HDFS与MAPREDUCE操作

二、实验内容

1.虚拟机集群搭建部署hadoop

安装VM

 

在VM中安装Linux

 

在linux中安装hadoop

 

2.HDFS文件操作

启动hadoop

 

上传文件

 

3.MAPREDUCE并行程序开发

3.1  求每年最高气温

import java.io.IOException;
 
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;
public class Temperature {
    /**
     * 四个泛型类型分别代表：
     * KeyIn        Mapper的输入数据的Key，这里是每行文字的起始位置（0,11,...）
     * ValueIn      Mapper的输入数据的Value，这里是每行文字
     * KeyOut       Mapper的输出数据的Key，这里是每行文字中的“年份”
     * ValueOut     Mapper的输出数据的Value，这里是每行文字中的“气温”
     */
    static class TempMapper extends
            Mapper<LongWritable, Text, Text, IntWritable> {
        @Override
        public void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            // 打印样本: Before Mapper: 0, 2000010115
            System.out.print("Before Mapper: " + key + ", " + value);
            String line = value.toString();
            String year = line.substring(0, 4);
            int temperature = Integer.parseInt(line.substring(8));
            context.write(new Text(year), new IntWritable(temperature));
            // 打印样本: After Mapper:2000, 15
            System.out.println(
                    "======" +
                    "After Mapper:" + new Text(year) + ", " + new IntWritable(temperature));
        }
    }
 
    /**
     * 四个泛型类型分别代表：
     * KeyIn        Reducer的输入数据的Key，这里是每行文字中的“年份”
     * ValueIn      Reducer的输入数据的Value，这里是每行文字中的“气温”
     * KeyOut       Reducer的输出数据的Key，这里是不重复的“年份”
     * ValueOut     Reducer的输出数据的Value，这里是这一年中的“最高气温”
       static class TempReducer extends
            Reducer<Text, IntWritable, Text, IntWritable> {
        @Override
        public void reduce(Text key, Iterable<IntWritable> values,
                Context context) throws IOException, InterruptedException {
            int maxValue = Integer.MIN_VALUE;
            StringBuffer sb = new StringBuffer();
            //取values的最大值
            for (IntWritable value : values) {
                maxValue = Math.max(maxValue, value.get());
                sb.append(value).append(", ");
            }
            // 打印样本： Before Reduce: 2000, 15, 23, 99, 12, 22, 
            System.out.print("Before Reduce: " + key + ", " + sb.toString());
            context.write(key, new IntWritable(maxValue));
            // 打印样本： After Reduce: 2000, 99
            System.out.println(
                    "======" +
                    "After Reduce: " + key + ", " + maxValue);
        }
    }
 
    public static void main(String[] args) throws Exception {
        //输入路径
        String dst = "hdfs://localhost:9000/intput.txt";
        //输出路径，必须是不存在的，空文件加也不行。
        String dstOut = "hdfs://localhost:9000/output";
        Configuration hadoopConfig = new Configuration();
         
        hadoopConfig.set("fs.hdfs.impl", 
            org.apache.hadoop.hdfs.DistributedFileSystem.class.getName()
        );
        hadoopConfig.set("fs.file.impl",
            org.apache.hadoop.fs.LocalFileSystem.class.getName()
        );
        Job job = new Job(hadoopConfig);
         
        //如果需要打成jar运行，需要下面这句
        //job.setJarByClass(NewMaxTemperature.class);
 
        //job执行作业时输入和输出文件的路径
        FileInputFormat.addInputPath(job, new Path(dst));
        FileOutputFormat.setOutputPath(job, new Path(dstOut));
 
        //指定自定义的Mapper和Reducer作为两个阶段的任务处理类
        job.setMapperClass(TempMapper.class);
        job.setReducerClass(TempReducer.class);
         
        //设置最后输出结果的Key和Value的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);       
        //执行job，直到完成
        job.waitForCompletion(true);
        System.out.println("Finished");
    }

 

运行

 

 

 

3.2 词频统计

import java.io.IOException;
 
import org.apache.commons.lang.StringUtils;
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 key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context)
            throws IOException, InterruptedException {
        // TODO Auto-generated method stub
        //super.map(key, value, context);
        //String[] words = StringUtils.split(value.toString());
          String[] words = StringUtils.split(value.toString(), " ");
        for(String word:words)
        {
              context.write(new Text(word), new LongWritable(1));
            
        }                
    }    
}

 
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 arg0, Iterable<LongWritable> arg1,
            Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {
        // TODO Auto-generated method stub
        //super.reduce(arg0, arg1, arg2);
        int sum=0;
        for(LongWritable num:arg1)
        {
            sum += num.get();
            
        }
        context.write(arg0,new LongWritable(sum));
        
        
    }
}

 
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
 
public class WordCountRunner {
 
    public static void main(String[] args) throws IllegalArgumentException, IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();  
        Job job = new Job(conf);  
        job.setJarByClass(WordCountRunner.class);  
        job.setJobName("wordcount");  
        job.setOutputKeyClass(Text.class);  
        job.setOutputValueClass(LongWritable.class);  
        job.setMapperClass(WordCountMapper.class);  
        job.setReducerClass(WordCountReducer.class);  
        job.setInputFormatClass(TextInputFormat.class);  
        job.setOutputFormatClass(TextOutputFormat.class);  
        FileInputFormat.addInputPath(job, new Path(args[1]));  
        FileOutputFormat.setOutputPath(job, new Path(args[2]));  
        job.waitForCompletion(true);  
    }
    
}

 

上传单词文件

 

修改路径

 

运行后

 

Cat一下结果