我主要有这个。。。

job.setMapperClass(AverageIntMapper.class);
    job.setCombinerClass(AverageIntCombiner.class);
    job.setReducerClass(AverageIntReducer.class);

合并器有不同的代码，但是合并器被完全忽略了，因为reducer使用的输出是Map器的输出。
我知道合流器可能不会被使用，但我认为合流器和减速机是一样的。我不太明白创建自定义组合器的意义，但是系统仍然可以跳过它的使用。
如果这是不应该发生的，什么可能是一个原因，合路器没有被使用？
代码。。。

import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.DoubleWritable;
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 AverageInt {

public static class AverageIntMapper extends Mapper<LongWritable, Text, Text, Text> {

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

        String n_string = value.toString();
        context.write(new Text("Value"), new Text(n_string));
    }
}

public static class AverageIntCombiner extends Reducer<Text, Text, Text, Text> {

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

        int sum = 0;
        int count = 0;

        for(IntWritable value : values) {
            int temp = Integer.parseInt(value.toString());
            sum += value.get();
            count += 1;
        }

        String sum_count = Integer.toString(sum) + "," + Integer.toString(count);

        context.write(key, new Text(sum_count));
    }
}

public static class AverageIntReducer extends Reducer<Text, Text, Text, Text> {

    public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {

        int total = 0;
        int count = 0;

        for(Text value : values) {
            String temp = value.toString();
            String[] split = temp.split(",");
            total += Integer.parseInt(split[0]);
            count += Integer.parseInt(split[1]);
        }

        Double average = (double)total/count;

        context.write(key, new Text(average.toString()));
    }
}

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

    if(args.length != 2) {
        System.err.println("Usage: AverageInt <input path> <output path>");
        System.exit(-1);
    }

    Job job = new Job();
    job.setJarByClass(AverageInt.class);
    job.setJobName("Average");

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

    job.setMapperClass(AverageIntMapper.class);
    job.setCombinerClass(AverageIntCombiner.class);
    job.setReducerClass(AverageIntReducer.class);

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

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