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 |
b.txt
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hello jerry hello jerry tom jerry |
c.txt
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hello jerry hello tom |
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 copyhello 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 |
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 |
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); } } |
3.1.1 输入数据
a.txt
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hello tom hello jerry hello tom |
b.txt
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hello jerry hello jerry tom jerry |
c.txt
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hello jerry hello tom |
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 |
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); } } |
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 |
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 |
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
