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If you read this plan from the bottom up, starting at ID=6, it shows the steps described in Figure 14-1. The full table scan would be split up into many smaller scans (step 5). Each of those would aggregate their COUNT(STATUS) values (step 4). These subresults would be transmitted to the parallel query coordinator (steps 2 and 3), which would aggregate these results further (step 1) and output the answer. If you were to actually execute this query on a freshly started system (where no other parallel execution had been done), you might observe the following (note that we are using the Linux ps command to find processes that represent parallel query processes we expect to find none running the query with parallel execution enabled, and then looking for those processes again):

WHERE Id=@id AND LastChanged=@lastChanged IF @@ROWCOUNT = 0 BEGIN RAISERROR('Row has been edited by another user', 16, 1) RETURN END SELECT @newLastChanged = LastChanged FROM Projects WHERE Id=@id RETURN Again, this procedure updates the record only in the Projects table; the related records in the Assignments table are updated separately. Notice the @lastChanged parameter required by the procedure. This represents the last known timestamp value for the row. In 18, you ll see how this value is maintained by the business object. When the object attempts to update the row, it provides the last known value for the LastChanged column. If that value hasn t changed in the database, then no other user has updated the row since the object read its data. But if the value has changed in the database, then some other user did change the data in the row since the object read the data. First-write-wins optimistic concurrency specifies that this second update can t be allowed, because it could overwrite changes made by that other user. The UPDATE statement itself uses this parameter in the WHERE clause to ensure that the row is updated only if the value matches. The procedure then checks to see whether the row was actually updated. If no rows were updated, it raises an error, which shows up as a database exception in the data access code of the business object. On the other hand, if the update goes through and the row is changed, then a SELECT statement is executed to return the new value of the LastChanged column as an output parameter so that the object can maintain the new value to allow possible future updates.

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ps -aef | grep '^ora11gr2.*ora_p00._ora11gr2'

big_table%ORA11GR2> select count(status) from big_table; COUNT(STATUS) ------------10000000 big_table%ORA11GR2> host ps -aef | grep '^ora11gr2.*ora_p00._ora11gr2' ora11gr2 20397 1 1 11:17 00:00:00 ora_p000_ora11gr2 ora11gr2 20399 1 1 11:17 00:00:00 ora_p001_ora11gr2 ora11gr2 20401 1 1 11:17 00:00:00 ora_p002_ora11gr2 ora11gr2 20403 1 1 11:17 00:00:00 ora_p003_ora11gr2 ora11gr2 20405 1 1 11:17 00:00:00 ora_p004_ora11gr2 ora11gr2 20407 1 2 11:17 00:00:00 ora_p005_ora11gr2 ora11gr2 20409 1 1 11:17 00:00:00 ora_p006_ora11gr2 ora11gr2 20411 1 1 11:17 00:00:00 ora_p007_ora11gr2 As you can see, there are now eight parallel execution servers that have been started by Oracle. If we are curious enough to want to watch parallel query, we can easily do so using two sessions. In the session we will run the parallel query in, we ll start by determining our SID: big_table@ORA11GR2> select sid from v$mystat where rownum = 1; SID ---------701 In another session, we get this query ready to run (but don t run it yet, just type it in!): ops$tkyte%ORA11GR2> select sid, qcsid, server#, degree 2 from v$px_session 3 where qcsid = 701 Now, going back to the original session that we queried the SID from (the big_table@ORA11GR2 session), we ll start the parallel query. In the session with the query setup, we can run it now and see output similar to this: 4 /

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