Hadoop--倒排索引过程详解

倒排索引就是根据单词内容来查找文档的方式，由于不是根据文档来确定文档所包含的内容，进行了相反的操作，所以被称为倒排索引

下面来看一个例子来理解什么是倒排索引

这里我准备了两个文件分别为1.txt和2.txt

1.txt的内容如下

    I Love Hadoop
    I like ZhouSiYuan
    I love me

2.txt的内容如下

I Love MapReduce
I like NBA
I love Hadoop

我这里使用的是默认的输入格式TextInputFormat，他是一行一行的读的，键是偏移量，如果对于这个不理解，可以看我之前发表的文章
MapReduce工作原理
 Hadoop数据类型和自定义输入输出

所以在map阶段之前的到结果如下
map阶段从1.txt的得到的输入

0   I Love Hadoop
15  I like ZhouSiYuan
34  I love me

map阶段从2.txt的得到的输入

0   I Love MapReduce
18  I like NBA
30  I love Hadoop

map阶段
把词频作为值
把单词和URI组成key值
比如
key : I+hdfs://192.168.52.140:9000/index/2.txt value:1

为什么要这样设置键和值？
因为这样设计可以使用MapReduce框架自带的map端排序，将同一单词的词频组成列表

经过map阶段1.txt得到的输出如下

I:hdfs://192.168.52.140:9000/index/1.txt            1
Love:hdfs://192.168.52.140:9000/index/1.txt         1
MapReduce:hdfs://192.168.52.140:9000/index/1.txt    1
I:hdfs://192.168.52.140:9000/index/1.txt            1
Like:hdfs://192.168.52.140:9000/index/1.txt         1
ZhouSiYuan:hdfs://192.168.52.140:9000/index/1.txt   1
I:hdfs://192.168.52.140:9000/index/1.txt            1
love:hdfs://192.168.52.140:9000/index/1.txt         1   
me:hdfs://192.168.52.140:9000/index/1.txt           1

经过map阶段2.txt得到的输出如下

I:hdfs://192.168.52.140:9000/index/2.txt            1
Love:hdfs://192.168.52.140:9000/index/2.txt         1
MapReduce:hdfs://192.168.52.140:9000/index/2.txt    1
I:hdfs://192.168.52.140:9000/index/2.txt            1
Like:hdfs://192.168.52.140:9000/index/2.txt         1
NBA:hdfs://192.168.52.140:9000/index/2.txt          1
I:hdfs://192.168.52.140:9000/index/2.txt            1
love:hdfs://192.168.52.140:9000/index/2.txt         1   
Hadoop:hdfs://192.168.52.140:9000/index/2.txt       1

1.txt经过MapReduce框架自带的map端排序得到的输出结果如下

I:hdfs://192.168.52.140:9000/index/1.txt            list{1,1,1}
Love:hdfs://192.168.52.140:9000/index/1.txt         list{1} 
MapReduce:hdfs://192.168.52.140:9000/index/1.txt    list{1}
Like:hdfs://192.168.52.140:9000/index/1.txt         list{1}
ZhouSiYuan:hdfs://192.168.52.140:9000/index/1.txt   list{1}
love:hdfs://192.168.52.140:9000/index/1.txt         list{1}
me:hdfs://192.168.52.140:9000/index/1.txt           list{1}

2.txt经过MapReduce框架自带的map端排序得到的输出结果如下

I:hdfs://192.168.52.140:9000/index/2.txt            list{1,1,1}
Love:hdfs://192.168.52.140:9000/index/2.txt         list{1} 
MapReduce:hdfs://192.168.52.140:9000/index/2.txt    list{1}
Like:hdfs://192.168.52.140:9000/index/2.txt         list{1}
NBA:hdfs://192.168.52.140:9000/index/2.txt          list{1}
love:hdfs://192.168.52.140:9000/index/2.txt         list{1}
Hadoop:hdfs://192.168.52.140:9000/index/2.txt       list{1}

combine阶段：
key值为单词，
value值由URI和词频组成
value: hdfs://192.168.52.140:9000/index/2.txt:3 key:I
为什么这样设计键值了？
因为在Shuffle过程将面临一个问题，所有具有相同单词的记录(由单词、URL和词频组成)应该交由同一个Reducer处理
所以重新把单词设置为键可以使用MapReduce框架默认的Shuffle过程,将相同单词的所有记录发送给同一个Reducer处理

combine阶段将key相同的value值累加

1.txt得到如下输出

I       hdfs://192.168.52.140:9000/index/1.txt:3
Love        hdfs://192.168.52.140:9000/index/1.txt:1 
MapReduce   hdfs://192.168.52.140:9000/index/1.txt:1
Like        hdfs://192.168.52.140:9000/index/1.txt:1
ZhouSiYuan  hdfs://192.168.52.140:9000/index/1.txt:1
love        hdfs://192.168.52.140:9000/index/1.txt:1
me          hdfs://192.168.52.140:9000/index/1.txt:1

2.txt得到如下输出

I           hdfs://192.168.52.140:9000/index/2.txt:3
Love        hdfs://192.168.52.140:9000/index/2.txt:1 
MapReduce   hdfs://192.168.52.140:9000/index/2.txt:1
Like        hdfs://192.168.52.140:9000/index/2.txt:1
NBA         hdfs://192.168.52.140:9000/index/2.txt:1
love        hdfs://192.168.52.140:9000/index/2.txt:1
Hadoop      hdfs://192.168.52.140:9000/index/2.txt:1

这样reducer过程就很简单了，它只用来生成文档列表
比如相同的单词I，这样生成文档列表
I hdfs://192.168.52.140:9000/index/2.txt:3;hdfs://192.168.52.140:9000/index/1.txt:3;

最后所有的输出结果如下

Hadoop  hdfs://192.168.52.140:9000/index/1.txt:1;hdfs://192.168.52.140:9000/index/2.txt:1;
I   hdfs://192.168.52.140:9000/index/2.txt:3;hdfs://192.168.52.140:9000/index/1.txt:3;
Love    hdfs://192.168.52.140:9000/index/1.txt:1;hdfs://192.168.52.140:9000/index/2.txt:1;
MapReduce   hdfs://192.168.52.140:9000/index/2.txt:1;
NBA hdfs://192.168.52.140:9000/index/2.txt:1;
ZhouSiYuan  hdfs://192.168.52.140:9000/index/1.txt:1;
like    hdfs://192.168.52.140:9000/index/1.txt:1;hdfs://192.168.52.140:9000/index/2.txt:1;
love    hdfs://192.168.52.140:9000/index/2.txt:1;hdfs://192.168.52.140:9000/index/1.txt:1;
me  hdfs://192.168.52.140:9000/index/1.txt:1;

下面是整个源代码

package com.hadoop.mapreduce.test8.invertedindex;

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 {
    /**
     * 
     * @author 汤高
     *
     */
    public static class InvertedIndexMapper extends Mapper<Object, Text, Text, Text>{

        private Text keyInfo = new Text();  // 存储单词和URI的组合
        private Text valueInfo = new Text(); //存储词频
        private FileSplit split;  // 存储split对象。
        @Override
        protected void map(Object key, Text value, Mapper<Object, Text, Text, Text>.Context context)
                throws IOException, InterruptedException {
            //获得<key,value>对所属的FileSplit对象。
            split = (FileSplit) context.getInputSplit();
            System.out.println("偏移量"+key);
            System.out.println("值"+value);
            //StringTokenizer是用来把字符串截取成一个个标记或单词的，默认是空格或多个空格(\t\n\r等等)截取
            StringTokenizer itr = new StringTokenizer( value.toString());
            while( itr.hasMoreTokens() ){
                // key值由单词和URI组成。
                keyInfo.set( itr.nextToken()+":"+split.getPath().toString());
                //词频初始为1
                valueInfo.set("1");
                context.write(keyInfo, valueInfo);
            }
            System.out.println("key"+keyInfo);
            System.out.println("value"+valueInfo);
        }
    }
    /**
     * 
     * @author 汤高
     *
     */
    public static class InvertedIndexCombiner extends Reducer<Text, Text, Text, Text>{
        private Text info = new Text();
        @Override
        protected void reduce(Text key, Iterable<Text> values, Reducer<Text, Text, Text, Text>.Context context)
                throws IOException, InterruptedException {

            //统计词频
            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和词频组成
            info.set( key.toString().substring( splitIndex + 1) +":"+sum );

            //重新设置key值为单词
            key.set( key.toString().substring(0,splitIndex));

            context.write(key, info);
            System.out.println("key"+key);
            System.out.println("value"+info);
        }
    }

    /**
     * 
     * @author 汤高
     *
     */
    public static class InvertedIndexReducer extends Reducer<Text, Text, Text, Text>{

        private Text result = new Text();

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

            //生成文档列表
            String fileList = new String();
            for (Text value : values) {
                fileList += value.toString()+";";
            }
            result.set(fileList);

            context.write(key, result);
        }

    }

    public static void main(String[] args) {
        try {
            Configuration conf = new Configuration();

            Job job = Job.getInstance(conf,"InvertedIndex");
            job.setJarByClass(InvertedIndex.class);

            //实现map函数，根据输入的<key,value>对生成中间结果。
            job.setMapperClass(InvertedIndexMapper.class);

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

            job.setCombinerClass(InvertedIndexCombiner.class);
            job.setReducerClass(InvertedIndexReducer.class);

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

            //我把那两个文件上传到这个index目录下了
            FileInputFormat.addInputPath(job, new Path("hdfs://192.168.52.140:9000/index/"));
            //把结果输出到out_index+时间戳的目录下
            FileOutputFormat.setOutputPath(job, new Path("hdfs://192.168.52.140:9000/out_index"+System.currentTimeMillis()+"/"));

            System.exit(job.waitForCompletion(true) ? 0 : 1);
        } catch (IllegalStateException e) {
            e.printStackTrace();
        } catch (IllegalArgumentException e) {
            e.printStackTrace();
        } catch (ClassNotFoundException e) {
            e.printStackTrace();
        } catch (IOException e) {
            e.printStackTrace();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }

    }
}

欢迎各位提出宝贵的意见
码字不易，转载请指明出处

Hadoop--倒排索引过程详解

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