mapreduce 实现矩阵乘法

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MatrixMultiply {
  /** mapper和reducer需要的三个必要变量，由conf.get()方法得到 **/
  public static int rowM = 0;
  public static int columnM = 0;
  public static int columnN = 0;

  public static class MatrixMapper extends Mapper<Object, Text, Text, Text> {
    private Text map_key = new Text();
    private Text map_value = new Text();

    /**
     * 执行map()函数前先由conf.get()得到main函数中提供的必要变量， 这也是MapReduce*享变量的一种方式
     */
    public void setup(Context context) throws IOException {
      Configuration conf = context.getConfiguration();
      columnN = Integer.parseInt(conf.get("columnN"));
      rowM = Integer.parseInt(conf.get("rowM"));
    }

    public void map(Object key, Text value, Context context)
        throws IOException, InterruptedException {
      /** 得到输入文件名，从而区分输入矩阵M和N **/
      FileSplit fileSplit = (FileSplit) context.getInputSplit();
      String fileName = fileSplit.getPath().getName();

      if (fileName.contains("M")) {
        String[] tuple = value.toString().split(",");
        int i = Integer.parseInt(tuple[0]);
        String[] tuples = tuple[1].split("\t");
        int j = Integer.parseInt(tuples[0]);
        int Mij = Integer.parseInt(tuples[1]);

        for (int k = 1; k < columnN + 1; k++) {
          map_key.set(i + "," + k);
          map_value.set("M" + "," + j + "," + Mij);
          context.write(map_key, map_value);
        }
      }

      else if (fileName.contains("N")) {
        String[] tuple = value.toString().split(",");
        int j = Integer.parseInt(tuple[0]);
        String[] tuples = tuple[1].split("\t");
        int k = Integer.parseInt(tuples[0]);
        int Njk = Integer.parseInt(tuples[1]);

        for (int i = 1; i < rowM + 1; i++) {
          map_key.set(i + "," + k);
          map_value.set("N" + "," + j + "," + Njk);
          context.write(map_key, map_value);
        }
      }
    }
  }

  public static class MatrixReducer extends Reducer<Text, Text, Text, Text> {
    private int sum = 0;

    public void setup(Context context) throws IOException {
      Configuration conf = context.getConfiguration();
      columnM = Integer.parseInt(conf.get("columnM"));
    }

    public void reduce(Text key, Iterable<Text> values, Context context)
        throws IOException, InterruptedException {
      int[] M = new int[columnM + 1];
      int[] N = new int[columnM + 1];

      for (Text val : values) {
        String[] tuple = val.toString().split(",");
        if (tuple[0].equals("M")) {
          M[Integer.parseInt(tuple[1])] = Integer.parseInt(tuple[2]);
        } else
          N[Integer.parseInt(tuple[1])] = Integer.parseInt(tuple[2]);
      }

      /** 根据j值，对M[j]和N[j]进行相乘累加得到乘积矩阵的数据 **/
      for (int j = 1; j < columnM + 1; j++) {
        sum += M[j] * N[j];
      }
      context.write(key, new Text(Integer.toString(sum)));
      sum = 0;
    }
  }

  /**
   * main函数
   * <p>
   * Usage:
   *
   * <p>
   * <code>MatrixMultiply  inputPathM inputPathN outputPath</code>
   *
   * <p>
   * 从输入文件名称中得到矩阵M的行数和列数，以及矩阵N的列数，作为重要参数传递给mapper和reducer
   *
   * @param args 输入文件目录地址M和N以及输出目录地址
   *
   * @throws Exception
   */

  public static void main(String[] args) throws Exception {

    if (args.length != 3) {
      System.err
          .println("Usage: MatrixMultiply <inputPathM> <inputPathN> <outputPath>");
      System.exit(2);
    } else {
      String[] infoTupleM = args[0].split("_");
      rowM = Integer.parseInt(infoTupleM[1]);
      columnM = Integer.parseInt(infoTupleM[2]);
      String[] infoTupleN = args[1].split("_");
      columnN = Integer.parseInt(infoTupleN[2]);
    }

    Configuration conf = new Configuration();
    /** 设置三个全局共享变量 **/
    conf.setInt("rowM", rowM);
    conf.setInt("columnM", columnM);
    conf.setInt("columnN", columnN);

    Job job = new Job(conf, "MatrixMultiply");
    job.setJarByClass(MatrixMultiply.class);
    job.setMapperClass(MatrixMapper.class);
    job.setReducerClass(MatrixReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(Text.class);
    FileInputFormat.setInputPaths(job, new Path(args[0]), new Path(args[1]));
    FileOutputFormat.setOutputPath(job, new Path(args[2]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

 

 

下面是从http://blog.fens.me/hadoop-mapreduce-matrix/及http://blog.csdn.net/xyilu/article/details/9066973截取的两张图：