DAX optimization problem

Hi Anonymous 
Thank you for reaching out to the Microsoft Fabric Community.

I have reproduced your scenario in Power BI using your logic for KPI calculations, From/To month filters, and the conditional status filtering. After implementing your measures and applying the visual-level filter based on Date_Filter, I was able to get the expected output as per your requirement.

For your reference, I’m attaching the .pbix file I used for this validation so you can review and adjust it to fit your full dataset.

If this information is helpful, please “Accept as solution”  to assist other community members in resolving similar issues more efficiently.
Thank you.

Hi Anonymous 

You're working with a Power BI report that includes a single table visual displaying over 170 columns, many of which are complex DAX measures that use filters, date ranges, and conditional logic. These measures are calculated dynamically based on slicer selections (like "From Month" and "To Month") and involve row-level computations, including filters using TOPN, CALCULATE, and VAR logic. Because all these measures are evaluated simultaneously for each row in the visual, and the table has a wide structure, the report is consuming excessive memory and computational resources, resulting in the "Resources exceeded" error—especially since you're not using Premium capacity. Additionally, you have a filter condition (Date_Filter) that adds further complexity by evaluating each row's status and date range to conditionally include or exclude data.

To improve performance, it's recommended to break the large visual into smaller sections using bookmarks or multiple pages, thereby reducing the memory footprint per visual. Also, pre-aggregating data at the query level (using Power Query or SQL) or creating intermediate summary tables in DAX can help avoid expensive row-level filtering and repeated logic across measures. You can also optimize the measures by avoiding redundant use of FILTER and TOPN, and instead use simpler filter expressions within CALCULATE. If possible, move logic like Date_Filter into a calculated column during data load, which will help offload the logic from the visual evaluation at runtime. Finally, use the Performance Analyzer in Power BI Desktop to identify which specific measures are causing the most delay, and optimize those first. These steps should collectively help you avoid memory overload and improve responsiveness, even without Premium capacity.