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https://issues.apache.org/jira/browse/YARN-8202?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16470644#comment-16470644
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Robert Kanter commented on YARN-8202:
-------------------------------------
+1
> DefaultAMSProcessor should properly check units of requested custom resource
> types against minimum/maximum allocation
> ---------------------------------------------------------------------------------------------------------------------
>
> Key: YARN-8202
> URL: https://issues.apache.org/jira/browse/YARN-8202
> Project: Hadoop YARN
> Issue Type: Bug
> Reporter: Szilard Nemeth
> Assignee: Szilard Nemeth
> Priority: Blocker
> Attachments: YARN-8202-001.patch, YARN-8202-002.patch,
> YARN-8202-003.patch, YARN-8202-004.patch, YARN-8202-005.patch,
> YARN-8202-006.patch, YARN-8202-007.patch, YARN-8202-008.patch,
> YARN-8202-009.patch, YARN-8202-010.patch
>
>
>
> When I execute a pi job with arguments:
> {code:java}
> -Dmapreduce.map.resource.memory-mb=200
> -Dmapreduce.map.resource.resource1=500M 1 1000{code}
> and I have one node with 5GB of resource1, I get the following exception on
> every second and the job hangs:
> {code:java}
> 2018-04-24 08:42:03,694 INFO org.apache.hadoop.ipc.Server: IPC Server handler
> 20 on 8030, call Call#386 Retry#0
> org.apache.hadoop.yarn.api.ApplicationMasterProtocolPB.allocate from
> 172.31.119.172:58138
> org.apache.hadoop.yarn.exceptions.InvalidResourceRequestException: Invalid
> resource request, requested resource type=[resource1] < 0 or greater than
> maximum allowed allocation. Requested resource=<memory:200, vCores:1,
> resource1: 500M>, maximum allowed allocation=<memory:6144, vCores:8,
> resource1: 5G>, please note that maximum allowed allocation is calculated by
> scheduler based on maximum resource of registered NodeManagers, which might
> be less than configured maximum allocation=<memory:8192, vCores:8192,
> resource1: 9223372036854775807G>
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.SchedulerUtils.validateResourceRequest(SchedulerUtils.java:286)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.SchedulerUtils.normalizeAndValidateRequest(SchedulerUtils.java:242)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.SchedulerUtils.normalizeAndvalidateRequest(SchedulerUtils.java:258)
> at
> org.apache.hadoop.yarn.server.resourcemanager.RMServerUtils.normalizeAndValidateRequests(RMServerUtils.java:249)
> at
> org.apache.hadoop.yarn.server.resourcemanager.DefaultAMSProcessor.allocate(DefaultAMSProcessor.java:230)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.constraint.processor.DisabledPlacementProcessor.allocate(DisabledPlacementProcessor.java:75)
> at
> org.apache.hadoop.yarn.server.resourcemanager.AMSProcessingChain.allocate(AMSProcessingChain.java:92)
> at
> org.apache.hadoop.yarn.server.resourcemanager.ApplicationMasterService.allocate(ApplicationMasterService.java:433)
> at
> org.apache.hadoop.yarn.api.impl.pb.service.ApplicationMasterProtocolPBServiceImpl.allocate(ApplicationMasterProtocolPBServiceImpl.java:60)
> at
> org.apache.hadoop.yarn.proto.ApplicationMasterProtocol$ApplicationMasterProtocolService$2.callBlockingMethod(ApplicationMasterProtocol.java:99)
> at
> org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:523)
> at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:991)
> at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:872)
> at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:818)
> at java.security.AccessController.doPrivileged(Native Method)
> at javax.security.auth.Subject.doAs(Subject.java:422)
> at
> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1682)
> at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2678)
> {code}
> *This is because
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.SchedulerUtils#validateResourceRequest
> does not take resource units into account.*
>
> However, if I start a job with arguments:
> {code:java}
> -Dmapreduce.map.resource.memory-mb=200 -Dmapreduce.map.resource.resource1=1G
> 1 1000{code}
> and I still have 5GB of resource1 on one node then the job runs successfully.
>
> I also tried a third job run, when I request 1GB of resource1 and I have no
> nodes with any amount of resource1, then I restart the node with 5GBs of
> resource1, the job ultimately completes, but just after the node with enough
> resources registered in RM, which is the desired behaviour.
>
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