Forum Discussion
SQL Endpoint error: Failed to complete the command because the underlying location does not exist.
Hello everyone.
I have a lakehouse that's connected to Dataverse with the Link to Fabric function. Also, I'm connected to Customer Voice preview feature.
The problem is: I ran a Logic App that starts the Fabric capacity in the morning, delay for 1min, refresh a Power BI semantic model and then suspend the capacity after all these steps.
But, after a few days (2-3), the semantic model doesn't refresh because of this:
"Data source error: DataSource.Error: Microsoft SQL: Failed to complete the command because the underlying location does not exist. Underlying data description: table 'dbo.msevtmgt_checkin', file 'https://onelake.dfs.fabric.microsoft.com/id-here/id-here/Tables/msevtmgt_checkin/PartitionId=2024/part-00000-e66b828a-e4f8-4333-bf18-a4269f915de1.c000.snappy.parquet'."
I extended the delay to wait 10min, but still get the error. Only after manually refreshing the SQL Endpoint I'm able to query again using the SQL Endp.
I've searched and saw that other users have the same problem, a few group managed by waiting 10-15min, but that's a lottery tbh.
Any ideas of what can I do? Is there a way to refresh the SQL Endpoint using notebooks or something else?
Thanks in advance.
Sorry for the late response. I don't find a specific API or something else that can help refresh the SQL Endpoint of a lakehouse automatically. I captured the trace of the "refresh" action in Explorer. It seems trigger a metadata refresh of the SQL endpoint. But I failed to covert it into a valid call we can use for the automation process.
You may try setting a longer waiting time after starting the capacity. If this still doesn't work, I would suggest creating a support ticket to ask escalation engineers to help check the failue. This looks more like a synchronization issue.
In addition, a lakehouse generates a default semantic model. You can manage the default semantic model or create customized semantic models from it. Maybe you can consider using the default semantic model or customized semantic models for your reports. Default Power BI semantic models - Microsoft Fabric | Microsoft Learn
Best Regards,
Jing
10 Replies
From this documentation, I learned that Dataverse will generate a lakehouse, SQL endpoint, and a default Power BI semantic model in the Fabric workspace. Do you run a Logic App to refresh the default semantic model generated by Dataverse? Or do you customize another semantic model then use a Logic App to refresh it?
May I know how you manually refresh the SQL Endpoint? To my knowledge, the associated SQL Endpoint will be updated automatically after the lakehouse has been updated. I don't find where I can refresh a SQL Endpoint. Can you help point it out? Maybe I have missed it.
Additionally, if you want to refresh a semantic model at a scheduled time, there is a preview Semantic model refresh pipeline activity in Data Factory currently. You can create a data pipeline in Data Factory and add a Semantic model refresh activity for refreshing a specific semantic model. Then schedule to run the pipeline. Maybe this could be an alternative.
Data Factory Spotlight: Semantic model refresh activity | Microsoft Fabric Blog | Microsoft Fabric
Best Regards,
Jing
If this post helps, please Accept it as Solution to help other members find it. Appreciate your Kudos!This has been discussed in other threads. The SQL endpoints refresh, but not immediately. There is a random delay that makes things unpredictable.
Hello!
I refresh first the Lakehouse semantic model, then the Power BI semantic model.I read in another thread that the Lakehouse semantic model doesn't affect the SQL endpoint refreshness, is that true? If so, refreshing it would not affect the error in the endpoint.
Edit: I refresh the SQL Endpoint by entering and manually click on refresh button:Sorry, I misunderstood what you meant. So you have created a Power BI dataset which is getting data from a Fabric lakehouse by connecting to the SQL Endpoint. Then you use a Logic App to refresh the Power BI dataset, right?
Regards,
Jing
