map函數：對于矩陣M中的每個元素m(ij)，產生一系列的key-value對<(i，k)，（M，j，m(ij)）>
其中k=1,2.....知道矩陣N的總列數;對于矩陣N中的每個元素n(jk)，產生一系列的key-value對<(i , k) , (N , j ,n(jk)>, 其中i=1,2.......直到i=1,2.......直到矩陣M的總列數。
map
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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; /** * 執行map()函數前先由conf.get()得到main函數中提供的必要變量 * 也就是從輸入文件名中得到的矩陣維度信息 */ @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，從而區分輸入矩陣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函數：對于每個鍵（i,k）相關聯的值（M,j,m(ij)）及（N,j,n(jk)）,根據相同的j值將m(ij)和n(jk)分別存入不同的數組中，然后將倆者的第j個元素抽取出來分別相乘，最后相加，即可得到p(jk)的值。
reducer
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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; } }} |
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原文鏈接：https://my.oschina.net/u/3264690/blog/909239
