sql查询优化

Question

请比较以下内容:

INNER JOIN table1 t1 ON t1.someID LIKE 'search.%' AND 
                        t1.someID = ( 'search.' || t0.ID )

对比

INNER JOIN table1 t1 ON t1.someID = ( 'search.' || t0.ID )

有人告诉我，第一种情况已经过优化。但你知道，我不明白为什么会这样。据我了解，第二个示例应该运行得更快。

我们使用 Oracle，但我认为目前这无关紧要。

如果我错了，请解释。

谢谢

Answer 1

因此，这里是仅连接连接字符串的查询的解释计划:

SQL> explain plan for
  2     select e.* from emp e
  3         join big_table bt on bt.col2 = 'search'||trim(to_char(e.empno))
  4  /

Explained.

SQL> select * from table(dbms_xplan.display)
  2  /

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
Plan hash value: 179424166

-------------------------------------------------------------------------------
| Id  | Operation          | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |          |  1052 | 65224 |    43   (0)| 00:00:01 |
|   1 |  NESTED LOOPS      |          |  1052 | 65224 |    43   (0)| 00:00:01 |
|   2 |   TABLE ACCESS FULL| EMP      |    20 |   780 |     3   (0)| 00:00:01 |
|*  3 |   INDEX RANGE SCAN | BIG_VC_I |    53 |  1219 |     2   (0)| 00:00:01 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   3 - access("BT"."COL2"='search'||TRIM(TO_CHAR("E"."EMPNO")))

15 rows selected.

SQL>

与在其连接中包含 LIKE 子句的查询计划进行比较和对比:

SQL> explain plan for
  2     select e.* from emp e
  3           join big_table bt on (bt.col2 like 'search%'
  4               and bt.col2 = 'search'||trim(to_char(e.empno)))
  5  /

Explained.

SQL> select * from table(dbms_xplan.display)
  2  /

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
Plan hash value: 179424166

-------------------------------------------------------------------------------
| Id  | Operation          | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |          |     1 |    62 |     5   (0)| 00:00:01 |
|   1 |  NESTED LOOPS      |          |     1 |    62 |     5   (0)| 00:00:01 |
|*  2 |   TABLE ACCESS FULL| EMP      |     1 |    39 |     3   (0)| 00:00:01 |
|*  3 |   INDEX RANGE SCAN | BIG_VC_I |     1 |    23 |     2   (0)| 00:00:01 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - filter('search'||TRIM(TO_CHAR("E"."EMPNO")) LIKE 'search%')
   3 - access("BT"."COL2"='search'||TRIM(TO_CHAR("E"."EMPNO")))
       filter("BT"."COL2" LIKE 'search%')

17 rows selected.

SQL>

第二次查询的成本远低于第一次。但这是因为优化器估计第二个查询将返回比第一个查询少得多的行。更多的信息可以让数据库做出更准确的预测。 (实际上查询不会返回任何行)。

当然，这确实假定连接的列已编入索引，否则不会有任何区别。

要记住的另一件事是被查询的列会影响计划。此版本从 BIG_TABLE 而不是 EMP 中选择。

SQL> explain plan for
  2     select bt.* from emp e
  3           join big_table bt on (bt.col2 like 'search%'
  4                        and bt.col2 = 'search'||trim(to_char(e.empno)))
  5  /

Explained.

SQL> select * from table(dbms_xplan.display)
  2  /

PLAN_TABLE_OUTPUT
---------------------------------------------------------------------------------------------------------------

Plan hash value: 4042413806

------------------------------------------------------------------------------------------
| Id  | Operation                    | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |           |     1 |    46 |     4   (0)| 00:00:01 |
|   1 |  NESTED LOOPS                |           |       |       |            |          |
|   2 |   NESTED LOOPS               |           |     1 |    46 |     4   (0)| 00:00:01 |
|*  3 |    INDEX FULL SCAN           | PK_EMP    |     1 |     4 |     1   (0)| 00:00:01 |
|*  4 |    INDEX RANGE SCAN          | BIG_VC_I  |     1 |       |     2   (0)| 00:00:01 |
|   5 |   TABLE ACCESS BY INDEX ROWID| BIG_TABLE |     1 |    42 |     3   (0)| 00:00:01 |
------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   3 - filter('search'||TRIM(TO_CHAR("E"."EMPNO")) LIKE 'search%')
   4 - access("BT"."COL2"='search'||TRIM(TO_CHAR("E"."EMPNO")))
       filter("BT"."COL2" LIKE 'search%')

19 rows selected.

SQL>