Multi-Table Transform in SeaTunnel

SeaTunnel’s transform feature supports multi-table transformations, which is especially useful when the upstream plugin outputs multiple tables. This allows you to complete all necessary transformation operations within a single transform configuration. Currently, many connectors in SeaTunnel support multi-table outputs, such as JDBCSource and MySQL-CDC. All transforms can be configured for multi-table transform as described below.

tip

Multi-table Transform has no limitations on Transform capabilities; any Transform configuration can be used in a multi-table Transform. The purpose of multi-table Transform is to handle multiple tables in the data stream individually and merge the Transform configurations of multiple tables into one Transform for easier management.

Properties

Name	Type	Required	Default	Description
table_match_regex	String	No	.*	A regular expression to match the tables that require transformation. By default, it matches all tables. Note that this table name refers to the actual upstream table name, not result_table_name.
table_transform	List	No	-	You can use a list in table_transform to specify rules for individual tables. If a transformation rule is configured for a specific table in table_transform, the outer rules will not apply to that table. The rules in table_transform take precedence.
table_transform.table_path	String	No	-	When configuring a transformation rule for a table in table_transform, you need to specify the table path using the table_path field. The table path should include databaseName[.schemaName].tableName.

Matching Logic

Suppose we read five tables from upstream: test.abc, test.abcd, test.xyz, test.xyzxyz, and test.www. They share the same structure, each having three fields: id, name, and age.

| id | name | age |

Now, let's say we want to copy the data from these five tables using the Copy transform with the following specific requirements:

• For tables test.abc and test.abcd, we need to copy the name field to a new field name1.
• For test.xyz, we want to copy the name field to name2.
• For test.xyzxyz, we want to copy the name field to name3.
• For test.www, no changes are needed.

We can configure this as follows:

transform {
  Copy {
    source_table_name = "fake"  // Optional dataset name to read from
    result_table_name = "fake1" // Optional dataset name for output

    table_match_regex = "test.a.*" // 1. Matches tables needing transformation, here matching `test.abc` and `test.abcd`
    src_field = "name" // Source field
    dest_field = "name1" // Destination field

    table_transform = [{
      table_path = "test.xyz" // 2. Specifies the table name for transformation
      src_field = "name"  // Source field
      dest_field = "name2" // Destination field
    }, {
      table_path = "test.xyzxyz"
      src_field = "name"
      dest_field = "name3"
    }]
  }
}

Explanation

1. With the regular expression and corresponding Copy transform options, we match tables test.abc and test.abcd and copy the name field to name1.
2. Using the table_transform configuration, we specify that for table test.xyz, the name field should be copied to name2.

This allows us to handle transformations for multiple tables within a single transform configuration.

For each table, the priority of configuration is: table_transform > table_match_regex. If no rules match a table, no transformation will be applied.

Below are the transform configurations for each table:

• test.abc and test.abcd

transform {
  Copy {
    src_field = "name"
    dest_field = "name1"
  }
}

Output structure:

| id | name | age | name1 |

• test.xyz

transform {
  Copy {
    src_field = "name"
    dest_field = "name2"
  }
}

Output structure:

| id | name | age | name2 |

• test.xyzxyz

transform {
  Copy {
    src_field = "name"
    dest_field = "name3"
  }
}

Output structure:

| id | name | age | name3 |

• test.www

transform {
  // No transformation needed
}

Output structure:

| id | name | age |

In this example, we used the Copy transform, but all transforms in SeaTunnel support multi-table transformations, and you can configure them similarly within the corresponding transform block.