MapReduce 简单数据清洗

MapReduce 简单数据清洗 思路简介

直接在Map阶段使用各种规则将数据进行过滤即可，不需使用Reduce阶段。

输入数据

Maven和log4j.properties配置

参考 MapReduce统计流量案例 中的配置  

自定义Mapper类实现(WebLogMapper)

package com.test.mapreduce.weblog;

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 WebLogMapper extends Mapper {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 1.获取每一行转换为字符串
        String line = value.toString();
        // 2.调用自定义解析日志函数
        boolean result = parseLog(line);
        // 3.判断日志不合法退出
        if (!result){
            return;
        }
        // 4.合法则写出
        context.write(value, NullWritable.get());
    }

    
    private boolean parseLog(String line) {
        // 2.1 字符串切割
        String[] fields = line.split(" ");
        // 2.2 判断日志长度大于11为合法
        if (fields.length > 11) {
            return true;
        }else {
            return false;
        }
    }
}

自定义Driver类实现(WebLogDriver) 

package com.test.mapreduce.weblog;

import com.atguigu.mapreduce.outputformat.LogDriver;
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 WebLogDriver {
    public static void main(String[] args) throws Exception {
        // 1.创建配置信息Configuration对象并获取Job单例对象
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        // 2.设置关联本Driver
        job.setJarByClass(LogDriver.class);

        // 3.设置关联Mapper
        job.setMapperClass(WebLogMapper.class);

        // 4.设置最终输出的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        // 5.设置reducetask个数为0
        job.setNumReduceTasks(0);

        // 6.设置输入和输出路径
        FileInputFormat.setInputPaths(job, new Path("D:\input"));
        FileOutputFormat.setOutputPath(job, new Path("D:\output"));

        // 7.提交
        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }
}

 数据输出