Archive for the ‘12c’ Category.

12c Adaptive Optimization – Part 3

This is the third and final post on follow up questions from the Redgate webinar I did on 12c Adaptive Optimization (the link goes to a recording of the webcast by the way).

Also, here are links to the 2 earlier posts:

    12c Adaptive Optimization – Part 1.
    12c Adaptive Optimization – Part 2 (Hints).

So here are the last set of questions along with my responses:

Q: Is this feature on by default or you have to set a parameter to make sure of it?
A: It’s on by default but can be turned off by the methods listed in the presentation.

Q: Is there any drawback of adaptive execution plan?
A: New features (especially auto-magic ones) always make people nervous, but I don’t see too many potential pitfalls with this one. The fact that it is enabled by default out of the box is also a good indicator that the developers themselves have a lot of confidence in it. There is certainly more work going on to collect statistics and buffer rows, but it seems quite minimal and only happens on the first execution. So my basic answer is no, I don’t foresee any major drawbacks.

Q: For adaptive plans, usually queries are more complex, with multiple combinations of hash joins and nested loops. But adaptive plans only switches to one “sub plan”, correct? How does it account for all the various combinations?
A: A sub-plan is limited to a single join. There can obviously be many joins in a single plan and thus many sub-plans. But each sub-plan will result in either a HJ or a NLJ. At the end there will be only one final plan. See my previous post (Part 1) for an example of a more complex plan with multiple sub-plans.

Q: parallel distribution methods: why not use broadcast all the time? :)
A: :)

Q: Would adaptive optim switch to a better index if it finds itself sitting on a wrong index?
A: I presume the question is with regard to Adaptive Plans kicking in on the first execution, if so, the answer is No. At this point only join methods and px distribution methods can be changed. I expect this will be expanded over time though.

Q: Does same plan_hash_value’s means same final plans?
A: Yes – plan hash value is computed based on the final plan with no regard to the fact that the plan was adaptive.

Q: How correlated plan_hash_values with final plans? How we can find same final plans?
A: Plan hash value is computed based on final plan, so the correlation is very high. :)

Q: Dynamic sampling would not put an excessive pressure on the CPU?
A: I guess it could, but it’s been around for some time and I haven’t been involved in any situations where the time spent on dynamic sampling was an issue. Setting it to 11 may give us some chances to see such a thing though. More often the issues arise when dynamic sampling does not come up with a good picture of the data due to the limited size of the sample.

Q: Is dynamic sampling = 11 actually a good blanket setting, or do you not trust the optimizer that much? What do you use and why?
A: The optimizer_dynamic_sampling parameter still defaults to 2 in 12c. That alone makes me cautious about setting it to the new totally auto-magic value of 11. If the developers have enough confidence in a new feature to make it the default, then I will be more trusting. I prefer to stick with default values unless I have to make a change to address a specific issue. I have worked on a few systems that change the default setting, but 11 has not been one of those values (yet). I need to do more testing with it.

Q: Gotta love Spinal Tap… crank it up to 11 !
A: Rock and Roll!

Q: Is there any effect on cpu utilisation becoz of adaptive optimisation??
A: There is definitely some extra overhead in collecting statistics and buffering rows but it should be minimal and it should only affect the initial execution.

Q: Can HJ be change to NL in 1-st execution? What is threshold for such change?
A: Yes – Adaptive Plans kick in the first execution. The threshold depends on the specific case. See the example earlier in part 2 of this series for an example of calculating the inflection point (from a 10053 trace).

Q: This means that if it is abandoned once it will also be abondoned if ran again?
A: Yes, assuming no other changes occur. But there are many things that can change such as Adaptive Cursor Sharing, Cardinality Feedback, etc… and of course the data itself and/or the statistics about the data can change over time as well. Just to be clear, the choice between the the two join methods is only made during the first execution after a hard parse, so once a statement is loaded into the cache, the plan will be static until something changes that causes a new child cursor to be created.

Q: At what data volumes does Adaptive Optimization become likely to be helpful.
A: Any volume that causes a NLJ to result in significantly different elapsed time than HJ.

Q: Does AWR show these updated adaptive plans with minus ?
A: That’s a good question. Yes, you can use the dbms_xplan.display_awr with the ‘adaptive’ format option (see the example below).

 
SYS@db12c1> select * from table(dbms_xplan.display_awr('&sql_id',nvl('&plan_hash_value',null),null,'adaptive'));
Enter value for sql_id: 6qg99cfg26kwb
Enter value for plan_hash_value: 
 
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID 6qg99cfg26kwb
--------------------
SELECT COUNT(UNQ) UNQ, COUNT(PFX) PFX FROM (SELECT /*+ first_rows(1)
leading(cc) */ CD.TYPE# UNQ, NULL PFX FROM SYS.CCOL$ CC, SYS.CDEF$ CD
WHERE CC.OBJ# = :B2 AND CC.INTCOL# = :B1 AND CD.CON# = CC.CON# AND
CD.OBJ# = CC.OBJ# AND CD.ENABLED IS NOT NULL AND CD.INTCOLS = 1 AND
CD.TYPE# IN (2,3) AND BITAND(CD.DEFER, 2+4) = 4 AND ROWNUM < 2 UNION
ALL SELECT /*+ first_rows(1) leading(i) */ CASE WHEN I.INTCOLS = 1 AND
BITAND(I.PROPERTY,1) = 1 THEN 3 ELSE NULL END UNQ, CASE WHEN IC.POS# =
1 THEN 1 ELSE NULL END PFX FROM SYS.IND$ I, SYS.ICOL$ IC WHERE I.BO# =
:B2 AND I.BO# = IC.BO# AND IC.INTCOL# = :B1 AND I.OBJ# = IC.OBJ# AND
BITAND(I.FLAGS,1025) = 0 AND ROWNUM < 2 )
 
Plan hash value: 1065215175
 
----------------------------------------------------------------------------------------------------
| Id  | Operation                                | Name    | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                         |         |       |       |     6 (100)|          |
|   1 |  SORT AGGREGATE                          |         |     1 |    16 |            |          |
|   2 |   VIEW                                   |         |     2 |    32 |     6   (0)| 00:00:01 |
|   3 |    UNION-ALL                             |         |       |       |            |          |
|   4 |     COUNT STOPKEY                        |         |       |       |            |          |
|-  5 |      HASH JOIN                           |         |     1 |    35 |     3   (0)| 00:00:01 |
|   6 |       NESTED LOOPS                       |         |     1 |    35 |     3   (0)| 00:00:01 |
|-  7 |        STATISTICS COLLECTOR              |         |       |       |            |          |
|   8 |         TABLE ACCESS CLUSTER             | CCOL$   |     1 |    13 |     2   (0)| 00:00:01 |
|   9 |          INDEX UNIQUE SCAN               | I_COBJ# |     1 |       |     1   (0)| 00:00:01 |
|  10 |        TABLE ACCESS CLUSTER              | CDEF$   |     1 |    22 |     1   (0)| 00:00:01 |
|- 11 |       TABLE ACCESS BY INDEX ROWID BATCHED| CDEF$   |     1 |    22 |     1   (0)| 00:00:01 |
|- 12 |        INDEX RANGE SCAN                  | I_CDEF2 |     1 |       |     1   (0)| 00:00:01 |
|  13 |     COUNT STOPKEY                        |         |       |       |            |          |
|- 14 |      HASH JOIN                           |         |     1 |    38 |     3   (0)| 00:00:01 |
|  15 |       NESTED LOOPS                       |         |     1 |    38 |     3   (0)| 00:00:01 |
|- 16 |        STATISTICS COLLECTOR              |         |       |       |            |          |
|  17 |         TABLE ACCESS CLUSTER             | IND$    |     1 |    21 |     2   (0)| 00:00:01 |
|  18 |          INDEX UNIQUE SCAN               | I_OBJ#  |     1 |       |     1   (0)| 00:00:01 |
|  19 |        TABLE ACCESS CLUSTER              | ICOL$   |     1 |    17 |     1   (0)| 00:00:01 |
|- 20 |       TABLE ACCESS CLUSTER               | ICOL$   |     1 |    17 |     1   (0)| 00:00:01 |
|- 21 |        INDEX UNIQUE SCAN                 | I_OBJ#  |     1 |       |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------
 
Note
-----
   - this is an adaptive plan (rows marked '-' are inactive)
 
 
46 rows selected.

Q: Is there any way to encourage the optimizer to collect the information but not act on it?
A: Yes, set optimizer_adaptive_reporting_only = true.

Q: Does adaptive distribution for parallel processing work as expected on a Virtual server where resources can be spread over several other servers?
A: No idea (in fact I’m not even sure I understand the question). Give it a test and let us know what you find out. :)

Q: Does Adaptive Optimization help oracle optimize somewhat complex nested views? I know nested views are not recommended but we sometimes have to live with what we inherited.
A: I don’t think this particular feature is going to help nested views specifically. But who knows. The optimizer seems to get lost occasionally with deeply nested views. By the way, there is an interesting new procedure in 12c called dbms_utility.expand_sql_text which spits out the fully expanded version of a SQL statement that accesses data through views. Tom Kyte has blogged about it here: 12c – SQL Text Expansion

Q: We regularly have hash join problems tracable to temp space limits. Shifting to nested loops has proven necessary in 10 and 11. Early detection and shifting to nested loops would be important for us.
A: I’m not sure this feature is really going to help you much in that regard unless the optimizer is erroneously picking the HJ based on incorrect estimates. If you’re just forcing the NLJ to avoid poor i/o performance on the temp stuff though it probably won’t help. In that case you need to figure out how to sort less or use more memory (increase pga, or use manual workarea size, or use more slaves in px, etc…).

Q: So if sort/merge join is used then this feature would not go to nested loop/hash join if sort/merge join is a bad plan ?
A: No it applies only to HJ and NLJ as of 12.1.0.1.

Q: What happens with the rows that were read up to inflection point? Does Oracle start reading from the scratch again?
A: The rows are buffered so they don’t need to be re-read.

Q: Will the SQL scripts that were demonstrated for reviewing the SQL plan information be made available?
A: Most are on this blog already (use the search box to locate them) but let me know if you can’t find any of the ones I used.

Q: It’s is a contraction for it is or it has. Its is a possessive pronoun meaning, more or less, of it or belonging to it.
A: Duly noted (and fixed in the presentation). :)

Q: Can we *force* plan change in mid-execution?
A: No. You can enable or disable the feature, but the optimizer decides whether to switch or not.

Q: How long statistics collector runs if it does not switch?
A: It should only run until the inflection point (the point at which it makes the decision), but I have not actually tested this.

Q: Is there a way adaptive can be disabled for PDB and enabled for others?
A: Yes, the optimizer_adaptive_features parameter can be set separately for each PDB (see the example below).

> rlwrap sqlplus / as sysdba
 
SQL*Plus: Release 12.1.0.1.0 Production on Mon Dec 9 19:53:03 2013
 
Copyright (c) 1982, 2013, Oracle.  All rights reserved.
 
 
Connected to:
Oracle Database 12c Enterprise Edition Release 12.1.0.1.0 - 64bit Production
With the Partitioning, Real Application Clusters, Automatic Storage Management, OLAP,
Advanced Analytics and Real Application Testing options
 
 
INSTANCE_NAME    STARTUP_TIME      CURRENT_TIME         DAYS    SECONDS
---------------- ----------------- ----------------- ------- ----------
CONTAIN1         02-DEC-2013 03:22 09-DEC-2013 19:53    7.69     664225
 
SYS@CONTAIN1> @whoami_pdb
 
    CON_ID CON_NAME   USERNAME             USER#        SID    SERIAL# PREV_HASH_VALUE SCHEMANAME                     OS_PID
---------- ---------- --------------- ---------- ---------- ---------- --------------- ------------------------------ -------
         1 CDB$ROOT   SYS                      0         24        295      3265981639 SYS                            4481
 
SYS@CONTAIN1> @connect_pdb
Enter value for pdb_name: plug1
 
Session altered.
 
SYS@CONTAIN1:PLUG1> @parms
Enter value for parameter: optimizer_adaptive_features
Enter value for isset: 
Enter value for show_hidden: 
 
NAME                                               VALUE                                                                  ISDEFAUL ISMODIFIED ISSET
-------------------------------------------------- ---------------------------------------------------------------------- -------- ---------- ----------
optimizer_adaptive_features                        TRUE                                                                   TRUE     TRUE       TRUE
 
SYS@CONTAIN1:PLUG1> alter system set optimizer_adaptive_features=false;
 
System altered.
 
SYS@CONTAIN1:PLUG1> @parms
Enter value for parameter: optimizer_adaptive_features
Enter value for isset: 
Enter value for show_hidden: 
 
NAME                                               VALUE                                                                  ISDEFAUL ISMODIFIED ISSET
-------------------------------------------------- ---------------------------------------------------------------------- -------- ---------- ----------
optimizer_adaptive_features                        FALSE                                                                  TRUE     TRUE       TRUE
 
SYS@CONTAIN1:PLUG1> @connect_pdb
Enter value for pdb_name: plug2
 
Session altered.
 
SYS@CONTAIN1:PLUG2> @whoami_pdb
 
    CON_ID CON_NAME   USERNAME             USER#        SID    SERIAL# PREV_HASH_VALUE SCHEMANAME                     OS_PID
---------- ---------- --------------- ---------- ---------- ---------- --------------- ------------------------------ -------
         4 PLUG2      SYS                      0         24        295      2710464132 SYS                            4481
 
SYS@CONTAIN1:PLUG2> @parms
Enter value for parameter: optimizer_adaptive_features
Enter value for isset: 
Enter value for show_hidden: 
 
NAME                                               VALUE                                                                  ISDEFAUL ISMODIFIED ISSET
-------------------------------------------------- ---------------------------------------------------------------------- -------- ---------- ----------
optimizer_adaptive_features                        TRUE                                                                   TRUE     TRUE       TRUE
 
SYS@CONTAIN1:PLUG2> connect / as sysdba
Connected.
 
INSTANCE_NAME    STARTUP_TIME      CURRENT_TIME         DAYS    SECONDS
---------------- ----------------- ----------------- ------- ----------
CONTAIN1         02-DEC-2013 03:22 09-DEC-2013 19:54    7.69     664324
 
SYS@CONTAIN1> @parms
Enter value for parameter: optimizer_adaptive_features
Enter value for isset: 
Enter value for show_hidden: 
 
NAME                                               VALUE                                                                  ISDEFAUL ISMODIFIED ISSET
-------------------------------------------------- ---------------------------------------------------------------------- -------- ---------- ----------
optimizer_adaptive_features                        TRUE                                                                   TRUE     TRUE       TRUE

So you can set the optimizer_adaptive_features parameter separately for each PDB. Note: here are links to the couple of scripts I used in this post:

    connect_pdb.sql
    whoami_pdb.sql

There was another good question that I don’t have time to look into at the moment.

Q: In the Pro*C sequence PREPARE, OPEN, FETCH, at what point(s) might Oracle switch plans? If during FETCH, how does Oracle return the next row/array?

Maybe I’ll get around to that later but if anyone wants to give it a shot and post the results in the comments section that would be great. :)

12c Adaptive Optimization – Part 2 – Hints

This is the second post on follow up questions from the Redgate webinar I did on 12c Adaptive Optimization. The first post is here: 12c Adaptive Optimization – Part 1. Since there were several comments and questions about hints and how they interact with Adaptive Plans, I decided to limit this 2nd post to that topic.

Q: Regarding turning off the adaptive optimization (particularly adaptive joins), will there also be a hint to disable it for a particular SQL?
Q: can we pick and choose SQL’s not to run this collector for

A: There are no specific hints to enable or disable Adaptive Plans as of 12.1.0.1. However, the OPT_PARAM hint does work with both the OPTIMIZER_ADAPTIVE_FEATURES parameter and the “_optimizer_adaptive_plans” parameter.

Here’s an example:

 
SYS@db12c1> -- statement that wants to generate an adaptive plan
SYS@db12c1> select product_name
  2  from oe.order_items o, oe.product_information p
  3  where o.unit_price=15 and o.quantity > 1
  4  and o.product_id = p.product_id
  5  /
 
PRODUCT_NAME
--------------------------------------------------
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
 
13 rows selected.
 
SYS@db12c1> @prev_sql
 
SQL_ID         CHILD  PLAN_HASH  EXECS  AVG_ETIME SQL_TEXT
------------- ------ ---------- ------ ---------- ----------------------------------------------------------------------
3ycnqgx5nc8nn      0 1553478007      1        .00 select product_name from oe.order_items o, oe.product_information p wh
 
SYS@db12c1> @dplan_adaptive
Enter value for sql_id: 3ycnqgx5nc8nn
Enter value for child_no: 
 
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  3ycnqgx5nc8nn, child number 0
-------------------------------------
select product_name from oe.order_items o, oe.product_information p
where o.unit_price=15 and o.quantity > 1 and o.product_id = p.product_id
 
Plan hash value: 1553478007
 
----------------------------------------------------------------------------------------------------------
|   Id  | Operation                     | Name                   | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------------
|     0 | SELECT STATEMENT              |                        |       |       |     7 (100)|          |
|  *  1 |  HASH JOIN                    |                        |     4 |   128 |     7   (0)| 00:00:01 |
|-    2 |   NESTED LOOPS                |                        |       |       |            |          |
|-    3 |    NESTED LOOPS               |                        |     4 |   128 |     7   (0)| 00:00:01 |
|-    4 |     STATISTICS COLLECTOR      |                        |       |       |            |          |
|  *  5 |      TABLE ACCESS STORAGE FULL| ORDER_ITEMS            |     4 |    48 |     3   (0)| 00:00:01 |
|- *  6 |     INDEX UNIQUE SCAN         | PRODUCT_INFORMATION_PK |     1 |       |     0   (0)|          |
|-    7 |    TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION    |     1 |    20 |     1   (0)| 00:00:01 |
|     8 |   TABLE ACCESS STORAGE FULL   | PRODUCT_INFORMATION    |     1 |    20 |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   1 - access("O"."PRODUCT_ID"="P"."PRODUCT_ID")
   5 - storage(("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1))
       filter(("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1))
   6 - access("O"."PRODUCT_ID"="P"."PRODUCT_ID")
 
Note
-----
   - this is an adaptive plan (rows marked '-' are inactive)
 
 
Reoptimized plan:
-----------------
This cursor is marked for automatic reoptimization.  The plan that is
expected to be chosen on the next execution is displayed below.
 
Plan hash value: 1553478007
 
--------------------------------------------------------------------------------------------------
| Id  | Operation                  | Name                | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |                     |    13 |   416 |     8   (0)| 00:00:01 |
|*  1 |  HASH JOIN                 |                     |    13 |   416 |     8   (0)| 00:00:01 |
|*  2 |   TABLE ACCESS STORAGE FULL| ORDER_ITEMS         |    13 |   156 |     3   (0)| 00:00:01 |
|   3 |   TABLE ACCESS STORAGE FULL| PRODUCT_INFORMATION |   288 |  5760 |     5   (0)| 00:00:01 |
--------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   1 - access("O"."PRODUCT_ID"="P"."PRODUCT_ID")
   2 - storage("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1)
       filter("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1)
 
Note
-----
   - this is an adaptive plan
 
60 rows selected.
 
SYS@db12c1> -- so the previous statement used an adaptive plan picking a HJ over the NLJ
SYS@db12c1>
SYS@db12c1> -- now let's turn off adaptive plans via the OPT_PARAM hint
SYS@db12c1> -- (set _optimizer_adaptive_plans or optimizer_adaptive_features to false)
SYS@db12c1>
SYS@db12c1> select /*+ OPT_PARAM('_optimizer_adaptive_plans','false') */ product_name
  2  from oe.order_items o, oe.product_information p
  3  where o.unit_price=15 and o.quantity > 1
  4  and o.product_id = p.product_id
  5  /
 
PRODUCT_NAME
--------------------------------------------------
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
 
13 rows selected.
 
SYS@db12c1> @x
 
PLAN_TABLE_OUTPUT
-----------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  04g4xyu3788qm, child number 0
-------------------------------------
select /*+ OPT_PARAM('_optimizer_adaptive_plans','false') */
product_name from oe.order_items o, oe.product_information p where
o.unit_price=15 and o.quantity > 1 and o.product_id = p.product_id
 
Plan hash value: 1255158658
 
-------------------------------------------------------------------------------------------------------
| Id  | Operation                    | Name                   | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |                        |       |       |     7 (100)|          |
|   1 |  NESTED LOOPS                |                        |       |       |            |          |
|   2 |   NESTED LOOPS               |                        |     4 |   128 |     7   (0)| 00:00:01 |
|*  3 |    TABLE ACCESS STORAGE FULL | ORDER_ITEMS            |     4 |    48 |     3   (0)| 00:00:01 |
|*  4 |    INDEX UNIQUE SCAN         | PRODUCT_INFORMATION_PK |     1 |       |     0   (0)|          |
|   5 |   TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION    |     1 |    20 |     1   (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   3 - storage(("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1))
       filter(("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1))
   4 - access("O"."PRODUCT_ID"="P"."PRODUCT_ID")
 
 
26 rows selected.
 
SYS@db12c1> -- So the plan has reverted to the NL Join and is not marked as adaptive

So, even though there is no specific hint at this point, the OPT_PARAM hint can be used to control this behavior on a statement by statement basis.

Q: how does AP (Adaptive Plans) treat query HINTS?
Q: Does adaptive join selection potentially override query hints?

A: As to whether AP can override hints, it does not appear that it can. If you specify a join method with a valid hint, a 10053 (Wolfgang) trace will show that AP’s are not used due to the hint. For example, if you use a hint to specify a nested loop join, the optimizer will not allow AP to override that directive and the 10053 trace will show this behavior.

Here’s an example:

SSYS@db12c1> select product_name
  2  from oe.order_items o, oe.product_information p
  3  where o.unit_price=15 and o.quantity > 1
  4  and o.product_id = p.product_id
  5  /
 
PRODUCT_NAME
--------------------------------------------------
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
 
13 rows selected.
 
SYS@db12c1> @x
 
PLAN_TABLE_OUTPUT
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  3ycnqgx5nc8nn, child number 0
-------------------------------------
select product_name from oe.order_items o, oe.product_information p
where o.unit_price=15 and o.quantity > 1 and o.product_id = p.product_id
 
Plan hash value: 1553478007
 
--------------------------------------------------------------------------------------------------
| Id  | Operation                  | Name                | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |                     |       |       |     8 (100)|          |
|*  1 |  HASH JOIN                 |                     |    13 |   416 |     8   (0)| 00:00:01 |
|*  2 |   TABLE ACCESS STORAGE FULL| ORDER_ITEMS         |    13 |   156 |     3   (0)| 00:00:01 |
|   3 |   TABLE ACCESS STORAGE FULL| PRODUCT_INFORMATION |   288 |  5760 |     5   (0)| 00:00:01 |
--------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   1 - access("O"."PRODUCT_ID"="P"."PRODUCT_ID")
   2 - storage(("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1))
       filter(("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1))
 
Note
-----
   - this is an adaptive plan
 
 
27 rows selected.
 
SYS@db12c1> -- from 10053
SYS@db12c1> !grep -i inflection adapt*trc
Searching for inflection point (join #1) between 0.00 and 12.76
AP: Computing costs for inflection point at min value 0.00
DP: Using binary search for inflection point search
DP: Costing Nested Loops Join for inflection point at card 0.00
DP: Costing Hash Join for inflection point at card 0.00
AP: Computing costs for inflection point at max value 12.76
DP: Costing Nested Loops Join for inflection point at card 12.76
DP: Costing Hash Join for inflection point at card 12.76
AP: Searching for inflection point at value 1.00
DP: Costing Nested Loops Join for inflection point at card 6.38
DP: Costing Hash Join for inflection point at card 6.38
AP: Searching for inflection point at value 6.38
DP: Costing Nested Loops Join for inflection point at card 3.19
DP: Costing Hash Join for inflection point at card 3.19
AP: Searching for inflection point at value 3.19
DP: Costing Nested Loops Join for inflection point at card 4.78
DP: Costing Hash Join for inflection point at card 4.78
AP: Searching for inflection point at value 4.78
DP: Costing Nested Loops Join for inflection point at card 5.58
DP: Costing Hash Join for inflection point at card 5.58
DP: Costing Nested Loops Join for inflection point at card 5.58
DP: Found point of inflection for NLJ vs. HJ: card = 5.58
 
SYS@db12c1> -- now with valid join hint
SYS@db12c1> select /*+ leading(o) use_nl(p) */ product_name
  2  from oe.order_items o, oe.product_information p
  3  where o.unit_price=15 and o.quantity > 1
  4  and o.product_id = p.product_id
  5  /
 
PRODUCT_NAME
--------------------------------------------------
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
Screws <B.28.S>
 
13 rows selected.
 
SYS@db12c1> @x
 
PLAN_TABLE_OUTPUT
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  bytr421c0c2n7, child number 0
-------------------------------------
select /*+ leading(o) use_nl(p) */ product_name from oe.order_items o,
oe.product_information p where o.unit_price=15 and o.quantity > 1 and
o.product_id = p.product_id
 
Plan hash value: 1255158658
 
-------------------------------------------------------------------------------------------------------
| Id  | Operation                    | Name                   | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |                        |       |       |    16 (100)|          |
|   1 |  NESTED LOOPS                |                        |       |       |            |          |
|   2 |   NESTED LOOPS               |                        |    13 |   416 |    16   (0)| 00:00:01 |
|*  3 |    TABLE ACCESS STORAGE FULL | ORDER_ITEMS            |    13 |   156 |     3   (0)| 00:00:01 |
|*  4 |    INDEX UNIQUE SCAN         | PRODUCT_INFORMATION_PK |     1 |       |     0   (0)|          |
|   5 |   TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION    |     1 |    20 |     1   (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   3 - storage(("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1))
       filter(("O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1))
   4 - access("O"."PRODUCT_ID"="P"."PRODUCT_ID")
 
 
26 rows selected.
 
SYS@db12c1> !grep -i inflection /u01/app/oracle/diag/rdbms/db12c/db12c1/trace/db12c1_ora_32529.trc
 
SYS@db12c1> !grep AP: non-adapt*.trc
AP: Adaptive joins bypassed for table P @ SEL$1 due to join method is hinted
AP: Adaptive joins bypassed for table P @ SEL$1 due to join method is hinted

So it appears that AP will not override valid hints (as evidenced by the lines in the 10053 trace file showing that “Adaptive join bypassed … due to join method is hinted”). Keep in mind though that this is only one test case, so it’s possible that in some circumstances AP could override hints, but now you know what to look for to validate. :)

Here are a few more hint related questions:

Q: Does adaptive optimization reduce the need for using hints
A: Maybe. If you are hinting to avoid short comings in the optimizer where it chooses the wrong join method, you might not need to do that any more. Likewise, if you are hinting to force a particular distribution method for PX statements, you may not need to do that any more. It’s certainly a step in the right direction.

Q: Can we force dynamic sampling for a statement, regardless of what Oracle thinks it should do?
A: Yes, the DYNAMIC_SAMPLING hint has been available since 9.2.

Q: If we had SQL Plan Baseline set for a paticular SQL in version 11g and we were to upgrade to 12c version….would SQL Baseline be used or this adaptive plans are used ?
A: The hints in the Baseline would be used and should reproduce the 11g plan. See the example above where valid hints disable AP.

Q: Is there a way to grab a previous plan (good) using the profile technique and assign it to the current statement that changed the exec plan which is bad? So far I have been doing this manually using your scripts.
A: Yes – Profiles are just a collection of hints that get applied to a statement. So they can be used to control plans even if AP is enabled.

Q: How does this play with SQL Plan Management.. ?
A: Final plans can be captured and baselines created for them. This feature behaves as expected. Subsequent parses will try to reproduce final plan (using hints in the baseline if necessary).

So that’s it for the hint related questions. The final post in this series will cover the remainder of the questions.

12c Adaptive Optimization – Part 1

Last week I did a webinar on 12c Adaptive Optimization. The talk was recorded. The slides are here: 12c Adaptive Optimization V2 PDF. The recording can be found here: 12c Adaptive Optimization Recording. There were a number of follow up questions and emails so I thought I’d summarize here. Since there were so many questions, (I guess I must not have done that good of a job of explaining how it works) I will break them up into 2 or 3 posts. So for this first one I will just cut and paste from a couple of email follow ups.

Here’s the first question(s):

Hi Kerry,

I followed your webinar today, and I have two questions about it.
First, what will happened if the plan changed during the fetch operation ?
Is it possible ? If then, how does it know which rows has been already fetched ?

Second question is more a practical question. Indeed, in the examples you showed, it uses basic queries, but in the case you have an execution plan with more than hundreds of operation, and if during the execution an adaptive plan is decided with changes in join method, the plan can change a lot.
If we want to identify the step that will modify the plan, do we have to identify it as the step just before the statistic collector op, or will it be more complex to identify ?

Thanks in advance for you answers

And here’s my Answer(s):

Hi

I’ll have to find a little time to test the prepare, open, fetch stuff to verify where the initial rows are actually retrieved, but if I had to guess it would be on the first fetch call, regardless of how many records the fetch requests. Could also be on the open though. It’s interesting to see how the optimizer comes up with the inflection point by the way (although I don’t know enough about the internal algorithm to know exactly what they are doing – but it’s clear they are guessing by splitting the difference ). But here’s a little output from a wolfgang (10053) trace file.

SYS@db12c1> !grep inflection adaptive.trc
Searching for inflection point (join #1) between 0.00 and 12.76
AP: Computing costs for inflection point at min value 0.00
DP: Using binary search for inflection point search
DP: Costing Nested Loops Join for inflection point at card 0.00
DP: Costing Hash Join for inflection point at card 0.00
AP: Computing costs for inflection point at max value 12.76
DP: Costing Nested Loops Join for inflection point at card 12.76
DP: Costing Hash Join for inflection point at card 12.76
AP: Searching for inflection point at value 1.00
DP: Costing Nested Loops Join for inflection point at card 6.38
DP: Costing Hash Join for inflection point at card 6.38
AP: Searching for inflection point at value 6.38
DP: Costing Nested Loops Join for inflection point at card 3.19
DP: Costing Hash Join for inflection point at card 3.19
AP: Searching for inflection point at value 3.19
DP: Costing Nested Loops Join for inflection point at card 4.78
DP: Costing Hash Join for inflection point at card 4.78
AP: Searching for inflection point at value 4.78
DP: Costing Nested Loops Join for inflection point at card 5.58
DP: Costing Hash Join for inflection point at card 5.58
DP: Costing Nested Loops Join for inflection point at card 5.58
DP: Found point of inflection for NLJ vs. HJ: card = 5.58


On the identification of what’s going on in more complicated plans, the general pattern appears to be like this:

Hash Join
NL Join
Statistics Collector

Regardless of whether the final plan would be to use HJ or NL. In some cases the NL is abandoned, in other cases the HJ is abandoned. (by the way, the optimization only appears to kick in on steps where the default plan would use a NL)

* Note that I was wrong in my assertion that the optimization only kicks in for NLJ steps as pointed out by Stephan in the comments section. It can kick in on HJ steps as well, although they don’t appear nearly as often. :)

So anyway, a NL would look like this:

- Hash Join
    NL Join
-     Statistics COllector

And a HJ like this:

  Hash Join 
-   NL Join
-     Statistics Collector

Here’s an example of a more complex plan – in this case a couple of hash joins are discarded in favor of NL.


SYS@db12c1> @dplan_adaptive
Enter value for sql_id: 95stx63r9dc34
Enter value for child_no: 1
 
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  95stx63r9dc34, child number 1
-------------------------------------
select /* test dp2c6pq28u5jr */ count(*), sum(blocks) FROM dba_segments
where    OWNER = 'XDB' and TABLESPACE_NAME = 'SYSAUX'
 
Plan hash value: 1481365994
 
----------------------------------------------------------------------------------------------------------------
|   Id  | Operation                               | Name               | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------------------
|     0 | SELECT STATEMENT                        |                    |       |       |  1441 (100)|          |
|     1 |  SORT AGGREGATE                         |                    |     1 |   104 |            |          |
|     2 |   VIEW                                  | SYS_DBA_SEGS       |     9 |   936 |  1441   (1)| 00:00:01 |
|     3 |    UNION-ALL                            |                    |       |       |            |          |
|     4 |     NESTED LOOPS                        |                    |     6 |   852 |  1356   (1)| 00:00:01 |
|     5 |      NESTED LOOPS                       |                    |     6 |   810 |  1356   (1)| 00:00:01 |
|  *  6 |       HASH JOIN                         |                    |    67 |  6767 |  1350   (1)| 00:00:01 |
|  *  7 |        FILTER                           |                    |       |       |            |          |
|  *  8 |         HASH JOIN RIGHT OUTER           |                    |   278 | 11954 |    89   (0)| 00:00:01 |
|     9 |          TABLE ACCESS STORAGE FULL      | USER$              |    71 |  1278 |     3   (0)| 00:00:01 |
|    10 |          NESTED LOOPS                   |                    | 19743 |   482K|    86   (0)| 00:00:01 |
|    11 |           TABLE ACCESS BY INDEX ROWID   | TS$                |     1 |    11 |     1   (0)| 00:00:01 |
|  * 12 |            INDEX UNIQUE SCAN            | I_TS1              |     1 |       |     0   (0)|          |
|    13 |           TABLE ACCESS STORAGE FULL     | OBJ$               | 19743 |   269K|    85   (0)| 00:00:01 |
|    14 |        VIEW                             | SYS_OBJECTS        |  4731 |   267K|  1261   (1)| 00:00:01 |
|    15 |         UNION-ALL                       |                    |       |       |            |          |
|  * 16 |          TABLE ACCESS STORAGE FULL      | TAB$               |  1533 | 33726 |   312   (0)| 00:00:01 |
|    17 |          TABLE ACCESS STORAGE FULL      | TABPART$           |   262 |  4192 |     5   (0)| 00:00:01 |
|    18 |          TABLE ACCESS STORAGE FULL      | CLU$               |    10 |   140 |   312   (0)| 00:00:01 |
|  * 19 |          TABLE ACCESS STORAGE FULL      | IND$               |  2164 | 41116 |   312   (0)| 00:00:01 |
|    20 |          TABLE ACCESS STORAGE FULL      | INDPART$           |   194 |  3104 |     4   (0)| 00:00:01 |
|  * 21 |          TABLE ACCESS STORAGE FULL      | LOB$               |   512 | 10752 |   309   (0)| 00:00:01 |
|    22 |          TABLE ACCESS STORAGE FULL      | TABSUBPART$        |    32 |   480 |     2   (0)| 00:00:01 |
|    23 |          TABLE ACCESS STORAGE FULL      | INDSUBPART$        |     1 |    52 |     2   (0)| 00:00:01 |
|    24 |          TABLE ACCESS STORAGE FULL      | LOBFRAG$           |    23 |   414 |     2   (0)| 00:00:01 |
|  * 25 |       TABLE ACCESS CLUSTER              | SEG$               |     1 |    34 |     1   (0)| 00:00:01 |
|  * 26 |        INDEX UNIQUE SCAN                | I_FILE#_BLOCK#     |     1 |       |     0   (0)|          |
|  * 27 |      INDEX UNIQUE SCAN                  | I_FILE2            |     1 |     7 |     0   (0)|          |
|  * 28 |     FILTER                              |                    |       |       |            |          |
|  * 29 |      HASH JOIN RIGHT OUTER              |                    |     3 |   405 |    85   (0)| 00:00:01 |
|    30 |       TABLE ACCESS STORAGE FULL         | USER$              |    71 |  1278 |     3   (0)| 00:00:01 |
|    31 |       VIEW                              | VW_JF_SET$A8769BAB |   246 | 28782 |    82   (0)| 00:00:01 |
|    32 |        UNION-ALL                        |                    |       |       |            |          |
|    33 |         NESTED LOOPS                    |                    |     4 |   272 |    33   (0)| 00:00:01 |
|- * 34 |          HASH JOIN                      |                    |     4 |   244 |    33   (0)| 00:00:01 |
|    35 |           NESTED LOOPS                  |                    |     4 |   244 |    33   (0)| 00:00:01 |
|-   36 |            STATISTICS COLLECTOR         |                    |       |       |            |          |
|    37 |             NESTED LOOPS                |                    |    36 |  1044 |     3   (0)| 00:00:01 |
|    38 |              TABLE ACCESS BY INDEX ROWID| TS$                |     1 |    11 |     1   (0)| 00:00:01 |
|  * 39 |               INDEX UNIQUE SCAN         | I_TS1              |     1 |       |     0   (0)|          |
|  * 40 |              TABLE ACCESS STORAGE FULL  | UNDO$              |    36 |   648 |     2   (0)| 00:00:01 |
|  * 41 |            TABLE ACCESS CLUSTER         | SEG$               |     1 |    32 |     1   (0)| 00:00:01 |
|  * 42 |             INDEX UNIQUE SCAN           | I_FILE#_BLOCK#     |     1 |       |     0   (0)|          |
|- * 43 |           TABLE ACCESS STORAGE FULL     | SEG$               |     1 |    32 |     1   (0)| 00:00:01 |
|  * 44 |          INDEX UNIQUE SCAN              | I_FILE2            |     1 |     7 |     0   (0)|          |
|- * 45 |         HASH JOIN                       |                    |   241 | 13255 |    25   (0)| 00:00:01 |
|    46 |          NESTED LOOPS                   |                    |   241 | 13255 |    25   (0)| 00:00:01 |
|-   47 |           STATISTICS COLLECTOR          |                    |       |       |            |          |
|    48 |            NESTED LOOPS                 |                    |     5 |    90 |     2   (0)| 00:00:01 |
|    49 |             TABLE ACCESS BY INDEX ROWID | TS$                |     1 |    11 |     1   (0)| 00:00:01 |
|  * 50 |              INDEX UNIQUE SCAN          | I_TS1              |     1 |       |     0   (0)|          |
|    51 |             INDEX FULL SCAN             | I_FILE2            |     5 |    35 |     1   (0)| 00:00:01 |
|  * 52 |           TABLE ACCESS CLUSTER          | SEG$               |    48 |  1776 |     5   (0)| 00:00:01 |
|  * 53 |            INDEX RANGE SCAN             | I_FILE#_BLOCK#     |     1 |       |     2   (0)| 00:00:01 |
|- * 54 |          TABLE ACCESS STORAGE FULL      | SEG$               |    48 |  1776 |     5   (0)| 00:00:01 |
|- * 55 |         HASH JOIN                       |                    |     1 |    55 |    25   (0)| 00:00:01 |
|    56 |          NESTED LOOPS                   |                    |     1 |    55 |    25   (0)| 00:00:01 |
|-   57 |           STATISTICS COLLECTOR          |                    |       |       |            |          |
|    58 |            NESTED LOOPS                 |                    |     5 |    90 |     2   (0)| 00:00:01 |
|    59 |             TABLE ACCESS BY INDEX ROWID | TS$                |     1 |    11 |     1   (0)| 00:00:01 |
|  * 60 |              INDEX UNIQUE SCAN          | I_TS1              |     1 |       |     0   (0)|          |
|    61 |             INDEX FULL SCAN             | I_FILE2            |     5 |    35 |     1   (0)| 00:00:01 |
|  * 62 |           TABLE ACCESS CLUSTER          | SEG$               |     1 |    37 |     5   (0)| 00:00:01 |
|  * 63 |            INDEX RANGE SCAN             | I_FILE#_BLOCK#     |     1 |       |     2   (0)| 00:00:01 |
|- * 64 |          TABLE ACCESS STORAGE FULL      | SEG$               |     1 |    37 |     5   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   6 - access("O"."OBJ#"="SO"."OBJECT_ID" AND "O"."TYPE#"="SO"."OBJECT_TYPE_ID")
   7 - filter(NVL("U"."NAME",'SYS')='XDB')
   8 - access("O"."OWNER#"="U"."USER#")
  12 - access("TS"."NAME"='SYSAUX')
  16 - filter(BITAND("T"."PROPERTY",1024)=0)
  19 - filter(("I"."TYPE#"=1 OR "I"."TYPE#"=2 OR "I"."TYPE#"=3 OR "I"."TYPE#"=4 OR "I"."TYPE#"=6 OR
              "I"."TYPE#"=7 OR "I"."TYPE#"=8 OR "I"."TYPE#"=9))
  21 - filter((BITAND("L"."PROPERTY",64)=0 OR BITAND("L"."PROPERTY",128)=128))
  25 - filter("S"."TYPE#"="SO"."SEGMENT_TYPE_ID")
  26 - access("S"."TS#"="TS"."TS#" AND "S"."FILE#"="SO"."HEADER_FILE" AND
              "S"."BLOCK#"="SO"."HEADER_BLOCK")
       filter("S"."TS#"="SO"."TS_NUMBER")
  27 - access("S"."TS#"="F"."TS#" AND "S"."FILE#"="F"."RELFILE#")
  28 - filter(NVL("U"."NAME",'SYS')='XDB')
  29 - access("ITEM_1"="U"."USER#")
  34 - access("S"."TS#"="TS"."TS#" AND "S"."TS#"="UN"."TS#" AND "S"."BLOCK#"="UN"."BLOCK#" AND
              "S"."FILE#"="UN"."FILE#")
  39 - access("TS"."NAME"='SYSAUX')
  40 - storage("UN"."STATUS$"<>1)
       filter("UN"."STATUS$"<>1)
  41 - filter(("S"."TYPE#"=1 OR "S"."TYPE#"=10))
  42 - access("S"."TS#"="UN"."TS#" AND "S"."FILE#"="UN"."FILE#" AND "S"."BLOCK#"="UN"."BLOCK#")
       filter("S"."TS#"="TS"."TS#")
  43 - filter(("S"."TYPE#"=1 OR "S"."TYPE#"=10))
  44 - access("UN"."TS#"="F"."TS#" AND "UN"."FILE#"="F"."RELFILE#")
  45 - access("S"."FILE#"="F"."RELFILE#" AND "S"."TS#"="F"."TS#" AND "S"."TS#"="TS"."TS#")
  50 - access("TS"."NAME"='SYSAUX')
  52 - filter(("S"."TYPE#"<>6 AND "S"."TYPE#"<>5 AND "S"."TYPE#"<>8 AND "S"."TYPE#"<>10 AND
              "S"."TYPE#"<>11 AND "S"."TYPE#"<>1))
  53 - access("S"."TS#"="TS"."TS#" AND "S"."FILE#"="F"."RELFILE#")
       filter("S"."TS#"="F"."TS#")
  54 - filter(("S"."TYPE#"<>6 AND "S"."TYPE#"<>5 AND "S"."TYPE#"<>8 AND "S"."TYPE#"<>10 AND
              "S"."TYPE#"<>11 AND "S"."TYPE#"<>1))
  55 - access("S"."FILE#"="F"."RELFILE#" AND "S"."TS#"="F"."TS#" AND "S"."TS#"="TS"."TS#")
  60 - access("TS"."NAME"='SYSAUX')
  62 - filter("S"."TYPE#"=11)
  63 - access("S"."TS#"="TS"."TS#" AND "S"."FILE#"="F"."RELFILE#")
       filter("S"."TS#"="F"."TS#")
  64 - filter("S"."TYPE#"=11)
 
Note
-----
   - this is an adaptive plan (rows marked '-' are inactive)

Hope that helps.

Kerry



The second email:


Thanks, Kerry!
So as i understand, just one execution can now create several child cursors with different final plans? (As many collectors there are in the plan?)
They will have different plan hash values?
And how other sessions will choose child for them during execution which creates many child cursors? Especially interesting, how we will analyze such plans through AWR if statistics will be splitted between several plan hash values…

And I said:

No – only one cursor is created. It can have multiple adaptations – i.e. there may be multiple places where a decision between NL and HJ are made – but in the end it decides on 1 plan ands that’s it. A new cursor will only be created if something more normal triggers a new cursor (adaptive cursor sharing, optimizer environment changes, cardinality feedback kicks in, etc…)

Kerry

That’s it for now. In Part 2 I’ll address some questions regarding interaction with hints.

12c – parallel_degree_level (control for auto DOP)

I heard JP Dijcks speak at RMOUG in 2012 about a new parameter that would show up in 12c called parallel_degree_level. It’s basically a knob that you can turn to dial up (or down) the calculated DOP when setting parallel_degree_policy=auto. Early on (11.2.0.1) auto DOP seemed to vastly overestimate what the DOP should be. In a later version (11.2.0.3) it seems to often underestimate what the DOP should be. I’ve said in the past that I thought auto DOP was too hard to control and thus too scary for production systems. I’ve also said that I thought auto DOP was the wave of the future. And I think this parameter alone may make it possible to use this feature in production because it gives us the ability to dial in the level of parallelism that works for our system. So here’s a quick demo:

SYS@db12c1> @parms
Enter value for parameter: parallel_degree
Enter value for isset: 
Enter value for show_hidden: 
 
NAME                                               VALUE                                                                  ISDEFAUL ISMODIFIED ISSET
-------------------------------------------------- ---------------------------------------------------------------------- -------- ---------- ----------
parallel_degree_level                              100                                                                    TRUE     FALSE      FALSE
parallel_degree_limit                              16                                                                     FALSE    FALSE      TRUE
parallel_degree_policy                             AUTO                                                                   FALSE    TRUE       TRUE
 
3 rows selected.
 
Elapsed: 00:00:00.05
SYS@db12c1> alter session set parallel_degree_policy=auto;
 
Session altered.
 
Elapsed: 00:00:00.00
SYS@db12c1> select count(*) from kso.TT_CLUSTER_ONAME;
 
    COUNT(*)
------------
    79429632
 
1 row selected.
 
Elapsed: 00:00:01.96
SYS@db12c1> @x
 
PLAN_TABLE_OUTPUT
-----------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  apvrg0vpxxw8k, child number 2
-------------------------------------
select count(*) from kso.TT_CLUSTER_ONAME
 
Plan hash value: 2036413816
 
--------------------------------------------------------------------
| Id  | Operation                      | Name             | E-Rows |
--------------------------------------------------------------------
|   0 | SELECT STATEMENT               |                  |        |
|   1 |  SORT AGGREGATE                |                  |      1 |
|   2 |   PX COORDINATOR               |                  |        |
|   3 |    PX SEND QC (RANDOM)         | :TQ10000         |      1 |
|   4 |     SORT AGGREGATE             |                  |      1 |
|   5 |      PX BLOCK ITERATOR         |                  |     79M|
|*  6 |       TABLE ACCESS STORAGE FULL| TT_CLUSTER_ONAME |     79M|
--------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   6 - storage(:Z>=:Z AND :Z<=:Z)
 
Note
-----
   - automatic DOP: Computed Degree of Parallelism is 14
   - parallel scans affinitized
 
31 rows selected.
 
Elapsed: 00:00:00.02
SYS@db12c1> alter session set parallel_degree_level=10;
 
Session altered.
 
Elapsed: 00:00:00.01
SYS@db12c1> select count(*) from kso.TT_CLUSTER_ONAME;
 
    COUNT(*)
------------
    79429632
 
1 row selected.
 
Elapsed: 00:00:19.95
SYS@db12c1> @x
 
PLAN_TABLE_OUTPUT
-----------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  apvrg0vpxxw8k, child number 4
-------------------------------------
select count(*) from kso.TT_CLUSTER_ONAME
 
Plan hash value: 2036413816
 
------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name             | Rows  | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |                  |       |   174K(100)|          |        |      |            |
|   1 |  SORT AGGREGATE                |                  |     1 |            |          |        |      |            |
|   2 |   PX COORDINATOR               |                  |       |            |          |        |      |            |
|   3 |    PX SEND QC (RANDOM)         | :TQ10000         |     1 |            |          |  Q1,00 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE             |                  |     1 |            |          |  Q1,00 | PCWP |            |
|   5 |      PX BLOCK ITERATOR         |                  |    79M|   174K  (1)| 00:00:07 |  Q1,00 | PCWC |            |
|*  6 |       TABLE ACCESS STORAGE FULL| TT_CLUSTER_ONAME |    79M|   174K  (1)| 00:00:07 |  Q1,00 | PCWP |            |
------------------------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   6 - storage(:Z>=:Z AND :Z<=:Z)
 
Note
-----
   - automatic DOP: Computed Degree of Parallelism is 2
   - parallel scans affinitized
 
 
31 rows selected.
 
Elapsed: 00:00:00.09
SYS@db12c1> alter session set parallel_degree_level=100;
 
Session altered.
 
Elapsed: 00:00:00.00
SYS@db12c1> select count(*) from kso.TT_CLUSTER_ONAME;
 
    COUNT(*)
------------
    79429632
 
1 row selected.
 
Elapsed: 00:00:04.07
SYS@db12c1> @x
Enter value for sql_id: apvrg0vpxxw8k
Enter value for child_no: 
 
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  apvrg0vpxxw8k, child number 2
-------------------------------------
select count(*) from kso.TT_CLUSTER_ONAME
 
Plan hash value: 2036413816
 
------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name             | Rows  | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |                  |       | 24875 (100)|          |        |      |            |
|   1 |  SORT AGGREGATE                |                  |     1 |            |          |        |      |            |
|   2 |   PX COORDINATOR               |                  |       |            |          |        |      |            |
|   3 |    PX SEND QC (RANDOM)         | :TQ10000         |     1 |            |          |  Q1,00 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE             |                  |     1 |            |          |  Q1,00 | PCWP |            |
|   5 |      PX BLOCK ITERATOR         |                  |    79M| 24875   (1)| 00:00:01 |  Q1,00 | PCWC |            |
|*  6 |       TABLE ACCESS STORAGE FULL| TT_CLUSTER_ONAME |    79M| 24875   (1)| 00:00:01 |  Q1,00 | PCWP |            |
------------------------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   6 - storage(:Z>=:Z AND :Z<=:Z)
 
Note
-----
   - automatic DOP: Computed Degree of Parallelism is 14
   - parallel scans affinitized
 
 
31 rows selected.
 
Elapsed: 00:00:00.09
SYS@db12c1> alter session set parallel_degree_level=200;
 
Session altered.
 
SYS@db12c1> select count(*) from kso.TT_CLUSTER_ONAME;
 
  COUNT(*)
----------
  79429632
 
Elapsed: 00:00:00.59
SYS@db12c1> @x
 
PLAN_TABLE_OUTPUT
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  apvrg0vpxxw8k, child number 5
-------------------------------------
select count(*) from kso.TT_CLUSTER_ONAME
 
Plan hash value: 2036413816
 
--------------------------------------------------------------------
| Id  | Operation                      | Name             | E-Rows |
--------------------------------------------------------------------
|   0 | SELECT STATEMENT               |                  |        |
|   1 |  SORT AGGREGATE                |                  |      1 |
|   2 |   PX COORDINATOR               |                  |        |
|   3 |    PX SEND QC (RANDOM)         | :TQ10000         |      1 |
|   4 |     SORT AGGREGATE             |                  |      1 |
|   5 |      PX BLOCK ITERATOR         |                  |     79M|
|*  6 |       TABLE ACCESS STORAGE FULL| TT_CLUSTER_ONAME |     79M|
--------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   6 - storage(:Z>=:Z AND :Z<=:Z)
 
Note
-----
   - automatic DOP: Computed Degree of Parallelism is 16 because of degree limit
   - parallel scans affinitized
 
 
31 rows selected.
 
Elapsed: 00:00:00.12
SYS@db12c1> alter session set parallel_degree_limit=32;
 
Session altered.
 
Elapsed: 00:00:00.00
SYS@db12c1> select count(*) from kso.TT_CLUSTER_ONAME;
 
  COUNT(*)
----------
  79429632
 
Elapsed: 00:00:07.53
SYS@db12c1> @x
 
PLAN_TABLE_OUTPUT
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  apvrg0vpxxw8k, child number 6
-------------------------------------
select count(*) from kso.TT_CLUSTER_ONAME
 
Plan hash value: 2036413816
 
--------------------------------------------------------------------
| Id  | Operation                      | Name             | E-Rows |
--------------------------------------------------------------------
|   0 | SELECT STATEMENT               |                  |        |
|   1 |  SORT AGGREGATE                |                  |      1 |
|   2 |   PX COORDINATOR               |                  |        |
|   3 |    PX SEND QC (RANDOM)         | :TQ10000         |      1 |
|   4 |     SORT AGGREGATE             |                  |      1 |
|   5 |      PX BLOCK ITERATOR         |                  |     79M|
|*  6 |       TABLE ACCESS STORAGE FULL| TT_CLUSTER_ONAME |     79M|
--------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   6 - storage(:Z>=:Z AND :Z<=:Z)
 
Note
-----
   - automatic DOP: Computed Degree of Parallelism is 28
   - parallel scans affinitized
 
 
31 rows selected.
 
Elapsed: 00:00:00.06

So as you can see, parallel_degree_level is basically a percentage. The default is 100 and setting it to a value of 10 decreases the calculated value to roughly 10% while increasing it to 200 doubles the calculated DOP.

So just to reiterate, the auto DOP calculations have gotten progressively better over the last couple of years, but I think the simple addition of this new parameter makes it a much more palatable option.

SQL Translation Framework

My favorite new Oracle Database 12c feature is the SQL Translation Framework. The feature grew out of SQL Developer’s ability to translate SQL from non-Oracle RDBMS’s. For example, there is a pre-built Sybase ASE translation package that is designed to translate the Sybase dialect of SQL into Oracle SQL dialect. So that’s what the feature is designed for. But the developers decided to move it to the database and to allow us to write our own translations which opens up a whole world of possibilities.

The first thought that occurred to me when I saw this feature listed in the 12c New Features doc, was that I might be able to use it to fix badly written SQL behind the scenes. I’ve written and talked quite a bit about using hint based mechanisms (Outlines, SQL Profiles, Baselines, and SQL Patches) to alter execution plans without having to change the code. Those technique work great most of the time, but there are cases where hints alone can’t fix the problem. In some cases it is necessary to change the SQL statement text to get the desired results. And the SQL Translation Framework gives us the tool kit we need to do just that. And by the way, although I do have a tendency to use Oracle features for purposes for which they were not originally intended, in this case, I think the developers knew full well that the features could be used to address performance issues by re-writing SQL. As proof, here is a snippet from the 12c Release 1 Migration guide.

In addition to translating non-Oracle SQL statements, the SQL Translation Framework can also be used to substitute an Oracle SQL statement with another Oracle statement to address a semantic or a performance issue. In this way, you can address an application issue without patching the client application.

So let’s dive in. There are two main components to the framework. The first is a pl/sql package to programmatically translate code (also called the Translator in the docs). The second is a set of maps for individual SQL statements (this is called a SQL Translation Profile). There are a couple of requirements to use this feature.

1. You must create a SQL Translation Profile (using dbms_sql_translator.create_profile)
2. You must assign a session to use the Translation Profile (generally with an alter session command)
3. You must set the 10601 system event

While the Translator Profile is required, it does not have to be assigned a translator. In other words, you can map individual statements without writing a PL/SQL package. Of course if you have a system that has a lot of problems caused by the same coding pattern, you could potentially use the framework to rewrite those statements on the fly.

Here’s a quick example for a simple case of mapping individual statements.

SYS@LAB1211> exec dbms_sql_translator.create_profile('FOO');
 
PL/SQL procedure successfully completed.
 
SYS@LAB1211> select object_name, object_type from dba_objects where object_name like 'FOO';
 
OBJECT_NAME                    OBJECT_TYPE
------------------------------ -----------------------
FOO                            SQL TRANSLATION PROFILE
 
SYS@LAB1211> exec dbms_sql_translator.register_sql_translation('FOO','select count(*) from hr.countries','select count(*) from hr.jobs');
 
PL/SQL procedure successfully completed.
 
SYS@LAB1211> exec dbms_sql_translator.register_sql_translation('FOO','select count(*) from countries','select count(*) from jobs');
 
PL/SQL procedure successfully completed.
 
SYS@LAB1211> exec dbms_sql_translator.register_sql_translation('FOO','select 1 from hr.countries','select count(*) from hr.countries');
 
PL/SQL procedure successfully completed.
 
SYS@LAB1211> grant all on sql translation profile foo to hr;
 
Grant succeeded.
 
SYS@LAB1211> alter session set sql_translation_profile = FOO;
 
Session altered.
 
SYS@LAB1211> alter session set events = '10601 trace name context forever, level 32';
 
Session altered.
 
SYS@LAB1211> set echo on
SYS@LAB1211> select count(*) from hr.countries;
 
  COUNT(*)
----------
        19
 
SYS@LAB1211> select /*+ fix_wrong_results */ count(*) from hr.countries;
 
  COUNT(*)
----------
        25
 
SYS@LAB1211> @x
 
PLAN_TABLE_OUTPUT
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  aaajpnhn25nza, child number 0
-------------------------------------
select /*+ fix_wrong_results */ count(*) from hr.countries
 
Plan hash value: 1399856367
 
----------------------------------------------------------------------------
| Id  | Operation        | Name            | Rows  | Cost (%CPU)| Time     |
----------------------------------------------------------------------------
|   0 | SELECT STATEMENT |                 |       |     1 (100)|          |
|   1 |  SORT AGGREGATE  |                 |     1 |            |          |
|   2 |   INDEX FULL SCAN| COUNTRY_C_ID_PK |    25 |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------
 
 
14 rows selected.
 
SYS@LAB1211> select count(*) from hr.countries;
 
  COUNT(*)
----------
        19
 
SYS@LAB1211> @x
 
PLAN_TABLE_OUTPUT
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  c95vwg4jwqqfd, child number 0
-------------------------------------
select count(*) from hr.jobs
 
Plan hash value: 3870222678
 
-----------------------------------------------------------
| Id  | Operation        | Name      | Rows  | Cost (%CPU)|
-----------------------------------------------------------
|   0 | SELECT STATEMENT |           |       |     1 (100)|
|   1 |  SORT AGGREGATE  |           |     1 |            |
|   2 |   INDEX FULL SCAN| JOB_ID_PK |    19 |     0   (0)|
-----------------------------------------------------------
 
 
14 rows selected.

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