本文主要是介绍ecplise提交JOB到spark on yarn/standalone，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
以前我通常是把scala或者java程序打包，这样在发布的时候可以结合传统运维的jekins发布规则，只需要运维手动点击发布即可，不需要每次手动发布。
最近我手动使用ecplise来提交JOB，碰到一些问题做个记录：
1. ecplise提交JOB到spark on yarn
下面是一个很简单的程序，统计a.sql行数
public class App {public static void main(String[] args) {//System.setProperty("HADOOP_USER_NAME", "hdfs");SparkConf sparkConf = new SparkConf().setAppName("JavaWordCount");sparkConf.setMaster("yarn-client"); //sparkConf.set("spark.yarn.jar", "hdfs:///tmp/spark-assembly_2.10-1.6.0-cdh5.10.2.jar");// sparkConf.set("spark.yarn.appMasterEnv.CLASSPATH","$CLASSPATH:/opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/*");JavaSparkContext ctx = new JavaSparkContext(sparkConf);JavaRDD<String> lines = ctx.textFile("hdfs:///tmp/a.sql");System.out.println(lines.count());}
}提交之后 yarn的日志显示找不到mapreduce的类的错误：
18/12/18 08:48:46 INFO yarn.ApplicationMaster$AMEndpoint: Add WebUI Filter. AddWebUIFilter(org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter,Map(PROXY_HOSTS -> tsczbdnndev1.trinasolar.com, PROXY_URI_BASES -> http://tsczbdnndev1.trinasolar.com:8088/proxy/application_1542688914382_0086),/proxy/application_1542688914382_0086)
Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hadoop/mapreduce/MRJobConfigat org.apache.spark.deploy.yarn.Client$$anonfun$21.apply(Client.scala:1206)at org.apache.spark.deploy.yarn.Client$$anonfun$21.apply(Client.scala:1205)at scala.util.Try$.apply(Try.scala:161)at org.apache.spark.deploy.yarn.Client$.getDefaultMRApplicationClasspath(Client.scala:1205)at org.apache.spark.deploy.yarn.Client$.getMRAppClasspath(Client.scala:1182)at org.apache.spark.deploy.yarn.Client$.populateHadoopClasspath(Client.scala:1167)at org.apache.spark.deploy.yarn.Client$.populateClasspath(Client.scala:1269)at org.apache.spark.deploy.yarn.ExecutorRunnable.prepareEnvironment(ExecutorRunnable.scala:284)at org.apache.spark.deploy.yarn.ExecutorRunnable.launchContextDebugInfo(ExecutorRunnable.scala:70)at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$registerAM$1.apply(ApplicationMaster.scala:297)at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$registerAM$1.apply(ApplicationMaster.scala:291)at org.apache.spark.Logging$class.logInfo(Logging.scala:58)at org.apache.spark.deploy.yarn.ApplicationMaster.logInfo(ApplicationMaster.scala:51)at org.apache.spark.deploy.yarn.ApplicationMaster.registerAM(ApplicationMaster.scala:291)at org.apache.spark.deploy.yarn.ApplicationMaster.runExecutorLauncher(ApplicationMaster.scala:377)at org.apache.spark.deploy.yarn.ApplicationMaster.run(ApplicationMaster.scala:199)at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$main$1.apply$mcV$sp(ApplicationMaster.scala:681)at org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:69)at org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:68)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:415)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1920)at org.apache.spark.deploy.SparkHadoopUtil.runAsSparkUser(SparkHadoopUtil.scala:68)at org.apache.spark.deploy.yarn.ApplicationMaster$.main(ApplicationMaster.scala:679)at org.apache.spark.deploy.yarn.ExecutorLauncher$.main(ApplicationMaster.scala:698)at org.apache.spark.deploy.yarn.ExecutorLauncher.main(ApplicationMaster.scala)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.mapreduce.MRJobConfigat java.net.URLClassLoader$1.run(URLClassLoader.java:366)at java.net.URLClassLoader$1.run(URLClassLoader.java:355)at java.security.AccessController.doPrivileged(Native Method)at java.net.URLClassLoader.findClass(URLClassLoader.java:354)at java.lang.ClassLoader.loadClass(ClassLoader.java:425)at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308)at java.lang.ClassLoader.loadClass(ClassLoader.java:358)查看spark提交的环境变量，spark.yarn.appMasterEnv.CLASSPATH，添加mapreduce的lib目录$CLASSPATH:/opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/*
2. ecplise提交给spark standalone
这个地方没有什么特殊的，就是要注意版本一定要一致。pom文件的JAR版本和服务器的版本能对应上。 否者容易出现不能初始化SparkContext的错误。
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