Hi @Philip O'Rourke ,
Executing Data flow would take few minutes to spin up the cluster. The time till your pipeline is in queued state, it is actually enabling the data flow debug that is making the spark cluster ready to perform the transformations.
Dataflow runs behind on spark clusters which are managed by ADF. Clusters are created on demand from scratch and will be destroyed after job is done. That's the reason that 4-5 minutes for acquiring compute. Once compute is acquired, the job runs and kill the cluster after the job run is completed.
There is a workaround though that user can set TimeToLive in Azure IR, and this will keep cluster alive for next job (if the job falls in this time period). Like if you set the TTL for 10 minutes, it will wait if there is any other job for same IR arrives and continue the cycle. If no job arrives in 10 minutes it kills the cluster.
From the ADF pipeline designer UI, go to Connections > Integration Runtimes > New. Select Azure IR and then open the Data Flow Run Time properties section. You will be able to see TimeToLive Option there which is The allowed idle time for the data flow compute. Specifies how long it stays alive after completion of a data flow run if there are no other active jobs.
Please refer to the following blog posts for more information:
- https://techcommunity.microsoft.com/t5/azure-data-factory-blog/adf-adds-ttl-to-azure-ir-to-reduce-data-flow-activity-times/ba-p/878380
- https://social.msdn.microsoft.com/Forums/en-US/91d388d9-730f-4d53-93b2-2a8697513511/azure-dataflow-execution-behaviour?forum=AzureDataFactory
Hope this will help. Please let us know if any further queries.
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