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.
1update t t1 set local_fare = (
2select 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 2rank() over (order by sum(local_fare) desc) fare_rank
07:35:44 3from t
07:35:45 4group by area_codee
07:35:50 5
07:35:52 SQL select area_code,sum(local_fare) local_fare,
07:36:02 2rank() over (order by sum(local_fare) desc) fare_rank
07:36:20 3from t
07:36:21 4group by area_code
07:36:25 5/
AREA_CODELOCAL_FAREFARE_RANK
---------- -------------- ----------
5765104548.721
5761 54225.412
5763 54225.412
5764 53156.774
5762 52039.625
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 2dense_rank() over (order by sum(local_fare) desc ) fare_rank
07:39:39 3from t
07:39:42 4group by area_code
07:39:46 5/
AREA_CODELOCAL_FAREFARE_RANK
---------- -------------- ----------
5765104548.721
5761 54225.412
5763 54225.412
5764 53156.773这是这里出现了第三名
5762 52039.624
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假如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
1select area_code,sum(local_fare) local_fare,
2 row_number() over (order by sum(local_fare) desc ) fare_rank
3from t
4* group by area_code
07:44:50 SQL /
AREA_CODELOCAL_FAREFARE_RANK
---------- -------------- ----------
5765104548.721
5761 54225.412
5763 54225.413
5764 53156.774
5762 52039.625
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. 下个帖子我们将具体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
3from t
4group by bill_month,area_code
5/
BILL_MONTHAREA_CODE LOCAL_FAREAREA_RANK
--------------- --------------- -------------- ----------
200405576525057.741
200405576113060.432
200405576313060.432
200405576212643.794
200405576412487.795
200406576526058.461
200406576113318.932
200406576313318.932
200406576413295.194
200406576212795.065
200407576526301.881
200407576113710.272
200407576313710.272
200407576413444.094
200407576213224.305
200408576527130.641
200408576114135.782
200408576314135.782
200408576413929.694
200408576213376.475
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
1select 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
6from (
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
--------- ---------- -------------- -------------- --------------- --------------- ---------------
576120040513060.4330 013318.93 13710.265
5761200406 13318.930 13060.433 13710.265 14135.781
576120040713710.26513060.43313318.93 14135.781 0
576120040814135.781 13318.93 13710.265 0 0
576220040512643.7910 012795.06 13224.297
5762200406 12795.060 12643.791 13224.297 13376.468
576220040713224.29712643.79112795.06 13376.468 0
576220040813376.468 12795.06 13224.297 0 0
576320040513060.4330 013318.93 13710.265
5763200406 13318.930 13060.433 13710.265 14135.781
576320040713710.26513060.43313318.93 14135.781 0
576320040814135.781 13318.93 13710.265 0
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