MapReduce编程之倒排索引的实现

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

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map

输出：

key:单词+文档URI，

value：词频

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combiner

输入：

key:单词+文档URI，

value：词频

key:单词+文档URI，

value，单个文档的中出现的词频汇总就是map的value-list合并的结果

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Reduce:

输入：
combiner的结果key value-list

输出

key:单词

value:文档URI:词频

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源代码

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

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 InvertedIndex {

public static class InvertedIndexMap extends Mapper<Object,Text,Text,Text>{

private Text valueInfo = new Text();
private Text keyInfo = new Text();
private FileSplit split;

public void map(Object key, Text value,Context context)
throws IOException, InterruptedException {
//获取<key value>对所属的FileSplit对象
split = (FileSplit) context.getInputSplit();
StringTokenizer stk = new StringTokenizer(value.toString());
while (stk.hasMoreElements()) {
//key值由（单词：URI）组成
keyInfo.set(stk.nextToken()+":"+split.getPath().toString());
//词频
valueInfo.set("1");
context.write(keyInfo, valueInfo);

}


}
}

public static class InvertedIndexCombiner extends Reducer<Text,Text,Text,Text>{

Text info = new Text();

public void reduce(Text key, Iterable<Text> values,Context contex)
throws IOException, InterruptedException {
int sum = 0;
for (Text value : values) {
sum += Integer.parseInt(value.toString());
}

int splitIndex = key.toString().indexOf(":");
//重新设置value值由（URI+:词频组成）
info.set(key.toString().substring(splitIndex+1) +":"+ sum);
//重新设置key值为单词
key.set(key.toString().substring(0,splitIndex));
contex.write(key, info);
}
}

public static class InvertedIndexReduce extends Reducer<Text,Text,Text,Text>{

private Text result = new Text();

public void reduce(Text key, Iterable<Text> values,Context contex)
throws IOException, InterruptedException {
//生成文档列表
String fileList = new String();
for (Text value : values) {
fileList += value.toString()+";";
}
result.set(fileList);
contex.write(key, result);
}
}

public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

Configuration conf = new Configuration();

Job job = new Job(conf,"InvertedIndex");

job.setJarByClass(InvertedIndex.class);

job.setMapperClass(InvertedIndexMap.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);

job.setCombinerClass(InvertedIndexCombiner.class);

job.setReducerClass(InvertedIndexReduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);

FileInputFormat.addInputPath(job, new Path("./in/invertedindex/"));
FileOutputFormat.setOutputPath(job, new Path("./out/"));

System.exit(job.waitForCompletion(true)?0:1);


}
}
测试文件：

1.txt:

Hello MapReduce Hello Hadoop

2.txt

Hello MapReduce Hello Hadoop

Hello MapReduce Hello Hadoop

3.txt

Hello MapReduce Hello Hadoop

Hello MapReduce Hello Hadoop

Hello MapReduce Hello Hadoop

part-r-00000

Hadoop file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/1.txt:1;file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/2.txt:2;file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/3.txt:3;
Hello file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/3.txt:6;file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/1.txt:2;file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/2.txt:4;
MapReduce file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/2.txt:2;file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/1.txt:1;file:/E:/KAI_FA/eclipse/workshop/InvertedIndex/in/invertedindex/3.txt:3;