【大数据实验】Mapreduce的应用：统计词频，输出出现次数最多的前十个单词

【大数据实验】Mapreduce的应用：统计词频，输出出现次数最多的前十个单词

目录

实验内容

实验命令 *** 作

实验源码 

实验结果

实验报告

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实验内容

熟悉在Linux使用Java编写Mapreduce程序，编写wordcount程序，找出词频排在前十的词。

实验命令 *** 作

打开终端，根据一下提示，输入命令：

启动hadoop
cd /usr/local/hadoop
./sbin/start-dfs.sh

判断hadoop是否启动成功，成功会有显示
jps

查看jar包

cd /usr/local/hadoop/myapp
ls 

上传词频文件cipin.txt  将其存放在input文件夹中存放的内容是你要统计词频的语段
./bin/hdfs dfs -put /home/hadoop/cipin.txt input

查看hadoop目录  查看input文件夹下的内容
./bin/hdfs dfs -ls
./bin/hdfs dfs -ls input

删除output文件夹，不删除会报错
./bin/hdfs dfs -rm -r output./

运行实验3程序
./bin/hadoop jar ./myapp/shiyan3.jar input output
显示运行结果
./bin/hdfs dfs -cat output/*

关闭hadoop
./sbin/stop-dfs.sh

PS:cipin.txt的内容需要自己编写。可以在本地编辑好了，再上传到hadoop。

实验源码 

package sy;
import java.io.IOException;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.linkedList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;
import org.apache.hadoop.util.GenericOptionsParser;


public class shiyan3 {

    public static class WsMapper extends Mapper {
        @Override
        public void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {
            String[] split = value.toString().split(" ");
            for (String word : split) {
                context.write(new Text(word), new IntWritable(1));
            }
        }
    }

    public static class WsReducer extends Reducer{

        Map map=new HashMap();
        public void reduce(Text key, Iterable iter,Context conext) throws IOException, InterruptedException {
            int  count=0;
            for (IntWritable wordCount : iter) {
                count+=wordCount.get();
            }
            String name=key.toString();
            map.put(name, count);
        }

        @Override
        public void cleanup(Context context)throws IOException, InterruptedException {

            //这里将map.entrySet()转换成list
            List> list=new linkedList>(map.entrySet());
            //通过比较器来实现排序
            Collections.sort(list,new Comparator>() {

                //降序排序
                @Override
                public int compare(Entry arg0,Entry arg1) {
                    return (int) (arg1.getValue() - arg0.getValue());
                }
            });

            for(int i=0;i<10;i++){
                context.write(new Text(list.get(i).getKey()), new IntWritable(list.get(i).getValue()));
            }
        }
    }
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        if (otherArgs.length != 2) {
            System.err.println("输入参数个数为："+otherArgs.length+"，Usage: wordcount  ");
            System.exit(2);//终止当前正在运行的java虚拟机
        }
        Job job = Job.getInstance(conf, "CleanUpJob");
        job.setJarByClass(shiyan3.class);
        job.setMapperClass(WsMapper.class);
        job.setReducerClass(WsReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        for(int i = 0; i < otherArgs.length - 1; ++i) {
            FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
        }
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1]));
        System.exit(job.waitForCompletion(true)?0:1);
    }
}

实验结果

实验报告

 《大数据导论》MapReduce的应用.docx-Hadoop文档类资源-CSDN下载