Configure the Amazon RDS for SQL Server Input Component

Prerequisites

• An Amazon RDS for SQL Server data source is created. Create an Amazon RDS for SQL Server Data Source.

• Your account has sync-read permission on the data source. Request, Renew, or Release Data Source Permissions.

Procedure

1. In the top menu bar on the Dataphin homepage, choose Develop > Data Integration.

2. On the Integration page, select a Project. In Dev-Prod mode, also select an environment.

3. In the left navigation pane, click Batch Pipeline. In the Batch Pipeline list, click the target 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, click Input, then drag the Amazon RDS for SQL Server component onto the canvas.

6. Click the icon in the Amazon RDS for SQL Server input widget to open the Amazon RDS for SQL Server Input Configuration dialog box.

7. In the Amazon RDS for SQL Server Input Configuration dialog box, configure the following parameters.

   Parameter	Description
   Step Name	Auto-generated. You can rename it. Rules: Use only Chinese characters, letters, underscores (_), and digits. Maximum length is 64 characters.
   Datasource	All Amazon RDS for SQL Server data sources, including those without sync-read permission. Click the icon to copy the name. If you lack sync-read permission, click Request. Request, Renew, or Release Data Source Permissions. To create a data source, click Create Data Source. Create an Amazon RDS for SQL Server Data Source.
   Schema (optional)	Select the schema of the target table. Cross-schema selection is supported. Defaults to the data source schema.
   Source Table Count	Select Single Table or Multiple Tables: Single Table: Syncs one source table to one destination table. Multiple Tables: Syncs multiple source tables to one destination table using the union algorithm. INTERSECT, UNION, and EXCEPT.
   Table Matching Method	Choose between Generic Rules and Database Regex. Note Available only when Source Table Count is set to Multiple Tables.
   Table	Select the source table: If you selected Single Table for Source Table Count, search by keyword or enter the exact table name and click Exact Match. The system checks the table status automatically. Click the icon to copy the table name. If Source Table Count is Multiple Tables, add tables using expressions based on the matching method. If you chose Generic Rules: Enter an expression to match tables with identical structure. Supported formats: enumeration, regex-like patterns, and combinations. Example: table_[001-100];table_102;. If you chose Database Regex: Enter a database-supported regex. At runtime, the task dynamically matches new tables against this pattern. After entering the expression, click Exact Match to view matched tables in the Confirm Match Details dialog box.
   Split Key (optional)	Column used to partition data for concurrent reads. Any source column works; primary keys or indexed columns perform best. Important For date-time type split keys, the system splits the full time range across concurrency threads. This split is not guaranteed to be even.
   Batch Read Size (optional)	Records per batch (for example, 1024). Larger batches reduce network round trips.
   Input Filter (optional)	Filter condition for input data. Example: ds=${bizdate}. Use Input Filter for: A fixed subset of data. Parameter-based filtering.
   Hint (optional)	Table hints override the query optimizer's default execution plan for table or view access. Place hints after the table name in the FROM clause. Separate multiple hints with commas. Example: SELECT * FROM MyTable WITH (INDEX(IX_MyIndex), NOLOCK)
   Output Fields	All fields from the selected table and filters. Field Management: Remove unneeded downstream fields: Remove One Field: Click the icon in the Actions column. Batch deletion: Click Field Management. In the Field Management dialog box, select fields, click the left-moving icon to move them to the unselected list, then click OK. Bulk Add: Click Bulk Add to add fields in JSON, TEXT, or DDL format. Note Clicking OK overwrites existing field configurations. JSON format example: // Example: [ { "index": 1, "name": "Id", "type": "int(10)", "mapType": "Long", "comment": "" }, { "index": 2, "name": "Data", "type": "nvarchar(100)", "mapType": "String", "comment": "" } ] Note index is the column number. name is the field name after import. type is the field type after import. Example: "index":3,"name":"user_id","type":"String" imports column 4 as user_id with type String. TEXT format example: // Example: 1,Id,int(10),Long, 2,Data,nvarchar(100),String, 3,RowVersion,timestamp,Bytes, Row delimiter separates field entries. Default is line feed (\n). Also supports semicolon (;) and period (.). Column delimiter separates field names and types. Default is comma (,). Also supports','. Field type is optional and defaults to','. DDL format example: CREATE TABLE tablename ( user_id serial, username VARCHAR(50), password VARCHAR(50), email VARCHAR (255), created_on TIMESTAMP, ); Add Output Field: Click + Add Output Field. Specify Column, Type, Comment, and Mapping Type. Click the icon to save.

8. Click OK.