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

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：

巴特西

Mapreduce数据分析实例

最新文章

热门文章