InternalError: Ipython kernel exits with code -9. Please restart your session.

Rufin Touomou 5 Reputation points
2023-06-27T17:32:11.69+00:00

Hello,

I am running into the error below when running a cell code in synapse notebook 8 times out of 10, and the 2 times it runs fine, there has been no change in the code. Can someone provide an explanation or ideas of why this can be happening?

Error
PV allocated
{
    "errorCode": "6002",
    "message": "InternalError: Ipython kernel exits with code -9. Please restart your session.",
    "failureType": "UserError",
    "target": "*******",
    "details": []
}

Thanks

Azure Synapse Analytics
Azure Synapse Analytics
An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Previously known as Azure SQL Data Warehouse.
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  1. PRADEEPCHEEKATLA 90,506 Reputation points
    2023-06-28T06:47:28.7933333+00:00

    @Rufin Touomou - Thanks for the question and using MS Q&A platform.

    It seems like you are encountering an error message "InternalError: Ipython kernel exits with code -91. Please restart your session." while running a cell code in Synapse Notebook. This error message indicates that the IPython kernel has exited with a code -9, which usually means that the kernel has been killed by the operating system.

    There could be several reasons why this is happening.

    One possible reason is that the kernel is running out of memory. You can try to reduce the memory usage of your code by optimizing your code or reducing the size of the data you are working with.

    Another possible reason is that there is a bug in operating system that is causing the kernel to crash. You can try to check the logs as mentioned in the document: Use Synapse Studio to monitor your Apache Spark applications.

    If the issue persists, you can try restarting your session or the kernel to see if that resolves the issue. You can also try running your code on a different spark pool or a new spark pool with different version to see if you are facing problem with specific spark pool or specific to spark runtime version.

    Hope this helps. Do let us know if you any further queries.


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