oracle分析函數學習

0、建表及插入測試數據

 --CREATE TEST TABLE AND INSERT TEST DATA. create table students (id number(15,0), area varchar2(10), stu_type varchar2(2), score number(20,2)); insert into students values(1, '111', 'g', 80 ); insert into students values(1, '111', 'j', 80 ); insert into students values(1, '222', 'g', 89 ); insert into students values(1, '222', 'g', 68 ); insert into students values(2, '111', 'g', 80 ); insert into students values(2, '111', 'j', 70 ); insert into students values(2, '222', 'g', 60 ); insert into students values(2, '222', 'j', 65 ); insert into students values(3, '111', 'g', 75 ); insert into students values(3, '111', 'j', 58 ); insert into students values(3, '222', 'g', 58 ); insert into students values(3, '222', 'j', 90 ); insert into students values(4, '111', 'g', 89 ); insert into students values(4, '111', 'j', 90 ); insert into students values(4, '222', 'g', 90 ); insert into students values(4, '222', 'j', 89 ); commit; col score format 999999999999.99

1、GROUP BY子句的增強

A、GROUPING SETS

 select id,area,stu_type,sum(score) score from students group by grouping sets((id,area,stu_type),(id,area),id) order by id,area,stu_type;

--------理解grouping sets

select a, b, c, sum( d ) from t

group by grouping sets ( a, b, c )

等效於

select * from (

select a, null, null, sum( d ) from t group by a

union all

select null, b, null, sum( d ) from t group by b

union all

select null, null, c, sum( d ) from t group by c

)

B、ROLLUP

 select id,area,stu_type,sum(score) score from students group by rollup(id,area,stu_type) order by id,area,stu_type;

--------理解rollup

select a, b, c, sum( d )

from t

group by rollup(a, b, c);

等效於

select * from (

select a, b, c, sum( d ) from t group by a, b, c

union all

select a, b, null, sum( d ) from t group by a, b

union all

select a, null, null, sum( d ) from t group by a

union all

select null, null, null, sum( d ) from t

)

C、CUBE

 select id,area,stu_type,sum(score) score from students group by cube(id,area,stu_type) order by id,area,stu_type;

--------理解cube

select a, b, c, sum( d ) from t

group by cube( a, b, c)

等效於

select a, b, c, sum( d ) from t

group by grouping sets(

( a, b, c ),

( a, b ), ( a ), ( b, c ),

( b ), ( a, c ), ( c ),

() )

D、GROUPING函數

從上面的結果中我們很容易發現,每個統計數據所對應的行都會出現null,如何來區分到底是根據那個字段做的彙總呢,grouping函數判斷是否合計列!

 select decode(grouping(id),1,'all id',id) id, decode(grouping(area),1,'all area',to_char(area)) area, decode(grouping(stu_type),1,'all_stu_type',stu_type) stu_type, sum(score) score from students group by cube(id,area,stu_type) order by id,area,stu_type;

2、OVER()函數的使用

A、RANK()、DENSE_RANK() 、ROW_NUMBER()、CUME_DIST()、MAX()、AVG()

 break on id skip 1 select id,area,score from students order by id,area,score desc; select id,rank() over(partition by id order by score desc) rk,score from students; --允許並列名次、名次不間斷 select id,dense_rank() over(partition by id order by score desc) rk,score from students; --即使SCORE相同，ROW_NUMBER()結果也是不同 select id,row_number() over(partition by ID order by SCORE desc) rn,score from students; select cume_dist() over(order by id) a, --該組最大row_number/所有記錄row_number row_number() over (order by id) rn,id,area,score from students; select id,max(score) over(partition by id order by score desc) as mx,score from students; select id,area,avg(score) over(partition by id order by area) as avg,score from students; --注意有無order by的區別 --按照ID求AVG select id,avg(score) over(partition by id order by score desc rows between unbounded preceding and unbounded following ) as ag,score from students;

B、SUM()

 select id,area,score from students order by id,area,score desc; select id,area,score, sum(score) over (order by id,area) 連續求和, --按照OVER後邊內容彙總求和 sum(score) over () 總和, -- 此處sum(score) over () 等同於sum(score) 100*round(score/sum(score) over (),4) "份額(%)" from students; select id,area,score, sum(score) over (partition by id order by area ) 連id續求和, --按照id內容彙總求和 sum(score) over (partition by id) id總和, --各id的分數總和 100*round(score/sum(score) over (partition by id),4) "id份額(%)", sum(score) over () 總和, -- 此處sum(score) over () 等同於sum(score) 100*round(score/sum(score) over (),4) "份額(%)" from students;

C、LAG(COL,n,default)、LEAD(OL,n,default) --取前後邊N條數據

 select id,lag(score,1,0) over(order by id) lg,score from students; select id,lead(score,1,0) over(order by id) lg,score from students;

D、FIRST_VALUE()、LAST_VALUE()

 select id,first_value(score) over(order by id) fv,score from students; select id,last_value(score) over(order by id) fv,score from students; --而對於last_value() over(order by id),結果是有問題的，因爲我們沒有按照id分區，所以應該出來的效果應該全部是90（最後一條） --再看個例子 select id,last_value(score) over(order by rownum),score from students; --當使用last_value分析函數的時候，缺省的WINDOWING範圍是RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW，在進行比較的時候從當前行向前進行比較，所以會出現上邊的結果。加上如下的參數，結果就正常了。呵呵。默認窗口範圍爲所有處理結果。 select id,last_value(score) over(order by rownum RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING),score from students;