MDX初步知识-3

最新推荐文章于 2016-07-05 10:16:00 发布

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最新推荐文章于 2016-07-05 10:16:00 发布

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分类专栏： SQL Server 2005 文章标签： filter string command 产品 class

SQL Server 2005 专栏收录该内容

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创建命名集

} 相当于是T-SQL中的表值函数（但可以更复杂）

} 是一系列元组或者成员的集合

} 例如：

◦ 某几个城市的组合

动态构造成员

WITH MEMBER [Measures].[Test] AS

[Measures].[Order Quantity]*1.25,

FORMAT_STRING = "Currency",

VISIBLE = 1

SELECT [Measures].[Test] ON 0,

[Dim Customer].[State Province Name - Dim Geography].[State Province Name] ON 1

FROM [Adventure Works DW]

WHERE ([Dim Product].[Class].&[H])

MDX 函数和模板

成员百分比分析
函数：CurrentMember、Parent等；
分析各城市的销售所占全部城市的总销售额百分比。
WITH MEMBER Measures.[Unit Sales Percent] AS '((Store.CURRENTMEMBER, Measures.[Unit Sales]) / (Store.CURRENTMEMBER.PARENT, Measures.[Unit Sales])) ', FORMAT_STRING = 'Percent'

SELECT {Measures.[Unit Sales], Measures.[Unit Sales Percent]} ON COLUMNS,
ORDER(DESCENDANTS(Store.[USA].[CA], Store.[Store City], SELF),[Measures].[Unit Sales], ASC) ON ROWS

FROM Sales

} 重要顾客分布分析

函数：Count、Sum、Filter、Descendants等；
分析各个省份中重要顾客的数量及他们的总购买量，"重要顾客"的定义是一个顾客的购买金额或者购买数目达到或超过一定的数值。

WITH MEMBER [Measures].[Qualified Count] AS

‘ COUNT(FILTER(DESCENDANTS(Customers.CURRENTMEMBER, [Customers].[Name]), ([Measures].[Store Sales]) > 10000 OR ([Measures].[Unit Sales]) > 10))'

MEMBER [Measures].[Qualified Sales] AS 'SUM(FILTER(DESCENDANTS(Customers.CURRENTMEMBER, [Customers].[Name]), ([Measures].[Store Sales]) > 10000 OR ([Measures].[Unit Sales]) > 10), ([Measures].[Store Sales]))'

SELECT {[Measures].[Qualified Count], [Measures].[Qualified Sales]} ON COLUMNS,

DESCENDANTS([Customers].[All Customers], [State Province], SELF_AND_BEFORE) ON ROWS

FROM Sales

排序
函数：Order
对各个产品类别按照Store Sales指标降序排列，排序分为维内排序/整体排序。
select {[Measures].[Unit Sales], [Measures].[Store Sales]} on columns,
Order([Product].[Product Department].members, [Measures].[Store Sales], DESC) on rows

from Sales

历史相关的累计值
函数：YTD、Sum、Descendants
求销售额的本年累计值YTD()，类似还可以求解历史累计YTD()、本月累计MTD()、本周累计WTD()等, 以及更通用的函数PeriodToDate()。
with member [Measures].[Accumulated Sales] as 'Sum(YTD(),[Measures].[Store Sales])'

select {[Measures].[Store Sales],[Measures].[Accumulated Sales]} on columns, {Descendants([Time].[1997],[Time].[Month])} on rows
from [Warehouse and Sales]

逻辑判断
函数：IIf
逻辑判断可以根据不同的条件产生不同的结果。下例判断各商店是否是啤酒及白酒的大卖家。

WITH MEMBER [Product].[BigSeller] AS 'IIf([Product].[Drink].[Alcoholic Beverages].[Beer and Wine] > 100, "Yes","No")'
SELECT {[Product].[BigSeller],[Product].children} ON COLUMNS,

{[Store].[All Stores].[USA].[CA].children} ON ROWS

FROM Sales

同期、前期

函数：PrevMember、ParellelPeriod

求解各产品销售额的去年同期值，年增长率。

with member [Measures].[Store Sales Last Period] as '([Measures].[Store Sales], Time.PrevMember)', format='#,###.00'

member [Measures].[Yearly Increase Rate] as ‘([Measures].[Store Sales] - [Measures].[Store Sales Last Period])/ [Measures].[Store Sales Last Period]', FORMAT_STRING = 'Percent'

select {[Measures].[Store Sales], [Measures].[Store Sales Last Period]} on columns,

{ [Product].members} on rows

from Sales

where ([Time].[1998])

◦ 环比

◦ 同比

如何在应用程序中查寻MDX

◦ 创建Connection

◦ 创建Command

◦ 执行Command

◦ 处理结果

带参数的查询

} 使用AdomdClient的访问接口

Top N分析

函数：TopCount

求解1998年总购买量处于前5名的顾客；

select {[Measures].[Store Sales]} on columns,

{TopCount([Customers].[Customer Name].members,5, [Measures].[Store Sales])} on rows

from Sales

where ([time].[1998])

成员过滤

函数：Filter、Except

求解1998年所有顾客中购买总额得到1万元以上的顾客，列出满足条件的顾客的名字、年购买数量、年购买金额。

Select {[measures].[Store Sales],[measures].[unit sales]} on columns,

FILTER(Customers.[Name].Members,[Measures].[Store Sales] > 10000) on rows

From sales

Whare ([time].[1998])

另外一种成员过滤（从所有的媒体类型中剔除No Media类型），确切的说应该是集合运算。

select {[Measures].[Unit Sales]} on columns,

except([Promotion Media].[Media Type].members,{[Promotion Media].[Media Type].[No Media]}) on rows

from Sales