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  1. Hadoop YARN
  2. YARN-8202

DefaultAMSProcessor should properly check units of requested custom resource types against minimum/maximum allocation

    Details

    • Type: Bug
    • Status: Resolved
    • Priority: Blocker
    • Resolution: Fixed
    • Affects Version/s: None
    • Fix Version/s: 3.1.1
    • Component/s: None
    • Labels:
      None
    • Target Version/s:
    • Hadoop Flags:
      Reviewed

      Description

       

      When I execute a pi job with arguments: 

      -Dmapreduce.map.resource.memory-mb=200 -Dmapreduce.map.resource.resource1=500M 1 1000

      and I have one node with 5GB of resource1, I get the following exception on every second and the job hangs:

      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)
      

      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: 

      -Dmapreduce.map.resource.memory-mb=200 -Dmapreduce.map.resource.resource1=1G 1 1000

      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.

       

        Attachments

        1. YARN-8202-001.patch
          83 kB
          Szilard Nemeth
        2. YARN-8202-002.patch
          92 kB
          Szilard Nemeth
        3. YARN-8202-003.patch
          93 kB
          Szilard Nemeth
        4. YARN-8202-004.patch
          94 kB
          Szilard Nemeth
        5. YARN-8202-005.patch
          129 kB
          Szilard Nemeth
        6. YARN-8202-006.patch
          130 kB
          Szilard Nemeth
        7. YARN-8202-007.patch
          130 kB
          Szilard Nemeth
        8. YARN-8202-008.patch
          112 kB
          Szilard Nemeth
        9. YARN-8202-009.patch
          109 kB
          Szilard Nemeth
        10. YARN-8202-010.patch
          108 kB
          Szilard Nemeth

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              • Assignee:
                snemeth Szilard Nemeth
                Reporter:
                snemeth Szilard Nemeth
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                Dates

                • Created:
                  Updated:
                  Resolved: