安装Hadoop系列 — 新建MapReduce项目

1、新建MR工程

依次点击 File → New → Ohter…  选择 “Map/Reduce Project”，然后输入项目名称:mrdemo，创建新项目:

 

 

2、（这步在以后的开发中可能会用到，但是现在不用，现在直接新建一个class文件即可）创建Mapper和Reducer
依次点击 File → New → Ohter… 选择Mapper，自动继承Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT>

 

 

 

创建Reducer的过程同Mapper，具体的业务逻辑自己实现即可。

 

3、新建一个class文件，包名为com.mrdemo，类名为WordCount，按finish。

 

4、编写map函数、reduce函数和主函数。本文就以官方自带的WordCount为例进行测试（将下面的源码复制到eclipse中）：

package com.mrdemo;

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);
    }
    //conf.set("fs.defaultFS", "hdfs://192.168.6.77:9000");
    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);
  }
}

 

5、准备测试数据。

在hdfs中新建一个input01文件夹，然后将/home/hadoop/Documents文件夹下新建的hello文件上传到hdfs中的input01文件夹中。

测试数据：

hello world!
hello hadoop
jobtracker
maptracker
reducetracker
task
namenode
datanode
block
beautiful world
hadoop:
HDFS
MapReduce

 

hadoop@hadoop-ThinkPad:~$ hadoop fs -mkdir input01

hadoop@hadoop-ThinkPad:~$ cd /home/hadoop/Documents

hadoop@hadoop-ThinkPad:~/Documents$ hadoop fs -copyFromLocal hello input01

hdfs://localhost:9000/user/yyq/input01
hdfs://localhost:9000/user/yyq/output01

 

6、配置运行参数
Run As → Run Configurations… ，在Arguments中配置运行参数，例如程序的输入参数：

 

7、运行
Run As -> Run on Hadoop ，执行完成后可以看到如下信息：

到此Eclipse中调用Hadoop-1.0.3本地伪分布式模式执行MR演示成功。

参考博客：

http://www.aboutyun.com/forum.php?mod=viewthread&tid=7541&