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https://issues.apache.org/jira/browse/MAPREDUCE-6190?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16698105#comment-16698105
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Zhaohui Xin edited comment on MAPREDUCE-6190 at 11/27/18 2:44 AM:
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Hi [~ajisakaa], [~jianhe], can you help me review this patch? :)
was (Author: uranus):
Hi [~ajisakaa], can you help me review this patch? :)
> MR Job is stuck because of one mapper stuck in STARTING
> -------------------------------------------------------
>
> Key: MAPREDUCE-6190
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-6190
> Project: Hadoop Map/Reduce
> Issue Type: Bug
> Affects Versions: 2.6.0, 2.7.0, 2.8.0, 2.9.0, 3.0.0, 3.1.1
> Reporter: Ankit Malhotra
> Assignee: Zhaohui Xin
> Priority: Major
> Attachments: MAPREDUCE-6190.001.patch, MAPREDUCE-6190.002.patch
>
>
> Trying to figure out a weird issue we started seeing on our CDH5.1.0 cluster
> with map reduce jobs on YARN.
> We had a job stuck for hours because one of the mappers never started up
> fully. Basically, the map task had 2 attempts, the first one failed and the
> AM tried to schedule a second one and the second attempt was stuck on STATE:
> STARTING, STATUS: NEW. A node never got assigned and the task along with the
> job was stuck indefinitely.
> The AM logs had this being logged again and again:
> {code}
> 2014-12-09 19:25:12,347 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Ramping down 0
> 2014-12-09 19:25:13,352 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Received
> completed container container_1408745633994_450952_02_003807
> 2014-12-09 19:25:13,352 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Reduce preemption
> successful attempt_1408745633994_450952_r_000048_1000
> 2014-12-09 19:25:13,352 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Ramping down all
> scheduled reduces:0
> 2014-12-09 19:25:13,352 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Going to preempt 1
> 2014-12-09 19:25:13,353 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Preempting
> attempt_1408745633994_450952_r_000050_1000
> 2014-12-09 19:25:13,353 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Recalculating
> schedule, headroom=0
> 2014-12-09 19:25:13,353 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator:
> completedMapPercent 0.99968 totalMemLimit:1722880 finalMapMemLimit:2560
> finalReduceMemLimit:1720320 netScheduledMapMem:2560
> netScheduledReduceMem:1722880
> 2014-12-09 19:25:13,353 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Ramping down 0
> 2014-12-09 19:25:13,353 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: After Scheduling:
> PendingReds:77 ScheduledMaps:1 ScheduledReds:0 AssignedMaps:0
> AssignedReds:673 CompletedMaps:3124 CompletedReds:0 ContAlloc:4789
> ContRel:798 HostLocal:2944 RackLocal:155
> 2014-12-09 19:25:14,353 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Before
> Scheduling: PendingReds:78 ScheduledMaps:1 ScheduledReds:0 AssignedMaps:0
> AssignedReds:673 CompletedMaps:3124 CompletedReds:0 ContAlloc:4789
> ContRel:798 HostLocal:2944 RackLocal:155
> 2014-12-09 19:25:14,359 INFO [RMCommunicator Allocator]
> org.apache.hadoop.mapreduce.v2.app.rm.RMContainerAllocator: Recalculating
> schedule, headroom=0
> {code}
> On killing the task manually, the AM started up the task again, scheduled and
> ran it successfully completing the task and the job with it.
> Some quick code grepping led us here:
> http://grepcode.com/file/repo1.maven.org/maven2/org.apache.hadoop/hadoop-mapreduce-client-app/2.3.0/org/apache/hadoop/mapreduce/v2/app/rm/RMContainerAllocator.java#397
> But still dont quite understand why this would happen once in a while and why
> the job would suddenly be ok once the stuck task is manually killed.
> Note: Other jobs succeed on the cluster while this job is stuck.
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