【快速入门大数据】Hadoop项目实战-用户行为日志

左手的ㄟ右手 2022-12-14 13:39 209阅读 0赞

### 文章目录 ###

*  用户日志
 *   *  用处
     *  日志生成渠道
     *  日志内容
     *  意义
     *  离线数据处理架构
 *  分析日志
 *   *  引入解析UserAgent
     *  单体实现
     *  hadoop-MapReduce实现
     *  效果图
     *  总结

# 用户日志 #

## 用处 ##

分析行为  
推荐

## 日志生成渠道 ##

服务端Ngnix统计  
前端统计Ajax

## 日志内容 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center]

## 意义 ##

判断购买的推广转化率，及时调整不同区域的投资推广  
判断页面包含的子页面是否合理，路径是否合理转化率  
分析日志，营销经费合理分配

## 离线数据处理架构 ##

数据处理流程
    1）数据采集
    	Flume： web日志写入到HDFS
    
    2）数据清洗
    	脏数据
    	Spark、Hive、MapReduce 或者是其他的一些分布式计算框架  
    	清洗完之后的数据可以存放在HDFS(Hive/Spark SQL)
    
    3）数据处理
    	按照我们的需要进行相应业务的统计和分析
    	Spark、Hive、MapReduce 或者是其他的一些分布式计算框架
    
    4）处理结果入库
    	结果可以存放到RDBMS、NoSQL
    
    5）数据的可视化
    	通过图形化展示的方式展现出来：饼图、柱状图、地图、折线图
    	ECharts、HUE、Zeppelin

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 1]

# 分析日志 #

可获得到信息  
地点、时间、访问设备、访问次数  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 2]

UserAgent
    
    hadoop jar /home/hadoop/lib/hadoop-train-1.0-jar-with-dependencies.jar com.imooc.hadoop.project.LogApp /10000_access.log /browserout

## 引入解析UserAgent ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 3]

// 下载克隆
    git clone https://github.com/LeeKemp/UserAgentParser.git
    
    // 打包
    mvn clean package -DskipTests
    
    // 去target找到该文件jar
    cd target/
    UserAgentParser-0.0.1.jar
    
    // 后退cd ..重新打包到本地的jar，让idea识别可用
    mvn clean install -DskipTest
    // 检验本地jar包生成
    (base) bennyrhysdeMacBook-Pro:UserAgentParser bennyrhys$ cd /Users/bennyrhys/.m2/repository/com/kumkee/UserAgentParser/0.0.1/
    (base) bennyrhysdeMacBook-Pro:0.0.1 bennyrhys$ ls
    UserAgentParser-0.0.1.jar	UserAgentParser-0.0.1.pom	_remote.repositories

引入pom本地依赖

处理10000条数据的100条

// 提取100条数据
    head -n 100 10000_access.log > 100_access.log
    // 检测数据条数
    wc -l 100_access.log

## 单体实现 ##

UserAgentTest

package com.bennyrhys.hadoop.project;
    
    import com.kumkee.userAgent.UserAgent;
    import com.kumkee.userAgent.UserAgentParser;
    import org.apache.commons.lang.StringUtils;
    import org.junit.Test;
    
    import java.io.*;
    import java.nio.file.Path;
    import java.util.HashMap;
    import java.util.Map;
    import java.util.regex.Matcher;
    import java.util.regex.Pattern;
    
    /**
     * @Author bennyrhys
     * @Date 1/16/21 6:37 PM
     * UserAgent测试类
     */
    public class UserAgentTest { 
    //    public static void main(String[] args) { 
    
        /**
         * 读取文件，根据指定区间，统计浏览器信息
         * @throws IOException
         */
        @Test
        public void testReadFile() throws IOException { 
            String path = "/Users/bennyrhys/Desktop/spark-practice/100_access.log";
            BufferedReader reader = new BufferedReader(
              new InputStreamReader(new FileInputStream(new File(path)))
            );
            String line = "";
            HashMap<String, Integer> browserMap = new HashMap<>();
    
            UserAgentParser userAgentParser = new UserAgentParser();
            while (line != null) { 
                line = reader.readLine(); // 一次读入一行数据
                if (StringUtils.isNotBlank(line)) { 
                    String source = line.substring(getCharacterPosition(line, "\"", 7) + 1);
                    UserAgent agent = userAgentParser.parse(source);
    
                    String browser = agent.getBrowser();
                    String engine = agent.getEngine();
                    String engineVersion = agent.getEngineVersion();
                    String os = agent.getOs();
                    String platform = agent.getPlatform();
                    String version = agent.getVersion();
                    boolean mobile = agent.isMobile();
    
                    System.out.println(browser + " " + engine  + " " + engineVersion  + " " + os  + " " + platform  + " " + version  + " " + mobile);
    
                    Integer browserCount = browserMap.get(browser);
                    if (browserCount != null) { 
                        browserMap.put(browser, browserCount + 1);
                    }else { 
                        browserMap.put(browser, 1);
                    }
                }
            }
            for (Map.Entry<String , Integer> entry : browserMap.entrySet()) { 
                System.out.println(entry.getKey() + " : " + entry.getValue());
            }
        }
    
        /**
         * 测试标识符的定位方法
         */
        @Test
        public void testGetCharacterPosition() { 
            String value = "183.162.52.7 - - [10/Nov/2016:00:01:02 +0800] \"POST /api3/getadv HTTP/1.1\" 200 813 \"www.imooc.com\" \"-\" cid=0&timestamp=1478707261865&uid=2871142&marking=androidbanner&secrect=a6e8e14701ffe9f6063934780d9e2e6d&token=f51e97d1cb1a9caac669ea8acc162b96 \"mukewang/5.0.0 (Android 5.1.1; Xiaomi Redmi 3 Build/LMY47V),Network 2G/3G\" \"-\" 10.100.134.244:80 200 0.027 0.027";
            int index = getCharacterPosition(value, "\"", 7);
            System.out.println(index);
        }
    
        /**
         * 获取字符串中的指定标识字符串出现的索引位置
         */
        private int getCharacterPosition(String value, String operator, int index) { 
            Matcher matcher = Pattern.compile(operator).matcher(value);
            int mIdx = 0;
            while (matcher.find()) { 
                mIdx++;
                if (mIdx == index) { 
                    break;
                }
            }
            return matcher.start();
        }
    
        /**
         * 单元测试：UserAgent 工具类的使用
         */
        @Test
        public void UserAgentParserT1() { 
            // String source = "mukewang/5.0.0 (Android 5.1.1; Xiaomi Redmi 3 Build/LMY47V),Network 2G/3G";
            String source = "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36";
    
            UserAgentParser userAgentParser = new UserAgentParser();
            UserAgent agent = userAgentParser.parse(source);
    
            String browser = agent.getBrowser();
            String engine = agent.getEngine();
            String engineVersion = agent.getEngineVersion();
            String os = agent.getOs();
            String platform = agent.getPlatform();
            String version = agent.getVersion();
            boolean mobile = agent.isMobile();
    
            System.out.println(browser + " " + engine  + " " + engineVersion  + " " + os  + " " + platform  + " " + version  + " " + mobile);
        }
    
    //    }
    }

## hadoop-MapReduce实现 ##

java

package com.bennyrhys.hadoop.project;
    
    import com.bennyrhys.hadoop.mapreduce.WordCount2App;
    import com.kumkee.userAgent.UserAgent;
    import com.kumkee.userAgent.UserAgentParser;
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.FileSystem;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.LongWritable;
    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.output.FileOutputFormat;
    
    import java.io.IOException;
    import java.util.regex.Matcher;
    import java.util.regex.Pattern;
    
    /** * @Author bennyrhys * @Date 1/18/21 11:21 AM * 使用MapReduce完成需求：通过日志统计浏览器访问次数 */
    public class LogApp { 
    
    
    
        /** * Map：读取输入的文件 */
        public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> { 
    
            LongWritable one = new LongWritable(1);
    
            private UserAgentParser userAgentParser;
    
            /** * setup，只初始化一次 */
            @Override
            protected void setup(Context context) throws IOException, InterruptedException { 
                userAgentParser = new UserAgentParser();
            }
    
            @Override
            protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { 
    
                // 接收到的每一行数据：其实就是一行日志信息
                String line = value.toString();
    
                String source = line.substring(getCharacterPosition(line, "\"", 7) + 1);
                UserAgent agent = userAgentParser.parse(source);
    
                String browser = agent.getBrowser();
    
                // 通过上下文把map的处理结果输出
                context.write(new Text(browser), one);
    
            }
    
            // gc的时候回收
            @Override
            protected void cleanup(Context context) throws IOException, InterruptedException { 
                userAgentParser = null;
            }
        }
    
        /** * 获取字符串中的指定标识字符串出现的索引位置 */
        private static int getCharacterPosition(String value, String operator, int index) { 
            Matcher matcher = Pattern.compile(operator).matcher(value);
            int mIdx = 0;
            while (matcher.find()) { 
                mIdx++;
                if (mIdx == index) { 
                    break;
                }
            }
            return matcher.start();
        }
    
        /** * Reduce：归并操作 */
        public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> { 
    
            @Override
            protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { 
    
                long sum = 0;
                for(LongWritable value : values) { 
                    // 求key出现的次数总和
                    sum += value.get();
                }
    
                // 最终统计结果的输出
                context.write(key, new LongWritable(sum));
            }
        }
    
        /** * 定义Driver：封装了MapReduce作业的所有信息 */
        public static void main(String[] args) throws Exception { 
    
            //创建Configuration
            Configuration configuration = new Configuration();
    
            // 准备清理已存在的输出目录
            Path outputPath = new Path(args[1]);
            FileSystem fileSystem = FileSystem.get(configuration);
            if(fileSystem.exists(outputPath)){ 
                fileSystem.delete(outputPath, true);
                System.out.println("output file exists, but is has deleted");
            }
    
            //创建Job
            Job job = Job.getInstance(configuration, "LogApp");
    
            //设置job的处理类
            job.setJarByClass(LogApp.class);
    
            //设置作业处理的输入路径
            FileInputFormat.setInputPaths(job, new Path(args[0]));
    
            //设置map相关参数
            job.setMapperClass(LogApp.MyMapper.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(LongWritable.class);
    
            //设置reduce相关参数
            job.setReducerClass(LogApp.MyReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(LongWritable.class);
    
            //设置作业处理的输出路径
            FileOutputFormat.setOutputPath(job, new Path(args[1]));
    
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        }
    }

pom.xml注意插件  
生成包含本地的jar

<?xml version="1.0" encoding="UTF-8"?>
    
    <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
      xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
      <modelVersion>4.0.0</modelVersion>
    
      <groupId>com.bennyrhys.hadoop</groupId>
      <artifactId>hdfs-api</artifactId>
      <version>1.0-SNAPSHOT</version>
    
      <name>hdfs-api</name>
      
      <url>http://www.example.com</url>
    
      <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>1.7</maven.compiler.source>
        <maven.compiler.target>1.7</maven.compiler.target>
        <hadoop.version>2.6.0-cdh5.7.0</hadoop.version>
      </properties>
    
      <repositories>
        <repository>
          <id>cloudera</id>
          <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
        </repository>
      </repositories>
    
      <dependencies>
    
        <dependency>
          <groupId>org.apache.hadoop</groupId>
          <artifactId>hadoop-client</artifactId>
          <version>${hadoop.version}</version>
          <scope>provided</scope>
        </dependency>
    
        
        <dependency>
          <groupId>com.kumkee</groupId>
          <artifactId>UserAgentParser</artifactId>
          <version>0.0.1</version>
        </dependency>
    
        <dependency>
          <groupId>junit</groupId>
          <artifactId>junit</artifactId>
          <version>4.11</version>
          <scope>test</scope>
        </dependency>
      </dependencies>
    
    
      <build>
        <plugins>
          <plugin>
            <artifactId>maven-assembly-plugin</artifactId>
            <configuration>
              <archive>
                <manifest>
                  <mainClass></mainClass>
                </manifest>
              </archive>
              <descriptorRefs>
                <descriptorRef>jar-with-dependencies</descriptorRef>
              </descriptorRefs>
            </configuration>
          </plugin>
        </plugins>
      </build>
    </project>

-rw-r--r--  1 bennyrhys  staff  34246 Jan 18 19:36 hdfs-api-1.0-SNAPSHOT-jar-with-dependencies.jar
    -rw-r--r--  1 bennyrhys  staff  24436 Jan 18 19:36 hdfs-api-1.0-SNAPSHOT.jar

hadoop 启动hdfs和yarn  
上传lib jar，data 日志文件  
写启动脚本shell  
log.sh

hadoop fs -rm -r /browserout
    hadoop jar /root/lib/hdfs-api-1.0-SNAPSHOT-jar-with-dependencies.jar com.bennyrhys.hadoop.project.LogApp  hdfs://hadoop01:9000/10000_access.log hdfs://hadoop01:9000/browserout

## 效果图 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70]

## 总结 ##

用户行为日志：用户每次访问网站时所有的行为数据（访问、浏览、搜索、点击...）
    	用户行为轨迹、流量日志
    
    
    日志数据内容：
    1）访问的系统属性： 操作系统、浏览器等等
    2）访问特征：点击的url、从哪个url跳转过来的(referer)、页面上的停留时间等
    3）访问信息：session_id、访问ip(访问城市)等
    
    2013-05-19 13:00:00     http://www.taobao.com/17/?tracker_u=1624169&type=1      B58W48U4WKZCJ5D1T3Z9ZY88RU7QA7B1        http://hao.360.cn/      1.196.34.243   
    
    
    数据处理流程
    1）数据采集
    	Flume： web日志写入到HDFS
    
    2）数据清洗
    	脏数据
    	Spark、Hive、MapReduce 或者是其他的一些分布式计算框架  
    	清洗完之后的数据可以存放在HDFS(Hive/Spark SQL)
    
    3）数据处理
    	按照我们的需要进行相应业务的统计和分析
    	Spark、Hive、MapReduce 或者是其他的一些分布式计算框架
    
    4）处理结果入库
    	结果可以存放到RDBMS、NoSQL
    
    5）数据的可视化
    	通过图形化展示的方式展现出来：饼图、柱状图、地图、折线图
    	ECharts、HUE、Zeppelin
    
    
    
    UserAgent
    
    
    
    hadoop jar /home/hadoop/lib/hadoop-train-1.0-jar-with-dependencies.jar com.imooc.hadoop.project.LogApp /10000_access.log /browserout

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center]: /images/20221123/94242ea2fe6a4abaa7cbb1081fad4dd0.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20221123/84f802554c954f81b2f4e8dd3b86030f.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 2]: /images/20221123/f4ab9d10d855486b9091d56cdc69e30a.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 3]: /images/20221123/b16315cd8444432099fce3a96981ea06.png
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