The implementation of Row-Level Security (RLS) on Power BI must consider performance on large datasets. The following tips will ensure that it is optimized:
1. Optimize RLS Filter Logic
Filter With a Single Column: RLS is better off filtering on a single column than on multiple columns or tables with complex expressions.
Avoid Bi-Directional Relationships: Bi-directional filtering makes the query more complex; therefore, a single-directional relationship is preferred.
Use Numeric Keys Instead of Text – Compared to filtering on text values, filtering on integer-based keys will enhance efficiency.
2. Reduce Data Volume & Cardinality
Pre-aggregate Data: Use aggregated tables to reduce the row count before applying RLS.
Filter Fact Tables, Not Dimensions: Apply RLS directly on fact tables instead of filtering through dimension tables; this may create additional overhead for queries.
Limit RLS Role Overlaps: Overlapping RLS roles leads to more checks for security evaluation; fewer overlaps will help here.
3. Optimize Data Model & Queries
Use Static Security Tables: Maintain predefined security tables holding the user-role mappings instead of dynamically filtering along DAX.
Reduce Virtual Relationships: If LOOKUPVALUE is in use within RLS filters, remove it in favor of physical relationships via bridge tables.
Test with Performance Analyzer – Leverage Performance Analyzer and DAX Studio to confirm query performance metrics and fine-tune filters subsequently.
