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Dabricks error in the session

Data Engineer 0 Reputation points
2026-03-17T15:27:35.3533333+00:00

I am getting this error in databricks workspace all the time Reference ID: 5f39c0ad-8c66-41e7-87cc-eec2e5bdb196

Azure Databricks
Azure Databricks

An Apache Spark-based analytics platform optimized for Azure.

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  1. Pilladi Padma Sai Manisha 10,110 Reputation points Microsoft External Staff Moderator
    2026-03-19T23:16:28.0833333+00:00

    Hi Data Engineer,

    It sounds like you’re running into a persistent error in your Azure Databricks workspace that’s surfaced as “Reference ID: 5f39c0ad-8c66-41e7-87cc-eec2e5bdb196.” Since a reference ID alone doesn’t reveal the root cause, here’s a general approach you can follow—and a few questions to help us narrow it down:

    What you can try right away

    1. Run the built-in notebook failure troubleshooter
      • In your workspace, go to the notebook run or job that failed.
      • Click View run details → Troubleshoot and repair. This will guide you step by step based on common failure patterns (like executor loss, GC issues, driver unavailability, etc.).
    2. Check service diagnostics on your Databricks resource
      • Open the Azure portal → your Databricks workspace → Diagnose and solve problems → Service diagnostics.
      • Ensure there are no underlying service outages or egress limits being hit for artifacts.
    3. Review Spark driver and executor logs
      • In the cluster UI, select the cluster used for your run → Driver logs and Executor logs.
      • Look for error messages (e.g., ExecutorLostFailure, FetchFailedException, GC overhead warnings) around the timestamp of your failure.
    4. Validate cluster/network configuration
      • If you see bootstrap or container-download failures, check your VNet’s DNS settings and user-defined routes.
      • Ensure the Azure recursive resolver (168.63.129.16) is reachable and that your workspace can connect to Databricks control planes and artifact storage.

    Follow-up questions

    To dig deeper, could you share:

    1. The exact error message or stack trace that appears in the driver/ executor logs (not just the reference ID)?
    2. Which operation you were running when the error occurred (e.g., notebook cell, job step, file write, Spark magic command, etc.)?
    3. Your cluster configuration details:
      • Databricks runtime version
      • Number of workers and driver size
      • Any custom Spark configurations you’ve applied
    4. Whether this error affects all notebooks/jobs or a single workspace region/time window
    5. Any recent changes to your environment (new libraries, network policies, mount configurations)

    Once we have those details, we can point you to a more specific fix.

    Reference list

    Note: This content was drafted with the help of an AI system. Please verify the information before relying on it for decision-making.

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