无法在hadoop中使用mapreduce获得预期的缩减输出

t98cgbkg 于 2021-05-29 发布在 Hadoop

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我正在努力学习mapreduce并完成这个任务。
我的意见如下（州、运动、金额（美元））： California Football 69.09 California Swimming 31.5 Illinois Golf 8.31 Illinois Tennis 15.75 Oklahoma Golf 15.44 Oklahoma Tennis 8.33 Texas Golf 16.71 Texas Swimming 71.59 Washington Football 50.32000000000001 我期待我的输出，这样的输出应该显示哪项运动是在特定的国家流行（取决于体育项目的最高销售）。例如： California Football 69.09 Illinois Tennis 15.75 Oklahoma Golf 15.44 等等
下面是我的Map器、减速器和驱动程序代码：
Map程序代码：

package org.assignment.sports;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class Sports_Mapper2 extends Mapper<LongWritable, Text, Text, Text>{
    public void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException{
        String[] s= value.toString().split(" ");
        String Sport_State = s[0];
        String other = s[1]+" "+s[2];
        context.write(new Text(Sport_State), new Text(other));
    }
}

减速机代码：

package org.assignment.sports;

import java.io.IOException;

import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class Sports_Reducer2 extends Reducer<Text, Text, Text, DoubleWritable>{

    private static double MAX=0.00;
    public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException
    {
        //String[] k= values.toString().split(" ");
        for (Text value:values){
            String[] k= value.toString().split(" ");
            DoubleWritable price = new DoubleWritable(Double.parseDouble(k[1]));
        if(price.get()>MAX){
            MAX = price.get();
        }
        else{
            continue;
        }
        String ss = key.toString()+" "+ k[0];
        context.write(new Text(ss), new DoubleWritable(MAX));
        }               
        }

}

驱动程序代码：

package org.assignment.sports;

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

public class Sports_Driver2 {
    public static void main(String[] args) throws Exception
    {
        Configuration conf = new Configuration();

        Job job = new Job(conf, "Sports_Driver2");

        String[] otherArgs =new GenericOptionsParser(conf, args).getRemainingArgs();

        job.setJarByClass(Sports_Driver2.class);
        job.setMapperClass(Sports_Mapper2.class);
        job.setReducerClass(Sports_Reducer2.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);

        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job,new Path(otherArgs[1]));

        System.exit(job.waitForCompletion(true)? 0: 1);
    }

}

我得到的输出如下： California Football 69.09 Texas Swimming 71.59 我哪里出错了？感谢您的帮助

Java hadoop mapreduce

来源：https://stackoverflow.com/questions/34935962/unable-to-get-the-expected-reduced-output-using-mapreduce-in-hadoop

2条答案

按热度按时间

ttp71kqs1#

要在reducer中取每个值的最大值作为键，您还需要跟踪运动的名称。否则会产生错误的结果。请尝试下面的代码。
司机

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

public class Sports_Driver2 {
    public static void main(String[] args) throws Exception
    {
        Configuration conf = new Configuration();

        FileSystem fs = FileSystem.get(conf);
        Job job = new Job(conf, "Sports_Driver2");

        String[] otherArgs =new GenericOptionsParser(conf, args).getRemainingArgs();

        job.setJarByClass(Sports_Driver2.class);
        job.setMapperClass(Sports_Mapper2.class);
        job.setReducerClass(Sports_Reducer2.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);

        if(fs.exists(new Path(otherArgs[1]))){
            fs.delete(new Path(otherArgs[1]), true);
        }

        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job,new Path(otherArgs[1]));

        System.exit(job.waitForCompletion(true)? 0: 1);
    }

}

制图器

public class Sports_Mapper2 extends Mapper<LongWritable, Text, Text, Text>{
    public void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException{
        String[] s= value.toString().split(" ");
        String Sport_State = s[0];
        String other = s[1]+" "+s[2];
        context.write(new Text(Sport_State), new Text(other));
    }
}

减速机

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.Map.Entry;

import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class Sports_Reducer2 extends Reducer<Text, Text, Text, DoubleWritable>{

    Text keyEmit = new Text();
    public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException
    {
        Map<String,Double> getMax = new HashMap<>();
        String sportName = "";
        for (Text value:values){
            String[] k= value.toString().split(" ");
            sportName = k[0];
            //store values
            getMax.put(sportName, Double.parseDouble(k[1]));
        }   
        /*
         * Get maximum
         */
        Map.Entry<String, Double> maxEntry = null;
        for (Entry<String, Double> entry : getMax.entrySet())
        {
            if (maxEntry == null || entry.getValue().compareTo(maxEntry.getValue()) > 0)
            {
                maxEntry = entry;
            }
        }
        keyEmit.set(key.toString()+" "+maxEntry.getKey());
        context.write(keyEmit, new DoubleWritable(maxEntry.getValue()));
    }

}

输出

California Football 69.09
Illinois Tennis 15.75
Oklahoma Golf   15.44
Texas Swimming  71.59
Washington Football 50.32000000000001

希望这有帮助。

赞(0）回复(0）举报 2021-05-30

yr9zkbsy2#

问题是在写入每个特定状态后，没有重置减速器中的最大值。

String ss = key.toString()+" "+ k[0];
context.write(new Text(ss), new DoubleWritable(MAX));
MAX = 0.00;

赞(0）回复(0）举报 2021-05-29

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无法在hadoop中使用mapreduce获得预期的缩减输出

2条答案

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