DAX: Use variables to improve your formulas
As a data modeler, writing and debugging some DAX calculations can be challenging. It's common that complex calculation requirements often involve writing compound or complex expressions. Compound expressions can involve the use of many nested functions, and possibly the reuse of expression logic.
Using variables in your DAX formulas helps you write complex and efficient calculations. Variables can:
In this article, we'll demonstrate the first three benefits by using an example measure for year-over-year (YoY) sales growth. (The formula for YoY sales growth is: period sales _fewer sales for the same period last year, divided by sales for the same period last year.)
Let's start with the following measure definition.
Sales YoY Growth % = DIVIDE( ([Sales] - CALCULATE([Sales], PARALLELPERIOD('Date'[Date], -12, MONTH))), CALCULATE([Sales], PARALLELPERIOD('Date'[Date], -12, MONTH)) )
The measure produces the correct result, yet let's now see how it can be improved.
Notice that the formula repeats the expression that calculates "same period last year". This formula is inefficient, as it requires Power BI to evaluate the same expression twice. The measure definition can be made more efficient by using a variable.
The following measure definition represents an improvement. It uses an expression to assign the "same period last year" result to a variable named SalesPriorYear. The variable is then used twice in the RETURN expression.
Sales YoY Growth % = VAR SalesPriorYear = CALCULATE([Sales], PARALLELPERIOD('Date'[Date], -12, MONTH)) RETURN DIVIDE(([Sales] - SalesPriorYear), SalesPriorYear)
The measure continues to produce the correct result, and does so in about half the query time.
In the previous measure definition, notice how the choice of variable name makes the RETURN expression simpler to understand. The expression is short and self-describing.
Variables can also help you debug a formula. To test an expression assigned to a variable, you temporarily rewrite the RETURN expression to output the variable.
The following measure definition returns only the SalesPriorYear variable. Notice how it comments-out the intended RETURN expression. This technique allows you to easily revert it back once your debugging is complete.
Sales YoY Growth % = VAR SalesPriorYear = CALCULATE([Sales], PARALLELPERIOD('Date'[Date], -12, MONTH)) RETURN --DIVIDE(([Sales] - SalesPriorYear), SalesPriorYear) SalesPriorYear
In earlier versions of DAX, variables were not yet supported. Complex expressions that introduced new filter contexts were required to use the EARLIER or EARLIEST DAX functions to reference outer filter contexts. Unfortunately, data modelers found these functions difficult to understand and use.
Variables are always evaluated outside the filters your RETURN expression applies. For this reason, when you use a variable within a modified filter context, it achieves the same result as the EARLIEST function. The use of the EARLIER or EARLIEST functions can therefore be avoided. It means you can now write formulas that are less complex, and that are easier to understand.
Consider the following calculated column definition added to the Subcategory table. It evaluates a rank for each product subcategory based on the Subcategory Sales column values.
Subcategory Sales Rank = COUNTROWS( FILTER( Subcategory, EARLIER(Subcategory[Subcategory Sales]) < Subcategory[Subcategory Sales] ) ) + 1
The EARLIER function is used to refer to the Subcategory Sales column value in the current row context.
The calculated column definition can be improved by using a variable instead of the EARLIER function. The CurrentSubcategorySales variable stores the Subcategory Sales column value in the current row context, and the RETURN expression uses it within a modified filter context.
Subcategory Sales Rank = VAR CurrentSubcategorySales = Subcategory[Subcategory Sales] RETURN COUNTROWS( FILTER( Subcategory, CurrentSubcategorySales < Subcategory[Subcategory Sales] ) ) + 1
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