Dataphin:Configure Greenplum Output Component

Prerequisites

• A Greenplum data source is created. Creating Greenplum Data Source.

• The account configuring the Greenplum output component must have read-through permission for the data source. To request permission, see Request Data Source Permission.

Procedure

1. On the Dataphin homepage, choose Development > Data Integration from the top menu bar.

2. On the Integration page, in the top menu bar, select Project (In Dev-Prod mode, select Environment).

3. In the left navigation pane, click Batch Pipeline. In the Batch Pipeline list, click the target Offline Pipeline to open its configuration page.

4. In the upper-right corner, click Component Library to open the Component Library panel.

5. In the Component Library panel, select Outputs, find Greenplum, and drag it to the canvas.

6. Drag the icon from an upstream input, transform, or flow component to connect it to the Greenplum output component.

7. Click the icon on the Greenplum output component to open the Greenplum Output Configuration dialog box.

8. In the Greenplum Output Configuration dialog box, configure parameters.

   Parameter		Description
   Basic Settings	Step Name	The component name. Dataphin generates a default name that you can modify. Naming rules: Can contain only Chinese characters, letters, underscores (_), and numbers. Cannot exceed 64 characters.
   	Datasource	Lists all Greenplum data sources, regardless of your write-through permission. For data sources without write-through permission, click Request to request access. Request Data Source Permission. To create a data source, click the icon. Create Greenplum Data Source.
   	Schema	Required. Select a database schema. If the data source connection includes schema information, that schema is used by default. You can select any schema you have permission for.
   	Table	Select the target table for output data. If no target table exists, use one-click table creation: Click One-Click Table Creation. Dataphin generates a CREATE TABLE statement with the source table name and auto-converted field types. Example: Modify the SQL statement as needed, then click New. Dataphin uses the new table as the output target. Note If a table with the same name already exists in the development environment, clicking New returns an error.
   	Production Table Missing Policy	Specifies the action when the production table does not exist. Select Do Not Process or Automatic Creation. Default: Automatic Creation. Do Not Process: The system warns that the target table does not exist on submission, but you can still publish. Create the target table in production before running the task. Automatic Creation: Click Edit The Table Creation Statement to modify the pre-populated DDL. Use ${table_name} as the table name placeholder — this is the only supported placeholder, replaced with the actual name at runtime. If the target table does not exist, the system creates it using this statement. If creation fails, the publish check fails — fix the statement and republish. If the table already exists, no action is taken. Note This parameter is supported only in Dev-Prod mode projects.
   	Loading Policy	Select the Append Data or Copy policy: Append Data: Reports a dirty data error on primary key or constraint conflicts. Copy Policy: Applies the conflict resolution policy. Supports tables only, not views.
   	Conflict Resolution Policy	Available when Loading Policy is Copy. Greenplum supports only Report Error on Conflict.
   	Batch Write Data Volume (Optional)	Data volume per batch write. Works with Batch Write Record Count — the system flushes when either limit is reached first. Default: 32 MB.
   	Batch Write Record Count (Optional)	Default: 2,048 records. The system flushes data when either Batch Write Record Count or Batch Write Data Volume is reached first. When either limit is reached, the system writes the accumulated batch to the destination. For optimal throughput, set record count higher than data volume divided by average record size. Example: with a 16 MB data volume limit and ~1 KB records, set record count above 16,384 (e.g., 20,000 records). The system then flushes primarily by data volume.
   	Prepare Statement (Optional)	The SQL script executed on the database before data import. Example: To swap tables with zero downtime, create Target_A, write to it, then rename Service_B to Temp_C, rename Target_A to Service_B, and drop Temp_C.
   	End Statement (Optional)	The SQL script executed on the database after data import.
   Field Mapping	Input Fields	Displays input fields from upstream components.
   	Output Fields	Displays output fields. Supports the following operations: Field Management: Click Field Management to select output fields. Click the icon to move Selected Input Fields to Unselected Input Fields. Click the icon to move Unselected Input Fields to Selected Input Fields. Batch Add: Click Batch Add to add fields in JSON, TEXT, or DDL format. Batch configure in JSON format. For example: // Example: [{ "name": "user_id", "type": "String" }, { "name": "user_name", "type": "String" }] Note name specifies the field name and type specifies the field type. For example, "name":"user_id","type":"String" imports user_id as String. Batch configure in TEXT format. For example: // Example: user_id,String user_name,String Row delimiter: line feed (\n) by default. Also supports semicolons (;) and periods (.). Column delimiter: comma (,) by default. Batch configure in DDL format. For example: CREATE TABLE tablename ( id INT PRIMARY KEY, name VARCHAR(50), age INT ); New Output Field: Click + New Output Field, enter the Column name, select a Type, and click the icon to save.
   	Field Mapping	Map upstream input fields to target table fields. Quick Mapping supports Row-Based Mapping and Name-Based Mapping. Name-Based Mapping: Maps fields with the same field name. Row-Based Mapping: Maps fields by row position when source and target field names differ.

9. Click Confirm to save the configuration.