dcsimg

Introduction to MSSQL Server 2000 Analysis Services: Distinct Count Basics: Two Perspectives - Page 5

January 10, 2005

Rendering Distinct Counts Using MDX

We now have a set of "answers" that we can attempt to replicate in direct MDX. Let's initialize the MDX Sample Application, as a platform from which to perform our practice exercises, taking the following steps:

1.     Start the MDX Sample Application.

We are initially greeted by the Connect dialog, shown in Illustration 14.

Click for larger image

Illustration 14: The Connect Dialog for the MDX Sample Application

The illustration above depicts the name of my server, MOTHER1, and properly indicates that we will be connecting via the MSOLAP provider (the default).

2.  Click OK.

The MDX Sample Application window appears.

3.  Ensure that FoodMart 2000 is selected as the database name in the DB box of the toolbar.

4.  Select the Warehouse cube in the Cube drop-down list box.

5.  Click File --> New to open a blank Query pane.

The MDX Sample Application window should resemble that depicted in Illustration 15, complete with the information from the Warehouse cube displaying in the Metadata tree (left section of the Metadata pane).


Illustration 15: The MDX Sample Application Window (Compressed View)

We will begin creating our query with a focus on returning results in the same general formation as the Data View we left in the Cube Editor. We will retrieve the Warehouse Profit and Product Count measures, as pictured in Illustration 13 above. Next, we will attempt to add a calculated measure that we craft directly in MDX, to replicate the distinct count information we obtained with the Product Count measure that we created in Analysis Manager earlier. This will afford us a side-by-side comparison between our "MDX solution" and the "Analysis Manager" approach we took in the last section.

1.  Create the following new query:


-- ANSYS31-1 Initial Attempt at Distinction
WITH MEMBER 
   [MEASURES].[ProdCount] 
AS  
   'DISTINCTCOUNT({[Product].MEMBERS})'
SELECT
   { [MEASURES].[Warehouse Profit], [MEASURES].[Product Count],
[MEASURES].[ProdCount] } ON COLUMNS,
   {[Product].CHILDREN} ON ROWS
FROM 
   [Warehouse]

The above represents an attempt to meet the information consumers' objectives - with what appears to be the straightforward use of the DISTINCTCOUNT() function. This might seem intuitive to a practitioner who has given up on the handful of non-working or nebulous examples that can be found on the web, (and which happen to be about all we seem to have as a basis for learning MDX, in many instances). While this approach ultimately fails to provide the desired solution, as we shall see, it should not be surprising that we might attempt this, given the definition in the Books Online, not to mention the words used in the name of the function itself. (Most will agree, also, that it is better to attempt it now, than when under the gun of an employer or a hurried client.)

The calculated member ProdCount embodies the function. I named it ProdCount to distinguish if from Product Count, the measure we created while within the user interface in the earlier section, which I have also decided to present within the results dataset for comparison purposes. Warehouse Profit is also presented to align with our Data View as we left it in the last section.

2.  Execute the query using the Run Query button.

The results dataset appears as shown in Illustration 16.


Illustration 16: The Results Dataset - DISTINCTCOUNT() Approach

3.  Save the query as ANSYS31-1.

It doesn't require a huge leap of logic to conclude that the ProdCount calculated measure is generating a transaction count. The count is correctly "distinct," within its own (actual) meaning, but not at all what the information consumers have requested in our practice example.

Having seen why the "intuitive" approach is lacking, let's resort to another, more cumbersome approach, which results in the distinct product values that we seek.

4.  Create the following new query:


-- ANSYS31-2  Distinction at its Finest 
WITH MEMBER
   [MEASURES].[CalcCount]
AS
   'COUNT(CROSSJOIN({[MEASURES].[Warehouse Profit]}, DESCENDANTS
    ([Product].CURRENTMEMBER, [Product].[Product Name])), EXCLUDEEMPTY)'
SELECT 
   {[MEASURES]. [Warehouse Profit], [MEASURES].[Product Count], [MEASURES].[CalcCount] } 
      ON COLUMNS,
   [Product].CHILDREN ON ROWS
FROM
   [Warehouse]

The above "attempt at distinction" is embodied by the calculated measure CalcCount, named, again, simply as a means of distinguishing it from the measure we created in the Cube Editor, and which we include once again for comparison purposes.

The above approach may not have been the initial impulse that many of us had in tackling what seemed to be a straightforward replication of the Data View we saw earlier. What we are doing, in short, with the CrossJoin() function is marrying the Warehouse Profit values with the products, and returning (thanks to EXCLUDEEMPTY) a count of the non-empty pairings. The Descendants() function builds in flexibility, allowing us to apply the logic equally well to a group of products as to the full set of products. The key to this is the selection of the current member's descendents, adding the "relativity" that so pointedly underscores the power of the .CurrentMember function.

5.  Execute the query using the Run Query button.

The results dataset appears as shown in Illustration 17.


Illustration 17: The Results Dataset - Distinction Attained

6.  Save the query as ANSYS31-2.

The values for the new measure are in alignment with those of the measure we created in the Cube Editor. (All that remains to make the measures identical is the addition of formatting syntax).

7.  Exit the MDX Sample Application and Analysis Manager when ready.

Conclusion

In this article, we introduced the concept of distinct counts, discussing why they are often a requirement in our multidimensional analysis efforts, and those of the information consumers whom we support. In our introduction and overview, and throughout our examination of the objects and MDX syntax we explored to achieve our illustrative ends, we highlighted some of the challenges that are inherent in distinct counts.

We performed practice exercises, to illustrate solutions for hypothetical business needs that called upon the use of a distinct count capability, obtaining exposure to the options afforded us by the MSAS user interface, as well the MDX syntax involved with using the alternative solutions that we proposed.

We now have a basis in distinct counts that will allow us to examine more detailed nuances surrounding the capability. In subsequent articles, we will examine specific performance considerations inherent in the production of distinct counts, as well as options that are available to tune our efforts for more efficient operation. The need for distinct counts is a fact of business life, and mastery of the costs and results of this vital capability represent a unique opportunity to add another tool to our MSAS skill sets.

» See All Articles by Columnist William E. Pearson, III

Discuss this article in the MSSQL Server 2000 Analysis Services and MDX Topics Forum.

Introduction to MSSQL Server Analysis Services Series
Introduction to Security in Analysis Services
Cube Storage: Planning Partitions from a SQL Server Management Studio Perspective
Cube Storage: Planning Partitions (Business Intelligence Development Studio Perspective)
Cube Storage: Introduction to Partitions
Introduction to Cube Storage
Attribute Discretization: Customize Grouping Names
Attribute Discretization: Using the "Clusters" Method
Attribute Discretization: Using the "Equal Areas" Method
Attribute Discretization: Using the Automatic Method
Introduction to Attribute Discretization
More Exposure to Settings and Properties in Analysis Services Attribute Relationships
Attribute Relationships: Settings and Properties
Introduction to Attribute Relationships in MSSQL Server Analysis Services
Attribute Member Values in Analysis Services
MSSQL Analysis Services - Attribute Member Names
Attribute Member Keys - Pt II: Composite Keys
Attribute Member Keys - Pt 1: Introduction and Simple Keys
Dimension Attributes: Introduction and Overview, Part V
Dimension Attributes: Introduction and Overview, Part IV
Dimension Attributes: Introduction and Overview, Part III
Dimension Attributes: Introduction and Overview, Part II
Dimension Attributes: Introduction and Overview, Part I
Dimensional Model Components: Dimensions Part II
Dimensional Model Components: Dimensions Part I
Manage Unknown Members in Analysis Services 2005, Part II
Manage Unknown Members in Analysis Services 2005, Part I
Alternatively Sorting Attribute Members in Analysis Services 2005
Introduction to Linked Objects in Analysis Services 2005
Distinct Counts in Analysis Services 2005
Positing the Intelligence: Conditional Formatting in the Analysis Services Layer
Administration and Optimization: SQL Server Profiler for Analysis Services Queries
Mastering Enterprise BI: Time Intelligence Pt. II
Mastering Enterprise BI: Time Intelligence Pt. I
Design and Documentation: Introducing the Visio 2007 PivotDiagram
Actions in Analysis Services 2005: The URL Action
Actions in Analysis Services 2005: The Drillthrough Action
Mastering Enterprise BI: Introducing Actions in Analysis Services 2005
Mastering Enterprise BI: Introduction to Translations
Mastering Enterprise BI: Introduction to Perspectives
Introduction to the Analysis Services 2005 Query Log
Mastering Enterprise BI: Working with Measure Groups
Mastering Enterprise BI: Introduction to Key Performance Indicators
Mastering Enterprise BI: Extend the Data Source with Named Calculations, Pt. II
Mastering Enterprise BI: Extend the Data Source with Named Calculations, Pt. I
Process Analysis Services Objects with Integration Services
Usage-Based Optimization in Analysis Services 2005
Introduction to MSSQL Server Analysis Services: Named Sets Revisited
Introduction to MSSQL Server Analysis Services: Migrating an Analysis Services 2000 Database to Analysis Services 2005
Introduction to MSSQL Server Analysis Services: Introducing Data Source Views
Introduction to MSSQL Server Analysis Services: Reporting Options for Analysis Services Cubes: MS Excel 2003 and More ...
Introduction to MSSQL Server Analysis Services: Mastering Enterprise BI: Create Aging "Buckets" in a Cube
Introduction to MSSQL Server Analysis Services: Mastering Enterprise BI: Relative Time Periods in an Analysis Services Cube, Part II
Introduction to MSSQL Server Analysis Services: Mastering Enterprise BI: Relative Time Periods in an Analysis Services Cube
Introduction to MSSQL Server Analysis Services: Process Analysis Services Cubes with DTS
Introduction to MSSQL Server Analysis Services: Presentation Nuances: CrossTab View - Same Dimension
Introduction to MSSQL Server Analysis Services: Point-and-Click Cube Schema Simplification
Introduction to MSSQL Server 2000 Analysis Services: Manage Distinct Count with a Virtual Cube
Introduction to MSSQL Server 2000 Analysis Services: Distinct Count Basics: Two Perspectives
Introduction to MSSQL Server 2000 Analysis Services: Semi-Additive Measures and Periodic Balances
Introduction to MSSQL Server 2000 Analysis Services: Performing Incremental Cube Updates - An Introduction
Introduction to MSSQL Server 2000 Analysis Services: Partitioning a Cube in Analysis Services - An Introduction
Introduction to MSSQL Server 2000 Analysis Services: Basic Storage Design
Introduction to MSSQL Server 2000 Analysis Services: Derived Measures vs. Calculated Measures
Introduction to MSSQL Server 2000 Analysis Services: Creating a Dynamic Default Member
Introduction to MSSQL Server 2000 Analysis Services: Another Approach to Local Cube Design and Creation
Introduction to MSSQL Server 2000 Analysis Services: Introduction to Local Cubes
Introduction to MSSQL Server 2000 Analysis Services: Actions in Virtual Cubes
Introduction to MSSQL Server 2000 Analysis Services: Putting Actions to Work in Regular Cubes
Introduction to MSSQL Server 2000 Analysis Services: Reporting Options for Analysis Services Cubes: ProClarity Part II
Introduction to MSSQL Server 2000 Analysis Services: Reporting Options for Analysis Services Cubes: ProClarity Professional, Part I
Introduction to MSSQL Server 2000 Analysis Services: Using Calculated Cells in Analysis Services , Part II
Introduction to MSSQL Server 2000 Analysis Services: Using Calculated Cells in Analysis Services, Part I
Introduction to MSSQL Server 2000 Analysis Services: MSAS Administration and Optimization: Toward More Sophisticated Analysis
Introduction to MSSQL Server 2000 Analysis Services: MSAS Administration and Optimization: Simple Cube Usage Analysis
Introduction to MSSQL Server 2000 Analysis Services: Build a Web Site Traffic Analysis Cube: Part II
Build a Web Site Traffic Analysis Cube: Part I
Reporting Options for Analysis Services Cubes: Cognos PowerPlay
Reporting Options for Analysis Services Cubes: MS FrontPage 2002
Reporting Options for Analysis Services Cubes: MS Excel 2002
Introduction to MSSQL Server 2000 Analysis Services: Drilling Through to Details: From Two Perspectives
Introduction to MSSQL Server 2000 Analysis Services: Custom Cubes: Financial Reporting - Part II
Introduction to MSSQL Server 2000 Analysis Services Custom Cubes: Financial Reporting (Part I)
Introduction to SQL Server 2000 Analysis Services: Exploring Virtual Cubes
Introduction to SQL Server 2000 Analysis Services: Working with the Cube Editor
Introduction to SQL Server 2000 Analysis Services: Parent-Child Dimensions
Introduction to SQL Server 2000 Analysis Services: Handling Time Dimensions
Introduction to SQL Server 2000 Analysis Services: Working with Dimensions
Introduction to SQL Server 2000 Analysis Services: Creating Our First Cube








The Network for Technology Professionals

Search:

About Internet.com

Legal Notices, Licensing, Permissions, Privacy Policy.
Advertise | Newsletters | E-mail Offers