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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) Here, we just want the data split up How the data is split up is not relevant to our processing, so our definition looks like this: ops$tkyte@ORA11GR2> create or replace 2 function parallel_pipelined( l_cursor in sys_refcursor ) 3 return t2_tab_type 4 pipelined 5 parallel_enable ( partition l_cursor by any ) We d like to be able to see what rows were processed by which parallel execution servers, so we ll declare a local variable L_SESSION_ID and initialize it from V$MYSTAT: 6 7 8 9 10 11 12 13 is l_session_id number; l_rec t1%rowtype; begin select sid into l_session_id from v$mystat where rownum =1;.

The initial implementations of the symmetric key cryptography in NTP version 3 (specified in RFC 1305 in 1992!) were based on the Cyber Block Chaining (CBC) operation mode of the Data Encryption Standard (DES). The DES-CBC produces a one-way hash (a message digest) that can be used to verify the identity of an NTP peer. The DES-CBC method has been subsequently replaced with the Message Digest 5 (MD5) algorithm, which was developed by Ronald L. Rivest and described in RFC 1321.

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Now we are ready to process the data. We simply fetch out a row (or rows, as we could certainly use BULK COLLECT here to array process the ref cursor), perform our complex process on it, and pipe it out. When the ref cursor is exhausted of data, we close the cursor and return: 14 loop 15 fetch l_cursor into l_rec; 16 exit when l_cursor%notfound; 17 -- complex process here 18 pipe row(t2_type(l_rec.id,l_rec.text,l_session_id)); 19 end loop; 20 close l_cursor; 21 return; 22 end; 23 / Function created. And that s it. We re ready to process the data in parallel, letting Oracle figure out based on the resources available what the most appropriate degree of parallelism is: ops$tkyte@ORA11GR2> alter session enable parallel dml; Session altered. ops$tkyte@ORA11GR2> insert /*+ append */ 2 into t2(id,text,session_id) 3 select * 4 from table(parallel_pipelined 5 (CURSOR(select /*+ parallel(t1) */ * 6 from t1 ) 7 )) 8 / 48250 rows created. ops$tkyte@ORA11GR2> commit; Commit complete. Just to see what happened here, we can query the newly inserted data out and group by SESSION_ID to see how many parallel execution servers were used and how many rows each one processed: ops$tkyte%ORA11GR2> select session_id, count(*) 2 from t2 3 group by session_id; SESSION_ID COUNT(*) ---------- ---------470 7044 703 7464 14 10100 704 11524 241 9421 471 8351 473 8918 9 9317 8 rows selected.

Figure 4-2. Object graph illustrating containment A common example of containment is a sales order, which contains a list of line item objects. Typically the SalesOrder object is a root and a parent, the LineItemList object is a child and a parent, and each LineItem object is a child. It is also possible for a list to be a root, as shown in Figure 4-3.

Apparently, we used eight parallel execution servers for the SELECT component of this parallel operation, and each one processed about 7,000 to 11,000 records each. As you can see, Oracle parallelized our process, but we underwent a fairly radical rewrite of our process. This is a long way from the original implementation. So, while Oracle can process our routine in parallel, we may well not have any routines that are coded to be parallelized. If a rather large rewrite of your procedure is not feasible, you may well be interested in the next implementation: DIY parallelism.

Say we have that same process as in the preceding section: the serial, simple procedure We cannot afford a rather extensive rewrite of the implementation, but we would like to execute it in parallel What can we do Starting with Oracle Database 11g Release 2 and above, we have a new way to implement parallelism via the DBMS_PARALLLEL_EXECUTE built-in package Using it, you can execute a SQL or PL/SQL statement in parallel by taking the data to be processed and breaking it up into multiple, smaller streams You can implement the same approach yourself, manually, and I show how in the section on Old School Do-It-Yourself Parallelism that follows The beauty of the new package, though, is that it eliminates much of the tedious work that you otherwise need to perform.

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