Hadoop编写矩阵乘法的实例

Hadoop编写矩阵乘法的实例主要分为三个步骤：

1. Map阶段：将矩阵A和矩阵B分别划分为若干个小块，每个Map任务负责计算其中的一块矩阵乘积，并将结果输出为键值对。

2. Shuffle阶段：将所有Map任务输出的键值对按照键进行排序并分组，确保相同的键（即矩阵乘积的行和列）被分到同一个Reduce任务中。

3. Reduce阶段：将每个Reduce任务分别计算相同键的值的和，即计算矩阵乘积中某个位置的值，并将结果输出。

下面是Hadoop编写矩阵乘法的示例代码：

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MatrixMultiplication {
    public static class MatrixMapper extends Mapper<LongWritable, Text, Text, Text> {
        private Text outKey = new Text();
        private Text outValue = new Text();
        private int matrixSize = 2; // 矩阵大小为2x2

        public void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String[] line = value.toString().split(",");
            if (line[0].equals("A")) {
                for (int i = 0; i < matrixSize; i++) {
                    outKey.set(line[1] + "," + i);
                    outValue.set(line[0] + "," + line[2] + "," + line[3]);
                    context.write(outKey, outValue);
                }
            } else {
                for (int i = 0; i < matrixSize; i++) {
                    outKey.set(i + "," + line[2]);
                    outValue.set(line[0] + "," + line[1] + "," + line[3]);
                    context.write(outKey, outValue);
                }
            }
        }
    }

    public static class MatrixReducer extends Reducer<Text, Text, Text, IntWritable> {
        public void reduce(Text key, Iterable<Text> values, Context context)
                throws IOException, InterruptedException {
            int[] a = new int[2];
            int[] b = new int[2];
            for (Text val : values) {
                String[] line = val.toString().split(",");
                if (line[0].equals("A")) {
                    a[Integer.parseInt(line[1])] = Integer.parseInt(line[2]);
                } else {
                    b[Integer.parseInt(line[1])] = Integer.parseInt(line[2]);
                }
            }
            int result = a[0] * b[0] + a[1] * b[1];
            context.write(key, new IntWritable(result));
        }
    }

    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);
    }
}

在上述代码中，MatrixMapper类实现了Map阶段的逻辑，将输入的矩阵A和矩阵B分别转换为以列或行为键的键值对，并将矩阵乘积的值作为值进行输出。

MatrixReducer类实现了Reduce阶段的逻辑，将相同键的值进行累加，并计算矩阵乘积的结果。

在main函数中，设置了Mapper和Reducer类，并指定输入和输出路径，最后启动了MapReduce任务。