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

     


     


  。

3.9.1数据清洗案例实操-简单解析版

1  
    
    
、需求

去除日志中字段长度小于等于11的日志 




 




 
。

（1）输入数据

web.log

（2）期望输出数据

每行字段长度都大于11  

   

   

。

2

     


     


  、需求分析

需要在Map阶段对输入的数据根据规则进行过滤清洗

     

     

 。

3 




 




 
、实现代码

（1）编写LogMapper类

package com.cuiyf41.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> { Text k = new Text(); @Override protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException { // 1 获取1行数据 String line = value.toString(); // 2 解析日志 boolean result = parseLog(line,context); // 3 日志不合法退出 if (!result) { return; } // 4 设置key k.set(line); // 5 写出数据 context.write(k, NullWritable.get()); } // 2 解析日志 private boolean parseLog(String line, Context context) { // 1 截取 String[] fields = line.split(" "); // 2 日志长度大于11的为合法 if (fields.length > 11) { // 系统计数器 context.getCounter("map", "true").increment(1); return true; }else { context.getCounter("map", "false").increment(1); return false; } } }

（2）编写LogDriver类

package com.cuiyf41.etl; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; 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; import java.io.IOException; public class LogDriver { public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException { // 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:/input/inputlog", "e:/output1" }; // 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 Path input = new Path(args[0]); Path output = new Path(args[1]); // 如果输出路径存在   

   

 ，则进行删除 FileSystem fs = FileSystem.get(conf); if (fs.exists(output)) { fs.delete(output,true); } FileInputFormat.setInputPaths(job, input); FileOutputFormat.setOutputPath(job, output); // 6 提交 job.waitForCompletion(true); } }

3.9.2数据清洗案例实操-复杂解析版

1  

   

   

、需求

对Web访问日志中的各字段识别切分    

     

     ，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

web.log

（2）期望输出数据

都是合法的数据

2

     

     

 、实现代码

（1）定义一个bean  


  


，用来记录日志数据中的各数据字段

package com.cuiyf41.etlu; 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(); } }

（2）编写LogMapper类

package com.cuiyf41.etlu; 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, Mapper<LongWritable, Text, Text, NullWritable>.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; } }

（3）编写LogDriver类

package com.cuiyf41.etlu; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; 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; import java.io.IOException; public class LogDriver { public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException { // 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 Path input = new Path(args[0]); Path output = new Path(args[1]); // 如果输出路径存在




   




   



，则进行删除 FileSystem fs = FileSystem.get(conf); if (fs.exists(output)) { fs.delete(output,true); } FileInputFormat.setInputPaths(job, input); FileOutputFormat.setOutputPath(job, output); // 6 提交 job.waitForCompletion(true); } }