Mapreduce program implementation of matrix multiplication

map function: For each element m(ij) in matrix M, generate a series of key-value pairs <(i, k), (M, j, m(ij))>

Where k=1,2.....know the total number of columns of matrix N; for each element n(jk) in matrix N, generate a series of key-value pairs <(i , k) , (N , j ,n(jk)>, where i=1,2.......until i=1,2.......until the total number of columns of matrix M.

map

package com.cb.matrix;

import static org.mockito.Matchers.intThat;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileSplit;
import org.apache.hadoop.mapreduce.Mapper;

import com.sun.org.apache.bcel.internal.generic.NEW;


public class MatrixMapper extends Mapper<Object, Text, Text, Text> {
	private Text map_key=new Text();
	private Text map_value= new Text();
	private int columnN;
	private int rowM;
	/**
	 * 执行map()函数前先由conf.get()得到main函数中提供的必要变量
	 * 也就是从输入文件名中得到的矩阵维度信息
	 */
	
	@Override
	protected void setup(Mapper<Object, Text, Text, Text>.Context context) throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		Configuration config=context.getConfiguration();
		columnN=Integer.parseInt(config.get("columnN"));
		rowM =Integer.parseInt(config.get("rowM"));
	}
	
	@Override
	protected void map(Object key, Text value, Mapper<Object, Text, Text, Text>.Context context)
			throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		//得到文件名，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);
			}
		}
		
	}

}

reduce function: For each key (i,k) associated with the value (M,j,m(ij)) and (N,j,n(jk)), m(ij) and n are divided according to the same j value (jk) are stored in different arrays respectively, and then the j-th elements of the two are extracted and multiplied respectively, and finally added to obtain the value of p(jk).

reducer

package com.cb.matrix;


import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;



public class MatrixReducer extends Reducer<Text, Text, Text, Text> {
	private int sum=0;
	private int columnM;
	@Override
	protected void setup(Reducer<Text, Text, Text, Text>.Context context) throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		Configuration conf =context.getConfiguration();
		columnM=Integer.parseInt(conf.get("columnM"));
	}
	@Override
	protected void reduce(Text arg0, Iterable<Text> arg1, Reducer<Text, Text, Text, Text>.Context arg2)
			throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		int[] M=new int[columnM+1];
		int[] N=new int[columnM+1];
		
		for(Text val:arg1){
			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]);
			}
			for(int j=1;j<columnM+1;j++){
				sum+=M[j]*N[j];
			}
			arg2.write(arg0, new Text(Integer.toString(sum)));
			sum=0;
		}
	}

}