我想实现dpc（通过快速搜索和密度峰的发现进行聚类）的算法。这是一项艰巨的工作，所以我决定从rho的计算开始。
这是Map：

public void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            String[] lineSplit = line.split(" ");
            if (Double.parseDouble(lineSplit[2]) < dcThreshold) {
                IntWritable one = new IntWritable(
                        Integer.parseInt(lineSplit[0]));
                IntWritable two = new IntWritable(
                        Integer.parseInt(lineSplit[1]));
                context.write(one, two);
            }
        }

这里是减速机：

public void reduce(IntWritable key, IntWritable values, Context context)
                throws IOException, InterruptedException {
            int[] indexs = new int[2];
            indexs[0] = Integer.parseInt(key.toString());
            indexs[1] = Integer.parseInt(values.toString());
            for (int i = 0; i < indexs.length; i++) {
                densityCountMap.put(indexs[i],
                        densityCountMap.get(indexs[i]) + 1);
            }
        }

问题

densitycountmap是一个哈希Map，只有在完成之后才能正确。如何输出densitycountmap？以什么方式？

---------解决方案---------

多亏了mbaxi，你提到reduce定义不正确，densitycountmap不是必需的，这让我深受启发。
我应该更清楚地说明，目标是如果linesplit[2]低于某个阈值，那么linesplit[0]和linesplit[1]都会增加。实际上，没有必要覆盖清理。
Map器：

public static class TokenizerMapper extends
        Mapper<LongWritable, Text, IntWritable, IntWritable> {
    private final static IntWritable count = new IntWritable(1);

    public void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String line = value.toString();
        String[] lineSplit = line.split(" ");
        if (Double.parseDouble(lineSplit[2]) < dcThreshold) {
            IntWritable one = new IntWritable(
                    Integer.parseInt(lineSplit[0]));
            IntWritable two = new IntWritable(
                    Integer.parseInt(lineSplit[1]));
            context.write(one, count);// Both should be increased 
            context.write(two, count);// both as key
        }
    }
}

减速器：

public static class IntSumReducer extends
            Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(IntWritable key, Iterable<IntWritable> values,
                Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);//densityCountMap is redundant if having known better the structure of Map/reduce
            context.write(key, result);//it equals to output densityCountMap
        }
    }

再次感谢，你带来的不仅仅是帮助，更是灵感。