经常别人说EXISTS比IN快!NOT EXISTS比NOT IN快!然而事实真的如此么?(EXISTS它主要是判断条件结果的真和假,隐式返回True&false)
我们先讨论IN和EXISTS。 select * from t1 where x in ( select y from t2 ) 事实上可以理解为: select * from t1, ( select distinct y from t2 ) t2 where t1.x = t2.y; ——如果你有一定的SQL优化经验,从这句很自然的可以想到t2绝对不能是个大表,因为需要对t2进行全表的“唯一排序”,如果t2很大这个排序的性能是不可忍受的。但是t1可以很大,为什么呢?最通俗的理解就是因为t1.x=t2.y可以走索引。但这并不是一个很好的解释。试想,如果t1.x和t2.y都有索引,我们知道索引是种有序的结构,因此t1和t2之间最佳的方案是走merge join。另外,如果t2.y上有索引,对t2的排序性能也有很大提高。 select * from t1 where exists ( select null from t2 where y = x ) 可以理解为: for x in ( select * from t1 ) loop if ( exists ( select null from t2 where y = x.x ) then OUTPUT THE RECORD! end if end loop ——这个更容易理解,t1永远是个表扫描!因此t1绝对不能是个大表,而t2可以很大,因为y=x.x可以走t2.y的索引。 综合以上对IN/EXISTS的讨论,我们可以得出一个基本通用的结论:IN适合于外表大而内表小的情况;EXISTS适合于外表小而内表大的情况。 如果你对上述说法表示怀疑,请看以下测试: ******************************************************************************** SQL> create table big as select * from all_objects;
表已创建。
SQL> insert /*+ append */ into big select * from big;
已创建26872行。
SQL> commit;
提交完成。
SQL> insert /*+ append */ into big select * from big;
已创建53744行。
SQL> commit;
提交完成。
SQL> insert /*+ append */ into big select * from big;
已创建107488行。
SQL> commit;
提交完成。
SQL> create index big_idx on big(object_id);
索引已创建。
SQL> create table small as select * from all_objects where rownum < 100;
表已创建。
SQL> create index small_idx on small(object_id);
索引已创建。 ******************************************************************************** 运行SQL并设置EVENT=10046,用TKPROF格式化TRACE文件,结果如下。
大表在外,小表在内的测试: ******************************************************************************** select count(subobject_name) from big where object_id in ( select object_id from small )
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.01 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.00 0.14 29 900 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.00 0.15 29 900 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
792 TABLE ACCESS (BY INDEX ROWID) OF 'BIG'
892 NESTED LOOPS
99 VIEW OF 'VW_NSO_1'
99 SORT (UNIQUE)
99 TABLE ACCESS (FULL) OF 'SMALL'
792 INDEX (RANGE SCAN) OF 'BIG_IDX' (NON-UNIQUE) select count(subobject_name) from big where exists ( select null from small where small.object_id = big.object_id )
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 1.90 2.72 2917 216125 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 1.90 2.72 2917 216125 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
792 FILTER
214976 TABLE ACCESS (FULL) OF 'BIG'
225 INDEX (RANGE SCAN) OF 'SMALL_IDX' (NON-UNIQUE)
********************************************************************************
用IN的性能数据:
cpu=0.00 elapsed=0.15 query=900 current=0 disk=29
用EXISTS的性能数据:
cpu=1.90 elapsed=2.72 query=216125 current=0 disk=2917
——在CPU的消耗和LIO、PIO上的对比十分明显,IN的效率高得多! 大表在内,小表在外的测试: ******************************************************************************** select count(subobject_name) from small where object_id in ( select object_id from big )
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.41 1.71 2917 2982 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.41 1.72 2917 2982 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
99 TABLE ACCESS (BY INDEX ROWID) OF 'SMALL'
26972 NESTED LOOPS
26872 VIEW OF 'VW_NSO_1'
26872 SORT (UNIQUE)
214976 TABLE ACCESS (FULL) OF 'BIG'
99 INDEX (RANGE SCAN) OF 'SMALL_IDX' (NON-UNIQUE) select count(subobject_name) from small where exists ( select null from big where small.object_id = big.object_id )
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.00 0.00 0 202 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.00 0.00 0 202 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
99 FILTER
99 TABLE ACCESS (FULL) OF 'SMALL'
99 INDEX (RANGE SCAN) OF 'BIG_IDX' (NON-UNIQUE)
********************************************************************************
用IN的性能数据:
cpu=0.41 elapsed=1.72 query=2982 current=26 disk=2917
用EXISTS的性能数据:
cpu=0.00 elapsed=0.00 query=202 current=0 disk=0
——在CPU的消耗和PIO、LIO上的对比十分明显,EXISTS效率高得多! 有些遗憾的是我这个测试是在RBO下进行的,RBO是个死板的只根据优先级来确定执行计划的优化器,RBO不会评估实际的执行计划对系统造成的影响。在RBO中NESTED LOOP的优先级要远远大于MERGE JOIN,只要能走NESTED LOOP RBO就绝不会走MERGE JOIN。如果你用的是CBO,并且对表、索引做过统计分析,上面IN的测试一定会选择走MERGE JOIN。我们用HINTS在RBO下强制走MERGE JOIN对比一下这个SQL分别走MJ和NL的性能: ******************************************************************************** select count(subobject_name) from small where object_id in ( select/*+ use_merge(small big) */ object_id from big )
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.01 0.17 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.09 0.27 187 473 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.10 0.44 187 473 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
99 MERGE JOIN
26872 SORT (UNIQUE)
214976 INDEX (FULL SCAN) OF 'BIG_IDX' (NON-UNIQUE)
99 SORT (JOIN)
99 TABLE ACCESS (FULL) OF 'SMALL'
********************************************************************************
可以看到:
NESTED LOOP:cpu=0.41 elapsed=1.72 query=2982 current=26 disk=2917
MERGE JOIN:cpu=0.10 elapsed=0.44 query=437 current=2 disk=187
——这也证实了我上面的说法。很多人不敢让自己的SQL走merge join,其实对于两个已经具有排序结构的表merge join是最佳选择。 下面我们讨论NOT IN和NOT EXISTS,我把它们放在一起讨论实属被逼无奈,因为很多人喜欢拿它们比较。其实NOT IN/NOT EXISTS与IN/EXISTS不一样,IN/EXISTS是完全可以作为等价替换结构的,而NOT IN/NOT EXISTS则不同,它们并不是等价替换结构!只有当子查询中不可能返回空值时,NOT IN/NOT EXISTS才可以等价替换。 为什么?请看: ******************************************************************************** SQL> conn scott/tiger@tdb; 已连接。 SQL> select count(*) 2 from emp 3 where mgr is null;
COUNT(*) ---------------- 1
SQL> select count(*) 2 from emp 3 where empno not in ( select mgr from emp );
COUNT(*) ---------------- 0
SQL> select count(*) 2 from emp t1 3 where not exists ( select null 4 from emp t2 5 where t2.mgr = t1.empno );
COUNT(*) ---------------- 7 ******************************************************************************** 如果子查询中返回的结果集含有空值NOT IN永远是0,因为NULL代表“未知”,任何值和NULL比较永远是false。 现在我们基于假设——子查询中不返回空值,来比较NOT IN和NOT EXISTS。 在RBO中如果不使用HINTS来改变NOT IN的执行计划,几乎任何时候NOT IN都比NOT EXISTS慢得多,在CBO中如果具有准确的统计信息NOT IN的效率和NOT EXISTS的一样,甚至会比NOT EXISTS快得多。 调整NOT IN性能的基本原则是:如果想让NOT IN跑得快就必须走合适的连接。 select * from t1 where x not in ( select y from t2 ) 以这个句子为例(y无空值) 这个句子可以等价替换为: a) select * from t1 where not exists ( select null from t2 where t2.y=t1.x) 或 b) select t1.* from t1, t2 where t1.x=t2.y(+) and t2.y is null 测试如下: ******************************************************************************** SQL> create table t1 as select * from all_objects where rownum <= 5000;
表已创建。
SQL> create table t2 as select * from all_objects where rownum <= 4950;
表已创建。
SQL> create index t2_idx on t2(object_id);
索引已创建。 ******************************************************************************** RBO下的测试: ******************************************************************************** select count(*) from t1 rbo where object_id not in ( select object_id from t2 )
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 6.13 19.12 127487 197502 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 6.13 19.13 127487 197502 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
50 FILTER
5000 TABLE ACCESS (FULL) OF 'T1'
4950 TABLE ACCESS (FULL) OF 'T2' select count(*) from t1 rbo where not exists ( select null from t2 where t2.object_id = rbo.object_id)
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.01 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.01 0.12 83 10075 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.02 0.12 83 10075 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
50 FILTER
5000 TABLE ACCESS (FULL) OF 'T1'
4950 INDEX (RANGE SCAN) OF 'T2_IDX' (NON-UNIQUE) select count(*) from t1, t2 rbo where t1.object_id = rbo.object_id(+) and rbo.object_id is null
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.00 0.05 72 5087 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.00 0.06 72 5087 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
50 FILTER
5000 NESTED LOOPS (OUTER)
5000 TABLE ACCESS (FULL) OF 'T1'
4950 INDEX (RANGE SCAN) OF 'T2_IDX' (NON-UNIQUE)
********************************************************************************
RBO自己选择的执行计划,性能数据:
NOT IN:cpu=6.13 elapsed=19.13 query=197502 current=0 disk=127487
NOT EXISTS:cpu=0.02 elapsed=0.12 query=10075 current=0 disk=83
OUTER JOIN:cpu=0.00 elapsed=0.06 query=5087 current=0 disk=72
——NOT EXISTS的效率比NOT IN好很多,但与OUTER JOIN相比NOT EXISTS的效率略低。 RBO,用HINTS改变NOT IN的执行计划: ******************************************************************************** select count(*) from t1 rbo where object_id not in ( select/*+ hash_aj(rbo t2) */ object_id from t2 )
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.04 0.45 0 3 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.01 0.09 48 191 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.05 0.55 48 194 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
0 SORT (AGGREGATE)
0 HASH JOIN (ANTI)
0 TABLE ACCESS (FULL) OF 'T1'
0 INDEX (FAST FULL SCAN) OF 'T2_IDX' (NON-UNIQUE)
********************************************************************************
在只有t2.object_id有索引的情况下,hash join-anti性能数据如下:
HJ-ANTI:cpu=0.05 elapsed=0.55 query=194 current=0 disk=48
——性能好了很多! 在t1.object_id上建立索引,使用merge join-anti: ******************************************************************************** select count(*) from t1 rbo where object_id not in ( select /*+ merge_aj(rbo t2) */ object_id from t2 )
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.00 0.00 0 28 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.00 0.00 0 28 0 1 Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT GOAL: CHOOSE
1 SORT (AGGREGATE)
50 MERGE JOIN (ANTI)
5000 INDEX (FULL SCAN) OF 'T1_IDX' (NON-UNIQUE)
4950 SORT (UNIQUE)
4950 INDEX (FAST FULL SCAN) OF 'T2_IDX' (NON-UNIQUE)
********************************************************************************
在t1.object_id上建立索引,merge join-anti的性能数据如下:
MJ-ANTI:cpu=0.00 elapsed=0.00 query=28 current=0 disk=0
——这个NOT IN语句在t1.object_id、t2.object_id都有索引的情况下,merge join-anti的效率高于上面的任何SQL。 综上,只要NOT IN走合适的连接,其效率很高甚至高于NOT EXISTS和OUTER JOIN。 |
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