hadoop的hello world

能进行hello world之前假设你的环境已经搭建完毕（我搭建的伪分布式）
我用hadoop源码中的WordCount作为hadoop的hello world。
（1）我们拿到hadoop源码中的WordCount类代码如下

package org.apache.hadoop.examples;

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<Object, Text, Text, IntWritable>{
    
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
      
    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<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> 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 <in> <out>");
      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);
  }
}

（2）我们把这个文件放到hadoop的工作目录下，并在工作目录下面新建文件夹WordCount
（3）

javac -classpath hadoop-core-0.20.203.0.jar:lib/commons-cli-1.2.jar -d WordCount WordCount.java

（4）进入到WordCount文件夹中执行
   

 jar -cvf wordcount.jar org/*

然后把生成的jar拷贝到hadoop工作目录下面
（5）然后在hadoop工作目录下面新建一个input目录 mkdir input，在目录里面新建一个文件vi file1,输入以下内容：
hello world
hello hadoop
hello mapreduce
，把该文件上传到hadoop的分布式文件系统中去

./bin/hadoop fs -put input/file* input

（6）然后我们开始执行

./bin/hadoop jar wordcount.jar org.apache.hadoop.examples.WordCount input wordcount_output

（7）最后我们查看运行结果

./bin/hadoop fs -cat wordcount_output/part-r-00000

hadoop  1
hello   3
mapreduce       1
world   1

评论

1 楼 Menuz 2013-04-16  

比WordCount例子更具体，Good~~