hadoop 2.2.0 编译运行wordcount

hadoop2.2.0 编译运行wordcount，因为hadoop2.2.0不支持eclipse的插件，所以运行wordcount，需要手动编译并将wordcount打包成jar包来运行（也可以自己编译生成eclipse成插件），下面记录一下编译运行的过程，希望能给大家有些帮助。

1、首先介绍下hadoop的版本问题，当前Hadoop版本比较混乱，让很多用户不知所措。实际上，当前Hadoop只有两个版本：Hadoop 1.0和Hadoop 2.0，其中，Hadoop 1.0由一个分布式文件系统HDFS和一个离线计算框架MapReduce组成，而Hadoop 2.0则包含一个支持NameNode横向扩展的HDFS，一个资源管理系统YARN和一个运行在YARN上的离线计算框架MapReduce。相比于Hadoop 1.0，Hadoop 2.0功能更加强大，且具有更好的扩展性、性能，并支持多种计算框架。由于hadoop 2.0不用于hadoop 1.0的API，所以，从hadoop 1.0升级到hadoop 2.0需要重写mapreduce程序。
关于从Hadoop 1.0升级到2.0（1）参考链接： http://dongxicheng.org/mapreduce-nextgen/hadoop-upgrade-to-version-2/
hadoop 2.2.0新功能介绍 参考链接http://docs.aws.amazon.com/zh_cn/ElasticMapReduce/latest/DeveloperGuide/emr-hadoop-2.2.0-features.html
2、然后就是准备程序WordCount.java在/root/test/下：

import java.io.IOException;  
import java.util.StringTokenizer;  

import org.apache.hadoop.conf.Configuration;  
import org.apache.hadoop.fs.Path;  
import org.apache.hadoop.io.IntWritable;  
import org.apache.hadoop.io.Text;  
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.output.FileOutputFormat;  
import org.apache.hadoop.util.GenericOptionsParser;  

public class WordCount {  

  public static class TokenizerMapper   
       extends Mapper{  

    private final static IntWritable one = new IntWritable(1);  
    private Text word = new Text();  
    // value已经是文件内容的一行  
    public void map(Object key, Text value, Context context  
                    ) throws IOException, InterruptedException {  
      StringTokenizer itr = new StringTokenizer(value.toString());  
      while (itr.hasMoreTokens()) {  
        word.set(itr.nextToken());  
        context.write(word, one);  
      }  
    }  
  }  

  public static class IntSumReducer   
       extends Reducer {  
    private IntWritable result = new IntWritable();  

    public void reduce(Text key, Iterable values,   
                       Context context  
                       ) throws IOException, InterruptedException {  
      int sum = 0;  
      for (IntWritable val : values) {  
        sum += val.get();  
      }  
      result.set(sum);  
      context.write(key, result);  
    }  
  }  

  public static void main(String[] args) throws Exception {  
    Configuration conf = new Configuration();  
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();  
    if (otherArgs.length != 2) {  
      System.err.println("Usage: wordcount  ");  
      System.exit(2);  
    }  
    Job job = new Job(conf, "word count");  
    job.setJarByClass(WordCount.class);  
    job.setMapperClass(TokenizerMapper.class);  
    job.setCombinerClass(IntSumReducer.class);  
    job.setReducerClass(IntSumReducer.class);  
    job.setOutputKeyClass(Text.class);  
    job.setOutputValueClass(IntWritable.class);  
    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));  
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));  
    System.exit(job.waitForCompletion(true) ? 0 : 1);  
  }  
}

3、新建bin文件夹在/root/test/下，将WordCount编译成class文件，命令如下：

root@ubuntupc:/home/ubuntu/software/cdh5-hadoop/share/hadoop# javac -classpath common/hadoop-common-2.2.0-cdh5.0.0-beta-2.jar:common/lib/commons-cli-1.2.jar:common/lib/hadoop-annotations-2.2.0-cdh5.0.0-beta-2.jar:mapreduce/hadoop-mapreduce-client-core-2.2.0-cdh5.0.0-beta-2.jar -d /root/test/bin/ /root/test/WordCount.java

4、将class文件打包成jar包，命令如下：

root@ubuntupc:~/test# jar -cvf WordCount.jar com/du/simple/*.class

5、运行jar文件

root@ubuntupc:~/test# hadoop jar WordCount.jar com/du/simple/WordCount /user/root/input /user/root/output

6、查看运行结果

root@ubuntupc:~/hadoop/WordCount# hadoop fs -cat output/part-r-00000