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);
    }
}