mapreduce典型应用案例之倒排索引_倒排索引 测试数据如下 1)文件一:data01.txt,内容:beijing is beautif

一、倒排索引的介绍

通俗的讲，就是根据单词找到包含这个单词的所有文档。

二、mapreduce实现框架

1、首先要确定map、reduce、combiner中的key和value是什么类型
2、然后确定key和value具体是什么？
Map ： key为 单词+文件名 value为空
combiner ： key为单词 value为次数+文件名
reduce： key为单词 value为相同单词的“次数+文件名”拼接而成

三、mapreduce代码实现

1、准备数据

2、具体代码实现

package com.qyl.master;

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.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class MyMapReduce {
       public static class MyMapper extends Mapper<LongWritable,Text,Text,Text>{
           private Text okey=new Text();
           private Text ovalue=new Text();
           @Override
           protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
               String filename = ((FileSplit) context.getInputSplit()).getPath().getName();
               String[] strs = value.toString().split(" ");
               for(String s:strs){
                   okey.set(s+"-"+filename);
                   context.write(okey,ovalue);
               }
           }
       }
       public static class MyCombiner extends Reducer<Text,Text,Text,Text>{
           private Text okey=new Text();
           private Text ovalue=new Text();

           @Override
           protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
               int count=0;
               for(Text text:values){
                   count++;
               }
               String strs[]=key.toString().split("-");
               okey.set(strs[0]);
               ovalue.set(strs[1]+"="+count);
               context.write(okey,ovalue);
           }
       }
       public static class MyReduce extends Reducer<Text,Text,Text,Text>{
           private Text ovalue=new Text();
           @Override
           protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
               StringBuilder sb=new StringBuilder();
               for(Text text:values){
                   sb.append(text.toString()).append(",");
               }
               sb.delete(sb.length()-1,sb.length());
               ovalue.set(sb.toString());
               context.write(key,ovalue);
           }
       }

    public static void main(String[] args) {
        Configuration conf=new Configuration();
        try {
            Job job=Job.getInstance(conf);
            job.setJarByClass(MyMapReduce.class);
            job.setMapperClass(MyMapper.class);
            job.setReducerClass(MyReduce.class);
            job.setCombinerClass(MyCombiner.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(Text.class);

            Path inPath =new Path("C:\\data");
            FileInputFormat.addInputPath(job, inPath);

            Path outpath=new Path("C:\\data\\result");
            if(outpath.getFileSystem(conf).exists(outpath)){
                outpath.getFileSystem(conf).delete(outpath, true);
            }
            FileOutputFormat.setOutputPath(job, outpath);

            job.waitForCompletion(true);

        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }

    }


}

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3、结果

New	b.txt=1
York	b.txt=1
and	b.txt=1,e.txt=1,a.txt=1
anycity	c.txt=1
beijing	b.txt=1,a.txt=1
china	a.txt=1,e.txt=1
dot	c.txt=1
familiy	e.txt=1
i	c.txt=1,a.txt=1,b.txt=1
like	b.txt=1,c.txt=1,d.txt=1
love	e.txt=2,b.txt=1,a.txt=2
not	b.txt=1
where	d.txt=1
you	d.txt=1

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