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转-用Hadoop构建电影推荐系统

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Hadoop家族系列文章,主要介绍Hadoop家族产品,常用的项目包括Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa,新增加的项目包括,YARN, Hcatalog, Oozie, Cassandra, Hama, Whirr, Flume, Bigtop, Crunch, Hue等。

从2011年开始,中国进入大数据风起云涌的时代,以Hadoop为代表的家族软件,占据了大数据处理的广阔地盘。开源界及厂商,所有数据软件,无一不向Hadoop靠拢。Hadoop也从小众的高富帅领域,变成了大数据开发的标准。在Hadoop原有技术基础之上,出现了Hadoop家族产品,通过“大数据”概念不断创新,推出科技进步。

作为IT界的开发人员,我们也要跟上节奏,抓住机遇,跟着Hadoop一起雄起!

关于作者:

  • 张丹(Conan), 程序员Java,R,PHP,Javascript
  • weibo:@Conan_Z
  • blog: http://blog.fens.me
  • email: bsspirit@gmail.com

转载请注明出处:
http://blog.fens.me/hadoop-mapreduce-recommend/

hadoop-recommand

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。

目录

  1. 推荐系统概述
  2. 需求分析:推荐系统指标设计
  3. 算法模型:Hadoop并行算法
  4. 架构设计:推荐系统架构
  5. 程序开发:MapReduce程序实现
  6. 补充内容:对Step4过程优化

1. 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:

image015

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:

image017

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

2. 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。

netflix_prize

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

3. 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

alogrithm_1

图片摘自”Mahout In Action”

MapReduce任务设计

aglorithm_2

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

4. 架构设计:推荐系统架构

hadoop-recommand-architect

上图中,左边是Application业务系统,右边是Hadoop的HDFS, MapReduce。

  1. 业务系统记录了用户的行为和对物品的打分
  2. 设置系统定时器CRON,每xx小时,增量向HDFS导入数据(userid,itemid,value,time)。
  3. 完成导入后,设置系统定时器,启动MapReduce程序,运行推荐算法。
  4. 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。

5. 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<String, String> path = new HashMap<String, String>();
        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<Object, Text, IntWritable, Text> {
        private final static IntWritable k = new IntWritable();
        private final static Text v = new Text();

        @Override
        public void map(Object key, Text value, OutputCollector<IntWritable, Text> 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<IntWritable, Text, IntWritable, Text> {
        private final static Text v = new Text();

        @Override
        public void reduce(IntWritable key, Iterator values, OutputCollector<IntWritable, Text> 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<String, String> 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<LongWritable, Text, Text, IntWritable> {
        private final static Text k = new Text();
        private final static IntWritable v = new IntWritable(1);

        @Override
        public void map(LongWritable key, Text values, OutputCollector<Text, IntWritable> 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<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        @Override
        public void reduce(Text key, Iterator values, OutputCollector<Text, IntWritable> 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<String, String> 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

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<LongWritable, Text, IntWritable, Text> {
        private final static IntWritable k = new IntWritable();
        private final static Text v = new Text();

        @Override
        public void map(LongWritable key, Text values, OutputCollector<IntWritable, Text> 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<String, String> 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<LongWritable, Text, Text, IntWritable> {
        private final static Text k = new Text();
        private final static IntWritable v = new IntWritable();

        @Override
        public void map(LongWritable key, Text values, OutputCollector<Text, IntWritable> 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<String, String> 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<LongWritable, Text, IntWritable, Text> {
        private final static IntWritable k = new IntWritable();
        private final static Text v = new Text();

        private final static Map<Integer, List> cooccurrenceMatrix = new HashMap<Integer, List>();

        @Override
        public void map(LongWritable key, Text values, OutputCollector<IntWritable, Text> 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<IntWritable, Text, IntWritable, Text> {
        private final static Text v = new Text();

        @Override
        public void reduce(IntWritable key, Iterator values, OutputCollector<IntWritable, Text> output, Reporter reporter) throws IOException {
            Map<String, Double> result = new HashMap<String, Double>();
            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<String, String> 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();
          }
    }
}

这样我们就自己编程实现了MapReduce化基于物品的协同过滤算法。

RHadoop的实现方案,请参考文章:RHadoop实践系列之三 R实现MapReduce的协同过滤算法

Mahout的实现方案,请参考文章:Mahout分步式程序开发 基于物品的协同过滤ItemCF

我已经把整个MapReduce的实现都放到了github上面:
https://github.com/bsspirit/maven_hadoop_template/releases/tag/recommend

6. 补充内容:对Step4过程优化

在Step4.java这一步运行过程中,Mapper过程在Step4_PartialMultiplyMapper类通过分别读取两个input数据,在内存中进行了计算。

这种方式有明显的限制条件:

  • a. 两个输入数据集,有严格的读入顺序。由于Hadoop不能指定读入顺序,因此在多节点的Hadoop集群环境,读入顺序有可能会发生错误,造成程序的空指针错误。
  • b. 这个计算过程,在内存中实现。如果矩阵过大,会造成单节点的内存不足。

做为优化的方案,我们需要对Step4的过程,实现MapReduce的矩阵乘法,矩阵算法原理请参考文章:用MapReduce实现矩阵乘法

对Step4优化的实现:把矩阵计算通过两个MapReduce过程实现。

  • 矩阵乘法过程类文件:Step4_Update.java
  • 矩阵加法过程类文件:Step4_Update2.java
  • 修改启动程序:Recommend.java

增加文件:Step4_Update.java


package org.conan.myhadoop.recommend;

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.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 Step4_Update {

    public static class Step4_PartialMultiplyMapper extends Mapper<LONGWRITABLE, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" text="" text,=""> {

        private String flag;// A同现矩阵 or B评分矩阵

        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            FileSplit split = (FileSplit) context.getInputSplit();
            flag = split.getPath().getParent().getName();// 判断读的数据集

            // System.out.println(flag);
        }

        @Override
        public void map(LongWritable key, Text values, Context context) throws IOException, InterruptedException {
            String[] tokens = Recommend.DELIMITER.split(values.toString());

            if (flag.equals("step3_2")) {// 同现矩阵
                String[] v1 = tokens[0].split(":");
                String itemID1 = v1[0];
                String itemID2 = v1[1];
                String num = tokens[1];

                Text k = new Text(itemID1);
                Text v = new Text("A:" + itemID2 + "," + num);

                context.write(k, v);
                // System.out.println(k.toString() + "  " + v.toString());

            } else if (flag.equals("step3_1")) {// 评分矩阵
                String[] v2 = tokens[1].split(":");
                String itemID = tokens[0];
                String userID = v2[0];
                String pref = v2[1];

                Text k = new Text(itemID);
                Text v = new Text("B:" + userID + "," + pref);

                context.write(k, v);
                // System.out.println(k.toString() + "  " + v.toString());
            }
        }

    }

    public static class Step4_AggregateReducer extends Reducer<TEXT, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" text="" text,=""> {

        @Override
        public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            System.out.println(key.toString() + ":");

            Map<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" string=""> mapA = new HashMap<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" string="">();
            Map<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" string=""> mapB = new HashMap<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" string="">();

            for (Text line : values) {
                String val = line.toString();
                System.out.println(val);

                if (val.startsWith("A:")) {
                    String[] kv = Recommend.DELIMITER.split(val.substring(2));
                    mapA.put(kv[0], kv[1]);

                } else if (val.startsWith("B:")) {
                    String[] kv = Recommend.DELIMITER.split(val.substring(2));
                    mapB.put(kv[0], kv[1]);

                }
            }

            double result = 0;
            Iterator iter = mapA.keySet().iterator();
            while (iter.hasNext()) {
                String mapk = iter.next();// itemID

                int num = Integer.parseInt(mapA.get(mapk));
                Iterator iterb = mapB.keySet().iterator();
                while (iterb.hasNext()) {
                    String mapkb = iterb.next();// userID
                    double pref = Double.parseDouble(mapB.get(mapkb));
                    result = num * pref;// 矩阵乘法相乘计算

                    Text k = new Text(mapkb);
                    Text v = new Text(mapk + "," + result);
                    context.write(k, v);
                    System.out.println(k.toString() + "  " + v.toString());
                }
            }
        }
    }

    public static void run(Map<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" string=""> path) throws IOException, InterruptedException, ClassNotFoundException {
        JobConf conf = Recommend.config();

        String input1 = path.get("Step5Input1");
        String input2 = path.get("Step5Input2");
        String output = path.get("Step5Output");

        HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
        hdfs.rmr(output);

        Job job = new Job(conf);
        job.setJarByClass(Step4_Update.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        job.setMapperClass(Step4_Update.Step4_PartialMultiplyMapper.class);
        job.setReducerClass(Step4_Update.Step4_AggregateReducer.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.setInputPaths(job, new Path(input1), new Path(input2));
        FileOutputFormat.setOutputPath(job, new Path(output));

        job.waitForCompletion(true);
    }

}

增加文件:Step4_Update2.java


package org.conan.myhadoop.recommend;

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.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.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 Step4_Update2 {

    public static class Step4_RecommendMapper extends Mapper<LONGWRITABLE, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" text="" text,=""> {

        @Override
        public void map(LongWritable key, Text values, Context context) throws IOException, InterruptedException {
            String[] tokens = Recommend.DELIMITER.split(values.toString());
            Text k = new Text(tokens[0]);
            Text v = new Text(tokens[1]+","+tokens[2]);
            context.write(k, v);
        }
    }

    public static class Step4_RecommendReducer extends Reducer<TEXT, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" text="" text,=""> {
        
        @Override
        public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            System.out.println(key.toString() + ":");
            Map<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" double=""> map = new HashMap<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" double="">();// 结果
            
            for (Text line : values) {
                System.out.println(line.toString());
                String[] tokens = Recommend.DELIMITER.split(line.toString());
                String itemID = tokens[0];
                Double score = Double.parseDouble(tokens[1]);
                
                 if (map.containsKey(itemID)) {
                     map.put(itemID, map.get(itemID) + score);// 矩阵乘法求和计算
                 } else {
                     map.put(itemID, score);
                 }
            }
            
            Iterator iter = map.keySet().iterator();
            while (iter.hasNext()) {
                String itemID = iter.next();
                double score = map.get(itemID);
                Text v = new Text(itemID + "," + score);
                context.write(key, v);
            }
        }
    }

    public static void run(Map<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" string=""> path) throws IOException, InterruptedException, ClassNotFoundException {
        JobConf conf = Recommend.config();

        String input = path.get("Step6Input");
        String output = path.get("Step6Output");

        HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
        hdfs.rmr(output);

        Job job = new Job(conf);
        job.setJarByClass(Step4_Update2.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        job.setMapperClass(Step4_Update2.Step4_RecommendMapper.class);
        job.setReducerClass(Step4_Update2.Step4_RecommendReducer.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.setInputPaths(job, new Path(input));
        FileOutputFormat.setOutputPath(job, new Path(output));

        job.waitForCompletion(true);
    }

}

修改Recommend.java


package org.conan.myhadoop.recommend;

import java.util.HashMap;
import java.util.Map;
import java.util.regex.Pattern;

import org.apache.hadoop.mapred.JobConf;
import org.conan.myhadoop.hdfs.HdfsDAO;

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<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" string=""> path = new HashMap<STRING, style="PADDING-BOTTOM: 0pt; MARGIN: 0pt; PADDING-LEFT: 0pt; PADDING-RIGHT: 0pt; PADDING-TOP: 0pt" string="">();
        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");
        
        path.put("Step5Input1", path.get("Step3Output1"));
        path.put("Step5Input2", path.get("Step3Output2"));
        path.put("Step5Output", path.get("Step1Input") + "/step5");
        
        path.put("Step6Input", path.get("Step5Output"));
        path.put("Step6Output", path.get("Step1Input") + "/step6");       

        Step1.run(path);
        Step2.run(path);
        Step3.run1(path);
        Step3.run2(path);
        //Step4.run(path);
        
        Step4_Update.run(path);
        Step4_Update2.run(path);
               
        System.exit(0);
    }

    public static JobConf config() {
        JobConf conf = new JobConf(Recommend.class);
        conf.setJobName("Recommand");
        conf.addResource("classpath:/hadoop/core-site.xml");
        conf.addResource("classpath:/hadoop/hdfs-site.xml");
        conf.addResource("classpath:/hadoop/mapred-site.xml");
        conf.set("io.sort.mb", "1024");
        return conf;
    }

}

运行Step4_Update.java,查看输出结果


~ hadoop fs -cat /user/hdfs/recommend/step5/part-r-00000

3       107,2.0
2       107,2.0
1       107,5.0
5       107,4.0
4       107,5.0
3       106,4.0
2       106,4.0
1       106,10.0
5       106,8.0
4       106,10.0
3       105,4.0
2       105,4.0
1       105,10.0
5       105,8.0
4       105,10.0
3       104,8.0
2       104,8.0
1       104,20.0
5       104,16.0
4       104,20.0
3       103,8.0
2       103,8.0
1       103,20.0
5       103,16.0
4       103,20.0
3       102,6.0
2       102,6.0
1       102,15.0
5       102,12.0
4       102,15.0
3       101,10.0
2       101,10.0
1       101,25.0
5       101,20.0
4       101,25.0
2       106,2.5
1       106,3.0
5       106,3.0
2       105,2.5
1       105,3.0
5       105,3.0
2       104,5.0
1       104,6.0
5       104,6.0
2       103,7.5
1       103,9.0
5       103,9.0
2       102,7.5
1       102,9.0
5       102,9.0
2       101,7.5
1       101,9.0
5       101,9.0
2       106,10.0
1       106,5.0
5       106,4.0
4       106,6.0
2       105,5.0
1       105,2.5
5       105,2.0
4       105,3.0
2       104,15.0
1       104,7.5
5       104,6.0
4       104,9.0
2       103,20.0
1       103,10.0
5       103,8.0
4       103,12.0
2       102,15.0
1       102,7.5
5       102,6.0
4       102,9.0
2       101,20.0
1       101,10.0
5       101,8.0
4       101,12.0
3       107,4.0
2       107,2.0
5       107,4.0
4       107,4.5
3       106,8.0
2       106,4.0
5       106,8.0
4       106,9.0
3       105,8.0
2       105,4.0
5       105,8.0
4       105,9.0
3       104,16.0
2       104,8.0
5       104,16.0
4       104,18.0
3       103,12.0
2       103,6.0
5       103,12.0
4       103,13.5
3       102,8.0
2       102,4.0
5       102,8.0
4       102,9.0
3       101,16.0
2       101,8.0
5       101,16.0
4       101,18.0
3       107,4.5
5       107,3.5
3       106,4.5
5       106,3.5
3       105,9.0
5       105,7.0
3       104,9.0
5       104,7.0
3       103,4.5
5       103,3.5
3       102,4.5
5       102,3.5
3       101,9.0
5       101,7.0
5       106,8.0
4       106,8.0
5       105,4.0
4       105,4.0
5       104,8.0
4       104,8.0
5       103,8.0
4       103,8.0
5       102,4.0
4       102,4.0
5       101,8.0
4       101,8.0
3       107,5.0
3       105,5.0
3       104,5.0
3       101,5.0

运行Step4_Update2.java,查看输出结果


~ hadoop fs -cat /user/hdfs/recommend/step6/part-r-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

这样我们就把原来内存中计算的部分,通过MapReduce实现了,结果与之间Step4的结果一致。

代码已经更新到github,请需要的同学更新查看。
https://github.com/bsspirit/maven_hadoop_template/tree/master/src/main/java/org/conan/myhadoop/recommend

转载请注明出处:
http://blog.fens.me/hadoop-mapreduce-recommend/

 

This entry was posted in Hadoop实践, JAVA语言实践, R语言实践, 数据挖掘, 架构设计, 程序算法

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