Pipeline Failed due to error: InternalServerError executing request at adf

Question

Around 15 triggers i have and only one dataset having trigger is failing everyday due to the error: InternalServerError executing request. The pipeline with attached triggers are published already. Any workaround for this issue will be more helpful.

Answer 1

@Lakshmi Moulya Nerella

Given that you've already checked the published version, source data, and activity configurations, and the issue is specific to only one pipeline trigger, here are some potential reasons why this might be happening:

1. Trigger-specific configuration issue: Double-check the trigger configuration for the problematic pipeline. Ensure that the trigger settings, such as the schedule, recurrence, and data slice, are correct and not conflicting with other triggers.
2. Dataset or linked service issue: Although you mentioned that the source data is fine, it's possible that there's an issue with the specific dataset or linked service used in the problematic pipeline. Verify that the dataset and linked service are correctly configured and that the credentials are valid.
3. Resource constraints: Since you have 15 pipeline triggers, it's possible that the Data Factory is experiencing resource constraints, leading to the InternalServerError. Check the Data Factory's resource utilization, such as CPU, memory, and concurrent runs, to ensure that it's not overwhelmed.
4. Dependency on other pipelines or activities: If the problematic pipeline has dependencies on other pipelines or activities, it's possible that one of those dependencies is causing the issue. Investigate the pipeline's dependencies and check if any of them are failing or causing delays.
5. Data volume or size issue: If the problematic pipeline is processing a large volume of data or handling large files, it might be causing the InternalServerError. Check the data volume and file sizes being processed by this pipeline and compare them to the other pipelines.
6. Databricks connection issue: As the target is a Silver layer in Databricks, it's possible that there's an issue with the Databricks connection or the Silver layer itself. Verify that the Databricks connection is stable and that the Silver layer is properly configured.

To further troubleshoot the issue,

• Enabling debug logging for the problematic pipeline to gather more detailed error messages
• Running the pipeline in debug mode to see if it fails consistently
• Creating a copy of the problematic pipeline and modifying it to isolate the issue

Hope this helps. Do let us know if you any further queries.