Hadoop MapReduce不写输出

Question

我创建了一个文件并添加了一些数字，如10,20,220和228。我想在我的mapper函数中读取此文件，如下所示，并检查数字是否为Amicable。但在编译类文件并构建jar之后，输出文件中没有任何内容。

public class FriendlyNumbers { 

    public static void main(String[] args) throws Exception { 
     Configuration conf = new Configuration(); 
     Job job = Job.getInstance(conf, "befriended numbers"); 
     job.setJarByClass(FriendlyNumbers.class); 
     job.setMapperClass(FriendlyNumberMapper.class); 
//  job.setCombinerClass(IntSumReducer.class); 
     job.setReducerClass(FriendlyNumberKeywordReducer.class); 

     job.setMapOutputKeyClass(IntWritable.class); 
     job.setMapOutputValueClass(NumberCouple.class); 
     job.setOutputKeyClass(IntWritable.class); 
     job.setOutputValueClass(IntWritable.class); 

     FileInputFormat.setInputPaths(job, new Path(args[0])); 
     FileOutputFormat.setOutputPath(job, new Path(args[1])); 
     System.exit(job.waitForCompletion(true) ? 0 : 1); 
    } 
} 

class FriendlyNumberMapper extends Mapper<Object, Text, IntWritable, NumberCouple> { 

    // process all the input data 
    // the data come's from the file file0 

    private IntWritable number = new IntWritable(); // number from file 
    private IntWritable sum = new IntWritable(); // number from calculateSum() 
    private NumberCouple numberCouple = new NumberCouple(); 

    public void map(Object key, Text value, Context context) throws IOException, InterruptedException { 

     StringTokenizer numberTokens = new StringTokenizer(value.toString()); 

     // loop trough all given numbers 
     while (numberTokens.hasMoreTokens()) { 

      int parsedNumberToken = Integer.parseInt(numberTokens.nextToken()); 
      int calculatedSum = calculateSum(parsedNumberToken); 

      // set stuff 
      number.set(parsedNumberToken); 
      sum.set(calculatedSum); 
      numberCouple.set(number, sum); 

      context.write(sum, numberCouple); 

      if (number.get() != sum.get()) { 
       context.write(number, numberCouple); 
      } 
     } 
    } 

    // the actual sum to check if a number is amicable 
    public int calculateSum(int number) { 
     int sum = 0; 

     for (int i = 1; i <= number/2; i++) { 
      if (number % i == 0) { 
       sum += i; 
      } 
     } 
     return sum; 
    } 
} 

class FriendlyNumberKeywordReducer extends Reducer<IntWritable, NumberCouple, IntWritable, IntWritable> { 

    // combine data 
    // in this case: get only the befriended numbers and remove others 

    public void reduce(IntWritable key, Iterable<NumberCouple> values, Context context) throws IOException, InterruptedException { 
     // 
    } 
} 

class NumberCouple implements WritableComparable<NumberCouple> { 

    private IntWritable number; 
    private IntWritable sum; 

    public NumberCouple() { 
     set(new IntWritable(), new IntWritable()); 
    } 

    public NumberCouple(NumberCouple couple) { 
     set(new IntWritable(couple.number.get()), new IntWritable(couple.sum.get())); 
    } 

    public NumberCouple(int number, int sum) { 
     set(new IntWritable(number), new IntWritable(sum)); 
    } 

    public void set(IntWritable number, IntWritable sum) { 
     this.number = number; 
     this.sum = sum; 
    } 

    public IntWritable getNumber() { 
     return this.number; 
    } 

    public IntWritable getSum() { 
     return this.sum; 
    } 

    @Override 
    public void write(DataOutput out) throws IOException { 
     number.write(out); 
     sum.write(out); 
    } 

    @Override 
    public void readFields(DataInput in) throws IOException { 
     number.readFields(in); 
     sum.readFields(in); 
    } 

    @Override 
    public int compareTo(NumberCouple o) { 
     return number.compareTo(o.number); 
    } 
} 

Answer 1

由于您没有将numReduceTask设置为“0”，所以它会转到Reducer并尝试运行reduce任务。

所以，如果你想运行只有地图的作业，请将numReduceTask设置为“0”。你不需要设置ReducerClass。在驱动程序中使用以下内容

Job job = Job.getInstance(conf, "befriended numbers"); 

// Set this property to Zero to run map-only job 
job.setNumReduceTasks(0); 

job.setJarByClass(FriendlyNumbers.class); 
job.setMapperClass(FriendlyNumberMapper.class); 
job.setCombinerClass(IntSumReducer.class); 
job.setMapOutputKeyClass(IntWritable.class); 
job.setMapOutputValueClass(NumberCouple.class); 
job.setOutputKeyClass(IntWritable.class); 
job.setOutputValueClass(IntWritable.class);