Version: 2.3.5

SQL Server

JDBC SQL Server Source Connector

Support SQL Server Version

server:2008 (Or later version for information only)

Support Those Engines

Spark
Flink
Seatunnel Zeta

Using Dependency

For Spark/Flink Engine

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

For SeaTunnel Zeta Engine

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

Key Features

supports query SQL and can achieve projection effect.

Description

Read external data source data through JDBC.

Supported DataSource Info

datasource	supported versions	driver	url	maven
SQL Server	support version >= 2008	com.microsoft.sqlserver.jdbc.SQLServerDriver	jdbc:sqlserver://localhost:1433	Download

Database dependency

Please download the support list corresponding to 'Maven' and copy it to the '$SEATNUNNEL_HOME/plugins/jdbc/lib/' working directory
For example SQL Server datasource: cp mssql-jdbc-xxx.jar $SEATNUNNEL_HOME/plugins/jdbc/lib/

Data Type Mapping

SQLserver Data type	Seatunnel Data type
BIT	BOOLEAN
TINYINT SMALLINT	SMALLINT
INTEGER INT	INT
BIGINT	BIGINT
NUMERIC(p,s) DECIMAL(p,s) MONEY SMALLMONEY	DECIMAL(p,s)
FLOAT(1~24) REAL	FLOAT
DOUBLE FLOAT(>24)	DOUBLE
CHAR NCHAR VARCHAR NTEXT NVARCHAR TEXT XML	STRING
DATE	DATE
TIME(s)	TIME(s)
DATETIME(s) DATETIME2(s) DATETIMEOFFSET(s) SMALLDATETIME	TIMESTAMP(s)
BINARY VARBINARY IMAGE	BYTES

Source Options

name	type	required	default	Description
url	String	Yes	-	The URL of the JDBC connection. Refer to a case: jdbc:sqlserver://127.0.0.1:1434;database=TestDB
driver	String	Yes	-	The jdbc class name used to connect to the remote data source, if you use SQLserver the value is `com.microsoft.sqlserver.jdbc.SQLServerDriver`.
user	String	No	-	Connection instance user name
password	String	No	-	Connection instance password
query	String	Yes	-	Query statement
connection_check_timeout_sec	Int	No	30	The time in seconds to wait for the database operation used to validate the connection to complete
partition_column	String	No	-	The column name for parallelism's partition, only support numeric type.
partition_lower_bound	Long	No	-	The partition_column min value for scan, if not set SeaTunnel will query database get min value.
partition_upper_bound	Long	No	-	The partition_column max value for scan, if not set SeaTunnel will query database get max value.
partition_num	Int	No	job parallelism	The number of partition count, only support positive integer. default value is job parallelism
fetch_size	Int	No	0	For queries that return a large number of objects,you can configure the row fetch size used in the query toimprove performance by reducing the number database hits required to satisfy the selection criteria. Zero means use jdbc default value.
properties	Map	No	-	Additional connection configuration parameters,when properties and URL have the same parameters, the priority is determined by the specific implementation of the driver. For example, in MySQL, properties take precedence over the URL.
table_path	Int	No	0	The path to the full path of table, you can use this configuration instead of `query`. examples: mysql: "testdb.table1" oracle: "test_schema.table1" sqlserver: "testdb.test_schema.table1" postgresql: "testdb.test_schema.table1"
table_list	Array	No	0	The list of tables to be read, you can use this configuration instead of `table_path` example: `[{ table_path = "testdb.table1"}, {table_path = "testdb.table2", query = "select * id, name from testdb.table2"}]`
where_condition	String	No	-	Common row filter conditions for all tables/queries, must start with `where`. for example `where id > 100`
split.size	Int	No	8096	The split size (number of rows) of table, captured tables are split into multiple splits when read of table.
split.even-distribution.factor.lower-bound	Double	No	0.05	The lower bound of the chunk key distribution factor. This factor is used to determine whether the table data is evenly distributed. If the distribution factor is calculated to be greater than or equal to this lower bound (i.e., (MAX(id) - MIN(id) + 1) / row count), the table chunks would be optimized for even distribution. Otherwise, if the distribution factor is less, the table will be considered as unevenly distributed and the sampling-based sharding strategy will be used if the estimated shard count exceeds the value specified by `sample-sharding.threshold`. The default value is 0.05.
split.even-distribution.factor.upper-bound	Double	No	100	The upper bound of the chunk key distribution factor. This factor is used to determine whether the table data is evenly distributed. If the distribution factor is calculated to be less than or equal to this upper bound (i.e., (MAX(id) - MIN(id) + 1) / row count), the table chunks would be optimized for even distribution. Otherwise, if the distribution factor is greater, the table will be considered as unevenly distributed and the sampling-based sharding strategy will be used if the estimated shard count exceeds the value specified by `sample-sharding.threshold`. The default value is 100.0.
split.sample-sharding.threshold	Int	No	10000	This configuration specifies the threshold of estimated shard count to trigger the sample sharding strategy. When the distribution factor is outside the bounds specified by `chunk-key.even-distribution.factor.upper-bound` and `chunk-key.even-distribution.factor.lower-bound`, and the estimated shard count (calculated as approximate row count / chunk size) exceeds this threshold, the sample sharding strategy will be used. This can help to handle large datasets more efficiently. The default value is 1000 shards.
split.inverse-sampling.rate	Int	No	1000	The inverse of the sampling rate used in the sample sharding strategy. For example, if this value is set to 1000, it means a 1/1000 sampling rate is applied during the sampling process. This option provides flexibility in controlling the granularity of the sampling, thus affecting the final number of shards. It's especially useful when dealing with very large datasets where a lower sampling rate is preferred. The default value is 1000.
common-options		No	-	Source plugin common parameters, please refer to Source Common Options for details

Parallel Reader

The JDBC Source connector supports parallel reading of data from tables. SeaTunnel will use certain rules to split the data in the table, which will be handed over to readers for reading. The number of readers is determined by the parallelism option.

Split Key Rules:

If partition_column is not null, It will be used to calculate split. The column must in Supported split data type.
If partition_column is null, seatunnel will read the schema from table and get the Primary Key and Unique Index. If there are more than one column in Primary Key and Unique Index, The first column which in the supported split data type will be used to split data. For example, the table have Primary Key(nn guid, name varchar), because guid id not in supported split data type, so the column name will be used to split data.

Supported split data type:

String
Number(int, bigint, decimal, ...)
Date

split.size

How many rows in one split, captured tables are split into multiple splits when read of table.

split.even-distribution.factor.lower-bound

Not recommended for use

The lower bound of the chunk key distribution factor. This factor is used to determine whether the table data is evenly distributed. If the distribution factor is calculated to be greater than or equal to this lower bound (i.e., (MAX(id) - MIN(id) + 1) / row count), the table chunks would be optimized for even distribution. Otherwise, if the distribution factor is less, the table will be considered as unevenly distributed and the sampling-based sharding strategy will be used if the estimated shard count exceeds the value specified by sample-sharding.threshold. The default value is 0.05.

split.even-distribution.factor.upper-bound

Not recommended for use

The upper bound of the chunk key distribution factor. This factor is used to determine whether the table data is evenly distributed. If the distribution factor is calculated to be less than or equal to this upper bound (i.e., (MAX(id) - MIN(id) + 1) / row count), the table chunks would be optimized for even distribution. Otherwise, if the distribution factor is greater, the table will be considered as unevenly distributed and the sampling-based sharding strategy will be used if the estimated shard count exceeds the value specified by sample-sharding.threshold. The default value is 100.0.

split.sample-sharding.threshold

This configuration specifies the threshold of estimated shard count to trigger the sample sharding strategy. When the distribution factor is outside the bounds specified by chunk-key.even-distribution.factor.upper-bound and chunk-key.even-distribution.factor.lower-bound, and the estimated shard count (calculated as approximate row count / chunk size) exceeds this threshold, the sample sharding strategy will be used. This can help to handle large datasets more efficiently. The default value is 1000 shards.

split.inverse-sampling.rate

The inverse of the sampling rate used in the sample sharding strategy. For example, if this value is set to 1000, it means a 1/1000 sampling rate is applied during the sampling process. This option provides flexibility in controlling the granularity of the sampling, thus affecting the final number of shards. It's especially useful when dealing with very large datasets where a lower sampling rate is preferred. The default value is 1000.

partition_column [string]

The column name for split data.

partition_upper_bound [BigDecimal]

The partition_column max value for scan, if not set SeaTunnel will query database get max value.

partition_lower_bound [BigDecimal]

The partition_column min value for scan, if not set SeaTunnel will query database get min value.

partition_num [int]

Not recommended for use, The correct approach is to control the number of split through split.size

How many splits do we need to split into, only support positive integer. default value is job parallelism.

tips

If the table can not be split(for example, table have no Primary Key or Unique Index, and partition_column is not set), it will run in single concurrency.
Use table_path to replace query for single table reading. If you need to read multiple tables, use table_list.

Task Example

Simple:

Simple single task to read the data table

# Defining the runtime environment
env {
  parallelism = 1
  job.mode = "BATCH"
}
source{
    Jdbc {
        driver = com.microsoft.sqlserver.jdbc.SQLServerDriver
        url = "jdbc:sqlserver://localhost:1433;databaseName=column_type_test"
        user = SA
        password = "Y.sa123456"
        query = "select * from full_types_jdbc"
    }
}

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/transform-v2/sql
}

sink {
    Console {}
}

Parallel:

Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table

env {
  parallelism = 10
  job.mode = "BATCH"
}

source {
    Jdbc {
        driver = com.microsoft.sqlserver.jdbc.SQLServerDriver
        url = "jdbc:sqlserver://localhost:1433;databaseName=column_type_test"
        user = SA
        password = "Y.sa123456"
        # Define query logic as required
        query = "select * from full_types_jdbc"
        # Parallel sharding reads fields
        partition_column = "id"
        # Number of fragments
        partition_num = 10
    }
}

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/transform-v2/sql
}

sink {
    Console {}
}

Fragmented Parallel Read Simple:

It is a shard that reads data in parallel fast

env {
  # You can set engine configuration here
  parallelism = 10
}

source {
  # This is a example source plugin **only for test and demonstrate the feature source plugin**
  Jdbc {
    driver = com.microsoft.sqlserver.jdbc.SQLServerDriver
    url = "jdbc:sqlserver://localhost:1433;databaseName=column_type_test"
    user = SA
    password = "Y.sa123456"
    query = "select * from column_type_test.dbo.full_types_jdbc"
    # Parallel sharding reads fields
    partition_column = "id"
    # Number of fragments
    partition_num = 10

  }
  # 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/connector-v2/source/Jdbc
}


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/transform-v2/sql
}

sink {
  Console {}
  # 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/connector-v2/sink/Jdbc
}

SQL Server

Support SQL Server Version​

Support Those Engines​

Using Dependency​

For Spark/Flink Engine​

For SeaTunnel Zeta Engine​

Key Features​

Description​

Supported DataSource Info​

Database dependency​

Data Type Mapping​

Source Options​

Parallel Reader​

Options Related To Split​

split.size​

split.even-distribution.factor.lower-bound​

split.even-distribution.factor.upper-bound​

split.sample-sharding.threshold​

split.inverse-sampling.rate​

partition_column [string]​

partition_upper_bound [BigDecimal]​

partition_lower_bound [BigDecimal]​

partition_num [int]​

tips​

Task Example​

Simple:​

Parallel:​

Fragmented Parallel Read Simple:​