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https://issues.apache.org/jira/browse/YARN-7739?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wangda Tan updated YARN-7739:
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Description: Currently, YARN RM reject requested resource if memory or
vcores are less than 0 or greater than maximum allocation. We should run the
check for customized resource types as well. (was: Currently, YARN Scheduler
normalizes requested resource based on the maximum allocation derived from
configured maximum allocation and maximum registered node resources. Basically,
the scheduler will silently cap asked resource by maximum allocation.
This could cause issues for applications, for example, a Spark job which needs
12 GB memory to run, however in the cluster, registered NMs have at most 8 GB
mem on each node. So scheduler allocates 8GB memory container to the requested
application.
Once app receives containers from RM, if it doesn't double check allocated
resources, it will lead to OOM and hard to debug because scheduler silently
caps maximum allocation.
When non-mandatory resources introduced, this becomes worse. For resources like
GPU, we typically set minimum allocation to 0 since not all nodes have GPU
devices. So it is possible that application asks 4 GPUs but get 0 GPU, it gonna
be a big problem.)
> DefaultAMSProcessor should properly check customized resource types against
> minimum/maximum allocation
> ------------------------------------------------------------------------------------------------------
>
> Key: YARN-7739
> URL: https://issues.apache.org/jira/browse/YARN-7739
> Project: Hadoop YARN
> Issue Type: Bug
> Reporter: Wangda Tan
> Assignee: Wangda Tan
> Priority: Blocker
> Attachments: YARN-7739.001.patch
>
>
> Currently, YARN RM reject requested resource if memory or vcores are less
> than 0 or greater than maximum allocation. We should run the check for
> customized resource types as well.
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