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MapReduce清洗数据进行可视化

时间：2019-11-16 23:06:41      阅读：71      评论：0      收藏：0      [点我收藏+]

标签：inf   chart   get   title   VID   数据可视化   split   apache   nal   

继上篇第一阶段清洗数据并导入hive

本篇是剩下的两阶段

2、数据处理：

·统计最受欢迎的视频/文章的Top10访问次数 （video/article）

·按照地市统计最受欢迎的Top10课程 （ip）

·按照流量统计最受欢迎的Top10课程 （traffic）

3、数据可视化：将统计结果倒入MySql数据库中，通过图形化展示的方式展现出来。

 

2、

·统计最受欢迎的视频/文章的Top10访问次数 （video/article）

package mapreduce;

import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
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 GetVideoResult {
    
    public static void main(String[] args) {
        try {
            Job job = Job.getInstance();
            job.setJobName("GetVideoResult");
            job.setJarByClass(GetVideoResult.class);
            job.setMapperClass(doMapper.class);
            job.setReducerClass(doReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            job.setInputFormatClass(TextInputFormat.class);  
            job.setOutputFormatClass(TextOutputFormat.class);
            Path in = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out1/part-r-00000");  
            Path out = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out1.2");  
            FileInputFormat.addInputPath(job,in);
            FileOutputFormat.setOutputPath(job,out);
            //System.exit(job.waitForCompletion(true) ? 0:1);
            if(job.waitForCompletion(true))
            {
                Job job1 = Job.getInstance();
                job1.setJobName("Sort");
                job1.setJarByClass(GetVideoResult.class);
                job1.setMapperClass(doMapper1.class);
                job1.setReducerClass(doReducer1.class);
                job1.setOutputKeyClass(IntWritable.class);
                job1.setOutputValueClass(Text.class);
                job1.setSortComparatorClass(IntWritableDecreasingComparator.class);
                job1.setInputFormatClass(TextInputFormat.class);  
                job1.setOutputFormatClass(TextOutputFormat.class);
                Path in1 = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out1.2/part-r-00000");  
                Path out1 = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out1.3");  
                FileInputFormat.addInputPath(job1,in1);
                FileOutputFormat.setOutputPath(job1,out1);
                System.exit(job1.waitForCompletion(true) ? 0:1);
            }
            
            
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    public static class doMapper extends Mapper<Object,Text,Text,IntWritable>{
        public static Text word = new Text();
        public static final IntWritable id = new IntWritable(1);
        @Override
        protected void map(Object key,Text value,Context context) throws IOException,InterruptedException{
            String[] data = value.toString().split("\t");
            word.set(data[5]);
            //id.set(Integer.parseInt(data[5]));
            
            context.write(word,id);
            
        }
    }
    
    public static class doReducer extends Reducer< Text, IntWritable, IntWritable, Text>{  
        private static IntWritable result= new IntWritable();  
    
        
        public void reduce(Text key,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{  
            int sum = 0;
            for(IntWritable value:values){
                sum += value.get();
            }
            
            result.set(sum);
            context.write(result,key);                     
            }  
    }
    
    
    public static class doMapper1 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[0]));  
            goods.set(arr[1]);  
            context.write(num,goods);  
        }  
    }
    
    
    
    public static class doReducer1 extends Reducer< IntWritable, Text, IntWritable, Text>{  
        private static IntWritable result= new IntWritable();  
        int i=0;
        
        public void reduce(IntWritable key,Iterable<Text> values,Context context) throws IOException, InterruptedException{  
            for(Text value:values){
                if(i<10) {
                context.write(key,value); 
                i++;
                }
            }
                    
            }  
    }
    
    private static class IntWritableDecreasingComparator extends IntWritable.Comparator {
         public int compare(WritableComparable a, WritableComparable b) {
             return -super.compare(a, b);
          }
         public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
                    return -super.compare(b1, s1, l1, b2, s2, l2);
           }
    }
    
}

 

自己一开始使用两个类完成的，先求和在排序，在网上查阅资料后发现可以有两个job，然后就在一个类中完成，然后MapReduce本来的排序是升序，而我们需要的是降序，所以在此引入了一个比较器。

 

技术图片

 

按照地市统计最受欢迎的Top10课程 （ip）

 

package mapreduce;

import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
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 GetVideoResultip {
    
    public static void main(String[] args) {
        try {
            Job job = Job.getInstance();
            job.setJobName("GetVideoResult");
            job.setJarByClass(GetVideoResultip.class);
            job.setMapperClass(doMapper.class);
            job.setReducerClass(doReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            job.setInputFormatClass(TextInputFormat.class);  
            job.setOutputFormatClass(TextOutputFormat.class);
            Path in = new Path("hdfs://192.168.137.67:9000/mymapreducel/in/result.txt");  
            Path out = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out2.1");  
            FileInputFormat.addInputPath(job,in);
            FileOutputFormat.setOutputPath(job,out);
            //System.exit(job.waitForCompletion(true) ? 0:1);
            if(job.waitForCompletion(true))
            {
                Job job1 = Job.getInstance();
                job1.setJobName("Sort");
                job1.setJarByClass(GetVideoResult.class);
                job1.setMapperClass(doMapper1.class);
                job1.setReducerClass(doReducer1.class);
                job1.setOutputKeyClass(IntWritable.class);
                job1.setOutputValueClass(Text.class);
                job1.setSortComparatorClass(IntWritableDecreasingComparator.class);
                job1.setInputFormatClass(TextInputFormat.class);  
                job1.setOutputFormatClass(TextOutputFormat.class);
                Path in1 = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out2.1/part-r-00000");  
                Path out1 = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out2.2");  
                FileInputFormat.addInputPath(job1,in1);
                FileOutputFormat.setOutputPath(job1,out1);
                System.exit(job1.waitForCompletion(true) ? 0:1);
            }
            
            
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    public static class doMapper extends Mapper<Object,Text,Text,IntWritable>{
        public static Text word = new Text();
        public static final IntWritable id = new IntWritable(1);
        @Override
        protected void map(Object key,Text value,Context context) throws IOException,InterruptedException{
            String[] data = value.toString().split(",");
            String str=data[0]+"\t"+data[5];
            System.out.println(str);
            word.set(str);
            //id.set(Integer.parseInt(data[5]));
            
            context.write(word,id);
        }
    }
    
    public static class doReducer extends Reducer< Text, IntWritable, IntWritable, Text>{  
        private static IntWritable result= new IntWritable();  
        public void reduce(Text key,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{  
            int sum = 0;
            for(IntWritable value:values){
                sum += value.get();
            }
            
            result.set(sum);
            context.write(result,key);                     
            }  
    }
    
    
    public static class doMapper1 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");
            String str=arr[1]+"\t"+arr[2];
            num.set(Integer.parseInt(arr[0]));  
            goods.set(str);  
            context.write(num,goods);  
        }  
    }
    
    
    
    public static class doReducer1 extends Reducer< IntWritable, Text, IntWritable, Text>{  
        private static IntWritable result= new IntWritable();  
        int i=0;
        
        public void reduce(IntWritable key,Iterable<Text> values,Context context) throws IOException, InterruptedException{  
            for(Text value:values){
                if(i<10) {
                context.write(key,value); 
                i++;
                }
            }
                    
            }  
    }
    
    private static class IntWritableDecreasingComparator extends IntWritable.Comparator {
         public int compare(WritableComparable a, WritableComparable b) {
             return -super.compare(a, b);
          }
         public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
                    return -super.compare(b1, s1, l1, b2, s2, l2);
           }
    }
    
}

 

技术图片

 

 

·按照流量统计最受欢迎的Top10课程 （traffic）

 

package mapreduce;

import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
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 GetVideoResulttraffic {
    
    public static void main(String[] args) {
        try {
            Job job = Job.getInstance();
            job.setJobName("GetVideoResult");
            job.setJarByClass(GetVideoResultip.class);
            job.setMapperClass(doMapper.class);
            job.setReducerClass(doReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            job.setInputFormatClass(TextInputFormat.class);  
            job.setOutputFormatClass(TextOutputFormat.class);
            Path in = new Path("hdfs://192.168.137.67:9000/mymapreducel/in/result.txt");  
            Path out = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out3.1");  
            FileInputFormat.addInputPath(job,in);
            FileOutputFormat.setOutputPath(job,out);
            //System.exit(job.waitForCompletion(true) ? 0:1);
            if(job.waitForCompletion(true))
            {
                Job job1 = Job.getInstance();
                job1.setJobName("Sort");
                job1.setJarByClass(GetVideoResult.class);
                job1.setMapperClass(doMapper1.class);
                job1.setReducerClass(doReducer1.class);
                job1.setOutputKeyClass(IntWritable.class);
                job1.setOutputValueClass(Text.class);
                job1.setSortComparatorClass(IntWritableDecreasingComparator.class);
                job1.setInputFormatClass(TextInputFormat.class);  
                job1.setOutputFormatClass(TextOutputFormat.class);
                Path in1 = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out3.1/part-r-00000");  
                Path out1 = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out3.2");  
                FileInputFormat.addInputPath(job1,in1);
                FileOutputFormat.setOutputPath(job1,out1);
                System.exit(job1.waitForCompletion(true) ? 0:1);
            }
            
            
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    public static class doMapper extends Mapper<Object,Text,Text,IntWritable>{
        public static Text word = new Text();
        public static final IntWritable id = new IntWritable();
        @Override
        protected void map(Object key,Text value,Context context) throws IOException,InterruptedException{
            String[] data = value.toString().split(",");
            //String str=data[0]+"\t"+data[5];
            data[3] = data[3].substring(0, data[3].length()-1);
            word.set(data[5]);
            id.set(Integer.parseInt(data[3]));
            
            context.write(word,id);
        }
    }
    
    public static class doReducer extends Reducer< Text, IntWritable, IntWritable, Text>{  
        private static IntWritable result= new IntWritable();  
        public void reduce(Text key,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{  
            int sum = 0;
            for(IntWritable value:values){
                sum += value.get();
            }
            
            result.set(sum);
            context.write(result,key);                     
            }  
    }
    
    
    public static class doMapper1 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[0]));  
            goods.set(arr[1]);  
            context.write(num,goods);  
        }  
    }
    
    
    
    public static class doReducer1 extends Reducer< IntWritable, Text, IntWritable, Text>{  
        private static IntWritable result= new IntWritable();  
        int i=0;
        
        public void reduce(IntWritable key,Iterable<Text> values,Context context) throws IOException, InterruptedException{  
            for(Text value:values){
                if(i<10) {
                context.write(key,value); 
                i++;
                }
            }
                    
            }  
    }
    
    private static class IntWritableDecreasingComparator extends IntWritable.Comparator {
         public int compare(WritableComparable a, WritableComparable b) {
             return -super.compare(a, b);
          }
         public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
                    return -super.compare(b1, s1, l1, b2, s2, l2);
           }
    }
    
}

 

技术图片

 

3、数据没有导入到mysql中，但是通过MapReduce进行了echarts可视化

先通过MapReduce进行清洗数据，然后在jsp中进行可视化

package mapreduce3;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;  
import org.apache.hadoop.io.IntWritable;  
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.mapreduce.Job;  
import org.apache.hadoop.mapreduce.Mapper;  
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
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 Pai {

    public static List<String> Names=new ArrayList<String>();
    public static  List<String> Values=new ArrayList<String>();
    
    public static class Sort extends WritableComparator 
    {
        public Sort()
        {
            super(IntWritable.class,true);
        }
        @Override
        public int compare(WritableComparable a, WritableComparable b) 
        {
            return -a.compareTo(b);
        }
    }
    public static class Map extends Mapper<Object , Text , IntWritable,Text >{  
        private static Text Name=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");  
            if(!arr[0].startsWith(" "))
            {
                  num.set(Integer.parseInt(arr[0]));  
                  Name.set(arr[1]);
                  context.write(num, Name);
            }
          
        }
    }
    public static class Reduce extends Reducer< IntWritable, Text, IntWritable, Text>{  
        private static IntWritable result= new IntWritable();  
        int i=0;
        
         public void reduce(IntWritable key,Iterable<Text> values,Context context) throws IOException, InterruptedException{  
                for(Text val:values)
                {  
                if(i<10)
                {i=i+1;
                    Names.add(val.toString());
                    Values.add(key.toString());
                }
                context.write(key,val);  
                }  
         }
    }

  
    
 
    
    public static int run()throws IOException, ClassNotFoundException, InterruptedException{
        Configuration conf=new Configuration();  
        conf.set("fs.defaultFS", "hdfs://192.168.137.67:9000");
        FileSystem fs =FileSystem.get(conf);
        Job job =new Job(conf,"OneSort");  
        job.setJarByClass(Pai.class);  
        job.setMapperClass(Map.class);  
        job.setReducerClass(Reduce.class);  
        job.setSortComparatorClass(Sort.class);
        job.setOutputKeyClass(IntWritable.class);  
        job.setOutputValueClass(Text.class);  
        job.setInputFormatClass(TextInputFormat.class);  
        job.setOutputFormatClass(TextOutputFormat.class);  
        Path in = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out1.2/part-r-00000");  
        Path out = new Path("hdfs://192.168.137.67:9000/mymapreducelShiYan/out1.4");  
        FileInputFormat.addInputPath(job,in);  
        fs.delete(out,true);
        FileOutputFormat.setOutputPath(job,out);  
        return(job.waitForCompletion(true) ? 0 : 1);  
        
       
        }
    
}

 

zhu.jsp

 

<%@page import="mapreduce3.Pai"%>
<%@page import="mapreduce3.GetVideoResult"%>
<%@ page language="java" import="java.util.*" contentType="text/html; charset=UTF-8"
    pageEncoding="UTF-8"%>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Insert title here</title>
<script src="${pageContext.request.contextPath}/resource/echarts.js"></script>
</head>
<body>
<%
    
    Pai ss=    new Pai();
    ss.run();
    String[] a=new String[11];
    String[] b=new String[11];
    int i=0,j=0;
    
        for(i = 0 ; i < 10 ; i++)
        {
            a[i] = ss.Values.get(i);
            b[i] = ss.Names.get(i);
        }
%>
<div id="main" style="width: 600px;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: ‘最受欢迎的文章/视频 TOP10‘
            },
            tooltip: {},
            legend: {
                data:[‘统计‘]
            },
            xAxis: {
                data: [   <%
                          for( i=0;i<10;i++)
                          {
                          %><%=b[i]%>,<%
                          
                          }
                          %>]
            },
            yAxis: {},
            series: [{
                name: ‘最受欢迎的文章‘,
                type: ‘bar‘,
                data: [
                <%
                for( i=0;i<10;i++)
                {
                %><%=a[i]%>,<%
                
                }
                %>

                        ]
            }]
        };

      // 使用刚指定的配置项和数据显示图表。
      myChart.setOption(option);
      </script>
</body>
</html>

 

技术图片

 

 

因为其他的数据清洗上边有，代码就不一一展示，只贴出jsp文件，如果想要改变可视化团，在echarts官网中直接复制代码到jsp中进行修改即可。

 

zhe.jsp

 

<%@page import="mapreduce3.Pai1"%>
<%@page import="mapreduce3.GetVideoResult"%>
<%@ page language="java" import="java.util.*" contentType="text/html; charset=UTF-8"
    pageEncoding="UTF-8"%>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Insert title here</title>
<script src="${pageContext.request.contextPath}/resource/echarts.js"></script>
</head>
<body>
<%
    
    Pai1 ss=    new Pai1();
    ss.run();
    String[] a=new String[11];
    String[] b=new String[11];
    int i=0,j=0;
    
        for(i = 0 ; i < 10 ; i++)
        {
            a[i] = ss.Values.get(i);
            b[i] = ss.Names.get(i);
        }
%>
<div id="main" style="width: 600px;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: ‘按照地市最受欢迎‘
            },
            tooltip: {},
            legend: {
                data:[‘统计‘]
            },
            xAxis: {
                data: [

　　　　　　　　　　　　　　<%
　　　　　　　　　　　　　　　　for( i=0;i<10;i++)
　　　　　　　　　　　　　　　　{
　　　　　　　　　　　　　　%>‘<%=b[i]%>‘,

　　　　　　　　　　　　　　　　<%
　　　　　　　　　　　　　　　　}
　　　　　　　　　　　　　　　　%>

]
            },
            yAxis: {},
            series: [{
                name: ‘最受欢迎的文章‘,
                type: ‘line‘,
                data: [
                <%
                for( i=0;i<10;i++)
                {
                %><%=a[i]%>,<%
                
                }
                %>

                        ]
            }]
        };

      // 使用刚指定的配置项和数据显示图表。
      myChart.setOption(option);
      </script>
</body>
</html>

技术图片

 

tu.jsp

<%@page import="mapreduce3.Pai2"%>
<%@page import="mapreduce3.GetVideoResult"%>
<%@ page language="java" import="java.util.*" contentType="text/html; charset=UTF-8"
    pageEncoding="UTF-8"%>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Insert title here</title>
<script src="${pageContext.request.contextPath}/resource/echarts.js"></script>
</head>
<body>
<%
    
    Pai2 ss=    new Pai2();
    ss.run();
    String[] a=new String[11];
    String[] b=new String[11];
    int i=0,j=0;
    
        for(i = 0 ; i < 10 ; i++)
        {
            a[i] = ss.Values.get(i);
            b[i] = ss.Names.get(i);
        }
%>
<div id="main" style="width: 600px;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
      option = {
            title : {
                text: ‘按照流量最受欢迎‘, 
                x:‘center‘
            },
            tooltip : {
        trigger: ‘item‘,
        formatter: "{a} <br/>{b} : {c} ({d}%)"
    },
    legend: {
        orient: ‘vertical‘,
        left: ‘left‘,
        data: [
            <%
            for( i=0;i<10;i++)
            {
            %>‘<%=b[i]%>‘,
            
            <%                     
            }
            %>
        ]
    },
    series : [
        {
            name: ‘访问来源‘,
            type: ‘pie‘,
            radius : ‘55%‘,
            center: [‘50%‘, ‘60%‘],
            data:[
              
                <%
                for( i=0;i<10;i++)
                {
                %>{value:<%=a[i]%>,name:‘<%=b[i]%>‘},
               
                <%                     
                }
                %>
            ],
            itemStyle: {
                emphasis: {
                    shadowBlur: 10,
                    shadowOffsetX: 0,
                    shadowColor: ‘rgba(0, 0, 0, 0.5)‘
                }
            }
        }
    ]
};

      // 使用刚指定的配置项和数据显示图表。
      myChart.setOption(option);
      </script>
</body>
</html>

技术图片

 

MapReduce清洗数据进行可视化

标签：inf   chart   get   title   VID   数据可视化   split   apache   nal   

原文地址：https://www.cnblogs.com/quyangzhangsiyuan/p/11874426.html
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