are you a monkey or an astronaut?
DESCRIPTION
Oracle continues to add more and more automated features and advisors to assist with diagnosing and fixing problems. Given all this automation and advice Oracle is providing, a question to ask is whether or not all this advice is "dumbing us down" or "smartening us up". Too often more automation can lead to using these features as a crutch and bypassing the acquisition of key skills and knowledge. Ideally, these features should be tools to help us do our jobs better instead of simply letting Oracle do it for us. The key is to know what help the advisors can provide and know how to use them properly and effectively. The risk is becoming trained to only follow the advisors output, but when a situation arises where our skills and knowledge are needed, it's not there. This presentation will review several cases where the Oracle SQL Tuning Advisor (in particular) isn't able to provide any help with identifying and correcting poorly performing SQL. We'll then review the knowledge and critical thinking skills needed by the DBA or Developer to understand how to make the needed improvements to the code to provide enhanced performance.TRANSCRIPT
Are you a monkey or an
astronaut?
The Oracle Advisors from
a different perspective.
Karen Morton [email protected]
Your speaker…
• Karen Morton
– Educator, DBA, developer,
consultant, researcher, author, speaker, …
– Director
– Software, education, consulting
• Come see us…
– method-r.com
– karenmorton.blogspot.com
– An Oracle user group near you
Committed to genuinely satisfying software performance
Agenda
• Introduction
• The “Self-Managing” Database
• Before and After
• What do we really want?
• When the Advisors mislead
• It takes human intelligence
• Conclusion
• Q&A
It is impossible to underrate
human intelligence – beginning
with one’s own.
Henry Brooks Adams
Introduction
Do you remember?
What about these guys?
Monkeys were first
Are you a monkey?
Or, are you an astronaut?
The Self-Managing
Database
The Oracle Advisors
Database performance optimization is a “complex, time consuming task and
requires expensive specialist consulting skills” to perform
Performance diagnosis and tuning is mostly about following a logical, methodical approach. Fortunately computers are pretty good at that!
Graham Wood & Kyle Hailey
What role will we choose and
what is the risk?
The system is slow!
Please fix it now!
DBAx
x
How would you diagnose the problem?
1) Check server to determine if adequate resources are available
2) Check database and notice many sessions are waiting on “latch free” waits
3) Drilling down into latches shows most are on “library cache” and “shared pool” latches
4) From knowledge, experience and reference books, you know these latches are associated
with hard parsing issues
5) Double-check the hypothesis that hard parsing is the problem by looking at
the rate at which “parse time elapsed” and “parse time cpu” are increasing
6) Now determine if one or more sessions are responsible for the majority of the hard
parses or check shared pool for existence of many similar statements with the
same SQL plan
7) By looking at the shared pool, you find there are a small number of plans each of which
has many different SQL texts associated with it (they differ only by a literal)
8) Now the problem is identified and you can move on to fixing it
With ADDM, how would the problem have been diagnosed?
SQL statements were not shared due to usage of literals. This resulted in additional hard parses which were consuming significant database time.
ADDM says…
ADDM recommends…
ACTION: Investigate application logic for possible use of bind variables instead of literals. Alternatively, you may set the parameter CURSOR_SHARING to FORCE.
…because…
RATIONALE: SQL statements with PLAN_HASH_VALUE 3106087033 were found to be using literals. Look in V$SQL for examples of such SQL statements.
Without ADDM
• Multiple investigative steps must be
completed
• Must have knowledge of what latch
free waits indicate
• Must locate the problem SQL
• Requires executing queries against
database that add overhead to the system
With ADDM
• Consult the latest ADDM report
• No mention of latching at all in the
report
• Exact SQL identified with
PLAN_HASH_VALUE
• No additional overhead needed as
report is based on previously collected snapshot data
Given this example, which method of diagnosis would you prefer?
ADDM did a great job!
Who wouldn’t want to spend less time getting to the root
cause of a problem?
But…
Could you have diagnosed the problem without ADDM?
If you could have diagnosed the problem, would using ADDM
have been quicker (and therefore, better)?
What would you have done if you fixed the problem as described
by ADDM, but the user says “it’s still slow”?
Instead of using the human brain, humans have created another brain to do the job. There is nothing around us that is not built to cater to the needs of the human race and to ease off the duties of what the human brain is supposed to do; for instance, thinking. Is laziness going to be the death of the human race?
Kurt Vonnegut
What do we really want?
A magic formula?
A checklist, a “how-to”, a tool or some secret parameter?
alter system set fast = true;
What most people really want is knowledge and experience
…that allows us to confidently approach a problem and
solve it efficiently
We want training that teaches us how and why things work as they do
…and teaches us how to “know” what to do and how to do it
when problems arise
The part of intuition that involves pattern matching and recognition of familiar and typical cases can be trained. If you want people to size up situations quickly and accurately, you need to expand their experience base. One way is to arrange for a person to receive more difficult cases.
Gary Klein
…or simply have them become an Oracle DBA or developer.
When the advisors
mislead
What would you do if the problem isn’t resolved via the advisors?
The advisors aren’t true artificial intelligence
They can only pattern match, aggregate and assimilate answers based on programmed heuristics
They can only suggest solutions within a realm of known
(i.e. built-in) possibilities
They can’t rewrite code
They can miss something or “get it wrong”
It takes human intelligence
SQL Tuning Advisor vs. Human
Example
So, how did the SQL Advisor do?
Let’s compare using AUTOTRACE
Elapsed: 00:00:01.51 -------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | -------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 160 | 2560 | 191 (78)| 00:00:03 | | 1 | SORT UNIQUE | | 160 | 2560 | 191 (78)| 00:00:03 | | 2 | UNION-ALL | | | | | | |* 3 | FILTER | | | | | | | 4 | HASH GROUP BY | | 10 | 160 | 48 (11)| 00:00:01 | |* 5 | HASH JOIN OUTER | | 12890 | 201K| 44 (3)| 00:00:01 | | 6 | INDEX FAST FULL SCAN| CUST2_PK | 1000 | 5000 | 2 (0)| 00:00:01 | | 7 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 | |* 8 | FILTER | | | | | | | 9 | HASH GROUP BY | | 50 | 800 | 48 (15)| 00:00:01 | | 10 | NESTED LOOPS | | 12890 | 201K| 44 (7)| 00:00:01 | | 11 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 | |* 12 | INDEX UNIQUE SCAN | CUST2_PK | 1 | 5 | 0 (0)| 00:00:01 | |* 13 | FILTER | | | | | | | 14 | HASH GROUP BY | | 50 | 800 | 48 (15)| 00:00:01 | | 15 | NESTED LOOPS | | 12890 | 201K| 44 (7)| 00:00:01 | | 16 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 | |* 17 | INDEX UNIQUE SCAN | CUST2_PK | 1 | 5 | 0 (0)| 00:00:01 | |* 18 | FILTER | | | | | | | 19 | HASH GROUP BY | | 50 | 800 | 48 (15)| 00:00:01 | | 20 | NESTED LOOPS | | 12890 | 201K| 44 (7)| 00:00:01 | | 21 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 | |* 22 | INDEX UNIQUE SCAN | CUST2_PK | 1 | 5 | 0 (0)| 00:00:01 | --------------------------------------------------------------------------------------
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
39419 consistent gets
0 physical reads
0 redo size
26289 bytes sent via SQL*Net to client
1107 bytes received via SQL*Net from client
68 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
1000 rows processed
Elapsed: 00:00:01.50 ------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 160 | 2560 | 184 (77)| 00:00:03 | | 1 | UNION-ALL | | | | | | |* 2 | FILTER | | | | | | | 3 | HASH GROUP BY | | 10 | 160 | 46 (7)| 00:00:01 | |* 4 | HASH JOIN OUTER | | 12890 | 201K| 44 (3)| 00:00:01 | | 5 | INDEX FAST FULL SCAN| CUST2_PK | 1000 | 5000 | 2 (0)| 00:00:01 | | 6 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 | |* 7 | FILTER | | | | | | | 8 | HASH GROUP BY | | 50 | 800 | 46 (11)| 00:00:01 | | 9 | NESTED LOOPS | | 12890 | 201K| 44 (7)| 00:00:01 | | 10 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 | |* 11 | INDEX UNIQUE SCAN | CUST2_PK | 1 | 5 | 0 (0)| 00:00:01 | |* 12 | FILTER | | | | | | | 13 | HASH GROUP BY | | 50 | 800 | 46 (11)| 00:00:01 | | 14 | NESTED LOOPS | | 12890 | 201K| 44 (7)| 00:00:01 | | 15 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 | |* 16 | INDEX UNIQUE SCAN | CUST2_PK | 1 | 5 | 0 (0)| 00:00:01 | |* 17 | FILTER | | | | | | | 18 | HASH GROUP BY | | 50 | 800 | 46 (11)| 00:00:01 | | 19 | NESTED LOOPS | | 12890 | 201K| 44 (7)| 00:00:01 | | 20 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 | |* 21 | INDEX UNIQUE SCAN | CUST2_PK | 1 | 5 | 0 (0)| 00:00:01 | -------------------------------------------------------------------------------------
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
39419 consistent gets
0 physical reads
0 redo size
24813 bytes sent via SQL*Net to client
1107 bytes received via SQL*Net from client
68 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1000 rows processed
Differences?
• Elapsed time went from 1.51s to 1.50s
• Memory sorts were reduced from 1 to 0
• Query cost reduced from 191 to 184
• Consistent gets stayed the same
I still need better response time and less resource consumption.
Now what?
Let’s give the human a try
I rewrite the query…
SQL> get cust_disc_rewrite.sql
1 select c.cust_no, sum(o.total_order_price) tot_orders,
2 (case when sum(o.total_order_price) = 0 then 0
3 when sum(o.total_order_price) <= 100000 then .10
4 when sum(o.total_order_price) <= 500000 then .15
5 when sum(o.total_order_price) > 500000 then .20
6 else 0 end) as disc_rate
7 from customer2 c, ord2 o
8 where c.cust_no = o.cust_no(+)
9* group by c.cust_no
…and make a huge difference!
Elapsed: 00:00:00.29
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1000 | 16000 | 46 (7)| 00:00:01 |
| 1 | HASH GROUP BY | | 1000 | 16000 | 46 (7)| 00:00:01 |
|* 2 | HASH JOIN OUTER | | 12890 | 201K| 44 (3)| 00:00:01 |
| 3 | INDEX FAST FULL SCAN| CUST2_PK | 1000 | 5000 | 2 (0)| 00:00:01 |
| 4 | TABLE ACCESS FULL | ORD2 | 12890 | 138K| 41 (0)| 00:00:01 |
-----------------------------------------------------------------------------------
The query uses 191 vs. 39,419 LIOs and runs in .29 seconds!
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
191 consistent gets
0 physical reads
0 redo size
26279 bytes sent via SQL*Net to client
1107 bytes received via SQL*Net from client
68 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1000 rows processed
The moral of the story
Something else to keep in mind…
Tools on the market today do not use a business-task focused approach to solve a performance problem
The Advisors and Enterprise Manager mainly monitor system-wide metrics
What do you do if your user’s performance problem
isn’t representative of the system-wide issues?
That’s exactly what Method R
is all about
1. Determine the business task that is performing sub-optimally that you want to fix next.
2. Collect detailed statistics about how this task spends its time while it is performing poorly.
3. Execute repairs to reduce the task’s response time and/or resources consumed, or prove that it is already as efficient as it can affordably be made.
4. If problems still exist, then go to step 1.
There is a belief that there is a downside to a business task-focused approach.
Many believe it’s too difficult to get access to the information or people needed.
It seems easier to focus on what the “system” has to tell us in order to find and fix problems.
Remember the monkeys?
Enter the astronaut.
The costs of following the wrong advise can be substantial.
If you are working on the wrong thing, the real problem remains and
the business continues to suffer.
Conclusion
Don’t let the advisors – or any ‘automatic’ feature
become the dictator of your career.
Tools are there to facilitate our knowledge, not replace it.
Use the tools wisely but not as a substitute for your own
lack of knowledge.
The bottom line
The key is about a thirst for knowledge… not just the knowledge itself.
It’s about how we strive to learn, understand, and know how and why
things work that makes the difference.
It is the fulfillment of the desire to know that leads us to the knowledge we need
to evolve from monkey to astronaut and to be able to solve the problems
that seem unsolvable.
It takes effort but the result is that you become a respected,
invaluable resource with the skills and confidence to impact your
business in a positive way.
Thank You!