Forum Discussion
Error fetching data
- 10 months ago
Hi jaryszek,
Thank you for reaching out to Microsoft Fabric Community.
Your dataset query is failing because it needs more memory i.e 38 GB than the current workspace capacity allows only 25 GB limit.
- Please move the workspace to a Fabric or Premium capacity with higher memory limits, or scale up your existing capacity.
- If not you will need to reduce the dataset size like for example by removing unused columns or enabling incremental refresh.
Thanks and regards,
Anjan Kumar Chippa
thank you but i need to find what is causing it.
Can somebody advice how to check size and query or measure which is causing the case?
I tried with Vertipaq Analyzer from SQLBI but there is no size...this is onelake model.
https://community.fabric.microsoft.com/t5/Data-Warehouse/Determine-size-of-semantic-model/m-p/4281042
Anybody manage to find a semantic model on onelake size?
I removed added measure which was:
% of Total Quantity AllSelected "=
VAR Denom =
CALCULATE ( [Total Quantity], ALLSELECTED ( Fct_EA_AmortizedCosts ) )
RETURN
DIVIDE ( [Total Quantity], Denom, 0 )"
Nothing difficult...
but it caused my report failing...
I need to :
1. Check semantic model size after loading from onelake and after running queries
2. Identify what is so slow that caused report failure.
Best,
Jacek
- v-achippa10 months agoCommunity Support
Hi jaryszek,
Since this is a OneLake(DirectLake) semantic model, you can’t see table sizes in Vertipaq Analyzer like the Import models.
- To check model size, look at the underlying lakehouse or warehouse tables, their row counts and file sizes reflect the semantic model size.
- To find which query or measure is causing issues, use Performance Analyzer in power bi desktop to see which visual or query runs long and check the Fabric Capacity Metrics app for any memory spikes.
- Your % of Total Quantity measure with ALLSELECTED can scan the entire fact table, which can cause the memory spike. Try testing with filters or try replacing ALLSELECTED with a lighter one.
Thanks and regards,
Anjan Kumar Chippa