file1.txt:MapReduce is simple
file2.txt:mapReduce is powerful is simple
file3.txt:Hello MapReduce bye MapReduce

Hello     file3.txt:1
MapReduce    file3.txt:2;file2.txt:1;file1.txt:1
bye     file3.txt:1
is     file2.txt:2;file1.txt:1
powerful    file2.txt:1
simple     file2.txt:1;file1.txt:1

  1 package pro;
  2
  3 import java.io.IOException;
  4 import java.util.StringTokenizer;
  5 import org.apache.hadoop.conf.Configuration;
  6 import org.apache.hadoop.fs.Path;
  7 import org.apache.hadoop.io.IntWritable;
  8 import org.apache.hadoop.io.Text;
  9 import org.apache.hadoop.mapreduce.Job;
 10 import org.apache.hadoop.mapreduce.Mapper;
 11 import org.apache.hadoop.mapreduce.Reducer;
 12 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 13 import org.apache.hadoop.mapreduce.lib.input.FileSplit;
 14 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 15 import org.apache.hadoop.util.GenericOptionsParser;
 16
 17 public class InvertedIndex {
 18     final static String INPUT_PATH = "hdfs://hadoop0:9000/index_in";
 19     final static String OUTPUT_PATH = "hdfs://hadoop0:9000/index_out";
 20
 21     public static class Map extends Mapper<Object, Text, Text, Text> {
 22
 23         private Text keyInfo = new Text(); // 存储单词和URL组合
 24         private Text valueInfo = new Text(); // 存储词频
 25         private FileSplit split; // 存储Split对象
 26
 27         // 实现map函数
 28         public void map(Object key, Text value, Context context)
 29                 throws IOException, InterruptedException {
 30             // 获得<key,value>对所属的FileSplit对象
 31             split = (FileSplit) context.getInputSplit();
 32             StringTokenizer itr = new StringTokenizer(value.toString());
 33             while (itr.hasMoreTokens()) {
 34
 35                 // 只获取文件的名称。
 36                 int splitIndex = split.getPath().toString().indexOf("file");
 37                 keyInfo.set(itr.nextToken() + ":"
 38                         + split.getPath().toString().substring(splitIndex));
 39                 // 词频初始化为1
 40                 valueInfo.set("1");
 41                 context.write(keyInfo, valueInfo);
 42             }
 43         }
 44     }
 45
 46     public static class Combine extends Reducer<Text, Text, Text, Text> {
 47         private Text info = new Text();
 48
 49         // 实现reduce函数
 50         public void reduce(Text key, Iterable<Text> values, Context context)
 51                 throws IOException, InterruptedException {
 52             // 统计词频
 53             int sum = 0;
 54             for (Text value : values) {
 55                 sum += Integer.parseInt(value.toString());
 56             }
 57
 58             int splitIndex = key.toString().indexOf(":");
 59             // 重新设置value值由URL和词频组成
 60             info.set(key.toString().substring(splitIndex + 1) + ":" + sum);
 61             // 重新设置key值为单词
 62             key.set(key.toString().substring(0, splitIndex));
 63             context.write(key, info);
 64         }
 65     }
 66
 67     public static class Reduce extends Reducer<Text, Text, Text, Text> {
 68         private Text result = new Text();
 69
 70         // 实现reduce函数
 71         public void reduce(Text key, Iterable<Text> values, Context context)
 72                 throws IOException, InterruptedException {
 73             // 生成文档列表
 74             String fileList = new String();
 75             for (Text value : values) {
 76                 fileList += value.toString() + ";";
 77             }
 78             result.set(fileList);
 79
 80             context.write(key, result);
 81         }
 82     }
 83
 84     public static void main(String[] args) throws Exception {
 85
 86         Configuration conf = new Configuration();
 87
 88         Job job = new Job(conf, "Inverted Index");
 89         job.setJarByClass(InvertedIndex.class);
 90
 91         // 设置Map、Combine和Reduce处理类
 92         job.setMapperClass(Map.class);
 93         job.setCombinerClass(Combine.class);
 94         job.setReducerClass(Reduce.class);
 95
 96         // 设置Map输出类型
 97         job.setMapOutputKeyClass(Text.class);
 98         job.setMapOutputValueClass(Text.class);
 99
100         // 设置Reduce输出类型
101         job.setOutputKeyClass(Text.class);
102         job.setOutputValueClass(Text.class);
103
104         // 设置输入和输出目录
105         FileInputFormat.addInputPath(job, new Path(INPUT_PATH));
106         FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH));
107         System.exit(job.waitForCompletion(true) ? 0 : 1);
108     }
109 }

Hello        file3.txt:1;
MapReduce    file3.txt:2;file1.txt:1;file2.txt:1;
bye        file3.txt:1;
is        file1.txt:1;file2.txt:2;
powerful    file2.txt:1;
simple        file2.txt:1;file1.txt:1;