Hadoop2.6.0版本号MapReudce演示样例之WordCount（一）

一、准备測试数据

1、在本地Linux系统/var/lib/hadoop-hdfs/file/路径下准备两个文件file1.txt和file2.txt，文件列表及各自内容例如以下图所看到的：

watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" >

2、在hdfs中。准备/input路径，并上传两个文件file1.txt和file2.txt。例如以下图所看到的：

watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" >

二、编写代码，封装Jar包并上传至linux

将代码封装成TestMapReduce.jar。并上传至linux的/usr/local路径下。例如以下图所看到的：

watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" >

三、执行命令

运行命令例如以下：hadoop jar /usr/local/TestMapReduce.jar com.jngreen.mapreduce.test.WordCount /input/file1.txt /input/file2.txt /output/output

命令运行过程截图例如以下：

watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" >

四、查看执行结果

查看hdfs输出路径/output下的结果，例如以下图所看到的：

执行结果为Hello 4、Hadoop 1、Man 1、Boy 1、Word 1，全然正确！

五、WordCount展示

源代码例如以下：

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;

public class WordCount {

  // TokenizerMapper作为Map阶段，须要继承Mapper，并重写map()函数
  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 {

      // 用StringTokenizer作为分词器，对value进行分词
      StringTokenizer itr = new StringTokenizer(value.toString());

      // 遍历分词后结果
      while (itr.hasMoreTokens()) {

    	// 将String设置入Text类型word
        word.set(itr.nextToken());
        // 将(word,1)。即(Text,IntWritable)写入上下文context，供兴许Reduce阶段使用
        context.write(word, one);
      }
    }
  }

  // IntSumReducer作为Reduce阶段，须要继承Reducer，并重写reduce()函数
  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;
      // 遍历map阶段输出结果中的values中每一个val，累加至sum
      for (IntWritable val : values) {
        sum += val.get();
      }

      // 将sum设置入IntWritable类型result
      result.set(sum);

      // 通过上下文context的write()方法，输出结果(key, result)，即(Text,IntWritable)
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    // 载入hadoop配置
	Configuration conf = new Configuration();

	// 校验命令行输入參数
	if (args.length < 2) {
      System.err.println("Usage: wordcount <in> [<in>...] <out>");
      System.exit(2);
    }

	// 构造一个Job实例job，并命名为"word count"
    Job job = new Job(conf, "word count");

    // 设置jar
    job.setJarByClass(WordCount.class);

    // 设置Mapper
    job.setMapperClass(TokenizerMapper.class);
    // 设置Combiner
    job.setCombinerClass(IntSumReducer.class);
    // 设置Reducer
    job.setReducerClass(IntSumReducer.class);
    // 设置OutputKey
    job.setOutputKeyClass(Text.class);
    // 设置OutputValue
    job.setOutputValueClass(IntWritable.class);

    // 加入输入路径
    for (int i = 0; i < args.length - 1; ++i) {
      FileInputFormat.addInputPath(job, new Path(args[i]));
    }

    // 加入输出路径
    FileOutputFormat.setOutputPath(job,
      new Path(args[args.length - 1]));

    // 等待作业job执行完毕并退出
    System.exit(job.waitForCompletion(true) ?

0 : 1);
  }
}