hadoop - Spark Shell - __spark_libs__.zip 不存在

标签 hadoop apache-spark hadoop-yarn

我是 Spark 的新手，我正忙于设置启用 HA 的 Spark 集群。

当通过以下方式启动 spark shell 进行测试时:bash spark-shell --master yarn --deploy-mode client

我收到以下错误(请参阅下面的完整错误):file:/tmp/spark-126d2844-5b37-461b-98a4-3f3de5ece91b/__spark_libs__3045590511279655158.zip 不存在

应用程序在 yarn web 应用程序上被标记为失败，并且没有容器启动。

当通过以下方式启动 shell 时:spark-shell --master local 它打开时没有错误。

我注意到文件只被写入创建 shell 的节点上的 tmp 文件夹。

任何帮助将不胜感激。如果需要更多信息，请告诉我。

环境变量:

HADOOP_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop/

YARN_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop/

SPARK_HOME=/opt/spark-2.0.2-bin-hadoop2.7/

完整错误信息:

16/11/30 21:08:47 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 
16/11/30 21:08:49 WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME. 
16/11/30 21:09:03 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Container marked as failed: container_e14_1480532715390_0001_02_000003 on host: slave2. Exit status: -1000. Diagnostics: File file:/tmp/spark-126d2844-5b37-461b-98a4-3f3de5ece91b/__spark_libs__3045590511279655158.zip does not exist 
java.io.FileNotFoundException: File file:/tmp/spark-126d2844-5b37-461b-98a4-3f3de5ece91b/__spark_libs__3045590511279655158.zip
does not exist
        at org.apache.hadoop.fs.RawLocalFileSystem.deprecatedGetFileStatus(RawLocalFileSystem.java:611)
        at org.apache.hadoop.fs.RawLocalFileSystem.getFileLinkStatusInternal(RawLocalFileSystem.java:824)
        at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:601)
        at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:421)
        at org.apache.hadoop.yarn.util.FSDownload.copy(FSDownload.java:253)
        at org.apache.hadoop.yarn.util.FSDownload.access$000(FSDownload.java:63)
        at org.apache.hadoop.yarn.util.FSDownload$2.run(FSDownload.java:361)
        at org.apache.hadoop.yarn.util.FSDownload$2.run(FSDownload.java:359)
        at java.security.AccessController.doPrivileged(Native Method)
        at javax.security.auth.Subject.doAs(Subject.java:422)
        at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1698)
        at org.apache.hadoop.yarn.util.FSDownload.call(FSDownload.java:358)
        at org.apache.hadoop.yarn.util.FSDownload.call(FSDownload.java:62)
        at java.util.concurrent.FutureTask.run(FutureTask.java:266)
        at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
        at java.util.concurrent.FutureTask.run(FutureTask.java:266)
        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)

16/11/30 22:29:28 ERROR cluster.YarnClientSchedulerBackend: Yarn application has already exited with state FINISHED! 16/11/30 22:29:28 ERROR spark.SparkContext: Error initializing SparkContext. java.lang.IllegalStateException: Spark context stopped while waiting for backend
        at org.apache.spark.scheduler.TaskSchedulerImpl.waitBackendReady(TaskSchedulerImpl.scala:584)
        at org.apache.spark.scheduler.TaskSchedulerImpl.postStartHook(TaskSchedulerImpl.scala:162)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:546)
        at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2258)
        at org.apache.spark.sql.SparkSession$Builder$$anonfun$8.apply(SparkSession.scala:831)
        at org.apache.spark.sql.SparkSession$Builder$$anonfun$8.apply(SparkSession.scala:823)
        at scala.Option.getOrElse(Option.scala:121)
        at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:823)
        at org.apache.spark.repl.Main$.createSparkSession(Main.scala:95)
        at $line3.$read$$iw$$iw.<init>(<console>:15)
        at $line3.$read$$iw.<init>(<console>:31)
        at $line3.$read.<init>(<console>:33)
        at $line3.$read$.<init>(<console>:37)
        at $line3.$read$.<clinit>(<console>)
        at $line3.$eval$.$print$lzycompute(<console>:7)
        at $line3.$eval$.$print(<console>:6)
        at $line3.$eval.$print(<console>)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:786)
        at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1047)
        at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:638)
        at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:637)
        at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
        at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
        at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:637)
        at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:569)
        at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:565)
        at scala.tools.nsc.interpreter.ILoop.interpretStartingWith(ILoop.scala:807)
        at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:681)
        at scala.tools.nsc.interpreter.ILoop.processLine(ILoop.scala:395)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply$mcV$sp(SparkILoop.scala:38)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
        at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
        at scala.tools.nsc.interpreter.IMain.beQuietDuring(IMain.scala:214)
        at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:37)
        at org.apache.spark.repl.SparkILoop.loadFiles(SparkILoop.scala:94)
        at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply$mcZ$sp(ILoop.scala:920)
        at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909)
        at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909)
        at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)
        at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909)
        at org.apache.spark.repl.Main$.doMain(Main.scala:68)
        at org.apache.spark.repl.Main$.main(Main.scala:51)
        at org.apache.spark.repl.Main.main(Main.scala)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:736)
        at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:185)
        at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:210)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:124)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

yarn-site.xml

<configuration>
  <property>
    <name>yarn.resourcemanager.connect.retry-interval.ms</name>
    <value>2000</value>
  </property>
  <property>
    <name>yarn.resourcemanager.ha.enabled</name>
    <value>true</value>
  </property>
  <property>
    <name>yarn.resourcemanager.ha.automatic-failover.enabled</name>
    <value>true</value>
  </property>
  <property>
    <name>yarn.resourcemanager.ha.automatic-failover.embedded</name>
    <value>true</value>
  </property>
  <property>
    <name>yarn.resourcemanager.cluster-id</name>
    <value>yarn-cluster</value>
  </property>
  <property>
    <name>yarn.resourcemanager.ha.rm-ids</name>
    <value>rm1,rm2</value>
  </property>
  <property>
    <name>yarn.resourcemanager.ha.id</name>
    <value>rm1</value>
  </property>
  <property>
    <name>yarn.resourcemanager.scheduler.class</name>
    <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
  </property>
  <property>
    <name>yarn.resourcemanager.recovery.enabled</name>
    <value>true</value>
  </property>
  <property>
    <name>yarn.resourcemanager.store.class</name>
    <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
  </property>
  <property>
    <name>yarn.resourcemanager.zk-address</name>
    <value>master:2181,slave1:2181,slave2:2181</value>
  </property>
  <property>
    <name>yarn.app.mapreduce.am.scheduler.connection.wait.interval-ms</name>
    <value>5000</value>
  </property>
  <property>
    <name>yarn.resourcemanager.work-preserving-recovery.enabled</name>
    <value>true</value>
  </property>

  <property>
    <name>yarn.resourcemanager.address.rm1</name>
    <value>master:23140</value>
  </property>
  <property>
    <name>yarn.resourcemanager.scheduler.address.rm1</name>
    <value>master:23130</value>
  </property>
  <property>
    <name>yarn.resourcemanager.webapp.https.address.rm1</name>
    <value>master:23189</value>
  </property>
  <property>
    <name>yarn.resourcemanager.webapp.address.rm1</name>
    <value>master:23188</value>
  </property>
  <property>
    <name>yarn.resourcemanager.resource-tracker.address.rm1</name>
    <value>master:23125</value>
  </property>
  <property>
    <name>yarn.resourcemanager.admin.address.rm1</name>
    <value>master:23141</value>
  </property>

  <property>
    <name>yarn.resourcemanager.address.rm2</name>
    <value>slave1:23140</value>
  </property>
  <property>
    <name>yarn.resourcemanager.scheduler.address.rm2</name>
    <value>slave1:23130</value>
  </property>
  <property>
    <name>yarn.resourcemanager.webapp.https.address.rm2</name>
    <value>slave1:23189</value>
  </property>
  <property>
    <name>yarn.resourcemanager.webapp.address.rm2</name>
    <value>slave1:23188</value>
  </property>
  <property>
    <name>yarn.resourcemanager.resource-tracker.address.rm2</name>
    <value>slave1:23125</value>
  </property>
  <property>
    <name>yarn.resourcemanager.admin.address.rm2</name>
    <value>slave1:23141</value>
  </property>

  <property>
    <description>Address where the localizer IPC is.</description>
    <name>yarn.nodemanager.localizer.address</name>
    <value>0.0.0.0:23344</value>
  </property>
  <property>
    <description>NM Webapp address.</description>
    <name>yarn.nodemanager.webapp.address</name>
    <value>0.0.0.0:23999</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
  </property>
  <property>
    <name>yarn.nodemanager.local-dirs</name>
    <value>/tmp/pseudo-dist/yarn/local</value>
  </property>
  <property>
    <name>yarn.nodemanager.log-dirs</name>
    <value>/tmp/pseudo-dist/yarn/log</value>
  </property>
  <property>
    <name>mapreduce.shuffle.port</name>
    <value>23080</value>
  </property>
  <property>
    <name>yarn.resourcemanager.work-preserving-recovery.enabled</name>
    <value>true</value>
  </property>
</configuration>

最佳答案

此错误是由于 core-site.xml 文件中的配置所致。

Please note that to find this file your HADOOP_CONF_DIR env variable must be set.

In my case I added HADOOP_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop/ to ./conf/spark-env.sh

See: Spark Job running on Yarn Cluster java.io.FileNotFoundException: File does not exits , eventhough the file exits on the master node

核心站点.xml

<configuration>
    <property>
        <name>fs.default.name</name>
        <value>hdfs://master:9000</value>
    </property> 
</configuration>

如果此端点不可访问，或者如果 Spark 检测到文件系统与当前系统相同，lib 文件将不会分发到集群中的其他节点，从而导致上述错误。

在我的情况下，我所在的节点无法到达指定主机上的端口 9000。

调试

将日志级别提高到信息。您可以通过以下方式执行此操作:

  1. 将./conf/log4j.properties.template复制到./conf/log4j.properties

  2. 在文件集 log4j.logger.org.apache.spark.repl.Main = INFO

正常启动 Spark Shell。如果您的问题与我的相同，您应该会看到一条信息消息，例如:INFO 客户端:源文件系统和目标文件系统相同。不复制文件:/tmp/spark-c1a6cdcd-d348-4253-8755-5086a8931e75/__spark_libs__1391186608525933727.zip

这应该会导致您遇到问题，因为它启动了由丢失文件引起的序列 react 。

关于hadoop - Spark Shell - __spark_libs__.zip 不存在，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/40905224/

上一篇：hadoop - 在行组大小小于 100 的 spark 中创建 Parquet 文件

下一篇：java - 亚马逊电子病历 : running Custom Jar with input and output from S3

相关文章：

amazon-web-services - Flume 可以将 ElasticSearch 与 Amazon Elastic Search Service 结合使用吗？

Hadoop 名称节点 : Single point of failure

r - R on Hive的处理能力

hadoop - 除了namenode以外，所有其他一切都从Hadoop伪分布式开始

hadoop - Hadoop:不要重新安排失败的Reducer

apache-spark - Spark 2.0 : 4 Rows. IllegalArgumentException:绑定(bind)必须为正

scala - 使用 Apache Zeppelin 重新运行带有 -deprecation 的 Scala 代码

sql - 如何让 Spark 处理更大的数据集？

java - yarn 上的 Spark ，在/0.0.0.0 :8032 连接到 ResourceManager

hadoop - apache hadoop examples 的源代码在哪里

©2024 IT工具网  联系我们