Forum Discussion
Helper IISelect the fabric kernel to debug spark job definition in VSCode
Currently, Spark Job Definitions cannot be debugged using the Fabric kernel in VSCode. The Fabric kernel is mainly designed for running notebooks, whereas Spark Job Definitions require execution on the remote Spark Runtime. If you try to debug a Spark Job Definition using the Fabric kernel, it won’t function as expected since it lacks the necessary Spark execution environment.
To properly develop and debug Spark jobs, you need to select the remote Spark Runtime, which provides the required Spark components and dependencies. This ensures that your job runs in an environment similar to the one it will be deployed in, allowing for accurate testing and debugging.
If you were expecting this functionality within the Fabric kernel itself, it would be worth checking Microsoft’s official roadmap or documentation for any future support. Having the ability to debug Spark Job Definitions directly in the Fabric kernel would be a valuable enhancement, as it would streamline development and testing workflows without requiring an external runtime.
Are there any specific issues you’re facing while debugging your Spark Job Definition? Let me know how I can assist further!
Helper IIThanks! This explains why I'm not able to run spark job definitions on the fabric kernel. My suggestion would be to change the following line:"When you run the notebook or Spark Job Definition, you can choose the local conda environment or remote Spark Runtime. " on page: https://learn.microsoft.com/en-us/fabric/data-engineering/fabric-runtime-in-vscode#considerations-for-choosing-local-conda-environment-or-remote-spark-compute as it's suggests it's possible to run the spark job definition on the fabric kernel.
Another question would be: will this be a feature in the (near) future? As it would open the door for us the develop spark job definitions in a proper way.
Thanks for your feedback! You’re absolutely right—this line could be misleading, as it implies that Spark Job Definitions can run on the Fabric kernel, which is not currently supported. This could cause confusion for users who expect seamless execution of Spark jobs in this environment. Updating this wording to clarify the distinction between running notebooks and Spark Job Definitions would definitely improve the documentation and help users understand the limitations of the current setup.
Additionally, I’d like to inquire whether there are any plans to support running Spark Job Definitions directly on the Fabric kernel in the future. Having this capability would be a major enhancement, as it would allow developers to create, test, and debug Spark jobs more efficiently within the Fabric ecosystem without relying on external runtimes. The ability to execute Spark Job Definitions seamlessly within Fabric would streamline development workflows and improve productivity, especially for teams that rely on structured Spark workloads.
If this feature is on the roadmap, it would be great to get some insights into its expected timeline or any upcoming improvements in this area. Looking forward to your thoughts on this!