaming + integration workloads


• 
  Cons: Strong version coupling; heavyweight for pure data synchronization tasks

4. Spark Engine

SeaTunnel integrates with Spark via the DataSource V2 API.

4.1 Architecture

• 
  Batch: Spark RDD / DataFrame execution


• 
  Streaming: Spark Structured Streaming (micro-batch)

4.2 Pros & Cons

• 
  Pros: Excellent batch processing performance for large-scale ETL


• 
  Cons: Higher latency due to micro-batching; slower resource scheduling

5. Engine Comparison

6. How to Choose?

1. If you are starting a new project, or your primary requirement is data synchronization (Data Integration):

• 👉 Zeta Engine is the top choice. It is the most lightweight, delivers the best performance, and provides dedicated optimizations for CDC and multi-table synchronization.

1. If you already have an existing Flink or Spark cluster, and your operations team does not want to maintain an additional engine:

• 👉 Choose the Flink or Spark engine to reuse your existing infrastructure.

1. If your jobs involve extremely complex custom computation logic (Complex Computation):

• 👉 Give priority to Flink (streaming) or Spark (batch) to leverage their rich operator ecosystems.
  However, Zeta + SQL Transform can also satisfy most requirements in many scenarios.

7. Beginner’s Quick Start Guide

If this is your first time using SeaTunnel, follow the steps below to quickly experience the power of the Zeta engine.

7.1 Environment Preparation

Make sure Java 8 or Java 11 is installed on your machine.

java -version

7.2 Download and Installation

1. 
   Download: Download the latest binary package (apache-seatunnel-x.x.x-bin.tar.gz) from the Apache SeaTunnel official website.


2. 
   Extract:
   

   tar -zxvf apache-seatunnel-*.tar.gz

   cd apache-seatunnel-*

7.3 Install Connector Plugins (Important!)

This is the step most beginners tend to overlook.

The default distribution does not include all connectors. You need to run the script to automatically download them.

# Automatically install all plugins defined in plugin_config

sh bin/install-plugin.sh

7.4 Run Your First Job Quickly

Create a simple configuration file config/quick_start.conf to generate data from a Fake source and print it to the console:

env {

  execution.parallelism = 1

  job.mode = "BATCH"

}



source {

  FakeSource {

    result_table_name = "fake"

    row.num = 100

    schema = {

      fields {

        name = "string"

        age = "int"

      }

    }

  }

}



transform {

  # Simple SQL processing

  Sql {

    source_table_name = "fake"

    result_table_name = "sql_result"

    query = "select name, age from fake where age > 50"

  }

}



sink {

  Console {

    source_table_name = "sql_result"

  }

}

Run the job (Local mode):

./bin/seatunnel.sh --config ./config/quick_start.conf -e local

If you see tabular data printed in the console, congratulations — you have successfully mastered the basic usage of SeaTunnel!

8. Deep Learning Path for the Zeta Engine Internals

If you want to gain a deeper understanding of how the Zeta engine works internally, or plan to contribute to the community, you can follow the learning path below to read and debug the source code.

8.1 Core Module Overview

The Zeta engine code is mainly located under the seatunnel-engine module:

• 
  seatunnel-engine-core: Defines core data structures (such as Job and Task) and communication protocols.


• 
  seatunnel-engine-server: Contains the concrete implementations of the Coordinator and Worker.


• 
  seatunnel-engine-client: Handles client-side job submission logic.

8.2 Recommended Source Code Reading Path

1. Job Submission and Parsing (Coordinator Side)

Start from the JobMaster class to understand how jobs are received and initialized.

• 
  Entry point: org.apache.seatunnel.engine.server.master.JobMaster
  


• 
  Key logic: Focus on the init and run methods to understand the transformation from LogicalDag to PhysicalPlan.

2. Task Execution (Worker Side)

Understand how Tasks are scheduled and executed.

• 
  

  Service entry:
  
  TaskExecutionService.java

  
  
  
  
  
  • This class is responsible for managing all TaskGroups on a Worker node.
  
  
  
  
  
  




• Execution context:
  
  
  org.apache.seatunnel.engine.server.execution.TaskExecutionContext

3. Checkpoint Mechanism (Core Challenge)

Zeta’s snapshot mechanism is critical for ensuring data consistency.

• 
  

  Coordinator:
  
  CheckpointCoordinator.java

  
  
  
  
  
  • Focus on the triggerCheckpoint method to understand how barriers are distributed.
  
  
  
  
  
  



• 
  
  

  Planning:
  
  CheckpointPlan.java

  
  
  
  
  
  • Understand how the scope of tasks involved in a checkpoint is calculated.
  
  
  
  
  
  

8.3 Debugging Tips

1. Adjust log level:
   
   In config/log4j2.properties, set the log level of org.apache.seatunnel to DEBUG to observe detailed RPC communication and state transition logs.




2. Local debugging:
   
   Run the org.apache.seatunnel.core.starter.seatunnel.SeaTunnelStarter class directly in your IDE, passing the parameters
   
   -c config/your_job.conf -e local,
   
   to set breakpoints and debug the entire execution flow.