Job failed due to reason: at Sink 'CachedSink': Exception thrown in awaitResult:

Question

Job failed due to reason: at Sink 'CachedSink': Exception thrown in awaitResult:

The job in ADF is failing multiple times post the re-runs, kindly reach out in case anyone has answer to this problem.

Answer 1

Hi Rakshat GOYAL,

Thanks for reaching out to Microsoft Q&A.

The error message you're seeing in Azure Data Factory (ADF) involving the "CachedSink" and an exception thrown in awaitResult typically points to an issue at the data writing stage. Here are a few steps you can take to diagnose and potentially narrow down this issue:

• Ensure that the sink configuration in your data flow or pipeline is correctly set up. This includes checking the dataset, linked service, and any additional parameters that control the behavior of the data write operation.
• Verify that the data being passed to the sink matches the expected schema. Sometimes, mismatches in data types or unexpected null values can cause failures.
• Determine if the issue might be related to resource constraints or timeouts, especially if you're dealing with large volumes of data. Adjusting timeout settings or scaling up resources temporarily might help identify if this is the case.
• Review the detailed debug logs for the job. These logs can provide more specific information about what part of the data write operation is failing. If debugging is not turned on, you may want to enable verbose logging for a more detailed analysis.
• If the failure is intermittent, implementing retry logic or improving error handling in the pipeline could help. ADF provides features to handle retries and to specify fault tolerance settings. Ensure that your ADF instance and any related components (like SDKs or connectors) are up to date. Sometimes, bugs related to specific versions can cause these types of errors.

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Answer 2

You have a problem during the process of writing data to the destination.

This can happen due to various reasons, such as large data volumes causing timeouts, mismatches in data types between the source and the sink, or connection issues between ADF and the destination service.

It could also be a result of locking or contention issues when multiple pipelines attempt to write simultaneously to the same resource, or insufficient resources on the destination service like Azure SQL Database running out of storage or performance capacity.

To troubleshoot, you can increase timeout settings, check for data type mismatches, and review the connection configuration.

Enabling detailed logging and using Data Flow Debug mode can help pinpoint the exact cause by providing more granular error information.