本文目的

對豐富的函數集可以提供的強大功能有一個直觀的認識
看看Microsoft是怎樣應用函數，也許可以參考借鑒；

應用舉例

• 成員百分比分析
  函數：CurrentMember、Parent等；
  分析各城市的銷售所占全部城市的總銷售額百分比。
  ?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
  
• 重要顧客分布分析
  函數：Count、Sum、Filter、Descendants等；
  分析各個省份中重要顧客的數量及他們的總購買量，"重要顧客"的定義是一個顧客的購買金額或者購買數目達到或超過一定的數值。
  ?
  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
  函數：Order
  對各個產品類別按照Store Sales指標降序排列，排序分為維內排序/整體排序。
  select {[Measures].[Unit Sales], [Measures].[Store Sales]} on columns,
  ?
  Order([Product].[Product Department].members, [Measures].[Store Sales], DESC) on rows
  ?
  from Sales
  ?
• 歷史相關的累計值
  函數：YTD、Sum、Descendants
  求銷售額的本年累計值YTD()，類似還可以求解歷史累計YTD()、本月累計MTD()、本周累計WTD()等, 以及更通用的函數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]
  ?
• 四則運算
  函數：四則運算函數；
  在成員上及指標上均可以進行四則運算，動態派生出新的成員及指標。
  ?
  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)
  ?
  
• 邏輯判斷
  函數：IIf
  邏輯判斷可以根據不同的條件產生不同的結果。下例判斷各商店是否是啤酒及白酒的大賣家。
  ?
  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
  ?
  
• 成員屬性
  函數：Properties、Dimension Properties
  成員屬性是與成員綁定的，其對應關系導致很難選擇合適的使用方式。以下是使用成員屬性的例子，它對應每個商店成員列出了商店類型屬性，相應的，商店經理、 商店規模、商店地址等屬性也可以被列出。該用法稍加靈活應用就可以解決過去遇到的企業名稱——〉企業代碼對應展示問題。
  ?
  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
  ?
  ?
  另外一種用法：
  ?
  SELECT {[Measures].[Units Shipped], [Measures].[Units Ordered]} ON COLUMNS,
  ?
  [Store].[Store Name].MEMBERS DIMENSION PROPERTIES [Store].[Store Name].[Store Type] ON ROWS
  ?
  FROM Warehouse
  ?
  
• 多步計算實現復雜邏輯
  ?
  函數：其實可以是任意函數合乎邏輯的組合
  ?
  ?
  求出從來沒有買過乳制品的顧客，求解過程是先求出每位顧客在過去購買的乳制品的數量累計，然后找出累計值為0的顧客。同樣，過去遇到的求稅額大于平均稅額的海關的問題可以類似求出。
  ?
  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
  ?
  函數：PrevMember、ParellelPeriod
  ?
  ?
  求解各產品銷售額的去年同期值，年增長率。
  ?
  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])
  ?
  ?
  另一個例子，使用ParellelPeriod函數。
  ?
  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
  ?
  函數：TopCount
  ?
  ?
  求解1998年總購買量處于前5名的顧客；
  ?
  select {[Measures].[Store Sales]} on columns,
  ?
  {TopCount([Customers].[Customer Name].members,5, [Measures].[Store Sales])} on rows
  ?
  from Sales
  ?
  where ([time].[1998])
  ?
  ?
  
• 成員過濾
  ?
  函數：Filter、Except
  ?
  ?
  求解1998年所有顧客中購買總額得到1萬元以上的顧客，列出滿足條件的顧客的名字、年購買數量、年購買金額。
  ?
  Select {[measures].[Store Sales],[measures].[unit sales]} on columns,
  ?
  FILTER(Customers.[Name].Members,[Measures].[Store Sales] > 10000) on rows
  ?
  From sales
  ?
  Whare ([time].[1998])
  ?
  ?
  另外一種成員過濾（從所有的媒體類型中剔除No Media類型），確切的說應該是集合運算。
  ?
  select {[Measures].[Unit Sales]} on columns,
  ?
  except([Promotion Media].[Media Type].members,{[Promotion Media].[Media Type].[No Media]}) on rows
  ?
  from Sales
  ?
  ?
  
• 時間段
  ?
  函數：sum、"："運算符
  ?
  ?
  求美國的商店在指定時間段內的銷售額。
  ?
  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])

總結

以上是生活随笔為你收集整理的转)微软Olap服务MDX函数应用举例的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。