Win7上eclipse无插件交付Hadoop2.2分布式作业

Win7上eclipse无插件提交Hadoop2.2分布式作业

一直以来，都以为，想在Win上提交hadoop集群的作业，必须得在eclipse上安装hadoop-eclipse-plugin插件才可以提交，但最近与同事交流，发现其实，不一定必须安装hadoop的eclipse插件，才能提交。今天试了一把，发现果然可以不用安装插件也可以正确提交作业到集群上，故在此总结一下。


既然，无须安装hadoop的eclipse插件，就能提交hadoop作业，那为毛，还出现了这个插件呢？   其实安装插件除了能直接提交作业外，还有一个比较方便的功能，就是能直接在eclipse上对HDFS上的文件，进行删除，上传，新建目录等，这一点是不安装插件做不到的，当然，如果你不需要这些操作，那么就无所谓了，仅仅提交个作业而已。


下面说下，如何在eclipse上使用无插件提交hadoop作业，（在hadoop集群的8088界面上可以看到提交的作业信息是否成功）。

序号操作说明1eclipse IDE散仙在这里是4.2版本的eclipse2hadoop2.2的64位完整包散仙在这里放在D盘根目录下3修改源码org/apache/hadoop/mapred/YARNRunner.java，改变linux与windows的路径不一致bug散仙已经修改好，文末散仙会上传这个修改好的类4把linux集群上的配置文件，core-site.xml，hdfs-site.xml,mapred.site.xml和yarn-site.xml文件，放在src根目录下，另外在D盘hadoop的/etc/hadoop目录下，覆盖一下注意一致5编写wordcount的MR例子，开始测试入门测试6高富帅工程师一名主角7配置hadoop的win上的环境变量HADOOP_HOME只配置这一个即可


上面的操作都完成后，就可以进行测试了,散仙在这里的WordCount源码如下：

package com.mywordcount;  import java.io.File;import java.io.FileInputStream;import <a target=_blank target="_blank" href="http://www.myexception.cn/j2ee/java.io.FileNotFoundException.html" style="margin: 0px; padding: 0px; color: rgb(23, 62, 128); text-decoration: none;">java.io.FileNotFoundException</a>;import java.io.FilenameFilter;import <a target=_blank target="_blank" href="http://www.myexception.cn/j2me/java.io.IOException.html" style="margin: 0px; padding: 0px; color: rgb(23, 62, 128); text-decoration: none;">java.io.IOException</a>;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapred.JobConf;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.util.Tool;/*** *  * Hadoop2.2.0  无插件提交集群作业 *  * @author qindongliang *  *         hadoop技术交流群： 376932160 *  *  * */public class MyWordCount2 {/** * Mapper *  * **/private static class WMapper extendsMapper<LongWritable, Text, Text, IntWritable> {private IntWritable count = new IntWritable(1);private Text text = new Text();@Overrideprotected void map(LongWritable key, Text value, Context context)throws <a target=_blank target="_blank" href="http://www.myexception.cn/j2se/IOException.html" style="margin: 0px; padding: 0px; color: rgb(23, 62, 128); text-decoration: none;">IOException</a>, InterruptedException {String values[] = value.toString().split("#");// System.out.println(values[0]+"========"+values[1]);count.set(Integer.parseInt(values[1]));text.set(values[0]);context.write(text, count);}}/** * Reducer *  * **/private static class WReducer extendsReducer<Text, IntWritable, Text, Text> {private Text t = new Text();@Overrideprotected void reduce(Text key, Iterable<IntWritable> value,Context context) throws IOException, InterruptedException {int count = 0;for (IntWritable i : value) {count += i.get();}t.set(count + "");context.write(key, t);}}public static void printEnv(Job job) {Configuration conf = job.getConfiguration();System.out.println("###########################################");System.out.println("fs.defaultFS:" + conf.get("fs.defaultFS"));System.out.println("mapred.job.tracker:"+ conf.get("mapred.job.tracker"));System.out.println("mapreduce.framework.name" + ":"+ conf.get("mapreduce.framework.name"));System.out.println("yarn.nodemanager.aux-services" + ":"+ conf.get("yarn.nodemanager.aux-services"));System.out.println("yarn.resourcemanager.address" + ":"+ conf.get("yarn.resourcemanager.address"));System.out.println("yarn.resourcemanager.scheduler.address" + ":"+ conf.get("yarn.resourcemanager.scheduler.address"));System.out.println("yarn.resourcemanager.resource-tracker.address"+ ":"+ conf.get("yarn.resourcemanager.resource-tracker.address"));System.out.println("yarn.application.classpath" + ":"+ conf.get("yarn.application.classpath"));System.out.println("zkhost:" + conf.get("zkhost"));System.out.println("namespace:" + conf.get("namespace"));System.out.println("project:" + conf.get("project"));System.out.println("collection:" + conf.get("collection"));System.out.println("shard:" + conf.get("shard"));System.out.println("###########################################");} /**  * 载入hadoop的配置文件  * 兼容hadoop1.x和hadoop2.x  *   * */public static void getConf(final Configuration conf) throws <a target=_blank target="_blank" href="http://www.myexception.cn/j2se/FileNotFoundException.html" style="margin: 0px; padding: 0px; color: rgb(23, 62, 128); text-decoration: none;">FileNotFoundException</a>{String HADOOP_CONF_DIR = System.getenv().get("HADOOP_CONF_DIR");String HADOOP_HOME = System.getenv().get("HADOOP_HOME");System.out.println("HADOOP_HOME:" + HADOOP_HOME);System.out.println("HADOOP_CONF_DIR:" + HADOOP_CONF_DIR);//此处兼容hadoop1.x//此处兼容hadoop2.xif (HADOOP_CONF_DIR == null || HADOOP_CONF_DIR.isEmpty()) {HADOOP_CONF_DIR = HADOOP_HOME + "/etc/hadoop";}//得到hadoop的conf目录的路径加载文件File file = new File(HADOOP_CONF_DIR);FilenameFilter filter = new FilenameFilter() {@Overridepublic boolean accept(File dir, String name) {return name.endsWith("xml");}};//获取hadoop的仅仅xml结尾的文件列表String[] list = file.list(filter);for (String fn : list) {System.out.println("Loading Configuration: " + HADOOP_CONF_DIR+ "/" + fn);//循环加载xml文件conf.addResource(new FileInputStream(HADOOP_CONF_DIR + "/" + fn));} //yarn的classpath路径，如果为空则加载拼接yarn的路径if (conf.get("yarn.application.classpath", "").isEmpty()) {StringBuilder sb = new StringBuilder();sb.append(System.getenv("CLASSPATH")).append(":");sb.append(HADOOP_HOME).append("/share/hadoop/common/lib/*").append(":");sb.append(HADOOP_HOME).append("/share/hadoop/common/*").append(":");sb.append(HADOOP_HOME).append("/share/hadoop/hdfs/*").append(":");sb.append(HADOOP_HOME).append("/share/hadoop/mapreduce/*").append(":");sb.append(HADOOP_HOME).append("/share/hadoop/yarn/*").append(":");sb.append(HADOOP_HOME).append("/lib/*").append(":");conf.set("yarn.application.classpath", sb.toString());}} public static void main(String[] args) throws Exception { { Configuration conf = new Configuration();conf.set("mapreduce.job.jar", "myjob.jar");//此处代码，一定放在Job任务前面，否则会报类找不到的异常Job job = Job.getInstance(conf, "345"); getConf(conf);job.setJarByClass(MyWordCount2.class);job.setMapperClass(WMapper.class);job.setReducerClass(WReducer.class);job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);String path = "/qin/output";FileSystem fs = FileSystem.get(conf);Path p = new Path(path);if (fs.exists(p)) {fs.delete(p, true);System.out.println("输出路径存在，已删除！");}FileInputFormat.setInputPaths(job, "/qin/input");FileOutputFormat.setOutputPath(job, p);printEnv(job);System.exit(job.waitForCompletion(true) ? 0 : 1);  }}}

项目结构目录，截图如下：



运行信息如下： 

HADOOP_HOME:D:\hadoop-2.2.0HADOOP_CONF_DIR:nullLoading Configuration: D:\hadoop-2.2.0/etc/hadoop/capacity-scheduler.xmlLoading Configuration: D:\hadoop-2.2.0/etc/hadoop/core-site.xmlLoading Configuration: D:\hadoop-2.2.0/etc/hadoop/hadoop-policy.xmlLoading Configuration: D:\hadoop-2.2.0/etc/hadoop/hdfs-site.xmlLoading Configuration: D:\hadoop-2.2.0/etc/hadoop/httpfs-site.xmlLoading Configuration: D:\hadoop-2.2.0/etc/hadoop/mapred-site.xmlLoading Configuration: D:\hadoop-2.2.0/etc/hadoop/yarn-site.xml2014-06-25 20:40:08,419 WARN  [main] conf.Configuration (Configuration.java:loadProperty(2172)) - java.io.FileInputStream@3ba08dab:an attempt to override final parameter: mapreduce.jobtracker.address;  Ignoring.输出路径存在，已删除！###########################################fs.defaultFS:hdfs://h1:90002014-06-25 20:40:08,897 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.addressmapred.job.tracker:h1:8021mapreduce.framework.name:yarnyarn.nodemanager.aux-services:mapreduce_shuffleyarn.resourcemanager.address:h1:8032yarn.resourcemanager.scheduler.address:h1:8030yarn.resourcemanager.resource-tracker.address:h1:8031yarn.application.classpath:$HADOOP_CONF_DIR,$HADOOP_COMMON_HOME/share/hadoop/common/*,$HADOOP_COMMON_HOME/share/hadoop/common/lib/*,$HADOOP_HDFS_HOME/share/hadoop/hdfs/*,$HADOOP_HDFS_HOME/share/hadoop/hdfs/lib/*,$HADOOP_YARN_HOME/share/hadoop/yarn/*,$HADOOP_YARN_HOME/share/hadoop/yarn/lib/*zkhost:nullnamespace:nullproject:nullcollection:nullshard:null###########################################2014-06-25 20:40:08,972 INFO  [main] client.RMProxy (RMProxy.java:createRMProxy(56)) - Connecting to ResourceManager at h1/192.168.46.32:80322014-06-25 20:40:09,153 WARN  [main] mapreduce.JobSubmitter (JobSubmitter.java:copyAndConfigureFiles(149)) - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.2014-06-25 20:40:09,331 INFO  [main] input.FileInputFormat (FileInputFormat.java:listStatus(287)) - Total input paths to process : 12014-06-25 20:40:09,402 INFO  [main] mapreduce.JobSubmitter (JobSubmitter.java:submitJobInternal(394)) - number of splits:12014-06-25 20:40:09,412 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - user.name is deprecated. Instead, use mapreduce.job.user.name2014-06-25 20:40:09,412 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.jar is deprecated. Instead, use mapreduce.job.jar2014-06-25 20:40:09,413 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class2014-06-25 20:40:09,413 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.mapoutput.value.class is deprecated. Instead, use mapreduce.map.output.value.class2014-06-25 20:40:09,413 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class2014-06-25 20:40:09,414 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.job.name is deprecated. Instead, use mapreduce.job.name2014-06-25 20:40:09,414 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class2014-06-25 20:40:09,414 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class2014-06-25 20:40:09,414 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir2014-06-25 20:40:09,414 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir2014-06-25 20:40:09,415 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class2014-06-25 20:40:09,416 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps2014-06-25 20:40:09,416 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class2014-06-25 20:40:09,416 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.mapoutput.key.class is deprecated. Instead, use mapreduce.map.output.key.class2014-06-25 20:40:09,416 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir2014-06-25 20:40:09,502 INFO  [main] mapreduce.JobSubmitter (JobSubmitter.java:printTokens(477)) - Submitting tokens for job: job_1403723552088_00162014-06-25 20:40:09,651 INFO  [main] impl.YarnClientImpl (YarnClientImpl.java:submitApplication(174)) - Submitted application application_1403723552088_0016 to ResourceManager at h1/192.168.46.32:80322014-06-25 20:40:09,683 INFO  [main] mapreduce.Job (Job.java:submit(1272)) - The url to track the job: http://h1:8088/proxy/application_1403723552088_0016/2014-06-25 20:40:09,683 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1317)) - Running job: job_1403723552088_00162014-06-25 20:40:17,070 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1338)) - Job job_1403723552088_0016 running in uber mode : false2014-06-25 20:40:17,072 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) -  map 0% reduce 0%2014-06-25 20:40:23,232 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) -  map 100% reduce 0%2014-06-25 20:40:30,273 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) -  map 100% reduce 100%2014-06-25 20:40:30,289 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1356)) - Job job_1403723552088_0016 completed successfully2014-06-25 20:40:30,403 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1363)) - Counters: 43File System CountersFILE: Number of bytes read=58FILE: Number of bytes written=160123FILE: Number of read operations=0FILE: Number of large read operations=0FILE: Number of write operations=0HDFS: Number of bytes read=136HDFS: Number of bytes written=27HDFS: Number of read operations=6HDFS: Number of large read operations=0HDFS: Number of write operations=2Job Counters Launched map tasks=1Launched reduce tasks=1Data-local map tasks=1Total time spent by all maps in occupied slots (ms)=4398Total time spent by all reduces in occupied slots (ms)=4263Map-Reduce FrameworkMap input records=4Map output records=4Map output bytes=44Map output materialized bytes=58Input split bytes=98Combine input records=0Combine output records=0Reduce input groups=3Reduce shuffle bytes=58Reduce input records=4Reduce output records=3Spilled Records=8Shuffled Maps =1Failed Shuffles=0Merged Map outputs=1GC time elapsed (ms)=94CPU time spent (ms)=980Physical memory (bytes) snapshot=310431744Virtual memory (bytes) snapshot=1681850368Total committed heap usage (bytes)=136450048Shuffle ErrorsBAD_ID=0CONNECTION=0IO_ERROR=0WRONG_LENGTH=0WRONG_MAP=0WRONG_REDUCE=0File Input Format Counters Bytes Read=38File Output Format Counters Bytes Written=27

至此，我们已经可以成功的在无插件的环境里提交hadoop任务了，如果提交过程中，出现权限异常，可以在eclipse的run环境里配置，linux上安装hadoop的用户名即可，截图如下：



注意，一定是安装hadoop的用户，写成其他的用户，可能会导致没有权限访问HDFS上的数据，从而使提交的作业运行失败。