oracle中如何改写exists降低逻辑读

这篇文章主要介绍oracle中如何改写exists降低逻辑读，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！通过将exists改写成in或这inner join优化sql。
Sql_id：056bs9dzz8mwy问题简述：逻辑读高。Sql文本：SELECT A.*, a.rowid FROM WBANK.WD_BANK_BASEINFOMATION AWHERE EXISTS (SELECT 1 FROM (select KEYWORD, TYPECODE, INNERCODE, COUNT(*) FROM WBANK.WD_BANK_BASEINFOMATION WHERE SUBSTR(TYPECODE, 1, 3) = ‘001’ GROUP BY KEYWORD, TYPECODE, INNERCODE HAVING COUNT(*) 1) B WHERE A.KEYWORD = B.KEYWORD AND A.TYPECODE = B.TYPECODE AND A.INNERCODE = B.INNERCODE);执行计划：Execution Plan———————————————————-Plan hash value: 1318914978————————————————————————————————-| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |————————————————————————————————-| 0 | SELECT STATEMENT | | 1 | 130 | 7930K (1)| 39:39:10 ||* 1 | FILTER | | | | | || 2 | TABLE ACCESS FULL | WD_BANK_BASEINFOMATION | 2640K| 327M| 6249 (2)| 00:01:53 ||* 3 | FILTER | | | | | || 4 | SORT GROUP BY NOSORT| | 1 | 47 | 3 (0)| 00:00:01 ||* 5 | INDEX RANGE SCAN | IDX_WD_B_BI | 1 | 47 | 3 (0)| 00:00:01 |————————————————————————————————-Predicate Information (identified by operation id):————————————————— 1 – filter( EXISTS (SELECT 0 FROM “WBANK”.”WD_BANK_BASEINFOMATION” “WD_BANK_BASEINFOMATION” WHERE “TYPECODE”=:B1 AND “KEYWORD”=:B2 AND “INNERCODE”=:B3 AND SUBSTR(“TYPECODE”,1,3)=’001′ GROUP BY “KEYWORD”,”TYPECODE”,”INNERCODE” HAVING COUNT(*)1)) 3 – filter(COUNT(*)1) 5 – access(“KEYWORD”=:B1 AND “TYPECODE”=:B2 AND “INNERCODE”=:B3) filter(“INNERCODE”=:B1 AND SUBSTR(“TYPECODE”,1,3)=’001′)Statistics———————————————————- 1 recursive calls 0 db block gets
2329554 consistent gets 13 physical reads 0 redo size 2507 bytes sent via SQL*Net to client 513 bytes received via SQL*Net from client 1 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 0 rows processed可以发现逻辑读高达200多万。刚看到这个sql的时候猜想会不会逻辑有问题，导致结果集为空。跑了一遍发现结果集确实为空。子查询的innercode列全部为null。根据条件A.INNERCODE = B.INNERCODE外部表（虽然是同一张表）是不会有匹配结果的。转念一想如果子查询innercode列有非空的，那就不会有问题了。当然了还是要询问开发结果集与该列为空是否有必然联系，如果有联系的话可以利用该逻辑关系改写sql。当然，这是后话了。看一下数据分布：SQL> select count(*) from WBANK.WD_BANK_BASEINFOMATION; COUNT(*)———- 2645546SQL> select count(*) from (select KEYWORD, TYPECODE, INNERCODE, COUNT(*) 2 FROM WBANK.WD_BANK_BASEINFOMATION 3 WHERE SUBSTR(TYPECODE, 1, 3) = ‘001’ 4 GROUP BY KEYWORD, TYPECODE, INNERCODE 5 HAVING COUNT(*) 1); COUNT(*)———- 128外层结果集是全表数据260多万。子查询结果集只有128条。而根据oracle对exists的处理，会以外部结果集为驱动，也就是说要执行260多万次，这显然是不合理的。如果外部结果集大，内部结果集小的话，这种情况下通常是要用in，以内部结果集为驱动，这样也就执行128次。验证一下执行次数的问题：SQL> alter session set statistics_level=all;SQL> SELECT A.*, a.rowid 2 FROM WBANK.WD_BANK_BASEINFOMATION A 3 WHERE EXISTS (SELECT 1 4 FROM (select KEYWORD, TYPECODE, INNERCODE, COUNT(*) 5 FROM WBANK.WD_BANK_BASEINFOMATION 6 WHERE SUBSTR(TYPECODE, 1, 3) = ‘001’ 7 GROUP BY KEYWORD, TYPECODE, INNERCODE 8 HAVING COUNT(*) 1) B 9 WHERE A.KEYWORD = B.KEYWORD10 AND A.TYPECODE = B.TYPECODE11 AND A.INNERCODE = B.INNERCODE);no rows selectedSQL> SELECT * FROM TABLE(dbms_xplan.display_cursor(null,null,’ALLSTATS LAST’));Plan hash value: 1318914978PLAN_TABLE_OUTPUT———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————–| Id | Operation | Name |
Starts
| E-Rows | A-Rows | A-Time | Buffers |———————————————————————————————————–| 0 | SELECT STATEMENT | | 1 | | 0 |00:00:09.75 | 2329K||* 1 | FILTER | | 1 | | 0 |00:00:09.75 | 2329K|| 2 | TABLE ACCESS FULL | WD_BANK_BASEINFOMATION | 1 | 2640K| 2645K|00:00:00.61 | 12226 ||* 3 | FILTER | |
2632K| | 0 |00:00:07.38 | 2317K|| 4 | SORT GROUP BY NOSORT| |
2632K| 1 | 1273K|00:00:06.64 | 2317K||* 5 | INDEX RANGE SCAN | IDX_WD_B_BI |
2632K| 1 | 1273K|00:00:03.42 | 2317K|———————————————————————————————————–PLAN_TABLE_OUTPUT————————————————————————————————————————————————————————————————————————————————————————————————————Predicate Information (identified by operation id):————————————————— 1 – filter( IS NOT NULL) 3 – filter(COUNT(*)1) 5 – access(“KEYWORD”=:B1 AND “TYPECODE”=:B2 AND “INNERCODE”=:B3) filter((“INNERCODE”=:B1 AND SUBSTR(“TYPECODE”,1,3)=’001′))31 rows selected.可以看到starts列部分，内部子查询2632k次，与外表数据量吻合。用in改写sqlSELECT A.*, a.rowid FROM WBANK.WD_BANK_BASEINFOMATION AWHERE (A.KEYWORD,A.TYPECODE,A.INNERCODE) in (SELECT B.KEYWORD,B.TYPECODE,B.INNERCODE FROM (select KEYWORD, TYPECODE, INNERCODE, COUNT(*) FROM WBANK.WD_BANK_BASEINFOMATION WHERE SUBSTR(TYPECODE, 1, 3) = ‘001’ GROUP BY KEYWORD, TYPECODE, INNERCODE HAVING COUNT(*) 1) B);执行计划：Set autotrace on执行sql。得到执行计划：Execution Plan———————————————————-Plan hash value: 1385212545——————————————————————————————————-| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |——————————————————————————————————-| 0 | SELECT STATEMENT | | 3 | 7008 | 6236 (2)| 00:01:53 || 1 | NESTED LOOPS | | 3 | 7008 | 6236 (2)| 00:01:53 || 2 | NESTED LOOPS | | 3 | 7008 | 6236 (2)| 00:01:53 || 3 | VIEW | VW_NSO_1 | 55 | 118K| 6228 (2)| 00:01:53 ||* 4 | FILTER | | | | | || 5 | HASH GROUP BY | | 1 | 2585 | 6228 (2)| 00:01:53 ||* 6 | TABLE ACCESS FULL | WD_BANK_BASEINFOMATION | 26410 | 1212K| 6226 (2)| 00:01:53 ||* 7 | INDEX RANGE SCAN | IDX_WD_B_BI | 1 | | 2 (0)| 00:00:01 || 8 | TABLE ACCESS BY INDEX ROWID| WD_BANK_BASEINFOMATION | 1 | 130 | 3 (0)| 00:00:01 |——————————————————————————————————-Predicate Information (identified by operation id):————————————————— 4 – filter(COUNT(*)1) 6 – filter(SUBSTR(“TYPECODE”,1,3)=’001′) 7 – access(“A”.”KEYWORD”=”KEYWORD” AND “A”.”TYPECODE”=”TYPECODE” AND “A”.”INNERCODE”=”INNERCODE”) filter(“A”.”INNERCODE” IS NOT NULL AND “A”.”INNERCODE”=”INNERCODE”)Statistics———————————————————- 1 recursive calls 0 db block gets
12226 consistent gets 0 physical reads 0 redo size 2507 bytes sent via SQL*Net to client 513 bytes received via SQL*Net from client 1 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 0 rows processed执行计划已经变成以内部子查询为驱动表了。而且逻辑读从200万降低1万。下面再来验证执行次数：SQL> alter session set statistics_level=all;执行sqlSQL> SELECT * FROM TABLE(dbms_xplan.display_cursor(null,null,’ALLSTATS LAST’));得到执行计划（部分）：———————————————————————————————————————| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem ||——————————————–免费主机域名————————————————————————-| 0 | SELECT STATEMENT | | 1 | | 0 |00:00:02.28 | 12226 | |||* 1 | HASH JOIN RIGHT SEMI | | 1 | 1311K| 0 |00:00:02.28 | 12226 | 391K|)|| 2 | VIEW | VW_NSO_1 | 1 | 80389 | 128 |00:00:02.28 | 12226 | |||* 3 | FILTER | | 1 | | 128 |00:00:02.28 | 12226 | ||| 4 | HASH GROUP BY | | 1 | 4020 | 1607K|00:00:02.17 | 12226 | 710M|)||* 5 | TABLE ACCESS FULL| WD_BANK_BASEINFOMATION | 1 | 1607K| 1607K|00:00:00.78 | 12226 | |||* 6 | TABLE ACCESS FULL | WD_BANK_BASEINFOMATION | 0 | 1311K| 0 |00:00:00.01 | 0 | ||发现执行计划并不一致，这个才是真正的执行计划。Predicate Information (identified by operation id):————————————————— 1 – access(“A”.”KEYWORD”=”KEYWORD” AND “A”.”TYPECODE”=”TYPECODE” AND “A”.”INNERCODE”=”INNERCODE”) 3 – filter(COUNT(*)1) 5 – filter(SUBSTR(“TYPECODE”,1,3)=’001′) 6 – filter(“A”.”INNERCODE” IS NOT NULL)NotePLAN_TABLE_OUTPUT—————————————————————————————————————————————————————————————————————————————————————————————————————– – cardinality feedback used for this statement后面发现了基数反馈的东西。估计值是实际值差别还是很大的。说明统计信息是有问题的。查看统计信息已经是4月份收集的了。收集统计信息SQL> exec dbms_stats.gather_table_stats(ownname => ‘WBANK’,tabname => ‘WD_BANK_BASEINFOMATION’,estimate_percent => 10,method_opt=> ‘for all columns size repeat’,no_invalidate=>false);PL/SQL procedure successfully completed.收集完统计信息后的执行计划Plan hash value: 1385212545——————————————————————————————————————————————–| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |——————————————————————————————————————————————–| 0 | SELECT STATEMENT | | 1 | | 2 |00:00:02.55 | 12232 | | | || 1 | NESTED LOOPS | | 1 | 3 | 2 |00:00:02.55 | 12232 | | | || 2 | NESTED LOOPS | | 1 | 3 | 2 |00:00:02.55 | 12230 | | | || 3 | VIEW | VW_NSO_1 | 1 | 53 | 129 |00:00:02.55 | 12226 | | | ||* 4 | FILTER | | 1 | | 129 |00:00:02.55 | 12226 | | | || 5 | HASH GROUP BY | | 1 | 1 | 1607K|00:00:02.40 | 12226 | 710M| 17M| 170M (0)||* 6 | TABLE ACCESS FULL | WD_BANK_BASEINFOMATION | 1 | 26458 | 1607K|00:00:00.80 | 12226 | | | ||* 7 | INDEX RANGE SCAN | IDX_WD_B_BI |
129
| 1 | 2 |00:00:00.01 | 4 | | | || 8 | TABLE ACCESS BY INDEX ROWID| WD_BANK_BASEINFOMATION | 2 | 1 | 2 |00:00:00.01 | 2 | | | |——————————————————————————————————————————————–Predicate Information (identified by operation id):————————————————— 4 – filter(COUNT(*)1) 6 – filter(SUBSTR(“TYPECODE”,1,3)=’001′) 7 – access(“A”.”KEYWORD”=”KEYWORD” AND “A”.”TYPECODE”=”TYPECODE” AND “A”.”INNERCODE”=”INNERCODE”) filter((“A”.”INNERCODE” IS NOT NULL AND “A”.”INNERCODE”=”INNERCODE”))34 rows selected.可以看到确实是129次。而且也不存在基数反馈导致执行计划改变了。逻辑读还是在1万多。突然想到还可以使用inner join的方法来改写sqlSELECT A.*, a.rowid FROM WBANK.WD_BANK_BASEINFOMATION Ainner join (select KEYWORD, TYPECODE, INNERCODE, COUNT(*) FROM WBANK.WD_BANK_BASEINFOMATION WHERE SUBSTR(TYPECODE, 1, 3) = ‘001’ GROUP BY KEYWORD, TYPECODE, INNERCODE HAVING COUNT(*) 1) B on A.KEYWORD = B.KEYWORD AND A.TYPECODE = B.TYPECODE AND A.INNERCODE = B.INNERCODE;执行计划：Plan hash value: 4254729379PLAN_TABLE_OUTPUT——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————–| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |———————————————免费主机域名———————————————————————————————–| 0 | SELECT STATEMENT | | 1 | | 2 |00:00:02.48 | 12232 | | | || 1 | NESTED LOOPS | | 1 | 59 | 2 |00:00:02.48 | 12232 | | | || 2 | NESTED LOOPS | | 1 | 59 | 2 |00:00:02.48 | 12230 | | | || 3 | VIEW | | 1 | 59 | 129 |00:00:02.48 | 12226 | | | ||* 4 | FILTER | | 1 | | 129 |00:00:02.48 | 12226 | | | || 5 | HASH GROUP BY | | 1 | 59 | 1607K|00:00:02.31 | 12226 | 710M| 17M| 168M (0)||* 6 | TABLE ACCESS FULL | WD_BANK_BASEINFOMATION | 1 | 26466 | 1607K|00:00:00.76 | 12226 | | | |PLAN_TABLE_OUTPUT————————————————————————————————————————————————————————————————————————————————————————————————————|* 7 | INDEX RANGE SCAN | IDX_WD_B_BI |
129
| 1 | 2 |00:00:00.01 | 4 | | | || 8 | TABLE ACCESS BY INDEX ROWID| WD_BANK_BASEINFOMATION | 2 | 1 | 2 |00:00:00.01 | 2 | | | |——————————————————————————————————————————————–Predicate Information (identified by operation id):————————————————— 4 – filter(COUNT(*)1) 6 – filter(SUBSTR(“TYPECODE”,1,3)=’001′) 7 – access(“A”.”KEYWORD”=”B”.”KEYWORD” AND “A”.”TYPECODE”=”B”.”TYPECODE” AND “A”.”INNERCODE”=”B”.”INNERCODE”) filter((“A”.”INNERCODE” IS NOT NULL AND “A”.”INNERCODE”=”B”.”INNERCODE”))PLAN_TABLE_OUTPUT————————————————————————————————————————————————————————————————————————————————————————————————————34 rows selected.逻辑读Statistics———————————————————- 1 recursive calls 0 db block gets
12232 consistent gets 0 physical reads 0 redo size 3083 bytes sent via SQL*Net to client 524 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 2 rows processed逻辑读也是1万多。看一下执行计划发现，瓶颈都在对表的全表扫且过滤条件filter(SUBSTR(“TYPECODE”,1,3)=’001′)。可以考虑在这列上建函数索引，SQL> select count(*) from wbank.WD_BANK_BASEINFOMATION WHERE SUBSTR(TYPECODE, 1, 3) = ‘001’; COUNT(*)———- 1607674表的数据一共只有2645546，返回1607674，所以建了索引也没用，所以不用建索引了。综上所述。优化建议是更改sql，将exists改成in或者inner join：SELECT A.*, a.rowid FROM WBANK.WD_BANK_BASEINFOMATION AWHERE (A.KEYWORD,A.TYPECODE,A.INNERCODE) in (SELECT B.KEYWORD,B.TYPECODE,B.INNERCODE FROM (select KEYWORD, TYPECODE, INNERCODE, COUNT(*) FROM WBANK.WD_BANK_BASEINFOMATION WHERE SUBSTR(TYPECODE, 1, 3) = ‘001’ GROUP BY KEYWORD, TYPECODE, INNERCODE HAVING COUNT(*) 1) B);或者SELECT A.*, a.rowid FROM WBANK.WD_BANK_BASEINFOMATION Ainner join (select KEYWORD, TYPECODE, INNERCODE, COUNT(*) FROM WBANK.WD_BANK_BASEINFOMATION WHERE SUBSTR(TYPECODE, 1, 3) = ‘001’ GROUP BY KEYWORD, TYPECODE, INNERCODE HAVING COUNT(*) 1) B on A.KEYWORD = B.KEYWORD AND A.TYPECODE = B.TYPECODE AND A.INNERCODE = B.INNERCODE;逻辑读将从200多万将至1万多。以上是“oracle中如何改写exists降低逻辑读”这篇文章的所有内容，感谢各位的阅读！希望分享的内容对大家有帮助，更多相关知识，欢迎关注云技术行业资讯频道！

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