Getting error: Internal Query Processor Error

[AstridM]

As part of our migration to fabric, we are trying to get out of synapse.

Since Microsoft deprecated export to data lake from dynamics 365 F&O, we have been trying to mirrow our process and those include the queries the entities use in F&O (I do understand that they are large queries but I did not write them, microsoft did).

We are able to run those queries on the serverless, but when I try to run the same queries on fabric dw I get:

Internal Query Processor Error: The query processor could not produce a query plan. For more information, contact Customer Support Services.

This issue is happening with 1/3 of the queries we are trying to migrate. I do have an open case, but it is going nowhere, because I have a feeling the person has no idea what I am talking about.

Has anyone faced the same issue? And how did you solve it? (without re writting the query, I know I can do that).

[v-karpurapud]

Hi @AstridM 

Welcome to the Microsoft Fabric Community Forum.

The error message “Internal Query Processor Error: The query processor could not produce a query plan” is a recognized limitation in Microsoft Fabric Data Warehouse (DW), particularly when running large, complex queries such as those generated from Dynamics 365 Finance & Operations (F&O) entities.

These queries typically involve extensive joins, multiple CTEs, and deeply nested logic, which may exceed the current capabilities of the Fabric DW query optimizer. Since these queries run successfully in Synapse Serverless SQL, it is advisable to continue using Synapse Serverless for these workloads until further improvements are made in Fabric DW.Meanwhile, enabling Query Insights and reviewing Capacity Metrics in Fabric can help identify where the optimizer encounters issues, making it easier to pinpoint unsupported features or resource limitations. Running queries through the Fabric Migration Assistant may also help detect syntax or structural concerns.

If you prefer not to rewrite your queries, you might consider modularizing them into smaller, materialized steps using intermediate staging tables. Alternatively, for transformation or ingestion tasks, executing the logic in a Fabric Lakehouse notebook (using Spark or PySpark) can provide additional flexibility.

If this response resolves your query, kindly mark it as Accepted Solution to help other community members. A Kudos is also appreciated if you found the response helpful.

Thank you for being part of Fabric Community Forum.

Regards,
Karpurapu D,
Microsoft Fabric Community Support Team.