Oracle 分析函数的使用

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.

 

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.

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