rolllup巧用

Posted 2020-10-15 ClarkYu

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--构造环境
drop table dept purge;
drop table emp purge;
create table dept as select * from scott.dept;
create table emp as select * from scott.emp;

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

set term off
set heading on
set verify off
set feedback off
set linesize 2000
set pagesize 30000
set long 999999999
set longchunksize 999999
set autotrace off

---写法1
SELECT a.dname,b.job,SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP BY a.dname,b.job;

DNAME JOB SUM_SAL
-------------- --------- ----------
SALES MANAGER 2850
SALES CLERK 950
ACCOUNTING MANAGER 2450
ACCOUNTING PRESIDENT 5000
ACCOUNTING CLERK 1300
SALES SALESMAN 5600
RESEARCH MANAGER 2975
RESEARCH ANALYST 6000
RESEARCH CLERK 1900

/*

不错不错，自我陶醉中....
停!先别得意，有人跑来说希望能增加一列总的汇总。
等等，更变态的需求来了，希望能得到不同DNAME的各自汇总!

*/

---写法2（没办法，先想到如下一个办法来实现楼上的变态需求）

set autotrace on
select * from (
SELECT a.dname,b.job,SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP BY a.dname,b.job
UNION ALL
--实现了部门的小计
SELECT a.dname,NULL, SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP BY a.dname
UNION ALL
--实现了所有部门总的合计
SELECT NULL,NULL, SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno)
order by dname;

DNAME JOB SUM_SAL
-------------- --------- ----------
ACCOUNTING CLERK 1300
ACCOUNTING MANAGER 2450
ACCOUNTING PRESIDENT 5000
ACCOUNTING 8750
RESEARCH CLERK 1900
RESEARCH MANAGER 2975
RESEARCH ANALYST 6000
RESEARCH 10875
SALES CLERK 950
SALES MANAGER 2850
SALES SALESMAN 5600
SALES 9400
29025

union all 合并笨办法产生的执行计划
-------------------------------------------------------------------------------
Plan hash value: 2979078843
-------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 29 | 812 | 23 (22)| 00:00:01 |
| 1 | SORT ORDER BY | | 29 | 812 | 23 (22)| 00:00:01 |
| 2 | VIEW | | 29 | 812 | 22 (19)| 00:00:01 |
| 3 | UNION-ALL | | | | | |
| 4 | HASH GROUP BY | | 14 | 756 | 8 (25)| 00:00:01 |
|* 5 | HASH JOIN | | 14 | 756 | 7 (15)| 00:00:01 |
| 6 | TABLE ACCESS FULL| DEPT | 4 | 88 | 3 (0)| 00:00:01 |
| 7 | TABLE ACCESS FULL| EMP | 14 | 448 | 3 (0)| 00:00:01 |
| 8 | HASH GROUP BY | | 14 | 672 | 8 (25)| 00:00:01 |
|* 9 | HASH JOIN | | 14 | 672 | 7 (15)| 00:00:01 |
| 10 | TABLE ACCESS FULL| DEPT | 4 | 88 | 3 (0)| 00:00:01 |
| 11 | TABLE ACCESS FULL| EMP | 14 | 364 | 3 (0)| 00:00:01 |
| 12 | SORT AGGREGATE | | 1 | 39 | | |
|* 13 | HASH JOIN | | 14 | 546 | 7 (15)| 00:00:01 |
| 14 | TABLE ACCESS FULL| DEPT | 4 | 52 | 3 (0)| 00:00:01 |
| 15 | TABLE ACCESS FULL| EMP | 14 | 364 | 3 (0)| 00:00:01 |
-------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
5 - access("A"."DEPTNO"="B"."DEPTNO")
9 - access("A"."DEPTNO"="B"."DEPTNO")
13 - access("A"."DEPTNO"="B"."DEPTNO")
统计信息
----------------------------------------------------------
0 recursive calls
0 db block gets
18 consistent gets
0 physical reads
0 redo size
783 bytes sent via SQL*Net to client
416 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
13 rows processed

---写法3（学本领很重要，如果你会rollup神功，性能就能大幅度提升，SQL书写也不麻烦了）

set autotrace on
SELECT a.dname,b.job, SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP BY ROLLUP(a.dname,b.job);

DNAME JOB SUM_SAL
-------------- --------- ----------
SALES CLERK 950
SALES MANAGER 2850
SALES SALESMAN 5600
SALES 9400
RESEARCH CLERK 1900
RESEARCH ANALYST 6000
RESEARCH MANAGER 2975
RESEARCH 10875
ACCOUNTING CLERK 1300
ACCOUNTING MANAGER 2450
ACCOUNTING PRESIDENT 5000
ACCOUNTING 8750
29025

rollup写法产生的执行计划
-----------------------------------------------------------------------------
Plan hash value: 1037965942
-----------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 14 | 756 | 8 (25)| 00:00:01 |
| 1 | SORT GROUP BY ROLLUP| | 14 | 756 | 8 (25)| 00:00:01 |
|* 2 | HASH JOIN | | 14 | 756 | 7 (15)| 00:00:01 |
| 3 | TABLE ACCESS FULL | DEPT | 4 | 88 | 3 (0)| 00:00:01 |
| 4 | TABLE ACCESS FULL | EMP | 14 | 448 | 3 (0)| 00:00:01 |
-----------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("A"."DEPTNO"="B"."DEPTNO")
统计信息
----------------------------------------------------------
0 recursive calls
0 db block gets
6 consistent gets
0 physical reads
0 redo size
778 bytes sent via SQL*Net to client
416 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
13 rows processed

--在这里应该可以清楚的发现，表的访问次数比union all硬平畴的要少，而且COST和逻辑读也少的多!

---写法4（如果你想再多一个维度，比如再增加雇佣年份的统计，之前union all硬拼凑的方法要崩溃了吧，不过rollup轻松搞定，如下）

SELECT to_char(b.hiredate,‘yyyy‘) hire_year,a.dname,b.job, SUM(sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP BY ROLLUP(to_char(b.hiredate,‘yyyy‘),a.dname,b.job);

HIRE DNAME JOB SUM_SAL
---- -------------- --------- ----------
1980 RESEARCH CLERK 800
1980 RESEARCH 800
1980 800
1981 SALES CLERK 950
1981 SALES MANAGER 2850
1981 SALES SALESMAN 5600
1981 SALES 9400
1981 RESEARCH ANALYST 3000
1981 RESEARCH MANAGER 2975
1981 RESEARCH 5975
1981 ACCOUNTING MANAGER 2450
1981 ACCOUNTING PRESIDENT 5000
1981 ACCOUNTING 7450
1981 22825
1982 ACCOUNTING CLERK 1300
1982 ACCOUNTING 1300
1982 1300
1987 RESEARCH CLERK 1100
1987 RESEARCH ANALYST 3000
1987 RESEARCH 4100
1987 4100
29025

执行计划
----------------------------------------------------------------------------
Plan hash value: 1037965942
-----------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 14 | 882 | 8 (25)| 00:00:01 |
| 1 | SORT GROUP BY ROLLUP| | 14 | 882 | 8 (25)| 00:00:01 |
|* 2 | HASH JOIN | | 14 | 882 | 7 (15)| 00:00:01 |
| 3 | TABLE ACCESS FULL | DEPT | 4 | 88 | 3 (0)| 00:00:01 |
| 4 | TABLE ACCESS FULL | EMP | 14 | 574 | 3 (0)| 00:00:01 |
-----------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("A"."DEPTNO"="B"."DEPTNO")
统计信息
----------------------------------------------------------
0 recursive calls
0 db block gets
6 consistent gets
0 physical reads
0 redo size
1107 bytes sent via SQL*Net to client
427 bytes received via SQL*Net from client
3 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
22 rows processed

--看官们注意到了吗，多了一个维度的统计，无论是COST还是逻辑读，都没有增加，够帅!

---写法5 （另外，不止是增加维度，更换维度的次序，对rollup 也是轻而易举的事，如下)

SELECT b.job,a.dname, SUM(b.sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP BY ROLLUP(b.job,a.dname);

JOB DNAME SUM_SAL
--------- -------------- ----------
CLERK SALES 950
CLERK RESEARCH 1900
CLERK ACCOUNTING 1300
CLERK 4150
ANALYST RESEARCH 6000
ANALYST 6000
MANAGER SALES 2850
MANAGER RESEARCH 2975
MANAGER ACCOUNTING 2450
MANAGER 8275
SALESMAN SALES 5600
SALESMAN 5600
PRESIDENT ACCOUNTING 5000
PRESIDENT 5000
29025



--------------------- 部分ROLLUP分组---------------------------------------
SELECT to_char(b.hiredate,‘yyyy‘) hire_year,a.dname,b.job, SUM(sal) sum_sal
FROM dept a,emp b
WHERE a.deptno = b.deptno
GROUP BY to_char(b.hiredate,‘yyyy‘),a.dname,ROLLUP(b.job);

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