前言
MapReduce打开了并行计算的大门，让我们个人开发者有了处理大数据的能力。但想用好MapReduce，把原来单机算法并行化，也不是一件容易事情。很多的时候，我们需要从单机算法能否矩阵化去思考，所以矩阵操作就变成了算法并行化的基础。
像推荐系统的协同过滤算法，就是基于矩阵思想实现MapReduce并行化。
目录
- 矩阵介绍
- 矩阵乘法的R语言计算
- 矩阵乘法的MapReduce计算
- 稀疏矩阵乘法的MapReduce计算
1. 矩阵介绍
矩阵: 数学上，一个m×n的矩阵是一个由m行n列元素排列成的矩形阵列。矩阵里的元素可以是数字、符号或数学式。以下是一个由6个数字符素构成的2行3列的矩阵：
1 2 3
4 5 6
矩阵加法
大小相同（行数列数都相同）的矩阵之间可以相互加减，具体是对每个位置上的元素做加减法。
举例：两个矩阵的加法
1 3 1 + 0 0 5 = 1+0 3+0 1+5 = 1 3 6
1 0 0 7 5 0 1+7 0+5 0+0 8 5 0
矩阵乘法
两个矩阵可以相乘，当且仅当第一个矩阵的列数等于第二个矩阵的行数。矩阵的乘法满足结合律和分配律，但不满足交换律。
举例：两个矩阵的乘法
1 0 2 * 3 1 = (1*3+0*2+2*1) (1*1+0*1+2*0) = 5 1
-1 3 1 2 1 (-1*3+3*2+1*1) (-1*1+3*1+1*0) 4 2
1 0
2. 矩阵乘法的R语言计算
> m1<-matrix(c(1,0,2,-1,3,1),nrow=2,byrow=TRUE);m1
[,1] [,2] [,3]
[1,] 1 0 2
[2,] -1 3 1
> m2<-matrix(c(3,1,2,1,1,0),nrow=3,byrow=TRUE);m2
[,1] [,2]
[1,] 3 1
[2,] 2 1
[3,] 1 0
> m3<-m1 %*% m2;m3
[,1] [,2]
[1,] 5 1
[2,] 4 2
由R语言实现矩阵的乘法是非常简单的。
3. 矩阵乘法的MapReduce计算
算法实现思路：
- 新建2个矩阵数据文件：m1.csv, m2.csv
- 新建启动程序:MainRun.java
- 新建MR程序:MartrixMultiply.java
1).新建2个矩阵数据文件m1.csv, m2.csv
m1.csv
1,0,2
-1,3,1
m2.csv
3,1
2,1
1,0
3).新建启动程序:MainRun.java
启动程序
package org.conan.myhadoop.matrix;
import java.util.HashMap;
import java.util.Map;
import java.util.regex.Pattern;
import org.apache.hadoop.mapred.JobConf;
public class MainRun {
public static final String HDFS = "hdfs://192.168.1.210:9000";
public static final Pattern DELIMITER = Pattern.compile("[\t,]");
public static void main(String[] args) {
martrixMultiply();
}
public static void martrixMultiply() {
Map<String, String> path = new HashMap<String, String>();
path.put("m1", "logfile/matrix/m1.csv");// 本地的数据文件
path.put("m2", "logfile/matrix/m2.csv");
path.put("input", HDFS + "/user/hdfs/matrix");// HDFS的目录
path.put("input1", HDFS + "/user/hdfs/matrix/m1");
path.put("input2", HDFS + "/user/hdfs/matrix/m2");
path.put("output", HDFS + "/user/hdfs/matrix/output");
try {
MartrixMultiply.run(path);// 启动程序
} catch (Exception e) {
e.printStackTrace();
}
System.exit(0);
}
public static JobConf config() {// Hadoop集群的远程配置信息
JobConf conf = new JobConf(MainRun.class);
conf.setJobName("MartrixMultiply");
conf.addResource("classpath:/hadoop/core-site.xml");
conf.addResource("classpath:/hadoop/hdfs-site.xml");
conf.addResource("classpath:/hadoop/mapred-site.xml");
return conf;
}
}
3).新建MR程序:MartrixMultiply.java
MapReduce程序
package org.conan.myhadoop.matrix;
import java.io.IOException;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.conan.myhadoop.hdfs.HdfsDAO;
public class MartrixMultiply {
public static class MatrixMapper extends Mapper<LongWritable, Text, Text, Text> {
private String flag;// m1 or m2
private int rowNum = 2;// 矩阵A的行数
private int colNum = 2;// 矩阵B的列数
private int rowIndexA = 1; // 矩阵A，当前在第几行
private int rowIndexB = 1; // 矩阵B，当前在第几行
@Override
protected void setup(Context context) throws IOException, InterruptedException {
FileSplit split = (FileSplit) context.getInputSplit();
flag = split.getPath().getName();// 判断读的数据集
}
@Override
public void map(LongWritable key, Text values, Context context) throws IOException, InterruptedException {
String[] tokens = MainRun.DELIMITER.split(values.toString());
if (flag.equals("m1")) {
for (int i = 1; i <= rowNum; i++) {
Text k = new Text(rowIndexA + "," + i);
for (int j = 1; j <= tokens.length; j++) {
Text v = new Text("A:" + j + "," + tokens[j - 1]);
context.write(k, v);
System.out.println(k.toString() + " " + v.toString());
}
}
rowIndexA++;
} else if (flag.equals("m2")) {
for (int i = 1; i <= tokens.length; i++) {
for (int j = 1; j <= colNum; j++) {
Text k = new Text(i + "," + j);
Text v = new Text("B:" + rowIndexB + "," + tokens[j - 1]);
context.write(k, v);
System.out.println(k.toString() + " " + v.toString());
}
}
rowIndexB++;
}
}
}
public static class MatrixReducer extends Reducer<Text, Text, Text, IntWritable> {
@Override
public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
Map<String, String> mapA = new HashMap<String, String>();
Map<String, String> mapB = new HashMap<String, String>();
System.out.print(key.toString() + ":");
for (Text line : values) {
String val = line.toString();
System.out.print("("+val+")");
if (val.startsWith("A:")) {
String[] kv = MainRun.DELIMITER.split(val.substring(2));
mapA.put(kv[0], kv[1]);
// System.out.println("A:" + kv[0] + "," + kv[1]);
} else if (val.startsWith("B:")) {
String[] kv = MainRun.DELIMITER.split(val.substring(2));
mapB.put(kv[0], kv[1]);
// System.out.println("B:" + kv[0] + "," + kv[1]);
}
}
int result = 0;
Iterator<String> iter = mapA.keySet().iterator();
while (iter.hasNext()) {
String mapk = iter.next();
result += Integer.parseInt(mapA.get(mapk)) * Integer.parseInt(mapB.get(mapk));
}
context.write(key, new IntWritable(result));
System.out.println();
// System.out.println("C:" + key.toString() + "," + result);
}
}
public static void run(Map<String, String> path) throws IOException, InterruptedException, ClassNotFoundException {
JobConf conf = MainRun.config();
String input = path.get("input");
String input1 = path.get("input1");
String input2 = path.get("input2");
String output = path.get("output");
HdfsDAO hdfs = new HdfsDAO(MainRun.HDFS, conf);
hdfs.rmr(input);
hdfs.mkdirs(input);
hdfs.copyFile(path.get("m1"), input1);
hdfs.copyFile(path.get("m2"), input2);
Job job = new Job(conf);
job.setJarByClass(MartrixMultiply.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setMapperClass(MatrixMapper.class);
job.setReducerClass(MatrixReducer.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(input1), new Path(input2));// 加载2个输入数据集
FileOutputFormat.setOutputPath(job, new Path(output));
job.waitForCompletion(true);
}
}
运行日志
Delete: hdfs://192.168.1.210:9000/user/hdfs/matrix
Create: hdfs://192.168.1.210:9000/user/hdfs/matrix
copy from: logfile/matrix/m1.csv to hdfs://192.168.1.210:9000/user/hdfs/matrix/m1
copy from: logfile/matrix/m2.csv to hdfs://192.168.1.210:9000/user/hdfs/matrix/m2
2014-1-15 10:48:03 org.apache.hadoop.util.NativeCodeLoader <clinit>
警告: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2014-1-15 10:48:03 org.apache.hadoop.mapred.JobClient copyAndConfigureFiles
警告: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
2014-1-15 10:48:03 org.apache.hadoop.mapred.JobClient copyAndConfigureFiles
警告: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
2014-1-15 10:48:03 org.apache.hadoop.mapreduce.lib.input.FileInputFormat listStatus
信息: Total input paths to process : 2
2014-1-15 10:48:03 org.apache.hadoop.io.compress.snappy.LoadSnappy <clinit>
警告: Snappy native library not loaded
2014-1-15 10:48:04 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Running job: job_local_0001
2014-1-15 10:48:04 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2014-1-15 10:48:04 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: io.sort.mb = 100
2014-1-15 10:48:04 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: data buffer = 79691776/99614720
2014-1-15 10:48:04 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: record buffer = 262144/327680
1,1 A:1,1
1,1 A:2,0
1,1 A:3,2
1,2 A:1,1
1,2 A:2,0
1,2 A:3,2
2,1 A:1,-1
2,1 A:2,3
2,1 A:3,1
2,2 A:1,-1
2,2 A:2,3
2,2 A:3,1
2014-1-15 10:48:04 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
2014-1-15 10:48:04 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2014-1-15 10:48:04 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
2014-1-15 10:48:05 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 0% reduce 0%
2014-1-15 10:48:07 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2014-1-15 10:48:07 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000000_0' done.
2014-1-15 10:48:07 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2014-1-15 10:48:07 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: io.sort.mb = 100
2014-1-15 10:48:07 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: data buffer = 79691776/99614720
2014-1-15 10:48:07 org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init>
信息: record buffer = 262144/327680
1,1 B:1,3
2014-1-15 10:48:07 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
1,2 B:1,1
2,1 B:1,3
2,2 B:1,1
1,1 B:2,2
1,2 B:2,1
2,1 B:2,2
2,2 B:2,1
1,1 B:3,1
1,2 B:3,0
2,1 B:3,1
2,2 B:3,0
2014-1-15 10:48:07 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2014-1-15 10:48:07 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000001_0 is done. And is in the process of commiting
2014-1-15 10:48:08 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 100% reduce 0%
2014-1-15 10:48:10 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2014-1-15 10:48:10 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000001_0' done.
2014-1-15 10:48:10 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2014-1-15 10:48:10 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2014-1-15 10:48:10 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Merging 2 sorted segments
2014-1-15 10:48:10 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Down to the last merge-pass, with 2 segments left of total size: 294 bytes
2014-1-15 10:48:10 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
1,1:(B:1,3)(B:2,2)(B:3,1)(A:1,1)(A:2,0)(A:3,2)
1,2:(A:1,1)(A:2,0)(A:3,2)(B:1,1)(B:2,1)(B:3,0)
2,1:(B:1,3)(B:2,2)(B:3,1)(A:1,-1)(A:2,3)(A:3,1)
2,2:(A:1,-1)(A:2,3)(A:3,1)(B:1,1)(B:2,1)(B:3,0)
2014-1-15 10:48:10 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
2014-1-15 10:48:10 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2014-1-15 10:48:10 org.apache.hadoop.mapred.Task commit
信息: Task attempt_local_0001_r_000000_0 is allowed to commit now
2014-1-15 10:48:10 org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter commitTask
信息: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://192.168.1.210:9000/user/hdfs/matrix/output
2014-1-15 10:48:13 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: reduce > reduce
2014-1-15 10:48:13 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_r_000000_0' done.
2014-1-15 10:48:14 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 100% reduce 100%
2014-1-15 10:48:14 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Job complete: job_local_0001
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Counters: 19
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: File Output Format Counters
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Bytes Written=24
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: FileSystemCounters
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: FILE_BYTES_READ=1713
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: HDFS_BYTES_READ=75
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: FILE_BYTES_WRITTEN=125314
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: HDFS_BYTES_WRITTEN=114
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: File Input Format Counters
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Bytes Read=30
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Map-Reduce Framework
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Map output materialized bytes=302
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Map input records=5
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Reduce shuffle bytes=0
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Spilled Records=48
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Map output bytes=242
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Total committed heap usage (bytes)=764215296
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: SPLIT_RAW_BYTES=220
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Combine input records=0
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Reduce input records=24
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Reduce input groups=4
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Combine output records=0
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Reduce output records=4
2014-1-15 10:48:14 org.apache.hadoop.mapred.Counters log
信息: Map output records=24
4. 稀疏矩阵乘法的MapReduce计算
我们在用矩阵处理真实数据的时候，一般都是非常稀疏矩阵，为了节省存储空间，通常只会存储非0的数据。
下面我们来做一个稀疏矩阵：
- R语言的实现矩阵乘法
- 新建2个矩阵数据文件sm1.csv, sm2.csv
- 修改启动程序:MainRun.java
- 新建MR程序:SparseMartrixMultiply.java
1). R语言的实现矩阵乘法
R语言程序
> m1<-matrix(c(1,0,0,3,2,5,0,4,0,0,0,1,4,7,1,2),nrow=4,byrow=TRUE);m1
[,1] [,2] [,3] [,4]
[1,] 1 0 0 3
[2,] 2 5 0 4
[3,] 0 0 0 1
[4,] 4 7 1 2
> m2<-matrix(c(5,0,0,2,0,0,3,1),nrow=4,byrow=TRUE);m2
[,1] [,2]
[1,] 5 0
[2,] 0 2
[3,] 0 0
[4,] 3 1
> m3<-m1 %*% m2;m3
[,1] [,2]
[1,] 14 3
[2,] 22 14
[3,] 3 1
[4,] 26 16
2).新建2个稀疏矩阵数据文件sm1.csv, sm2.csv
只存储非0的数据，3列存储，第一列“原矩阵行”，第二列“原矩阵列”,第三列“原矩阵值”。
sm1.csv
1,1,1
1,4,3
2,1,2
2,2,5
2,4,4
3,4,1
4,1,4
4,2,7
4,3,1
4,4,2
sm2.csv
1,1,5
2,2,2
4,1,3
4,2,1
3).修改启动程序:MainRun.java
增加SparseMartrixMultiply的启动配置
public static void main(String[] args) {
sparseMartrixMultiply();
}
public static void sparseMartrixMultiply() {
Map<String, String> path = new HashMap<String, String>();
path.put("m1", "logfile/matrix/sm1.csv");// 本地的数据文件
path.put("m2", "logfile/matrix/sm2.csv");
path.put("input", HDFS + "/user/hdfs/matrix");// HDFS的目录
path.put("input1", HDFS + "/user/hdfs/matrix/m1");
path.put("input2", HDFS + "/user/hdfs/matrix/m2");
path.put("output", HDFS + "/user/hdfs/matrix/output");
try {
SparseMartrixMultiply.run(path);// 启动程序
} catch (Exception e) {
e.printStackTrace();
}
System.exit(0);
}
4). 新建MR程序:SparseMartrixMultiply.java
- map函数有修改，reduce函数没有变化
- 去掉判断所在行和列的变量
package org.conan.myhadoop.matrix;
import java.io.IOException;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.conan.myhadoop.hdfs.HdfsDAO;
public class SparseMartrixMultiply {
public static class SparseMatrixMapper extends Mapper>LongWritable, Text, Text, Text< {
private String flag;// m1 or m2
private int rowNum = 4;// 矩阵A的行数
private int colNum = 2;// 矩阵B的列数
@Override
protected void setup(Context context) throws IOException, InterruptedException {
FileSplit split = (FileSplit) context.getInputSplit();
flag = split.getPath().getName();// 判断读的数据集
}
@Override
public void map(LongWritable key, Text values, Context context) throws IOException, InterruptedException {
String[] tokens = MainRun.DELIMITER.split(values.toString());
if (flag.equals("m1")) {
String row = tokens[0];
String col = tokens[1];
String val = tokens[2];
for (int i = 1; i >= colNum; i++) {
Text k = new Text(row + "," + i);
Text v = new Text("A:" + col + "," + val);
context.write(k, v);
System.out.println(k.toString() + " " + v.toString());
}
} else if (flag.equals("m2")) {
String row = tokens[0];
String col = tokens[1];
String val = tokens[2];
for (int i = 1; i >= rowNum; i++) {
Text k = new Text(i + "," + col);
Text v = new Text("B:" + row + "," + val);
context.write(k, v);
System.out.println(k.toString() + " " + v.toString());
}
}
}
}
public static class SparseMatrixReducer extends Reducer>Text, Text, Text, IntWritable< {
@Override
public void reduce(Text key, Iterable>Text< values, Context context) throws IOException, InterruptedException {
Map>String, String< mapA = new HashMap>String, String<();
Map>String, String< mapB = new HashMap>String, String<();
System.out.print(key.toString() + ":");
for (Text line : values) {
String val = line.toString();
System.out.print("(" + val + ")");
if (val.startsWith("A:")) {
String[] kv = MainRun.DELIMITER.split(val.substring(2));
mapA.put(kv[0], kv[1]);
// System.out.println("A:" + kv[0] + "," + kv[1]);
} else if (val.startsWith("B:")) {
String[] kv = MainRun.DELIMITER.split(val.substring(2));
mapB.put(kv[0], kv[1]);
// System.out.println("B:" + kv[0] + "," + kv[1]);
}
}
int result = 0;
Iterator>String< iter = mapA.keySet().iterator();
while (iter.hasNext()) {
String mapk = iter.next();
String bVal = mapB.containsKey(mapk) ? mapB.get(mapk) : "0";
result += Integer.parseInt(mapA.get(mapk)) * Integer.parseInt(bVal);
}
context.write(key, new IntWritable(result));
System.out.println();
// System.out.println("C:" + key.toString() + "," + result);
}
}
public static void run(Map>String, String< path) throws IOException, InterruptedException, ClassNotFoundException {
JobConf conf = MainRun.config();
String input = path.get("input");
String input1 = path.get("input1");
String input2 = path.get("input2");
String output = path.get("output");
HdfsDAO hdfs = new HdfsDAO(MainRun.HDFS, conf);
hdfs.rmr(input);
hdfs.mkdirs(input);
hdfs.copyFile(path.get("m1"), input1);
hdfs.copyFile(path.get("m2"), input2);
Job job = new Job(conf);
job.setJarByClass(MartrixMultiply.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setMapperClass(SparseMatrixMapper.class);
job.setReducerClass(SparseMatrixReducer.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(input1), new Path(input2));// 加载2个输入数据集
FileOutputFormat.setOutputPath(job, new Path(output));
job.waitForCompletion(true);
}
}
运行输出:
Delete: hdfs://192.168.1.210:9000/user/hdfs/matrix
Create: hdfs://192.168.1.210:9000/user/hdfs/matrix
copy from: logfile/matrix/sm1.csv to hdfs://192.168.1.210:9000/user/hdfs/matrix/m1
copy from: logfile/matrix/sm2.csv to hdfs://192.168.1.210:9000/user/hdfs/matrix/m2
2014-1-15 11:57:31 org.apache.hadoop.util.NativeCodeLoader >clinit<
警告: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2014-1-15 11:57:31 org.apache.hadoop.mapred.JobClient copyAndConfigureFiles
警告: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
2014-1-15 11:57:31 org.apache.hadoop.mapred.JobClient copyAndConfigureFiles
警告: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
2014-1-15 11:57:31 org.apache.hadoop.mapreduce.lib.input.FileInputFormat listStatus
信息: Total input paths to process : 2
2014-1-15 11:57:31 org.apache.hadoop.io.compress.snappy.LoadSnappy >clinit<
警告: Snappy native library not loaded
2014-1-15 11:57:31 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Running job: job_local_0001
2014-1-15 11:57:31 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2014-1-15 11:57:31 org.apache.hadoop.mapred.MapTask$MapOutputBuffer >init<
信息: io.sort.mb = 100
2014-1-15 11:57:31 org.apache.hadoop.mapred.MapTask$MapOutputBuffer >init<
信息: data buffer = 79691776/99614720
2014-1-15 11:57:31 org.apache.hadoop.mapred.MapTask$MapOutputBuffer >init<
信息: record buffer = 262144/327680
1,1 A:1,1
1,2 A:1,1
1,1 A:4,3
1,2 A:4,3
2,1 A:1,2
2,2 A:1,2
2,1 A:2,5
2,2 A:2,5
2,1 A:4,4
2,2 A:4,4
3,1 A:4,1
3,2 A:4,1
4,1 A:1,4
4,2 A:1,4
4,1 A:2,7
4,2 A:2,7
4,1 A:3,1
4,2 A:3,1
4,1 A:4,2
4,2 A:4,2
2014-1-15 11:57:31 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
2014-1-15 11:57:31 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2014-1-15 11:57:31 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
2014-1-15 11:57:32 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 0% reduce 0%
2014-1-15 11:57:34 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2014-1-15 11:57:34 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000000_0' done.
2014-1-15 11:57:34 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2014-1-15 11:57:34 org.apache.hadoop.mapred.MapTask$MapOutputBuffer >init<
信息: io.sort.mb = 100
2014-1-15 11:57:34 org.apache.hadoop.mapred.MapTask$MapOutputBuffer >init<
信息: data buffer = 79691776/99614720
2014-1-15 11:57:34 org.apache.hadoop.mapred.MapTask$MapOutputBuffer >init<
信息: record buffer = 262144/327680
1,1 B:1,5
2,1 B:1,5
3,1 B:1,5
4,1 B:1,5
2014-1-15 11:57:34 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
1,2 B:2,2
2,2 B:2,2
3,2 B:2,2
4,2 B:2,2
1,1 B:4,3
2,1 B:4,3
3,1 B:4,3
4,1 B:4,3
1,2 B:4,1
2,2 B:4,1
3,2 B:4,1
4,2 B:4,1
2014-1-15 11:57:34 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2014-1-15 11:57:34 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000001_0 is done. And is in the process of commiting
2014-1-15 11:57:35 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 100% reduce 0%
2014-1-15 11:57:37 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2014-1-15 11:57:37 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000001_0' done.
2014-1-15 11:57:37 org.apache.hadoop.mapred.Task initialize
信息: Using ResourceCalculatorPlugin : null
2014-1-15 11:57:37 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2014-1-15 11:57:37 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Merging 2 sorted segments
2014-1-15 11:57:37 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Down to the last merge-pass, with 2 segments left of total size: 436 bytes
2014-1-15 11:57:37 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
1,1:(B:1,5)(B:4,3)(A:1,1)(A:4,3)
1,2:(A:1,1)(A:4,3)(B:2,2)(B:4,1)
2,1:(B:1,5)(B:4,3)(A:1,2)(A:2,5)(A:4,4)
2,2:(A:1,2)(A:2,5)(A:4,4)(B:4,1)(B:2,2)
3,1:(B:1,5)(B:4,3)(A:4,1)
3,2:(A:4,1)(B:2,2)(B:4,1)
4,1:(B:4,3)(B:1,5)(A:1,4)(A:2,7)(A:3,1)(A:4,2)
4,2:(A:1,4)(A:2,7)(A:3,1)(A:4,2)(B:2,2)(B:4,1)
2014-1-15 11:57:37 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
2014-1-15 11:57:37 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息:
2014-1-15 11:57:37 org.apache.hadoop.mapred.Task commit
信息: Task attempt_local_0001_r_000000_0 is allowed to commit now
2014-1-15 11:57:37 org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter commitTask
信息: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://192.168.1.210:9000/user/hdfs/matrix/output
2014-1-15 11:57:40 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: reduce < reduce
2014-1-15 11:57:40 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_r_000000_0' done.
2014-1-15 11:57:41 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: map 100% reduce 100%
2014-1-15 11:57:41 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Job complete: job_local_0001
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Counters: 19
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: File Output Format Counters
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Bytes Written=53
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: FileSystemCounters
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: FILE_BYTES_READ=2503
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: HDFS_BYTES_READ=266
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: FILE_BYTES_WRITTEN=126274
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: HDFS_BYTES_WRITTEN=347
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: File Input Format Counters
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Bytes Read=98
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Map-Reduce Framework
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Map output materialized bytes=444
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Map input records=14
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Reduce shuffle bytes=0
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Spilled Records=72
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Map output bytes=360
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Total committed heap usage (bytes)=764215296
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: SPLIT_RAW_BYTES=220
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Combine input records=0
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Reduce input records=36
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Reduce input groups=8
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Combine output records=0
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Reduce output records=8
2014-1-15 11:57:41 org.apache.hadoop.mapred.Counters log
信息: Map output records=36
程序源代码，已上传到github:
https://github.com/bsspirit/maven_hadoop_template/tree/master/src/main/java/org/conan/myhadoop/matrix
这样就用MapReduce的程序，实现了矩阵的乘法！有了矩阵计算的基础，接下来，我们就可以做更多的事情了！
参考文章：MapReduce实现大矩阵乘法
作者介绍：
张丹，R语言中文社区专栏特邀作者，《R的极客理想》系列图书作者，民生银行大数据中心数据分析师，前况客创始人兼CTO。
10年IT编程背景，精通R ,Java, Nodejs 编程，获得10项SUN及IBM技术认证。丰富的互联网应用开发架构经验，金融大数据专家。个人博客 http://fens.me， Alexa全球排名70k。
著有《R的极客理想-工具篇》、《R的极客理想-高级开发篇》，合著《数据实践之美》，新书《R的极客理想-量化投资篇》(即将出版)。
《R的极客理想-工具篇》京东购买快速通道：https://item.jd.com/11524750.html
《R的极客理想-高级开发篇》京东购买快速通道：https://item.jd.com/11731967.html
《数据实践之美》京东购买快速通道：https://item.jd.com/12106224.html
本站文章版权归原作者及原出处所有 。内容为作者个人观点， 并不代表本站赞同其观点和对其真实性负责。本站是一个个人学习交流的平台，并不用于任何商业目的，如果有任何问题，请及时联系我们，我们将根据著作权人的要求，立即更正或者删除有关内容。本站拥有对此声明的最终解释权。