hadoop编程之词频统计

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数据集实例

java代码，编程

实例

我们要先创建三个类分别为WordCoutMain、WordCoutMapper、WordCoutReducer这三个类

对应的代码如下

WordCoutMain

import java.io.IOException;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class WordCountMain{public static void main(String[] args) throws Exception {Configuration conf = new Configuration();Job job = Job.getInstance(conf);job.setJarByClass(WordCountMain.class);job.setMapperClass(WordCountMapper.class);job.setReducerClass(WordCountReducer.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(LongWritable.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(LongWritable.class);FileInputFormat.setInputPaths(job, new Path(args[0]));FileOutputFormat.setOutputPath(job, new Path(args[1]));job.waitForCompletion(true);}}

WordCoutMapper

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;public class WordCountMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
@Override
protected void map(LongWritable key1, Text value1, Context context)
throws IOException, InterruptedException {String data = value1.toString();String[] words = data.split(" ");for(String w:words){context.write(new Text(w),new LongWritable(1));}}
}

WordCoutReducer

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;public class WordCountReducer extends Reducer<Text, LongWritable,Text, LongWritable> {
@Override
protected void reduce(Text k3, Iterable<LongWritable> v3,Context   context) throws IOException,InterruptedException {long  total = 0;for(LongWritable v:v3){total+=v.get();}context.write(k3, new LongWritable(total));}
}

对应的使用命令

hadoop jar 1.jar  ch01.WordCountMain  /user/data/input/hamlet.txt  /user/data/output/ch1hadoop jar 包名   引用主类   输入文件路径  输出文件路径

结果展示

 

学习链接：

在Ubuntu上用mapreduce进行词频统计（伪分布式）_mapreduce怎么统计txt文件词频终端-CSDN博客

利用mapreduce统计部门的最高工资_使用mapreduce查询某个部门中薪资最高的员工姓名,如果输出结果的格式为“薪资 员-CSDN博客

 hadoop编程之工资序列化排序-CSDN博客

hadoop编程之部门工资求和-CSDN博客

hadoop编程之词频统计-CSDN博客

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