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MS SQL

Posted Oct 4, 2004

MDX Essentials: Basic Set Functions: The EXTRACT() Function - Page 4

By William Pearson

Procedure

We are now ready to apply the EXTRACT() function to the general population of Product-Store City combinations that we have created with the CROSSJOIN() function in our preparatory steps. We effectively have a set that contains all 1998 Store City Warehouse Sales, filtered, of course, for the empties. To meet the expressed needs of the information consumers with which we are working, we now are faced with extracting fresh Store City tuples, and then presenting the five Store Cities whose products have experienced the highest Warehouse Sales in the 1998 time frame.

1. Insert the EXTRACT() function, as shown:

     EXTRACT(

between the third line of the query (counting the comment line, once again, and shown below):

{[Measures].[Warehouse Sales]} ON COLUMNS,

and the fourth line of the query (which we just added in the last section, as follows):

               FILTER(

2. Insert the following into the line beginning with NOT ISEMPTY , which we also added in the last section, between the last parentheses in the line (just before ON ROWS) and ON ROWS:

                           , [Store]) 

The complete line, together with our addition, should appear as follows:

            NOT ISEMPTY([Measures]. [Warehouse Sales])), [Store])  ON ROWS

With the addition of the EXTRACT() function, together with our specification of the Store dimension in the line ending in ON ROWS, we are, in effect, saying "return a dataset composed of tuples from the Store dimension only." This again generates fresh tuples of only the Store Cities - through the specification of the Store dimension, and the implicit elimination of the Food component from the original tuples.

The Query pane appears as shown in Illustration 4, with additions circled in red.


Illustration 4: Modified Query in Query Pane (Compressed View)

3.  Execute the query by clicking the Run Query button in the toolbar

The Results pane is populated, and the dataset shown in Illustration 5 appears.


Illustration 5: Results Dataset, With EXTRACT() in Place

We see there are still a total of twenty-three Store Cities; the difference now is that the Food component has vanished. (Actually, the Store City tuples have been created anew, without the Food component that came along with the CROSSJOIN() function earlier).

4.  Select File --> Save to save the query as MDX24-3. Leave the file open for the next step.

5.  Modify the comment line to read:

-- MDX24-4:  Tutorial Query Step 4

6.  Save the file as MDX24-4, to protect MDX24-3.

We have extracted the Store dimension members from the crossjoin-produced original set, and thus are nigh unto meeting the needs of the information consumers. The remaining consideration, to present the five Store Cities whose products have experienced the highest Warehouse Sales, is only a single function away from realization. Let's add the appropriate TOPCOUNT() function and give the users precisely what they have requested.

7.  Insert the TOPCOUNT() function, as shown:

     TOPCOUNT(

Once again, between the third line of the query (shown below):

  {[Measures].[Warehouse Sales]} ON COLUMNS,

And the fourth line of the query (which we just added in the last section, as follows):

               EXTRACT(

8. Insert a comma (,) after [Store]) in the ON ROWS line of the query.

9. Insert the following into the same line, just behind the comma we inserted in the last step, and just before ON ROWS:

5, [Measures].[Warehouse Sales] ) ON ROWS

The complete line, together with our addition, should appear as follows:

NOT ISEMPTY([Measures]. [Warehouse Sales])), [Store]), 
    5, [Measures].[Warehouse Sales] ) ON ROWS

With the addition of the TOPCOUNT() function, together with our specifications of the "5" and the measure Warehouse Sales, in the line ending in ON ROWS, we are saying "return the Store Cities with the top five Warehouse Sales values."

The Query pane appears as depicted in Illustration 6, with additions circled in red.


Illustration 6: Finalized Query in Query Pane (Compressed View)

10.  Execute the query by clicking the Run Query button in the toolbar.

The Results pane is populated, and the dataset shown in Illustration 7 appears.


Illustration 7: Results Dataset, Final Enhancements in Place

We now see the Store Cities, in descending order, with the top five Warehouse Sales values.

11.  Select File --> Save to save the query as MDX24-4.

12.  Exit the Sample Application when ready.

In the forgoing manner we meet an expressed business requirement in a simple example that illustrates the operation of the EXTRACT() function.

Summary ...

In this article, we explored the useful EXTRACT() function, whose purpose is to return a set from an initial set we specify, much in a manner that is opposite to the behavior of the CROSSJOIN() function. The returned set is composed of tuples from dimension(s) that we specify as arguments within the function. We discussed the EXTRACT() function in general, and emphasized its capabilities within MDX, and its usefulness within our analysis toolsets.

In addition to discussing the purpose and operation of the EXTRACT() function, we mentioned the elimination of duplicates by the function. We practiced the use of the function within a multi-step exercise, building an original set in preparation to meet a hypothetical business need. Throughout the steps of the practice exercise, we discussed the results we obtained with each step's execution, remarking on the distinguishing characteristics of each.

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

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



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