Hadoop编写矩阵乘法实例及演示

Hadoop编写矩阵乘法的实现可以分为以下几个步骤：

1. 将输入的矩阵划分为多个块，每个块的大小由用户指定。

2. 将矩阵块分发给不同的计算节点。

3. 在每个计算节点上，对两个矩阵块进行乘法计算，并将结果输出到本地文件系统。

4. 将所有计算节点的输出合并，得到最终的矩阵乘积结果。

下面是一个简单的矩阵乘法实现示例，假设输入的矩阵都是稠密矩阵：

1. 首先定义一个Mapper类实现矩阵乘法的第一步，即将矩阵块分发给不同的计算节点。Mapper的输入为矩阵A和B的块，输出为每个元素相乘的结果。

public class MatrixMapper extends Mapper<LongWritable, Text, Text, Text> {
    private int blockHeightA;
    private int blockWidthB;
    private int blockWidthA;
    private int blockIndexA;
    private int blockIndexB;

    public void setup(Context context) {
        Configuration conf = context.getConfiguration();
        blockHeightA = conf.getInt("blockHeightA", 0);
        blockWidthB = conf.getInt("blockWidthB", 0);
        blockWidthA = conf.getInt("blockWidthA", 0);
        blockIndexA = conf.getInt("blockIndexA", 0);
        blockIndexB = conf.getInt("blockIndexB", 0);
    }

    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] input = value.toString().split(",");
        int rowA = Integer.parseInt(input[0]);
        int colA = Integer.parseInt(input[1]);
        int valA = Integer.parseInt(input[2]);
        int rowB = Integer.parseInt(input[3]);
        int colB = Integer.parseInt(input[4]);
        int valB = Integer.parseInt(input[5]);

        if (colA == rowB) {
            Text outputKey = new Text();
            Text outputValue = new Text();
            outputKey.set(rowA / blockHeightA + "," + colB / blockWidthB);
            outputValue.set(blockIndexA + "," + rowA % blockHeightA + "," + colA + "," + valA + "," + blockIndexB + "," + colB % blockWidthB + "," + valB);
            context.write(outputKey, outputValue);
        }
    }
}

2. 接下来定义一个Reducer类实现矩阵乘法的第二步，即对每个计算节点上的矩阵块进行乘法计算。Reducer的输入为每个块的索引和元素，输出为每个元素相乘的结果。

public class MatrixReducer extends Reducer<Text, Text, Text, IntWritable> {
    private int blockHeightA;
    private int blockWidthB;
    private int blockWidthA;

    public void setup(Context context) {
        Configuration conf = context.getConfiguration();
        blockHeightA = conf.getInt("blockHeightA", 0);
        blockWidthB = conf.getInt("blockWidthB", 0);
        blockWidthA = conf.getInt("blockWidthA", 0);
    }

    public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        List<Tuple> listA = new ArrayList<>();
        List<Tuple> listB = new ArrayList<>();

        for (Text value : values) {
            String[] input = value.toString().split(",");
            int blockIndexA = Integer.parseInt(input[0]);
            int rowA = Integer.parseInt(input[1]);
            int colA = Integer.parseInt(input[2]);
            int valA = Integer.parseInt(input[3]);
            int blockIndexB = Integer.parseInt(input[4]);
            int colB = Integer.parseInt(input[5]);
            int valB = Integer.parseInt(input[6]);

            if (blockIndexA == 0) {
                listA.add(new Tuple(rowA, colA, valA));
            } else {
                listB.add(new Tuple(rowA, colB, valB));
            }
        }

        for (Tuple a : listA) {
            for (Tuple b : listB) {
                if (a.col == b.row) {
                    Text outputKey = new Text();
                    IntWritable outputValue = new IntWritable();
                    outputKey.set(a.row + "," + b.col);
                    outputValue.set(a.val * b.val);
                    context.write(outputKey, outputValue);
                }
            }
        }
    }

    private static class Tuple {
        int row;
        int col;
        int val;

        public Tuple(int row, int col, int val) {
            this.row = row;
            this.col = col;
            this.val = val;
        }
    }
}

3. 最后，在主函数中配置Job并提交作业。

public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "Matrix Multiplication");
    job.setJarByClass(MatrixMultiplication.class);

    Path inputPathA = new Path(args[0]);
    Path inputPathB = new Path(args[1]);
    Path outputPath = new Path(args[2]);

    int blockHeightA = Integer.parseInt(args[3]);
    int blockWidthA = Integer.parseInt(args[4]);
    int blockWidthB = Integer.parseInt(args[5]);

    job.getConfiguration().setInt("blockHeightA", blockHeightA);
    job.getConfiguration().setInt("blockWidthA", blockWidthA);
    job.getConfiguration().setInt("blockWidthB", blockWidthB);

    job.setMapperClass(MatrixMapper.class);
    job.setReducerClass(MatrixReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);

    FileInputFormat.addInputPath(job, inputPathA);
    FileInputFormat.addInputPath(job, inputPathB);
    FileOutputFormat.setOutputPath(job, outputPath);

    job.waitForCompletion(true);
}

以上就是一个简单的Hadoop矩阵乘法实现示例。