. rank介绍
介绍完rollup和cube使用,下面我们来看看rank系列使用思路方法.
问题2.我想查出这几个月份中各个地区总话费排名.
Quote:
为了将rank,dense_rank,row_number差别显示出来,我们对已有基础数据做些修改,将5763数据改成和5761数据相同.
1 update t t1 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来计算各个地区话费排名.
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 3名
5762 52039.62 4

Elapsed: 00:00:00.00


在这个例子中,出现了个第 3名,这就是rank和dense_rank差别,
rank如果出现两个相同数据,那么后面数据就会直接跳过这个排名,而dense_rank则不会,
差别更大是,row_number哪怕是两个数据完全相同,排名也会不样,这个特性在我们想找出对应没个条件唯记录时候又很大用处


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中,我们发现,哪怕sum(local_fare)完全相同,我们还是得到了不样排名,我们可以利用这个特性剔除数据库中重复记录.
这个帖子中几个例子是为了介绍说明这 3个基本使用方法. 下个帖子我们将详细介绍他们些使用方法.

2. rank介绍

a. 取出数据库中最后入网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删除数据库中重复记录
create table t as select obj#,name from sys.obj$;
再insert o 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介绍

取出每个月上个月和下个月话费总额
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 0
5764 200405 12487.791 0 0 13295.187 13444.093
5764 200406 13295.187 0 12487.791 13444.093 13929.694
5764 200407 13444.093 12487.791 13295.187 13929.694 0
5764 200408 13929.694 13295.187 13444.093 0 0
5765 200405 25057.736 0 0 26058.46 26301.881
5765 200406 26058.46 0 25057.736 26301.881 27130.638
5765 200407 26301.881 25057.736 26058.46 27130.638 0
5765 200408 27130.638 26058.46 26301.881 0 0
20 rows selected.

利用lag和lead,我们可以在同行中显示前n行数据,也可以显示后n行数据.


4. sum,avg,max,min移动计算数据介绍

计算出各个连续3个月通话费用平均数
1 select area_code,bill_month, local_fare,
2 sum(local_fare)
3 over ( partition by area_code
4 order by to_number(bill_month)
5 range between 1 preceding and 1 following ) "3month_sum",
6 avg(local_fare)
7 over ( partition by area_code
8 order by to_number(bill_month)
9 range between 1 preceding and 1 following ) "3month_avg",
10 max(local_fare)
11 over ( partition by area_code
12 order by to_number(bill_month)
13 range between 1 preceding and 1 following ) "3month_max",
14 min(local_fare)
15 over ( partition by area_code
16 order by to_number(bill_month)
17 range between 1 preceding and 1 following ) "3month_min"
18 from (
19 select area_code,bill_month,sum(local_fare) local_fare
20 from t
21 group by area_code,bill_month
22* )
SQL> /

AREA_CODE BILL_MONTH LOCAL_FARE 3month_sum 3month_avg 3month_max 3month_min
--------- ---------- ---------------- ---------- ---------- ---------- ----------
5761 200405 13060.433 26379.363 13189.6815 13318.93 13060.433
5761 200406 13318.930 40089.628 13363.2093 13710.265 13060.433
5761 200407 13710.265 41164.976 13721.6587 14135.781 13318.93
40089.628 = 13060.433 + 13318.930 + 13710.265
13363.2093 = (13060.433 + 13318.930 + 13710.265) / 3
13710.265 = max(13060.433 + 13318.930 + 13710.265)
13060.433 = min(13060.433 + 13318.930 + 13710.265)
5761 200408 14135.781 27846.046 13923.023 14135.781 13710.265
5762 200405 12643.791 25438.851 12719.4255 12795.06 12643.791
5762 200406 12795.060 38663.148 12887.716 13224.297 12643.791
5762 200407 13224.297 39395.825 13131.9417 13376.468 12795.06
5762 200408 13376.468 26600.765 13300.3825 13376.468 13224.297
5763 200405 13060.433 26379.363 13189.6815 13318.93 13060.433
5763 200406 13318.930 40089.628 13363.2093 13710.265 13060.433
5763 200407 13710.265 41164.976 13721.6587 14135.781 13318.93
5763 200408 14135.781 27846.046 13923.023 14135.781 13710.265
5764 200405 12487.791 25782.978 12891.489 13295.187 12487.791
5764 200406 13295.187 39227.071 13075.6903 13444.093 12487.791
5764 200407 13444.093 40668.974 13556.3247 13929.694 13295.187
5764 200408 13929.694 27373.787 13686.8935 13929.694 13444.093
5765 200405 25057.736 51116.196 25558.098 26058.46 25057.736
5765 200406 26058.460 77418.077 25806.0257 26301.881 25057.736
5765 200407 26301.881 79490.979 26496.993 27130.638 26058.46
5765 200408 27130.638 53432.519 26716.2595 27130.638 26301.881

20 rows selected.

5. ratio_to_report介绍




Quote:
1 select bill_month,area_code,sum(local_fare) local_fare,
2 ratio_to_report(sum(local_fare)) over
3 ( partition by bill_month ) area_pct
4 from t
5* group by bill_month,area_code
SQL> on bill_month skip 1
SQL> compute sum of local_fare on bill_month
SQL> compute sum of area_pct on bill_month
SQL> /

BILL_MONTH AREA_CODE LOCAL_FARE AREA_PCT
---------- --------- ---------------- ----------
200405 5761 13060.433 .171149279
5762 12643.791 .165689431
5763 13060.433 .171149279
5764 12487.791 .163645143
5765 25057.736 .328366866
********** ---------------- ----------
sum 76310.184 1

200406 5761 13318.930 .169050772
5762 12795.060 .162401542
5763 13318.930 .169050772
5764 13295.187 .168749414
5765 26058.460 .330747499
********** ---------------- ----------
sum 78786.567 1

200407 5761 13710.265 .170545197
5762 13224.297 .164500127
5763 13710.265 .170545197
5764 13444.093 .167234221
5765 26301.881 .327175257
********** ---------------- ----------
sum 80390.801 1

200408 5761 14135.781 .170911147
5762 13376.468 .161730539
5763 14135.781 .170911147
5764 13929.694 .168419416
5765 27130.638 .328027751
********** ---------------- ----------
sum 82708.362 1


20 rows selected.



6 first,last使用介绍




Quote:
取出每月通话费最高和最低两个用户.
1 select bill_month,area_code,sum(local_fare) local_fare,
2 first_value(area_code)
3 over (order by sum(local_fare) desc
4 rows unbounded preceding) firstval,
5 first_value(area_code)
6 over (order by sum(local_fare) asc
7 rows unbounded preceding) lastval
8 from t
9 group by bill_month,area_code
10* order by bill_month
SQL> /