MapReduce中怎么实现倒排索引

今天就跟大家聊聊有关MapReduce中怎么实现倒排索引，可能很多人都不太了解，为了让大家更加了解，小编给大家总结了以下内容，希望大家根据这篇文章可以有所收获。

需求: 为a, b, c 3个文本文件中的单词建倒排索引

输出格式:

a:

hello world
hello hadoop
hello world

b:

spark hadoop
hello hadoop
world hadoop

c:

spark world
hello world
hello spark

map阶段

context.write("hello:a","1")context.write("hello:a","1")context.write("hello:a","1")

map阶段输出: <"hello:a",{1,1,1}>

combine阶段

context.write("hello","a:3");context.write("hello","b:1");context.write("hello","c:2");

combine阶段输出: <"hello",{"a:3","b:1","c:2"}>

reduce阶段

context.write("hello","a:3,b:1,c:2");

reduce阶段输出: <"hello","a:3,b:1,c:2">

定义Mapper类, 该类继承org.apache.hadoop.mapreduce.Mapper

并重写map()方法

public class IIMapper extends Mapper {  @Override  protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {   String line = value.toString();   String[] words = StringUtils.split(line, " ");   // 从context中获取文件切片inputSplit   FileSplit inputSplit = (FileSplit) context.getInputSplit();   // 从inputSplit中获取文件的绝对路径path   String path = inputSplit.getPath().toString();   int index = path.lastIndexOf("/");   // 从path中截取文件名   String fileName = path.substring(index + 1);   for (String word : words) {    context.write(new Text(word + ":" + fileName), new Text("1"));   }   // map输出结果 <"hello:a",{1,1,1}>  } }

定义Combiner类, 该类继承org.apache.hadoop.mapreduce.Reducer

combine阶段是map阶段和reduce阶段的中间过程

并重写reduce()方法

 public class IICombiner extends Reducer {  @Override  protected void reduce(Text key, Iterable values, Context context)    throws IOException, InterruptedException {   String[] data = key.toString().split(":");   String word = data[0];   String fileName = data[1];   int count = 0;   for (Text value : values) {    count += Integer.parseInt(value.toString());   }   context.write(new Text(word), new Text(fileName + ":" + count));   // combine输出结果 <"hello",{"a:3","b:1","c:2"}>  } }

定义Reducer类, 该类继承org.apache.hadoop.mapreduce.Reducer

并重写reduce()方法

 public class IIReducer extends Reducer {  @Override  protected void reduce(Text key, Iterable values, Context context)    throws IOException, InterruptedException {   StringBuilder sb = new StringBuilder();   for (Text value : values) {    sb.append(value.toString() + "\t");   }   context.write(key, new Text(sb.toString()));   // reduce输出结果 <"hello","a:3,b:1,c:2">  } }

测试倒排索引

 public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {  Job job = Job.getInstance(new Configuration());  job.setJarByClass(InverseIndexRunner.class);    // 设置job的主类  job.setMapperClass(IIMapper.class);    // 设置Mapper类  job.setCombinerClass(IICombiner.class);    // 设置Combiner类  job.setReducerClass(IIReducer.class);    // 设置Reducer类  job.setMapOutputKeyClass(Text.class);    // 设置map阶段输出Key的类型  job.setMapOutputValueClass(Text.class);    // 设置map阶段输出Value的类型  job.setOutputKeyClass(Text.class);    // 设置reduce阶段输出Key的类型  job.setOutputValueClass(Text.class);    // 设置reduce阶段输出Value的类型  // 设置job输入路径(从main方法参数args中获取)  FileInputFormat.setInputPaths(job, new Path(args[0]));  // 设置job输出路径(从main方法参数args中获取)  FileOutputFormat.setOutputPath(job, new Path(args[1]));  job.waitForCompletion(true);    // 提交job }

job输出的结果文件:

hadoop a:1 b:3
hello b:1 c:2 a:3
spark b:1 c:2
world c:2 b:1 a:2

看完上述内容，你们对MapReduce中怎么实现倒排索引有进一步的了解吗？如果还想了解更多知识或者相关内容，请关注行业资讯频道，感谢大家的支持。