通过MapReduce统计每个单子在每个文件中出现的次数（FileSplit的使用），单词作为key，所在文本和次数作为value进行统计

代码如下：

package cn.toto.bigdata.mr.index;

import java.io.IOException;

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

public class IndexCreateStepOne {

	public static class IndexCreateMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
		Text k = new Text();
		IntWritable v = new IntWritable(1);
		
		@Override
		protected void map(LongWritable key, Text value, Context context) 
				throws IOException, InterruptedException {
			String line = value.toString();
			String[] words = line.split(" ");
			
			FileSplit inputSplit = (FileSplit) context.getInputSplit();
			//获取到word(单词)所在的文件的名称
			String fileName = inputSplit.getPath().getName();
			
			//最终输出的格式效果如：       key:单词---文件名   value:1
			for(String word : words) {
				k.set(word + "--" + fileName);
				context.write(k, v);
			}
		}	
	}
	
	public static class IndexCreateReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
		IntWritable v = new IntWritable();
		
		@Override
		protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
			int count = 0;
			for (IntWritable value : values) {

				count += value.get();

			}
			v.set(count);

			context.write(key, v);
		}
	}
	
	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		
		Job job = Job.getInstance(conf);
		//告诉框架，我们的程序所在jar包的路径
		// job.setJar("c:/wordcount.jar");
		job.setJarByClass(IndexCreateStepOne.class);
		
		//告诉框架，我们的程序所用的mapper类和reducer类
		job.setMapperClass(IndexCreateMapper.class);
		job.setReducerClass(IndexCreateReducer.class);
		job.setCombinerClass(IndexCreateReducer.class);
		
		//告诉框架，我们的mapperreducer输出的数据类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		
		FileInputFormat.setInputPaths(job, new Path("E:/wordcount/inverindexinput/"));
		
		//告诉框架，我们的处理结果要输出到哪里
		FileOutputFormat.setOutputPath(job, new Path("E:/wordcount/index-1/"));
		
		boolean res = job.waitForCompletion(true);
		System.exit(res ? 0 : 1);
	}
}

1、要处理的数据文件

b.txt的内容如下：

其它的c.txt,d.txt和上面的类似

运行后的结果如下：

这样，可以列出各各单词在每个文件中的数量了

接着，做如下的功能：单词作为key,在文件和文件中的个数的数值作为value，然后去做统计，实例代码如下：

package cn.toto.bigdata.mr.index;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
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 org.mockito.internal.stubbing.StubbedInvocationMatcher;

import io.netty.handler.codec.http.HttpHeaders.Values;

public class IndexCreateStepTwo {

	public static class IndexCreateStepTwoMapper extends Mapper<LongWritable, Text, Text, Text> {
		Text k = new Text();
		Text v = new Text();
		
		@Override
		protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
				throws IOException, InterruptedException {
			String line = value.toString();
			String[] fields = line.split("\t");
			String word_file = fields[0];
			String count = fields[1];
			String[] split = word_file.split("--");
			String word = split[0];
			String file = split[1];
			
			k.set(word);
			v.set(file + "--" + count);
			context.write(k, v);
		}
	}
	
	public static class IndexCreateStepTwoReducer extends Reducer<Text, Text, Text, Text> {
		Text v = new Text();
		
		@Override
		protected void reduce(Text key, Iterable<Text> values, Context context)
				throws IOException, InterruptedException {
			StringBuffer sb = new StringBuffer();
			for (Text value : values) {
				sb.append(value.toString()).append(" ");
			}
			v.set(sb.toString());
			context.write(key, v);
		}
	}
	
	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		
		Job job = Job.getInstance(conf);
		//告诉框架，我们的程序所在jar包的路径
		// job.setJar("c:/wordcount.jar");
		job.setJarByClass(IndexCreateStepTwo.class);

		//告诉框架，我们的程序所用的mapper类和reducer类
		job.setMapperClass(IndexCreateStepTwoMapper.class);
		job.setReducerClass(IndexCreateStepTwoReducer.class);
		job.setCombinerClass(IndexCreateStepTwoReducer.class);
		
		//告诉框架，我们的mapperreducer输出的数据类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Text.class);
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		
		FileInputFormat.setInputPaths(job, new Path("E:/wordcount/index-1/"));
		
		//告诉框架，我们的处理结果要输出到哪里去
		FileOutputFormat.setOutputPath(job, new Path("E:/wordcount/index-2/"));
		
		boolean res = job.waitForCompletion(true);
		System.exit(res ? 0 : 1);
	}
}