Boosting DAX Performance: Practical Optimizations for Faster Queries

We are excited to share a few key tips for optimizing DAX queries that significantly improved the efficiency of our Azure Analysis Services (AAS) Tabular Model project.

Our project involves various financial measures that calculate key metrics such as LTM Sales, LTM EBITDA, and Net Debt. Each of these measures incorporates logic for FX conversions and period filtering. While individual measures performed well, combining many measures in a single dashboard negatively impacted performance. After analyzing our DAX expressions, we identified two major factors affecting execution time. Below, we outline how we optimized performance in each case.

1. Use Variables in IF Statements for Efficiency

Unlike traditional programming languages such as C#, DAX’s IF statements do not short-circuit. This means that both the TRUE and FALSE branches are evaluated, even if only one is required.

For example, our project required an FX rate calculation based on a conversion methodology parameter (“Standard” or “Constant”). Initially, we wrote the IF statement as follows:

var conversion_method = SELECTEDVALUE(CONVERSION_METHOD[CONVERSION_METHOD], "Constant")
var result = CALCULATE(
   IF(conversion_method = "Constant", 
      [Currency Rate Fin Constant], 
      [Currency Rate Fin Standard]
   )
)
return result

Since DAX eagerly evaluates both branches, it calculated [Currency Rate Fin Constant] and [Currency Rate Fin Standard] even when only one was necessary.

To improve efficiency, we stored each measure in a variable before using them in the IF statement:

VAR fxRateConst = [Currency Rate Fin Constant]
VAR fxRateStandard = [Currency Rate Fin Standard]
var conversion_method = SELECTEDVALUE(CONVERSION_METHOD[CONVERSION_METHOD], "Constant")
var result = CALCULATE(
   IF(conversion_method = "Constant", 
      fxRateConst, 
      fxRateStandard
   )
)
return result

DAX only evaluates variables when needed. For example, fxRateConst is only evaluated when the “Constant” conversion method is used. Besides improving performance, using local variables enhances code readability and allows reuse, further optimizing execution time.

2. Prefer Boolean Filter Expressions Over FILTER()

In our CALCULATE() expression for LTM Sales, we originally used the FILTER() function to select a subset of the calendar table representing the past 12 months. Since our business logic required specific adjustments to the start and end months, we referenced two internal measures where this logic is implemented: [Fin Period Adj. Start (L12M)] and [Fin Period Adj. End].

Initially, we wrote the filter using FILTER():

CALCULATE(/amount_expression_goes_here/,
   FILTER(
      ALL(D_CALENDAR[YEAR_MONTH_NUMBER]), 
      D_Calendar[YEAR_MONTH_NUMBER] <= [Fin Period Adj. End] &&
      D_Calendar[YEAR_MONTH_NUMBER]>= [Fin Period Adj. Start (L12M)]
   )
)

While FILTER() provides flexibility, it comes at a performance cost. A more efficient approach replaces FILTER() with Boolean expressions by assigning measure results to local variables:

VAR period_start = [Fin Period Adj. Start (L12M)]
VAR period_end = [Fin Period Adj. End]
...
CALCULATE(/amount_expression_goes_here/,
    D_Calendar[YEAR_MONTH_NUMBER] <= period_end,
    D_Calendar[YEAR_MONTH_NUMBER] >= period_start
)

Boolean filters are generally faster than FILTER() because they operate on column-based logic and avoid iterating over entire tables. While this approach depends on specific filter context requirements, it is a valuable optimization strategy for improving performance.

Conclusion

DAX variables play a crucial role in performance optimization by preventing unnecessary calculations, improving readability, and allowing result reuse. Whether optimizing IF statements or replacing FILTER() with Boolean expressions, these techniques can significantly speed up DAX queries.

Keep an eye out for future insights and best practices in Azure Analysis Services and DAX performance tuning!