Query Execution Plan

Previous installment: Day Three: Just a Mess Without a Clue

I know a funny little man,
As quiet as a mouse,
Who does the mischief that is done
In everybody’s house!
There’s no one ever sees his face,
And yet we all agree
That every plate we break was cracked
By Mr. Nobody.

—Unknown author

P.O.I.S.E.D. is an acronym for a six-step performance tuning method; it stands for Problem, Observation, Interpretation, Solution, Execution, and Documentation. Every problem-solving exercise has these six stages, whether the exercise takes six minutes or six weeks.(read more)

SQLT adoption has been limited even though it is the best tool for investigating SQL performance: It is only available as a download from Oracle Support. It creates tables and PL/SQL procedures in the database. It does not support Statspack, only AWR. (read more)

In his fifteen years with Oracle Support, Carlos Sierra created and perfected a tool called SQLTXPLAIN (SQL Tuning and Explain Plan; SQLT for short) for SQL performance investigations. SQLT collects all the information that could possibly be required to investigate SQL performance including environment information, execution plan history, statistics history (system statistics, table statistics, index statistics), and object definitions.(read more)

If the inputs to the CBO change, the plan can change. For example, changes to bind variables can cause the plan to change when it is hard parsed. Therefore one should never be surprised when plans change.(read more)

In the August 2015 issue of the NoCOUG Journal, we asked Stéphane Faroult why it is so hard to get SQL performance right the first time. His answer implies that SQL itself and the way it is taught are the problems.(read more)

We can’t help attaching meaning to optimizer statistics. After all, they represent our data, don’t they? So we refresh them as frequently as possible and use the largest sample sizes that we can use. Recently, for the first time in my life, I encountered a group of DBAs who understood that statistics do not have any intrinsic meaning; that they are nothing more than a collection of numbers that influence the generation of query plans. In the case of these DBAs, they found that the strategy that worked best for their applications was to treat the optimizer statistics just as they would treat program code. Statistics were checked into the source code repository just as program code was checked into the source code repository.(read more)

Previous installment: POISED: A problem-solving method First installment: DON’T PANIC
“ Those friends thou hast, and their adoption tried, Grapple them unto thy soul with hoops of steel; But do not dull thy palm with entertainment Of each new-hatch’d, unfledg’d comrade.”
—some of the advice of Polonius, counselor to King Claudius, to his son Laertes who is leaving for France in Act 1, Scene 3 of The Tragedy of Hamlet, Prince of Denmark by William Shakespeare...(read more)

P.O.I.S.E.D. is the acronym I invented for the performance tuning method described by Chris Lawson in The Art and Science of Oracle Performance Tuning; it stands for Problem, Observation, Interpretation, Solution, Execution, and Documentation. The Observation and Interpretation phases feed each other. The Interpretation phase concludes with a determination of root cause. If we are investigating poor performance a single SQL statement, the tool of choice for the observation phase is SQLT for all but the simplest cases.(read more)

The mother of all SQL “anti-patterns” is the ad-hoc query. Here, the end-user of the application can specify any combination of parameters—for example, the job_id, the dept_id, or a combination of both—and the data is retrieved using a catchall query. This antipattern has two sisters who are almost as evil. These two sisters arise from the requirement to paginate through the records retrieved by a query.(read more)