Visual Calculations DAX perfomance

[PshemekFLK]

Hi,

The new visual calculations in DAX (preview feature) were advertised as bringing improvement to the performance since the calculation happens on the visual only, not on the whole model. Yet every test I did trying to replace my slow measure with visual calculation result in even slower performance. Did you notice the same?

[Greg_Deckler · Accepted solution]

@PshemekFLK I think this is an earnest question that deserves a non-generative AI response. Yes. I have also noticed some issues with performance as well. I'm sure it depends on the exact nature of the calculations being performed. It would be helpful if you could provide some examples. There may be some optimization that can be done depending on the DAX involved. Remember that if your VC is based on measures that you have hidden in your table, those measures still have to be evaluated in addition to your VC so of course in that situation the visual will take longer to render than if it does not have the VC.

[123abc]

It's unfortunate that you're experiencing slower performance with the new visual calculations feature in DAX. While visual calculations are designed to improve performance by limiting calculations to the visual level rather than the entire model, there can be various factors contributing to slower performance.

Here are a few suggestions to help troubleshoot and potentially improve the performance:

1. Data Model Optimization: Ensure that your data model is optimized for performance. This includes properly defining relationships, using appropriate data types, and minimizing the number of unnecessary columns and tables.

2. Measure Optimization: Review your DAX measures to ensure they are written efficiently. Avoid using functions that iterate over large tables, such as SUMX or FILTER, whenever possible. Consider using aggregation functions like SUM, AVERAGE, or COUNTROWS instead.

3. Visual Optimization: Optimize your visuals by limiting the number of data points displayed, reducing the complexity of visuals, and avoiding unnecessary formatting or customizations that may impact performance.

4. Data Refresh: Check if the performance issues are related to data refresh operations. If so, consider optimizing your data refresh process by scheduling refreshes during off-peak hours or optimizing data loading queries.

5. Hardware and Environment: Consider the hardware specifications of your Power BI environment, including the resources allocated to your Power BI service or Power BI Desktop. In some cases, upgrading hardware or optimizing resource allocation may improve performance.

6. Benchmarking and Profiling: Use performance monitoring and profiling tools to identify specific bottlenecks or areas for optimization. Tools like DAX Studio or the Performance Analyzer in Power BI Desktop can help identify inefficient DAX expressions or resource-intensive queries.

7. Testing and Validation: Perform thorough testing and validation when implementing changes to measure calculations or adopting new features like visual calculations. Compare performance metrics before and after making changes to assess the impact on performance.

If you continue to experience performance issues despite these efforts, you may consider reaching out to Microsoft Support or the Power BI community for additional assistance and guidance specific to your scenario.

If this post helps, then please consider Accepting it as the solution to help the other members find it more quickly.

In case there is still a problem, please feel free and explain your issue in detail, It will be my pleasure to assist you in any way I can.