Hadoop实现矩阵相乘

Hadoop可以通过MapReduce框架来实现矩阵相乘，具体步骤如下：

1. 将矩阵A和矩阵B分别存储在HDFS中，并将它们进行分块，每个块的大小可以根据实际情况来确定。

2. 编写Map函数，将矩阵A和矩阵B的分块读入内存中，进行矩阵相乘操作，并将结果输出为键值对（key-value）的形式。其中，key表示输出矩阵的行和列，value表示矩阵相乘后的结果。

3. 编写Reduce函数，将Map函数输出的键值对进行合并，得到最终的矩阵相乘结果。

具体实现细节可以参考以下代码：

Map函数：

public static class MatrixMapper extends Mapper<LongWritable, Text, Text, Text> {
    private Text outKey = new Text();
    private Text outValue = new Text();
    private int row = 0;
    private int col = 0;
    private int n = 0;
    private String flag = "";

    public void setup(Context context) throws IOException, InterruptedException {
        Configuration conf = context.getConfiguration();
        row = Integer.parseInt(conf.get("row"));
        col = Integer.parseInt(conf.get("col"));
        n = Integer.parseInt(conf.get("n"));
        flag = conf.get("flag");
    }

    public void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String line = value.toString();
        String[] tokens = line.split(",");
        if (flag.equals("A")) {
            int i = Integer.parseInt(tokens[0]);
            int j = Integer.parseInt(tokens[1]);
            int v = Integer.parseInt(tokens[2]);
            for (int k = 1; k <= n; k++) {
                outKey.set(i + "," + k);
                outValue.set("A," + j + "," + v);
                context.write(outKey, outValue);
            }
        } else {
            int j = Integer.parseInt(tokens[0]);
            int k = Integer.parseInt(tokens[1]);
            int v = Integer.parseInt(tokens[2]);
            for (int i = 1; i <= row; i++) {
                outKey.set(i + "," + k);
                outValue.set("B," + j + "," + v);
                context.write(outKey, outValue);
            }
        }
    }
}

Reduce函数：

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

    public void reduce(Text key, Iterable<Text> values, Context context)
            throws IOException, InterruptedException {
        int[] a = new int[n + 1];
        int[] b = new int[n + 1];
        for (Text value : values) {
            String[] tokens = value.toString().split(",");
            if (tokens[0].equals("A")) {
                int j = Integer.parseInt(tokens[1]);
                int v = Integer.parseInt(tokens[2]);
                a[j] = v;
            } else {
                int j = Integer.parseInt(tokens[1]);
                int v = Integer.parseInt(tokens[2]);
                b[j] = v;
            }
        }
        int sum = 0;
        for (int i = 1; i <= n; i++) {
            sum += a[i] * b[i];
        }
        outValue.set(String.valueOf(sum));
        context.write(key, outValue);
    }
}

调用MapReduce作业：

Configuration conf = new Configuration();
conf.set("row", String.valueOf(row));
conf.set("col", String.valueOf(col));
conf.set("n", String.valueOf(n));
Job job = Job.getInstance(conf, "MatrixMultiply");
job.setJarByClass(MatrixMultiply.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);

以上代码实现了矩阵相乘的MapReduce作业，其中参数row、col和n分别表示矩阵A的行数、矩阵B的列数和矩阵A的列数（也是矩阵B的行数）。