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解析一个通过添加本地分区索引提高SQL性能的案例

程序员文章站 2023-01-08 11:35:51
该sql如下:复制代码 代码如下:select  /*+ parallel(src, 8) */ distinct  src.systemname as...

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。