使用MapReduce开发WordCount应用程序_运行mapreduce应用程序 wordcount-优快云博客

本文介绍了一个使用Hadoop MapReduce框架实现的WordCount应用程序。该程序包括Mapper和Reducer两个阶段，能够从输入文件中统计单词频率，并将结果输出到指定路径。

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package com.hadoop.mapreduce;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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 java.io.IOException;

/**
 * 使用MapReduce开发WordCount应用程序
 */
public class WordCountApp {
    /**
     * Map:读取输入的文件
     * Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT>
     */
    public static class MyMapper extends Mapper<LongWritable,Text,Text,LongWritable>{
        LongWritable one = new LongWritable(1);

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //接收到的每一行数据
            String line = value.toString();

            //按照指定分隔符进行拆分
            String[] words = line.split(" ");

            for ( String word : words ) {
                //通过上下文把map的处理结果输出
                context.write(new Text(word),one);
            }
        }
    }
    /**
     * Reduce: 归并操作
     */
    public static class MyReducer extends Reducer<Text,LongWritable,Text,LongWritable>{
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {

            long sum = 0;
            for (LongWritable value:values) {
                //求key出现的次数总和
                sum += value.get();
            }

            //最终统计结果的输出
            context.write(key,new LongWritable(sum));
        }
    }

    /**
     * 定义Driver：封装了MapReduce作业的所有信息
     * @param args
     */
    public static void main(String[] args) throws Exception {

        //创建Configuration
        Configuration configuration = new Configuration();

        //准备清理已存在的输出目录
        Path outputPath = new Path(args[1]);
        FileSystem fileSystem = FileSystem.get(configuration);
        if (fileSystem.exists(outputPath)){
            fileSystem.delete(outputPath,true);
            System.out.println("output file exists, but is has deleted");
        }

        //创建Job
        Job job = Job.getInstance(configuration, "wordcount");

        //设置job的处理类
        job.setJarByClass(WordCountApp.class);

        //设置作业处理的输入路径
        FileInputFormat.setInputPaths(job,new Path(args[0]));

        //设置map相关参数
        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //设置reduce相关参数
        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        //设置作业处理的输出路径
        FileOutputFormat.setOutputPath(job,new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

    /**
     * 打包：mvn clean package -DskipTests
     * 上传到服务器：scp target/hadoop-train.1.0.jar hadoop@hadoop000:~/lib
     * 运行：hadoop jar /home/hadoop/lib/hadoop-train-1.0.jar com.imooc.hadoop.mapreduce.WordCountApp
     *          hdfs://hadoop000:8020/hello.txt hdfs://hadoop000:8020/output/wc
     *
     * 创建文件：mkdir wc_shell.sh
     *                  hadoop fs -rm -r /output/wc    #先删除
     *                  hadoop jar /home/hadoop/lib/hadoop-train-1.0.jar com.imooc.hadoop.mapreduce.WordCountApp
     *                      hdfs://hadoop000:8020/hello.txt hdfs://hadoop000:8020/output/wc
     * 增加执行权限：chmod u+x wc_shell.sh
     */

}