2018/7/12 23:00:29当前位置媒体热门新闻热点浏览文章

转载请务必注明原创地址为:https://dongkelun.com/2018/04/16/sparkOnYarnConf/

前言

YARN 是在Hadoop 2.0 中引入的集群管理器,它能让多种数据解决框架运行在一个共享的资源池上,并且通常安装在与Hadoop 文件系统(简称HDFS)相同的物理节点上。在这样配置的YARN 集群上运行Spark 是很有意义的,它能让Spark 在存储数据的物理节点上运行,以快速访问HDFS 中的数据。

1、配置

1.1 配置HADOOP_CONF_DIR

vim /etc/profile
export HADOOP_CONF_DIR=/opt/hadoop-2.7.5/etc/hadoop
source /etc/profile

1.2 命令行启动

spark-shell --yarn

但是在spark2.x里会报一个错误

18/04/16 07:59:23 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable18/04/16 07:59:27 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.18/04/16 07:59:54 ERROR SparkContext: Error initializing SparkContext.org.apache.spark.SparkException: Yarn application has already ended! It might have been killed or unable to launch application master.    at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:85)    at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:62)    at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:173)    at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)    at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2516)    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:918)    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:910)    at scala.Option.getOrElse(Option.scala:121)    at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:910)    at org.apache.spark.repl.Main$.createSparkSession(Main.scala:101)    at $line3.$read$$iw$$iw.<init>(<console>:15)    at $line3.$read$$iw.<init>(<console>:42)    at $line3.$read.<init>(<console>:44)    at $line3.$read$.<init>(<console>:48)    at $line3.$read$.<clinit>(<console>)    at $line3.$eval$.$print$lzycompute(<console>:7)    at $line3.$eval$.$print(<console>:6)    at $line3.$eval.$print(<console>)    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)    at java.lang.reflect.Method.invoke(Method.java:497)    at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:786)    at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1047)    at scala.tools.nsc.interpreter.IMain$WrappedRequest$$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:98)    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$$anonfun$process$1.apply(ILoop.scala:909)    at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)    at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909)    at org.apache.spark.repl.Main$.doMain(Main.scala:74)    at org.apache.spark.repl.Main$.main(Main.scala:54)    at org.apache.spark.repl.Main.main(Main.scala)    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)    at java.lang.reflect.Method.invoke(Method.java:497)    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:775)    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119)    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)18/04/16 07:59:54 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!18/04/16 07:59:54 WARN MetricsSystem: Stopping a MetricsSystem that is not runningorg.apache.spark.SparkException: Yarn application has already ended! It might have been killed or unable to launch application master.  at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:85)  at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:62)  at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:173)  at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)  at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2516)  at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:918)  at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:910)  at scala.Option.getOrElse(Option.scala:121)  at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:910)  at org.apache.spark.repl.Main$.createSparkSession(Main.scala:101)  ... 47 elided<console>:14: error: not found: value spark       import spark.implicits._              ^<console>:14: error: not found: value spark       import spark.sql              ^Welcome to      ____              __     / __/__  ___ _____/ /__    _\ \/ _ \/ _ `/ __/  _/   /___/ .__/\_,_/_/ /_/\_\   version 2.2.1      /_/         Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_45)Type in expressions to have them evaluated.Type :help for more information.scala> 

2、错误会决

2.1 增加spark.yarn.jars

首先看到第二条warn

Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.

联想到是不是这条warn信息导致的,而后根据这条warn信息上网查了一下,再根据错误信息也查了一下

Yarn application has already ended! It might have been killed or unable to ...

发现,都是说要配置spark.yarn.jars,于是按照如下命令配置

hdfs dfs -mkdir /hadoophdfs dfs -mkdir /hadoop/spark_jarshdfs dfs -put /opt/spark-2.2.1-bin-hadoop2.7/jars/ /hadoop/spark_jarscd /opt/spark-2.2.1-bin-hadoop2.7/conf/cp spark-defaults.conf.template spark-defaults.confvim spark-defaults.conf

在最下面增加:

spark.yarn.jars hdfs://192.168.44.128:8888/hadoop/spark_jars/

(注意后面的不可以去掉)
而后启动spark-shell,发现还是报类似错误(没了warn)

2.2 配置hadoop的yarn-site.xml

由于java8导致的问题

vim /opt/hadoop-2.7.5/etc/hadoop/yarn-site.xml

增加:

<property>    <name>yarn.nodemanager.pmem-check-enabled</name>    <value>false</value></property><property>    <name>yarn.nodemanager.vmem-check-enabled</name>    <value>false</value></property> 

再次启动spark-shell,成功!


image

3、意外之喜

因为要写博客记录,所以需要将错误复原,第一次只将spark.yarn.jars注释掉,启动spark-shell,发现是成功的,只是会有条warn而已,也就是说,这个错误的根本起因,是java8导致没有配置2.2中的yarn-site.xml!!


image

参考资料

https://blog.csdn.net/lxhandlbb/article/details/54410644
https://blog.csdn.net/gg584741/article/details/72825713

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