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.
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 plugin_output. |
| 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.abcandtest.abcd, we need to copy thenamefield to a new fieldname1. - For
test.xyz, we want to copy thenamefield toname2. - For
test.xyzxyz, we want to copy thenamefield toname3. - For
test.www, no changes are needed.
We can configure this as follows:
transform {
Copy {
plugin_input = "fake" // Optional dataset name to read from
plugin_output = "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
- With the regular expression and corresponding Copy transform options, we match tables
test.abcandtest.abcdand copy thenamefield toname1. - Using the
table_transformconfiguration, we specify that for tabletest.xyz, thenamefield should be copied toname2.
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.