RE: Strategies for dealing with (NOT EQUAL) conditions and indexes
- From: "Taylor, Chris David" <ChrisDavid.Taylor@xxxxxxxxxxxxxxx>
- To: 'Sidney Chen' <huanshengchen@xxxxxxxxx>, "'taral.desai@xxxxxxxxx'" <taral.desai@xxxxxxxxx>
- Date: Thu, 17 Nov 2011 07:57:34 -0600
Is there a difference in the way Oracle deals with "!=" versus "<>" inequality
/nonequality conditions in the optimizer?
Chris Taylor
Sr. Oracle DBA
Ingram Barge Company
Nashville, TN 37205
"Quality is never an accident; it is always the result of intelligent effort."
-- John Ruskin (English Writer 1819-1900)
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From: Sidney Chen [mailto:huanshengchen@xxxxxxxxx]
Sent: Thursday, November 17, 2011 6:53 AM
To: taral.desai@xxxxxxxxx
Cc: Taylor, Chris David; Stephens, Chris; Mark W. Farnham;
oracle-l@xxxxxxxxxxxxx
Subject: Re: Strategies for dealing with (NOT EQUAL) conditions and indexes
it's all about cost, the index is not used because the Optimizer think it's
cheaper(lower cost) to do a full table scan, not because it can't do a index
scan for an not equal predicate.
just as Mark said, if you make val1 popular enough, the index will be used. I
remove the primary key to make sure around 90% var1 = 12. this tick to show how
index scan can happen for not equal predicate.
oe@CS10G> select count(*)
2 from test1
3 where val1
4 and val2 = 'A12E'
5 /
COUNT(*)
----------
90094
1 row selected.
oe@CS10G> explain plan for
2 select /*+ dynamic_sampling(4)*/ val2, val3, val4, val5
3 from test1
4 where val1 = 12
5 and val2 = 'A12E'
6 /
Explained.
oe@CS10G> @x typical
oe@CS10G> select * from table(dbms_xplan.display('plan_table',null,'&1'));
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 1966964964
-------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes |
Cost (%CPU)| Time |
-------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 90087 | 1847K| 99 (0)|
00:00:02 |
|* 1 | TABLE ACCESS BY INDEX ROWID| TEST1 | 90087 | 1847K| 99
(0)| 00:00:02 |
|* 2 | INDEX RANGE SCAN | TEST1_IDX01 | 1000 | | 3
(0)| 00:00:01 |
-------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("VAL2"='A12E')
2 - access("VAL1")
Note
-----
- dynamic sampling used for this statement
19 rows selected.
oe@CS10G> explain plan for
2 select /*+ dynamic_sampling(4)*/val2, val3, val4, val5
3 from test1
4 where val1 != 12
5 and val2 = 'A12E'
6 /
Explained.
oe@CS10G> @x typical
oe@CS10G> select * from table(dbms_xplan.display('plan_table',null,'&1'));
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 2016337987
-------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes |
Cost (%CPU)| Time |
-------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 10 | 210 | 100 (0)|
00:00:02 |
|* 1 | TABLE ACCESS BY INDEX ROWID| TEST1 | 10 | 210 | 100
(0)| 00:00:02 |
|* 2 | INDEX RANGE SCAN | TEST1_IDX02 | 1000 | | 4
(0)| 00:00:01 |
-------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter("VAL1"<>12)
2 - access("VAL2"='A12E')
Note
-----
- dynamic sampling used for this statement
19 rows selected.
--update created table scripts
---create table and load with data----
drop table test1
/
/* Let us create a wider table */
create table test1
(val1 number(3) not null,
val2 varchar2(8) not null,
val3 number(3) not null,
val4 varchar2(5) not null,
val5 varchar2(5) not null,
val6 varchar2(5) not null,
val7 varchar2(5),
val8 varchar2(5)
)
/
/* Now let us add a primary key like might exist */
--alter table test1 add constraint test1_pk primary key (val1, val2, val3)
--using index
/* Now let us add some additional indexes like might exist */
create index test1_idx01 on test1 (val1)
/
create index test1_idx02 on test1 (val2)
/
create index test1_idx03 on test1 (val3)
/
create index test1_idx04 on test1 (val1, val3)
/
create index test1_idx05 on test1 (val2, val3)
/
create index test1_idx06 on test1 (val1, val2, val4)
/
create index test1_idx07 on test1 (val1, val3, val4)
/
create index test1_idx08 on test1 (val1, val2, val3, val4)
/
create index test1_idx09 on test1 (val1, val2, val3, val5)
/
create index test1_idx10 on test1 (val1, val2, val3, val6)
/
/* Now let us add some data */
insert into test1
with generator as
(select
case when mod(rownum,10) < 9 then 12 else round(dbms_random.value(1,100)) end
n1,
round(dbms_random.value(1,100)) n2
from dual connect by level < 100000
)
select
n1,
'A'||n1||'E',
n2,
'B'||n1||'D',
'C'||n2||'C',
'D'||n2||'B',
'E'||n2||'A',
null
from generator;
begin
dbms_Stats.gather_table_stats(ownname=>'',tabname=>'TEST1',estimate_percent=>100,cascade=>TRUE,
method_opt=>'FOR ALL COLUMNS SIZE AUTO', granularity=>'ALL',
block_sample=>TRUE);
end;
/
--
Regards
Sidney Chen
--
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