JDBC Vertica Sink Connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Description

Write data through jdbc. Support Batch mode and Streaming mode, support concurrent writing, support exactly-once semantics (using XA transaction guarantee).

Using Dependency

For Spark/Flink Engine

1. You need to ensure that the jdbc driver jar package has been placed in directory ${SEATUNNEL_HOME}/plugins/.

For SeaTunnel Zeta Engine

1. You need to ensure that the jdbc driver jar package has been placed in directory ${SEATUNNEL_HOME}/lib/.

Key Features

• exactly-once
• cdc

Use Xa transactions to ensure exactly-once. So only support exactly-once for the database which is support Xa transactions. You can set is_exactly_once=true to enable it.

Supported DataSource Info

Database Dependency

Please download the support list corresponding to ‘Maven’ and copy it to the ‘$SEATNUNNEL_HOME/plugins/jdbc/lib/‘ working directory
For example Vertica datasource: cp vertica-jdbc-xxx.jar $SEATNUNNEL_HOME/plugins/jdbc/lib/

Data Type Mapping

Sink Options

Tips

If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks.

Task Example

Simple:

This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to JDBC Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target table is test_table will also be 16 rows of data in the table. Before run this job, you need create database test and table test_table in your vertical. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in Install SeaTunnel to install and deploy SeaTunnel. And then follow the instructions in Quick Start With SeaTunnel Engine to run this job.

# Defining the runtime environment
env {
  parallelism = 1
  job.mode = "BATCH"
}
source {
  # This is a example source plugin **only for test and demonstrate the feature source plugin**
  FakeSource {
    parallelism = 1
    result_table_name = "fake"
    row.num = 16
    schema = {
      fields {
        name = "string"
        age = "int"
      }
    }
  }
  # If you would like to get more information about how to configure seatunnel and see full list of source plugins,
  # please go to https://seatunnel.apache.org/docs/category/source-v2
}
transform {
  # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
    # please go to https://seatunnel.apache.org/docs/category/transform-v2
}
sink {
    jdbc {
        url = "jdbc:vertica://localhost:5433/vertica"
        driver = "com.vertica.jdbc.Driver"
        user = "root"
        password = "123456"
        query = "insert into test_table(name,age) values(?,?)"
        }
  # If you would like to get more information about how to configure seatunnel and see full list of sink plugins,
  # please go to https://seatunnel.apache.org/docs/category/sink-v2
}

Generate Sink SQL

This example not need to write complex sql statements, you can configure the database name table name to automatically generate add statements for you

sink {
    jdbc {
        url = "jdbc:vertica://localhost:5433/vertica"
        driver = "com.vertica.jdbc.Driver"
        user = "root"
        password = "123456"
        # Automatically generate sql statements based on database table names
        generate_sink_sql = true
        database = test
        table = test_table
    }
}

Exactly-once :

For accurate write scene we guarantee accurate once

sink {
    jdbc {
        url = "jdbc:vertica://localhost:5433/vertica"
        driver = "com.vertica.jdbc.Driver"
        max_retries = 0
        user = "root"
        password = "123456"
        query = "insert into test_table(name,age) values(?,?)"
        is_exactly_once = "true"
        xa_data_source_class_name = "com.vertical.cj.jdbc.VerticalXADataSource"
    }
}