Databend

Databend sink connector

Supported Engines

Spark
Flink
SeaTunnel Zeta

Key Features

• Exactly-Once
• Support Multi-table Writing
• CDC
• Parallelism

Description

A sink connector for writing data to Databend. Supports both batch and streaming processing modes. The Databend sink internally implements bulk data import through stage attachment.

Dependencies

For Spark/Flink

1. You need to download the Databend JDBC driver jar package and add it to the directory ${SEATUNNEL_HOME}/plugins/.

For SeaTunnel Zeta

1. You need to download the Databend JDBC driver jar package and add it to the directory ${SEATUNNEL_HOME}/lib/.

Sink Options

Name	Type	Required	Default Value	Description
url	String	Yes	-	Databend JDBC connection URL
username	String	Yes	-	Databend database username
password	String	Yes	-	Databend database password
database	String	No	-	Databend database name, defaults to the database name specified in the connection URL
table	String	No	-	Databend table name
batch_size	Integer	No	1000	Number of records for batch writing
auto_commit	Boolean	No	true	Whether to auto-commit transactions
max_retries	Integer	No	3	Maximum retry attempts on write failure
schema_save_mode	Enum	No	CREATE_SCHEMA_WHEN_NOT_EXIST	Schema save mode
data_save_mode	Enum	No	APPEND_DATA	Data save mode
custom_sql	String	No	-	Custom write SQL, typically used for complex write scenarios
execute_timeout_sec	Integer	No	300	SQL execution timeout (seconds)
jdbc_config	Map	No	-	Additional JDBC connection configuration, such as connection timeout parameters

schema_save_mode[Enum]

Before starting the synchronization task, choose different processing schemes for existing table structures. Option descriptions:
RECREATE_SCHEMA: Create when table doesn't exist, drop and recreate when table exists.
CREATE_SCHEMA_WHEN_NOT_EXIST: Create when table doesn't exist, skip when table exists.
ERROR_WHEN_SCHEMA_NOT_EXIST: Report error when table doesn't exist.
IGNORE: Ignore table processing.

data_save_mode[Enum]

Before starting the synchronization task, choose different processing schemes for existing data on the target side. Option descriptions:
DROP_DATA: Retain database structure and delete data.
APPEND_DATA: Retain database structure and data.
CUSTOM_PROCESSING: User-defined processing.
ERROR_WHEN_DATA_EXISTS: Report error when data exists.

Data Type Mapping

SeaTunnel Data Type	Databend Data Type
BOOLEAN	BOOLEAN
TINYINT	TINYINT
SMALLINT	SMALLINT
INT	INT
BIGINT	BIGINT
FLOAT	FLOAT
DOUBLE	DOUBLE
DECIMAL	DECIMAL
STRING	STRING
BYTES	VARBINARY
DATE	DATE
TIME	TIME
TIMESTAMP	TIMESTAMP

Task Examples

Simple Example

env {
  execution.parallelism = 1
  job.mode = "BATCH"
}

source {
  FakeSource {
    row.num = 10
    schema = {
      fields {
        name = string
        age = int
        score = double
      }
    }
  }
}

sink {
  Databend {
    url = "jdbc:databend://localhost:8000"
    username = "root"
    password = ""
    database = "default"
    table = "target_table"
    batch_size = 1000
  }
}

Writing with Custom SQL

sink {
  Databend {
    url = "jdbc:databend://localhost:8000"
    username = "root"
    password = ""
    database = "default"
    table = "target_table"
    custom_sql = "INSERT INTO default.target_table(name, age, score) VALUES(?, ?, ?)"
  }
}

Using Schema Save Mode

sink {
  Databend {
    url = "jdbc:databend://localhost:8000"
    username = "root"
    password = ""
    database = "default"
    table = "target_table"
    schema_save_mode = "RECREATE_SCHEMA"
    data_save_mode = "APPEND_DATA"
  }
}

Related Links

• Databend Official Website
• Databend JDBC Driver

Changelog

Change Log

Change	Commit	Version
[Feature][Connector-V2]Support Databend sink/source (#9331)	https://github.com/apache/seatunnel/pull/9331	TODO