为什么我的输出没有写在hadoop的输出文件中？

如何解决为什么我的输出没有写在hadoop的输出文件中？

我编写了以下代码来查找最高温度，但是当我尝试检索输出时，文件已创建但为空。我不太明白为什么会这样...有人可以帮忙吗？

我的跑步者代码：

import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;

public class MaxTemp {
    public static void main(String[] args) throws IOException {
        JobConf conf = new JobConf(MaxTemp.class);
        conf.setJobName("MaxTemp1");
        conf.setInputFormat(TextInputFormat.class);
        conf.setOutputFormat(TextOutputFormat.class);
        conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(IntWritable.class);
        conf.setMapperClass(MaxTempMapper.class);
        conf.setCombinerClass(MaxTempReducer.class);
        conf.setReducerClass(MaxTempReducer.class);

        FileInputFormat.setInputPaths(conf,new Path(args[0]));
        FileOutputFormat.setOutputPath(conf,new Path(args[1]));
        JobClient.runJob(conf);
    }
}

映射器代码：

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.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;

public class MaxTempMapper extends MapReduceBase implements Mapper<LongWritable,Text,IntWritable> {
    public void map(LongWritable key,Text value,OutputCollector<Text,IntWritable> output,Reporter reporter) throws IOException {
        String record = value.toString();
        String[] parts = record.split(",");
        output.collect(new Text(parts[0]),new IntWritable(Integer.parseInt(parts[1])));
    }
}

我的减速器代码：

import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;

public class MaxTempReducer extends MapReduceBase implements Reducer<Text,IntWritable,IntWritable> {
    public void reduce1(Text key,Iterator<IntWritable> values,Reporter reporter) throws IOException {
        int maxValue = 0;
        while (values.hasNext()) {
            maxValue=Math.max(maxValue,values.next().get());
        }
        output.collect(key,new IntWritable(maxValue));
    }


    @Override
    public void reduce(Text arg0,Iterator<IntWritable> arg1,IntWritable> arg2,Reporter arg3) throws IOException {
    // TODO Auto-generated method stub
    
    }
}

我将附加输出屏幕截图

解决方法

我不禁注意到，在MaxTempReducer类内部，您拥有reduce1函数，同时覆盖了要在外部使用的正确reduce函数 减速器等级。这就是为什么您没有在 HDFS 中获得任何输出的原因，因为该程序看到了reducer类，但是没有看到描述要处理的reduce函数它（也就是找到温度的最大值）。

还有一个问题，您正在使用deprecated classes from old versions of Hadoop，因为在令人满意的基础上对框架及其组件进行了更新测试（您可以自己检查here ）。

因此，通过解决这两个问题，您的程序可能看起来像这样：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.IntWritable;
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;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.Counters;
import java.io.*;
import java.io.IOException;
import java.util.*;
import java.nio.charset.StandardCharsets;

public class MaxTemp 
{
    /* input:  <byte_offset,line_of_dataset>
     * output: <City,Temperature>
     */
    public static class Map extends Mapper<Object,Text,IntWritable> 
    {
        public void map(Object key,Text value,Context context) throws IOException,InterruptedException 
        {
            String record = value.toString();
            String[] parts = record.split(",");

            context.write(new Text(parts[0]),new IntWritable(Integer.parseInt(parts[1])));
        }
    }

    /* input:  <City,Temperature>
     * output: <City,Max Temperature>
     */
    public static class Reduce extends Reducer<Text,IntWritable,IntWritable>
    {
        public void reduce(Text key,Iterable<IntWritable> values,InterruptedException 
        {
            int max_value = 0;
            
            for(IntWritable value : values)
            {
                if(value.get() > max_value)
                    max_value = value.get();
            }

            context.write(key,new IntWritable(max_value));
        }
    }


    public static void main(String[] args) throws Exception
    {
        // set the paths of the input and output directories in the HDFS
        Path input_dir = new Path("temperatures");
        Path output_dir = new Path("temp_out");

        // in case the output directory already exists,delete it
        Configuration conf = new Configuration();
        FileSystem fs = FileSystem.get(conf);
        if(fs.exists(output_dir))
            fs.delete(output_dir,true);

        // configure the MapReduce job
        Job maxtemp_job = Job.getInstance(conf,"Max Temperature");
        maxtemp_job.setJarByClass(MaxTemp.class);
        maxtemp_job.setMapperClass(Map.class);
        maxtemp_job.setCombinerClass(Reduce.class);
        maxtemp_job.setReducerClass(Reduce.class);    
        maxtemp_job.setMapOutputKeyClass(Text.class);
        maxtemp_job.setMapOutputValueClass(IntWritable.class);
        maxtemp_job.setOutputKeyClass(Text.class);
        maxtemp_job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(maxtemp_job,input_dir);
        FileOutputFormat.setOutputPath(maxtemp_job,output_dir);
        maxtemp_job.waitForCompletion(true);
    }
}

HDFS中的temperatures目录如下所示：

Boston,3
Athens,15
Tokyo,20
Tokyo,10
Athens,32
Boston,9

temp_out目录中的结果如下所示（来自Hadoop HDFS浏览器的图像）：