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Notebooks Spark SQL cells set to plaintext

EDIT3: Reply from Microsoft Support: "Upon checking internally with notebook team, we identified the issue and applied the fix. You can expect the fix to be deployed around mid of October. Meanwhile, we recommend you switch the cell language to another one then switch back to Spark SQL as a mitigation."

 

In my existing and new Notebooks, which I've set default Language to Spark SQL, all cells are plaintext.

I can change default language to other languages (PySpark) and cell language will update--but if I selected Spark SQL they all become plaintext

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Status: Delivered

Hi  @jeffshieldsdev ,

 

Thank you for sharing, for other users with similar experiences you can check the comments below. This thread will now be closed.

 

Best regards.
Community Support Team_ Caitlyn

Comments
jeffshieldsdev
Solution Sage

EDIT: Issue keeps happening...may have something to do with Git integration.

 

Clearing browser cache seems to have resolved the issue.

v-xiaoyan-msft
Community Support
Status changed to: Delivered

Hi  @jeffshieldsdev ,

 

Thank you for sharing, for other users with similar experiences you can check the comments below. This thread will now be closed.

 

Best regards.
Community Support Team_ Caitlyn

jeffshieldsdev
Solution Sage

EDIT: confirmed with support engineer--happening before commit to Git...not related to Git integration afterall.

 

This keeps happening--it may have something to do with the Git integration.

jeffshieldsdev
Solution Sage

@v-xiaoyan-msft I will submit a support ticket on this problem

EDIT: Ticket submitted: 2409110040005680

Fareval
Frequent Visitor

This issue hasn't been fixed, I managed to run a simple query but after that it changed to Plaintext

jeffshieldsdev
Solution Sage

Reply from support:

Upon checking internally with notebook team, we identified the issue and applied the fix. You can expect the fix to be deployed around mid of October.

Meanwhile, we recommend you switch the cell language to another one then switch back to Spark SQL as a mitigation.