Analysis Services 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。
  WITHMEMBER Measures.[Unit Sales Percent] AS '((Store.CURRENTMEMBER,Measures.[Unit Sales]) / (Store.CURRENTMEMBER.PARENT, Measures.[UnitSales])) ', 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。
  WITHMEMBER [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。
  
  WITHMEMBER MEASURES.ProfitPercent AS '([Measures].[StoreSales]-[Measures].[Store Cost])/([Measures].[StoreCost])',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.[StoreCost]) / 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 LastPeriod]', 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。
  
  WITHMEMBER [Measures].[YTD Unit Sales] AS 'COALESCEEMPTY(SUM(YTD(),[Measures].[Unit Sales]), 0)' MEMBER [Measures].[Previous YTD UnitSales] 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])