如何统计全天各个时间段产品销量情况(sqlserver)

时间:2021-05-23

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

WITH x0 AS ( SELECT number AS h FROM master..spt_values WHERE type = 'P' AND number >= 0 AND number <= 23 ), x1 AS ( SELECT DISTINCT CONVERT(VARCHAR(100), ctime, 23) AS d FROM snake ), 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 ), 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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