Forum Discussion

mcrreddyt's avatar
mcrreddyt
Microsoft Employee
2 years ago

Getting error when we try to Connect to the Power BI Semantic model created in the Fabric Lakehouse

We are seeing error when we try to Connect to the Power BI Semantic model created in the Fabric Lakehouse using Power BI Desktop OneLake data hub Power BI semantic model. A payload exceeded for the a...
data engineering
environment
lakehouse
v-cboorla-msft's avatar
v-cboorla-msft
Microsoft Employee
2 years ago

Hi mcrreddyt 

 

Thanks for using Microsoft Fabric Community.

The error message indicates that the Power BI Desktop is struggling to handle the size or complexity of the Power BI Semantic model you're trying to connect to in the Fabric Lakehouse using the OneLake data hub.

 

This error typically occurs when the size of the data being processed exceeds the allowed limit. Reducing the number of warehouses or artifacts within the workspace. This could involve simplifying the semantic model by removing unused tables, columns, or measures.

 

If modifying the model directly isn't feasible, check if you have permissions to enable fallback to DirectQuery mode in the Fabric portal. This allows Power BI Desktop to bypass the semantic model and query the lakehouse directly for data, albeit with potential performance trade-offs.

Note : Enabling DirectQuery might lead to slower report loading and interaction times. Evaluate if the performance penalty is acceptable compared to the current error.

For additional information please refer : Fallback

 

For semantic model limitations please refer : Known issues and limitations

 

I hope this information helps.

 

Thank you.

mcrreddyt's avatar
mcrreddyt
Microsoft Employee
2 years ago

Thanks for sharing the details. We are using Small Semantic model storage format and Fabric capacity. Please let us know the limitations for this Fabric Capacity with CU of F512 units.  in terms of number of Lakehouses and Semantic Models and artifacts can be created. Please let us know the limitations or size of the semantic model supported for Small Semantic model storage format and any other limitations which is causing the performance issues.

  • frithjof_v's avatar
    frithjof_v
    Community Champion
    2 years ago

    I think the Fallback link has some information about the limits:

     

     

    Ref. also Semantic model SKU limitation

     

    What is the size (number of rows) of the lakehouse tables you are attempting to query in Direct Lake mode?

    And how many columns from the lakehouse table are involved in your queries (=visuals)?

     

    This blog post explains about paging and memory consumption when using Direct Lake.

     

     

    I am not sure if the "Small Semantic model storage format" or "Large Semantic model storage format" matters for Direct Lake semantic models. 
    I think maybe it just matters if you store the data within the semantic model (e.g. Import mode semantic model).
    Ref.     Solved: Storage format for workspace with Fabric direct la... - Microsoft Fabric Community


    However this thread could potentially imply that the "Small Semantic model storage format" may have an effect also on Direct Lake queries: Solved: Change semantic model size from small to large - Microsoft Fabric Community

     

    I must admit I am unsure about the role of Small vs. Large semantic model storage format when it comes to Direct Lake. Hope someone can clarify.