Forum Discussion
Failure happened on 'destination' side. ErrorCode=LakehouseTimeoutError
Hello everyone,
I'm encountering an issue while transferring data from multiple databases, including Oracle and IBM i DB2, to the same Lakehouse in Microsoft Fabric using Data Pipelines. I'm running four pipelines simultaneously, each processing a different schema from the data source, and they all fail with the following errors:
Failure happened on 'destination' side. ErrorCode=LakehouseTimeoutError,'Type=Microsoft.DataTransfer.Common.Shared.HybridDeliveryException,Message=Request to Lakehouse workspace 'x' met timeout error. It is mostly caused by the poor network between the Data Factory runtime machine and the Lakehouse storage account. Check the network to resolve such error.,Source=Microsoft.DataTransfer.ClientLibrary,'
Followed by:
Failure happened on 'destination' side. ErrorCode=LakehouseOperationFailed,'Type=Microsoft.DataTransfer.Common.Shared.HybridDeliveryException,Message=Lakehouse operation failed for: The stream does not support concurrent IO read or write operations. Workspace: 'x'. Path: 'x/Tables/wms/HISTORY/3349f6ef-ac97-4606-a913-c3e3a5598621.parquet'..,Source=Microsoft.DataTransfer.ClientLibrary,''Type=System.NotSupportedException,Message=The stream does not support concurrent IO read or write operations.,Source=System,'
Could this be related to running multiple pipelines simultaneously? Are there any recommended configurations or best practices to avoid timeouts when transferring large volumes of data? Should I adjust the parallelism settings or modify the batch size per pipeline?
Any insights or troubleshooting steps would be greatly appreciated.
Thanks in advance 😊!
Hi KerenLopez
LakehouseTimeoutError is often caused by poor network performance between the Data Factory runtime and the Lakehouse storage account. Ensure that your network connection is stable and has sufficient bandwidth to handle the data transfer.
LakehouseOperationFailed indicates that the stream does not support concurrent IO read or write operations. This can happen when multiple pipelines try to access the same resource simultaneously.You can configure the concurrency settings in your pipeline to limit the number of parallel executions. This can help prevent conflicts and reduce the load on your network and Lakehouse storage.
Adjusting the batch size per pipeline can help manage the volume of data being transferred at any given time. Smaller batch sizes can reduce the likelihood of timeouts and improve overall stability.
Instead of running all four pipelines simultaneously, consider staggering their execution times. This can help distribute the load more evenly and reduce the chances of network congestion and concurrent access issues.
Use the most efficient data transfer methods available in Microsoft Fabric. For example, using the Copy tool in pipelines can help optimize the data transfer process.Regards,
Nono Chen
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
1 Reply
Hi KerenLopez
LakehouseTimeoutError is often caused by poor network performance between the Data Factory runtime and the Lakehouse storage account. Ensure that your network connection is stable and has sufficient bandwidth to handle the data transfer.
LakehouseOperationFailed indicates that the stream does not support concurrent IO read or write operations. This can happen when multiple pipelines try to access the same resource simultaneously.You can configure the concurrency settings in your pipeline to limit the number of parallel executions. This can help prevent conflicts and reduce the load on your network and Lakehouse storage.
Adjusting the batch size per pipeline can help manage the volume of data being transferred at any given time. Smaller batch sizes can reduce the likelihood of timeouts and improve overall stability.
Instead of running all four pipelines simultaneously, consider staggering their execution times. This can help distribute the load more evenly and reduce the chances of network congestion and concurrent access issues.
Use the most efficient data transfer methods available in Microsoft Fabric. For example, using the Copy tool in pipelines can help optimize the data transfer process.Regards,
Nono Chen
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.