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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: 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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