java编写mapreduce并在hadoop中运行

原文：https://www.cnblogs.com/jetdw/p/7238105.html?utm_source=itdadao&utm_medium=referral

1、mapreduce代码

package test.mapreduce;

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

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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;

/**
 * 该例子为网上经典例子统计单词出现次数
 *
 */
public class WordCount {
    public static class TokenizerMapper extends Mapper {

        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        /**
         * key 偏移量包括了回车所占的字符数(Windows和Linux环境会不同)
         * value 一行数据
         * context存储新Map的对象
         */
        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();

        /**
         * key 为Map中的key，hadoop会把相同key的内容合并为一个list，该list就为values。
         * context为存放结果的对象
         */
        public void reduce(Text key, Iterable values, Context context) throws IOException,
                InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
            	//累加每一个value
                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 = Job.getInstance();
        
        //设置本次job作业使用的mapper类是那个
        job.setJarByClass(WordCount.class);
        //本次job作业使用的mapper类是那个？
        job.setMapperClass(TokenizerMapper.class);
        //本次job作业使用的reducer类是那个
        job.setCombinerClass(IntSumReducer.class);
        //本次job作业使用的reducer类是那个
        job.setReducerClass(IntSumReducer.class);
        //本次job作业使用的reducer类的输出数据key类型
        job.setOutputKeyClass(Text.class);
        //本次job作业使用的reducer类的输出数据value类型
        job.setOutputValueClass(IntWritable.class);
        
        job.setNumReduceTasks(1);//设置reduce的个数
        
        
        //判断output文件夹是否存在，如果存在则删除
        Path path = new Path(otherArgs[1]);
        FileSystem fileSystem =path.getFileSystem(conf);//根据path找到这个文件夹
        if(fileSystem.exists(path)) {
        	fileSystem.delete(path,true);//true的意思是，就算output里面有东西，也一带删除
        }
        
        //本次job作业要处理的原始数据所在的路径
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        //本次job作业产生的结果输出路径
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        
        //提交本次作业
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

2、在hadoop中运行

hadoop jar /usr/local/hadoop_demo.jar test.mapreduce.WordCount /bbb.txt /output

hadoop jar 【jar包文件】 【jar包中main函数所在的类（包括包名）】 【需统计的文件】 【输出路径】