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https://issues.apache.org/jira/browse/YARN-2230?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14265186#comment-14265186
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Jian He commented on YARN-2230:
-------------------------------
[~kawaa], thanks for your patch!
looks good overall. Would mind fixing the doc for the memory and also for the
min-allocation too ? thx
> Fix description of yarn.scheduler.maximum-allocation-vcores in
> yarn-default.xml (or code)
> -----------------------------------------------------------------------------------------
>
> Key: YARN-2230
> URL: https://issues.apache.org/jira/browse/YARN-2230
> Project: Hadoop YARN
> Issue Type: Bug
> Components: client, documentation, scheduler
> Affects Versions: 2.4.0
> Reporter: Adam Kawa
> Priority: Minor
> Attachments: YARN-2230.001.patch
>
>
> When a user requests more vcores than the allocation limit (e.g.
> mapreduce.map.cpu.vcores is larger than
> yarn.scheduler.maximum-allocation-vcores), then
> InvalidResourceRequestException is thrown -
> https://svn.apache.org/repos/asf/hadoop/common/trunk/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-resourcemanager/src/main/java/org/apache/hadoop/yarn/server/resourcemanager/scheduler/SchedulerUtils.java
> {code}
> if (resReq.getCapability().getVirtualCores() < 0 ||
> resReq.getCapability().getVirtualCores() >
> maximumResource.getVirtualCores()) {
> throw new InvalidResourceRequestException("Invalid resource request"
> + ", requested virtual cores < 0"
> + ", or requested virtual cores > max configured"
> + ", requestedVirtualCores="
> + resReq.getCapability().getVirtualCores()
> + ", maxVirtualCores=" + maximumResource.getVirtualCores());
> }
> {code}
> According to documentation - yarn-default.xml
> http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-common/yarn-default.xml,
> the request should be capped to the allocation limit.
> {code}
> <property>
> <description>The maximum allocation for every container request at the RM,
> in terms of virtual CPU cores. Requests higher than this won't take
> effect,
> and will get capped to this value.</description>
> <name>yarn.scheduler.maximum-allocation-vcores</name>
> <value>32</value>
> </property>
> {code}
> This means that:
> * Either documentation or code should be corrected (unless this exception is
> handled elsewhere accordingly, but it looks that it is not).
> This behavior is confusing, because when such a job (with
> mapreduce.map.cpu.vcores is larger than
> yarn.scheduler.maximum-allocation-vcores) is submitted, it does not make any
> progress. The warnings/exceptions are thrown at the scheduler (RM) side e.g.
> {code}
> 2014-06-29 00:34:51,469 WARN
> org.apache.hadoop.yarn.server.resourcemanager.ApplicationMasterService:
> Invalid resource ask by application appattempt_1403993411503_0002_000001
> org.apache.hadoop.yarn.exceptions.InvalidResourceRequestException: Invalid
> resource request, requested virtual cores < 0, or requested virtual cores >
> max configured, requestedVirtualCores=32, maxVirtualCores=3
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.SchedulerUtils.validateResourceRequest(SchedulerUtils.java:237)
> at
> org.apache.hadoop.yarn.server.resourcemanager.RMServerUtils.validateResourceRequests(RMServerUtils.java:80)
> at
> org.apache.hadoop.yarn.server.resourcemanager.ApplicationMasterService.allocate(ApplicationMasterService.java:420)
> .....
> at
> org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:585)
> at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1026)
> at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1986)
> at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1982)
> at java.security.AccessController.doPrivileged(Native Method)
> at javax.security.auth.Subject.doAs(Subject.java:416)
> at
> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)
> at org.apache.hadoop.ipc.Server$Handler.run(Server.java:1980)
> {code}
> * IMHO, such an exception should be forwarded to client. Otherwise, it is non
> obvious to discover why a job does not make any progress.
> The same looks to be related to memory.
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