Mapreduce数据分析实例

数据包

百度网盘

链接：https://pan.baidu.com/s/1v9M3jNdT4vwsqup9N0mGOA
提取码：hs9c
复制这段内容后打开百度网盘手机App，操作更方便哦

1、     数据清洗说明：

（1）      第一列是时间；

（2）      第二列是卖出方；

（3）      第三列是买入方；

（4）      第四列是票的数量；

（5）      第五列是金额。

卖出方，买入方一共三个角色，机场（C开头），代理人（O开头）和一般顾客（PAX）

2、     数据清洗要求：

（1）统计最繁忙的机场Top10（包括买入卖出）；

（2）统计最受欢迎的航线；（起点终点一致（或相反））

（3）统计最大的代理人TOP10；

（4）统计某一天的各个机场的卖出数据top10。

3、     数据可视化要求：

（1）上述四中统计要求可以用饼图、柱状图等显示；

（2）可用关系图展示各个机场之间的联系程度（以机票数量作为分析来源）。

 

实验关键部分代码（列举统计最繁忙机场的代码，其他代码大同小异）：

数据初步情理，主要是过滤出各个机场个总票数

1.    package mapreduce;
2.    import java.io.IOException;
3.    import java.net.URI;
4.    import org.apache.hadoop.conf.Configuration;
5.    import org.apache.hadoop.fs.Path;
6.    import org.apache.hadoop.io.LongWritable;
7.    import org.apache.hadoop.io.Text;
8.    import org.apache.hadoop.mapreduce.Job;
9.    import org.apache.hadoop.mapreduce.Mapper;
10.    import org.apache.hadoop.mapreduce.Reducer;
11.    import org.apache.hadoop.mapreduce.lib.chain.ChainMapper;
12.    import org.apache.hadoop.mapreduce.lib.chain.ChainReducer;
13.    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
14.    import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
15.    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
16.    import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
17.    import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
18.    import org.apache.hadoop.fs.FileSystem;
19.    import org.apache.hadoop.io.IntWritable;
20.    public class ChainMapReduce {
21.        private static final String INPUTPATH = "hdfs://localhost:9000/mapreducetest/region.txt";
22.        private static final String OUTPUTPATH = "hdfs://localhost:9000/mapreducetest/out1";
23.        public static void main(String[] args) {
24.            try {
25.                Configuration conf = new Configuration();
26.                FileSystem fileSystem = FileSystem.get(new URI(OUTPUTPATH), conf);
27.                if (fileSystem.exists(new Path(OUTPUTPATH))) {
28.                    fileSystem.delete(new Path(OUTPUTPATH), true);
29.                }
30.                Job job = new Job(conf, ChainMapReduce.class.getSimpleName());
31.                FileInputFormat.addInputPath(job, new Path(INPUTPATH));
32.                job.setInputFormatClass(TextInputFormat.class);
33.                ChainMapper.addMapper(job, FilterMapper1.class, LongWritable.class, Text.class, Text.class, IntWritable.class, conf);
34.                ChainReducer.setReducer(job, SumReducer.class, Text.class, IntWritable.class, Text.class, IntWritable.class, conf);
35.                job.setMapOutputKeyClass(Text.class);
36.                job.setMapOutputValueClass(IntWritable.class);
37.                job.setPartitionerClass(HashPartitioner.class);
38.                job.setNumReduceTasks(1);
39.                job.setOutputKeyClass(Text.class);
40.                job.setOutputValueClass(IntWritable.class);
41.                FileOutputFormat.setOutputPath(job, new Path(OUTPUTPATH));
42.                job.setOutputFormatClass(TextOutputFormat.class);
43.                System.exit(job.waitForCompletion(true) ? 0 : 1);
44.            } catch (Exception e) {
45.                e.printStackTrace();
46.            }
47.        }
48.        public static class FilterMapper1 extends Mapper<LongWritable, Text, Text, IntWritable> {
49.            private Text outKey = new Text();
50.            private IntWritable outValue = new IntWritable();
51.            @Override
52.            protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
53.            throws IOException,InterruptedException {
54.                String line = value.toString();
55.                if (line.length() > 0) {
56.                    String[] arr = line.split(",");
57.                    int visit = Integer.parseInt(arr[3]);
58.                    if(arr[1].substring(0, 1).equals("C")||arr[2].substring(0, 1).equals("C")){
59.                        outKey.set(arr[1]);
60.                        outValue.set(visit);
61.                        context.write(outKey, outValue);
62.                    }
63.                }
64.            }
65.        }
66.
67.        public  static class SumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
68.            private IntWritable outValue = new IntWritable();
69.            @Override
70.            protected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context)
71.        throws IOException, InterruptedException {
72.        int sum = 0;
73.        for (IntWritable val : values) {
74.        sum += val.get();
75.        }
76.        outValue.set(sum);
77.        context.write(key, outValue);
78.        }
79.        }
80.
81.
82.        }    

数据二次清理，进行排序

package mapreduce;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class OneSort {
    public static class Map extends Mapper<Object , Text , IntWritable,Text >{
    private static Text goods=new Text();
    private static IntWritable num=new IntWritable();
    public void map(Object key,Text value,Context context) throws IOException, InterruptedException{
    String line=value.toString();
    String arr[]=line.split("\t");
    num.set(Integer.parseInt(arr[1]));
    goods.set(arr[0]);
    context.write(num,goods);
    }
    }
    public static class Reduce extends Reducer< IntWritable, Text, IntWritable, Text>{
    private static IntWritable result= new IntWritable();
    public void reduce(IntWritable key,Iterable<Text> values,Context context) throws IOException, InterruptedException{
        for(Text val:values){
        context.write(key,val);
        }
        }
        }
        public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{
        Configuration conf=new Configuration();
        Job job =new Job(conf,"OneSort");
        job.setJarByClass(OneSort.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(Text.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        Path in=new Path("hdfs://localhost:9000/mapreducetest/out1/part-r-00000");
        Path out=new Path("hdfs://localhost:9000/mapreducetest/out2");
        FileInputFormat.addInputPath(job,in);
        FileOutputFormat.setOutputPath(job,out);
        System.exit(job.waitForCompletion(true) ? 0 : 1);    

        }
        }    

从hadoop中读取文件

1. package mapreduce;  
   
   import java.io.BufferedReader;
   import java.io.IOException;
   import java.io.InputStreamReader;
   import java.net.URI;
   import java.util.ArrayList;
   import java.util.List;  
   
   import org.apache.hadoop.conf.Configuration;
   import org.apache.hadoop.fs.FSDataInputStream;
   import org.apache.hadoop.fs.FileSystem;
   import org.apache.hadoop.fs.Path;  
   
   public class ReadFile {
       public static List<String> ReadFromHDFS(String file) throws IOException
       {
           //System.setProperty("hadoop.home.dir", "H:\\文件\\hadoop\\hadoop-2.6.4");
           List<String> list=new ArrayList();
           int i=0;
            Configuration conf = new Configuration();
           StringBuffer buffer = new StringBuffer();
           FSDataInputStream fsr = null;
           BufferedReader bufferedReader = null;
           String lineTxt = null;  
   
           try
           {
               FileSystem fs = FileSystem.get(URI.create(file),conf);
               fsr = fs.open(new Path(file));
               bufferedReader = new BufferedReader(new InputStreamReader(fsr));
               while ((lineTxt = bufferedReader.readLine()) != null)
               {
                   String[] arg=lineTxt.split("\t");
                   list.add(arg[0]);
                   list.add(arg[1]);
               }
           } catch (Exception e)
           {
               e.printStackTrace();
           } finally
           {
               if (bufferedReader != null)
               {
                   try
                   {
                       bufferedReader.close();
                   } catch (IOException e)
                   {
                       e.printStackTrace();
                   }
               }
           }
           return list;  
   
       }  
   
       public static void main(String[] args) throws IOException {
           List<String> ll=new  ReadFile().ReadFromHDFS("hdfs://localhost:9000/mapreducetest/out2/part-r-00000");
           for(int i=0;i<ll.size();i++)
           {
               System.out.println(ll.get(i));
           }  
   
       }  
   
   }  

前台网页代码

<%@page import="mapreduce.ReadFile"%>
<%@page import="java.util.List"%>
<%@page import="java.util.ArrayList"%>
<%@page import="org.apache.hadoop.fs.FSDataInputStream" %>
<%@ page language="java" contentType="text/html; charset=UTF-8"
    pageEncoding="UTF-8"%>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Insert title here</title>
<% List<String> ll= ReadFile.ReadFromHDFS("hdfs://localhost:9000/mapreducetest/out2/part-r-00000");%>
 <script src="../js/echarts.js"></script>
</head>
<body>
<div id="main" style="width: 900px;height:400px;"></div>
 <script type="text/javascript">
        // 基于准备好的dom，初始化echarts实例
        var myChart = echarts.init(document.getElementById('main'));  

        // 指定图表的配置项和数据
        var option = {
            title: {
                text: '最繁忙的机场TOP10'
            },
            tooltip: {},
            legend: {
                data:['票数']
            },
            xAxis: {
                data:["<%=ll.get(ll.size()-1)%>"<%for(int i=ll.size()-3;i>=ll.size()-19;i--){
                    if(i%2==1){
                        %>,"<%=ll.get(i)%>"
                    <%
                    }
                    }
                    %>]  

            },
            yAxis: {},
            series: [{
                name: '票数',
                type: 'bar',
                data: [<%=ll.get(ll.size()-2)%>
                <%for(int i=ll.size()-1;i>=ll.size()-19;i--){
                    if(i%2==0){
                    %>,<%=ll.get(i)%>
                <%
                }
                }
                %>]
            }]
        };  

        // 使用刚指定的配置项和数据显示图表。
        myChart.setOption(option);
    </script>
    <h2 color="red"><a href="NewFile.jsp">返回</a></h2>
</body>  

结果截图：