(转)微软Olap服务MDX函数应用举例
本文目的
对丰富的函数集可以提供的强大功能有一个直观的认识
看看Microsoft是怎样应用函数，也许可以参考借鉴；
应用举例
- 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]
- 四则运算
函数：四则运算函数；
在成员上及指标上均可以进行四则运算，动态派生出新的成员及指标。
WITH MEMBER MEASURES.ProfitPercent AS '([Measures].[Store Sales]-[Measures].[Store Cost])/([Measures].[Store Cost])',FORMAT_STRING = '#.00%'
MEMBER [Time].[First Half 97] AS '[Time].[1997].[Q1] + [Time].[1997].[Q2]'
MEMBER [Time].[Second Half 97] AS '[Time].[1997].[Q3] + [Time].[1997].[Q4]'
SELECT {[Time].[First Half 97], [Time].[Second Half 97], [Time].[1997].CHILDREN} ON COLUMNS,
{[Store].[Store Country].[USA].CHILDREN} ON ROWS
FROM [Sales]
WHERE (MEASURES.ProfitPercent)
- 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
- 成员属性
函数：Properties、Dimension Properties
成员属性是与成员绑定的，其对应关系导致很难选择合适的使用方式。以下是使用成员属性的例子，它对应每个商店成员列出了商店类型属性，相应的，商店经理、商店规模、商店地址等属性也可以被列出。该用法稍加灵活应用就可以解决过去遇到的企业名称——〉企业代码对应展示问题。
WITH MEMBER [Measures].[StoreType] AS '[Store].CurrentMember.Properties("Store Type")',
MEMBER [Measures].[ProfitPct] AS '(Measures.[Store Sales] - Measures.[Store Cost]) / Measures.[Store Sales]', FORMAT_STRING = '##.00%'
SELECT { Descendants([Store].[USA], [Store].[Store Name])} ON COLUMNS,
{[Measures].[Store Sales], [Measures].[Store Cost], [Measures].[StoreType], [Measures].[ProfitPct] } ON ROWS"
FROM Sales
另外一种用法：
SELECT {[Measures].[Units Shipped], [Measures].[Units Ordered]} ON COLUMNS,
[Store].[Store Name].MEMBERS DIMENSION PROPERTIES [Store].[Store Name].[Store Type] ON ROWS
FROM Warehouse
- 多步计算实现复杂逻辑
函数：其实可以是任意函数合乎逻辑的组合
求出从来没有买过乳制品的顾客，求解过程是先求出每位顾客在过去购买的乳制品的数量累计，然后找出累计值为0的顾客。同样，过去遇到的求税额大于平均税额的海关的问题可以类似求出。
with member [Measures].[Dairy ever] as 'sum([Time].members, ([Measures].[Unit Sales],[Product].[Food].[Dairy]))'
set [Customers who never bought dairy] as 'filter([Customers].members, [Measures].[Dairy ever] = 0)'
select {[Measures].[Unit Sales], [Measures].[Dairy ever]} on columns,
[Customers who never bought dairy] 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])
另一个例子，使用ParellelPeriod函数。
WITH MEMBER [Measures].[YTD Unit Sales] AS 'COALESCEEMPTY(SUM(YTD(), [Measures].[Unit Sales]), 0)' MEMBER [Measures].[Previous YTD Unit Sales] AS '(Measures.[YTD Unit Sales], PARALLELPERIOD([Time].[Year]))'
MEMBER [Measures].[YTD Growth] AS '[Measures].[YTD Unit Sales] - ([Measures].[Previous YTD Unit Sales])'
SELECT {[Time].[1998]} ON COLUMNS,
{[Measures].[YTD Unit Sales], [Measures].[Previous YTD Unit Sales], [Measures].[YTD Growth]} ON ROWS
FROM Sales;
- 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
- 时间段
函数：sum、"："运算符
求美国的商店在指定时间段内的销售额。
WITH MEMBER [Time].[1997].[Six Month] AS 'SUM([Time].[1]:[Time].[6])'
MEMBER [Time].[1997].[Nine Month] AS 'SUM([Time].[1]:[Time].[9])'
SELECT {[Time].[1997].[Six Month],[Time].[1997].[Nine Month]} ON COLUMNS,
{[measures].[store salse]} ON ROWS
FROM Sales
Where ([Store].[USA])
