Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

CHAPTER 14 ■ PARALLEL EXECUTION 641
3 ( user, 'T1', numrows=>10000000,numblks=>100000 );
4 end;
5 /
PL/SQL procedure successfully completed.
ops$tkyte-ORA10G> create table t2
2 as
3 select t1.*, 0 session_id
4 from t1
5 where 1=0;
Table created.
We used DBMS_STATS to “trick” the optimizer into thinking that there are 10,000,000 rows in
that input table and that it consumes 100,000 database blocks. We want to simulate a big table
here. The second table, T2, is simply a copy of the first table’s structure with the addition of a
SESSION_ID column. That column will be useful to actually “see” the parallelism that takes
place.
Next, we need to set up object types for our pipelined function to return. The object type
is simply a structural definition of the “output” of the procedure we are converting. In this
case, it looks just like T2:
ops$tkyte-ORA10G> CREATE OR REPLACE TYPE t2_type
2 AS OBJECT (
3 id number,
4 text varchar2(30),
5 session_id number
6 )
7 /
Type created.
ops$tkyte-ORA10G> create or replace type t2_tab_type
2 as table of t2_type
3 /
Type created.
And now for the pipelined function, which is simply the original PROCESS_DATA procedure
rewritten. The procedure is now a function that produces rows. It accepts as an input the data
to process in a ref cursor. The function returns a T2_TAB_TYPE, the type we just created. It is a
pipelined function that is PARALLEL_ENABLED. The partition clause we are using says to Oracle,
“Partition, or slice up, the data by any means that work best. We don’t need to make any
assumptions about the order of the data.”
You may also use hash or range partitioning on a specific column in the ref cursor. That
would involve using a strongly typed ref cursor, so the compiler knows what columns are
available. Hash partitioning would just send equal rows to each parallel execution server to
process based on a hash of the column supplied. Range partitioning would send nonoverlapping
ranges of data to each parallel execution server, based on the partitioning key. For
example, if you range partitioned on ID, each parallel execution server might get ranges
1. . .1000, 1001. . .20000, 20001. . .30000, and so on (ID values in that range).