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Recall that the Microsoft BI Semantic Model consists of three flavors: Multidimensional, Tabular, and Power Pivot. The default storage mode of Tabular and Power Pivot is the xVelocity in-memory engine. The default Multidimensional storage is MOLAP. You might want to analyze which objects consume the most storage space. This especially important for Tabular since computer memory is still a scarce resource.

Windows Explorer

The easiest way to get the storage breakdown for Multidimensional and Tabular is to use Windows Explorer and examine the size of the corresponding data folder under the SSAS Data folder, e.g. C:\Program Files\Microsoft SQL Server\MSAS11.TABULAR\OLAP\Data\AdventureWorks Tabular Model SQL 2012.0.db. Unfortunately, Windows Explorer in Windows 7 and 8 doesn’t show the folder size requiring you either to hover on the folder to see its size or to go the folder properties. As a workaround, consider utilities such as HDGraph or Folder Size. Folder Size, for example, pops up a window in Windows Explorer on the side of each folder that has subfolders to show you the folder size. In the case of the AdventureWorks Tabular Model SQL 2012 Tabular database, we can see that Product Inventory table consumes the most space.

If you drill down the Product Inventory folder and sort by size, you can see the storage breakdown by column.

BISM Server Memory Report

Another way to analyze Tabular and Multidimensional storage in an user-friendly way is to use the BISM Server Memory Report developed by Kasper de Junge (Senior Program Manager at Microsoft).

DMV Views

Behind the scenes, the BISM Server Memory Report uses SSAS DMV Views which expose expose information about local server operations and server health. Specifically, it uses the Discover_object_memory_usage DMV which you can query directly to obtain the same information.

Power Pivot Macro

If you use Power Pivot, the easiest way to analyze Power Pivot storage in Excel 2013 is to use the Power Pivot macro made available by Kasper de Junge. When you run the macro, it generates Excel pivot report which you can sort by the object memory size. For example, the report below allows us to see the most memory is consumed by CalculatedColumn1 (although xVelocity calculated columns are stored to disk like regular columns, calculated columns are not compressed).

Power Pivot PowerShell Script

While the above macro relies on Excel 2013 native integration with Power Pivot and only works with Excel 2013, the methodology and PowerShell script developed by Vidas Matelis (SQL Server MVP) works with both Excel 2010 and 2013.

Scenario

You need to assign a dimension dynamically to a cube measure group, such as to calculate binning. In the extreme example, you might need to perform fact row level evaluation to determine where the dimension “fits” in. Consider the following example from the insurance industry. An ODS-style Claim table interprets every change to the claim table as a Type 2 change by creating a new row and expiring the previous row of the claim.

Options

Given this scenario, you might want to count the number of claims as of any date. You have two options:

1. Create a Claim Snapshot fact table taken at a daily or monthly interval.
2. Dynamically associate the Date dimension. In Multidimensional, this will require creating a Claim dimension and a Claim measure group, both bound to the same Claim table. Then, you can use the following script to “associate” the Date dimension (added as a unrelated dimension to the cube) to the Claim measure group:

CREATE MEMBER CURRENTCUBE.[Measures].[ClosingAccountingDate] AS [Date].[Calendar].MemberValue, VISIBLE = 0 ;

Scope

(

(

MeasureGroupMeasures(“Fact Claim”)

–

{

[Measures].[Start Date],

[Measures].[End Date]

}

),

[Date].[Calendar].Members,

Leaves([Claim])

);

this = iif ([Measures].[ClosingAccountingDate]>=[Measures].[Start Date] and [Measures].[ClosingAccountingDate]<=[Measures].[End Date],

Measures.CurrentMember, null);

End Scope;

End Scope;

To simplify the evaluation, the MemberValue property of all attributes of the Date dimension is bound to the closing date of the period, e.g. 12/31/2014 for year 2014, 3/31/2014 for any day in Q1, and 1/31/2014 for any day in January 2014. The scope assignment includes all measures in the Fact Claim measure group, except the Start Date and End Date measures since we don’t want to overwrite them. It also scopes on all members of the Calendar hierarchy of the Date dimension so that the calculation is performed at any level of the Calendar hierarchy. For example, if the user selects 2014 as a year, the calculation is performed as of 12/31/2014. Moreover, the Leaves([Claim]) scope positions at the measure leaves so we can evaluate any row in the fact table by comparing if the ‘as of’ date falls in the range of Start Date and End Date for that row.

Are we happy? We might be but end users might not be so excited. The problem is that this remarkably simple scope assignment can put a remarkable dent in the query performance. For any date, the server has to perform the evaluation for every row in the fact table. With a Claim dimension and a measure group with 500,000 rows, calculating the counts executes relatively fast (within a few seconds). However, running a trend query that requests counts for every day in a quarter might take a minute or so. The larger the claim dimension and the cube subspace that the query requires, the worse the performance will be. The performance is further impacted if you have other calculations on top of the claim counts.

Scope assignments are a very powerful and useful feature of Multidimensional but you shouldn’t abuse them. Keep the scope as narrow of possible to limit the cube space where the calculation is applied. Although it might require ETL effort, materialize whenever you can. For example, although it might result in millions of rows, a snapshot would perform much better with the above scenario. Further, performance can be further enhanced by partitions and aggregations while dynamic granular calculations won’t.