Hadoop学习：Map/Reduce初探与小Demo实现

Hadoop学习：Map/Reduce初探与小Demo实现
原文地址：https://blog.csdn.net/liyong199012/article/details/25423221

一、    概念知识介绍

        Hadoop MapReduce是一个用于处理海量数据的分布式计算框架。这个框架解决了诸如数据分布式存储、作业调度、容错、机器间通信等复杂问题，可以使没有并行处理或者分布式计算经验的工程师，也能很轻松地写出结构简单的、应用于成百上千台机器处理大规模数据的并行分布式程序。

       Hadoop MapReduce基于“分而治之”的思想，将计算任务抽象成map和reduce两个计算过程，可以简单理解为“分散运算—归并结果”的过程。一个 MapReduce程序首先会把输入数据分割成不相关的若干键/值对（key1/value1）集合，这些键/值对会由多个map任务来并行地处理。 MapReduce会对map的输出（一些中间键/值对key2/value2集合）按照key2进行排序，排序是用memcmp的方式对key在内存中字节数组比较后进行升序排序，并将属于同一个key2的所有value2组合在一起作为reduce任务的输入，由reduce任务计算出最终结果并输出 key3/value3。作为一个优化，同一个计算节点上的key2/value2会通过combine在本地归并。基本流程如下：

       Hadoop和单机程序计算流程对比：

       常计算任务的输入和输出都是存放在文件里的，并且这些文件被存放在Hadoop分布式文件系统HDFS（Hadoop Distributed File System）中，系统会尽量调度计算任务到数据所在的节点上运行，而不是尽量将数据移动到计算节点上，减少大量数据在网络中传输，尽量节省带宽消耗。

       应用程序开发人员一般情况下需要关心的是图中灰色的部分，单机程序需要处理数据读取和写入、数据处理；Hadoop程序需要实现map和 reduce，而数据读取和写入、map和reduce之间的数据传输、容错处理等由Hadoop MapReduce和HDFS自动完成。

二、    开发环境搭建

       Map/Reduce程序依赖Hadoop集群，另外Eclipse需要安装依赖的hadoop包。

       Hadoop集群搭建：参考Hadoop 2.2.0集群搭建

1.   安装、配置Eclipse

       在官网下载合适的Eclipse，将hadoop开发所依赖的插件jar包拷贝到eclipse的安装文件夹plugins下。下载地址参考：hadoop2.2.0开发依赖的jar包，当然也可以自己编译。

       启动eclipse，选择Window—>Prefrances，若出现如下Hadoop Map/Reduce说明插件安装成功

2.   配置DFS，主要是数据文件的输入输出管理。

       Window—>Open Perspective—>other—>Map/Reduce，显示Map/Reduce视图。点击Map/Reduce Locations 的小象图标，新建Hadoop Location，输入如下：

       项目视图会出现DFS Location，用来管理输入、输出数据文件。

       需要配置hadoop安装文件夹：新建Map/Reduce工程单击Configure Hadoop install direction，输入hadoop的安装路径。

       右键单击DFS Location下的空文件夹上传一个文本文件，然后刷新，若文件出现了则说明环境配置成功。

三、    编程模型

       MapReduce编程模型的原理是：利用一个输入key/value pair集合来产生一个输出的key/value pair集合。MapReduce库的用户用两个函数表达这个计算：Map和Reduce。

       用户自定义的Map函数接受一个输入的key/value pair值，然后产生一个中间key/value pair值的集合。MapReduce库把所有具有相同中间key值I的中间value值集合在一起后传递给reduce函数。

       用户自定义的Reduce函数接受一个中间key的值I和相关的一个value值的集合。Reduce函数合并这些value值，形成一个较小的 value值的集合。一般的，每次Reduce函数调用只产生0或1个输出value值。通常我们通过一个迭代器把中间value值提供给Reduce函数，这样我们就可以处理无法全部放入内存中的大量的value值的集合。

四、    小例子

1.      数据准备

       以Tomcat日志为例，日志格式如下：
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99. 127.0.0.1,-,-,[08/May/2014:14:27:51 +0800],GET /jygl/jaxrs/teaching/teachingPlanService/teachingPlanListByEducationLevelAndGradeId?grade=4af2a042437c2c0801437ed1cdea0017&educationLevel= HTTP/1.1,200,4849
100. 0:0:0:0:0:0:0:1,-,-,[08/May/2014:14:27:52 +0800],GET /jyglFront/teaching/teachingPlanList HTTP/1.1,200,22794
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102. 127.0.0.1,-,-,[08/May/2014:14:28:02 +0800],GET /jygl/jaxrs/exam/examArrangeService/getExamBatchIdByLatest HTTP/1.1,200,43
103. 127.0.0.1,-,-,[08/May/2014:14:28:02 +0800],GET /jygl/jaxrs/exam/examArrangeService/getExamBatchNameByEBId/4af2a0424323412e014327739b1702bd HTTP/1.1,200,16
104. 127.0.0.1,-,-,[08/May/2014:14:28:02 +0800],GET /jygl/jaxrs/exam/examSubscribeService/getUtilObjectThirExamBatchsByEBNN/201403 HTTP/1.1,200,653
105. 0:0:0:0:0:0:0:1,-,-,[08/May/2014:14:28:02 +0800],GET /jyglFront/exam_initgroupsubscribestatistic HTTP/1.1,200,13551
106. 127.0.0.1,-,-,[08/May/2014:14:28:19 +0800],POST /jygl/jaxrs/right/addUserLog HTTP/1.1,200,-
107. 127.0.0.1,-,-,[08/May/2014:14:31:42 +0800],GET /jygl/jaxrs/exam/examSubscribeService/groupSubscribe/201403/0/0/201309/1 HTTP/1.1,200,-
2.      要解决的问题：统计资源（URL）被访问的次数。

3.      编程实现

       想法：解析Tomcat日志，map的工作是将每一行日志中的URL截取作为key值，value为1表示1次，reduce的工作是将相同key值的行合并，value为总次数。

代码如下：
```
package org.ly.ccnu;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
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.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class SecondTest extends Configured implements Tool{
    enum Counter{
        LINESKIP,
    }    
    public static class Map extends Mapper<LongWritable,Text,Text,IntWritable>{
        private static final IntWritable one = new IntWritable(1); 
        public void map(LongWritable key,Text value,Context context) throws IOException, InterruptedException{
            String line = value.toString();
            try{
                String[] lineSplit = line.split(",");
                String requestUrl = lineSplit[4];
                requestUrl = requestUrl.substring(requestUrl.indexOf(' ')+1, requestUrl.lastIndexOf(' '));
                Text out = new Text(requestUrl);
                context.write(out,one);
            }catch(java.lang.ArrayIndexOutOfBoundsException e){
                context.getCounter(Counter.LINESKIP).increment(1);
            }            
        }
    }    
    public static class Reduce extends Reducer<Text,IntWritable,Text,IntWritable>{        
        public void reduce(Text key, Iterable<IntWritable> values,Context context)throws IOException{
            int count =  0;  
            for(IntWritable v : values){  
                count = count + 1;  
            }  
            try {
                context.write(key, new IntWritable(count));
            } catch (InterruptedException e) {
                 e.printStackTrace();
            }             
        }        
    }     
    @Override
    public int run(String[] args) throws Exception {
        Configuration conf = getConf();
        Job job = new Job(conf, "logAnalysis");
        job.setJarByClass(SecondTest.class);        
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));        
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputFormatClass(TextOutputFormat.class);        
        //keep the same format with the output of Map and Reduce
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);        
        job.waitForCompletion(true);
        return job.isSuccessful()?0:1;
    }    
    public static void main(String[] args)throws Exception{        
        int res = ToolRunner.run(new Configuration(), new SecondTest(),args);        
        System.exit(res);
    }
}
```
4. 处理结果
1. / 2
2. /jygl/jaxrs/article/getArticleList/10-1 3
3. /jygl/jaxrs/article/getTotalArticleRecords 3
4. /jygl/jaxrs/enroll/educationLevelService/allEducationLevels 5
5. /jygl/jaxrs/enroll/gradeInfoService/allGradeInfos 2
6. /jygl/jaxrs/enroll/gradeInfoService/currentGradeInfo 1
7. /jygl/jaxrs/enroll/studyCenterService/allStudyCentersByUtilObject 1
8. /jygl/jaxrs/exam/examArrangeService/getExamBatchIdByLatest 2
9. /jygl/jaxrs/exam/examArrangeService/getExamBatchNameByEBId/4af2a0424323412e014327739b1702bd 2
10. /jygl/jaxrs/exam/examParameterService/getAllGradeInfo 3
11. /jygl/jaxrs/exam/examParameterService/getAllStudyCenters 3
12. /jygl/jaxrs/exam/examSubscribeService/getUtilObjectThirExamBatchsByEBNN/201403 2
13. /jygl/jaxrs/exam/examSubscribeService/groupSubscribe/201403/0/0/201309/1 1
14. /jygl/jaxrs/graduate/graduateBatchService/getAllGraduateBatch 1
15. /jygl/jaxrs/graduate/graduateBatchService/getGraduateBatchByConditions?graduateBatchName=&pageSize=10&pageNo=1 1
16. /jygl/jaxrs/graduate/graduateBatchService/getTotalGraduateBatchByCondition?graduateBatchName= 1
17. /jygl/jaxrs/nationInfo/getAllNationInPage?pageSize=10&pageNo=1 1
18. /jygl/jaxrs/nationInfo/getTotalNations 1
19. /jygl/jaxrs/right/addUserLog 1
20. /jygl/jaxrs/right/getUserByLoginName/admin 3
21. /jygl/jaxrs/right/isValidUserByType/1-admin-superadmin 3
22. /jygl/jaxrs/teaching/teachingPlanService/getSpeicalListByTwo?gradeID=&educationLevelID= 1
23. /jygl/jaxrs/teaching/teachingPlanService/hasTeachingPlanInGrade?gradeId=4af2a0423f41d66d013f5a1f766c00ce 1
24. /jygl/jaxrs/teaching/teachingPlanService/hasTeachingPlanInGrade?gradeId=4af2a042437c2c0801437ed1cdea0017 1
25. /jygl/jaxrs/teaching/teachingPlanService/teachingPlanListByEducationLevelAndGradeId?grade=4af2a042437c2c0801437ed1cdea0017&educationLevel= 1
26. /jygl/jaxrs/teachingManage/ClassBatchPlanService/getCurClassBatchPlanVOList?newClassBatchName=&gradeName=&term=-1 2
27. /jygl/jaxrs/teachingManage/ClassBatchPlanService/getCurClassPlanVO 2
28. /jygl/jaxrs/teachingManage/ClassBatchPlanService/getCurrentClassPlanVO 2
29. /jyglFront/baseInfo_articleList?flag=1 3
30. /jyglFront/baseInfo_nationInfoList 1
31. /jyglFront/common/css/_images/botton2.gif 1
32. /jyglFront/common/css/basic.css 1
33. /jyglFront/common/css/menuStyle2.css 1
34. /jyglFront/exam_initgroupsubscribestatistic 2
35. /jyglFront/exam_initsubstudentsubscribe 1
36. /jyglFront/graduate_initGraduateBatch 1
37. /jyglFront/graduate_initGraduateInfo 1
38. /jyglFront/graduate_initGraduateQulifyCheck 1
39. /jyglFront/graduate_initLeaveSchoolInfo 1
40. /jyglFront/js/jquery.form.js 1
41. /jyglFront/mainView/navigate/images/allmenu.gif 3
42. /jyglFront/mainView/navigate/images/leftmenu_bg.gif 3
43. /jyglFront/mainView/navigate/images/logo.png 3
44. /jyglFront/mainView/navigate/images/toggle_menu.gif 3
45. /jyglFront/mainView/navigate/js/frame.js 3
46. /jyglFront/mainView/navigate/js/jquery.js 3
47. /jyglFront/mainView/navigate/js/tree.js 3
48. /jyglFront/mainView/navigate/menuList.jsp 3
49. /jyglFront/mainView/navigate/style/images/header_bg.jpg 3
50. /jyglFront/mainView/navigate/style/style.css 3
51. /jyglFront/mainView/studentView/style/images/nav_10.png 3
52. /jyglFront/right_login2home?loginName=admin&password=superadmin&type=1 3
53. /jyglFront/supervisor/intoInitAssignmentDetail 1
54. /jyglFront/teaching/openChooseCourse 1
55. /jyglFront/teaching/openReplaceChooseCourse 1
56. /jyglFront/teaching/teachingPlanList 1
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原文地址：https://www.cnblogs.com/boonya/p/9513059.html

Hadoop学习：Map/Reduce初探与小Demo实现

一、概念知识介绍

二、开发环境搭建

1. 安装、配置Eclipse

2. 配置DFS，主要是数据文件的输入输出管理。

三、编程模型

四、小例子

Hadoop学习：Map/Reduce初探与小Demo实现

一、 概念知识介绍

二、 开发环境搭建

1. 安装、配置Eclipse

2. 配置DFS，主要是数据文件的输入输出管理。

三、 编程模型

四、 小例子

一、概念知识介绍

二、开发环境搭建

三、编程模型

四、小例子