hadoop编写矩阵乘法

Hadoop编写矩阵乘法的一般步骤如下：

1. 将矩阵数据按行块划分为多个文件，每个文件包含一部分行数据。这样可以将大矩阵分解为小矩阵，方便并行计算。

2. Map阶段：每个Mapper读取一组矩阵，将其转换为稠密矩阵或稀疏矩阵，并将其转化为键值对（key-value）。其中键表示矩阵中每个元素所在的行和列，值表示该元素的值。

3. Shuffle阶段：将Map输出的键值对按照键进行排序，并按照键分组，每组对应一个Reducer。

4. Reduce阶段：对于每个Reducer，将其对应的所有键值对中的值进行乘法运算，最终得到输出的结果。

以下是Hadoop编写矩阵乘法的示例代码：

Mapper部分：

public class MatrixMapper extends Mapper<LongWritable, Text, Text, Text> {
    private Text outKey = new Text();
    private Text outValue = new Text();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 读取输入的矩阵数据
        String[] tokens = value.toString().split(",");
        String matrixName = tokens[0];
        int row = Integer.parseInt(tokens[1]);
        int col = Integer.parseInt(tokens[2]);
        int val = Integer.parseInt(tokens[3]);

        // 将矩阵数据转换为键值对
        if (matrixName.equals("A")) {
            for (int i = 1; i <= col; i++) {
                outKey.set(row + "," + i);
                outValue.set(matrixName + "," + col + "," + val);
                context.write(outKey, outValue);
            }
        } else {
            for (int i = 1; i <= row; i++) {
                outKey.set(i + "," + col);
                outValue.set(matrixName + "," + row + "," + val);
                context.write(outKey, outValue);
            }
        }
    }
}

Reducer部分：

public class MatrixReducer extends Reducer<Text, Text, Text, Text> {
    private Text outKey = new Text();
    private Text outValue = new Text();

    @Override
    protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        // 从输入的键值对中提取矩阵数据
        List<String> listA = new ArrayList<>();
        List<String> listB = new ArrayList<>();
        for (Text value : values) {
            String[] tokens = value.toString().split(",");
            String matrixName = tokens[0];
            int len = Integer.parseInt(tokens[1]);
            int val = Integer.parseInt(tokens[2]);

            // 将矩阵数据按照矩阵名称分别存储
            if (matrixName.equals("A")) {
                listA.add(len + "," + val);
            } else {
                listB.add(len + "," + val);
            }
        }

        // 对于每个矩阵元素进行乘法运算
        int result = 0;
        for (String a : listA) {
            String[] tokensA = a.split(",");
            int lenA = Integer.parseInt(tokensA[0]);
            int valA = Integer.parseInt(tokensA[1]);
            for (String b : listB) {
                String[] tokensB = b.split(",");
                int lenB = Integer.parseInt(tokensB[0]);
                int valB = Integer.parseInt(tokensB[1]);
                if (lenA == lenB) {
                    result += valA * valB;
                }
            }
        }

        // 输出结果
        outKey.set(key);
        outValue.set(String.valueOf(result));
        context.write(outKey, outValue);
    }
}

Driver部分：

public class MatrixMultiplication {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "matrix multiplication");
        job.setJarByClass(MatrixMultiplication.class);
        job.setMapperClass(MatrixMapper.class);
        job.setReducerClass(MatrixReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

以上代码仅供参考，实际情况需要根据具体需求进行修改。