【Hadoop学习之MapReduce】_25MR之数据清洗案例（ETL）

数据清洗（ETL）：提取-转换-装载（Extract-Transform-Load）

在运行核心业务MapReduce程序之前，往往要先对数据进行清洗，清理掉不符合用户要求的数据。清理的过程往往只需要运行Mapper程序，不需要运行Reduce程序。

一、数据清洗案例实操——简单案例

0. 需求

   去除网站日志中字段长度小于等于11的日志信息。

1. 输入数据

   58.177.135.108 - - [19/Sep/2013:06:19:56 +0000] "GET /data-scientist-problems/?cf_action=sync_comments&post_id=59 HTTP/1.1" 200 48 "http://blog.fens.me/data-scientist-problems/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.65 Safari/537.36"
   111.192.165.229 - - [19/Sep/2013:06:20:16 +0000] "POST /wp-admin/admin-ajax.php HTTP/1.1" 200 95 "http://blog.fens.me/wp-admin/post.php?post=2445&action=edit&message=10" "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.95 Safari/537.36"
   58.177.135.108 - - [19/Sep/2013:06:20:33 +0000] "GET /favicon.ico HTTP/1.1" 200 0 "-" "Mozilla/5.0 (iPhone; CPU iPhone OS 7_0 like Mac OS X) AppleWebKit/537.51.1 (KHTML, like Gecko) CriOS/30.0.1599.12 Mobile/11A465 Safari/8536.25"
   58.177.135.108 - - [19/Sep/2013:06:20:33 +0000] "GET /wp-content/uploads/2013/05/favicon.ico HTTP/1.1" 304 0 "-" "Mozilla/5.0 (iPhone; CPU iPhone OS 7_0 like Mac OS X) AppleWebKit/537.51.1 (KHTML, like Gecko) CriOS/30.0.1599.12 Mobile/11A465 Safari/8536.25"
   58.177.135.108 - - [19/Sep/2013:06:20:52 +0000] "-" 400 0 "-" "-"
   163.177.71.12 - - [19/Sep/2013:06:21:14 +0000] "HEAD / HTTP/1.1" 200 20 "-" "DNSPod-Monitor/1.0"
   ......
   

2. 期望输出数据

   每行字段长度都大于11.

3. 创建包名：com.easysir.etl

4. 创建LogMapper类：

   package com.easysir.etl;
   
   import org.apache.hadoop.io.LongWritable;
   import org.apache.hadoop.io.NullWritable;
   import org.apache.hadoop.io.Text;
   import org.apache.hadoop.mapreduce.Mapper;
   
   import java.io.IOException;
   
   public class LogMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
   
       @Override
       protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
   
           // 1 获取一行
           String line = value.toString();
   
           // 2 解析数据
           boolean result = parseLog(line, context);
   
           // 3 解析未通过
           if (!result){
               return;
           }
   
           // 3 解析通过
           context.write(value, NullWritable.get());
       }
   
       private boolean parseLog(String line, Context context){
   
           String[] fields = line.split(" ");
   
           if (fields.length > 11){
               // 实现计数器功能
               context.getCounter("map", "true").increment(1);
               return true;
           }else {
               // 实现计数器功能
               context.getCounter("map", "false").increment(1);
               return false;
           }
       }
   }
   

5. 创建LogDriver类：

   package com.easysir.etl;
   
   import org.apache.hadoop.conf.Configuration;
   import org.apache.hadoop.fs.Path;
   import org.apache.hadoop.io.NullWritable;
   import org.apache.hadoop.io.Text;
   import org.apache.hadoop.mapreduce.Job;
   import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
   import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
   
   public class LogDriver {
   
       public static void main(String[] args) throws Exception {
   
           // 输入输出路径需要根据自己电脑上实际的输入输出路径设置
           args = new String[] { "E:\\idea-workspace\\mrWordCount\\input\\web.log", "E:\\idea-workspace\\mrWordCount\\output" };
   
           // 1 获取job信息
           Configuration conf = new Configuration();
           Job job = Job.getInstance(conf);
   
           // 2 加载jar包
           job.setJarByClass(LogDriver.class);
   
           // 3 关联map
           job.setMapperClass(LogMapper.class);
   
           // 4 设置最终输出类型
           job.setOutputKeyClass(Text.class);
           job.setOutputValueClass(NullWritable.class);
   
           // 设置reducetask个数为0
           job.setNumReduceTasks(0);
   
           // 5 设置输入和输出路径
           FileInputFormat.setInputPaths(job, new Path(args[0]));
           FileOutputFormat.setOutputPath(job, new Path(args[1]));
   
           // 6 提交
           job.waitForCompletion(true);
       }
   }
   

6. 运行结果（计数器）：
   

二、数据清洗案例实操——复杂案例

0. 需求

   对web访问日志中的各个字段识别切分，去除日志中不合法的记录，根据清洗规则，输出过滤后的数据.

1. 输入数据

   58.177.135.108 - - [19/Sep/2013:06:19:56 +0000] "GET /data-scientist-problems/?cf_action=sync_comments&post_id=59 HTTP/1.1" 200 48 "http://blog.fens.me/data-scientist-problems/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.65 Safari/537.36"
   111.192.165.229 - - [19/Sep/2013:06:20:16 +0000] "POST /wp-admin/admin-ajax.php HTTP/1.1" 200 95 "http://blog.fens.me/wp-admin/post.php?post=2445&action=edit&message=10" "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.95 Safari/537.36"
   58.177.135.108 - - [19/Sep/2013:06:20:33 +0000] "GET /favicon.ico HTTP/1.1" 200 0 "-" "Mozilla/5.0 (iPhone; CPU iPhone OS 7_0 like Mac OS X) AppleWebKit/537.51.1 (KHTML, like Gecko) CriOS/30.0.1599.12 Mobile/11A465 Safari/8536.25"
   58.177.135.108 - - [19/Sep/2013:06:20:33 +0000] "GET /wp-content/uploads/2013/05/favicon.ico HTTP/1.1" 304 0 "-" "Mozilla/5.0 (iPhone; CPU iPhone OS 7_0 like Mac OS X) AppleWebKit/537.51.1 (KHTML, like Gecko) CriOS/30.0.1599.12 Mobile/11A465 Safari/8536.25"
   58.177.135.108 - - [19/Sep/2013:06:20:52 +0000] "-" 400 0 "-" "-"
   163.177.71.12 - - [19/Sep/2013:06:21:14 +0000] "HEAD / HTTP/1.1" 200 20 "-" "DNSPod-Monitor/1.0"
   ......
   

2. 期望输出

   均为合法数据。

3. 创建包名：com.easysir.etl2

4. 创建LogBean类，用来记录日志数据中各数据字段：

   package com.easysir.etl2;
   
   public class LogBean {
       private String remote_addr;// 记录客户端的ip地址
       private String remote_user;// 记录客户端用户名称,忽略属性"-"
       private String time_local;// 记录访问时间与时区
       private String request;// 记录请求的url与http协议
       private String status;// 记录请求状态；成功是200
       private String body_bytes_sent;// 记录发送给客户端文件主体内容大小
       private String http_referer;// 用来记录从那个页面链接访问过来的
       private String http_user_agent;// 记录客户浏览器的相关信息
   
       private boolean valid = true;// 判断数据是否合法
   
       public String getRemote_addr() {
           return remote_addr;
       }
   
       public void setRemote_addr(String remote_addr) {
           this.remote_addr = remote_addr;
       }
   
       public String getRemote_user() {
           return remote_user;
       }
   
       public void setRemote_user(String remote_user) {
           this.remote_user = remote_user;
       }
   
       public String getTime_local() {
           return time_local;
       }
   
       public void setTime_local(String time_local) {
           this.time_local = time_local;
       }
   
       public String getRequest() {
           return request;
       }
   
       public void setRequest(String request) {
           this.request = request;
       }
   
       public String getStatus() {
           return status;
       }
   
       public void setStatus(String status) {
           this.status = status;
       }
   
       public String getBody_bytes_sent() {
           return body_bytes_sent;
       }
   
       public void setBody_bytes_sent(String body_bytes_sent) {
           this.body_bytes_sent = body_bytes_sent;
       }
   
       public String getHttp_referer() {
           return http_referer;
       }
   
       public void setHttp_referer(String http_referer) {
           this.http_referer = http_referer;
       }
   
       public String getHttp_user_agent() {
           return http_user_agent;
       }
   
       public void setHttp_user_agent(String http_user_agent) {
           this.http_user_agent = http_user_agent;
       }
   
       public boolean isValid() {
           return valid;
       }
   
       public void setValid(boolean valid) {
           this.valid = valid;
       }
   
       @Override
       public String toString() {
   
           StringBuilder sb = new StringBuilder();
           sb.append(this.valid);
           sb.append("\001").append(this.remote_addr);
           sb.append("\001").append(this.remote_user);
           sb.append("\001").append(this.time_local);
           sb.append("\001").append(this.request);
           sb.append("\001").append(this.status);
           sb.append("\001").append(this.body_bytes_sent);
           sb.append("\001").append(this.http_referer);
           sb.append("\001").append(this.http_user_agent);
   
           return sb.toString();
       }
   }
   

5. 创建LogMapper类：

   package com.easysir.etl2;
   
   import org.apache.hadoop.io.LongWritable;
   import org.apache.hadoop.io.NullWritable;
   import org.apache.hadoop.io.Text;
   import org.apache.hadoop.mapreduce.Mapper;
   
   import java.io.IOException;
   
   public class LogMapper extends Mapper<LongWritable, Text, Text, NullWritable>{
       Text k = new Text();
   
       @Override
       protected void map(LongWritable key, Text value, Context context)	throws IOException, InterruptedException {
   
           // 1 获取1行
           String line = value.toString();
   
           // 2 解析日志是否合法
           LogBean bean = parseLog(line);
   
           if (!bean.isValid()) {
               return;
           }
   
           k.set(bean.toString());
   
           // 3 输出
           context.write(k, NullWritable.get());
       }
   
       // 解析日志
       private LogBean parseLog(String line) {
   
           LogBean logBean = new LogBean();
   
           // 1 截取
           String[] fields = line.split(" ");
   
           if (fields.length > 11) {
   
               // 2封装数据
               logBean.setRemote_addr(fields[0]);
               logBean.setRemote_user(fields[1]);
               logBean.setTime_local(fields[3].substring(1));
               logBean.setRequest(fields[6]);
               logBean.setStatus(fields[8]);
               logBean.setBody_bytes_sent(fields[9]);
               logBean.setHttp_referer(fields[10]);
   
               if (fields.length > 12) {
                   logBean.setHttp_user_agent(fields[11] + " "+ fields[12]);
               }else {
                   logBean.setHttp_user_agent(fields[11]);
               }
   
               // 大于400，HTTP错误
               if (Integer.parseInt(logBean.getStatus()) >= 400) {
                   logBean.setValid(false);
               }
           }else {
               logBean.setValid(false);
           }
   
           return logBean;
       }
   }
   

6. 创建LogDriver类：

   package com.easysir.etl2;
   
   import org.apache.hadoop.conf.Configuration;
   import org.apache.hadoop.fs.Path;
   import org.apache.hadoop.io.NullWritable;
   import org.apache.hadoop.io.Text;
   import org.apache.hadoop.mapreduce.Job;
   import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
   import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
   
   public class LogDriver {
       public static void main(String[] args) throws Exception {
   
           // 输入输出路径需要根据自己电脑上实际的输入输出路径设置
           args = new String[] { "E:\\idea-workspace\\mrWordCount\\input\\web.log", "E:\\idea-workspace\\mrWordCount\\output" };
   
           // 1 获取job信息
           Configuration conf = new Configuration();
           Job job = Job.getInstance(conf);
   
           // 2 加载jar包
           job.setJarByClass(LogDriver.class);
   
           // 3 关联map
           job.setMapperClass(LogMapper.class);
   
           // 4 设置最终输出类型
           job.setOutputKeyClass(Text.class);
           job.setOutputValueClass(NullWritable.class);
   
           // 5 设置输入和输出路径
           FileInputFormat.setInputPaths(job, new Path(args[0]));
           FileOutputFormat.setOutputPath(job, new Path(args[1]));
   
           // 6 提交
           job.waitForCompletion(true);
       }
   }