Role-Playing Dimensions in Multidimensional Modeling

Introduction

In SQL Server Analysis Services (SSAS) multidimensional modeling, a role-playing dimension is a dimension that can be used in multiple ways within a cube. This is particularly useful when a single dimension, like a Date dimension, needs to represent different business concepts such as an Order Date, Ship Date, or Due Date. Instead of creating separate, redundant dimension tables for each role, you can create a single dimension and define multiple instances of it within the cube, each playing a specific role.

Why Use Role-Playing Dimensions?

The primary benefits of using role-playing dimensions include:

  • Reduced Redundancy: Avoids duplicating dimension data, saving storage space and simplifying maintenance.
  • Consistency: Ensures that the same dimension attributes are used across different roles, maintaining data integrity.
  • Simplified Management: Changes to the dimension are applied to all its roles simultaneously.
  • Improved Performance: While not always a direct performance boost, managing fewer distinct dimensions can simplify query optimization.

Implementing Role-Playing Dimensions

Implementing role-playing dimensions in SSAS involves creating a single dimension and then referencing it multiple times within your cube definition, each with a distinct name and potentially a different usage property.

Step 1: Create the Base Dimension

First, create a standard dimension that will serve as the base for all its roles. For example, a 'DimDate' dimension with attributes like 'Full Date', 'Year', 'Month', 'Day', etc.

Base Date Dimension Structure

Figure 1: Structure of a base Date dimension.

Step 2: Reference the Dimension in the Cube

When you add this dimension to your cube, you can choose to add it multiple times. Each addition creates a new "instance" of the dimension within the cube's scope.

Step 3: Define Roles

For each instance of the dimension in the cube, you assign a specific role. This is done by renaming the dimension instance within the cube context and setting its properties, particularly the 'Name' property and potentially the 'Description'.

For instance, if you have a 'DimDate' dimension, you can add it to your cube three times and name these instances:

  • Date_OrderDate (for order dates)
  • Date_ShipDate (for ship dates)
  • Date_DueDate (for due dates)

In the Cube Designer, this will look something like:

Cube Designer showing Role-Playing Dimensions

Figure 2: Role-playing dimensions in the Cube Designer.

Step 4: Associate with Measures

Once defined, you can associate these role-playing dimensions with the relevant measures in your fact tables. For example, a measure for 'Sales Amount' might be sliced by 'Date_OrderDate' and 'Date_ShipDate'.

Example Scenario

Consider a sales data warehouse with a fact table `FactSales` and a dimension table `DimDate`.

  • `FactSales` table might have columns like `OrderDateKey`, `ShipDateKey`, `SaleAmount`.
  • `DimDate` table has `DateKey`, `FullDate`, `Year`, `Month`, `Day`.

In Analysis Services, you would:

  1. Create a dimension based on `DimDate`.
  2. Add this dimension to your `SalesCube` three times.
  3. Rename the instances to `OrderDate`, `ShipDate`, and `DueDate`.
  4. Link `OrderDate` dimension to `OrderDateKey` in `FactSales`.
  5. Link `ShipDate` dimension to `ShipDateKey` in `FactSales`.
  6. The `DueDate` might be linked to a `DueDateKey` if available, or used for other analytical purposes.

Using Role-Playing Dimensions in Queries (MDX)

When querying your cube using MDX, you refer to the dimensions by their role names within the cube context.

WITH MEMBER [Measures].[Total Sales] AS SUM( [Measures].[Sales Amount] )
SELECT
    { [Measures].[Total Sales] } ON COLUMNS,
    {
        [Date_OrderDate].[Year].MEMBERS *
        [Date_ShipDate].[Year].MEMBERS
    } ON ROWS
FROM [SalesCube]
WHERE ([Measures].[Total Sales])

This MDX query shows how you can analyze sales by both the year orders were placed and the year they were shipped, using the distinct role-playing instances of the date dimension.

Conclusion

Role-playing dimensions are a fundamental technique in SSAS multidimensional modeling for handling dimensions that represent multiple business concepts. By implementing them, you create more efficient, consistent, and manageable data models.