Eclipse远程提交hadoop集群任务

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发布时间：2019-06-12

本文共 12980 字，大约阅读时间需要 43 分钟。

文章概览：

1、前言

2、Eclipse查看远程hadoop集群文件

3、Eclipse提交远程hadoop集群任务

4、小结

1 前言

　　Hadoop高可用品台搭建完备后，参见《》，下一步是在集群上跑任务，本文主要讲述Eclipse远程提交hadoop集群任务。

2 Eclipse查看远程hadoop集群文件

2.1 编译hadoop eclipse 插件

　　Hadoop集群文件查看可以通过webUI或hadoop Cmd，为了在Eclipse上方便增删改查集群文件，我们需要编译hadoop eclipse 插件，步骤如下：

　　① 环境准备

　　　　JDK环境配置　　配置JAVA_HOME，并将bin目录配置到path

　　　　ANT环境配置　　配置ANT_HOME，并将bin目录配置到path

　　　　在cmd查看：

　　② 软件准备

　　　　hadoop2x-eclipse-plugin-master　　

　　　　hadoop-common-2.2.0-bin-master　　

　　　　hadoop-2.6.0

　　　　eclipse-jee-luna-SR2-win32-x86_64

　　③ 编译

　　注：软件位置为自己机器上位置，请勿照搬。

E:\>cd E:\hadoop\hadoop2x-eclipse-plugin-master\src\contrib\eclipse-plugin E:\hadoop\hadoop2x-eclipse-plugin-master\src\contrib\eclipse-plugin>ant jar -Dve

rsion=2.6.0 -Declipse.home=E:\eclipse -Dhadoop.home=E:\hadoop\hadoop-2.6.0Buildfile: E:\hadoop\hadoop2x-eclipse-plugin-master\src\contrib\eclipse-plugin\build.xmlcheck-contrib:init:     [echo] contrib: eclipse-plugininit-contrib:ivy-probe-antlib:ivy-init-antlib:ivy-init:[ivy:configure] :: Ivy 2.1.0 - 20090925235825 :: http://ant.apache.org/ivy/ ::[ivy:configure] :: loading settings :: file = E:\hadoop\hadoop2x-eclipse-plugin-master\ivy\ivysettings.xmlivy-resolve-common:ivy-retrieve-common:[ivy:cachepath] DEPRECATED: 'ivy.conf.file' is deprecated, use 'ivy.settings.file' instead[ivy:cachepath] :: loading settings :: file = E:\hadoop\hadoop2x-eclipse-plugin-master\ivy\ivysettings.xmlcompile:     [echo] contrib: eclipse-plugin    [javac] E:\hadoop\hadoop2x-eclipse-plugin-master\src\contrib\eclipse-plugin\build.xml:76: warning: 'includeantruntime' was not set, defaulting to build.sysclasspath=last; set to false for repeatable buildsjar:BUILD SUCCESSFULTotal time: 10 seconds

　　　　成功编译，生成如下图：

　　④ 将改文件拷贝到Eclipse中plugins目录下，重启Eclipse会出现：

2.2 配置hadoop选项

　　打开Map/Reduce Locations

　　编辑Map/Reduce配置项：

　　根据上一篇，我们配置用户hadoop，Active HDFS和Active NM位置信息。

　　完成后，就可以在Eclipse中查看HDFS文件信息:

2.3 hdfs简单实例

　　我们编写一个hdfs简单实例，来远程操作hadoop。

1 package com.diexun.cn.mapred; 2  3 import java.io.IOException; 4 import java.net.URI; 5 import java.net.URISyntaxException; 6  7 import org.apache.hadoop.conf.Configuration; 8 import org.apache.hadoop.fs.FSDataOutputStream; 9 import org.apache.hadoop.fs.FileSystem;10 import org.apache.hadoop.fs.Path;11 12 public class MR2Test {13     14     static final String INPUT_PATH = "hdfs://192.168.137.101:9000/hello";15     static final String OUTPUT_PATH = "hdfs://192.168.137.101:9000/output";16     17     public static void main(String[] args) throws IOException, URISyntaxException {18         Configuration conf = new Configuration();19         final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);20         final Path outPath = new Path(OUTPUT_PATH);21         if (fileSystem.exists(outPath)) {22             fileSystem.delete(outPath, true);23         }24         25         FSDataOutputStream fsDataOutputStream = fileSystem.create(new Path(INPUT_PATH));26         fsDataOutputStream.writeBytes("welcome to here ...");27     }28 29 }

　　用Eclipse查看HDFS文件，发现hello文件被修改为“welcome to here ...”。

3 Eclipse提交远程hadoop集群任务

　　正式进入本文的正题，新建一个Map/Reduce Project，会引用很多jar（注：平常我们都是新建Maven项目进行开发，有利于程序迁移及体积，后面的文章会以Maven构建），将自带WordCount实例拷贝到Eclipse，

配置运行参数：(注：填写hdfs集群上路径，本地路径无效)

　　执行，出现线面结果：

log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).log4j:WARN Please initialize the log4j system properly.log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.Exception in thread "main" java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z    at org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Native Method)    at org.apache.hadoop.io.nativeio.NativeIO$Windows.access(NativeIO.java:557)    at org.apache.hadoop.fs.FileUtil.canRead(FileUtil.java:977)    at org.apache.hadoop.util.DiskChecker.checkAccessByFileMethods(DiskChecker.java:187)    at org.apache.hadoop.util.DiskChecker.checkDirAccess(DiskChecker.java:174)    at org.apache.hadoop.util.DiskChecker.checkDir(DiskChecker.java:108)    at org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.confChanged(LocalDirAllocator.java:285)    at org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.getLocalPathForWrite(LocalDirAllocator.java:344)    at org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:150)    at org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:131)    at org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:115)    at org.apache.hadoop.mapred.LocalDistributedCacheManager.setup(LocalDistributedCacheManager.java:131)    at org.apache.hadoop.mapred.LocalJobRunner$Job.
     
      (LocalJobRunner.java:163)    at org.apache.hadoop.mapred.LocalJobRunner.submitJob(LocalJobRunner.java:731)    at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:536)    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1296)    at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1293)    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:1628)    at org.apache.hadoop.mapreduce.Job.submit(Job.java:1293)    at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1314)    at WordCount.main(WordCount.java:76)

　　方便后面打印，先添加log4j.properties文件：

log4j.rootLogger=DEBUG,stdout,R log4j.appender.stdout=org.apache.log4j.ConsoleAppender log4j.appender.stdout.layout=org.apache.log4j.PatternLayout log4j.appender.stdout.layout.ConversionPattern=%5p - %m%n log4j.appender.R=org.apache.log4j.RollingFileAppender log4j.appender.R.File=mapreduce_test.log log4j.appender.R.MaxFileSize=1MB log4j.appender.R.MaxBackupIndex=1 log4j.appender.R.layout=org.apache.log4j.PatternLayout log4j.appender.R.layout.ConversionPattern=%p %t %c - %m%n log4j.logger.com.codefutures=INFO

　　根据出错提示，是由于NativeIO.java中return access0(path, desiredAccess.accessRight());导致，此句注，改为返回return true。　

　　修改源码后，在项目里创建和Apache中一样的包，此包会覆盖Apache源码包，如下：

　　再次执行：

INFO - Job job_local401325246_0001 completed successfullyDEBUG - PrivilegedAction as:wangxiaolong (auth:SIMPLE) from:org.apache.hadoop.mapreduce.Job.getCounters(Job.java:764) INFO - Counters: 38    File System Counters        FILE: Number of bytes read=16290        FILE: Number of bytes written=545254        FILE: Number of read operations=0        FILE: Number of large read operations=0        FILE: Number of write operations=0        HDFS: Number of bytes read=38132        HDFS: Number of bytes written=6834        HDFS: Number of read operations=15        HDFS: Number of large read operations=0        HDFS: Number of write operations=4    Map-Reduce Framework        Map input records=174        Map output records=1139        Map output bytes=23459        Map output materialized bytes=7976        Input split bytes=99        Combine input records=1139        Combine output records=286        Reduce input groups=286        Reduce shuffle bytes=7976        Reduce input records=286        Reduce output records=286        Spilled Records=572        Shuffled Maps =1        Failed Shuffles=0        Merged Map outputs=1        GC time elapsed (ms)=18        CPU time spent (ms)=0        Physical memory (bytes) snapshot=0        Virtual memory (bytes) snapshot=0        Total committed heap usage (bytes)=468713472    Shuffle Errors        BAD_ID=0        CONNECTION=0        IO_ERROR=0        WRONG_LENGTH=0        WRONG_MAP=0        WRONG_REDUCE=0    File Input Format Counters         Bytes Read=19066    File Output Format Counters         Bytes Written=6834

　　确实已经成功执行了，可发现“INFO - Job job_local401325246_0001 completed successfully”，

　　观察http://nns:8088/cluster/apps也没有发现该任务，说明此任务并未提交到集群执行。

　　添加配置文件，如下：

　配置文件直接从集群下载（注：集群中yarn-site.xml配置中“yarn.resourcemanager.ha.id”是有所不同的），该下载哪份配置？

　　由于集群中Active RM是nns，故下载nns中yarn-site.xml配置。执行：

Error: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.diexun.cn.mapred.WordCount$TokenizerMapper not found    at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2074)    at org.apache.hadoop.mapreduce.task.JobContextImpl.getMapperClass(JobContextImpl.java:186)    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:742)    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)    at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:163)    at java.security.AccessController.doPrivileged(Native Method)    at javax.security.auth.Subject.doAs(Subject.java:422)    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)    at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)Caused by: java.lang.ClassNotFoundException: Class com.diexun.cn.mapred.WordCount$TokenizerMapper not found    at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:1980)    at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2072)    ... 8 more

　　没有找到对应的代码文件，我们把代码打包，并设置conf，conf.set("mapred.jar", "**.jar"); 再次执行：

Exception message: /bin/bash: line 0: fg: no job controlStack trace: ExitCodeException exitCode=1: /bin/bash: line 0: fg: no job control    at org.apache.hadoop.util.Shell.runCommand(Shell.java:538)    at org.apache.hadoop.util.Shell.run(Shell.java:455)    at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:715)    at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:211)    at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)    at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)    at java.util.concurrent.FutureTask.run(FutureTask.java:266)    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)    at java.lang.Thread.run(Thread.java:745)

　　出现如下错误，是由于平台引起，在hadoop2.2~2.5中需修改源码编译(略)，hadoop2.6已经可以直接添加配置，conf.set("mapreduce.app-submission.cross-platform", "true");或直接到mapred-site.xml中配置。再次执行：

INFO - Job job_1438912697979_0023 completed successfullyDEBUG - PrivilegedAction as:wangxiaolong (auth:SIMPLE) from:org.apache.hadoop.mapreduce.Job.getCounters(Job.java:764)DEBUG - IPC Client (1894045259) connection to dn2/192.168.137.104:56327 from wangxiaolong sending #217DEBUG - IPC Client (1894045259) connection to dn2/192.168.137.104:56327 from wangxiaolong got value #217DEBUG - Call: getCounters took 139ms INFO - Counters: 49    File System Counters        FILE: Number of bytes read=149        FILE: Number of bytes written=325029        FILE: Number of read operations=0        FILE: Number of large read operations=0        FILE: Number of write operations=0        HDFS: Number of bytes read=255        HDFS: Number of bytes written=86        HDFS: Number of read operations=9        HDFS: Number of large read operations=0        HDFS: Number of write operations=2    Job Counters         Launched map tasks=2        Launched reduce tasks=1        Data-local map tasks=2        Total time spent by all maps in occupied slots (ms)=45308        Total time spent by all reduces in occupied slots (ms)=9324        Total time spent by all map tasks (ms)=45308        Total time spent by all reduce tasks (ms)=9324        Total vcore-seconds taken by all map tasks=45308        Total vcore-seconds taken by all reduce tasks=9324        Total megabyte-seconds taken by all map tasks=46395392        Total megabyte-seconds taken by all reduce tasks=9547776    Map-Reduce Framework        Map input records=3        Map output records=12        Map output bytes=119        Map output materialized bytes=155        Input split bytes=184        Combine input records=12        Combine output records=12        Reduce input groups=11        Reduce shuffle bytes=155        Reduce input records=12        Reduce output records=11        Spilled Records=24        Shuffled Maps =2        Failed Shuffles=0        Merged Map outputs=2        GC time elapsed (ms)=827        CPU time spent (ms)=4130        Physical memory (bytes) snapshot=479911936        Virtual memory (bytes) snapshot=6192558080        Total committed heap usage (bytes)=261115904    Shuffle Errors        BAD_ID=0        CONNECTION=0        IO_ERROR=0        WRONG_LENGTH=0        WRONG_MAP=0        WRONG_REDUCE=0    File Input Format Counters         Bytes Read=71    File Output Format Counters         Bytes Written=86

　　至此，任务已经成功提交至集群执行。

　　有时我们想用我们特定用户去执行任务(注：dfs.permissions.enabled为true时，往往会涉及用户权限问题)，可以在VM arguments中设置，这样任务的提交这就变成了设定者。