Question

How can I improve the speed of reports that rely heavily on DISTINCTCOUNT()?
This question addresses ways to optimize reports that heavily use the DISTINCTCOUNT() function, a common DAX function in Power BI that can negatively affect performance when dealing with large datasets. It seeks advice on efficient use of DISTINCTCOUNT() or alternative DAX functions and techniques to enhance performance without compromising data accuracy.

Answer 1

To improve the speed of reports that rely heavily on DISTINCTCOUNT() in Power BI, consider the following optimization techniques:

1. Use Aggregated Tables: Pre-aggregate data at the source or create summary tables in Power BI to reduce the dataset size. Performing DISTINCTCOUNT() on smaller tables significantly improves performance.

2. Leverage Vertipaq Storage Engine: Ensure your data model is optimized for the Vertipaq storage engine by removing unnecessary columns, reducing cardinality, and using proper data types. Vertipaq handles distinct count operations more efficiently when datasets are well-optimized.

3. Use Alternatives to DISTINCTCOUNT(): Consider using calculated columns or measures with conditional logic if they provide the same results. For example, using COUNTROWS() on a distinct list or filtering values in advance can sometimes outperform DISTINCTCOUNT().

4. Optimize Relationships and Filtering: Ensure relationships in the data model are properly configured. Avoid bidirectional relationships unless necessary, as they can complicate calculations and slow down performance.

5. Analyze with Performance Analyzer: Use the Performance Analyzer tool to identify visuals or measures that are slow due to DISTINCTCOUNT() and refine the DAX expressions.