为什么spark-shell -master yarn-client会失败(而pyspark -master yarn似

Question

我试图通过Yarn在Hadoop集群上运行spark shell。我使用

Hadoop 2.4.1
• 火花1.0.0

我的Hadoop集群已经工作了。为了使用Spark，我按照下面的描述构建了Spark:

mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.1 -DskipTests clean package

编译工作正常，我可以毫无困难地运行spark-shell。但是，在yarn上运行它:

spark-shell --master yarn-client

得到以下错误:

14/07/07 11:30:32 INFO cluster.YarnClientSchedulerBackend: Application report from ASM:
         appMasterRpcPort: -1
         appStartTime: 1404725422955
         yarnAppState: ACCEPTED
14/07/07 11:30:33 INFO cluster.YarnClientSchedulerBackend: Application report from ASM:
         appMasterRpcPort: -1
         appStartTime: 1404725422955
         yarnAppState: FAILED
org.apache.spark.SparkException: Yarn application already ended,might be killed or not able to launch application master
.
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApp(YarnClientSchedulerBackend.scala:105
)
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:82)
        at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:136)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:318)
        at org.apache.spark.repl.SparkILoop.createSparkContext(SparkILoop.scala:957)
        at $iwC$$iwC.<init>(<console>:8)
        at $iwC.<init>(<console>:14)
        at <init>(<console>:16)
        at .<init>(<console>:20)
        at .<clinit>(<console>)
        at .<init>(<console>:7)
        at .<clinit>(<console>)
        at $print(<console>)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:606)
        at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:788)
        at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1056)
        at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:614)
        at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:645)
        at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:609)
        at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:796)
        at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:841)
        at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:753)
        at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:121)
        at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:120)
        at org.apache.spark.repl.SparkIMain.beQuietDuring(SparkIMain.scala:263)
        at org.apache.spark.repl.SparkILoopInit$class.initializeSpark(SparkILoopInit.scala:120)
        at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:56)
        at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$apply$mcZ$sp$5.apply$mcV$sp(SparkILoop.scala:913)
        at org.apache.spark.repl.SparkILoopInit$class.runThunks(SparkILoopInit.scala:142)
        at org.apache.spark.repl.SparkILoop.runThunks(SparkILoop.scala:56)
        at org.apache.spark.repl.SparkILoopInit$class.postInitialization(SparkILoopInit.scala:104)
        at org.apache.spark.repl.SparkILoop.postInitialization(SparkILoop.scala:56)
        at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:930)
        at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
        at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
        at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
        at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:884)
        at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:982)
        at org.apache.spark.repl.Main$.main(Main.scala:31)
        at org.apache.spark.repl.Main.main(Main.scala)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:606)
        at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:292)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:55)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

Spark设法与我的集群通信，但它不工作。另一个有趣的事情是，我可以使用pyspark --master yarn访问我的集群。但是，我得到以下警告

14/07/07 14:10:11 WARN cluster.YarnClientClusterScheduler: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory

和无限的计算时间，当做像

这样简单的事情时

sc.wholeTextFiles('hdfs://vm7x64.fr/').collect()

是什么导致了这个问题

Answer 1

请检查Hadoop集群是否正常运行。在主节点上，下一个YARN进程必须运行:

$ jps
24970 ResourceManager

从节点/执行器:

$ jps
14389 NodeManager

还要确保你在Spark配置目录下创建了一个Hadoop配置的引用(或复制了这些文件):

$ ll /spark/conf/ | grep site
lrwxrwxrwx  1 hadoop hadoop   33 Jun  8 18:13 core-site.xml -> /hadoop/etc/hadoop/core-site.xml
lrwxrwxrwx  1 hadoop hadoop   33 Jun  8 18:13 hdfs-site.xml -> /hadoop/etc/hadoop/hdfs-site.xml

您也可以在8088 - http://master:8088/cluster/nodes端口上查看ResourceManager的Web UI。必须有一个可用节点和资源的列表。

您必须使用下一个命令(您可以在Web UI中找到的应用程序ID)查看您的日志文件:

$ yarn logs -applicationId <yourApplicationId>

或者你可以直接查看Master/ResourceManager主机上的整个日志文件:

$ ll /hadoop/logs/ | grep resourcemanager
-rw-rw-r--  1 hadoop hadoop  368414 Jun 12 18:12 yarn-hadoop-resourcemanager-master.log
-rw-rw-r--  1 hadoop hadoop    2632 Jun 12 17:52 yarn-hadoop-resourcemanager-master.out

在Slave/NodeManager主机上:

$ ll /hadoop/logs/ | grep nodemanager
-rw-rw-r--  1 hadoop hadoop  284134 Jun 12 18:12 yarn-hadoop-nodemanager-slave.log
-rw-rw-r--  1 hadoop hadoop     702 Jun  9 14:47 yarn-hadoop-nodemanager-slave.out

还要检查所有环境变量是否正确:

HADOOP_CONF_LIB_NATIVE_DIR=/hadoop/lib/native
HADOOP_MAPRED_HOME=/hadoop
HADOOP_COMMON_HOME=/hadoop
HADOOP_HDFS_HOME=/hadoop
YARN_HOME=/hadoop
HADOOP_INSTALL=/hadoop
HADOOP_CONF_DIR=/hadoop/etc/hadoop
YARN_CONF_DIR=/hadoop/etc/hadoop
SPARK_HOME=/spark