解析一个通过添加本地分区索引提高SQL性能的案例
解析一个通过添加本地分区索引提高SQL性能的案例
发布时间:2016-12-28 来源:查字典编辑
摘要:该sql如下:复制代码代码如下:Select/*+parallel(src,8)*/distinctsrc.systemnameassyst...

该sql如下:

复制代码 代码如下:

Select /*+ parallel(src, 8) */ distinct

src.systemname as systemname

, src.databasename as databasename

, src.tablename as tablename

, src.username as username

from <STRONG>meta_dbql_table_usage_exp_hst</STRONG> src

inner <STRONG>join DR_QRY_LOG_EXP_HST</STRONG> rl on

<STRONG>src.acctstringdate = rl.acctstringdate

and src.queryid = rl.queryid</STRONG>

And Src.Systemname = Rl.Systemname

and src.acctstringdate > sysdate - 30

And Rl.Acctstringdate > Sysdate - 30

inner join <STRONG>meta_dr_qry_log_tgt_all_hst </STRONG>tgt on

upper(tgt.systemname) = upper('MOZART')

And Upper(tgt.Databasename) = Upper('GDW_TABLES')

And Upper(tgt.Tablename) = Upper('SSA_SLNG_LSTG_MTRC_SD')

<STRONG>AND src.acctstringdate = tgt.acctstringdate

and rl.statement_id = tgt.statement_id</STRONG>

and rl.systemname = tgt.systemname

And Tgt.Acctstringdate > Sysdate - 30

And Not(

Upper(Tgt.Systemname)=Upper(src.systemname)

And

Upper(Tgt.Databasename) = Upper(Src.Databasename)

And

Upper(Tgt.Tablename) = Upper(Src.Tablename)

)

And tgt.Systemname is not null

And tgt.Databasename Is Not Null

And tgt.tablename is not null

SQL的简单分析

总得来看,这个SQL就是三个表(meta_dbql_table_usage_exp_hst,DR_QRY_LOG_EXP_HST,meta_dr_qry_log_tgt_all_hst)的INNER JOIN,这三个表数据量都在百万级别,且都是分区表(以acctstringdate为分区键),执行计划如下:

复制代码 代码如下:

------------------------------------------------------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost | Pstart| Pstop |

------------------------------------------------------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 1 | 159 | 8654 | | |

| 1 | PX COORDINATOR | | | | | | |

| 2 | PX SEND QC (RANDOM) | :TQ10002 | 1 | 159 | 8654 | | |

| 3 | SORT UNIQUE | | 1 | 159 | 8654 | | |

| 4 | PX RECEIVE | | 1 | 36 | 3 | | |

| 5 | PX SEND HASH | :TQ10001 | 1 | 36 | 3 | | |

|* 6 | TABLE ACCESS BY LOCAL INDEX ROWID| DR_QRY_LOG_EXP_HST | 1 | 36 | 3 | | |

| 7 | NESTED LOOPS | | 1 | 159 | 8633 | | |

| 8 | NESTED LOOPS | | 8959 | 1076K| 4900 | | |

| 9 | BUFFER SORT | | | | | | |

| 10 | PX RECEIVE | | | | | | |

| 11 | PX SEND BROADCAST | :TQ10000 | | | | | |

| 12 | PARTITION RANGE ITERATOR | | 1 | 56 | 4746 | KEY | 14 |

|* 13 | TABLE ACCESS FULL | META_DR_QRY_LOG_TGT_ALL_HST | 1 | 56 | 4746 | KEY | 14 |

| 14 | PX BLOCK ITERATOR | | 8959 | 586K| 154 | KEY | KEY |

|* 15 | TABLE ACCESS FULL | META_DBQL_TABLE_USAGE_EXP_HST | 8959 | 586K| 154 | KEY | KEY |

| 16 | PARTITION RANGE ITERATOR | | 1 | | 2 | KEY | KEY |

|* 17 | INDEX RANGE SCAN | DR_QRY_LOG_EXP_HST_IDX | 1 | | 2 | KEY | KEY |

------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

---------------------------------------------------

6 - filter("RL"."STATEMENT_ID"="TGT"."STATEMENT_ID" AND "RL"."SYSTEMNAME"="TGT"."SYSTEMNAME" AND "SRC"."SYSTEMNAME"="RL"."SYSTEMNAME")

13 - filter(UPPER("TGT"."SYSTEMNAME")='MOZART' AND UPPER("TGT"."DATABASENAME")='GDW_TABLES' AND

UPPER("TGT"."TABLENAME")='SSA_SLNG_LSTG_MTRC_SD' AND "TGT"."ACCTSTRINGDATE">SYSDATE@!-30 AND "TGT"."SYSTEMNAME" IS NOT NULL

"TGT"."DATABASENAME" IS NOT NULL AND "TGT"."TABLENAME" IS NOT NULL)

15 - filter("SRC"."ACCTSTRINGDATE"="TGT"."ACCTSTRINGDATE" AND (UPPER("TGT"."SYSTEMNAME")<>UPPER("SRC"."SYSTEMNAME") OR

UPPER("TGT"."DATABASENAME")<>UPPER("SRC"."DATABASENAME") OR UPPER("TGT"."TABLENAME")<>UPPER("SRC"."TABLENAME")) AND

"SRC"."ACCTSTRINGDATE">SYSDATE@!-30)

17 - access("SRC"."QUERYID"="RL"."QUERYID" AND "SRC"."ACCTSTRINGDATE"="RL"."ACCTSTRINGDATE")

filter("RL"."ACCTSTRINGDATE">SYSDATE@!-30)

定位问题

从上面执行计划中的表连接方式可以知道,这三个表之间进行了两次NESTED LOOP,问题出现在最里层的NESTED LOOP(对两个表都做了TABLE FULL SCAN),因为表都是百万级别的(即时过滤后的数据量也不小),性能问题就出现在内表(即被驱动表)META_DBQL_TABLE_USAGE_EXP_HST做了太多次的全表扫描。如果能把全表扫描转换成索引,则性能可以大幅度提高。

下面是NESTED LOOP的介绍:

嵌套连接把要处理的数据集分为外部循环(驱动数据源)和内部循环(被驱动数据源),外部循环只执行一次,内部循环执行的次数等于外部循环执行返回的数据个数。

这种连接的好处是内存使用非常少。

如果驱动数据源有限,且被驱动表在连接列上有相应的索引,则这种连接方式才是高效的。

下面是这三个表上索引的情况:

复制代码 代码如下:

SQL> select index_name, table_name from user_indexes where table_name in ('DR_QRY_LOG_EXP_HST',upper('meta_dbql_table_usage_exp_hst'), upper('meta_dr_qry_log_tgt_all_hs

INDEX_NAME TABLE_NAME

------------------------------------------------------------ ------------------------------------------------------------

META_DR_QRY_LOG_TGT_ALL_IDX META_DR_QRY_LOG_TGT_ALL_HST

META_DBQL_TUSAGE_EHST_IDX META_DBQL_TABLE_USAGE_EXP_HST

DR_QRY_LOG_EXP_HST_IDX DR_QRY_LOG_EXP_HST

CREATE INDEX "GV"."META_DR_QRY_LOG_TGT_ALL_IDX" ON "GV"."META_DR_QRY_LOG_TGT_ALL_HST" ("STATEMENT_ID", "ACCTSTRINGDATE")

CREATE INDEX "GV"."META_DBQL_TUSAGE_EHST_IDX" ON "GV"."META_DBQL_TABLE_USAGE_EXP_HST" ("QUERYID", "ACCTSTRINGDATE")

CREATE INDEX "GV"."DR_QRY_LOG_EXP_HST_IDX" ON "GV"."DR_QRY_LOG_EXP_HST" ("QUERYID", "ACCTSTRINGDATE")

这三个索引都是本地分区索引(都包含分区键acctstringdate),很显然,DR_QRY_LOG_EXP_HST表少了个索引,因为它与表meta_dr_qry_log_tgt_all_hst 在statement_id上做join,因此应该在它的statement_id上也创建本地分区索引如下:

复制代码 代码如下:

create index DR_QRY_LOG_EXP_HST_IDX2 on gv.DR_QRY_LOG_EXP_HST (statement_id,ACCTSTRINGDATE) local;

性能对比

新的执行计划如下:

复制代码 代码如下:

------------------------------------------------------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost | Pstart| Pstop |

------------------------------------------------------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 1 | 159 | 4838 | | |

| 1 | SORT UNIQUE | | 1 | 159 | 4838 | | |

|* 2 | TABLE ACCESS BY LOCAL INDEX ROWID | META_DBQL_TABLE_USAGE_EXP_HST | 1 | 67 | 3 | | |

| 3 | NESTED LOOPS | | 1 | 159 | 4816 | | |

| 4 | NESTED LOOPS | | 18 | 1656 | 4762 | | |

| 5 | PARTITION RANGE ITERATOR | | 1 | 56 | 4746 | KEY | 14 |

|* 6 | TABLE ACCESS FULL | META_DR_QRY_LOG_TGT_ALL_HST | 1 | 56 | 4746 | KEY | 14 |

| 7 | PARTITION RANGE ITERATOR | | 18 | 648 | 16 | KEY | 14 |

|* 8 | TABLE ACCESS BY LOCAL INDEX ROWID| DR_QRY_LOG_EXP_HST | 18 | 648 | 16 | KEY | 14 |

|* 9 | <STRONG>INDEX RANGE SCAN | DR_QRY_LOG_EXP_HST_IDX2</STRONG> | 31 | | 15 | KEY | 14 |

| 10 | PARTITION RANGE ITERATOR | | 1 | | 2 | KEY | KEY |

|* 11 | INDEX RANGE SCAN | META_DBQL_TUSAGE_EHST_IDX | 1 | | 2 | KEY | KEY |

------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):

---------------------------------------------------

2 - filter((UPPER("TGT"."SYSTEMNAME")<>UPPER("SRC"."SYSTEMNAME") OR

UPPER("TGT"."DATABASENAME")<>UPPER("SRC"."DATABASENAME") OR UPPER("TGT"."TABLENAME")<>UPPER("SRC"."TABLENAME"))

AND "SRC"."SYSTEMNAME"="RL"."SYSTEMNAME")

6 - filter(UPPER("TGT"."SYSTEMNAME")='MOZART' AND UPPER("TGT"."DATABASENAME")='GDW_TABLES' AND

UPPER("TGT"."TABLENAME")='SSA_SLNG_LSTG_MTRC_SD' AND "TGT"."ACCTSTRINGDATE">SYSDATE@!-30 AND "TGT"."SYSTEMNAME"

IS NOT NULL AND "TGT"."DATABASENAME" IS NOT NULL AND "TGT"."TABLENAME" IS NOT NULL)

8 - filter("RL"."SYSTEMNAME"="TGT"."SYSTEMNAME")

9 - access("RL"."STATEMENT_ID"="TGT"."STATEMENT_ID" AND "RL"."ACCTSTRINGDATE">SYSDATE@!-30 AND

"RL"."ACCTSTRINGDATE" IS NOT NULL)

11 - access("SRC"."QUERYID"="RL"."QUERYID" AND "SRC"."ACCTSTRINGDATE"="RL"."ACCTSTRINGDATE")

filter("SRC"."ACCTSTRINGDATE"="TGT"."ACCTSTRINGDATE" AND "SRC"."ACCTSTRINGDATE">SYSDATE@!-30)

从新的的执行计划可以看出,它的第一个NESTED LOOP果然用了最新创建的索引。

下面是执行时间:

复制代码 代码如下:

已用时间: 00: 00: 02.16

两秒种搞定,远远超出他期望的5s :)

方法总结

NESTED LOOP高效的条件:驱动数据源有限,且被驱动表在连接列上有相应的索引。

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