Forum Discussion
unable to query Temporary tables
Hello community,
I am currently working on resolving a few data issues. As per the existing logic, the data is first loaded into a temporary view and then inserted into the main table. The problem is that temporary views are dropped as soon as the Spark session ends, which means I cannot query or analyze the data after the session expires. Because of this, debugging the data becomes difficult. I am looking for a better approach to debug the data instead of relying on temporary views.
Regards,
Vinay Pabbu
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 lifecycleThis 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:
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.
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.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.
15 Replies
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).
In this situation, there are limited options available. If you wish to debug after the Spark session has concluded, it would be necessary to persist the data within those temporary tables.
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 lifecycleThis way, you can inspect the data even after the Spark job finishes and safely drop the debug artifacts once the issue is resolved.
Hello VinayPabbu
There are two main approaches you might consider:
1. You could write to a persistent staging or debug table rather than using a temporary view. This allows you to insert or merge data from the staging table into your main table and retain the data for as long as you need for debugging purposes. Importantly, you’ll be able to query these staging or debug tables even after the Spark session has ended.
2. Alternatively, you may wish to use Materialised Lake Views (MLV) instead of temporary tables. This option provides valuable features such as data lineage, refresh history, and operational run details, which are beneficial for debugging. MLVs also enable you to implement data quality constraints, further supporting your data transformation process.
Please note, however, that MLVs are currently available in Preview.
For further details, please refer to the MLV documentation below:
Overview of Materialized Lake Views - Microsoft Fabric | Microsoft Learn
deborshi_nag, I cant add staging table in between as this was not part of our Project architectural design and most of the development was done, we will plan this in Phase 2 devlopment cycle.
Since MLVs are in preview, its not adaptable for me right now.
ok. so you can't use both the options I suggested. Here's a third one - however, I have not tried this one yet! Let me know how it goes.
Microsoft’s supported way to use breakpoints with Fabric notebooks is to open the notebook in VS Code for the web and run a remote debug session against the Fabric Spark compute.Here's the Microsoft documentation on how to use VS Code for the Web and debug notebooksIn principle you can inspect a temporary view at (or around) a breakpoint, as long as that temp view exists in the same Spark session that your debug run is using.
Hi ,
I hope you are using lakehouse to store your data.
After the session , can you persist the data in tables / files to debug later as required. After a week or some timeperiod you can always delete the staging data. For deleting the data refer the notebookutils module. It is simple and handy to use.
Notebookutils : https://learn.microsoft.com/en-us/fabric/data-engineering/notebook-utilitiesAs at the end you are writing the data in tables which are delta tables. You can always use history of the table to query some X version of data and then debug. Although it include first knowing the version and then debuging , it is added complexity here.
Sample query :
DESCRIBE HISTORY main_table;
SELECT * FROM main_table VERSION AS OF 13;Either the last option I can think of it is materialized view.
Hope this helps, let me know if you have any other doubts.
This will be a lot of manual work and not a valid solution currently,
FYI - Cleanups will be very risky in production environment which is not a long term solution.
Hi VinayPabbu ,
Yeah it is some manual work as overhead , apart from this I cant think any other solution for now.
Hi,
You can simplify debugging by persisting intermediate results in the Lakehouse as temporary tables or files. This lets you inspect data at different stages without re-running the full process. Once debugging is complete, the data can be cleaned up easily using notebookutils: https://learn.microsoft.com/en-us/fabric/data-engineering/notebook-utilities
Since the final output is stored as Delta tables, you can also use time travel to query previous versions when needed. As an alternative, materialized views can help keep commonly debugged transformations accessible.
Hope this helps.
Dhanush Sdhanushencode , I cant add staging table in between as this was not part of our Project architectural design and most of the development was done, we will plan this in Phase 2 devlopment cycle.
Since MLVs are in preview, its not adaptable for me right now.
Hi VinayPabbu ,
Thank you igokhan_fabric for the response provided!
Has your issue been resolved? If the response provided by the community member addressed your query, could you please confirm? It helps us ensure that the solutions provided are effective and beneficial for everyone.
Thank you.Hi VinayPabbu ,
I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions.
Thank you.
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:
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.
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.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.
