hadoop入门级代码
一、hadoop中的hello,world
/***************************************************************************
*
* Copyright (c) 2013 Baidu.com, Inc. All Rights Reserved
* $Id$
*
**************************************************************************/
/**
* @file MaxTemperatureMapper.java
* @author zhangliang08(zhangliang08@baidu.com)
* @date 2013/04/25 21:11:57
* @version $Revision$
* @brief
*
**/
import java.io.IOException;
import org.apache.hadoop.io.*;
import org.apache.hadoop.util.*;
import org.apache.hadoop.conf.*;
import java.util.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.fs.Path;
public class WordCount {
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
output.collect(word, one);
}
}
}
public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output,
Reporter reporter)
throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
output.collect(key, new IntWritable(sum));
}
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("wordcount");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(Map.class);
conf.setReducerClass(Reduce.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
}
/* vim: set ts=4 sw=4 sts=4 tw=100 */
利用ant工具打包，手动命令打包可能执行不了，mapreduce思想很简单，这里就不扯谈了，代码纯属copy
二、Hadoop实战中的例子：
1. 单表关联
单表关联这个例子是理解mapreduce的一个很好的例子
输入文件：
child parent
Tom Lucy
Lucy Mary
Tom Lucy
Lucy Mary
期望输出：
Tom Mary
代码如下：
package com.baidu.rawget;
import java.io.IOException;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.io.*;
import java.util.*;
import org.apache.hadoop.util.*;
import java.io.*;
public class STjoin {
public static int time = 0;
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, Text> {
private static IntWritable data = new IntWritable();
public void map(LongWritable key, Text value, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
String childname = new String();
String parentname = new String();
String relationtype = new String();
String line = value.toString();
int i = 0;
while (line.charAt(i) != ' ') {
i++;
}
String[] values = {line.substring(0, i), line.substring(i + 1)};
if (values[0].compareTo("child") != 0) {
childname = values[0];
parentname = values[1];
relationtype = "1";
output.collect(new Text(values[1]), new Text(relationtype + "+" + childname + "+" + parentname));
relationtype = "2";
output.collect(new Text(values[0]), new Text(relationtype + "+" + childname + "+" + parentname));
}
}
}
public static class Reduce extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
private static IntWritable linenum = new IntWritable(1);
static int linenum2;
public void reduce(Text key, Iterator<Text> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
if (time == 0) {
output.collect(new Text("grandchild"), new Text("grandparent"));
time++;
}
int grandchildnum = 0;
String grandchild[] = new String[10];
int grandparentnum = 0;
String grandparent[] = new String[10];
while (values.hasNext()) {
String record = values.next().toString();
System.err.println("record: " + record);
int len = record.length();
int i = 2;
if (len == 0)
continue;
char relationtype = record.charAt(0);
String childname = new String();
String parentname = new String();
while (record.charAt(i) != '+') {
childname = childname + record.charAt(i);
i++;
}
i = i + 1;
while (i < len) {
parentname = parentname + record.charAt(i);
i++;
}
if (relationtype == '1') {
grandchild[grandchildnum] = childname;
grandchildnum++;
}
else {
grandparent[grandparentnum] = parentname;
grandparentnum++;
}
}
System.err.println("table 1:");
for (int m = 0; m < grandchildnum; m++) {
System.err.println(grandchild[m]);
}
System.err.println("table 2:");
for (int m = 0; m < grandparentnum; m++) {
System.err.println(grandparent[m]);
}
if (grandparentnum != 0 && grandchildnum != 0) {
for (int m = 0; m < grandchildnum; m++) {
for (int n = 0; n < grandparentnum; n++) {
output.collect(new Text(grandchild[m]), new Text(grandparent[n]));
}
}
}
}
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(STjoin.class);
conf.setJobName("STjoin");
//conf.setNumReduceTasks(3);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(Text.class);
conf.setMapperClass(Map.class);
conf.setReducerClass(Reduce.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
}
为了更好的理解这个代码，我将每次reduce的输入和grandchild、grandparent都打印出来~
record: 2+Lucy+Mary record: 1+Tom+Lucy table 1: Tom table 2: Mary record: 1+Lucy+Mary table 1: Lucy table 2: record: 2+Tom+Lucy table 1: table 2: Lucy
不难看出，相同的key被combine在一起了，一目了然
2. 排序
如何利用mapreduce来排序呢，书上给出了个例子，也很好理解，不过书上的代码有点小问题，修正后的代码如下：
package com.baidu.rawget;
import java.io.IOException;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.io.*;
import java.util.*;
import org.apache.hadoop.util.*;
import java.io.*;
public class Sort
{
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, IntWritable, IntWritable> {
private static IntWritable data = new IntWritable();
public void map(LongWritable key, Text value, OutputCollector<IntWritable, IntWritable> output, Reporter reporter) throws IOException {
String line = value.toString();
data.set(Integer.parseInt(line));
output.collect(data, new IntWritable(1));
}
}
public static class Reduce extends MapReduceBase implements Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {
private static IntWritable linenum = new IntWritable(1);
static int linenum2;
public void reduce(IntWritable key, Iterator<IntWritable> values, OutputCollector<IntWritable, IntWritable> output, Reporter reporter) throws IOException {
System.err.println("Reduce");
while (values.hasNext()) {
System.err.println("key = ");
System.err.println(key);
output.collect(linenum, key);
linenum = new IntWritable(linenum.get() + 1);
linenum2 = values.next().get();
}
}
}
public static class Partition implements Partitioner<IntWritable, IntWritable> {
public void configure(JobConf job) {}
public int getPartition(IntWritable key, IntWritable value, int numPartitions) {
System.err.println("numPartitions = ");
System.err.println(numPartitions);
int Maxnumber = 65223;
int bound = Maxnumber / numPartitions + 1;
int keynumber = key.get();
for (int i = 1; i <= numPartitions; i++) {
if (keynumber < bound * i && keynumber >= bound * (i - 1)) {
return i - 1;
}
}
return -1;
}
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(Sort.class);
conf.setJobName("Sort");
conf.setNumReduceTasks(3);
conf.setOutputKeyClass(IntWritable.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(Map.class);
conf.setReducerClass(Reduce.class);
conf.setPartitionerClass(Partition.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
}
三、MapReduce进阶：
为了深入理解mapreduce思想，矩阵相乘以及kmeans等算法，以此加深对mapreduce算法的深入理解
1. 矩阵相乘：