一：理解【倒排索引】的功能

1.1 倒排索引：

    由于不是根据文档来确定文档所包含的内容，而是进行相反的操作,因而称为倒排索引
    简单来说根据单词，返回它在哪个文件中出现过，而且频率是多少的结果。例如：就像百度里的搜索，你输入一个关键字，那么百度引擎就迅速的在它的服务器里找到有该关键字的文件，并根据频率和其他一些策略（如页面点击投票率）等来给你返回结果

二：熟悉mapreduce 中的combine 功能

2.1 mapreduce的combine 功能

   1 Map过程:Map过程首先分析输入的<key,value>对，得到索引中需要的信息：单词，文档URI 和词频。key：单词和URI.value：出现同样单词的次数。

2 Combine过程：经过map方法处理后，Combine过程将key值相同的value值累加，得到一个单词在文档中的词频。

3 Reduce过程：经过上述的俩个过程后，Reduce过程只需要将相同的key值的value值组合成倒排引索文件的格式即可，其余的事情直接交给MapReduce框架进行处理

三：根据需求编码实现【倒排索引】的功能，旨在理解mapreduce 的功能。

3.1 Java的编程代码

InvertedIndexMapReduce.java

package org.apache.hadoop.studyhadoop.index;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.Mapper.Context;
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.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * 
 * @author zhangyy
 * 
 */
public class InvertedIndexMapReduce extends Configured implements Tool {
	// step 1 : mapper
	/**
	 * public class Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT>
	 */
	public static class WordCountMapper extends //
			Mapper<LongWritable, Text, Text, Text> {

		private Text mapOutputKey = new Text();
		private Text mapOutputValue = new Text("1");

		@Override
		public void map(LongWritable key, Text value, Context context)
				throws IOException, InterruptedException {

			// split1
			String[] lines = value.toString().split("##");
			// get url
			String url = lines[0];

			// split2
			String[] strs = lines[1].split(" ");

			for (String str : strs) {
				mapOutputKey.set(str + "," + url);
				context.write(mapOutputKey, mapOutputValue);
			}

		}
	}

	// set combiner class
	public static class InvertedIndexCombiner extends //
			Reducer<Text, Text, Text, Text> {

		private Text CombinerOutputKey = new Text();
		private Text CombinerOutputValue = new Text();

		@Override
		public void reduce(Text key, Iterable<Text> values, Context context)
				throws IOException, InterruptedException {

			// split
			String[] strs = key.toString().split(",");

			// set key
			CombinerOutputKey.set(strs[0] + "\n");

			// set value
			int sum = 0;
			for (Text value : values) {
				sum += Integer.valueOf(value.toString());
			}

			CombinerOutputValue.set(strs[1] + ":" + sum);

			context.write(CombinerOutputKey, CombinerOutputValue);

		}
	}

	// step 2 : reducer
	public static class WordCountReducer extends //
			Reducer<Text, Text, Text, Text> {

		private Text outputValue = new Text();

		@Override
		public void reduce(Text key, Iterable<Text> values, Context context)
				throws IOException, InterruptedException {
			// TODO

			String result = new String();

			for (Text value : values) {
				result += value.toString() + "\t";
			}
			
			outputValue.set(result);

			context.write(key, outputValue);
		}
	}

	// step 3 : job

	public int run(String[] args) throws Exception {

		// 1 : get configuration
		Configuration configuration = super.getConf();

		// 2 : create job
		Job job = Job.getInstance(//
				configuration,//
				this.getClass().getSimpleName());
		job.setJarByClass(InvertedIndexMapReduce.class);

		// job.setNumReduceTasks(tasks);

		// 3 : set job
		// input --> map --> reduce --> output
		// 3.1 : input
		Path inPath = new Path(args[0]);
		FileInputFormat.addInputPath(job, inPath);

		// 3.2 : mapper
		job.setMapperClass(WordCountMapper.class);
		// TODO
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Text.class);

		// ====================shuffle==========================
		// 1: partition
		// job.setPartitionerClass(cls);
		// 2: sort
		// job.setSortComparatorClass(cls);
		// 3: combine
		job.setCombinerClass(InvertedIndexCombiner.class);
		// 4: compress
		// set by configuration
		// 5 : group
		// job.setGroupingComparatorClass(cls);

		// ====================shuffle==========================

		// 3.3 : reducer
		job.setReducerClass(WordCountReducer.class);
		// TODO
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);

		// 3.4 : output
		Path outPath = new Path(args[1]);
		FileOutputFormat.setOutputPath(job, outPath);

		// 4 : submit job
		boolean isSuccess = job.waitForCompletion(true);
		return isSuccess ? 0 : 1;

	}

	public static void main(String[] args) throws Exception {

		args = new String[] {
				"hdfs://:8020/input/index.txt",
				"hdfs://:8020/outputindex/" 
				};
		
		// get configuration
		Configuration configuration = new Configuration();

		// configuration.set(name, value);

		// run job
		int status = ToolRunner.run(//
				configuration,//
				new InvertedIndexMapReduce(),//
				args);

		// exit program
		System.exit(status);
	}

}

3.2 运行案例测试

上传文件：
hdfs dfs -put index.txt /input 

代码运行结果：

输出结果：