org.apache.spark.SparkException:作业由于阶段失败而中止:来自应用程序的任务 [英] org.apache.spark.SparkException: Job aborted due to stage failure: Task from application
问题描述
我在独立集群上运行spark应用程序时遇到问题. (我使用spark 1.1.0版本). 我通过命令成功运行了主服务器:
I have a problem with running spark application on standalone cluster. (I use spark 1.1.0 version). I succesfully run master server by command:
bash start-master.sh
然后我通过命令运行一个工人:
Then I run one worker by command:
bash spark-class org.apache.spark.deploy.worker.Worker spark://fujitsu11:7077
在主用户的网络用户界面上:
At master’s web UI:
http://localhost:8080
我知道,主人和工人正在运行.
I see, that master and worker are running.
然后,我从Eclipse Luna运行我的应用程序.我通过命令成功连接到集群
Then I run my application from Eclipse Luna. I successfully connect to cluster by command
JavaSparkContext sc = new JavaSparkContext("spark://fujitsu11:7077", "myapplication");
在该应用程序正常工作之后,但是当程序实现以下代码时:
And after that application works, but when program achieve following code:
JavaRDD<Document> collectionRdd = sc.parallelize(list);
它崩溃并显示以下错误消息:
It's crashing with following error message:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in stage 0.0 failed 4 times, most recent failure: Lost task 7.3 in stage 0.0 (TID 11, fujitsu11.inevm.ru):java.lang.ClassNotFoundException: maven.maven1.Document
java.net.URLClassLoader$1.run(URLClassLoader.java:366)
java.net.URLClassLoader$1.run(URLClassLoader.java:355)
java.security.AccessController.doPrivileged(Native Method)
java.net.URLClassLoader.findClass(URLClassLoader.java:354)
java.lang.ClassLoader.loadClass(ClassLoader.java:425)
java.lang.ClassLoader.loadClass(ClassLoader.java:358)
java.lang.Class.forName0(Native Method)
java.lang.Class.forName(Class.java:270)
org.apache.spark.serializer.JavaDeserializationStream$$anon$1.resolveClass(JavaSerializer.scala:59)
java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1612)
java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1517)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1771)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.readArray(ObjectInputStream.java:1706)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1344)
java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
java.io.ObjectInputStream.defaultReadObject(ObjectInputStream.java:500)
org.apache.spark.rdd.ParallelCollectionPartition.readObject(ParallelCollectionRDD.scala:74)
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
java.lang.reflect.Method.invoke(Method.java:606)
java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017)
java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)
org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:87)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:159)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:744)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1173)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:688)
at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1391)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
在外壳中,我发现:
14/11/12 18:46:06 INFO ExecutorRunner: Launch command: "C:\PROGRA~1\Java\jdk1.7.0_51/bin/java" "-cp" ";;D:\spark\bin\..\conf;D:\spark\bin\..\lib\spark-assembly-
1.1.0-hadoop1.0.4.jar;;D:\spark\bin\..\lib\datanucleus-api-jdo-3.2.1.jar;D:\spar
k\bin\..\lib\datanucleus-core-3.2.2.jar;D:\spark\bin\..\lib\datanucleus-rdbms-3.
2.1.jar" "-XX:MaxPermSize=128m" "-Dspark.driver.port=50913" "-Xms512M" "-Xmx512M
" "org.apache.spark.executor.CoarseGrainedExecutorBackend" "akka.tcp://sparkDriv
er@fujitsu11.inevm.ru:50913/user/CoarseGrainedScheduler" "0" "fujitsu11.inevm.ru
" "8" "akka.tcp://sparkWorker@fujitsu11.inevm.ru:50892/user/Worker" "app-2014111
2184605-0000"
14/11/12 18:46:40 INFO Worker: Asked to kill executor app-20141112184605-0000/0
14/11/12 18:46:40 INFO ExecutorRunner: Runner thread for executor app-2014111218
4605-0000/0 interrupted
14/11/12 18:46:40 INFO ExecutorRunner: Killing process!
14/11/12 18:46:40 INFO Worker: Executor app-20141112184605-0000/0 finished with
state KILLED exitStatus 1
14/11/12 18:46:40 INFO LocalActorRef: Message [akka.remote.transport.ActorTransp
ortAdapter$DisassociateUnderlying] from Actor[akka://sparkWorker/deadLetters] to
Actor[akka://sparkWorker/system/transports/akkaprotocolmanager.tcp0/akkaProtoco
l-tcp%3A%2F%2FsparkWorker%40192.168.3.5%3A50955-2#1066511138] was not delivered.
[1] dead letters encountered. This logging can be turned off or adjusted with c
onfiguration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-
shutdown'.
14/11/12 18:46:40 INFO LocalActorRef: Message [akka.remote.transport.Association
Handle$Disassociated] from Actor[akka://sparkWorker/deadLetters] to Actor[akka:/
/sparkWorker/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2
FsparkWorker%40192.168.3.5%3A50955-2#1066511138] was not delivered. [2] dead let
ters encountered. This logging can be turned off or adjusted with configuration
settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
14/11/12 18:46:41 ERROR EndpointWriter: AssociationError [akka.tcp://sparkWorker
@fujitsu11.inevm.ru:50892] -> [akka.tcp://sparkExecutor@fujitsu11.inevm.ru:50954
]: Error [Association failed with [akka.tcp://sparkExecutor@fujitsu11.inevm.ru:5
0954]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sp
arkExecutor@fujitsu11.inevm.ru:50954]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon
$2: Connection refused: no further information: fujitsu11.inevm.ru/192.168.3.5:5
0954
]
14/11/12 18:46:42 ERROR EndpointWriter: AssociationError [akka.tcp://sparkWorker
@fujitsu11.inevm.ru:50892] -> [akka.tcp://sparkExecutor@fujitsu11.inevm.ru:50954
]: Error [Association failed with [akka.tcp://sparkExecutor@fujitsu11.inevm.ru:5
0954]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sp
arkExecutor@fujitsu11.inevm.ru:50954]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon
$2: Connection refused: no further information: fujitsu11.inevm.ru/192.168.3.5:5
0954
]
14/11/12 18:46:43 ERROR EndpointWriter: AssociationError [akka.tcp://sparkWorker
@fujitsu11.inevm.ru:50892] -> [akka.tcp://sparkExecutor@fujitsu11.inevm.ru:50954
]: Error [Association failed with [akka.tcp://sparkExecutor@fujitsu11.inevm.ru:5
0954]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sp
arkExecutor@fujitsu11.inevm.ru:50954]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon
$2: Connection refused: no further information: fujitsu11.inevm.ru/192.168.3.5:5
0954
]
在日志中:
14/11/12 18:46:41 ERROR EndpointWriter: AssociationError [akka.tcp://sparkMaster@fujitsu11:7077] -> [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]: Error [Association failed with [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: Connection refused: no further information: fujitsu11.inevm.ru/192.168.3.5:50913
]
14/11/12 18:46:42 INFO Master: akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913 got disassociated, removing it.
14/11/12 18:46:42 ERROR EndpointWriter: AssociationError [akka.tcp://sparkMaster@fujitsu11:7077] -> [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]: Error [Association failed with [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: Connection refused: no further information: fujitsu11.inevm.ru/192.168.3.5:50913
]
14/11/12 18:46:43 ERROR EndpointWriter: AssociationError [akka.tcp://sparkMaster@fujitsu11:7077] -> [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]: Error [Association failed with [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sparkDriver@fujitsu11.inevm.ru:50913]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: Connection refused: no further information: fujitsu11.inevm.ru/192.168.3.5:50913
]
我在Google上搜索了很多,但不知道怎么了... 我在这里发现了一些类似的讨论:
I googled a lot but I have no idea whats wrong... I found a bit similar discussion here:
https://github.com/datastax/spark-cassandra-connector/issues/187
但这不能解决我的问题...
But it doesn't solve my problem...
有人知道怎么了吗?
谢谢.
推荐答案
找到了一种使用IDE/Maven运行它的方法
Found a way to run it using IDE / Maven
- 创建一个胖子罐(一个包含所有依赖项的胖子).为此使用阴影插件. pom示例:
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.2</version>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
<executions>
<execution>
<id>job-driver-jar</id>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<shadedArtifactAttached>true</shadedArtifactAttached>
<shadedClassifierName>driver</shadedClassifierName>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
<!--
Some care is required:
http://doc.akka.io/docs/akka/snapshot/general/configuration.html
-->
<transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
<resource>reference.conf</resource>
</transformer>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>mainClass</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
<execution>
<id>worker-library-jar</id>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<shadedArtifactAttached>true</shadedArtifactAttached>
<shadedClassifierName>worker</shadedClassifierName>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
- 现在,我们必须将已编译的jar文件发送到集群.为此,请在spark配置中指定jar文件,如下所示:
SparkConf conf =新 SparkConf().setAppName("appName").setMaster("spark://machineName:7077").setJars(new String [] {"target/appName-1.0-SNAPSHOT-driver.jar"});
SparkConf conf = new SparkConf().setAppName("appName").setMaster("spark://machineName:7077").setJars(new String[] {"target/appName-1.0-SNAPSHOT-driver.jar"});
-
运行mvn clean软件包以创建Jar文件.它将在您的目标文件夹中创建.
Run mvn clean package to create the Jar file. It will be created in your target folder.
使用您的IDE或使用maven命令运行:
Run using your IDE or using maven command :
mvn exec:java -Dexec.mainClass ="className"
mvn exec:java -Dexec.mainClass="className"
这不需要提交火花.只记得运行前先打包文件
This does not require spark-submit. Just remember to package file before running
如果您不想对jar路径进行硬编码,则可以执行以下操作:
If you don't want to hardcode the jar path, you can do this :
- 在配置中,输入:
SparkConf conf =新的SparkConf() .setAppName("appName") .setMaster("spark://machineName:7077") .setJars(JavaSparkContext.jarOfClass(this.getClass()));
SparkConf conf = new SparkConf() .setAppName("appName") .setMaster("spark://machineName:7077") .setJars(JavaSparkContext.jarOfClass(this.getClass()));
- 创建胖子罐(如上)并在运行package命令后使用maven运行:
java -jar target/application-1.0-SNAPSHOT-driver.jar
java -jar target/application-1.0-SNAPSHOT-driver.jar
这将从加载类的jar中获取jar.
This will take the jar from the jar the class was loaded.
这篇关于org.apache.spark.SparkException:作业由于阶段失败而中止:来自应用程序的任务的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!