1. rank函数的介绍
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. 三个函数的基本用法
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
14135.781
5763
200407
13710.265
13060.433
13318.93
14135.781
0
5763
200408
14135.781
13318.93
13710.265
0
