用Hadoop构建电影推荐系统
前言
Netflix电影推荐的百万美金比赛，把“推荐”变成了时下最热门的数据挖掘算法之一。也正是由于Netflix的比赛，让企业界和学科界有了更深层次的技术碰撞。引发了各种网站“推荐”热，个性时代已经到来。
一、 推荐系统概述
电子商务网站是个性化推荐系统重要地应用的领域之一，亚马逊就是个性化推荐系统的积极应用者和推广者，亚马逊的推荐系统深入到网站的各类商品，为亚马逊带来了至少30%的销售额。
不光是电商类，推荐系统无处不在。QQ，人人网的好友推荐；新浪微博的你可能感觉兴趣的人；优酷，土豆的电影推荐；豆瓣的图书推荐；大从点评的餐饮推荐；世纪佳缘的相亲推荐；天际网的职业推荐等。
推荐算法分类：
按数据使用划分：
协同过滤算法：UserCF, ItemCF, ModelCF
基于内容的推荐: 用户内容属性和物品内容属性
社会化过滤：基于用户的社会网络关系
按模型划分：
最近邻模型:基于距离的协同过滤算法
Latent Factor Mode(SVD)：基于矩阵分解的模型
Graph：图模型，社会网络图模型
基于用户的协同过滤算法UserCF
基于用户的协同过滤，通过不同用户对物品的评分来评测用户之间的相似性，基于用户之间的相似性做出推荐。简单来讲就是：给用户推荐和他兴趣相似的其他用户喜欢的物品。
用例说明：
算法实现及使用介绍，请参考文章：Mahout推荐算法API详解
基于物品的协同过滤算法ItemCF
基于item的协同过滤，通过用户对不同item的评分来评测item之间的相似性，基于item之间的相似性做出推荐。简单来讲就是：给用户推荐和他之前喜欢的物品相似的物品。
用例说明：
算法实现及使用介绍，请参考文章：Mahout推荐算法API详解
注：基于物品的协同过滤算法，是目前商用最广泛的推荐算法。
协同过滤算法实现，分为2个步骤
1. 计算物品之间的相似度
2. 根据物品的相似度和用户的历史行为给用户生成推荐列表
有关协同过滤的另一篇文章，请参考：RHadoop实践系列之三 R实现MapReduce的协同过滤算法
二、 需求分析：推荐系统指标设计
下面我们将从一个公司案例出发来全面的解释，如何进行推荐系统指标设计。
案例介绍
Netflix电影推荐百万奖金比赛，http://www.netflixprize.com/
Netflix官方网站：www.netflix.com
Netflix，2006年组织比赛是的时候，是一家以在线电影租赁为生的公司。他们根据网友对电影的打分来判断用户有可能喜欢什么电影，并结合会员看过的电影以及口味偏好设置做出判断，混搭出各种电影风格的需求。
收集会员的一些信息，为他们指定个性化的电影推荐后，有许多冷门电影竟然进入了候租榜单。从公司的电影资源成本方面考量，热门电影的成本一般较高，如果Netflix公司能够在电影租赁中增加冷门电影的比例，自然能够提升自身盈利能力。
Netflix公司曾宣称60%左右的会员根据推荐名单定制租赁顺序，如果推荐系统不能准确地猜测会员喜欢的电影类型，容易造成多次租借冷门电影而并不符合个人口味的会员流失。为了更高效地为会员推荐电影，Netflix一直致力于不断改进和完善个性化推荐服务，在2006年推出百万美元大奖，无论是谁能最好地优化Netflix推荐算法就可获奖励100万美元。到2009年，奖金被一个7人开发小组夺得，Netflix随后又立即推出第二个百万美金悬赏。这充分说明一套好的推荐算法系统是多么重要，同时又是多么困难。
上图为比赛的各支队伍的排名！
补充说明：
1. Netflix的比赛是基于静态数据的，就是给定“训练级”，匹配“结果集”，“结果集”也是提前就做好的，所以这与我们每天运营的系统，其实是不一样的。
2. Netflix用于比赛的数据集是小量的，整个全集才666MB，而实际的推荐系统都要基于大量历史数据的，动不动就会上GB,TB等
Netflix数据下载
部分训练集：http://graphlab.org/wp-content/uploads/2013/07/smallnetflix_mm.train_.gz
部分结果集：http://graphlab.org/wp-content/uploads/2013/07/smallnetflix_mm.validate.gz
完整数据集：http://www.lifecrunch.biz/wp-content/uploads/2011/04/nf_prize_dataset.tar.gz
所以，我们在真实的环境中设计推荐的时候，要全面考量数据量，算法性能，结果准确度等的指标。
推荐算法选型：基于物品的协同过滤算法ItemCF，并行实现
数据量：基于Hadoop架构，支持GB,TB,PB级数据量
算法检验：可以通过 准确率，召回率，覆盖率，流行度 等指标评判。
结果解读：通过ItemCF的定义，合理给出结果解释
三、 算法模型：Hadoop并行算法
这里我使用”Mahout In Action”书里，第一章第六节介绍的分步式基于物品的协同过滤算法进行实现。Chapter 6: Distributing recommendation computations
测试数据集:small.csv
1,101,5.0
1,102,3.0
1,103,2.5
2,101,2.0
2,102,2.5
2,103,5.0
2,104,2.0
3,101,2.0
3,104,4.0
3,105,4.5
3,107,5.0
4,101,5.0
4,103,3.0
4,104,4.5
4,106,4.0
5,101,4.0
5,102,3.0
5,103,2.0
5,104,4.0
5,105,3.5
5,106,4.0
每行3个字段，依次是用户ID,电影ID,用户对电影的评分(0-5分，每0.5为一个评分点！)
算法的思想：
1. 建立物品的同现矩阵
2. 建立用户对物品的评分矩阵
3. 矩阵计算推荐结果
1). 建立物品的同现矩阵
按用户分组，找到每个用户所选的物品，单独出现计数及两两一组计数。
[101] [102] [103] [104] [105] [106] [107]
[101] 5 3 4 4 2 2 1
[102] 3 3 3 2 1 1 0
[103] 4 3 4 3 1 2 0
[104] 4 2 3 4 2 2 1
[105] 2 1 1 2 2 1 1
[106] 2 1 2 2 1 2 0
[107] 1 0 0 1 1 0 1
2). 建立用户对物品的评分矩阵
按用户分组，找到每个用户所选的物品及评分
U3
[101] 2.0
[102] 0.0
[103] 0.0
[104] 4.0
[105] 4.5
[106] 0.0
[107] 5.0
3). 矩阵计算推荐结果
同现矩阵*评分矩阵=推荐结果
MapReduce任务设计
图片摘自”Mahout In Action”
解读MapRduce任务：
步骤1: 按用户分组，计算所有物品出现的组合列表，得到用户对物品的评分矩阵
步骤2: 对物品组合列表进行计数，建立物品的同现矩阵
步骤3: 合并同现矩阵和评分矩阵
步骤4: 计算推荐结果列表
四、 架构设计：推荐系统架构
上图中，左边是Application业务系统，右边是Hadoop的HDFS, MapReduce。
业务系统记录了用户的行为和对物品的打分
设置系统定时器CRON，每xx小时，增量向HDFS导入数据(userid,itemid,value,time)。
完成导入后，设置系统定时器，启动MapReduce程序，运行推荐算法。
完成计算后，设置系统定时器，从HDFS导出推荐结果数据到数据库，方便以后的及时查询。
五、 程序开发：MapReduce程序实现
win7的开发环境 和 Hadoop的运行环境 ，请参考文章：用Maven构建Hadoop项目
新建Java类：
Recommend.java，主任务启动程序
Step1.java，按用户分组，计算所有物品出现的组合列表，得到用户对物品的评分矩阵
Step2.java，对物品组合列表进行计数，建立物品的同现矩阵
Step3.java，合并同现矩阵和评分矩阵
Step4.java，计算推荐结果列表
HdfsDAO.java，HDFS操作工具类
1). Recommend.java，主任务启动程序
源代码：
package org.conan.myhadoop.recommend;
import java.util.HashMap;
import java.util.Map;
import java.util.regex.Pattern;
import org.apache.hadoop.mapred.JobConf;
public class Recommend {
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) throws Exception {
Map path = new HashMap();
path.put("data", "logfile/small.csv");
path.put("Step1Input", HDFS + "/user/hdfs/recommend");
path.put("Step1Output", path.get("Step1Input") + "/step1");
path.put("Step2Input", path.get("Step1Output"));
path.put("Step2Output", path.get("Step1Input") + "/step2");
path.put("Step3Input1", path.get("Step1Output"));
path.put("Step3Output1", path.get("Step1Input") + "/step3_1");
path.put("Step3Input2", path.get("Step2Output"));
path.put("Step3Output2", path.get("Step1Input") + "/step3_2");
path.put("Step4Input1", path.get("Step3Output1"));
path.put("Step4Input2", path.get("Step3Output2"));
path.put("Step4Output", path.get("Step1Input") + "/step4");
Step1.run(path);
Step2.run(path);
Step3.run1(path);
Step3.run2(path);
Step4.run(path);
System.exit(0);
}
public static JobConf config() {
JobConf conf = new JobConf(Recommend.class);
conf.setJobName("Recommend");
conf.addResource("classpath:/hadoop/core-site.xml");
conf.addResource("classpath:/hadoop/hdfs-site.xml");
conf.addResource("classpath:/hadoop/mapred-site.xml");
return conf;
}
}
2). Step1.java，按用户分组，计算所有物品出现的组合列表，得到用户对物品的评分矩阵源代码：package org.conan.myhadoop.recommend;
import java.io.IOException;
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.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.RunningJob;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.conan.myhadoop.hdfs.HdfsDAO;
public class Step1 {
public static class Step1_ToItemPreMapper extends MapReduceBase implements Mapper {
private final static IntWritable k = new IntWritable();
private final static Text v = new Text();
@Override
public void map(Object key, Text value, OutputCollector output, Reporter reporter) throws IOException {
String[] tokens = Recommend.DELIMITER.split(value.toString());
int userID = Integer.parseInt(tokens[0]);
String itemID = tokens[1];
String pref = tokens[2];
k.set(userID);
v.set(itemID + ":" + pref);
output.collect(k, v);
}
}
public static class Step1_ToUserVectorReducer extends MapReduceBase implements Reducer {
private final static Text v = new Text();
@Override
public void reduce(IntWritable key, Iterator values, OutputCollector output, Reporter reporter) throws IOException {
StringBuilder sb = new StringBuilder();
while (values.hasNext()) {
sb.append("," + values.next());
}
v.set(sb.toString().replaceFirst(",", ""));
output.collect(key, v);
}
}
public static void run(Map path) throws IOException {
JobConf conf = Recommend.config();
String input = path.get("Step1Input");
String output = path.get("Step1Output");
HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
hdfs.rmr(input);
hdfs.mkdirs(input);
hdfs.copyFile(path.get("data"), input);
conf.setMapOutputKeyClass(IntWritable.class);
conf.setMapOutputValueClass(Text.class);
conf.setOutputKeyClass(IntWritable.class);
conf.setOutputValueClass(Text.class);
conf.setMapperClass(Step1_ToItemPreMapper.class);
conf.setCombinerClass(Step1_ToUserVectorReducer.class);
conf.setReducerClass(Step1_ToUserVectorReducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(input));
FileOutputFormat.setOutputPath(conf, new Path(output));
RunningJob job = JobClient.runJob(conf);
while (!job.isComplete()) {
job.waitForCompletion();
}
}
}
计算结果：
~ hadoop fs -cat /user/hdfs/recommend/step1/part-00000
1 102:3.0,103:2.5,101:5.0
2 101:2.0,102:2.5,103:5.0,104:2.0
3 107:5.0,101:2.0,104:4.0,105:4.5
4 101:5.0,103:3.0,104:4.5,106:4.0
5 101:4.0,102:3.0,103:2.0,104:4.0,105:3.5,106:4.0
3). Step2.java，对物品组合列表进行计数，建立物品的同现矩阵
源代码：
package org.conan.myhadoop.recommend;
import java.io.IOException;
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.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.RunningJob;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.conan.myhadoop.hdfs.HdfsDAO;
public class Step2 {
public static class Step2_UserVectorToCooccurrenceMapper extends MapReduceBase implements Mapper {
private final static Text k = new Text();
private final static IntWritable v = new IntWritable(1);
@Override
public void map(LongWritable key, Text values, OutputCollector output, Reporter reporter) throws IOException {
String[] tokens = Recommend.DELIMITER.split(values.toString());
for (int i = 1; i < tokens.length; i++) {
String itemID = tokens[i].split(":")[0];
for (int j = 1; j < tokens.length; j++) {
String itemID2 = tokens[j].split(":")[0];
k.set(itemID + ":" + itemID2);
output.collect(k, v);
}
}
}
}
public static class Step2_UserVectorToConoccurrenceReducer extends MapReduceBase implements Reducer {
private IntWritable result = new IntWritable();
@Override
public void reduce(Text key, Iterator values, OutputCollector output, Reporter reporter) throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
result.set(sum);
output.collect(key, result);
}
}
public static void run(Map path) throws IOException {
JobConf conf = Recommend.config();
String input = path.get("Step2Input");
String output = path.get("Step2Output");
HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
hdfs.rmr(output);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(Step2_UserVectorToCooccurrenceMapper.class);
conf.setCombinerClass(Step2_UserVectorToConoccurrenceReducer.class);
conf.setReducerClass(Step2_UserVectorToConoccurrenceReducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(input));
FileOutputFormat.setOutputPath(conf, new Path(output));
RunningJob job = JobClient.runJob(conf);
while (!job.isComplete()) {
job.waitForCompletion();
}
}
}
计算结果：
~ hadoop fs -cat /user/hdfs/recommend/step2/part-00000
101:101 5
101:102 3
101:103 4
101:104 4
101:105 2
101:106 2
101:107 1
102:101 3
102:102 3
102:103 3
102:104 2
102:105 1
102:106 1
103:101 4
103:102 3
103:103 4
103:104 3
103:105 1
103:106 2
104:101 4
104:102 2
104:103 3
104:104 4
104:105 2
104:106 2
104:107 1
105:101 2
105:102 1
105:103 1
105:104 2
105:105 2
105:106 1
105:107 1
106:101 2
106:102 1
106:103 2
106:104 2
106:105 1
106:106 2
107:101 1
107:104 1
107:105 1
107:107 1
复制代码
4). Step3.java，合并同现矩阵和评分矩阵
源代码：
package org.conan.myhadoop.recommend;
import java.io.IOException;
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.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.RunningJob;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.conan.myhadoop.hdfs.HdfsDAO;
public class Step3 {
public static class Step31_UserVectorSplitterMapper extends MapReduceBase implements Mapper {
private final static IntWritable k = new IntWritable();
private final static Text v = new Text();
@Override
public void map(LongWritable key, Text values, OutputCollector output, Reporter reporter) throws IOException {
String[] tokens = Recommend.DELIMITER.split(values.toString());
for (int i = 1; i < tokens.length; i++) {
String[] vector = tokens[i].split(":");
int itemID = Integer.parseInt(vector[0]);
String pref = vector[1];
k.set(itemID);
v.set(tokens[0] + ":" + pref);
output.collect(k, v);
}
}
}
public static void run1(Map path) throws IOException {
JobConf conf = Recommend.config();
String input = path.get("Step3Input1");
String output = path.get("Step3Output1");
HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
hdfs.rmr(output);
conf.setOutputKeyClass(IntWritable.class);
conf.setOutputValueClass(Text.class);
conf.setMapperClass(Step31_UserVectorSplitterMapper.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(input));
FileOutputFormat.setOutputPath(conf, new Path(output));
RunningJob job = JobClient.runJob(conf);
while (!job.isComplete()) {
job.waitForCompletion();
}
}
public static class Step32_CooccurrenceColumnWrapperMapper extends MapReduceBase implements Mapper {
private final static Text k = new Text();
private final static IntWritable v = new IntWritable();
@Override
public void map(LongWritable key, Text values, OutputCollector output, Reporter reporter) throws IOException {
String[] tokens = Recommend.DELIMITER.split(values.toString());
k.set(tokens[0]);
v.set(Integer.parseInt(tokens[1]));
output.collect(k, v);
}
}
public static void run2(Map path) throws IOException {
JobConf conf = Recommend.config();
String input = path.get("Step3Input2");
String output = path.get("Step3Output2");
HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
hdfs.rmr(output);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(Step32_CooccurrenceColumnWrapperMapper.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(input));
FileOutputFormat.setOutputPath(conf, new Path(output));
RunningJob job = JobClient.runJob(conf);
while (!job.isComplete()) {
job.waitForCompletion();
}
}
}
计算结果：
~ hadoop fs -cat /user/hdfs/recommend/step3_1/part-00000
101 5:4.0
101 1:5.0
101 2:2.0
101 3:2.0
101 4:5.0
102 1:3.0
102 5:3.0
102 2:2.5
103 2:5.0
103 5:2.0
103 1:2.5
103 4:3.0
104 2:2.0
104 5:4.0
104 3:4.0
104 4:4.5
105 3:4.5
105 5:3.5
106 5:4.0
106 4:4.0
107 3:5.0
~ hadoop fs -cat /user/hdfs/recommend/step3_2/part-00000
101:101 5
101:102 3
101:103 4
101:104 4
101:105 2
101:106 2
101:107 1
102:101 3
102:102 3
102:103 3
102:104 2
102:105 1
102:106 1
103:101 4
103:102 3
103:103 4
103:104 3
103:105 1
103:106 2
104:101 4
104:102 2
104:103 3
104:104 4
104:105 2
104:106 2
104:107 1
105:101 2
105:102 1
105:103 1
105:104 2
105:105 2
105:106 1
105:107 1
106:101 2
106:102 1
106:103 2
106:104 2
106:105 1
106:106 2
107:101 1
107:104 1
107:105 1
107:107 1
5). Step4.java，计算推荐结果列表
源代码：
package org.conan.myhadoop.recommend;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
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.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.RunningJob;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.conan.myhadoop.hdfs.HdfsDAO;
public class Step4 {
public static class Step4_PartialMultiplyMapper extends MapReduceBase implements Mapper {
private final static IntWritable k = new IntWritable();
private final static Text v = new Text();
private final static Map cooccurrenceMatrix = new HashMap();
@Override
public void map(LongWritable key, Text values, OutputCollector output, Reporter reporter) throws IOException {
String[] tokens = Recommend.DELIMITER.split(values.toString());
String[] v1 = tokens[0].split(":");
String[] v2 = tokens[1].split(":");
if (v1.length > 1) {// cooccurrence
int itemID1 = Integer.parseInt(v1[0]);
int itemID2 = Integer.parseInt(v1[1]);
int num = Integer.parseInt(tokens[1]);
List list = null;
if (!cooccurrenceMatrix.containsKey(itemID1)) {
list = new ArrayList();
} else {
list = cooccurrenceMatrix.get(itemID1);
}
list.add(new Cooccurrence(itemID1, itemID2, num));
cooccurrenceMatrix.put(itemID1, list);
}
if (v2.length > 1) {// userVector
int itemID = Integer.parseInt(tokens[0]);
int userID = Integer.parseInt(v2[0]);
double pref = Double.parseDouble(v2[1]);
k.set(userID);
for (Cooccurrence co : cooccurrenceMatrix.get(itemID)) {
v.set(co.getItemID2() + "," + pref * co.getNum());
output.collect(k, v);
}
}
}
}
public static class Step4_AggregateAndRecommendReducer extends MapReduceBase implements Reducer {
private final static Text v = new Text();
@Override
public void reduce(IntWritable key, Iterator values, OutputCollector output, Reporter reporter) throws IOException {
Map result = new HashMap();
while (values.hasNext()) {
String[] str = values.next().toString().split(",");
if (result.containsKey(str[0])) {
result.put(str[0], result.get(str[0]) + Double.parseDouble(str[1]));
} else {
result.put(str[0], Double.parseDouble(str[1]));
}
}
Iterator iter = result.keySet().iterator();
while (iter.hasNext()) {
String itemID = iter.next();
double score = result.get(itemID);
v.set(itemID + "," + score);
output.collect(key, v);
}
}
}
public static void run(Map path) throws IOException {
JobConf conf = Recommend.config();
String input1 = path.get("Step4Input1");
String input2 = path.get("Step4Input2");
String output = path.get("Step4Output");
HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
hdfs.rmr(output);
conf.setOutputKeyClass(IntWritable.class);
conf.setOutputValueClass(Text.class);
conf.setMapperClass(Step4_PartialMultiplyMapper.class);
conf.setCombinerClass(Step4_AggregateAndRecommendReducer.class);
conf.setReducerClass(Step4_AggregateAndRecommendReducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(input1), new Path(input2));
FileOutputFormat.setOutputPath(conf, new Path(output));
RunningJob job = JobClient.runJob(conf);
while (!job.isComplete()) {
job.waitForCompletion();
}
}
}
class Cooccurrence {
private int itemID1;
private int itemID2;
private int num;
public Cooccurrence(int itemID1, int itemID2, int num) {
super();
this.itemID1 = itemID1;
this.itemID2 = itemID2;
this.num = num;
}
public int getItemID1() {
return itemID1;
}
public void setItemID1(int itemID1) {
this.itemID1 = itemID1;
}
public int getItemID2() {
return itemID2;
}
public void setItemID2(int itemID2) {
this.itemID2 = itemID2;
}
public int getNum() {
return num;
}
public void setNum(int num) {
this.num = num;
}
}
计算结果：
~ hadoop fs -cat /user/hdfs/recommend/step4/part-00000
1 107,5.0
1 106,18.0
1 105,15.5
1 104,33.5
1 103,39.0
1 102,31.5
1 101,44.0
2 107,4.0
2 106,20.5
2 105,15.5
2 104,36.0
2 103,41.5
2 102,32.5
2 101,45.5
3 107,15.5
3 106,16.5
3 105,26.0
3 104,38.0
3 103,24.5
3 102,18.5
3 101,40.0
4 107,9.5
4 106,33.0
4 105,26.0
4 104,55.0
4 103,53.5
4 102,37.0
4 101,63.0
5 107,11.5
5 106,34.5
5 105,32.0
5 104,59.0
5 103,56.5
5 102,42.5
5 101,68.0
对Step4过程优化，请参考本文最后的补充内容。
6). HdfsDAO.java，HDFS操作工具类
详细解释，请参考文章：Hadoop编程调用HDFS
源代码：
package org.conan.myhadoop.hdfs;
import java.io.IOException;
import java.net.URI;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.mapred.JobConf;
public class HdfsDAO {
private static final String HDFS = "hdfs://192.168.1.210:9000/";
public HdfsDAO(Configuration conf) {
this(HDFS, conf);
}
public HdfsDAO(String hdfs, Configuration conf) {
this.hdfsPath = hdfs;
this.conf = conf;
}
private String hdfsPath;
private Configuration conf;
public static void main(String[] args) throws IOException {
JobConf conf = config();
HdfsDAO hdfs = new HdfsDAO(conf);
hdfs.copyFile("datafile/item.csv", "/tmp/new");
hdfs.ls("/tmp/new");
}
public static JobConf config(){
JobConf conf = new JobConf(HdfsDAO.class);
conf.setJobName("HdfsDAO");
conf.addResource("classpath:/hadoop/core-site.xml");
conf.addResource("classpath:/hadoop/hdfs-site.xml");
conf.addResource("classpath:/hadoop/mapred-site.xml");
return conf;
}
public void mkdirs(String folder) throws IOException {
Path path = new Path(folder);
FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);
if (!fs.exists(path)) {
fs.mkdirs(path);
System.out.println("Create: " + folder);
}
fs.close();
}
public void rmr(String folder) throws IOException {
Path path = new Path(folder);
FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);
fs.deleteOnExit(path);
System.out.println("Delete: " + folder);
fs.close();
}
public void ls(String folder) throws IOException {
Path path = new Path(folder);
FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);
FileStatus[] list = fs.listStatus(path);
System.out.println("ls: " + folder);
System.out.println("==========================================================");
for (FileStatus f : list) {
System.out.printf("name: %s, folder: %s, size: %d\n", f.getPath(), f.isDir(), f.getLen());
}
System.out.println("==========================================================");
fs.close();
}
public void createFile(String file, String content) throws IOException {
FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);
byte[] buff = content.getBytes();
FSDataOutputStream os = null;
try {
os = fs.create(new Path(file));
os.write(buff, 0, buff.length);
System.out.println("Create: " + file);
} finally {
if (os != null)
os.close();
}
fs.close();
}
public void copyFile(String local, String remote) throws IOException {
FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);
fs.copyFromLocalFile(new Path(local), new Path(remote));
System.out.println("copy from: " + local + " to " + remote);
fs.close();
}
public void download(String remote, String local) throws IOException {
Path path = new Path(remote);
FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);
fs.copyToLocalFile(path, new Path(local));
System.out.println("download: from" + remote + " to " + local);
fs.close();
}
public void cat(String remoteFile) throws IOException {
Path path = new Path(remoteFile);
FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);
FSDataInputStream fsdis = null;
System.out.println("cat: " + remoteFile);
try {
fsdis =fs.open(path);
IOUtils.copyBytes(fsdis, System.out, 4096, false);
} finally {
IOUtils.closeStream(fsdis);
fs.close();
}
}
}
