编写Hadoop实现矩阵相乘的java带啊吗

当然可以，以下是一个简单的矩阵相乘的Hadoop实现的Java代码示例：

import java.io.IOException;
import java.util.*;
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 Mapper1 extends Mapper<LongWritable, Text, Text, Text> {

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] data = value.toString().split(",");
            if (data[0].equals("M")) {
                for (int k = 0; k < Integer.parseInt(context.getConfiguration().get("n")); k++) {
                    context.write(new Text(data[1] + "," + k), new Text(data[0] + "," + data[2] + "," + data[3]));
                }
            } else {
                for (int i = 0; i < Integer.parseInt(context.getConfiguration().get("n")); i++) {
                    context.write(new Text(i + "," + data[2]), new Text(data[0] + "," + data[1] + "," + data[3]));
                }
            }
        }
    }

    public static class Reducer1 extends Reducer<Text, Text, Text, Text> {

        public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            List<String> listM = new ArrayList<String>();
            List<String> listN = new ArrayList<String>();

            for (Text val : values) {
                String[] data = val.toString().split(",");
                if (data[0].equals("M")) {
                    listM.add(val.toString());
                } else {
                    listN.add(val.toString());
                }
            }

            for (String m : listM) {
                String[] dataM = m.split(",");
                for (String n : listN) {
                    String[] dataN = n.split(",");
                    context.write(new Text(dataM[1] + "," + dataN[2]), new Text(Integer.toString(Integer.parseInt(dataM[2]) * Integer.parseInt(dataN[2]))));
                }
            }
        }
    }

    public static class Mapper2 extends Mapper<LongWritable, Text, Text, Text> {

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] data = value.toString().split(",");
            context.write(new Text(data[0] + "," + data[1]), new Text(data[2]));
        }
    }

    public static class Reducer2 extends Reducer<Text, Text, Text, IntWritable> {

        public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (Text val : values) {
                sum += Integer.parseInt(val.toString());
            }
            context.write(key, new IntWritable(sum));
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        conf.set("n", args[3]);

        Job job1 = Job.getInstance(conf, "Matrix Multiplication Job 1");
        job1.setJarByClass(MatrixMultiplication.class);
        job1.setMapperClass(Mapper1.class);
        job1.setReducerClass(Reducer1.class);
        job1.setOutputKeyClass(Text.class);
        job1.setOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job1, new Path(args[0]));
        FileOutputFormat.setOutputPath(job1, new Path(args[1]));
        job1.waitForCompletion(true);

        Job job2 = Job.getInstance(conf, "Matrix Multiplication Job 2");
        job2.setJarByClass(MatrixMultiplication.class);
        job2.setMapperClass(Mapper2.class);
        job2.setReducerClass(Reducer2.class);
        job2.setOutputKeyClass(Text.class);
        job2.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job2, new Path(args[1]));
        FileOutputFormat.setOutputPath(job2, new Path(args[2]));
        System.exit(job2.waitForCompletion(true) ? 0 : 1);
    }
}

这个示例代码假设您已经将两个矩阵存储为CSV格式的文件，并将它们作为输入传递给MapReduce作业。您需要将矩阵的大小作为命令行参数传递给程序（这里使用“n”来表示矩阵的大小）。