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。
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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，请关注创新互联行业资讯频道，感谢大家的支持。
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