【CUBE】oracle分组函数之CUBE演示及与ROLLUP的比较

CUBE与ROLLUP功能很相似，也是在统计数据时的一把好手。
对ROLLUP的统计效果请参考《【实验】【ROLLUP】oracle分组函数之ROLLUP演示》http://space.itpub.net/?uid-519536-action-viewspace-itemid-610995

1.先显示一下ROLLUP的效果
sec@ora10g> select * from group_test;

GROUP_ID JOB NAME SALARY
---------- ---------- ---------- ----------
10 Coding Bruce 1000
10 Programmer Clair 1000
10 Architect Gideon 1000
10 Director Hill 1000
20 Coding Jason 2000
20 Programmer Joey 2000
20 Architect Martin 2000
20 Director Michael 2000
30 Coding Rebecca 3000
30 Programmer Rex 3000
30 Architect Richard 3000
30 Director Sabrina 3000
40 Coding Samuel 4000
40 Programmer Susy 4000
40 Architect Tina 4000
40 Director Wendy 4000

16 rows selected.

sec@ora10g> select group_id,job,grouping(GROUP_ID),grouping(JOB),sum(salary) from group_test group by rollup(group_id, job);

GROUP_ID JOB GROUPING(GROUP_ID) GROUPING(JOB) SUM(SALARY)
---------- ---------- ------------------ ------------- -----------
10 Coding 0 0 1000
10 Director 0 0 1000
10 Architect 0 0 1000
10 Programmer 0 0 1000
10 0 1 4000
20 Coding 0 0 2000
20 Director 0 0 2000
20 Architect 0 0 2000
20 Programmer 0 0 2000
20 0 1 8000
30 Coding 0 0 3000
30 Director 0 0 3000
30 Architect 0 0 3000
30 Programmer 0 0 3000
30 0 1 12000
40 Coding 0 0 4000
40 Director 0 0 4000
40 Architect 0 0 4000
40 Programmer 0 0 4000
40 0 1 16000
1 1 40000

21 rows selected.

2.再看一下CUBE的效果
sec@ora10g> select group_id,job,grouping(GROUP_ID),grouping(JOB),sum(salary) from group_test group by cube(group_id, job) order by 1;

GROUP_ID JOB GROUPING(GROUP_ID) GROUPING(JOB) SUM(SALARY)
---------- ---------- ------------------ ------------- -----------
10 Architect 0 0 1000
10 Coding 0 0 1000
10 Director 0 0 1000
10 Programmer 0 0 1000
10 0 1 4000
20 Architect 0 0 2000
20 Coding 0 0 2000
20 Director 0 0 2000
20 Programmer 0 0 2000
20 0 1 8000
30 Architect 0 0 3000
30 Coding 0 0 3000
30 Director 0 0 3000
30 Programmer 0 0 3000
30 0 1 12000
40 Architect 0 0 4000
40 Coding 0 0 4000
40 Director 0 0 4000
40 Programmer 0 0 4000
40 0 1 16000
Architect 1 0 10000
Coding 1 0 10000
Director 1 0 10000
Programmer 1 0 10000
1 1 40000

25 rows selected.

3.仔细观察一下，这儿两个的细微差别是什么？
rollup(a,b) 统计列包含：(a,b)、(a)、()
rollup(a,b,c) 统计列包含：(a,b,c)、(a,b)、(a)、()
……以此类推ing……

cube(a,b) 统计列包含：(a,b)、(a)、(b)、()
cube(a,b,c) 统计列包含：(a,b,c)、(a,b)、(a,c)、(b,c)、(a)、(b)、(c)、()
……以此类推ing……

So，上面例子中CUBE的结果比ROLLUP多了下面关于第一列GROUP_ID的统计信息：
Architect 1 0 10000
Coding 1 0 10000
Director 1 0 10000

4.另外可以参考oracle官方文档中的例子，链接如下：
《CUBE Extension to GROUP BY》
http://download.oracle.com/docs/cd/B19306_01/server.102/b14223/aggreg.htm#i1007428

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