Does RLS usage degrade the performance of Fabric semantic model?

Hi Sreeja_G ,

It's important to understand that implementing RLS can indeed influence the performance of your reports, especially when applied to complex models or large datasets.

When you apply RLS, Power BI needs to dynamically filter data based on the user's role, which can introduce additional processing overhead. This is particularly noticeable with custom semantic models where the data structure and relationships might be more complex than in default semantic models.

Here are a few steps you can take to mitigate the performance degradation:

1. Optimize Your Model: Ensure that your model is as streamlined as possible. Remove any unnecessary columns or tables and make sure that relationships are efficiently defined. More information on optimizing your Power BI model can be found here: Understand star schema and the importance for Power BI - Power BI | Microsoft Learn

2. Review and Simplify RLS Rules: Sometimes, the logic within RLS rules can be simplified without changing the outcome. Review your RLS expressions and see if there are ways to make them more efficient.

3. Consider Using Aggregations: For large datasets, aggregations can help improve query performance by reducing the amount of data that needs to be processed for each query. This can be particularly effective in mitigating the performance impact of RLS.  Learn more about aggregations in Power BI here: Automatic aggregations overview - Power BI | Microsoft Learn

Best regards,
Community Support Team_Binbin Yu
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