sqlserver统计时间段产品销量情况的实例分享
数据库环境：SQL SERVER 2005
现有一个产品销售实时表，表数据如下：
字段name是产品名称，字段type是销售类型，1表示售出，2表示退货，字段num是数量，字段ctime是操作时间。
要求：
在一行中统计24小时内所有货物的销售（售出，退货）数据，把日期考虑在内。
分析：
这实际上是行转列的一个应用，在进行行转列之前，需要补全24小时的所有数据。补全数据可以通过系统的数字辅助表
spt_values来实现，进行行转列时，根据type和处理后的ctime分组即可。
1.建表，导入数据
CREATE TABLE snake (name VARCHAR(10 ),type INT,num INT, ctime DATETIME )
INSERT INTO snake VALUES(' 方便面', 1,10 ,'2015-08-10 16:20:05')
INSERT INTO snake VALUES(' 香烟A ', 2,2 ,'2015-08-10 18:21:10')
INSERT INTO snake VALUES(' 香烟A ', 1,5 ,'2015-08-10 20:21:10')
INSERT INTO snake VALUES(' 香烟B', 1,6 ,'2015-08-10 20:21:10')
INSERT INTO snake VALUES(' 香烟B', 2,9 ,'2015-08-10 20:21:10')
INSERT INTO snake VALUES(' 香烟C', 2,9 ,'2015-08-10 20:21:10')
2.补全24小时的数据
/*枚举0-23自然数列*/
WITH x0
AS ( SELECT number AS h
FROM master..spt_values
WHERE type = 'P'
AND number >= 0
AND number <= 23
),/*找出表所有的日期*/
x1
AS ( SELECT DISTINCT
CONVERT(VARCHAR(100), ctime, 23) AS d
FROM snake
),/*补全所有日期的24小时*/
x2
AS ( SELECT x1.d ,
x0.h
FROM x1
CROSS JOIN x0
),
x3
AS ( SELECT name ,
type ,
num ,
DATEPART(hour, ctime) AS h
FROM snake
),/*整理行转列需要用到的数据*/
x4
AS ( SELECT x2.d ,
x2.h ,
x3.name ,
x3.type ,
x3.num
FROM x2
LEFT JOIN x3 ON x3.h = x2.h
)
3.行转列
SELECT ISNULL([0], 0) AS [00] ,
ISNULL([1], 0) AS [01] ,
ISNULL([2], 0) AS [02] ,
ISNULL([3], 0) AS [03] ,
ISNULL([4], 0) AS [04] ,
ISNULL([5], 0) AS [05] ,
ISNULL([6], 0) AS [06] ,
ISNULL([3], 7) AS [07] ,
ISNULL([8], 0) AS [08] ,
ISNULL([9], 0) AS [09] ,
ISNULL([10], 0) AS [10] ,
ISNULL([3], 11) AS [11] ,
ISNULL([12], 0) AS [12] ,
ISNULL([13], 0) AS [13] ,
ISNULL([14], 0) AS [14] ,
ISNULL([3], 15) AS [15] ,
ISNULL([16], 0) AS [16] ,
ISNULL([17], 0) AS [17] ,
ISNULL([18], 0) AS [18] ,
ISNULL([19], 15) AS [19] ,
ISNULL([20], 0) AS [20] ,
ISNULL([21], 0) AS [21] ,
ISNULL([22], 0) AS [22] ,
ISNULL([23], 15) AS [23] ,
type ,
d AS date
FROM ( SELECT d ,
h ,
type ,
num
FROM x4
) t PIVOT( SUM(num) FOR h IN ( [0], [1], [2], [3], [4], [5], [6],
[7], [8], [9], [10], [11], [12],
[13], [14], [15], [16], [17], [18],
[19], [20], [21], [22], [23] ) ) t
WHERE type IS NOT NULL
来看一下最终效果，只有1天的数据，可能看起来不是很直观。
本文的技术点有2个：
1.利用数字辅助表补全缺失的记录
2.pivot行转列函数的使用
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