相信接触过搜索引擎开发的同学对倒排索引并不陌生，谷歌、百度等搜索引擎都是用的倒排索引，关于倒排索引的有关知识，这里就不再深入讲解，有兴趣的同学到网上了解一下。这篇博文就带着大家一起学习下如何利用Hadoop的MR程序来实现倒排索引的功能。
一、数据准备
1、输入文件数据
这里我们准备三个输入文件，分别如下所示
a.txt
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hello tom
hello jerry
hello tom
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b.txt
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3
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hello jerry
hello jerry
tom jerry
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c.txt
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2
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hello jerry
hello tom
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2、最终输出文件数据
最终输出文件的结果为：
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[plain] view plain copy
hello c.txt-->2 b.txt-->2 a.txt-->3
jerry c.txt-->1 b.txt-->3 a.txt-->1
tom c.txt-->1 b.txt-->1 a.txt-->2
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二、倒排索引过程分析
根据输入文件数据和最终的输出文件结果可知，此程序需要利用两个MR实现，具体流程可总结归纳如下：
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-------------第一步Mapper的输出结果格式如下：--------------------
context.wirte("hello->a.txt", "1")
context.wirte("hello->a.txt", "1")
context.wirte("hello->a.txt", "1")
context.wirte("hello->b.txt", "1")
context.wirte("hello->b.txt", "1")
context.wirte("hello->c.txt", "1")
context.wirte("hello->c.txt", "1")
-------------第一步Reducer的得到的输入数据格式如下：-------------
<"hello->a.txt", {1,1,1}>
<"hello->b.txt", {1,1}>
<"hello->c.txt", {1,1}>
-------------第一步Reducer的输出数据格式如下---------------------
context.write("hello->a.txt", "3")
context.write("hello->b.txt", "2")
context.write("hello->c.txt", "2")
-------------第二步Mapper得到的输入数据格式如下：-----------------
context.write("hello->a.txt", "3")
context.write("hello->b.txt", "2")
context.write("hello->c.txt", "2")
-------------第二步Mapper输出的数据格式如下：--------------------
context.write("hello", "a.txt->3")
context.write("hello", "b.txt->2")
context.write("hello", "c.txt->2")
-------------第二步Reducer得到的输入数据格式如下：-----------------
<"hello", {"a.txt->3", "b.txt->2", "c.txt->2"}>
-------------第二步Reducer输出的数据格式如下：-----------------
context.write("hello", "a.txt->3 b.txt->2 c.txt->2")
最终结果为：
hello a.txt->3 b.txt->2 c.txt->2
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三、程序开发
3.1、第一步MR程序与输入输出
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package com.lyz.hdfs.mr.ii;
import java.io.IOException;
import org.apache.commons.lang.StringUtils;
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.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
/**
* 倒排索引第一步Map Reduce程序,此处程序将所有的Map/Reduce/Runner程序放在一个类中
* @author liuyazhuang
*
*/
public class InverseIndexStepOne {
/**
* 完成倒排索引第一步的mapper程序
* @author liuyazhuang
*
*/
public static class StepOneMapper extends Mapper<LongWritable, Text, Text, LongWritable>{
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context)
throws IOException, InterruptedException {
//获取一行数据
String line = value.toString();
//切分出每个单词
String[] fields = StringUtils.split(line, " ");
//获取数据的切片信息
FileSplit fileSplit = (FileSplit) context.getInputSplit();
//根据切片信息获取文件名称
String fileName = fileSplit.getPath().getName();
for(String field : fields){
context.write(new Text(field + "-->" + fileName), new LongWritable(1));
}
}
}
/**
* 完成倒排索引第一步的Reducer程序
* 最终输出结果为：
* hello-->a.txt 3
hello-->b.txt 2
hello-->c.txt 2
jerry-->a.txt 1
jerry-->b.txt 3
jerry-->c.txt 1
tom-->a.txt 2
tom-->b.txt 1
tom-->c.txt 1
* @author liuyazhuang
*
*/
public static class StepOneReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
@Override
protected void reduce(Text key, Iterable<LongWritable> values,
Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {
long counter = 0;
for(LongWritable value : values){
counter += value.get();
}
context.write(key, new LongWritable(counter));
}
}
//运行第一步的MR程序
public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(InverseIndexStepOne.class);
job.setMapperClass(StepOneMapper.class);
job.setReducerClass(StepOneReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(LongWritable.class);
FileInputFormat.addInputPath(job, new Path("D:/hadoop_data/ii"));
FileOutputFormat.setOutputPath(job, new Path("D:/hadoop_data/ii/result"));
job.waitForCompletion(true);
}
}
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3.1.1 输入数据
a.txt
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hello tom
hello jerry
hello tom
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b.txt
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hello jerry
hello jerry
tom jerry
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c.txt
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hello jerry
hello tom
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3.1.2
输出结果：
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hello-->a.txt 3
hello-->b.txt 2
hello-->c.txt 2
jerry-->a.txt 1
jerry-->b.txt 3
jerry-->c.txt 1
tom-->a.txt 2
tom-->b.txt 1
tom-->c.txt 1
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3.2 第二步MR程序与输入输出
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package com.lyz.hdfs.mr.ii;
import java.io.IOException;
import org.apache.commons.lang.StringUtils;
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;
/**
* 倒排索引第二步Map Reduce程序,此处程序将所有的Map/Reduce/Runner程序放在一个类中
* @author liuyazhuang
*
*/
public class InverseIndexStepTwo {
/**
* 完成倒排索引第二步的mapper程序
*
* 从第一步MR程序中得到的输入信息为：
* hello-->a.txt 3
hello-->b.txt 2
hello-->c.txt 2
jerry-->a.txt 1
jerry-->b.txt 3
jerry-->c.txt 1
tom-->a.txt 2
tom-->b.txt 1
tom-->c.txt 1
* @author liuyazhuang
*
*/
public static class StepTwoMapper extends Mapper<LongWritable, Text, Text, 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 = StringUtils.split(line, "\t");
String[] wordAndFileName = StringUtils.split(fields[0], "-->");
String word = wordAndFileName[0];
String fileName = wordAndFileName[1];
long counter = Long.parseLong(fields[1]);
context.write(new Text(word), new Text(fileName + "-->" + counter));
}
}
/**
* 完成倒排索引第二步的Reducer程序
* 得到的输入信息格式为:
* <"hello", {"a.txt->3", "b.txt->2", "c.txt->2"}>,
* 最终输出结果如下：
* hello c.txt-->2 b.txt-->2 a.txt-->3
jerry c.txt-->1 b.txt-->3 a.txt-->1
tom c.txt-->1 b.txt-->1 a.txt-->2
* @author liuyazhuang
*
*/
public static class StepTwoReducer extends Reducer<Text, Text, Text, Text>{
@Override
protected void reduce(Text key, Iterable<Text> values, Reducer<Text, Text, Text, Text>.Context context)
throws IOException, InterruptedException {
String result = "";
for(Text value : values){
result += value + " ";
}
context.write(key, new Text(result));
}
}
//运行第一步的MR程序
public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(InverseIndexStepTwo.class);
job.setMapperClass(StepTwoMapper.class);
job.setReducerClass(StepTwoReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path("D:/hadoop_data/ii/result/part-r-00000"));
FileOutputFormat.setOutputPath(job, new Path("D:/hadoop_data/ii/result/final"));
job.waitForCompletion(true);
}
}
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3.2.1 输入数据
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hello-->a.txt 3
hello-->b.txt 2
hello-->c.txt 2
jerry-->a.txt 1
jerry-->b.txt 3
jerry-->c.txt 1
tom-->a.txt 2
tom-->b.txt 1
tom-->c.txt 1
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3.2.2 输出结果
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hello c.txt-->2 b.txt-->2 a.txt-->3
jerry c.txt-->1 b.txt-->3 a.txt-->1
tom c.txt-->1 b.txt-->1 a.txt-->2
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总结
以上就是本文关于Hadoop编程基于MR程序实现倒排索引示例的全部内容，希望对大家有所帮助。有什么问题可以直接留言，小编会及时回复大家的。感谢朋友们对本站的支持！
原文链接：http://blog.csdn.net/l1028386804/article/details/78239792