Spark Shell - __spark_libs__.zip 不存在
Spark Shell - __spark_libs__.zip does not exist
我是 Spark 的新手，我正忙于设置启用 HA 的 Spark 集群。
启动火花 shell 进行测试时通过：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 does not exist
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。
通过以下方式启动 shell 时：spark-shell --master local 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$YarnSchedulerEndpoint: Container marked as failed: container_e14_1480532715390_0001_02_000003 on host: slave2. Exit status: -1000. Diagnostics: File file:/tmp/spark-126d2844-5b37-461b-98a4-3f3de5ece91b/__spark_libs__3045590511279655158.zip does not exist
java.io.FileNotFoundException: File file:/tmp/spark-126d2844-5b37-461b-98a4-3f3de5ece91b/__spark_libs__3045590511279655158.zip
does not exist
at org.apache.hadoop.fs.RawLocalFileSystem.deprecatedGetFileStatus(RawLocalFileSystem.java:611)
at org.apache.hadoop.fs.RawLocalFileSystem.getFileLinkStatusInternal(RawLocalFileSystem.java:824)
at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:601)
at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:421)
at org.apache.hadoop.yarn.util.FSDownload.copy(FSDownload.java:253)
at org.apache.hadoop.yarn.util.FSDownload.access[=12=]0(FSDownload.java:63)
at org.apache.hadoop.yarn.util.FSDownload.run(FSDownload.java:361)
at org.apache.hadoop.yarn.util.FSDownload.run(FSDownload.java:359)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1698)
at org.apache.hadoop.yarn.util.FSDownload.call(FSDownload.java:358)
at org.apache.hadoop.yarn.util.FSDownload.call(FSDownload.java:62)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.Executors$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$$anonfun.apply(SparkSession.scala:831)
at org.apache.spark.sql.SparkSession$Builder$$anonfun.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$loadAndRunReq.apply(IMain.scala:638)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq.apply(IMain.scala:637)
at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:637)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:569)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:565)
at scala.tools.nsc.interpreter.ILoop.interpretStartingWith(ILoop.scala:807)
at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:681)
at scala.tools.nsc.interpreter.ILoop.processLine(ILoop.scala:395)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark.apply$mcV$sp(SparkILoop.scala:38)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark.apply(SparkILoop.scala:37)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark.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$$anonfun$process.apply$mcZ$sp(ILoop.scala:920)
at scala.tools.nsc.interpreter.ILoop$$anonfun$process.apply(ILoop.scala:909)
at scala.tools.nsc.interpreter.ILoop$$anonfun$process.apply(ILoop.scala:909)
at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)
at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909)
at org.apache.spark.repl.Main$.doMain(Main.scala:68)
at org.apache.spark.repl.Main$.main(Main.scala:51)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:736)
at org.apache.spark.deploy.SparkSubmit$.doRunMain(SparkSubmit.scala:185)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:210)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:124)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
纱-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>
我在你的日志中没有看到任何错误，只有警告，你可以通过添加环境变量来避免这些警告：
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"
例外情况：尝试手动设置 yarn 的 spark 配置：
http://badrit.com/blog/2015/2/29/running-spark-on-yarn#.WD_e66IrJsM
hdfs dfs -mkdir -p /user/spark/share/lib<br>
hdfs dfs -put $SPARK_HOME/assembly/lib/spark-assembly_*.jar /user/spark/share/lib/spark-assembly.jar<br>
export SPARK_JAR=hdfs://your-server:port/user/spark/share/lib/spark-assembly.jar
希望对您有所帮助。
此错误是由于 core-site.xml 文件中的配置造成的。
Please note that to find this file your HADOOP_CONF_DIR env variable
must be set.
In my case I added HADOOP_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop/ to
./conf/spark-env.sh
See:
核心-site.xml
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://master:9000</value>
</property>
</configuration>
如果此端点无法访问，或者如果 Spark 检测到文件系统与当前系统相同，lib 文件将不会分发到集群中的其他节点，从而导致上述错误。
在我的情况下，我所在的节点无法到达指定主机上的端口 9000。
调试
将日志级别提高到信息。您可以通过以下方式做到这一点：
复制./conf/log4j.properties.template到./conf/log4j.properties
文件中设置log4j.logger.org.apache.spark.repl.Main = INFO
正常启动您的 Spark Shell。如果您的问题与我的相同，您应该会看到一条信息消息，例如：INFO Client: Source and destination file systems are the same. Not copying file:/tmp/spark-c1a6cdcd-d348-4253-8755-5086a8931e75/__spark_libs__1391186608525933727.zip
这应该会导致您遇到问题，因为它启动了由丢失文件引起的序列反应。
您必须已将您的配置设置为 master("local[*]") 来自 spark session.I 已删除并且有效。
在我的例子中，spark 用户帐户无法 read/recurse 进入 HADOOP_HOME，因此无法读取核心-site.xml。
spark@ubuntu$ ls -lrt /opt/hadoop/
ls: cannot open directory '/opt/hadoop/': Permission denied <--- Cannot read the directory
spark@ubuntu$ ls -lrt /opt
total 20
drwxrwx--- 3 hadoop 1003 4096 Jun 18 20:38 hadoop <---- Invalid group
drwxr-xr-x 3 spark spark 4096 Jun 19 04:24 spark
会推荐给运行 ls -la $HADOOP_CONF_DIR 以确保提交 Spark 作业的帐户可以读取核心-site.xml。
我是 Spark 的新手，我正忙于设置启用 HA 的 Spark 集群。
启动火花 shell 进行测试时通过：bash spark-shell --master yarn --deploy-mode client
我收到以下错误（请参阅下面的完整错误）：file:/tmp/spark-126d2844-5b37-461b-98a4-3f3de5ece91b/__spark_libs__3045590511279655158.zip does not exist
应用程序在 yarn web 应用程序上被标记为失败，并且没有容器启动。
通过以下方式启动 shell 时：spark-shell --master local 打开时没有错误。
我注意到文件只被写入创建 shell 的节点上的 tmp 文件夹。
任何帮助将不胜感激。如果需要更多信息，请告诉我。
环境变量：
HADOOP_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop/
YARN_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop/
SPARK_HOME=/opt/spark-2.0.2-bin-hadoop2.7/
完整错误信息：
16/11/30 21:08:47 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/11/30 21:08:49 WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
16/11/30 21:09:03 WARN cluster.YarnSchedulerBackend$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[=12=]0(FSDownload.java:63)
at org.apache.hadoop.yarn.util.FSDownload.run(FSDownload.java:361)
at org.apache.hadoop.yarn.util.FSDownload.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$$anonfun.apply(SparkSession.scala:831)
at org.apache.spark.sql.SparkSession$Builder$$anonfun.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$loadAndRunReq.apply(IMain.scala:638)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq.apply(IMain.scala:637)
at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:637)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:569)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:565)
at scala.tools.nsc.interpreter.ILoop.interpretStartingWith(ILoop.scala:807)
at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:681)
at scala.tools.nsc.interpreter.ILoop.processLine(ILoop.scala:395)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark.apply$mcV$sp(SparkILoop.scala:38)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark.apply(SparkILoop.scala:37)
at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark.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$$anonfun$process.apply$mcZ$sp(ILoop.scala:920)
at scala.tools.nsc.interpreter.ILoop$$anonfun$process.apply(ILoop.scala:909)
at scala.tools.nsc.interpreter.ILoop$$anonfun$process.apply(ILoop.scala:909)
at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)
at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909)
at org.apache.spark.repl.Main$.doMain(Main.scala:68)
at org.apache.spark.repl.Main$.main(Main.scala:51)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:736)
at org.apache.spark.deploy.SparkSubmit$.doRunMain(SparkSubmit.scala:185)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:210)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:124)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
纱-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>
我在你的日志中没有看到任何错误，只有警告，你可以通过添加环境变量来避免这些警告：
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"
例外情况：尝试手动设置 yarn 的 spark 配置： http://badrit.com/blog/2015/2/29/running-spark-on-yarn#.WD_e66IrJsM
hdfs dfs -mkdir -p /user/spark/share/lib<br>
hdfs dfs -put $SPARK_HOME/assembly/lib/spark-assembly_*.jar /user/spark/share/lib/spark-assembly.jar<br>
export SPARK_JAR=hdfs://your-server:port/user/spark/share/lib/spark-assembly.jar
希望对您有所帮助。
此错误是由于 core-site.xml 文件中的配置造成的。
Please note that to find this file your
HADOOP_CONF_DIRenv variable must be set.In my case I added
HADOOP_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop/to./conf/spark-env.shSee:
核心-site.xml
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://master:9000</value>
</property>
</configuration>
如果此端点无法访问，或者如果 Spark 检测到文件系统与当前系统相同，lib 文件将不会分发到集群中的其他节点，从而导致上述错误。
在我的情况下，我所在的节点无法到达指定主机上的端口 9000。
调试
将日志级别提高到信息。您可以通过以下方式做到这一点：
复制
./conf/log4j.properties.template到./conf/log4j.properties文件中设置
log4j.logger.org.apache.spark.repl.Main = INFO
正常启动您的 Spark Shell。如果您的问题与我的相同，您应该会看到一条信息消息，例如：INFO Client: Source and destination file systems are the same. Not copying file:/tmp/spark-c1a6cdcd-d348-4253-8755-5086a8931e75/__spark_libs__1391186608525933727.zip
这应该会导致您遇到问题，因为它启动了由丢失文件引起的序列反应。
您必须已将您的配置设置为 master("local[*]") 来自 spark session.I 已删除并且有效。
在我的例子中，spark 用户帐户无法 read/recurse 进入 HADOOP_HOME，因此无法读取核心-site.xml。
spark@ubuntu$ ls -lrt /opt/hadoop/
ls: cannot open directory '/opt/hadoop/': Permission denied <--- Cannot read the directory
spark@ubuntu$ ls -lrt /opt
total 20
drwxrwx--- 3 hadoop 1003 4096 Jun 18 20:38 hadoop <---- Invalid group
drwxr-xr-x 3 spark spark 4096 Jun 19 04:24 spark
会推荐给运行 ls -la $HADOOP_CONF_DIR 以确保提交 Spark 作业的帐户可以读取核心-site.xml。