Forum Discussion
Helper IIUnable to call a Temp View in Fabric Notebook using Spark SQL
Hi @AyusmanBasu0604,
Thank you for your response, you're encountering known limitations in Microsoft Fabric's Lakehouse when working with temporary views and Materialized Lake Views (MLVs) using Spark SQL.
Reference: Lakehouse schemas (Preview) - Microsoft Fabric | Microsoft LearnFabric Lakehouse does not support reading from temporary or global temporary views in certain operations especially when working with Materialized Lake Views, which have stricter limitations, referencing views created using Spark SQL in another SQL statement or notebook cell or trying to use TEMP VIEW or GLOBAL TEMP VIEW in MLV creation or inside CREATE OR REPLACE VIEW.
Maintain consistent namespace conventions for all views and tables. This will ensure they are accessible within your SQL environment. If necessary, re-declare or remap contexts to resolve any discrepancies.Activate detailed logging of PySpark operations to identify any hidden discrepancies in SQL calls. This can help pinpoint the exact location where the table or view is not being found, allowing you to adjust your code accordingly. Ensure that the Materialized Lake View is created with the correct SQL schema definitions. Check for any syntax errors or schema mismatches that could cause issues.
You can try this workaround, Use a Staging Delta Table, instead of using TEMP VIEW, create a real physical Delta table (possibly under a temporary/staging schema), then refer to it. Then, use it in your MLV. This works because you’re using a physical Delta table. Spark and Fabric support referencing it in MLVs.
Hope this helps.
Best Regards
Chaithra E.
Helper IIFirst of all thanks for your detailed suggestions HarishKM
point# 3 - With Global temp views also the issue is same as I had tired this multiple times earlier.
point# 5 - I tried saving the dataframe into a temp location, while that worked but while calling, I got the error: Py4JJavaError: An error occurred while calling o348.sql.
: java.lang.AssertionError: assertion failed: Only the following table types are supported: MANAGED, MATERIALIZED_LAKE_VIEW
I tried using a Materialized lake view and it comes back to the original error which I was getting while trying to reference a global/temp view directly into a Materialized Lake View: [TABLE_OR_VIEW_NOT_FOUND]
Super UserAyusmanBasu0604 Hey,
I will below steps to troubleshoot.
- Ensure that the global/temp views are being created correctly with the proper scope and namespaces. Verify the default database context and that you aren't trying to access views across different sessions without the necessary context.
- Double-check that you are within the same session or context when you try to reference these views in your SQL operations. Scoping issues can lead to views not being found if they aren't set globally or session persisting views.
- Since saving to a temporary location seems to work, consolidate this approach by efficient file storage. You might store intermediate steps in universally accessible storage, such as HDFS or cloud storage, instead of solely relying on temp views.
- Ensure that the Materialized Lake View is being created with the correct SQL schema declarations and verify that the errors are not stemming from syntax or schema mismatch issues.
- Use consistent and clear namespace practices, ensuring that all views/tables are accessible in your SQL environment. Re-declare or map contexts if needed.
- Enable detailed logging of PySpark operations to uncover hidden discrepancies in SQL calls. It can provide you the exact point where the table or view is not found, helping you adjust your code accordingly.
- Sometimes platform-specific solutions might be available in official documentation or user forums. Consult these resources for additional pointers on handling scope and view references.
If the issues persist, examining logs for the root of the TABLE_OR_VIEW_NOT_FOUND error and reviewing SQL statements
Thanks
Harish M