unable to query Temporary tables

Hi VinayPabbu,

OneLake tables support time travel, but they have a limited retention period (30-40 days by default, extendable). MLV too, are not very reliable.

If storage is not an issue for you, you can implement a full history retention by maintaining a separate table. Append all incoming data with metadata columns like Stage_date (load date) and an incremental Index to distinguish multiple loads on the same day.

If you go ahead with this approach:

• Use unionByName with allowMissingColumns = true to handle schema changes safely.
• Identify latest data (for both main table insert, and QC purposes) using MAX(Stage_date) and MAX(Index).

Hi VinayPabbu ,

This is expected behavior.

Temporary views in Spark are session-scoped, so they are dropped as soon as the Spark session ends. Because of that, they are not suitable for debugging or post-run analysis.

For debugging purposes, a better approach is to persist the intermediate data instead of using temp views.

Common options are:

- Write the data to a temporary Delta table (or a debug table) in the Lakehouse
- Save intermediate results as Parquet/Delta files under a dedicated debug folder
- Use a permanent table or global temp view if you only need it during the same application lifecycle

This way, you can inspect the data even after the Spark job finishes and safely drop the debug artifacts once the issue is resolved.

This is not a bug, but a misunderstanding of how Spark temporary views work in Microsoft Fabric.

The core issue is that temporary views are session-scoped. Once the Spark session ends (which can be very short-lived in Fabric), all temporary views are automatically dropped. Because of this, they cannot be queried or inspected after the job finishes, which makes them unsuitable for post-run debugging or data analysis.

So the real problem here is using temporary views as a debugging mechanism beyond the Spark session lifecycle.

Unfortunately, there is no way to “keep” or query a temporary view after the session has ended. That behavior cannot be changed.

The only reliable and supported solutions are:

1. Persist intermediate results for debugging
   Instead of relying on temporary views, write the intermediate data to a persistent location:

   • A small debug Delta table in the Lakehouse, or

   • Parquet/Delta files under a dedicated debug folder (for example partitioned by runId or date)

   These artifacts can be inspected after execution and safely cleaned up later.

2. Use Delta time travel on the final table (when applicable)
   If the data is ultimately written to a Delta table, DESCRIBE HISTORY and VERSION AS OF can be used to analyze previous versions without introducing additional staging tables.

3. Debug within the same Spark session only
   Temporary views can be inspected during execution (for example using VS Code for the Web with a debug session), but this only works while the Spark session is still active and does not help with post-run debugging.

In summary, Spark temporary views are not designed for debugging after execution. If post-run inspection is required, the intermediate data must be persisted in some form. There is no alternative that allows querying temp views once the Spark session has ended.

This behavior is expected and consistent with Spark’s design.