An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Previously known as Azure SQL Data Warehouse.
Use the pipeline run details first, because for pipeline-triggered Synapse Spark failures the inline error is surfaced in the pipeline activity view.
- In Synapse/ADF monitoring, open Monitor → Pipeline runs and open the failed run for
Novo_ERP_Export. - Select the
RunErpExportactivity and open Output.- If the failure is a user error, the result field contains the detailed traceback.
- This is the primary place to inspect when the Spark application was never created.
- If the run detail page is available, check the Properties panel for the high-level error and failure stage.
- If logs exist, open Logs → Driver logs and search for
ERROR,Exception, orout-of-memory.- If no logs are available, that usually means the job was queued or cluster creation failed, which points to an infrastructure or capacity issue rather than notebook code.
Based on the symptoms described — failure in 4–15 seconds, no Spark application created, and monitor unable to find the Spark application name — the most relevant documented causes to check are:
- Workspace or pool capacity/resource shortage
- Error code 3250: not enough resources in the workspace.
- Error code 3251: not enough resources in the pool.
- Error code 3252: not enough vCores for the Spark job.
- Recommended actions: end running jobs, reduce requested vCores, increase workspace quota or pool maximum size, or use another workspace/pool.
- Throttling/concurrency pressure
- Error code 3253 indicates throttling due to substantial concurrent executions. The recommendation is to retry after a wait period.
- Linked service configuration issue
- Error code 3254 indicates an invalid or missing property in the
AzureSynapseArtifactslinked service. Check that required linked service properties are defined correctly.
- Error code 3254 indicates an invalid or missing property in the
- Notebook structure limitation
- If the notebook used by the pipeline has more than 256 code cells, pipeline execution can fail with error code 6002 and
MessageQueueFullException. Reduce the notebook below 256 cells by merging cells.
- If the notebook used by the pipeline has more than 256 code cells, pipeline execution can fail with error code 6002 and
If this started suddenly and no changes were made, there is also precedent for temporary platform-side Synapse Spark session issues where sessions fail before normal execution. In that case, monitor Azure service health and retry after the incident is mitigated.
- Transform data by running a Synapse Spark job definition
- Spark monitoring and performance optimization best practices
- Synapse Studio troubleshooting
- Troubleshoot Azure Data Factory and Synapse pipelines
- SparkPool is getting dead after running for a long time in Synapse Notebook - Microsoft Q&A
- Azure Synapse Error : This query cannot be executed because we can't get enough capacity for optimal performance in time. This can be an intermittent issue. Please retry later. - Microsoft Q&A