Iterator-heavy measures with filter complexity
"Iterator-heavy measures with filter complexity" is one of the performance checks Auditabi runs on a Power BI semantic model. Measures iterate over the fact table (SUMX, FILTER, ADDCOLUMNS) with complex conditions in the row context. It is a medium-severity finding: the model still works, but results are wrong in some filter contexts, slow, or fragile under change. Prefer column aggregations with CALCULATE filters, pre-compute row-level values as columns when they are static, and keep iterators over small tables.
What is detected
Measures iterate over the fact table (SUMX, FILTER, ADDCOLUMNS) with complex conditions in the row context. They evaluate row by row, are slow in matrices and consume memory on every interaction.
Why it matters
Wrong in some filter contexts, slow, or fragile under change. Fix in the next iteration.
How to fix it
Prefer column aggregations with CALCULATE filters, pre-compute row-level values as columns when they are static, and keep iterators over small tables.
Does your model fail this check?
Free static audit with the exact objects, no account, nothing stored.