Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer

Copyright © 2017, Oracle and/or its affiliates. All rights reserved.
Taming the beast
The 12c Optimizer
Connor McDonald
1

Connor McDonald

3

4

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
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150 hours free access so far
9

this session
10

3 things
11

12c optimizer …
better performance
12

12c optimizer …
worse performance
13

worse performance …
14

15

16

goal of the optimizer
17

simple
18

good execution plan
19

so how did we end up here ?
20

Hi, I'm the
optimizer. How
I can help ?
21

Hi, I'm the
optimizer. How
I can help ?
What I would
really like …
22

Hi, I'm the
optimizer. How
I can help ?
What I would
really like …
… is for you to
suffer like I do
23

PROBLEM #1
24

missing or poor statistics
25

cardinality mis-estimates
26

cardinality is everything
27

28

better stats
29

30

1) better histograms
31

SQL> create table T
2 as select * from dba_objects;
Table created.
SQL> exec dbms_stats.gather_table_stats('','T');
PL/SQL procedure successfully completed.
32

SQL> select COLUMN_NAME,NUM_DISTINCT,
2 ( select count(*)
3 from all_tab_histograms
4 where owner = a.owner
5 and table_name = a.table_name
6 and column_name = a.column_name )
7 from all_tab_cols a
8 where table_name = 'T'
9 order by column_id;
COLUMN_NAME NUM_DISTINCT HIST_CNT
------------------------------ ------------ ----------
OWNER 25 2
OBJECT_NAME 53000 2
SUBOBJECT_NAME 230 2
OBJECT_ID 90808 2 33

34

35

SQL> select intcol#,equality_preds,
2 range_preds,timestamp
3 from sys.col_usage$
4 where obj# = (select object_id
5 from obj
6 where object_name = 'T' );
INTCOL# EQUALITY_PREDS RANGE_PREDS TIMESTAMP
---------- -------------- ----------- ---------
1 1 0 24-SEP-17
36

SQL> select COLUMN_NAME,NUM_DISTINCT,
2 ( select count(*)
3 from all_tab_histograms
4 where owner = a.owner
5 and table_name = a.table_name
6 and column_name = a.column_name )
7 from all_tab_cols a
8 where table_name = 'T'
9 order by column_id;
COLUMN_NAME NUM_DISTINCT HIST_CNT
------------------------------ ------------ ----------
OWNER 25 25
OBJECT_NAME 53000 2
SUBOBJECT_NAME 230 2
OBJECT_ID 90808 2
37

we don't know what your "app" is
38

still requires care
39

12c improvements
40

SQL> select status, count(*)
2 from orders
3 group by status
4 order by 1;
STATUS COUNT(*)
-------------------- ----------
1-New 1000
2-Assigned 10
3-InProgress 4000
4-Processed 3
5-Shipped 800
6-Received 5000
7-Completed 15000
8-Archived 20000
41

skewed ...
42

... need histogram
43

11g
44

SQL> begin
2 dbms_stats.gather_table_stats('','ORDERS',
3 method_opt=>'for all columns size 15');
4 end;
5 /
SQL> select histogram from USER_TAB_COL_STATISTICS
2 where table_name = 'ORDERS';
HISTOGRAM
---------------
FREQUENCY
45

SQL> select
2 endpoint_number,
3 substr(
4 utl_raw.cast_to_varchar2(
5 hextoraw(to_char(endpoint_value,rpad('FM',65,'X')))),1,5),
6 endpoint_number -
7 nvl(lag(endpoint_number) over ( order by endpoint_number),0)
8 from user_histograms
9 where table_name = 'ORDERS'
10 order by 1;
ENDPOINT_NUMBER END_VAL FREQ
--------------- -------------------- ----------
1000 1-Nev 1000
1010 2-Ass 10
5010 3-InP 4000
5013 4-Pro 3
5813 5-Shi 800
10813 6-Rec 5000
25813 7-Com 15000
45813 8-Arc 20000
46

SQL> select
2 endpoint_number,
3 substr(
6 endpoint_number -
10 order by 1;
--------------- -------------------- ----------
1000 1-Nev 1000
1010 2-Ass 10
5010 3-InP 4000
5013 4-Pro 3
5813 5-Shi 800
10813 6-Rec 5000
25813 7-Com 15000
45813 8-Arc 20000
47

SQL> select
2 endpoint_number,
3 substr(
6 endpoint_number -
10 order by 1;
--------------- -------------------- ----------
1000 1-Nev 1000
1010 2-Ass 10
5010 3-InP 4000
5013 4-Pro 3
5813 5-Shi 800
10813 6-Rec 5000
25813 7-Com 15000
45813 8-Arc 20000
48

SQL> select * from orders
2 where status = '5-Shipped';
----------------------------------------------------
| Id | Operation | Name | Rows | Bytes |
----------------------------------------------------
| 0 | SELECT STATEMENT | | 800 | 8800 |
|* 1 | TABLE ACCESS FULL| ORDERS | 800 | 8800 |
----------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("STATUS"='5-Shipped')
49
true = 800

so far … so good
50

NDV > 254*
51

SQL> begin
4 end;
5 /
HISTOGRAM
---------------
HEIGHT BALANCED
52

SQL> select
2 endpoint_number,
3 substr(
6 endpoint_number -
10 order by 1;
ENDPOINT_NUMBER END_VAL BKTS
--------------- -------------------- ----------
0 1-Nev 0
1 6-Rec 1
3 7-Com 2
6 8-Arc 3
53

1-New
6-Received
7-Complete
8-Archived
8-Archived
8-Archived
7-Complete
54

2 where status = '8-Archived';
----------------------------------------------------
----------------------------------------------------
| 0 | SELECT STATEMENT | | 19089 | 205K|
|* 1 | TABLE ACCESS FULL| ORDERS | 19089 | 205K|
----------------------------------------------------
---------------------------------------------------
1 - filter("STATUS"='8-Archived')
true = 20000
55

2 where status = '7-Completed';
-----------------------------------------------------
-----------------------------------------------------
| 0 | SELECT STATEMENT | | 15271 | 164K|
|* 1 | TABLE ACCESS FULL| ORDERS | 15271 | 164K|
-----------------------------------------------------
---------------------------------------------------
1 - filter("STATUS"='7-Completed')
true = 10000
56

57

1-New
6-Received
7-Complete
8-Archived
8-Archived
8-Archived
7-Completetotal ~ 45,000 ~7000 per bucket 58

1-New
6-Received
7-Complete
8-Archived
8-Archived
8-Archived
7-Completetotal = 45,000 ~7000 per bucket 59

2 where status = '2-Assigned';
----------------------------------------------------
----------------------------------------------------
----------------------------------------------------
---------------------------------------------------
1 - filter("STATUS"='2-Assigned')
true = 3
60

height balanced means ...
61

bucket spanning = dramas
62

bucket swallowing = dramas
63

11g
HEIGHT-BALANCED
FREQUENCY
64

Footnote: 11g
FOR ALL COLUMNS SIZE 25AUTO
65

66

SQL> begin
4 end;
5 /
HISTOGRAM
---------------
FREQUENCY
67

SQL> begin
4 end;
5 /
HISTOGRAM
---------------
TOP-FREQUENCY
68

SQL> select
2 endpoint_number,
3 substr(
5 hextoraw(to_char(endpoint_value,rpad('FM',65,'X')))),1,5)
6 endpoint_number -
10 order by 1;
--------------- -------------------- ----------
1000 1-Nev 1000
5000 3-InP 4000
5800 5-Shi 800
10800 6-Rec 5000
25800 7-Com 15000
45800 8-Arc 20000 69

1-New
6-Received
3-InProgress
5-Shipped
8-Archived
7-Completed
70

other values ?
71

SQL> select num_rows
2 from user_tables
NUM_ROWS
----------
45813
SQL> select num_distinct
2 from user_tab_cols
NUM_DISTINCT
------------
8
FREQ
----------
800
1000
4000
5000
15000
20000
========
45800
( 45813 - 45800 ) / ( 8 - 6 ) =
72

2 where status = '4-Processed';
-----------------------------------------------------
-----------------------------------------------------
-----------------------------------------------------
---------------------------------------------------
1 - filter("STATUS"='4-Processed')
73
true = 3

what defines "TOP"
% top values > ((bkt-1)/bkt)* num
eg 1000 rows/ 12 bucket
12 most frequent > 11/12 * 1000
~920 rows
74

what if TOP FREQ not possible ?
75

SQL> insert into orders select '1-New'
2 from dual connect by level <= 9000;
SQL> insert into orders select '2-Assigned'
SQL> insert into orders select '3-InProgress'
SQL> insert into orders select '4-Processed'
SQL> insert into orders select '5-Shipped'
SQL> insert into orders select '6-Received'
76

SQL> select status, count(*)
2 from orders
3 group by status
4 order by 1;
STATUS COUNT(*)
-------------------- ----------
1-New 10000
2-Assigned 9510
3-InProgress 10000
4-Processed 9503
5-Shipped 9600
6-Received 9500
7-Completed 15000
8-Archived 20000
77

SQL> begin
4 end;
5 /
HISTOGRAM
---------------
HYBRID
78

SQL> select
2 endpoint_number,
3 substr(
6 endpoint_number -
7 nvl(lag(endpoint_number) over ( order by endpoint_number),0),
8 endpoint_repeat_count
11 order by 1;
ENDPOINT_NUMBER END_VAL FREQ ENDPOINT_REPEAT_COUNT
--------------- -------------------- ---------- ---------------------
590 1-Nev 590 590
1743 3-InP 1153 626
2314 4-Pro 571 571
2895 5-Shi 581 581
3449 6-Rec 554 554
5547 8-Arc 2098 1213 79

1-New
6-Received
3-InProgress
5-Shipped
8-Archived
4-Processed
ENDPOINT_NUMBER END_VAL FREQ ENDPOINT_REPEAT_COUNT
--------------- -------------------- ---------- ---------------------
590 1-Nev 590 590
1743 3-InP 1153 626
2314 4-Pro 571 571
2895 5-Shi 581 581
3449 6-Rec 554 554
5547 8-Arc 2098 1213
2-Assigned
590 1153 571 581 554 2098
7-Completed
80

8-Archived
END_VAL FREQ ENDPOINT_REPEAT_COUNT
---------------- -------- ---------------------
1-Nev 590 590
3-InP 1153 626
4-Pro 571 571
5-Shi 581 581
6-Rec 554 554
8-Arc 2098 1213
2098
7-Completed
"Bucket 8-Arc has 2098 occurrences...
1213 of them are the end value 8-Arc...
therefore 885 are > 6-Rec and < 8-Arc"
81
6-Received
554

no bucket spanning
82

key point
83

must use auto sample size
84

still need care
85

representative values
86

87

"today's hot deals"
88

gather statistics
SQL> select count(*)
2 from items
3 where type = 'HOT_DEAL'
COUNT(*)
----------
0
89

activate "deals of the day"
SQL> update items
2 set type = 'HOT_DEAL'
3 where ...
SQL> select count(*)
2 from items
COUNT(*)
----------
14
90

biggest day of the year !
SQL> select min(discount)
2 from items
4 and ...
where type = 'HOT_DEAL'
SQL> select free_shipping
2 from items
3 where hot_deal = 'Y'
4 and ...
SQL> select ...
2 from ...
SQL> select ...
2 from ...
SQL> select ...
2 from ...
SQL> select ...
2 from ...SQL> select ...
2 from ...
91

92

where type = 'HOT_DEAL'
93

94

SQL> select type, count(*)
2 from items
3 group by type;
TYPE COUNT(*)
------ ----------
ORDERED 21353
ARCHIVED 4213
NORMAL 513234
SOLD 528291
HOT_DEAL 0
95

normally...
96

"ok... I got 1,000,000 total rows...
and 7 distinct values.
I got a frequency histogram...
There's 4 popular values,
adding up to 910,000
That's 3 values left to cover the rest
3 / ( 1,000,000 - 910,000) = density
... I'm done!"
97

SINGLE TABLE ACCESS PATH
Single Table Cardinality Estimation for ITEMS[ITEMS]
SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE
kkecdn: Single Table Predicate:"ITEMS"."TYPE"='HOT_DEAL'
Column (#2):
NewDensity:0.001974, OldDensity:0.000000 BktCnt:1067091.000000,
PopBktCnt:1067091.000000, PopValCnt:4, NDV:4
Column (#2): TYPE(VARCHAR2)
AvgLen: 7 NDV: 4 Nulls: 0 Density: 0.001974
Histogram: Freq #Bkts: 4 UncompBkts: 1067091 EndPtVals: 4
Using density:
0.001974 of col #2 as selectivity of pred having unreasonably low value
NDV / (1067091 - 1067091)"Aaggghhhh!"
98

SQL> select * from items where type = 'HOT_DEAL';
----------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|
----------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2107 | 23177 | 672 (2)|
|* 1 | TABLE ACCESS FULL| ITEMS | 2107 | 23177 | 672 (2)|
----------------------------------------------------------------
TYPE COUNT(*)
------ ----------
ORDERED 21353
ARCHIVED 4213 * 50%
NORMAL 513234
SOLD 528291
99

representative values
... at the right time
100

2) extensions
101

11g
102

"extended" statistics
103

aka "column groups"
104

solve GIGO
105

SQL> select *
2 from ADDRESS
3 where CITY = 'Bangalore'
4 and COUNTRY = 'Australia';
106

107

automatic column groups
108

12.2
109

expression tracking
110

SQL> select s.item_id,
2 s.category,
3 p.prod_list_price - p.prod_min_price
4 from products p,
5 sales s
6 where ....;
SQL> select expression_text, evaluation_count, fixed_cost
2 from user_expression_statistics
2 where table_name = 'PRODUCTS';
EXPRESSION_TEXT EVALUATION_COUNT FIXED_COST
---------------------------------- ---------------- ----------
"PROD_LIST_PRICE"-"PROD_MIN_PRICE" 766 .000041667
111

112

3) online gather
113

the old problem
114

SQL> insert /*+ APPEND */ into MY_TABLE
2 select *
3 from MY_WHOPPING_GREAT_FAT_TABLE;MY_WHOPPING_GREAT_FAT_TABLE;
115

116

117

118

119

120

SQL> insert /*+ APPEND */ into MY_TABLE
2 select *
3 from MY_WHOPPING_GREAT_FAT_TABLE
4 ...
7102984123 rows created.
Elapsed: 06:12:34.00
121

and then...
122

SQL> select num_rows from user_tables
2 where table_name = 'MY_TABLE';
NUM_ROWS
----------
123

so ...
124

SQL> begin
2 dbms_stats.gather_table_stats('',
3 tname=>'MY_TABLE'
4 ...
5 ...
6 end;
7 /
Elapsed: a bloody long time
125

126

on load
127

SQL> create table T (
2 ...
3 ...
4 ... );
Table created.
2 from user_tables
3 where table_name = 'T';
NUM_ROWS
----------
SQL> insert /*+ APPEND */ into T
2 select * from dba_objects;
78876 rows created.
SQL> commit;
Commit complete.
2 from user_tables
NUM_ROWS
----------
78876 128

things to note
129

must be direct
insert /*+ APPEND */
create table as select
130

SQL> insert into T select * from dba_objects;
78876 rows created.
SQL> commit;
NUM_ROWS
----------
131

must be empty
132

SQL> insert into T values (....)
1 row created.
SQL> insert /*+ APPEND */ into T select * from dba_objects;
78876 rows created.
SQL> commit;
NUM_ROWS
----------
133

execution plan
134

SQL> insert /*+ APPEND */ into T
2 select * from t_src;
------------------------------------------------------------------------------
------------------------------------------------------------------------------
| 0 | INSERT STATEMENT | | 78877 | 9M| 428 (1)|
| 1 | LOAD AS SELECT | T | | | |
| 2 | OPTIMIZER STATISTICS GATHERING | | 78877 | 9M| 428 (1)|
| 3 | TABLE ACCESS FULL | T_SRC| 78877 | 9M| 428 (1)|
------------------------------------------------------------------------------
135

SQL> select column_name, notes
2 from user_tab_col_statistics
COLUMN_NAME NOTES
------------------------------ -------------
OWNER STATS_ON_LOAD
OBJECT_NAME STATS_ON_LOAD
SUBOBJECT_NAME STATS_ON_LOAD
OBJECT_ID STATS_ON_LOAD
DATA_OBJECT_ID STATS_ON_LOAD
OBJECT_TYPE STATS_ON_LOAD
CREATED STATS_ON_LOAD
...
...
136

note: no histograms
137

3) session GTT
138

11g
139

SQL> create global temporary table t ( x int, y int )
2 on commit preserve rows;
Table created.
SQL> insert into t
2 select rownum, rownum
3 from dual
4 connect by level <= 10000;
10000 rows created.
140

SQL> set autotrace traceonly explain
SQL> select * from t;
---------------------------------------------------------------
---------------------------------------------------------------
| 1 | TABLE ACCESS FULL| T | 10000 | 80000 | 7 (0)|
---------------------------------------------------------------
141

looks great until ...
142

SQL> connect scott/tiger
Connected.
SQL> select count(*) from t;
COUNT(*)
----------
0
---------------------------------------------------------------
---------------------------------------------------------------
---------------------------------------------------------------
143

144

SQL> create global temporary table t ( x int, y int )
2 on commit preserve rows;
Table created.
SQL> insert into t
3 from dual
10000 rows created.
145

---------------------------------------------------------------
---------------------------------------------------------------
---------------------------------------------------------------
as before
146

SQL> connect scott/tiger
Connected.
SQL> insert into t
3 from dual
50 rows created.
147

---------------------------------------------------------------
---------------------------------------------------------------
---------------------------------------------------------------
Note
-----
- Global temporary table session private statistics used
148

back to session 1
149

---------------------------------------------------------------
---------------------------------------------------------------
---------------------------------------------------------------
150

note: default = private
151

so far ...
152

153

improved optimizer "inputs"
histograms
gather on load
private GTT
154

PROBLEM #2
155

statistics needed = "∞"
156

learn ... as we go
157

learn ... from experiences
158

Adaptive Query Optimization #1
159
learn as we go

"Find all of the products with
a unit price of 15 that we have
sold more than 1 unit"
160

SQL> select ...
2 from order_iterms o,
3 product_info p
4 where o.unit_price = 15
5 and o.quantity > 1
6 and o.product_id = p.product_id
-----------------------------------------------------------------
-----------------------------------------------------------------
| 1 | NESTED LOOPS | | 12 | 200 |
| 2 | NESTED LOOPS | | 12 | 200 |
| 3 | TABLE ACCESS FULL | ORDER_ITEMS | 12 | 800 |
| 4 | INDEX UNIQUE SCAN | PRODUCT_PK | 1 | 40 |
| 5 | TABLE ACCESS BY ROWID | PRODUCT_INFO | 1 | 10 |
-----------------------------------------------------------------
161

162
what if there's 5000 ?
4 where o.unit_price = 15
5 and o.quantity > 1
-----------------------------------------------------------------
-----------------------------------------------------------------
| 1 | NESTED LOOPS | | 12 | 200 |
| 2 | NESTED LOOPS | | 12 | 200 |
| 3 | TABLE ACCESS FULL | ORDER_ITEMS | 12 | 800 |
| 4 | INDEX UNIQUE SCAN | PRODUCT_PK | 1 | 40 |
| 5 | TABLE ACCESS BY ROWID | PRODUCT_INFO | 1 | 10 |
-----------------------------------------------------------------

the runtime plan ?
163

SQL> select * from table(
2 dbms_xplan.display_cursor('...'));
--------------------------------------------------------------------------
--------------------------------------------------------------------------
| 1 | HASH JOIN | | 2000 | 16000 | 7 (0)|
| 2 | TABLE ACCESS FULL | ORDER_ITEMS | 10000 | 80000 | 7 (0)|
| 3 | TABLE ACCESS FULL | PRODUCT_INFO | 10000 | 80000 | 7 (0)|
--------------------------------------------------------------------------
164

SQL> desc V$SQL
Name Null? Type
----------------------------------------- -------- ----------------------------
SQL_TEXT VARCHAR2(1000)
SQL_FULLTEXT CLOB
SQL_ID VARCHAR2(13)
SHARABLE_MEM NUMBER
PERSISTENT_MEM NUMBER
...
IS_BIND_SENSITIVE VARCHAR2(1)
IS_BIND_AWARE VARCHAR2(1)
...
IS_REOPTIMIZABLE VARCHAR2(1)
IS_RESOLVED_ADAPTIVE_PLAN VARCHAR2(1)
...
SQL>
165

cool
166
"dodged a bullet"

Adaptive Query Optimization #2
167
learn from experience

SQL> select /*+ gather_plan_statistics */ ...
3 product_info p
4 where o.sales_date > date '2017-01-01'
5 and o.amt > 50
-------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 7500 | 250 |
| 1 | HASH JOIN | | 1 | 7500 | 250 |
| 2 | TABLE ACCESS FULL | ORDER_ITEMS | 1 | 7500 | 250 |
| 3 | TABLE ACCESS FULL | PRODUCT_INFO | 1 | 200 | 212 |
-------------------------------------------------------------------------
168

SQL> desc V$SQL
Name Null? Type
----------------------------------------- -------- ----------------------------
SQL_TEXT VARCHAR2(1000)
SQL_FULLTEXT CLOB
SQL_ID VARCHAR2(13)
SHARABLE_MEM NUMBER
PERSISTENT_MEM NUMBER
...
IS_BIND_SENSITIVE VARCHAR2(1)
IS_BIND_AWARE VARCHAR2(1)
...
IS_REOPTIMIZABLE VARCHAR2(1)
IS_RESOLVED_ADAPTIVE_PLAN VARCHAR2(1)
...
SQL>
169

SQL> select /*+ gather_plan_statistics */ ...
3 product_info p
5 and o.amt > 50
-------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 250 | 250 |
| 1 | HASH JOIN | | 1 | 250 | 250 |
| 2 | TABLE ACCESS FULL | ORDER_ITEMS | 1 | 250 | 250 |
| 3 | TABLE ACCESS FULL | PRODUCT_INFO | 1 | 212 | 212 |
-------------------------------------------------------------------------
Note:
- statistics feedback used for this statement
170

seems cool
171

we mightta got carried away :-(
172

SQL> select
2 directive_id,
3 type,
4 reason
5 from DBA_SQL_PLAN_DIRECTIVES;
DIRECTIVE_ID TYPE REASON
---------------------- ----------------------- ------------------------------------
14774885086507357845 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE
17607835686132426105 DYNAMIC_SAMPLING SINGLE TABLE CARDINALITY MISESTIMATE
28719982731298412 DYNAMIC_SAMPLING_RESULT SINGLE TABLE CARDINALITY MISESTIMATE
7592221435558555884 DYNAMIC_SAMPLING SINGLE TABLE CARDINALITY MISESTIMATE
...
...
173

SQL> select ...
3 product_info p
Note:
- 3 Sql Plan Directives used for this statement
174

same SQL ...
175

... different plans
176

... then more different plans
177

... then even more different plans
178

179

A common criticism ....
180

upgrade to 12.1
181

new optimizations
182

new statistics
183

default = everything enabled
184

185

"the plans keep changing"
186

just one switch
187

188

options
189

optimizer_adaptive_features = false
190

optimizer_features_enable = 11.2.0.4
191

don't panic ... that's fine
endorsed
192

12.1 ... perhaps too aggressive
193

12.2 ... more control
194

12.2 ... more passive
195

TRUE FALSE 196

12.2 ... much better
197

12.1
patch 22652097
198

which brings us to ...
199

upgrade scenarios
200

there are only two
201

1) your system sucks :-)
202
upgrade to 12.2
explore full adaptive

2) your system is OK
203
how can I lower risk ?

SQL Plan Baselines
204

apology #1
205

206

but ultimately ...
207

... just three things
208

try run stuff good
1
209

when it's running good …
2
210

… keep running it like that !
211

If there is something better
212
3

then tell me
213

214

215

baselines do exactly this !
216

rarely used
217

apology #2
218
evolve
accepted
repeatable
unaccepted
capture
signature

close to our goals
219
working good ? Keep it
found something better ? Tell me
found something worse ? Don’t use it

not new
220
11g

(quick) terminology
221

signature
222

SQL> select sal
from emp
where empno = ...
SQL> select SAL
from EMP where EMPNO = ...
SQL> SELECT /* comment */ SAL FROM EMP
WHERE EMPNO = ...
etc
etc
223
signature

plan(s) for signature
224

225
SELECT A.DATA,
B.DATA
FROM TAB1 A, TAB2
WHERE B.TAB1_ID = A.ID
AND A.CODE = :1
-----------------------------------------------------------------------
-----------------------------------------------------------------------
| 0 | SELECT STATEMENT | | | |
| 1 | NESTED LOOPS | | 25 | 425 |
| 2 | NESTED LOOPS | | 25 | 425 |
|* 3 | TABLE ACCESS FULL | TAB1 | 1 | 11 |
|* 4 | INDEX RANGE SCAN | TAB2_TAB1_FKI | 25 | |
| 5 | TABLE ACCESS BY INDEX ROWID| TAB2 | 25 | 150 |
-----------------------------------------------------------------------
select a.data, b.data
from tab1 a, tab2
where b.tab1_id = a.id
and a.code = :1
"capture" a baseline

226
SELECT A.DATA,
B.DATA
FROM TAB1 A, TAB2
AND A.CODE = :1
-----------------------------------------------------------------------
-----------------------------------------------------------------------
| 1 | NESTED LOOPS | | 25 | 425 |
| 2 | NESTED LOOPS | | 25 | 425 |
-----------------------------------------------------------------------
------------------------------------------------------------------------
------------------------------------------------------------------------
| * 1 | HASH JOIN | | 25 | 425 |
| * 5 | TABLE ACCESS FULL | TAB1 | 1 | 11 |
| 8 | TABLE ACCESS FULL | TAB2 | 25 | 150 |
------------------------------------------------------------------------
from tab1 a, tab2
and a.code = :1

when it's running good …
227
… keep running it like that !

228
SELECT A.DATA,
B.DATA
FROM TAB1 A, TAB2
AND A.CODE = :1
-----------------------------------------------------------------------
-----------------------------------------------------------------------
| 1 | NESTED LOOPS | | 25 | 425 |
| 2 | NESTED LOOPS | | 25 | 425 |
-----------------------------------------------------------------------
------------------------------------------------------------------------
------------------------------------------------------------------------
| * 1 | HASH JOIN | | 25 | 425 |
------------------------------------------------------------------------
from tab1 a, tab2
and a.code = :1
but we saved this !

maybe it is better ?
229
------------------------------------------------------------------------
------------------------------------------------------------------------
| * 1 | HASH JOIN | | 25 | 425 |
------------------------------------------------------------------------

evolve a plan
230

DBMS_SPM.EVOLVE_SQL_PLAN_BASELINE
231

232
SELECT A.DATA,
B.DATA
FROM TAB1 A, TAB2
AND A.CODE = :1
-----------------------------------------------------------------------
-----------------------------------------------------------------------
| 1 | NESTED LOOPS | | 25 | 425 |
| 2 | NESTED LOOPS | | 25 | 425 |
-----------------------------------------------------------------------
------------------------------------------------------------------------
------------------------------------------------------------------------
| * 1 | HASH JOIN | | 25 | 425 |
------------------------------------------------------------------------
from tab1 a, tab2
and a.code = :1

unless …
233
… that makes it bad or
there's something better

if only we had done ...
234
DBMS_SPM.I_LOVE_THIS_PLAN(sql_id)
DBMS_SPM. THIS_PLAN_SUCKS(sql_id)
DBMS_SPM. SWAP_THIS_PLAN_IN(sql_id)

so back to upgrade
235

2) your system is OK
236
how can I lower risk ?

SQL> alter system set
2 optimizer_capture_sql_plan_baselines = true
237

SQL> exec DBMS_SPM.PACK_STGTAB_BASELINE
(datapump)
SQL> exec DBMS_SPM.UNPACK_STGTAB_BASELINE
238

the same performance
239

240

wrap up
241

12c optimizer is better
242

12c optimizer is different
243

12.2 preferred
244

12.1 ... backport
245

SPM is your friend
246

That's a wrap!
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Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer

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Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer

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