Mondrian and OLAP
???Mondrian 是使用java編寫的OLAP引擎，它執(zhí)行MDX語言描述的查詢，可以從關系數(shù)據(jù)庫中獲取數(shù)據(jù)。
Online Analytical Processing (OLAP)
???OLAP是指實時地分析大數(shù)據(jù)量，與Online Transaction Processing (OLTP)不同。
|
Year
|
2000
|
2001
|
Growth
|
|
Product
|
Dollar sales
|
Unit sales
|
Dollar sales
|
Unit sales
|
Dollar sales
|
Unit sales
|
|
Total
|
$7,073
|
2,693
|
$7,636
|
3,008
|
8%
|
12%
|
|
Books
|
$2,753
|
824
|
$3,331
|
966
|
21%
|
17%
|
|
Fiction
|
$1,341
|
424
|
$1,202
|
380
|
-10%
|
-10%
|
|
Non-fiction
|
$1,412
|
400
|
$2,129
|
586
|
51%
|
47%
|
|
Magazines
|
$2,753
|
824
|
$2,426
|
766
|
-12%
|
-7%
|
|
— Greetings cards
|
$1,567
|
1,045
|
$1,879
|
1,276
|
20%
|
22%
|
從上表可以看到y(tǒng)ear和Product為dimensions （維度），measures 為'Unit sales' and 'Dollar sales'。
Layers of a Mondrian system
???Mondrian OLAP System 包含4個層：
???1、the presentation layer
?????????用于描述最終用戶所看到的，其表現(xiàn)形式有很多，如：pivot表、pie、line和bar圖、maps和動態(tài)圖。由用戶提問，OLAP服務器返回答案。
???2、the dimensional layer
?????????這一層解析、驗證和執(zhí)行MDX?查詢，首先計算軸，再計算軸上所有單元的值。metadata用于描述空間模型，及空間模型怎樣映射到關系模型上。
???3、the star layer
?????????它負責維護一個聚合的cache，aggregation 聚合是一些內存中的測量值（cells），及一些維度值。如果請求的cells不在cache中，則aggregation manager?向the storage layer發(fā)出請求。
???4、and the storage layer
?????????the storage layer為RDBMS。
What is MDX?
???'multi-dimensional expressions'為Mondrian執(zhí)行的查詢。
???下面為一個基本的查詢：
SELECT {[Measures].[Unit Sales], [Measures].[Store Sales]} ON COLUMNS,
{[Product].members} ON ROWS
FROM [Sales]
WHERE [Time].[1997].[Q2]
What is a schema??
?schema定義了一個多維數(shù)據(jù)庫。它包含一個邏輯模型，由多個cube、hierarchies、members及一個到物理模型的映射組成。
?邏輯模型包含：cubes, dimensions, hierarchies, levels, and members.
A cube is a collection of dimensions and measures in a particular subject area.
A measure is a quantity that you are interested in measuring, for example, unit sales of a product, or cost price of inventory items.
A dimension is an attribute, or set of attributes, by which you can divide measures into sub-categories. For example, you might wish to break down product sales by their color, the gender of the customer, and the store in which the product was sold; color, gender, and store are all dimensions. 例如：
<Schema>
? <Cube name="Sales">
??? <Table name="sales_fact_1997"/>
??? <Dimension name="Gender" foreignKey="customer_id">
????? <Hierarchy hasAll="true" allMemberName="All Genders" primaryKey="customer_id">
??????? <Table name="customer"/>
??????? <Level name="Gender" column="gender" uniqueMembers="true"/>
????? </Hierarchy>
??? </Dimension>
??? <Dimension name="Time" foreignKey="time_id">
????? <Hierarchy hasAll="false" primaryKey="time_id">
??????? <Table name="time_by_day"/>
??????? <Level name="Year" column="the_year" type="Numeric" uniqueMembers="true"/>
??????? <Level name="Quarter" column="quarter" uniqueMembers="false"/>
??????? <Level name="Month" column="month_of_year" type="Numeric" uniqueMembers="false"/>
????? </Hierarchy>
??? </Dimension>
??? <Measure name="Unit Sales" column="unit_sales" aggregator="sum" formatString="#,###"/>
??? <Measure name="Store Sales" column="store_sales" aggregator="sum" formatString="#,###.##"/>
??? <CalculatedMember name="Profit" dimension="Measures" formula="[Measures].[Store Sales]-[Measures].[Store Cost]">
????? <CalculatedMemberProperty name="FORMAT_STRING" value="$#,##0.00"/>
??? </CalculatedMember>
? </Cube>
</Schema>
? MDX查詢?yōu)椋?br />
SELECT {[Measures].[Unit Sales], [Measures].[Store Sales]} ON COLUMNS,
? {[Time].[1997].[Q1].descendants} ON ROWS
FROM [Sales]
WHERE [Gender].[F]
[Time] | [Measures].[Unit Sales] | [Measures].[Store Sales] | [1997].[Q1] | 0 | 0 | [1997].[Q1].[Jan] | 0 | 0 | [1997].[Q1].[Feb] | 0 | 0 | [1997].[Q1].[Mar] | 0 | 0 |
A member is a point within a dimension determined by a particular set of attribute values. The gender hierarchy has the two members 'M' and 'F'. 'San Francisco', 'California' and 'USA' are all members of the store hierarchy. A hierarchy is a set of members organized into a structure for convenient analysis. For example, the store hierarchy consists of the store name, city, state, and nation. The hierarchy allows you form intermediate sub-totals: the sub-total for a state is the sum of the sub-totals of all of the cities in that state, each of which is the sum of the sub-totals of the stores in that city. A level is a collection of members which have the same distance from the root of the hierarchy. A dimension is a collection of hierarchies which discriminate on the same fact table attribute (say, the day that a sale occurred).
mondrian.properties
???mondrian有一個配置文件可以定義它是如何運行的。
a simple star schema
???
Cube [Sales] has two measures [Unit sales] and [Dollar sales]
Dimension [Product] has levels [All Products], [Manufacturer], [Brand], [Prodid]
Dimension [Time] has levels [All Time], [Year], [Quarter], [Month], [Day]
Dimension [Customer] has levels [All Customers], [State], [City], [Custid]
Dimension [Payment Method] has levels [All Payment Methods], [Payment Method]
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