Oracle 分析函数 | 红颜丽人

Oracle 分析函数。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

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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.

Elapsed: 00:00:00.01
07:07:31 SQL>

可以看到,所有的空值现在都根据grouping函数做出了很好的区分,这样利用rollup,cube和grouping函数,我们做数据统计的时候就可以轻松很多了.

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.
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    14135.781
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
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函数的介绍

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> 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_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函数使用介绍

Quote:

取出每月通话费最高和最低的两个用户.
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> /

BILL_MONTH AREA_CODE    LOCAL_FARE FIRSTVAL       LASTVAL
———- ——— —————- ————— —————
200405    5764          12487.791 5765          5764
200405    5762          12643.791 5765          5764
200405    5761          13060.433 5765          5764
200405    5765          25057.736 5765          5764
200405    5763          13060.433 5765          5764
200406    5762          12795.060 5765          5764
200406    5763          13318.930 5765          5764
200406    5764          13295.187 5765          5764
200406    5765          26058.460 5765          5764
200406    5761          13318.930 5765          5764
200407    5762          13224.297 5765          5764
200407    5765          26301.881 5765          5764
200407    5761          13710.265 5765          5764
200407    5763          13710.265 5765          5764
200407    5764          13444.093 5765          5764
200408    5762          13376.468 5765          5764
200408    5764          13929.694 5765          5764
200408    5761          14135.781 5765          5764
200408    5765          27130.638 5765          5764
200408    5763          14135.781 5765          5764

20 rows selected.

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