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Your goal is to extract data from this table to a target table with a different format. The new table is named yearly_store_sales, and it lists the company sales figures differently each company s sales figure is listed by year. For example, in the original table, the store modern tools showed two yearly sales numbers in the same row: 1000000 and 1200000. In the new transformed table, these numbers should appear in different rows that is, the data should show the store/sales year combinations. To do this, the company name may appear more than once in this table: SQL> CREATE TABLE yearly_store_sales 2 (store_name VARCHAR2(25), 3 sales_year NUMBER, 4* total_sales NUMBER); Table created. SQL> Because table functions return sets of records, you need to create some special object structures to use table functions to transform data. The first object you need to create is an object type called yearly_store_sales_row, which reflects the records. Note that the structure of this type is the same as your target table, yearly_store_sales. SQL> 2 3 4 5* Type SQL> CREATE TYPE yearly_store_sales_row AS OBJECT( store_name varchar2(25), sales_year number, total_sales number); created.

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T2 +M1(in udt1 : UDT1)

The next step is to create a table type named yearly_store_sales_table. This table type is based on the object type you just created. SQL> 2 3 Type SQL> CREATE TYPE yearly_store_sales_table AS TABLE OF yearly_store_sales_row; created.

The package creation statement shown in Listing 13-12 is somewhat complex, and it is the heart of the table function feature. The table function uses a REF CURSOR to fetch the input rows. It then transforms the data and sends it out interactively (that is, it pipelines the data).

Listing 13-12. Creating the Table Function SQL> CREATE OR REPLACE PACKAGE sales_package 2 AS 3 TYPE sales_cursor_type IS REF CURSOR 4 RETURN sales_data%ROWTYPE; 5 FUNCTION modify_sales_data 6 (INPUTDATA IN sales_cursor_type) 7 RETURN yearly_store_sales_table 8 PIPELINED; 9* END; SQL> / Package created. SQL> 1 2 3 4 5 6 7 8 9

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Figure 1-74. Complex static coupling among components, due to careless packaging To keep coupling diagrams clean, you can package together all UDTs used in more than one component. In a system with lots of components, the static coupling to the UDT component assumes a star shape, as shown in Figure 1-75.

CREATE OR REPLACE PACKAGE BODY sales_package AS FUNCTION modify_sales_data( inputdata IN sales_cursor_type) RETURN yearly_store_sales_table PIPELINED IS inputrec sales_data%ROWTYPE; outputrow_2001 yearly_store_sales_row := yearly_store_sales_row(NULL,NULL,NULL); outputrow_2002 yearly_store_sales_row := yearly_store_sales_row(NULL,NULL,NULL); BEGIN LOOP FETCH inputdata INTO inputrec; EXIT WHEN inputdata%NOTFOUND; IF INPUTREC.SALES_2001 IS NOT NULL THEN outputrow_2001.store_name := inputrec.store_name; outputrow_2001.sales_year := 2001; outputrow_2001.total_sales:= inputrec.sales_2001; pipe row (outputrow_2001); END IF; IF INPUTREC.SALES_2002 IS NOT NULL THEN outputrow_2002.store_name := inputrec.store_name; outputrow_2002.sales_year := 2002; outputrow_2002.total_sales:= inputrec.sales_2002; pipe row (outputrow_2002); END IF; END LOOP; RETURN; END; END;

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Let s look at each part of the package carefully: In order to return sets of rows from the source table as inputs to the table function, you need to create a REF CURSOR based on the source table rows. The REF CURSOR in the example is named sales_cursor. The function modify_sales_data is the table function. It has one input parameter, the REF CURSOR sales_cursor. The function returns data in the format of the source table, yearly_store_sales. The keyword PIPELINED at the end means that data flows through the data transformation process. As the input data is processed, the transformed results are continuously fed into the target table. The package body shows the details of the function modify_sales_data. The function will transform the original structure of data in the source table into the desired format and insert it into the target table. In the following INSERT statement, the function modify_sales_data is used. Note how the function is applied to the row data from the original table sales_data. The data is transformed before it is inserted into the yearly_store_sales table. SQL> INSERT INTO yearly_store_sales t 2 SELECT * 3 FROM TABLE(sales_package.modify_sales_data( 4 CURSOR(select store_name,sales_2001,sales_2002 5 FROM sales_data))); 6 rows created. SQL> COMMIT; Commit complete. SQL>

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