Introduction to MSSQL Server 2000 Analysis Services: Distinct Count Basics: Two Perspectives - Page 2
January 10, 2005
Distinct Counts Concepts
Overview and Discussion
Anyone working within the realm of business intelligence and general analysis realizes, in short order, that we often encounter the need to quantify precisely the members of various sets of data. Those of us who have become familiar with MSAS are aware of its capabilities when it comes to categorizing and aggregating data within the hierarchical contexts of dimensions and levels. We can, for the most part, readily tap these capabilities from the user interface that MSAS provides. Through the exploitation of more advanced approaches, including the use of calculated members / measures, and multidimensional expressions ("MDX") in general, we can extend our analysis even further, and leverage MSAS to reach far more specific objectives.
One of the basic requirements that come into play, at least in some form, in many analysis scenarios, is the need to count the members of a set targeted for analysis. An example might be the need to count the number of products we have shipped from a given warehouse, or group of warehouses, to a given geographical location, or a specific group of stores. This can be accomplished readily enough with the Count() function, as most of us are aware.
Count() does a great job of giving us a total count. Of course, the results we would achieve in using Count() with products, in the scenarios above, would represent total number of products shipped. What we would not get, and what we might find far more useful in some situations, would be a count of the different products that were shipped. Count(), in providing a total number, would also be providing multiple counts of the same products, because products will have been shipped multiple times, in many instances. To reach our objective of counting different products, then, we would need to count each different product shipped, only once. To count them multiple times not only misstates the number of different products, but it also likely renders averages, and other metrics based upon the count value, meaningless or misleading.
The word "different" here is easily supplanted by "distinct." Moreover, as many of us are aware, the performance of distinct counts has historically presented a challenge in the OLAP world. Let's discuss an example that illustrates the challenge, and then transform that challenge to an opportunity to meet an illustrative business need, using the distinct count capabilities found within MSAS.
Considerations and Comments
For purposes of this exercise, we will be working with the Warehouse cube, within the FoodMart 2000 MSAS database; these working samples accompany a typical installation of MSAS. If the samples are not installed in, or have been removed from, your environment, they can be obtained from the installation CD, as well as from the Analysis Services section of the Microsoft website. If you prefer not to alter the structure of your sample cubes as they currently exist, make copies of the cube we reference in the article before beginning the practice exercises. For instructions on copying cubes, see the Preparation section of Introduction to MSSQL Server 2000 Analysis Services: Semi-Additive Measures and Periodic Balances.