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MDX Calculated Member Spanning Multiple Date Dimensions

It’s common in most cubes to have a number of different date dimensions, whether role playing, distinct, or a combination of both. Say for example, Entry Date, Posting Date and Accounting Period. There may also be numerous hierarchies in each date dimension, such as calendar and fiscal calendar, leading to a relatively complicated array of dates to worry about when calculating semi-additive measures.

If we create a date related calculation (i.e. total to date) how do we ensure that this calculation works across all date dimensions?

Lets assume we have a stock movement measure, where each record in the fact table is the change in stock (plus or minus). The current stock level is found by using a calculation totaling every record to date.

     , [Measures].[Stock Movement]

[Note that {NULL:xxx} just creates a set of everything before the xxx member, i.e. everything to date]

This works just fine, if the user selects the [Date].[Calendar] hierarchy. What if the user selects the [Date].[Fiscal] hierarchy, or the [Period] dimension? Basically the calculation wont work, as the MDX expression is only aware of the [Date].[Calendar] hierarchy.

The simple solution is to use the Aggregate function over all of the dimensions that the calculation needs to be aware of:

       * {NULL:[Date].[Calendar].CurrentMember}
       * {NULL:[Period].[Period].CurrentMember}
    , [Measures].[Stock Movement]

The calculation will then use whichever date or time hierarchy is selected. It will even cope if multiple dimensions are selected, say the calendar on 0 and the periods on 1, both axis will honor the aggregation as expected.

Frog-Blog out.

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Team Purple Frog specialise in designing and implementing Microsoft Data Analytics solutions, including Data Warehouses, Cubes, SQL Server, SSIS, ADF, SSAS, Power BI, MDX, DAX, Machine Learning and more.

This is a collection of thoughts, ramblings and ideas that we think would be useful to share.


Alex Whittles
Reiss McSporran
Jeet Kainth
Jon Fletcher
Nick Edwards
Liam McGrath

Data Platform MVP

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