如何统计全天各个时间段产品销量情况（sqlserver）

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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