The exit code 52 comes from org.apache.spark.util.SparkExitCode, and it is
val OOM=52 - i.e. an OutOfMemoryError
Refer
https://github.com/apache/spark/blob/d6dc12ef0146ae409834c78737c116050961f350/core/src/main/scala/org/apache/spark/util/SparkExitCode.scala



On 19 September 2016 at 14:57, Cyanny LIANG <lgrcya...@gmail.com> wrote:

> My job is 1TB join + 10 GB table on spark1.6.1
> run on yarn mode:
>
> *1. if I open shuffle service, the error is *
> Job aborted due to stage failure: ShuffleMapStage 2 (writeToDirectory at
> NativeMethodAccessorImpl.java:-2) has failed the maximum allowable number
> of times: 4. Most recent failure reason: 
> org.apache.spark.shuffle.FetchFailedException:
> java.lang.RuntimeException: Executor is not registered 
> (appId=application_1473819702737_1239,
> execId=52)
>     at org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.
> getBlockData(ExternalShuffleBlockResolver.java:105)
>     at org.apache.spark.network.shuffle.ExternalShuffleBlockHandler.
> receive(ExternalShuffleBlockHandler.java:74)
>     at org.apache.spark.network.server.TransportRequestHandler.
> processRpcRequest(TransportRequestHandler.java:114)
>     at org.apache.spark.network.server.TransportRequestHandler.handle(
> TransportRequestHandler.java:87)
>     at org.apache.spark.network.server.TransportChannelHandler.
> channelRead0(TransportChannelHandler.java:101)
>
> *2. if I close shuffle service, *
> *set spark.executor.instances 80*
> the error is :
> ExecutorLostFailure (executor 71 exited caused by one of the running
> tasks) Reason: Container marked as failed: 
> container_1473819702737_1432_01_406847560
> on host: nmg01-spark-a0021.nmg01.baidu.com. Exit status: 52. Diagnostics:
> Exception from container-launch: ExitCodeException exitCode=52:
> ExitCodeException exitCode=52:
>
> These errors are reported on shuffle stage
> My data is skew, some ids have 400million rows, but some ids only have
> 1million rows, is anybody has some ideas to solve the problem?
>
>
> *3. My config is *
> Here is my config
> I use tungsten-sort in off-heap mode, in on-heap mode, the oom problem
> will be more serious
>
> spark.driver.cores 4
>
> spark.driver.memory 8g
>
>
> # use on client mode
>
>
> spark.yarn.am.memory 8g
>
>
> spark.yarn.am.cores 4
>
>
> spark.executor.memory 8g
>
>
> spark.executor.cores 4
>
> spark.yarn.executor.memoryOverhead 6144
>
>
> spark.memory.offHeap.enabled true
>
>
> spark.memory.offHeap.size 4000000000
>
> Best & Regards
> Cyanny LIANG
>

Reply via email to