发布时间 : 星期三 文章Oracle分析函数更新完毕开始阅读3b3de16f58fafab069dc0221
介绍完rollup和cube函数的使用,下面我们来看看rank系列函数的使用方法.
问题2.我想查出这几个月份中各个地区的总话费的排名.
Quote:
为了将rank,dense_rank,row_number函数的差别显示出来,我们对已有的基础数据做一些修改,将5763的数据改成与5761的数据相同. 1 update t t1 set local_fare = ( 2 select local_fare from t t2
3 where t1.bill_month = t2.bill_month 4 and t1.net_type = t2.net_type 5 and t2.area_code = '5761' 6* ) where area_code = '5763' 07:19:18 SQL> /
8 rows updated.
Elapsed: 00:00:00.01
我们先使用rank函数来计算各个地区的话费排名.
07:34:19 SQL> select area_code,sum(local_fare) local_fare,
07:35:25 2 rank() over (order by sum(local_fare) desc) fare_rank 07:35:44 3 from t
07:35:45 4 group by area_codee 07:35:50 5
07:35:52 SQL> select area_code,sum(local_fare) local_fare,
07:36:02 2 rank() over (order by sum(local_fare) desc) fare_rank 07:36:20 3 from t
07:36:21 4 group by area_code 07:36:25 5 /
AREA_CODE LOCAL_FARE FARE_RANK ---------- -------------- ---------- 5765 104548.72 1 5761 54225.41 2 5763 54225.41 2 5764 53156.77 4 5762 52039.62 5
Elapsed: 00:00:00.01
我们可以看到红色标注的地方出现了,跳位,排名3没有出现 下面我们再看看dense_rank查询的结果.
07:36:26 SQL> select area_code,sum(local_fare) local_fare,
07:39:16 2 dense_rank() over (order by sum(local_fare) desc ) fare_rank 07:39:39 3 from t
07:39:42 4 group by area_code 07:39:46 5 /
AREA_CODE LOCAL_FARE FARE_RANK ---------- -------------- ---------- 5765 104548.72 1 5761 54225.41 2 5763 54225.41 2
5764 53156.77 3 这是这里出现了第三名 5762 52039.62 4
Elapsed: 00:00:00.00
在这个例子中,出现了一个第三名,这就是rank和dense_rank的差别,
rank如果出现两个相同的数据,那么后面的数据就会直接跳过这个排名,而dense_rank则不会, 差别更大的是,row_number哪怕是两个数据完全相同,排名也会不一样,这个特性在我们想找出对应没个条件的唯一记录的时候又很大用处
1 select area_code,sum(local_fare) local_fare,
2 row_number() over (order by sum(local_fare) desc ) fare_rank 3 from t
4* group by area_code 07:44:50 SQL> /
AREA_CODE LOCAL_FARE FARE_RANK ---------- -------------- ---------- 5765 104548.72 1 5761 54225.41 2 5763 54225.41 3 5764 53156.77 4 5762 52039.62 5
在row_nubmer函数中,我们发现,哪怕sum(local_fare)完全相同,我们还是得到了不一样排名,我们可以利用这个特性剔除数据库中的重复记录.
这个帖子中的几个例子是为了说明这三个函数的基本用法的. 下个帖子我们将详细介绍他们的一些用法.
2. rank函数的介绍
a. 取出数据库中最后入网的n个用户
select user_id,tele_num,user_name,user_status,create_date from (
select user_id,tele_num,user_name,user_status,create_date, rank() over (order by create_date desc) add_rank from user_info )
where add_rank <= :n;
b.根据object_name删除数据库中的重复记录
create table t as select obj#,name from sys.obj$; 再insert into t1 select * from t1 数次. delete from t1 where rowid in ( select row_id from (
select rowid row_id,row_number() over (partition by obj# order by rowid ) rn ) where rn <> 1 );
c. 取出各地区的话费收入在各个月份排名.
SQL> select bill_month,area_code,sum(local_fare) local_fare,
2 rank() over (partition by bill_month order by sum(local_fare) desc) area_rank 3 from t
4 group by bill_month,area_code 5 /
BILL_MONTH AREA_CODE LOCAL_FARE AREA_RANK --------------- --------------- -------------- ---------- 200405 5765 25057.74 1 200405 5761 13060.43 2 200405 5763 13060.43 2 200405 5762 12643.79 4 200405 5764 12487.79 5 200406 5765 26058.46 1 200406 5761 13318.93 2 200406 5763 13318.93 2 200406 5764 13295.19 4 200406 5762 12795.06 5 200407 5765 26301.88 1 200407 5761 13710.27 2
200407 5763 13710.27 2 200407 5764 13444.09 4 200407 5762 13224.30 5 200408 5765 27130.64 1 200408 5761 14135.78 2 200408 5763 14135.78 2 200408 5764 13929.69 4 200408 5762 13376.47 5
20 rows selected. SQL>
3. lag和lead函数介绍
取出每个月的上个月和下个月的话费总额
1 select area_code,bill_month, local_fare cur_local_fare,
2 lag(local_fare,2,0) over (partition by area_code order by bill_month ) pre_local_fare, 3 lag(local_fare,1,0) over (partition by area_code order by bill_month ) last_local_fare, 4 lead(local_fare,1,0) over (partition by area_code order by bill_month ) next_local_fare, 5 lead(local_fare,2,0) over (partition by area_code order by bill_month ) post_local_fare 6 from (
7 select area_code,bill_month,sum(local_fare) local_fare 8 from t
9 group by area_code,bill_month 10* ) SQL> /
AREA_CODE BILL_MONTH CUR_LOCAL_FARE PRE_LOCAL_FARE LAST_LOCAL_FARE NEXT_LOCAL_FARE POST_LOCAL_FARE
--------- ---------- -------------- -------------- --------------- --------------- --------------- 5761 200405 13060.433 0 0 13318.93 13710.265 5761 200406 13318.93 0 13060.433 13710.265 14135.781
5761 200407 13710.265 13060.433 13318.93 14135.781 0
5761 200408 14135.781 13318.93 13710.265 0 0 5762 200405 12643.791 0 0 12795.06 13224.297 5762 200406 12795.06 0 12643.791 13224.297 13376.468
5762 200407 13224.297 12643.791 12795.06 13376.468 0
5762 200408 13376.468 12795.06 13224.297 0 0 5763 200405 13060.433 0 0 13318.93 13710.265 5763 200406 13318.93 0 13060.433 13710.265 14135.78