大数据框架之Hadoop：MapReduceMapReduce框架原理——数据清洗（ETL）

Posted 2023-03-08 yiluohan0307

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);