Forum Discussion
Resource Governing
- Anonymous2 years ago
Hi, Dan_86
Thanks for the reply from SaiTejaTalasila and pallavi_r , please allow me to provide addition:
Simplify your data model by removing unnecessary columns, tables, or relationships. This helps to reduce the memory footprint of the dataset. You can reter to the following links:
Optimization guide for Power BI - Power BI | Microsoft Learn
Consider reducing data granularity. When working with large datasets, you can do this by summarizing the data or by using aggregate tables. Here is the link:Weird Problem: Why Does Power BI Use So Much Memory? (quicklylearnpowerbi.com)
If you're using Power BI Premium, you can increase the memory of the Premium capacity that hosts your dataset. You can do this by selecting the capacity you want to change in the Power BI admin portal, and then selecting the Change size option.Manage your Fabric capacity - Microsoft Fabric | Microsoft Learn
Power BI provides a feature called semantic model scale-out, which helps provide fast performance when reports and dashboards are used by a large audience. It uses Premium capacity to host one or more read replicas of the main semantic model.Power BI semantic model scale-out - Power BI | Microsoft Learn
Power BI's incremental refresh feature is a useful tool for managing large datasets. It allows you to refresh only the data that has changed and not the entire data set.Large semantic models in Power BI Premium - Power BI | Microsoft Learn
How to Get Your Question Answered Quickly
Best Regards
Yongkang Hua
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi Dan_86
Here are couple of points to be taken care:
1. Size of the dataset - The F64 equivalent to P1 capacity can handle max 25 gb of memory. I used to get this error with dataset size crossing 13+ gb because during refresh, memory gets doubled.
2. Incremental refresh - hope you have taken care of incremental refresh for large dataset, otherwise these error is inevitable.
3.Calculated columns - Calculated columns makes the model size bigger. So prefer to use measure for aggregated values.
Can you please let us know on these points so we can detect what is the root cause of this error.
Thanks,
Pallavi