Spark YARN設定の問題：コンテナが失敗し続ける

Question

データフレームをtextFileとして保存しようとしていますが、小さなデータでも時間がかかります。私は自分の設定に何か問題があると信じています。ここで間違っていることを教えてくれる人もいますか？

spark.default.parallelism 640 
spark.hadoop.fs.s3.cse.plaintextLength.enabled false 
spark.hadoop.fs.s3n.filestatuscache.enable true 
spark.hadoop.mapreduce.input.fileinputformat.split.maxsize 33554432 
spark.executor.id driver 
spark.executor.instances 10 
spark.executor.memory 18g 
spark.executor.extraJavaOptions -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p' 

spark.driver.memory 219695M 
spark.driver.port 51885 
spark.dynamicAllocation.enabled true 
spark.dynamicAllocation.initialExecutors spark.dynamicAllocation.minExecutors 
spark.dynamicAllocation.maxExecutors infinity 
spark.dynamicAllocation.minExecutors 120 
spark.dynamicAllocation.schedulerBacklogTimeout 1s 
spark.eventLog.enabled true 
spark.executor.cores 32 

私は取得していますr3.8xLarge 10人の奴隷

エラーと= 1つのマスターを持っています：

org.apache.spark.SparkException: Job aborted due to stage failure: Task 50 in stage 5.0 failed 4 times, most recent failure: Lost task 50.3 in stage 5.0 (TID 321, ip-172-31-3-183.ec2.internal): ExecutorLostFailure (executor 4 exited caused by one of the running tasks) Reason: Container marked as failed: container_1475709908651_0002_01_000005 on host: ip-172-31-3-183.ec2.internal. Exit status: 137. Diagnostics: Container killed on request. Exit code is 137 Container exited with a non-zero exit code 137 Killed by external signal Driver stacktrace: 
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431) 
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419) 
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418) 
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) 
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) 
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418) 
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) 
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) 
at scala.Option.foreach(Option.scala:236) 
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799) 
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640) 
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599) 
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588) 
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) 
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620) 
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832) 
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845) 
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858) 
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929) 
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927) 
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) 
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) 
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) 
at org.apache.spark.rdd.RDD.collect(RDD.scala:926) 
at org.apache.spark.RangePartitioner$.sketch(Partitioner.scala:264) 
at org.apache.spark.RangePartitioner.<init>(Partitioner.scala:126) 
at org.apache.spark.rdd.OrderedRDDFunctions$$anonfun$sortByKey$1.apply(OrderedRDDFunctions.scala:62) 
at org.apache.spark.rdd.OrderedRDDFunctions$$anonfun$sortByKey$1.apply(OrderedRDDFunctions.scala:61) 
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) 
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) 
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) 
at org.apache.spark.rdd.OrderedRDDFunctions.sortByKey(OrderedRDDFunctions.scala:61) 
at amazon.remo.spark.RemoPairRDDFunctions.saveAsRODB(RemoPairRDDFunctions.scala:62) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:192) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:197) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:199) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:201) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:203) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:205) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:207) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:209) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:211) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:213) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:215) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:217) 
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:219) 
    at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:221) at $iwC$$iwC$$iwC$$iwC.<init>(<console>:223) at $iwC$$iwC$$iwC.<init>(<console>:225) at $iwC$$iwC.<init>(<console>:227) at $iwC.<init>(<console>:229) at <init>(<console>:231) at .<init>(<console>:235) 
at .<clinit>(<console>) 
    at .<init>(<console>:7) 
    at .<clinit>(<console>) 
    at $print(<console>) 
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method 
) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) 
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) 
at java.lang.reflect.Method.invoke(Method.java:483) 
    at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065) 
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1346) 
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840) 
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871) 
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819) 
    at org.apache.zeppelin.spark.SparkInterpreter.interpretInput(SparkInterpreter.java:664) 
    at org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:629) 
    at org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:622) 
    at org.apache.zeppelin.interpreter.ClassloaderInterpreter.interpret(ClassloaderInterpreter.java:57) 
    at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:93) 
    at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:276) 
at org.apache.zeppelin.scheduler.Job.run(Job.java:170) 
    at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:118) 
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) 
    at java.util.concurrent.FutureTask.run(FutureTask.java:266) 
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180) 
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293) 
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) 
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) 
    at java.lang.Thread.run(Thread.java:745) 

私はEMR 4.3.0に

Answer 1

を使用していますそれはあなたのようです設定がクラスタで使用可能なリソースよりも大きい場合「終了コード137」は、Javaヒープ・スペース・エラーです。はるかに小さな設定で試してみてください。

spark.driver.memory 16G 
spark.dynamicAllocation.enabled true 
spark.dynamicAllocation.initialExecutors spark.dynamicAllocation.minExecutors 
spark.dynamicAllocation.maxExecutors 20 
spark.dynamicAllocation.minExecutors 2 
spark.executor.cores 2