OG Yatra - upgrading to the new 12c+ optimizer

22/07/2019
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Copyright © 2017, Oracle and/or its affiliates. All rights reserved.
Taming the beast
The Optimizer in 12c and beyond
Connor McDonald
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Connor McDonald
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Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
Stuff
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400+ posts mainly on database & development
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etc...
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Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
In Memoriam
this session
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3 things
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12c(18c,19c) optimizer …
better performance
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12c(18c,19c) optimizer …
worse performance
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worse performance …
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goal of the optimizer
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simple
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good execution plan
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so how did we end up here ?
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You will upgrade the
database with the new
optimizer features
LOL !LOL !
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LMAOLMAO!
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That dude
is toast!
That dude
is toast!
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Is this why I don't
have a desk ?
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lots of fear
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lots of mis-information
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only 3 issues
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ISSUE #1
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poor statistics
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cardinality mis-estimates
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for the optimizer...
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...cardinality is everything
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better stats
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1) better histograms
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2015
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Histograms are so
awesome. See
how much they
make me smile !
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No …
… honest :-)
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the sucky part
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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.
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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 45
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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
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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
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we don't know what your "app" is
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still requires care
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12c improvements
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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
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skewed ...
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... need histogram
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11g
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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
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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
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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
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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
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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')
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true = 800
so far … so good
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NDV > 254*
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SQL> begin
4 end;
5 /
HISTOGRAM
---------------
HEIGHT BALANCED
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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
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1-New
6-Received
7-Complete
8-Archived
8-Archived
8-Archived
7-Complete
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2 where status = '8-Archived';
----------------------------------------------------
----------------------------------------------------
| 0 | SELECT STATEMENT | | 19089 | 205K|
|* 1 | TABLE ACCESS FULL| ORDERS | 19089 | 205K|
----------------------------------------------------
---------------------------------------------------
1 - filter("STATUS"='8-Archived')
true = 20000
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2 where status = '7-Completed';
-----------------------------------------------------
-----------------------------------------------------
| 0 | SELECT STATEMENT | | 15271 | 164K|
|* 1 | TABLE ACCESS FULL| ORDERS | 15271 | 164K|
-----------------------------------------------------
---------------------------------------------------
1 - filter("STATUS"='7-Completed')
true = 10000
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1-New
6-Received
7-Complete
8-Archived
8-Archived
8-Archived
7-Complete
total ~ 45,000 ~7000 per bucket 70
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1-New
6-Received
7-Complete
8-Archived
8-Archived
8-Archived
7-Complete
total = 45,000 ~7000 per bucket 71
2 where status = '2-Assigned';
----------------------------------------------------
----------------------------------------------------
----------------------------------------------------
---------------------------------------------------
1 - filter("STATUS"='2-Assigned')
true = 3
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height balanced = risk
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11g
HEIGHT-BALANCED
FREQUENCY
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Footnote: 11g
FOR ALL COLUMNS SIZE 254AUTO
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SQL> begin
4 end;
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HISTOGRAM
---------------
FREQUENCY
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SQL> begin
4 end;
5 /
HISTOGRAM
---------------
TOP-FREQUENCY
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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 79
1-New
6-Received
3-InProgress
5-Shipped
8-Archived
7-Completed
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other values ?
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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 ) = 7 approx.
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2 where status = '4-Processed';
-----------------------------------------------------
-----------------------------------------------------
-----------------------------------------------------
---------------------------------------------------
1 - filter("STATUS"='4-Processed')
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true = 3
what defines "TOP" ?
% top values > ((bkt-1)/bkt)* num rows
eg 1000 rows/ 12 buckets
12 most frequent > 11/12 * 1000
~920 rows
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what if TOP FREQ not possible ?
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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'
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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
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SQL> begin
4 end;
5 /
HISTOGRAM
---------------
HYBRID
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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 89
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
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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"
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6-Received
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key point
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must use auto sample size
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still need care
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representative values
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"today's hot deals"
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gather statistics
SQL> select count(*)
2 from items
3 where type = 'HOT_DEAL'
COUNT(*)
----------
0
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activate "deals of the day"
SQL> update items
2 set type = 'HOT_DEAL'
3 where ...
SQL> select count(*)
2 from items
COUNT(*)
----------
14
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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 ...
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where type = 'HOT_DEAL'
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? 103
SQL> select type, count(*)
2 from items
3 group by type;
TYPE COUNT(*)
------ ----------
ORDERED 21353
ARCHIVED 4213
NORMAL 513234
SOLD 528291
HOT_DEAL 0
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normally...
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"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!"
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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!"
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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
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representative values
... at the right time
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19c can help
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Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 111
TIME
Gather
Stale
overnight
Conventional
DML
Real-time
Statistics
Gathered Statistics
Lightweight
Statistics
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real time statistics
DML driven
cheap essential statistics
high/low values
row/column counts
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lightweight rapid gather
resource manager
2) extensions
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11g
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"extended" statistics
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aka "column groups"
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SQL> select *
2 from ADDRESS
3 where CITY = 'Pune'
4 and COUNTRY = 'Australia';
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automatic column groups
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12.2
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expression tracking
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SQL> select s.item_id,
2 s.category,
3 stddev(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
------------------------------------------ ---------------- ----------
STDDEV("PROD_LIST_PRICE"-"PROD_MIN_PRICE") 766 .000041667
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ISSUE #2
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missing statistics
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3) online gather
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the old problem
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SQL> insert /*+ APPEND */ into MY_TABLE
2 select *
3 from MY_WHOPPING_GREAT_FAT_TABLE;MY_WHOPPING_GREAT_FAT_TABLE;
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SQL> insert /*+ APPEND */ into MY_TABLE
2 select *
3 from MY_WHOPPING_GREAT_FAT_TABLE
4 ...
7102984123 rows created.
Elapsed: 06:12:34.00
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and then...
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SQL> select num_rows from user_tables
2 where table_name = 'MY_TABLE';
NUM_ROWS
----------
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so ...
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SQL> begin
2 dbms_stats.gather_table_stats('',
3 tname=>'MY_TABLE'
4 ...
5 ...
6 end;
7 /
Elapsed: a really long time
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on load
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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 143
things to note
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must be direct
insert /*+ APPEND */
create table as select
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SQL> insert into T select * from dba_objects;
78876 rows created.
SQL> commit;
NUM_ROWS
----------
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must be empty
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SQL> insert into T values (....)
1 row created.
SQL> insert /*+ APPEND */ into T select * from dba_objects;
78876 rows created.
SQL> commit;
NUM_ROWS
----------
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execution plan
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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)|
------------------------------------------------------------------------------
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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
...
...
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note: no histograms
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3) session GTT
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11g
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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.
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SQL> set autotrace traceonly explain
SQL> select * from t;
---------------------------------------------------------------
---------------------------------------------------------------
| 1 | TABLE ACCESS FULL| T | 10000 | 80000 | 7 (0)|
---------------------------------------------------------------
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looks great until ...
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SQL> connect scott/tiger
Connected.
SQL> select count(*) from t;
COUNT(*)
----------
0
---------------------------------------------------------------
---------------------------------------------------------------
---------------------------------------------------------------
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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.
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---------------------------------------------------------------
---------------------------------------------------------------
---------------------------------------------------------------
as before
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SQL> connect scott/tiger
Connected.
SQL> insert into t
3 from dual
50 rows created.
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---------------------------------------------------------------
---------------------------------------------------------------
---------------------------------------------------------------
Note
-----
- Global temporary table session private statistics used
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back to session 1
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---------------------------------------------------------------
---------------------------------------------------------------
---------------------------------------------------------------
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note: default = private
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so far ...
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missing or poor statistics
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improved optimizer "inputs"
histograms
gather on load
private GTT
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ISSUE #3
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statistics needed = "∞"
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learn ... as we go
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learn ... from experiences
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Adaptive Query Optimization #1
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learn as we go
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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 |
-----------------------------------------------------------------
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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 |
-----------------------------------------------------------------
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the runtime plan ?
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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)|
--------------------------------------------------------------------------
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SQL> desc V$SQL
Name Null? Type
----------------------------------------- -------- ----------------------------
SQL_TEXT VARCHAR2(1000)
SQL_FULLTEXT CLOB
SQL_ID VARCHAR2(13)
SHARABLE_MEM NUMBER
PERSISTENT_MEM NUMBER
...
IS_BIND_SENSITIVE VARCHAR2(1)
IS_BIND_AWARE VARCHAR2(1)
...
IS_REOPTIMIZABLE VARCHAR2(1)
IS_RESOLVED_ADAPTIVE_PLAN VARCHAR2(1)
...
SQL>
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cool
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"dodged a bullet"
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Adaptive Query Optimization #2
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learn from experience
SQL> select /*+ gather_plan_statistics */ ...
3 product_info p
4 where o.sales_date > date '2017-01-01'
5 and o.amt > 50
-------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows |
-------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 7500 | 250 |
| 1 | HASH JOIN | | 1 | 7500 | 250 |
| 2 | TABLE ACCESS FULL | ORDER_ITEMS | 1 | 7500 | 250 |
| 3 | TABLE ACCESS FULL | PRODUCT_INFO | 1 | 200 | 212 |
-------------------------------------------------------------------------
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SQL> desc V$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>
183
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
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93
seems cool
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we mightta got carried away :-(
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94
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
...
...
187
SQL> select ...
3 product_info p
Note:
- 3 Sql Plan Directives used for this statement
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22/07/2019
95
same SQL ...
189
... different plans
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22/07/2019
96
... then more different plans
191
... then even more different plans
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97
So ... this happens
193
upgrade to 12.1
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98
"the plans keep changing"
199
"um...stability?"
200
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200

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just one switch
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100
options
203
optimizer_adaptive_features = false
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12.1 ... perhaps too aggressive
207
12.2 ... more control
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102
TRUE FALSE 209
12.1
patch 22652097
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103
which brings us to ...
211
upgrade scenarios
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there are only two
213
1) your system sucks :-)
214
upgrade to target version (12.2+)
explore full adaptive
213
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22/07/2019
105
2) your system is OK
215
how can I lower risk ?
SQL Plan Baselines
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22/07/2019
106
apology #1
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107
rarely used
219
apology #2
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evolve
accepted
repeatable
unaccepted
capture
signature
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220

22/07/2019
108
close to our goals
221
working good ? Keep it
found something better ? Tell me
found something worse ? Don’t use it
not new
222
11g
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223
-----------------------------------------------------------------------
-----------------------------------------------------------------------
| 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
224
-----------------------------------------------------------------------
-----------------------------------------------------------------------
| 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
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225
-----------------------------------------------------------------------
-----------------------------------------------------------------------
| 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 ?
226
------------------------------------------------------------------------
------------------------------------------------------------------------
| * 1 | HASH JOIN | | 25 | 425 |
------------------------------------------------------------------------
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111
evolve a plan
227
DBMS_SPM.EVOLVE_SQL_PLAN_BASELINE
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229
-----------------------------------------------------------------------
-----------------------------------------------------------------------
| 1 | NESTED LOOPS | | 25 | 425 |
| 2 | NESTED LOOPS | | 25 | 425 |
-----------------------------------------------------------------------
------------------------------------------------------------------------
------------------------------------------------------------------------
| * 1 | HASH JOIN | | 25 | 425 |
------------------------------------------------------------------------
from tab1 a, tab2
and a.code = :1
a better "API"...
230
DBMS_SPM.I_LOVE_THIS_PLAN(sql_id)
DBMS_SPM. THIS_PLAN_SUCKS(sql_id)
DBMS_SPM. SWAP_THIS_PLAN_IN(sql_id)
DBMS_SPM. AUTO_SWAP_THIS_PLAN_IN(sql_id)
229
230

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113
so back to upgrade
231
2) your system is OK
232
how can I lower risk ?
231
232

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114
SQL> alter system set
2 optimizer_capture_sql_plan_baselines = true
233
SQL> exec DBMS_SPM.PACK_STGTAB_BASELINE
(datapump)
SQL> exec DBMS_SPM.UNPACK_STGTAB_BASELINE
234
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22/07/2019
115
the same performance
235
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22/07/2019
116
wrap up
237
the optimizer is better
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22/07/2019
117
the optimizer is different
239
12.2+ preferred
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22/07/2019
118
12.1 ... backport
241
SPM is your friend
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22/07/2019
119
Thank you!
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243

OG Yatra - upgrading to the new 12c+ optimizer

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