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30 Common Error Codes in Databricks

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Here are 30 common error codes in Databricks, along with their possible causes and solutions:


1. DBFS001 – Databricks File System (DBFS) Access Denied

  • Cause: Insufficient permissions to access DBFS.
  • Solution: Check workspace permissions and adjust IAM roles.

2. DBFS002 – DBFS Mount Failure

  • Cause: The underlying storage account is unavailable.
  • Solution: Re-authenticate using dbutils.fs.mount() and verify credentials.

3. CLUSTER001 – Cluster Creation Failed

  • Cause: Insufficient capacity or incorrect configuration.
  • Solution: Try a different instance type or increase quota.

4. CLUSTER002 – Cluster Termination Error

  • Cause: The cluster failed to shut down.
  • Solution: Check logs and force terminate via Databricks UI.

5. CLUSTER003 – Driver Unavailable

  • Cause: The driver node crashed due to memory exhaustion.
  • Solution: Use a larger driver node or optimize memory usage.

6. CLUSTER004 – Worker Node Failure

  • Cause: Worker nodes failed due to spot instance termination.
  • Solution: Switch to on-demand instances.

7. SPARK001 – Job Execution Timeout

  • Cause: The query took too long.
  • Solution: Optimize the query using caching and partitioning.

8. SPARK002 – Out of Memory (OOM) Error

  • Cause: Data processing exceeds available memory.
  • Solution: Increase cluster memory or optimize partitions.

9. SPARK003 – Job Execution Failure

  • Cause: A bad Spark job configuration.
  • Solution: Check Spark logs for failed stages.

10. SPARK004 – Shuffle Read Failure

  • Cause: Insufficient disk space for shuffle operations.
  • Solution: Use a higher spark.sql.shuffle.partitions value.

11. SPARK005 – Too Many Open Files

  • Cause: Too many connections or file handlers open.
  • Solution: Adjust ulimit settings or close unused connections.

12. SQL001 – SQL Query Syntax Error

  • Cause: Incorrect SQL syntax.
  • Solution: Review SQL syntax and fix any errors.

13. SQL002 – Query Execution Timeout

  • Cause: Long-running queries exceeding Databricks SQL timeout.
  • Solution: Optimize query logic and indexing.

14. SQL003 – Table Not Found

  • Cause: The referenced table doesn’t exist.
  • Solution: Check table name and database location.

15. SQL004 – Partition Key Missing

  • Cause: Querying partitioned tables without specifying a key.
  • Solution: Use WHERE partition_column = 'value'.

16. SQL005 – Delta Table Merge Conflict

  • Cause: Two concurrent updates to the same Delta table.
  • Solution: Enable Optimistic Concurrency Control in Delta Lake.

17. DELTA001 – Delta Table Corruption

  • Cause: Inconsistent Delta transaction logs.
  • Solution: Run VACUUM and FSCK REPAIR TABLE.

18. DELTA002 – Delta Table Version Mismatch

  • Cause: Trying to write to an older Delta version.
  • Solution: Upgrade your Delta format using ALTER TABLE.

19. DELTA003 – Delta Lake Write Conflict

  • Cause: Multiple processes trying to write to the same file.
  • Solution: Enable ACID transactions.

20. PERM001 – Access Denied

  • Cause: Insufficient permissions.
  • Solution: Check IAM roles and Databricks workspace permissions.

21. PERM002 – Workspace Access Denied

  • Cause: User lacks permission to access workspace.
  • Solution: Grant appropriate permissions in Databricks Admin settings.

22. SCALA001 – Notebook Execution Failure

  • Cause: Scala code has compilation errors.
  • Solution: Check and fix syntax errors.

23. PYSPARK001 – Python Notebook Crash

  • Cause: Python process ran out of memory.
  • Solution: Increase the cluster size or optimize the script.

24. MLFLOW001 – MLflow Experiment Not Found

  • Cause: MLflow experiment path is incorrect.
  • Solution: Verify the MLflow experiment name.

25. MLFLOW002 – Model Deployment Failure

  • Cause: Incompatible model format.
  • Solution: Convert the model using MLflow’s export functions.

26. AUTOSCALE001 – Autoscaling Failure

  • Cause: Insufficient available instances.
  • Solution: Increase compute capacity or use static clusters.

27. INTEG001 – API Rate Limit Exceeded

  • Cause: Too many API requests to Databricks REST API.
  • Solution: Reduce API request frequency.

28. NETWORK001 – Connection Timeout

  • Cause: Network issues between Databricks and storage.
  • Solution: Check firewall settings and VNET peering.

29. S3001 – S3 Access Denied

  • Cause: Incorrect IAM role or missing bucket policy.
  • Solution: Validate S3 IAM role permissions.

30. ADB001 – Azure Databricks Resource Limit Exceeded

  • Cause: Reached Azure Databricks workspace limits.
  • Solution: Increase Azure resource quotas.

These Databricks error codes will help diagnose and fix issues related to clusters, Spark jobs, SQL queries, permissions, and Delta Lake transactions.

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