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.partitionsvalue.
11. SPARK005 – Too Many Open Files
- Cause: Too many connections or file handlers open.
- Solution: Adjust
ulimitsettings 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
VACUUMandFSCK 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.
