Partly Review of Mondrian Olap Engine

Mondrian Olap Engine Partly Review

 

1. Consisted of 4 layers

n  Presentation layer

    Take charge of the end-user interaction in various presentation forms. The logic model is multidimensional 'grammar' of dimensions, measures and cells.

n  Calculation layer

    Validates and executes MDX queries. Query transformer.

n  Aggregation layer

       Manage aggregation in the cache.

n  Storage layer

       Usually is an RDBMS.

 

2. Aggregation strategy

General idea is to delegate unto the database what the database has. Thus the side-effect is loading data processing is easier, and the system is suited to do Olap on dataset changing in real time.

n  Fact data is stored in the RDBMS

n  Read aggregated data into the cache by submitting group by queries

n  Use materialized views as many as possible

 

3. APIs

n  The Usage is similar to JDBC interfaces (see the following example)

n  Use MDX, in place of SQL

n  Presents the database schema as a set of objects: Schema, Cube, Dimension, Hierarchy, Level, Member

n  Now suppot XML/A

Example:

/* establish a connection with Mondrian, submit an MDX query and retrieve the multi-dimension dataset from Mondrian.  */ import mondrian.olap.*; import java.io.PrintWriter;   Connection connection = DriverManager.getConnection(     "Provider=mondrian;" +     "Jdbc=jdbc:odbc:MondrianFoodMart;" +     "Catalog=/WEB-INF/FoodMart.xml;",     null,     false); Query query = connection.parseQuery(     "SELECT {[Measures].[Unit Sales], [Measures].[Store Sales]} on columns," +     "  {[Product].children} on rows " +     "FROM [Sales] " +     "WHERE ([Time].[1997].[Q1], [Store].[CA].[San Francisco])"); Result result = connection.execute(query); result.print(new PrintWriter(System.out));

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

4. MDX extensions

Mondrian extends Mdx in two aspects.

n  Support parameters

This is done through the functions Parameter():

       Parameter(<name>, <type>, <defaultValue>[, <description>])

For example:

/* the top 10 brands in California */ SELECT {[Measures].[Unit Sales]} on columns,   TopCount([Product].[Brand].members,  Parameter("Count", NUMERIC, 10, "Number of products to show"),  ( Parameter("Region", [Store], [Store].[USA].[CA]), [Measures].[Unit Sales])           ) on rows FROM Sales

          

n  Extend Build in functions StrToSet and StrToTuple

Add a optional parameter <hierarchy> to the functions, thus user can get the result of the given hierarchy.

       StrToSet(<String Expression>[, <Hierarchy>])

       StrToTuple(<String Expression>[, <Hierarchy>])

 

5. Schema

Mondrian use xml files to define its schema, including Logical model, Physical model and the mapping between them.

n  Logical Model: the structure of multi-dimensional database cubes, including dimensions, hierarchies, levels, and members.

n  Physical Model: the source of the data

Example:

/* foodmart, Sales cube defination */ <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="#,###.##"/>   </Cube>  </Schema>

 

Attention: You can write custom member readers to extend the datasource.