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https://issues.apache.org/jira/browse/YARN-7560?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16272149#comment-16272149
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Wilfred Spiegelenburg commented on YARN-7560:
---------------------------------------------
[~c61...@163.com] I don't think a sweeping change like that is a good idea as
part of this change. If you want to tackle that problem I would suggest that
you open a new jira that shows where the issue are located and work on it there.
> Resourcemanager hangs when resourceUsedWithWeightToResourceRatio return a
> overflow value
> ------------------------------------------------------------------------------------------
>
> Key: YARN-7560
> URL: https://issues.apache.org/jira/browse/YARN-7560
> Project: Hadoop YARN
> Issue Type: Bug
> Components: fairscheduler, resourcemanager
> Affects Versions: 3.0.0
> Reporter: zhengchenyu
> Assignee: zhengchenyu
> Fix For: 3.0.0
>
> Attachments: YARN-7560.000.patch, YARN-7560.001.patch
>
>
> In our cluster, we changed the configuration, then refreshQueues, we found
> the resourcemanager hangs. And the Resourcemanager can't restart
> successfully. We got jstack information, always show like this:
> {code}
> "main" #1 prio=5 os_prio=0 tid=0x00007f98e8017000 nid=0x2f5 runnable
> [0x00007f98eed9a000]
> java.lang.Thread.State: RUNNABLE
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.policies.ComputeFairShares.resourceUsedWithWeightToResourceRatio(ComputeFairShares.java:182)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.policies.ComputeFairShares.computeSharesInternal(ComputeFairShares.java:140)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.policies.ComputeFairShares.computeSteadyShares(ComputeFairShares.java:66)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.policies.FairSharePolicy.computeSteadyShares(FairSharePolicy.java:148)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FSParentQueue.recomputeSteadyShares(FSParentQueue.java:102)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.QueueManager.getQueue(QueueManager.java:148)
> - locked <0x00007f8c4a8177a0> (a java.util.HashMap)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.QueueManager.getLeafQueue(QueueManager.java:101)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.QueueManager.updateAllocationConfiguration(QueueManager.java:387)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler$AllocationReloadListener.onReload(FairScheduler.java:1728)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.AllocationFileLoaderService.reloadAllocations(AllocationFileLoaderService.java:422)
> - locked <0x00007f8c4a7eb2e0> (a
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.AllocationFileLoaderService)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler.initScheduler(FairScheduler.java:1597)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler.serviceInit(FairScheduler.java:1621)
> at
> org.apache.hadoop.service.AbstractService.init(AbstractService.java:163)
> - locked <0x00007f8c4a76ac48> (a java.lang.Object)
> at
> org.apache.hadoop.service.CompositeService.serviceInit(CompositeService.java:107)
> at
> org.apache.hadoop.yarn.server.resourcemanager.ResourceManager$RMActiveServices.serviceInit(ResourceManager.java:569)
> at
> org.apache.hadoop.service.AbstractService.init(AbstractService.java:163)
> - locked <0x00007f8c49254268> (a java.lang.Object)
> at
> org.apache.hadoop.yarn.server.resourcemanager.ResourceManager.createAndInitActiveServices(ResourceManager.java:997)
> at
> org.apache.hadoop.yarn.server.resourcemanager.ResourceManager.serviceInit(ResourceManager.java:257)
> at
> org.apache.hadoop.service.AbstractService.init(AbstractService.java:163)
> - locked <0x00007f8c467495e0> (a java.lang.Object)
> at
> org.apache.hadoop.yarn.server.resourcemanager.ResourceManager.main(ResourceManager.java:1220)
> {code}
> When we debug the cluster, we found resourceUsedWithWeightToResourceRatio
> return a negative value. So the loop can't return. We found in our cluster,
> the sum of all minRes is over int.max, so
> resourceUsedWithWeightToResourceRatio return a negative value.
> below is the loop. Because totalResource is long, so always postive. But
> resourceUsedWithWeightToResourceRatio return int type. Our cluster is so big
> that resourceUsedWithWeightToResourceRatio will return a overflow value, just
> a negative. So the loop will never break.
> {code}
> while (resourceUsedWithWeightToResourceRatio(rMax, schedulables, type)
> < totalResource) {
> rMax *= 2.0;
> }
> {code}
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