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.kwa...@gmail.com>
wrote:
> Hi,
> Just upgrade to 1.1.1 - it was fixed some time ago
>
> /Raf
>
>
> sandy.r...@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.ch...@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://sparkdri...@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://sparkdri...@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 <swearinge...@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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>>
>
>
