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From "Wangda Tan (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (YARN-7739) Revisit scheduler resource normalization behavior for max allocation
Date Fri, 12 Jan 2018 01:04:00 GMT

     [ https://issues.apache.org/jira/browse/YARN-7739?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Wangda Tan updated YARN-7739:
-----------------------------
    Description: 
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.

  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.



> Revisit scheduler resource normalization behavior for max allocation
> --------------------------------------------------------------------
>
>                 Key: YARN-7739
>                 URL: https://issues.apache.org/jira/browse/YARN-7739
>             Project: Hadoop YARN
>          Issue Type: Bug
>            Reporter: Wangda Tan
>            Priority: Critical
>
> 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.



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