Hadoop实战演练：搜索数据分析----每秒搜索量计算与展示（5）

た入场券 2021-09-11 03:44 298阅读 0赞

林炳文Evankaka原创作品。转载请注明出处[http://blog.csdn.net/evankaka][http_blog.csdn.net_evankaka]

摘要：本文使用hadoop分析每秒的搜索量并保存到mysql存储和展示

工程源码下载：[https://github.com/appleappleapple/BigDataLearning/tree/master/Hadoop-Demo][https_github.com_appleappleapple_BigDataLearning_tree_master_Hadoop-Demo]

一、环境与数据

1、本地开发环境  
Windows7 + Eclipse Luna  
hadoop版本：2.6.2  
JDK版本：1.8  
2、数据来源：  
搜狗实验室  
http://www.sogou.com/labs/resource/q.php  
3、数据格式  
访问时间\\t用户ID\\t\[查询词\]\\t该URL在返回结果中的排名\\t用户点击的顺序号\\t用户点击的URL  
其中，用户ID是根据用户使用浏览器访问搜索引擎时的Cookie信息自动赋值，即同一次使用浏览器输入的不同查询对应同一个用户ID

样例：（注意，比[ Hadoop实战演练：搜索数据分析----数据去重 （1）][Hadoop_----_ _1]多了第一列搜索的时间）

![20160915155008094][]

4、统计

本文要统计出每一秒的搜索人数并将结果存储到mysql

二、编程实现

1、统计

package com.lin.counttime;
    
    import java.io.IOException;
    import java.util.StringTokenizer;
    
    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;
    import org.apache.hadoop.util.GenericOptionsParser;
    
    /**
     * 功能概要：
     * 
     * @author linbingwen
     * @since  2016年8月1日 
     */
    public class CountTime {
    	public static class Map extends Mapper<Object, Text, Text, IntWritable> {
    		private final static IntWritable one = new IntWritable(1);
    		// 实现map函数
            @Override
    		public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
    			// 将输入的纯文本文件的数据转化成String
    			String line = value.toString();
    			
    			// 将输入的数据首先按行进行分割
    			StringTokenizer tokenizerArticle = new StringTokenizer(line, "\n");
    			// 分别对每一行进行处理
    			while (tokenizerArticle.hasMoreElements()) {
    			    // 每行按空格划分
    			    StringTokenizer tokenizerLine = new StringTokenizer(tokenizerArticle.nextToken());
    			    String c1 = tokenizerLine.nextToken();//
    			    Text newline = new Text(c1);
    			    context.write(newline, one);
    			}
    		}
    
    	}
    	
    	public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
            private IntWritable result =new IntWritable();
            
    		// 实现reduce函数
            @Override
    		public void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
    			int count = 0;
    			 for(IntWritable val:values){
    				  count += val.get();
    			 }
    	        result.set(count);
    			context.write(key, result);
    		}
    	}
    	
    	public static void main(String[] args) throws Exception {
    		Configuration conf = new Configuration();
    		//设置hadoop的机器、端口
    		conf.set("mapred.job.tracker", "10.75.201.125:9000");
    		//设置输入输出文件目录
    		String[] ioArgs = new String[] { "hdfs://hmaster:9000/one_in", "hdfs://hmaster:9000/one_out" };
    		String[] otherArgs = new GenericOptionsParser(conf, ioArgs).getRemainingArgs();
    		if (otherArgs.length != 2) {
    			System.err.println("Usage:  <in> <out>");
    			System.exit(2);
    		}
    		//设置一个job
    		Job job = Job.getInstance(conf, "key Word count");
    		job.setJarByClass(CountTime.class);
    		
    		// 设置Map、Combine和Reduce处理类
    		job.setMapperClass(CountTime.Map.class);
    		job.setCombinerClass(CountTime.Reduce.class);
    		job.setReducerClass(CountTime.Reduce.class);
    		
    		// 设置输出类型
    		job.setOutputKeyClass(Text.class);
    		job.setOutputValueClass(IntWritable.class);
    		
    		// 将输入的数据集分割成小数据块splites，提供一个RecordReder的实现
    		job.setInputFormatClass(TextInputFormat.class);
    		
    		// 提供一个RecordWriter的实现，负责数据输出
    		job.setOutputFormatClass(TextOutputFormat.class);
    		
    		// 设置输入和输出目录
    		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
    		FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    		System.exit(job.waitForCompletion(true) ? 0 : 1);
    
    	}
    
    
    }

输出结果：

![20160915155557751][]

2、结果输出到mysql:

package com.lin.counttime;
    
    import java.io.DataInput;
    import java.io.DataOutput;
    import java.io.IOException;
    import java.sql.PreparedStatement;
    import java.sql.ResultSet;
    import java.sql.SQLException;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.io.Writable;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.Mapper;
    import org.apache.hadoop.mapreduce.Reducer;
    import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
    import org.apache.hadoop.mapreduce.lib.db.DBOutputFormat;
    import org.apache.hadoop.mapreduce.lib.db.DBWritable;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.util.GenericOptionsParser;
    
    /**
     * 功能概要：将top 搜索词保存到数据库
     * 
     * @author linbingwen
     */
    public class SaveCountTimeResult {
        /**  
        * 实现DBWritable  
        *   
        * TblsWritable需要向mysql中写入数据  
        */  
       public static class TblsWritable implements Writable, DBWritable {  
           String tbl_name;  
           int tbl_age;  
     
           public TblsWritable() {  
           }  
     
           public TblsWritable(String name, int age) {  
               this.tbl_name = name;  
               this.tbl_age = age;  
           }  
    
           @Override  
           public void write(PreparedStatement statement) throws SQLException {  
               statement.setString(1, this.tbl_name);  
               statement.setInt(2, this.tbl_age);  
           }  
    
           @Override  
           public void readFields(ResultSet resultSet) throws SQLException {  
               this.tbl_name = resultSet.getString(1);  
               this.tbl_age = resultSet.getInt(2);  
           }  
    
           @Override  
           public void write(DataOutput out) throws IOException {  
               out.writeUTF(this.tbl_name);  
               out.writeInt(this.tbl_age);  
           }  
    
           @Override  
           public void readFields(DataInput in) throws IOException {  
               this.tbl_name = in.readUTF();  
               this.tbl_age = in.readInt();  
           }  
     
           public String toString() {  
               return new String(this.tbl_name + " " + this.tbl_age);  
           }  
       }
       
       public static class StudentMapper extends Mapper<LongWritable, Text, LongWritable, Text>{
           @Override
           protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException {
               context.write(key, value);
           }
       }
       
       public static class StudentReducer extends Reducer<LongWritable, Text, TblsWritable, TblsWritable> {
           @Override
           protected void reduce(LongWritable key, Iterable<Text> values,Context context) throws IOException, InterruptedException {
               // values只有一个值，因为key没有相同的
               StringBuilder value = new StringBuilder();
               for(Text text : values){
                   value.append(text);
               }
               
               String[] studentArr = value.toString().split("\t");
               
               if(studentArr[0] != null){
                   String name = studentArr[0].trim();
                   
                   int age = 0;
                   try{
                       age = Integer.parseInt(studentArr[1].trim());
                   }catch(NumberFormatException e){
                   }
                   
                   context.write(new TblsWritable(name, age), null);  
               }
           }
       }
       
       public static void main(String[] args) throws Exception {
    	   Configuration conf = new Configuration();
    		
    	   DBConfiguration.configureDB(conf, "com.mysql.cj.jdbc.Driver","jdbc:mysql://localhost:3306/learning?serverTimezone=UTC","root", "linlin");  
    		
    		//设置hadoop的机器、端口
    		conf.set("mapred.job.tracker", "10.75.201.125:9000");
    		//设置输入输出文件目录
    		String[] ioArgs = new String[] { "hdfs://hmaster:9000/one_out"};
    		String[] otherArgs = new GenericOptionsParser(conf, ioArgs).getRemainingArgs();
    		if (otherArgs.length != 1) {
    			System.err.println("Usage:  <in> <out>");
    			System.exit(2);
    		}
    		//设置一个job
    		Job job = Job.getInstance(conf, "SaveResult");
    		job.setJarByClass(SaveCountTimeResult.class);
    		
    		// 输入路径
    		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
            
            // Mapper
            job.setMapperClass(StudentMapper.class);
            // Reducer
            job.setReducerClass(StudentReducer.class);
            
            // mapper输出格式
            job.setOutputKeyClass(LongWritable.class);
            job.setOutputValueClass(Text.class);
            
            // 输入格式，默认就是TextInputFormat
            job.setOutputFormatClass(DBOutputFormat.class);  
            
            // 输出到哪些表、字段
            DBOutputFormat.setOutput(job, "count_time", "time", "total");
            System.exit(job.waitForCompletion(true) ? 0 : 1);
       }
       
    
    }

结果成功保存

建表sql:

CREATE TABLE `count_time` (
      `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT,
      `time` varchar(255) DEFAULT NULL,
      `total` bigint(20) DEFAULT NULL,
      PRIMARY KEY (`id`)
    ) ENGINE=InnoDB  DEFAULT CHARSET=utf8

![20160915160042943][]

3、结果展示

笔者用百度Echars做了一个页面展示如下：

![20160915160138479][]

[http_blog.csdn.net_evankaka]: http://blog.csdn.net/evankaka
[https_github.com_appleappleapple_BigDataLearning_tree_master_Hadoop-Demo]: https://github.com/appleappleapple/BigDataLearning/tree/master/Hadoop-Demo
[Hadoop_----_ _1]: http://blog.csdn.net/evankaka/article/details/52549670
[20160915155008094]: /images/20210811/4fda7b15ebc6403c9d78170b9853e57e.png
[20160915155557751]: /images/20210811/5ff2b0cca078470ea36f5d92996a5e2c.png
[20160915160042943]: /images/20210811/ed23d3ae889e4ece8ae53c941e9fef4d.png
[20160915160138479]: /images/20210811/1e99db4eeecc4d24a2366fadc2cbd470.png