我正在尝试实现不同的模式：

map(key, record):
  emit record,null
reduce(key, records):
  emit key

我的钥匙是复杂的，定制的 Writable . 如果我在reduce中发出键及其哈希代码：

context.write(key, new IntWtitable(key.hashCode());

我收到以下输出：

key1 -1808937256
key2 -768063202
key3 906064410
key2 -768063202
key3 906064410

理论上，输出应该只包含 key1 , key2 ，和 key3 因为我用的是 HashPartitioner ：具有相等哈希代码的键组合到同一分区中。这里显然不是这样。
如果我要转换我的情结 Writable 变成一个 Text 对象（并相应地调整Map器/还原器类），并在 Mapper :

context.write(new Text(key.toString()), NullWritable.get());

... 输出如预期：

key1 1013632023
key2 762485389
key3 -1193948769

好的，这里有一个简单的例子来说明这个行为。
输入：

A A A A A
B B B B B
C C C C C
A A A A A
B B B B B

mapreduce作业：

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.ArrayWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
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.Tool;
import org.apache.hadoop.util.ToolRunner;

public class DistinctPattern extends Configured implements Tool {
public static class DistinctMapper extends Mapper<Object, Text, ComplexObject, NullWritable> {

    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        ComplexObject o = new ComplexObject(value.toString());
        context.write(o, NullWritable.get());
    }
}

public static class DistinctReducer extends Reducer<ComplexObject, NullWritable, ComplexObject, IntWritable> {

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

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

public static class MyArrayWritable extends ArrayWritable {

    public MyArrayWritable(Writable[] values) {
        super(DatumObject.class, values);
    }

    public MyArrayWritable() {
        super(DatumObject.class);
    }

    @Override
    public String toString() {
        return Arrays.toString(get());
    }

}

public static class DatumObject implements Writable {
    private String datum;

    public DatumObject() {}

    public DatumObject(String d) {
        datum = d;
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        datum = in.readUTF();
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(datum);    
    }

    @Override
    public String toString() {
        return datum;
    }

    @Override
    public int hashCode() {
        return 31 * datum.hashCode();
    }

}

public static class ComplexObject implements WritableComparable<ComplexObject> {
    private List<DatumObject> data = new ArrayList<>();

    public ComplexObject() {}

    public ComplexObject(String d) {
        String[] elements = d.split(" ");
        for(int i = 0; i < elements.length; i++)
            data.add(new DatumObject(elements[i]));
    }

    public int size() {
        return data.size();
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        data.clear();
        MyArrayWritable m = new MyArrayWritable();
        m.readFields(in);
        Writable[] w = m.get();
        for(int i = 0; i < w.length; i++)
            data.add((DatumObject) w[i]);

    }

    @Override
    public void write(DataOutput out) throws IOException {
        MyArrayWritable m = new MyArrayWritable(data.toArray(new DatumObject[data.size()]));
        m.write(out);
    }

    @Override
    public int compareTo(ComplexObject o) {
        if(this.equals(o))
            return 0;

        if(o.size() < this.size())
            return -1;

        return 1;
    }

    @Override
    public boolean equals(Object obj) {
        if(!(obj instanceof ComplexObject))
            return false;

        ComplexObject other = (ComplexObject) obj;
        return other.data.equals(data);
    }

    @Override
    public int hashCode() {
        return 31 * data.hashCode();
    }

    @Override
    public String toString() {
        StringBuilder s= new StringBuilder();
        data.forEach( entry -> {
            s.append(entry); 
            s.append(" ");
        });

        return s.toString();
    }

}

@Override
public int run(String[] args) throws Exception {
    Job job = Job.getInstance();
    job.setJar("distinct.jar");
    job.setJarByClass(DistinctPattern.class);
    job.setMapperClass(DistinctMapper.class);
    job.setReducerClass(DistinctReducer.class);
    job.setMapOutputKeyClass(ComplexObject.class);
    job.setMapOutputValueClass(NullWritable.class);
    job.setOutputKeyClass(ComplexObject.class);
    job.setOutputValueClass(IntWritable.class);

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

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

public static void main(String[] args) throws Exception {       
    int exitCode = ToolRunner.run(new DistinctPattern(), args);
    System.exit(exitCode);
}
}

预期产量：

A A A A A       368623362
B B B B B       1285710467
C C C C C       -2092169724

实际输出：

A A A A A       368623362
B B B B B       1285710467
C C C C C       -2092169724
A A A A A       368623362
B B B B B       1285710467

我错过了什么？
ps:hadoop 2.7.3版