Welcome to the world of Apache SeaTunnel! This guide helps beginners quickly understand SeaTunnel’s core features, architecture, and run their first data sync job. 1. What is Apache SeaTunnel? Apache SeaTunnel is a high-performance, easy-to-use data integration platform supporting both real-time streaming and offline batch processing. It solves common data integration challenges such as diverse data sources, complex sync scenarios, and high resource consumption. Core Features Wide Data Source Support: 100+ connectors covering databases, cloud storage, SaaS services, etc.Batch & Stream Unified: Same connector code supports both batch and streaming processing.High Performance: Supports multiple engines (Zeta, Flink, Spark) for high throughput and low latency.Easy to Use: Define complex sync tasks with simple configuration files. Wide Data Source Support: 100+ connectors covering databases, cloud storage, SaaS services, etc. Wide Data Source Support Batch & Stream Unified: Same connector code supports both batch and streaming processing. Batch & Stream Unified High Performance: Supports multiple engines (Zeta, Flink, Spark) for high throughput and low latency. High Performance Easy to Use: Define complex sync tasks with simple configuration files. Easy to Use 2. Architecture & Environment 2.1 Architecture SeaTunnel uses a decoupled design: Source, Transform, Sink plugins are separated from execution engines. 2.2 OS Support OSUse CaseNotesLinux(CentOS, Ubuntu, etc.)Production(recommended)Stable, suitable for long-running services.macOSDevelopment/TestSuitable for local debugging and config development. OSUse CaseNotesLinux(CentOS, Ubuntu, etc.)Production(recommended)Stable, suitable for long-running services.macOSDevelopment/TestSuitable for local debugging and config development. OSUse CaseNotes OSUse CaseNotes OS Use Case Notes Linux(CentOS, Ubuntu, etc.)Production(recommended)Stable, suitable for long-running services.macOSDevelopment/TestSuitable for local debugging and config development. Linux(CentOS, Ubuntu, etc.)Production(recommended)Stable, suitable for long-running services. Linux(CentOS, Ubuntu, etc.) Linux Production(recommended) Production Stable, suitable for long-running services. macOSDevelopment/TestSuitable for local debugging and config development. macOS macOS Development/Test Suitable for local debugging and config development. 2.3 Environment Preparation Before installation, ensure: JDK Version: Java 8 or 11 installed.Check with java -version.Set JAVA_HOME environment variable. JDK Version: Java 8 or 11 installed. JDK Version Check with java -version. java -version Set JAVA_HOME environment variable. JAVA_HOME 3. Core Components Deep Dive 3.1 Source Reads external data and converts it into SeaTunnel’s internal row format (SeaTunnelRow). SeaTunnelRow Enumerator: Runs on Master, discovers data splits. For JDBC, calculates query ranges based on partition_column.Reader: Runs on Worker, processes assigned splits. Parallel readers improve throughput.Checkpoint Support: For streaming jobs, stores state (e.g., Kafka offsets) for fault recovery. Enumerator: Runs on Master, discovers data splits. For JDBC, calculates query ranges based on partition_column. Enumerator partition_column Reader: Runs on Worker, processes assigned splits. Parallel readers improve throughput. Reader Checkpoint Support: For streaming jobs, stores state (e.g., Kafka offsets) for fault recovery. Checkpoint Support 3.2 Transform Processes data between Source and Sink. Stateless: Most transforms (Sql, Filter, Replace) don’t rely on other rows.Schema Changes: Transform can modify schema; downstream Sink detects these changes. Stateless: Most transforms (Sql, Filter, Replace) don’t rely on other rows. Stateless Sql Filter Replace Schema Changes: Transform can modify schema; downstream Sink detects these changes. Schema Changes 3.3 Sink Writes processed data to external systems. Writer: Runs on Worker, writes data in batches for throughput.Committer: Optional, runs on Master for transactional Sinks. Supports Exactly-Once semantics. Writer: Runs on Worker, writes data in batches for throughput. Writer Committer: Optional, runs on Master for transactional Sinks. Supports Exactly-Once semantics. Committer Exactly-Once 3.4 Execution Flow Parse config → build logical plan.Master allocates resources.Enumerator generates splits → Reader processes them.Data flows: Reader -> Transform -> Writer.Periodic checkpoints save state & commit transactions. Parse config → build logical plan. Master allocates resources. Enumerator generates splits → Reader processes them. Data flows: Reader -> Transform -> Writer. Reader -> Transform -> Writer Periodic checkpoints save state & commit transactions. 4. Supported Connectors & Analysis 4.1 Relational Databases (JDBC) Supported: MySQL, PostgreSQL, Oracle, SQLServer, DB2, Teradata, Dameng, OceanBase, TiDB, etc. Supported Pros: Universal via JDBC, parallel reads, auto table creation, Exactly-Once support.Cons: JDBC limitations may affect performance; high parallelism can stress source DB. Pros: Universal via JDBC, parallel reads, auto table creation, Exactly-Once support. Pros Cons: JDBC limitations may affect performance; high parallelism can stress source DB. Cons 4.2 Message Queues Supported: Kafka, Pulsar, RocketMQ, DynamoDB Streams. Supported Pros: High throughput, multiple serialization formats, Exactly-Once support.Cons: Complex config (offsets, schemas, consumer groups); debugging less intuitive. Pros: High throughput, multiple serialization formats, Exactly-Once support. Pros Cons: Complex config (offsets, schemas, consumer groups); debugging less intuitive. Cons 4.3 Change Data Capture (CDC) Supported: MySQL-CDC, PostgreSQL-CDC, Oracle-CDC, MongoDB-CDC, SQLServer-CDC, TiDB-CDC. Supported Pros: Millisecond-level capture, lock-free snapshot, supports resume & schema evolution.Cons: Requires high DB privileges, relies on Binlog/WAL. Pros: Millisecond-level capture, lock-free snapshot, supports resume & schema evolution. Pros Cons: Requires high DB privileges, relies on Binlog/WAL. Cons 4.4 File Systems & Cloud Storage Supported: LocalFile, HDFS, S3, OSS, GCS, FTP, SFTP. Supported Pros: Massive storage, supports multiple formats & compression.Cons: Small file problem in streaming; merging adds complexity. Pros: Massive storage, supports multiple formats & compression. Pros Cons: Small file problem in streaming; merging adds complexity. Cons 4.5 NoSQL & Others Supported: Elasticsearch, Redis, MongoDB, Cassandra, HBase, InfluxDB, ClickHouse, Doris, StarRocks. Supported Optimized for each DB, e.g., Stream Load for ClickHouse/StarRocks, batch writes for Elasticsearch. Optimized for each DB, e.g., Stream Load for ClickHouse/StarRocks, batch writes for Elasticsearch. 5. Transform Hands-On 5.1 SQL Transform transform {
  Sql {
    plugin_input = "fake"
    plugin_output = "fake_transformed"
    query = "select name, age, 'new_field_val' as new_field from fake"
  }
} transform {
  Sql {
    plugin_input = "fake"
    plugin_output = "fake_transformed"
    query = "select name, age, 'new_field_val' as new_field from fake"
  }
} 5.2 Filter Transform transform {
  Filter {
    plugin_input = "fake"
    plugin_output = "fake_filter"
    include_fields = ["name", "age"]
  }
} transform {
  Filter {
    plugin_input = "fake"
    plugin_output = "fake_filter"
    include_fields = ["name", "age"]
  }
} 5.3 Replace Transform transform {
  Replace {
    plugin_input = "fake"
    plugin_output = "fake_replace"
    replace_field = "name"
    pattern = " "
    replacement = "_"
    is_regex = true
    replace_first = true
  }
} transform {
  Replace {
    plugin_input = "fake"
    plugin_output = "fake_replace"
    replace_field = "name"
    pattern = " "
    replacement = "_"
    is_regex = true
    replace_first = true
  }
} 5.4 Split Transform transform {
  Split {
    plugin_input = "fake"
    plugin_output = "fake_split"
    separator = " "
    split_field = "name"
    output_fields = ["first_name", "last_name"]
  }
} transform {
  Split {
    plugin_input = "fake"
    plugin_output = "fake_split"
    separator = " "
    split_field = "name"
    output_fields = ["first_name", "last_name"]
  }
} 6. Quick Installation Download latest SeaTunnel binary.Extract & enter folder: Download latest SeaTunnel binary. SeaTunnel binary Extract & enter folder: tar -xzvf apache-seatunnel-2.3.x-bin.tar.gz
cd apache-seatunnel-2.3.x tar -xzvf apache-seatunnel-2.3.x-bin.tar.gz
cd apache-seatunnel-2.3.x Install plugins: Install plugins: sh bin/install-plugin.sh sh bin/install-plugin.sh 💡 Tip: Configure Maven mirror (e.g., Aliyun) for faster downloads. 7. First SeaTunnel Job Create hello_world.conf under config folder. Example config generates fake data and prints to console. hello_world.conf config Run locally using Zeta engine: ./bin/seatunnel.sh --config ./config/hello_world.conf -e local ./bin/seatunnel.sh --config ./config/hello_world.conf -e local Monitor logs: Job execution started, SeaTunnelRowoutputs, and Job Execution Status: FINISHED. Monitor logs: Job execution started, SeaTunnelRowoutputs, and Job Execution Status: FINISHED. Job execution started SeaTunnelRow Job Execution Status: FINISHED 8. Troubleshooting command not found: java → Check Java installation & JAVA_HOME.ClassNotFoundException → Connector plugin not installed.Config file not valid → Check HOCON syntax.Task hangs → Check resources or streaming mode. command not found: java → Check Java installation & JAVA_HOME. command not found: java JAVA_HOME ClassNotFoundException → Connector plugin not installed. ClassNotFoundException Config file not valid → Check HOCON syntax. Config file not valid Task hangs → Check resources or streaming mode. 9. Advanced Resources Official DocsConnector list: docs/en/connector-v2Example configs: config/*.template Official Docs Official Docs Connector list: docs/en/connector-v2 docs/en/connector-v2 Example configs: config/*.template config/*.template Apache SeaTunnel unifies batch & streaming, supports rich connectors, and is easy to deploy. Dive in, explore, and make your data flow effortlessly!

Apache SeaTunnel for Beginners: Your First Data Sync in 30 Minutes

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