Hadoop 正在跳过Reducer，并且只生成映射器结果

我看过一些相关的帖子，但在我的例子中，一切都很好，但是，当我运行Map reduce作业时，Reducer部分被跳过了。我正在努力解决这个问题。任何人都请找出问题并帮助作业运行

映射任务：

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我已正确设置了所有配置和参数，但我的Reduce任务不再执行。如果您有任何建议，我们将不胜感激。

有人能提供此方面的最新信息吗？这是必需的，谢谢。您有没有整理好？我也遇到了同样的问题。。。

package org.netflix.rating;

import java.io.IOException;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
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 NetflixAvgRatingMap extends Mapper<LongWritable,Text,LongWritable,IntWritable>{

    public void map(LongWritable key,Text value,Context context){
        String NetflixEntrypattern="^(\\s*)([0-9]+)(\\s+)([0-9]+)(\\s+)(\\d{1})(\\s+)(.*)";
        Pattern p = Pattern.compile(NetflixEntrypattern);
        Matcher matcher = p.matcher(value.toString());
        if (!matcher.matches()) {
            return;
    }
    Long movie_id=Long.parseLong(matcher.group(4));
    int rating=Integer.parseInt(matcher.group(6));
    try {
        context.write(new LongWritable(movie_id),new IntWritable(rating));
    } catch (IOException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    } catch (InterruptedException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    }

}

package org.netflix.rating;

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.io.FloatWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.Reducer;



public class NetflixAvgRatingReducer extends Reducer<LongWritable,IntWritable,LongWritable,FloatWritable> {
          public void reduce(LongWritable key,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{
              int sum=0,count=0;
                while(values.iterator().hasNext()){
                sum+=values.iterator().next().get();
                count++;
              }
              float avg=(float)sum/count;

                context.write(key,new FloatWritable(avg));

              }

          }

package org.netflix.rating;


import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.FloatWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.Job;


public class NetflixAvgRating {

    public static void main(String[] args) throws Exception {


        Configuration conf = new Configuration();

        // Create job
        Job job = new Job(conf, "NetflixAvgRating");
        job.setJarByClass(NetflixAvgRating.class);

        // Setup MapReduce job
        job.setMapperClass(NetflixAvgRatingMap.class);
        job.setReducerClass(NetflixAvgRatingReducer.class);


        job.setOutputKeyClass(LongWritable.class);
        job.setOutputValueClass(FloatWritable.class);
        job.setMapOutputKeyClass(LongWritable.class);
        job.setMapOutputValueClass(IntWritable.class);

        // Input
        FileInputFormat.addInputPath(job, new Path(args[0]));
        //job.setInputFormatClass(TextInputFormat.class);

        // Output
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        //job.setOutputFormatClass(TextOutputFormat.class);

        // Execute job
        int code = job.waitForCompletion(true) ? 0 : 1;
        System.exit(code);
    }
}