023_数量类型练习——Hadoop MapReduce手机流量统计

1） 分析业务需求：用户使用手机上网，存在流量的消耗。流量包括两部分：其一是上行流量（发送消息流量），其二是下行流量（接收消息的流量）。每种流量在网络传输过程中，有两种形式说明：包的大小，流量的大小。使用手机上网，以手机号为唯一标识符，进行记录。有记录，包括很多信息，需要的信息字段。

　　实际需要的字段：

    　　手机号码、上行数据包数、下行数据包数、上行总流量、下行总流量。

2） 自定义数据类型（五个字段）

DataWritable implement WritableComparable接口。

  1 package org.dragon.hadoop.mapreduce.app;
  2 
  3 import java.io.DataInput;
  4 import java.io.DataOutput;
  5 import java.io.IOException;
  6 
  7 import org.apache.hadoop.io.Writable;
  8 
  9 /**
 10  * 
 11  * @author ZhuXY
 12  * @time 2016-3-10 下午3:49:55
 13  * 
 14  */
 15 public class DataWritable implements Writable {
 16 
 17     // telsphone
 18 
 19     // upload
 20     private int upPackNum;
 21     private int upPayLoad;
 22 
 23     // download
 24     private int downPackNum;
 25     private int downPayLoad;
 26 
 27     public DataWritable() {
 28 
 29     }
 30 
 31     public void set(int upPackNum, int upPayLoad, int downPackNum,
 32             int downPayload) {
 33         this.upPackNum = upPackNum;
 34         this.upPayLoad = upPayLoad;
 35         this.downPackNum = downPackNum;
 36         this.downPayLoad = downPayload;
 37 
 38     }
 39 
 40     public int getUpPackNum() {
 41         return upPackNum;
 42     }
 43 
 44     public int getUpPayLoas() {
 45         return upPayLoad;
 46     }
 47 
 48     public int getDownPackNum() {
 49         return downPackNum;
 50     }
 51 
 52     public int getDownPayload() {
 53         return downPayLoad;
 54     }
 55 
 56     @Override
 57     public void write(DataOutput out) throws IOException {
 58         out.writeInt(upPackNum);
 59         out.writeInt(upPayLoad);
 60         out.writeInt(downPackNum);
 61         out.writeInt(downPayLoad);
 62     }
 63 
 64     /**
 65      * 讀出的順序要和寫入的順序相同
 66      */
 67     @Override
 68     public void readFields(DataInput in) throws IOException {
 69         // TODO Auto-generated method stub
 70         this.upPackNum = in.readInt();
 71         this.upPayLoad = in.readInt();
 72         this.downPackNum = in.readInt();
 73         this.downPayLoad = in.readInt();
 74     }
 75 
 76     @Override
 77     public String toString() {
 78         return upPackNum + "\t" + upPayLoad + "\t" + downPackNum + "\t"
 79                 + downPayLoad;
 80     }
 81 
 82     @Override
 83     public int hashCode() {
 84         final int prime = 31;
 85         int result = 1;
 86         result = prime * result + downPackNum;
 87         result = prime * result + downPayLoad;
 88         result = prime * result + upPackNum;
 89         result = prime * result + upPayLoad;
 90         return result;
 91     }
 92 
 93     @Override
 94     public boolean equals(Object obj) {
 95         if (this == obj)
 96             return true;
 97         if (obj == null)
 98             return false;
 99         if (getClass() != obj.getClass())
100             return false;
101         DataWritable other = (DataWritable) obj;
102         if (downPackNum != other.downPackNum)
103             return false;
104         if (downPayLoad != other.downPayLoad)
105             return false;
106         if (upPackNum != other.upPackNum)
107             return false;
108         if (upPayLoad != other.upPayLoad)
109             return false;
110         return true;
111     }
112 
113 }
DataWritable Code

3） 分析MapReduce写法，哪些业务逻辑在Map阶段执行，哪些业务逻辑在reduce阶段执行。

Map阶段：从文件中获取数据，抽取出需要的五个字段，输出的Key为手机号码，输出的Value为数据量的类型DataWritable对象。

Reduce阶段：将相同手机号码的Value中的数据流量进行相加，得出手机流量的总数（数据包和数据流量）。输出到文件中，以制表符分开。

  1 package org.dragon.hadoop.mapreduce.app.topk;
  2 
  3 import java.io.IOException;
  4 import java.util.Iterator;
  5 import java.util.TreeMap;
  6 import java.util.TreeSet;
  7 
  8 import javax.security.auth.callback.LanguageCallback;
  9 
 10 import org.apache.hadoop.classification.InterfaceAudience.Private;
 11 import org.apache.hadoop.conf.Configuration;
 12 import org.apache.hadoop.fs.Path;
 13 import org.apache.hadoop.io.LongWritable;
 14 import org.apache.hadoop.io.NullWritable;
 15 import org.apache.hadoop.io.Text;
 16 import org.apache.hadoop.mapreduce.Job;
 17 import org.apache.hadoop.mapreduce.Mapper;
 18 import org.apache.hadoop.mapreduce.Reducer;
 19 import org.apache.hadoop.mapreduce.Mapper.Context;
 20 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 21 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 22 import org.dragon.hadoop.mapreduce.app.topk.TopKMapReduceV3.TopKReducer;
 23 
 24 import com.sun.jersey.core.header.LanguageTag;
 25 
 26 import sun.reflect.LangReflectAccess;
 27 
 28 /**
 29  * 
 30  * @author ZhuXY
 31  * @time 2016-3-13 下午12:57:26
 32  * 
 33  */
 34 
 35 /**
 36  * 统计 & TopKey
 37  * 
 38  * 数据格式： 语言类别 歌曲名称 收藏次数 播放次数 歌手名称 需求： 统计前十首播放次数最多的歌曲名称和次数。
 39  * 
 40  * 思想:在Mapper中输出：key---歌曲类型+歌曲名称
 41  *                     value---播放次数
 42  * Reducer中：key----封装成TopKWritable对象
 43  *             value---nullwritable
 44  * reduce方法中进行集合存储，然后删除多余的
 45  * 
 46  */
 47 public class TopKMapReduceV4 {
 48     private static final int KEY = 4;
 49 
 50     // Mapper class
 51     public static class TopKMapper extends
 52             Mapper<LongWritable, Text, Text, LongWritable> {
 53 
 54         @Override
 55         protected void cleanup(Context context) throws IOException,
 56                 InterruptedException {
 57             super.cleanup(context);
 58         }
 59 
 60         @Override
 61         protected void map(LongWritable key, Text value, Context context)
 62                 throws IOException, InterruptedException {
 63             //文件的输入类型为TextInputFormat，默认到map中的为<Longwritable,Text>
 64             String lineValue = value.toString();
 65 
 66             if (null == lineValue) {
 67                 return;
 68             }
 69             
 70             //split
 71             String[] splitValue = lineValue.split("\t");
 72 
 73             if (splitValue != null && splitValue.length == 5) {
 74                 String languageType = splitValue[0];
 75                 String songName = splitValue[1];
 76                 Long playNum = Long.parseLong(splitValue[3]);
 77 
 78                 context.write(new Text(languageType + "\t" + songName),
 79                         new LongWritable(playNum));
 80             }
 81         }
 82 
 83         @Override
 84         protected void setup(Context context) throws IOException,
 85                 InterruptedException {
 86             // TODO Auto-generated method stub
 87             super.setup(context);
 88         }
 89     }
 90 
 91     // Reducer class
 92     public static class TopKReducer extends
 93             Reducer<Text, LongWritable, TopKWritable, NullWritable> {
 94         
 95         //此集合的排序规则即为TopKWritable中comparaTo的排序规则
 96         TreeSet<TopKWritable> treeSet=new TreeSet<TopKWritable>();
 97                 
 98         @Override
 99         protected void setup(Context context)
100                 throws IOException, InterruptedException {
101             // TODO Auto-generated method stub
102             super.setup(context);
103         }
104         
105         @Override
106         protected void reduce(Text key, Iterable<LongWritable> values,
107                 Context context) throws IOException, InterruptedException {
108             
109             Long palyNum=(long) 0;
110             if (key==null) {
111                 return;
112             }
113             
114             //get key
115             String[] keyArr=key.toString().split("\t");
116             String languageType=keyArr[0];
117             String songName=keyArr[1];
118             
119             //sum
120             for(LongWritable value:values){
121                 palyNum+=value.get();
122             }
123             
124             //歌曲类型、歌曲名称、歌曲播放次数封装成TopKWritable对象，保存在treeSet集合中，此集合自动排序
125             treeSet.add(new TopKWritable(
126                     languageType,songName,palyNum
127                     ));
128             
129             if (treeSet.size()>KEY) {
130                 treeSet.remove(treeSet.last());//remove the current small longNum
131             }
132         }
133 
134         @Override
135         protected void cleanup(Context context)
136                 throws IOException, InterruptedException {
137             for (TopKWritable topKWritable : treeSet) {
138                 context.write(topKWritable,NullWritable.get());
139             }
140         }
141 
142         
143     }
144     // Driver Code
145     public int run(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
146         // get conf
147         Configuration conf=new Configuration();
148         
149         // create job
150         Job job =new Job(conf, TopKMapReduceV4.class.getSimpleName());//Job name
151         
152         // set job
153         job.setJarByClass(TopKMapReduceV4.class);
154         
155         //    1)set inputPath
156         FileInputFormat.addInputPath(job, new Path(args[0]));
157         
158         //    2)set map
159         job.setMapperClass(TopKMapper.class);
160         job.setMapOutputKeyClass(Text.class);
161         job.setMapOutputValueClass(LongWritable.class);
162         
163         //    3)set outputPath
164         FileOutputFormat.setOutputPath(job, new Path(args[1]));
165         
166         //    4)set reduce 
167         job.setReducerClass(TopKReducer.class);
168         job.setOutputKeyClass(TopKWritable.class);
169         job.setOutputValueClass(NullWritable.class);
170         
171         // submit job
172         boolean isSuccess=job.waitForCompletion(true);
173         
174         //return status
175         return isSuccess?0:1;
176     }
177     
178     public static void main(String[] args) throws IOException, InterruptedException, Exception {
179         
180         args=new String[]{
181                 "hdfs://hadoop-master.dragon.org:9000/wc/wcinput/",
182                 "hdfs://hadoop-master.dragon.org:9000/wc/wcoutput"
183         };
184         int status =new TopKMapReduceV4().run(args);
185         System.exit(status);
186     }
187 }
View Mapper、Reducer、Driver Code

实际的业务中，原始数据存储在文件或者关系型数据库中，需要进行多次的数据的清理和筛选，符合我们需要的数据，将不合格的数据全部进行过滤，

Sqoop 框架，将关系型数据和Hbase、Hive以及HDFS中导入导出数据。

　　针对实际的负责的实际业务，都需要自己编写代码进行数据的清洗。

 

posted @ 2016-03-15 12:10  YouxiBug  阅读(482)  评论(0)  编辑  收藏  举报