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From Jon Chase <jon.ch...@gmail.com>
Subject Re: "Fetch Failure"
Date Fri, 19 Dec 2014 16:33:29 GMT
Yes, same problem.

On Fri, Dec 19, 2014 at 11:29 AM, Sandy Ryza <sandy.ryza@cloudera.com>
wrote:

> Do you hit the same errors?  Is it now saying your containers are exceed
> ~10 GB?
>
> On Fri, Dec 19, 2014 at 11:16 AM, Jon Chase <jon.chase@gmail.com> wrote:
>>
>> I'm actually already running 1.1.1.
>>
>> I also just tried --conf spark.yarn.executor.memoryOverhead=4096, but no
>> luck.  Still getting "ExecutorLostFailure (executor lost)".
>>
>>
>>
>> On Fri, Dec 19, 2014 at 10:43 AM, Rafal Kwasny <rafal.kwasny@gmail.com>
>> wrote:
>>
>>> Hi,
>>> Just upgrade to 1.1.1 - it was fixed some time ago
>>>
>>> /Raf
>>>
>>>
>>> sandy.ryza@cloudera.com wrote:
>>>
>>> Hi Jon,
>>>
>>> The fix for this is to increase spark.yarn.executor.memoryOverhead to
>>> something greater than it's default of 384.
>>>
>>> This will increase the gap between the executors heap size and what it
>>> requests from yarn. It's required because jvms take up some memory beyond
>>> their heap size.
>>>
>>> -Sandy
>>>
>>> On Dec 19, 2014, at 9:04 AM, Jon Chase <jon.chase@gmail.com> wrote:
>>>
>>> I'm getting the same error ("ExecutorLostFailure") - input RDD is 100k
>>> small files (~2MB each).  I do a simple map, then keyBy(), and then
>>> rdd.saveAsHadoopDataset(...).  Depending on the memory settings given to
>>> spark-submit, the time before the first ExecutorLostFailure varies (more
>>> memory == longer until failure) - but this usually happens after about 100
>>> files being processed.
>>>
>>> I'm running Spark 1.1.0 on AWS EMR w/Yarn.    It appears that Yarn is
>>> killing the executor b/c it thinks it's exceeding memory.  However, I can't
>>> repro any OOM issues when running locally, no matter the size of the data
>>> set.
>>>
>>> It seems like Yarn thinks the heap size is increasing according to the
>>> Yarn logs:
>>>
>>> 2014-12-18 22:06:43,505 INFO
>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
>>> (Container Monitor): Memory usage of ProcessTree 24273 for container-id
>>> container_1418928607193_0011_01_000002: 6.1 GB of 6.5 GB physical memory
>>> used; 13.8 GB of 32.5 GB virtual memory used
>>> 2014-12-18 22:06:46,516 INFO
>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
>>> (Container Monitor): Memory usage of ProcessTree 24273 for container-id
>>> container_1418928607193_0011_01_000002: 6.2 GB of 6.5 GB physical memory
>>> used; 13.9 GB of 32.5 GB virtual memory used
>>> 2014-12-18 22:06:49,524 INFO
>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
>>> (Container Monitor): Memory usage of ProcessTree 24273 for container-id
>>> container_1418928607193_0011_01_000002: 6.2 GB of 6.5 GB physical memory
>>> used; 14.0 GB of 32.5 GB virtual memory used
>>> 2014-12-18 22:06:52,531 INFO
>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
>>> (Container Monitor): Memory usage of ProcessTree 24273 for container-id
>>> container_1418928607193_0011_01_000002: 6.4 GB of 6.5 GB physical memory
>>> used; 14.1 GB of 32.5 GB virtual memory used
>>> 2014-12-18 22:06:55,538 INFO
>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
>>> (Container Monitor): Memory usage of ProcessTree 24273 for container-id
>>> container_1418928607193_0011_01_000002: 6.5 GB of 6.5 GB physical memory
>>> used; 14.2 GB of 32.5 GB virtual memory used
>>> 2014-12-18 22:06:58,549 INFO
>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
>>> (Container Monitor): Memory usage of ProcessTree 24273 for container-id
>>> container_1418928607193_0011_01_000002: 6.5 GB of 6.5 GB physical memory
>>> used; 14.3 GB of 32.5 GB virtual memory used
>>> 2014-12-18 22:06:58,549 WARN
>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
>>> (Container Monitor): Process tree for container:
>>> container_1418928607193_0011_01_000002 has processes older than 1 iteration
>>> running over the configured limit. Limit=6979321856, current usage =
>>> 6995812352
>>> 2014-12-18 22:06:58,549 WARN
>>> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl
>>> (Container Monitor): Container
>>> [pid=24273,containerID=container_1418928607193_0011_01_000002] is running
>>> beyond physical memory limits. Current usage: 6.5 GB of 6.5 GB physical
>>> memory used; 14.3 GB of 32.5 GB virtual memory used. Killing container.
>>> Dump of the process-tree for container_1418928607193_0011_01_000002 :
>>> |- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS)
>>> SYSTEM_TIME(MILLIS) VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) FULL_CMD_LINE
>>> |- 24273 4304 24273 24273 (bash) 0 0 115630080 302 /bin/bash -c
>>> /usr/java/latest/bin/java -server -XX:OnOutOfMemoryError='kill %p'
>>> -Xms6144m -Xmx6144m  -verbose:gc -XX:+HeapDumpOnOutOfMemoryError
>>> -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC
>>> -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70
>>> -Djava.io.tmpdir=/mnt1/var/lib/hadoop/tmp/nm-local-dir/usercache/hadoop/appcache/application_1418928607193_0011/container_1418928607193_0011_01_000002/tmp
>>> org.apache.spark.executor.CoarseGrainedExecutorBackend
>>> akka.tcp://sparkDriver@ip-xx-xxx-xxx-xxx.eu-west-1.compute.internal:54357/user/CoarseGrainedScheduler
>>> 1 ip-xx-xxx-xxx-xxx.eu-west-1.compute.internal 4 1>
>>> /mnt/var/log/hadoop/userlogs/application_1418928607193_0011/container_1418928607193_0011_01_000002/stdout
>>> 2>
>>> /mnt/var/log/hadoop/userlogs/application_1418928607193_0011/container_1418928607193_0011_01_000002/stderr
>>> |- 24277 24273 24273 24273 (java) 13808 1730 15204556800 1707660
>>> /usr/java/latest/bin/java -server -XX:OnOutOfMemoryError=kill %p -Xms6144m
>>> -Xmx6144m -verbose:gc -XX:+HeapDumpOnOutOfMemoryError -XX:+PrintGCDetails
>>> -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC
>>> -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70
>>> -Djava.io.tmpdir=/mnt1/var/lib/hadoop/tmp/nm-local-dir/usercache/hadoop/appcache/application_1418928607193_0011/container_1418928607193_0011_01_000002/tmp
>>> org.apache.spark.executor.CoarseGrainedExecutorBackend
>>> akka.tcp://sparkDriver@ip-xx-xxx-xxx-xxx.eu-west-1.compute.internal:54357/user/CoarseGrainedScheduler
>>> 1 ip-xx-xxx-xxx-xxx.eu-west-1.compute.internal 4
>>>
>>>
>>> I've analyzed some heap dumps and see nothing out of the ordinary.
>>> Would love to know what could be causing this.
>>>
>>>
>>> On Fri, Dec 19, 2014 at 7:46 AM, bethesda <swearingendw@mac.com> wrote:
>>>
>>>> I have a job that runs fine on relatively small input datasets but then
>>>> reaches a threshold where I begin to consistently get "Fetch failure"
>>>> for
>>>> the Failure Reason, late in the job, during a saveAsText() operation.
>>>>
>>>> The first error we are seeing on the "Details for Stage" page is
>>>> "ExecutorLostFailure"
>>>>
>>>> My Shuffle Read is 3.3 GB and that's the only thing that seems high, we
>>>> have
>>>> three servers and they are configured on this job for 5g memory, and
>>>> the job
>>>> is running in spark-shell.  The first error in the shell is "Lost
>>>> executor 2
>>>> on (servername): remote Akka client disassociated.
>>>>
>>>> We are still trying to understand how to best diagnose jobs using the
>>>> web ui
>>>> so it's likely that there's some helpful info here that we just don't
>>>> know
>>>> how to interpret -- is there any kind of "troubleshooting guide" beyond
>>>> the
>>>> Spark Configuration page?  I don't know if I'm providing enough info
>>>> here.
>>>>
>>>> thanks.
>>>>
>>>>
>>>>
>>>> --
>>>> View this message in context:
>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Fetch-Failure-tp20787.html
>>>> Sent from the Apache Spark User List mailing list archive at Nabble.com
>>>> .
>>>>
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>>>>
>>>
>>>
>>

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