orcale分析函数（一）

分析函数是 oracle816 引入的一个全新的概念 , 为我们分析数据提供了一种简单高效的处理方式 . 在分析函数出现以前 , 我们必须使用自联查询 , 子查询或者内联视图 , 甚至复杂的 存储过程 实现的语句 , 现在只要一条简单的 sql 语句就可以实现了 , 而且在执行效率方面也有相当大的提高 . 下面我将针对分析函数做一些具体的说明 .

今天我主要给大家介绍一下以下几个函数的使用方法

1. 自动汇总函数 rollup,cube,

2. rank 函数 , rank,dense_rank,row_number

3. lag,lead 函数

4. sum,avg, 的移动增加 , 移动平均数

5. ratio_to_report 报表处理函数

6. first,last 取基数的分析函数

基础数据

Code: [Copy to clipboard]

06:34:23 SQL> select * from t;

BILL_MONTH AREA_CODE NET_TYPE LOCAL_FARE

--------------- ---------- ---------- --------------

200405 5761 G 7393344.04

200405 5761 J 5667089.85

200405 5762 G 6315075.96

200405 5762 J 6328716.15

200405 5763 G 8861742.59

200405 5763 J 7788036.32

200405 5764 G 6028670.45

200405 5764 J 6459121.49

200405 5765 G 13156065.77

200405 5765 J 11901671.70

200406 5761 G 7614587.96

200406 5761 J 5704343.05

200406 5762 G 6556992.60

200406 5762 J 6238068.05

200406 5763 G 9130055.46

200406 5763 J 7990460.25

200406 5764 G 6387706.01

200406 5764 J 6907481.66

200406 5765 G 13562968.81

200406 5765 J 12495492.50

200407 5761 G 7987050.65

200407 5761 J 5723215.28

200407 5762 G 6833096.68

200407 5762 J 6391201.44

200407 5763 G 9410815.91

200407 5763 J 8076677.41

200407 5764 G 6456433.23

200407 5764 J 6987660.53

200407 5765 G 14000101.20

200407 5765 J 12301780.20

200408 5761 G 8085170.84

200408 5761 J 6050611.37

200408 5762 G 6854584.22

200408 5762 J 6521884.50

200408 5763 G 9468707.65

200408 5763 J 8460049.43

200408 5764 G 6587559.23

BILL_MONTH AREA_CODE NET_TYPE LOCAL_FARE

--------------- ---------- ---------- --------------

200408 5764 J 7342135.86

200408 5765 G 14450586.63

200408 5765 J 12680052.38

40 rows selected.

Elapsed: 00:00:00.00

1. 使用 rollup 函数的介绍

Quote:

下面是直接使用普通 sql 语句求出各地区的汇总数据的例子

06:41:36 SQL> set autot on

06:43:36 SQL> select area_code,sum(local_fare) local_fare

06:43:50 2 from t

06:43:51 3 group by area_code

06:43:57 4 union all

06:44:00 5 select ' 合计 ' area_code,sum(local_fare) local_fare

06:44:06 6 from t

06:44:08 7 /

AREA_CODE LOCAL_FARE

---------- --------------

5761 54225413.04

5762 52039619.60

5763 69186545.02

5764 53156768.46

5765 104548719.19

合计 333157065.31

6 rows selected.

Elapsed: 00:00:00.03

Execution Plan

----------------------------------------------------------

0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=7 Card=1310 Bytes=

24884)

1 0 UNION-ALL

2 1 SORT (GROUP BY) (Cost=5 Card=1309 Bytes=24871)

3 2 TABLE ACCESS (FULL) OF 'T' (Cost=2 Card=1309 Bytes=248

71)

4 1 SORT (AGGREGATE)

5 4 TABLE ACCESS (FULL) OF 'T' (Cost=2 Card=1309 Bytes=170

17)

Statistics

----------------------------------------------------------

0 recursive calls

0 db block gets

6 consistent gets

0 physical reads

0 redo size

561 bytes sent via SQL*Net to client

503 bytes received via SQL*Net from client

2 SQL*Net roundtrips to/from client

1 sorts (memory)

0 sorts (disk)

6 rows processed

下面是使用分析函数 rollup 得出的汇总数据的例子

06:44:09 SQL> select nvl(area_code,' 合计 ') area_code,sum(local_fare) local_fare

06:45:26 2 from t

06:45:30 3 group by rollup(nvl(area_code,' 合计 '))

06:45:50 4 /

AREA_CODE LOCAL_FARE

---------- --------------

5761 54225413.04

5762 52039619.60

5763 69186545.02

5764 53156768.46

5765 104548719.19

333157065.31

6 rows selected.

Elapsed: 00:00:00.00

Execution Plan

----------------------------------------------------------

0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=5 Card=1309 Bytes=

24871)

1 0 SORT (GROUP BY ROLLUP) (Cost=5 Card=1309 Bytes=24871)

2 1 TABLE ACCESS (FULL) OF 'T' (Cost=2 Card=1309 Bytes=24871

)

Statistics

----------------------------------------------------------

0 recursive calls

0 db block gets

4 consistent gets

0 physical reads

0 redo size

557 bytes sent via SQL*Net to client

503 bytes received via SQL*Net from client

2 SQL*Net roundtrips to/from client

1 sorts (memory)

0 sorts (disk)

6 rows processed

从上面的例子我们不难看出使用 rollup 函数 , 系统的 sql 语句更加简单 , 耗用的资源更少 , 从 6 个 consistent gets 降到 4 个 consistent gets, 如果基表很大的话 , 结果就可想而知了 .

1. 使用 cube 函数的介绍

Quote:

为了介绍 cube 函数我们再来看看另外一个使用 rollup 的例子

06:53:00 SQL> select area_code,bill_month,sum(local_fare) local_fare

06:53:37 2 from t

06:53:38 3 group by rollup(area_code,bill_month)

06:53:49 4 /

AREA_CODE BILL_MONTH LOCAL_FARE

---------- --------------- --------------

5761 200405 13060433.89

5761 200406 13318931.01

5761 200407 13710265.93

5761 200408 14135782.21

5761 54225413.04

5762 200405 12643792.11

5762 200406 12795060.65

5762 200407 13224298.12

5762 200408 13376468.72

5762 52039619.60

5763 200405 16649778.91

5763 200406 17120515.71

5763 200407 17487493.32

5763 200408 17928757.08

5763 69186545.02

5764 200405 12487791.94

5764 200406 13295187.67

5764 200407 13444093.76

5764 200408 13929695.09

5764 53156768.46

5765 200405 25057737.47

5765 200406 26058461.31

5765 200407 26301881.40

5765 200408 27130639.01

5765 104548719.19

333157065.31

26 rows selected.

Elapsed: 00:00:00.00

系统只是根据 rollup 的第一个参数 area_code 对结果集的数据做了汇总处理 , 而没有对 bill_month 做汇总分析处理 ,cube 函数就是为了这个而 设计 的 .

下面 , 让我们看看使用 cube 函数的结果

06:58:02 SQL> select area_code,bill_month,sum(local_fare) local_fare

06:58:30 2 from t

06:58:32 3 group by cube(area_code,bill_month)

06:58:42 4 order by area_code,bill_month nulls last

06:58:57 5 /

AREA_CODE BILL_MONTH LOCAL_FARE

---------- --------------- --------------

5761 200405 13060.43

5761 200406 13318.93

5761 200407 13710.27

5761 200408 14135.78

5761 54225.41

5762 200405 12643.79

5762 200406 12795.06

5762 200407 13224.30

5762 200408 13376.47

5762 52039.62

5763 200405 16649.78

5763 200406 17120.52

5763 200407 17487.49

5763 200408 17928.76

5763 69186.54

5764 200405 12487.79

5764 200406 13295.19

5764 200407 13444.09

5764 200408 13929.69

5764 53156.77

5765 200405 25057.74

5765 200406 26058.46

5765 200407 26301.88

5765 200408 27130.64

5765 104548.72

200405 79899.53

200406 82588.15

200407 84168.03

200408 86501.34

333157.05

30 rows selected.

Elapsed: 00:00:00.01

可以看到 , 在 cube 函数的输出结果比使用 rollup 多出了几行统计数据 . 这就是 cube 函数根据 bill_month 做的汇总统计结果 ]

1 rollup 和 cube 函数的再深入

Quote:

从上面的结果中我们很容易发现 , 每个统计数据所对应的行都会出现 null, 我们如何来区分到底是根据那个字段做的汇总呢 , 这时候 ,oracle 的 grouping 函数就粉墨登场了 .

如果当前的汇总记录是利用该字段得出的 ,grouping 函数就会返回 1, 否则返回 0

1 select decode(grouping(area_code),1,'all area',to_char(area_code)) area_code,

2 decode(grouping(bill_month),1,'all month',bill_month) bill_month,

3 sum(local_fare) local_fare

4 from t

5 group by cube(area_code,bill_month)

6* order by area_code,bill_month nulls last

07:07:29 SQL> /

AREA_CODE BILL_MONTH LOCAL_FARE

---------- --------------- --------------

5761 200405 13060.43

5761 200406 13318.93

5761 200407 13710.27

5761 200408 14135.78

5761 all month 54225.41

5762 200405 12643.79

5762 200406 12795.06

5762 200407 13224.30

5762 200408 13376.47

5762 all month 52039.62

5763 200405 16649.78

5763 200406 17120.52

5763 200407 17487.49

5763 200408 17928.76

5763 all month 69186.54

5764 200405 12487.79

5764 200406 13295.19

5764 200407 13444.09

5764 200408 13929.69

5764 all month 53156.77

5765 200405 25057.74

5765 200406 26058.46

5765 200407 26301.88

5765 200408 27130.64

5765 all month 104548.72

all area 200405 79899.53

all area 200406 82588.15

all area 200407 84168.03

all area 200408 86501.34

all area all month 333157.05

30 rows selected.

2. 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
3where t1.bill_month = t2.bill_month
4and t1.net_type = t2.net_type
5and 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,
07:35:252rank() over (order by sum(local_fare) desc) fare_rank
07:35:443from t
07:35:454group by area_codee
07:35:505
07:35:52 SQL> select area_code,sum(local_fare) local_fare,
07:36:022rank() over (order by sum(local_fare) desc) fare_rank
07:36:203from t
07:36:214group by area_code
07:36:255/

AREA_CODELOCAL_FAREFARE_RANK
---------- -------------- ----------
5765104548.721
576154225.412
576354225.412
576453156.774
576252039.625

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,
07:39:162dense_rank() over (order by sum(local_fare) desc ) fare_rank
07:39:393from t
07:39:424group by area_code
07:39:465/

AREA_CODELOCAL_FAREFARE_RANK
---------- -------------- ----------
5765104548.721
576154225.412
576354225.412
576453156.773这是这里出现了第三名
576252039.624

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


1select area_code,sum(local_fare) local_fare,
2row_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
576154225.412
576354225.413
576453156.774
576252039.625

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,
2rank() over (partition by bill_month order by sum(local_fare) desc) area_rank
3from t
4group by bill_month,area_code
5/

BILL_MONTHAREA_CODELOCAL_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,
2lag(local_fare,2,0) over (partition by area_code order by bill_month ) pre_local_fare,
3lag(local_fare,1,0) over (partition by area_code order by bill_month ) last_local_fare,
4lead(local_fare,1,0) over (partition by area_code order by bill_month ) next_local_fare,
5lead(local_fare,2,0) over (partition by area_code order by bill_month ) post_local_fare
6from (
7select area_code,bill_month,sum(local_fare) local_fare
8from t
9group 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
--------- ---------- -------------- -------------- --------------- --------------- ---------------
576120040513060.4330013318.9313710.265
576120040613318.93013060.43313710.26514135.781
576120040713710.26513060.43313318.9314135.7810
576120040814135.78113318.9313710.26500
576220040512643.7910012795.0613224.297
576220040612795.06012643.79113224.29713376.468
576220040713224.29712643.79112795.0613376.4680
576220040813376.46812795.0613224.29700
576320040513060.4330013318.9313710.265
576320040613318.93013060.43313710.26514135.781
576320040713710.26513060.43313318.9314135.7810
576320040814135.78113318.9313710.26500
576420040512487.7910013295.18713444.093
576420040613295.187012487.79113444.09313929.694
576420040713444.09312487.79113295.18713929.6940
576420040813929.69413295.18713444.09300
576520040525057.7360026058.4626301.881
576520040626058.46025057.73626301.88127130.638
576520040726301.88125057.73626058.4627130.6380
576520040827130.63826058.4626301.88100
20 rows selected.

利用lag和lead函数,我们可以在同一行中显示前n行的数据,也可以显示后n行的数据.


4. sum,avg,max,min移动计算数据介绍

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

AREA_CODE BILL_MONTHLOCAL_FARE 3month_sum 3month_avg 3month_max 3month_min
--------- ---------- ---------------- ---------- ---------- ---------- ----------
576120040513060.43326379.363 13189.681513318.9313060.433
576120040613318.93040089.628 13363.209313710.26513060.433
576120040713710.26541164.976 13721.658714135.78113318.93
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)
576120040814135.78127846.04613923.02314135.78113710.265
576220040512643.79125438.851 12719.425512795.0612643.791
576220040612795.06038663.14812887.71613224.29712643.791
576220040713224.29739395.825 13131.941713376.46812795.06
576220040813376.46826600.765 13300.382513376.46813224.297
576320040513060.43326379.363 13189.681513318.9313060.433
576320040613318.93040089.628 13363.209313710.26513060.433
576320040713710.26541164.976 13721.658714135.78113318.93
576320040814135.78127846.04613923.02314135.78113710.265
576420040512487.79125782.97812891.48913295.18712487.791
576420040613295.18739227.071 13075.690313444.09312487.791
576420040713444.09340668.974 13556.324713929.69413295.187
576420040813929.69427373.787 13686.893513929.69413444.093
576520040525057.73651116.19625558.09826058.4625057.736
576520040626058.46077418.077 25806.025726301.88125057.736
576520040726301.88179490.97926496.99327130.63826058.46
576520040827130.63853432.519 26716.259527130.63826301.881

20 rows selected.

5. ratio_to_report函数的介绍




Quote:
1select bill_month,area_code,sum(local_fare) local_fare,
2ratio_to_report(sum(local_fare)) over
3( partition by bill_month ) area_pct
4from t
5* group by bill_month,area_code
SQL> break 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_CODELOCAL_FAREAREA_PCT
---------- --------- ---------------- ----------
200405576113060.433 .171149279
576212643.791 .165689431
576313060.433 .171149279
576412487.791 .163645143
576525057.736 .328366866
**********---------------- ----------
sum76310.1841

200406576113318.930 .169050772
576212795.060 .162401542
576313318.930 .169050772
576413295.187 .168749414
576526058.460 .330747499
**********---------------- ----------
sum78786.5671

200407576113710.265 .170545197
576213224.297 .164500127
576313710.265 .170545197
576413444.093 .167234221
576526301.881 .327175257
**********---------------- ----------
sum80390.8011

200408576114135.781 .170911147
576213376.468 .161730539
576314135.781 .170911147
576413929.694 .168419416
576527130.638 .328027751
**********---------------- ----------
sum82708.3621


20 rows selected.



6 first,last函数使用介绍




Quote:
取出每月通话费最高和最低的两个用户.
1select bill_month,area_code,sum(local_fare) local_fare,
2first_value(area_code)
3over (order by sum(local_fare) desc
4rows unbounded preceding) firstval,
5first_value(area_code)
6over (order by sum(local_fare) asc
7rows unbounded preceding) lastval
8from t
9group by bill_month,area_code
10* order by bill_month
SQL> /

BILL_MONTH AREA_CODELOCAL_FARE FIRSTVALLASTVAL
---------- --------- ---------------- --------------- ---------------
200405576412487.791 57655764
200405576212643.791 57655764
200405576113060.433 57655764
200405576525057.736 57655764
200405576313060.433 57655764
200406576212795.060 57655764
200406576313318.930 57655764
200406576413295.187 57655764
200406576526058.460 57655764
200406576113318.930 57655764
200407576213224.297 57655764
200407576526301.881 57655764
200407576113710.265 57655764
200407576313710.265 57655764
200407576413444.093 57655764
200408576213376.468 57655764
200408576413929.694 57655764
200408576114135.781 57655764
200408576527130.638 57655764
200408576314135.781 57655764

20 rows selected.