如何解析Oracle中exists與in的執(zhí)行效率問(wèn)題,很多新手對(duì)此不是很清楚,為了幫助大家解決這個(gè)難題,下面小編將為大家詳細(xì)講解,有這方面需求的人可以來(lái)學(xué)習(xí)下,希望你能有所收獲。
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in是把外表和內(nèi)表作hash join,而exists是對(duì)外表作loop,每次loop再對(duì)內(nèi)表進(jìn)行查詢。一般大家都認(rèn)為exists比in語(yǔ)句的效率要高,這種說(shuō)法其實(shí)是不準(zhǔn)確的,這個(gè)是要區(qū)分環(huán)境的。
exists對(duì)外表用loop逐條查詢,每次查詢都會(huì)查看exists的條件語(yǔ)句,當(dāng) exists里的條件語(yǔ)句能夠返回記錄行時(shí)(無(wú)論記錄行是的多少,只要能返回),條件就為真,返回當(dāng)前l(fā)oop到的這條記錄,反之如果exists里的條件語(yǔ)句不能返回記錄行,則當(dāng)前l(fā)oop到的這條記錄被丟棄,exists的條件就像一個(gè)bool條件,當(dāng)能返回結(jié)果集則為true,不能返回結(jié)果集則為 false。
例如:
select * from user where exists (select 1);
對(duì)user表的記錄逐條取出,由于子條件中的select 1永遠(yuǎn)能返回記錄行,那么user表的所有記錄都將被加入結(jié)果集,所以與 select * from user;是一樣的
又如下
select * from user where exists (select * from user where userId = 0);
可以知道對(duì)user表進(jìn)行l(wèi)oop時(shí),檢查條件語(yǔ)句(select * from user where userId = 0),由于userId永遠(yuǎn)不為0,所以條件語(yǔ)句永遠(yuǎn)返回空集,條件永遠(yuǎn)為false,那么user表的所有記錄都將被丟棄
not exists與exists相反,也就是當(dāng)exists條件有結(jié)果集返回時(shí),loop到的記錄將被丟棄,否則將loop到的記錄加入結(jié)果集
總的來(lái)說(shuō),如果A表有n條記錄,那么exists查詢就是將這n條記錄逐條取出,然后判斷n遍exists條件
in查詢相當(dāng)于多個(gè)or條件的疊加,這個(gè)比較好理解,比如下面的查詢
select * from user where userId in (1, 2, 3);
等效于
select * from user where userId = 1 or userId = 2 or userId = 3;
not in與in相反,如下
select * from user where userId not in (1, 2, 3);
等效于
select * from user where userId != 1 and userId != 2 and userId != 3;
總的來(lái)說(shuō),in查詢就是先將子查詢條件的記錄全都查出來(lái),假設(shè)結(jié)果集為B,共有m條記錄,然后在將子查詢條件的結(jié)果集分解成m個(gè),再進(jìn)行m次查詢
值得一提的是,in查詢的子條件返回結(jié)果必須只有一個(gè)字段,例如
select * from user where userId in (select id from B);
而不能是
select * from user where userId in (select id, age from B);
而exists就沒(méi)有這個(gè)限制
下面來(lái)考慮exists和in的性能:
對(duì)于以上兩種情況,in是在內(nèi)存里遍歷比較,而exists需要查詢數(shù)據(jù)庫(kù),所以當(dāng)B表數(shù)據(jù)量較大時(shí),exists效率優(yōu)于in。
考慮如下SQL語(yǔ)句
select * from A where exists (select * from B where B.id = A.id);
select * from A where A.id in (select id from B);
1、select * from A where exists (select * from B where B.id = A.id);
exists()會(huì)執(zhí)行A.length次,它并不緩存exists()結(jié)果集,因?yàn)閑xists()結(jié)果集的內(nèi)容并不重要,重要的是其內(nèi)查詢語(yǔ)句的結(jié)果集空或者非空,空則返回false,非空則返回true。
它的查詢過(guò)程類似于以下過(guò)程:
for ($i = 0; $i < count(A); $i++) {
$a = get_record(A, $i); #從A表逐條獲取記錄
if (B.id = $a[id]) #如果子條件成立
$result[] = $a;
}
return $result;
當(dāng)B表比A表數(shù)據(jù)大時(shí)適合使用exists(),因?yàn)樗鼪](méi)有那么多遍歷操作,只需要再執(zhí)行一次查詢就行。
如:A表有10000條記錄,B表有1000000條記錄,那么exists()會(huì)執(zhí)行10000次去判斷A表中的id是否與B表中的id相等。
如:A表有10000條記錄,B表有100000000條記錄,那么exists()還是執(zhí)行10000次,因?yàn)樗粓?zhí)行A.length次,可見(jiàn)B表數(shù)據(jù)越多,越適合exists()發(fā)揮效果。
再如:A表有10000條記錄,B表有100條記錄,那么exists()還是執(zhí)行10000次,還不如使用in()遍歷10000*100次,因?yàn)閕n()是在內(nèi)存里遍歷比較,而exists()需要查詢數(shù)據(jù)庫(kù),我們都知道查詢數(shù)據(jù)庫(kù)所消耗的性能更高,而內(nèi)存比較很快。
結(jié)論:exists()適合B表比A表數(shù)據(jù)大的情況
2、select * from A where id in (select id from B);
in()只執(zhí)行一次,它查出B表中的所有id字段并緩存起來(lái)。之后,檢查A表的id是否與B表中的id相等,如果相等則將A表的記錄加入結(jié)果集中,直到遍歷完A表的所有記錄。
它的查詢過(guò)程類似于以下過(guò)程:
Array A=(select * from A);
Array B=(select id from B);
for(int i=0;i<a.length;i++) { </a.length;i++) { <>
for(int j=0;j<b.length;j++) { </b.length;j++) { <>
if(A[i].id==B[j].id) {
resultSet.add(A[i]);
break;
}
}
}
return resultSet;
可以看出,當(dāng)B表數(shù)據(jù)較大時(shí)不適合使用in(),因?yàn)樗鼤?huì)B表數(shù)據(jù)全部遍歷一次
如:A表有10000條記錄,B表有1000000條記錄,那么最多有可能遍歷10000*1000000次,效率很差。
再如:A表有10000條記錄,B表有100條記錄,那么最多有可能遍歷10000*100次,遍歷次數(shù)大大減少,效率大大提升。
結(jié)論:in()適合B表比A表數(shù)據(jù)小的情況
當(dāng)A表數(shù)據(jù)與B表數(shù)據(jù)一樣大時(shí),in與exists效率差不多,可任選一個(gè)使用。
在插入記錄前,需要檢查這條記錄是否已經(jīng)存在,只有當(dāng)記錄不存在時(shí)才執(zhí)行插入操作,可以通過(guò)使用 EXISTS 條件句防止插入重復(fù)記錄。
insert into A (name,age) select name,age from B where not exists (select 1 from
A where A.id=B.id);
EXISTS與IN的使用效率的問(wèn)題,通常情況下采用exists要比in效率高,因?yàn)镮N不走索引。但要看實(shí)際情況具體使用:IN適合于外表大而內(nèi)表小的情況;EXISTS適合于外表小而內(nèi)表大的情況。
下面再看not exists和 not in
1、select * from A where not exists (select * from B where B.id = A.id);
2、select * from A where A.id not in (select id from B);
看查詢1,還是和上面一樣,用了B的索引;而對(duì)于查詢2,可以轉(zhuǎn)化成如下語(yǔ)句
select * from A where A.id != 1 and A.id != 2 and A.id != 3;
可以知道not in是個(gè)范圍查詢,這種!=的范圍查詢無(wú)法使用任何索引,等于說(shuō)A表的每條記錄,都要在B表里遍歷一次,查看B表里是否存在這條記錄
not in 和not exists:如果查詢語(yǔ)句使用了not in 那么內(nèi)外表都進(jìn)行全表掃描,沒(méi)有用到索引;而not extsts 的子查詢依然能用到表上的索引。所以無(wú)論那個(gè)表大,用not exists都比not in要快,故not exists比not in效率高。
in 與 =的區(qū)別
select name from student where name in ('zhang','wang','li','zhao');
與
select name from student where name='zhang' or name='li' or name='wang' or name='zhao'
的結(jié)果是相同的。
在我們一般的觀點(diǎn)中,總是認(rèn)為使用EXISTS(或NOT EXISTS)通常將提高查詢的效率,所以一般推薦使用exists來(lái)代替in。但實(shí)際情況是不是這個(gè)樣子呢?我們分別在兩種不同的優(yōu)化器模式下用實(shí)際的例子來(lái)看一下:
SEIANG@seiang11g>create table wjq1 as select * from dba_objects;
Table created.
SEIANG@seiang11g>create table wjq2 as select * from dba_tables ;
Table created.
SEIANG@seiang11g>create index idx_object_name on wjq1(object_name);
Index created.
SEIANG@seiang11g>create index idx_table_name on wjq2(table_name);
Index created.
SEIANG@seiang11g>select count(*) from wjq1;
COUNT(*)
----------
86976
SEIANG@seiang11g>select count(*) from wjq2;
COUNT(*)
----------
2868
SEIANG@seiang11g>select * from wjq1 where object_name in (select table_name from wjq2 where table_name like 'M%');
815 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 1638414738
---------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1238 | 270K| 354 (1)| 00:00:05 |
|* 1 | HASH JOIN RIGHT SEMI| | 1238 | 270K| 354 (1)| 00:00:05 |
|* 2 | INDEX RANGE SCAN | IDX_TABLE_NAME | 772 | 13124 | 7 (0)| 00:00:01 |
|* 3 | TABLE ACCESS FULL | WJQ1 | 5503 | 1112K| 347 (1)| 00:00:05 |
---------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("OBJECT_NAME"="TABLE_NAME")
2 - access("TABLE_NAME" LIKE 'M%')
filter("TABLE_NAME" LIKE 'M%')
3 - filter("OBJECT_NAME" LIKE 'M%')
Note
-----
- dynamic sampling used for this statement (level=2)
Statistics
----------------------------------------------------------
17 recursive calls
0 db block gets
1462 consistent gets
1256 physical reads
0 redo size
46140 bytes sent via SQL*Net to client
1117 bytes received via SQL*Net from client
56 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
815 rows processed
SEIANG@seiang11g>select * from wjq1 where exists (select 1 from wjq2 where wjq1.object_name=wjq2.table_name and wjq2.table_name like 'M%');
815 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 1638414738
---------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1238 | 270K| 354 (1)| 00:00:05 |
|* 1 | HASH JOIN RIGHT SEMI| | 1238 | 270K| 354 (1)| 00:00:05 |
|* 2 | INDEX RANGE SCAN | IDX_TABLE_NAME | 772 | 13124 | 7 (0)| 00:00:01 |
|* 3 | TABLE ACCESS FULL | WJQ1 | 5503 | 1112K| 347 (1)| 00:00:05 |
---------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("WJQ1"."OBJECT_NAME"="WJQ2"."TABLE_NAME")
2 - access("WJQ2"."TABLE_NAME" LIKE 'M%')
filter("WJQ2"."TABLE_NAME" LIKE 'M%')
3 - filter("WJQ1"."OBJECT_NAME" LIKE 'M%')
Note
-----
- dynamic sampling used for this statement (level=2)
Statistics
----------------------------------------------------------
13 recursive calls
0 db block gets
1462 consistent gets
1242 physical reads
0 redo size
46140 bytes sent via SQL*Net to client
1117 bytes received via SQL*Net from client
56 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
815 rows processed
通過(guò)上面執(zhí)行計(jì)劃對(duì)比發(fā)現(xiàn):
在CBO模式下,我們可以看到這兩者的執(zhí)行計(jì)劃完全相同,統(tǒng)計(jì)數(shù)據(jù)也相同。
我們?cè)賮?lái)看一下RBO模式下的情況,這種情況相對(duì)復(fù)雜一些。
SEIANG@seiang11g>select /*+ rule*/ * from wjq1 where object_name in (select table_name from wjq2 where table_name like 'M%');
815 rows selected.
Elapsed: 00:00:00.01
Execution Plan
----------------------------------------------------------
Plan hash value: 144941173
--------------------------------------------------------
| Id | Operation | Name |
--------------------------------------------------------
| 0 | SELECT STATEMENT | |
| 1 | NESTED LOOPS | |
| 2 | NESTED LOOPS | |
| 3 | VIEW | VW_NSO_1 |
| 4 | SORT UNIQUE | |
|* 5 | INDEX RANGE SCAN | IDX_TABLE_NAME |
|* 6 | INDEX RANGE SCAN | IDX_OBJECT_NAME |
| 7 | TABLE ACCESS BY INDEX ROWID| WJQ1 |
--------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
5 - access("TABLE_NAME" LIKE 'M%')
filter("TABLE_NAME" LIKE 'M%')
6 - access("OBJECT_NAME"="TABLE_NAME")
Note
-----
- rule based optimizer used (consider using cbo)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
698 consistent gets
0 physical reads
0 redo size
55187 bytes sent via SQL*Net to client
1117 bytes received via SQL*Net from client
56 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
815 rows processed
SEIANG@seiang11g>select /*+ rule*/ * from wjq1 where exists (select 1 from wjq2 where wjq1.object_name=wjq2.table_name and wjq2.table_name like 'M%');
815 rows selected.
Elapsed: 00:00:00.15
Execution Plan
----------------------------------------------------------
Plan hash value: 3545670754
---------------------------------------------
| Id | Operation | Name |
---------------------------------------------
| 0 | SELECT STATEMENT | |
|* 1 | FILTER | |
| 2 | TABLE ACCESS FULL| WJQ1 |
|* 3 | INDEX RANGE SCAN | IDX_TABLE_NAME |
---------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter( EXISTS (SELECT 0 FROM "WJQ2" "WJQ2" WHERE
"WJQ2"."TABLE_NAME"=:B1 AND "WJQ2"."TABLE_NAME" LIKE 'M%'))
3 - access("WJQ2"."TABLE_NAME"=:B1)
filter("WJQ2"."TABLE_NAME" LIKE 'M%')
Note
-----
- rule based optimizer used (consider using cbo)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
91002 consistent gets
1242 physical reads
0 redo size
46140 bytes sent via SQL*Net to client
1117 bytes received via SQL*Net from client
56 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
815 rows processed
通過(guò)上面兩個(gè)執(zhí)行計(jì)劃的對(duì)比發(fā)現(xiàn):
在這里,我們可以看到實(shí)際上,使用in效率比exists效率更高。我們可以這樣來(lái)理解這種情況:
對(duì)于in,RBO優(yōu)化器選擇的內(nèi)存查詢的結(jié)果作為驅(qū)動(dòng)表來(lái)進(jìn)行nest loops連接,所以當(dāng)內(nèi)存查詢的結(jié)果集比較小的時(shí)候,這個(gè)in的效率還是比較高的。
對(duì)于exists,RBO優(yōu)化器則是利用外查詢表的全表掃描結(jié)果集過(guò)濾內(nèi)查詢的結(jié)果集,當(dāng)外查詢的表比較大的時(shí)候,相對(duì)效率比較低。
SEIANG@seiang11g>select * from wjq2 where table_name in (select object_name from wjq1 where object_name like 'S%');
278 rows selected.
Elapsed: 00:00:00.03
Execution Plan
----------------------------------------------------------
Plan hash value: 1807911610
--------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
--------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 278 | 164K| 55 (0)| 00:00:01 |
|* 1 | HASH JOIN SEMI | | 278 | 164K| 55 (0)| 00:00:01 |
|* 2 | TABLE ACCESS FULL| WJQ2 | 278 | 146K| 31 (0)| 00:00:01 |
|* 3 | INDEX RANGE SCAN | IDX_OBJECT_NAME | 4435 | 285K| 24 (0)| 00:00:01 |
--------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("TABLE_NAME"="OBJECT_NAME")
2 - filter("TABLE_NAME" LIKE 'S%')
3 - access("OBJECT_NAME" LIKE 'S%')
filter("OBJECT_NAME" LIKE 'S%')
Note
-----
- dynamic sampling used for this statement (level=2)
Statistics
----------------------------------------------------------
67 recursive calls
0 db block gets
403 consistent gets
446 physical reads
0 redo size
22852 bytes sent via SQL*Net to client
721 bytes received via SQL*Net from client
20 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
278 rows processed
SEIANG@seiang11g>
SEIANG@seiang11g>select * from wjq2 where exists (select 1 from wjq1 where wjq1.object_name=wjq2.table_name and wjq1.object_name like 'S%');
278 rows selected.
Elapsed: 00:00:00.02
Execution Plan
----------------------------------------------------------
Plan hash value: 1807911610
--------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
--------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 278 | 164K| 55 (0)| 00:00:01 |
|* 1 | HASH JOIN SEMI | | 278 | 164K| 55 (0)| 00:00:01 |
|* 2 | TABLE ACCESS FULL| WJQ2 | 278 | 146K| 31 (0)| 00:00:01 |
|* 3 | INDEX RANGE SCAN | IDX_OBJECT_NAME | 4435 | 285K| 24 (0)| 00:00:01 |
--------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("WJQ1"."OBJECT_NAME"="WJQ2"."TABLE_NAME")
2 - filter("WJQ2"."TABLE_NAME" LIKE 'S%')
3 - access("WJQ1"."OBJECT_NAME" LIKE 'S%')
filter("WJQ1"."OBJECT_NAME" LIKE 'S%')
Note
-----
- dynamic sampling used for this statement (level=2)
Statistics
----------------------------------------------------------
13 recursive calls
0 db block gets
295 consistent gets
2 physical reads
0 redo size
22852 bytes sent via SQL*Net to client
721 bytes received via SQL*Net from client
20 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
278 rows processed
通過(guò)上面兩個(gè)執(zhí)行計(jì)劃的對(duì)比發(fā)現(xiàn):
雖然他們的執(zhí)行計(jì)劃相同,但是使用exists比使用in的物理讀和邏輯讀明顯小很多,所以使用exists效率更高一下。
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