Question

我的代码如下：

object WordCount extends App{
  val conf = new SparkConf().setAppName("WordCount").setMaster("spark://sparkmaster:7077")
  val sc = new SparkContext(conf)
  sc.addJar("/home/found/FromWindows/testsSpark/out/artifacts/untitled_jar/untitled.jar")
  val file = sc.textFile("hdfs://192.168.1.101:9000/Texts.txt")
  val count = file.flatMap(line => line.split(" ")).map(word => (word,1)).reduceByKey(_+_)
  val res = count.collect()
  res.foreach(println(_))
}

当我以本地模式运行时，一切正常。但是，当在群集上运行时，它会崩溃。我收到以下错误消息：

1.来自intellij控制台的错误消息

16/04/24 19:17:34 WARN TaskSetManager: Lost task 1.0 in stage 0.0 (TID 1, 192.168.1.101): java.lang.ClassNotFoundException: org.klordy.test.WordCount$$anonfun$2
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
...
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
16/04/24 19:17:34 INFO TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0) on executor 192.168.1.101: java.lang.ClassNotFoundException (org.klordy.test.WordCount$$anonfun$2) [duplicate 1]
16/04/24 19:17:34 INFO TaskSetManager: Starting task 0.1 in stage 0.0 (TID 2, 192.168.1.101, ANY, 2133 bytes)
16/04/24 19:17:34 INFO TaskSetManager: Starting task 1.1 in stage 0.0 (TID 3, 192.168.1.101, ANY, 2133 bytes)
16/04/24 19:17:34 INFO TaskSetManager: Lost task 1.1 in stage 0.0 (TID 3) on executor 192.168.1.101: java.lang.ClassNotFoundException (org.klordy.test.WordCount$$anonfun$2) [duplicate 2]
16/04/24 19:17:34 INFO TaskSetManager: Starting task 1.2 in stage 0.0 (TID 4, 192.168.1.101, ANY, 2133 bytes)
16/04/24 19:17:34 ERROR TaskSetManager: Task 1 in stage 0.0 failed 4 times; aborting job
16/04/24 19:17:34 INFO TaskSchedulerImpl: Cancelling stage 0
16/04/24 19:17:34 INFO TaskSchedulerImpl: Stage 0 was cancelled
16/04/24 19:17:34 INFO DAGScheduler: ShuffleMapStage 0 (map at WordCount.scala:17) failed in 2.904 s
16/04/24 19:17:34 INFO DAGScheduler: Job 0 failed: collect at WordCount.scala:18, took 3.103414 s
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 0.0 failed 4 times, most recent failure: Lost task 1.3 in stage 0.0 (TID 6, 192.168.1.101): java.lang.ClassNotFoundException: org.klordy.test.WordCount$$anonfun$2
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
...
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:140)
Caused by: java.lang.ClassNotFoundException: org.klordy.test.WordCount$$anonfun$2
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
...
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
16/04/24 19:17:34 INFO SparkContext: Invoking stop() from shutdown hook
16/04/24 19:17:34 INFO TaskSetManager: Lost task 0.3 in stage 0.0 (TID 7) on executor 192.168.1.101: java.lang.ClassNotFoundException (org.klordy.test.WordCount$$anonfun$2) [duplicate 7]
16/04/24 19:17:34 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool 
16/04/24 19:17:34 INFO SparkDeploySchedulerBackend: Registered executor: AkkaRpcEndpointRef(Actor[akka.tcp://sparkExecutor@192.168.1.117:45350/user/Executor#2034500302]) with ID 0
16/04/24 19:17:34 INFO SparkUI: Stopped Spark web UI at http://192.168.1.101:4040
16/04/24 19:17:34 INFO DAGScheduler: Stopping DAGScheduler
16/04/24 19:17:34 INFO SparkDeploySchedulerBackend: Shutting down all executors
16/04/24 19:17:34 INFO SparkDeploySchedulerBackend: Asking each executor to shut down
16/04/24 19:17:34 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!

2.来自火花日志的消息：

    16/04/24 19:17:27 INFO Worker: Asked to launch executor app-20160424191727-0005/1 for WordCount
16/04/24 19:17:27 DEBUG AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1: [actor] handled message (1.327376 ms) AkkaMessage(LaunchExecutor(spark://sparkworker:7077,app-20160424191727-0005,1,ApplicationDescription(WordCount),2,1024),false) from Actor[akka://sparkWorker/deadLetters]
16/04/24 19:17:27 INFO SecurityManager: Changing view acls to: root
16/04/24 19:17:27 INFO SecurityManager: Changing modify acls to: root
16/04/24 19:17:27 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
16/04/24 19:17:27 DEBUG SSLOptions: No SSL protocol specified
16/04/24 19:17:27 DEBUG SSLOptions: No SSL protocol specified
16/04/24 19:17:27 DEBUG SSLOptions: No SSL protocol specified
16/04/24 19:17:27 DEBUG SecurityManager: SSLConfiguration for file server: SSLOptions{enabled=false, keyStore=None, keyStorePassword=None, trustStore=None, trustStorePassword=None, protocol=None, enabledAlgorithms=Set()}
16/04/24 19:17:27 DEBUG SecurityManager: SSLConfiguration for Akka: SSLOptions{enabled=false, keyStore=None, keyStorePassword=None, trustStore=None, trustStorePassword=None, protocol=None, enabledAlgorithms=Set()}
16/04/24 19:17:27 INFO ExecutorRunner: Launch command: "/usr/java/jdk1.7.0_51/bin/java" "-cp" "/usr/local/Spark/spark-1.5.1-bin-hadoop2.4/sbin/../conf/:/usr/local/Spark/spark-1.5.1-bin-hadoop2.4/lib/spark-assembly-1.5.1-hadoop2.4.0.jar:/usr/local/Spark/spark-1.5.1-bin-hadoop2.4/lib/datanucleus-api-jdo-3.2.6.jar:/usr/local/Spark/spark-1.5.1-bin-hadoop2.4/lib/datanucleus-core-3.2.10.jar:/usr/local/Spark/spark-1.5.1-bin-hadoop2.4/lib/datanucleus-rdbms-3.2.9.jar:/usr/local/Spark/hadoop-2.5.2/etc/hadoop/" "-Xms1024M" "-Xmx1024M" "-Dspark.driver.port=49473" "-XX:MaxPermSize=256m" "org.apache.spark.executor.CoarseGrainedExecutorBackend" "--driver-url" "akka.tcp://sparkDriver@192.168.1.101:49473/user/CoarseGrainedScheduler" "--executor-id" "1" "--hostname" "192.168.1.101" "--cores" "2" "--app-id" "app-20160424191727-0005" "--worker-url" "akka.tcp://sparkWorker@192.168.1.101:49838/user/Worker"
16/04/24 19:17:28 DEBUG FileAppender: Started appending thread
16/04/24 19:17:28 DEBUG FileAppender: Opened file /usr/local/Spark/spark-1.5.1-bin-hadoop2.4/work/app-20160424191727-0005/1/stdout
16/04/24 19:17:28 DEBUG FileAppender: Started appending thread
16/04/24 19:17:28 DEBUG FileAppender: Opened file /usr/local/Spark/spark-1.5.1-bin-hadoop2.4/work/app-20160424191727-0005/1/stderr
16/04/24 19:17:34 DEBUG AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1: Received RPC message: AkkaMessage(KillExecutor(spark://sparkworker:7077,app-20160424191727-0005,1),false)
16/04/24 19:17:34 INFO Worker: Asked to kill executor app-20160424191727-0005/1
16/04/24 19:17:34 DEBUG AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1: [actor] handled message (0.172782 ms) AkkaMessage(KillExecutor(spark://sparkworker:7077,app-20160424191727-0005,1),false) from Actor[akka://sparkWorker/deadLetters]
16/04/24 19:17:34 INFO ExecutorRunner: Runner thread for executor app-20160424191727-0005/1 interrupted
16/04/24 19:17:34 INFO ExecutorRunner: Killing process!
16/04/24 19:17:34 ERROR FileAppender: Error writing stream to file /usr/local/Spark/spark-1.5.1-bin-hadoop2.4/work/app-20160424191727-0005/1/stderr
java.io.IOException: Stream closed
    at java.io.BufferedInputStream.getBufIfOpen(BufferedInputStream.java:162)
    at java.io.BufferedInputStream.read1(BufferedInputStream.java:272)
    at java.io.BufferedInputStream.read(BufferedInputStream.java:334)
    at java.io.FilterInputStream.read(FilterInputStream.java:107)
    at org.apache.spark.util.logging.FileAppender.appendStreamToFile(FileAppender.scala:70)
    at org.apache.spark.util.logging.FileAppender$$anon$1$$anonfun$run$1.apply$mcV$sp(FileAppender.scala:39)
    at org.apache.spark.util.logging.FileAppender$$anon$1$$anonfun$run$1.apply(FileAppender.scala:39)
    at org.apache.spark.util.logging.FileAppender$$anon$1$$anonfun$run$1.apply(FileAppender.scala:39)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699)
    at org.apache.spark.util.logging.FileAppender$$anon$1.run(FileAppender.scala:38)
16/04/24 19:17:34 DEBUG FileAppender: Closed file /usr/local/Spark/spark-1.5.1-bin-hadoop2.4/work/app-20160424191727-0005/1/stderr
16/04/24 19:17:34 DEBUG AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1: [actor] received message AkkaMessage(ApplicationFinished(app-20160424191727-0005),false) from Actor[akka://sparkWorker/deadLetters]
16/04/24 19:17:34 DEBUG AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1: Received RPC message: AkkaMessage(ApplicationFinished(app-20160424191727-0005),false)
16/04/24 19:17:34 DEBUG AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1: [actor] handled message (0.186628 ms) AkkaMessage(ApplicationFinished(app-20160424191727-0005),false) from Actor[akka://sparkWorker/deadLetters]
16/04/24 19:17:35 DEBUG FileAppender: Closed file /usr/local/Spark/spark-1.5.1-bin-hadoop2.4/work/app-20160424191727-0005/1/stdout
16/04/24 19:17:35 DEBUG AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1: [actor] received message AkkaMessage(ExecutorStateChanged(app-20160424191727-0005,1,KILLED,None,Some(143)),false) from Actor[akka://sparkWorker/deadLetters]
16/04/24 19:17:35 DEBUG AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1: Received RPC message: AkkaMessage(ExecutorStateChanged(app-20160424191727-0005,1,KILLED,None,Some(143)),false)
16/04/24 19:17:35 INFO Worker: Executor app-20160424191727-0005/1 finished with state KILLED exitStatus 143
16/04/24 19:17:35 INFO Worker: Cleaning up local directories for application app-20160424191727-0005

真的很困惑，解决这个问题有什么好主意吗？我的集群环境是：Spark 1.5.1 / hadoop 2.5.2 / scala 2.10.4 / jdk1.7.0_51。

Answer 1

我的猜测是sc.addJar("/home/found/FromWindows/testsSpark/out/artifacts/untitled_jar/untitled.jar")在您的执行程序上失败，因为执行程序节点上不存在该路径/ jar。尝试将jar放入HDFS并使用HDFS路径指定它，以便执行者可以访问它。

或者甚至更好，使用--jars spark-submit的{{1}}命令行选项并在那里指定jar，然后您就不必更改代码并重新填写更改您的jar。

Answer 2

问题已经解决了。主要原因是我的项目导入了两个版本的scala（sbt下载的一个额外版本）。感谢帮助我解决这个问题的人。

Spark：将流写入文件/ XXX / stderr java.io.IOException时出错：流已关闭

2 个答案: