使用MapReduce对日志进行清洗(大数据学习16)

最新推荐文章于 2023-11-23 10:11:04 发布

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本文介绍了一种使用Hadoop MapReduce对Web访问日志进行清洗的方法，包括字段解析、非法记录剔除及数据格式化，适用于大规模日志处理。

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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KPI统计需求，生成各类访问请求过滤数据

2、实现代码：

包结构：

a) 定义一个bean，用来记录日志数据中的各数据字段

package com.lyz.bigdata.mr.weblogwash;
/**
 *@Author:759057893@qq.com Lyz
 *@Date: 2019/3/13 10:59
 *@Description:
 **/
public class WebLogBean {

        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();
        }
    }

b)定义一个parser用来解析过滤web访问日志原始记录

package com.lyz.bigdata.mr.weblogwash;

/**
 * @Author:759057893@qq.com Lyz
 * @Date: ${Date} 10:59
 * @Description:
 **/


import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Locale;

public class WebLogParser {

    static SimpleDateFormat sd1 = new SimpleDateFormat("dd/MMM/yyyy:HH:mm:ss", Locale.US);

    static SimpleDateFormat sd2 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");

    public static WebLogBean parser(String line) {
        WebLogBean webLogBean = new WebLogBean();
        String[] arr = line.split(" ");
        if (arr.length > 11) {
            webLogBean.setRemote_addr(arr[0]);
            webLogBean.setRemote_user(arr[1]);
            webLogBean.setTime_local(parseTime(arr[3].substring(1)));
            webLogBean.setRequest(arr[6]);
            webLogBean.setStatus(arr[8]);
            webLogBean.setBody_bytes_sent(arr[9]);
            webLogBean.setHttp_referer(arr[10]);

            if (arr.length > 12) {
                webLogBean.setHttp_user_agent(arr[11] + " " + arr[12]);
            } else {
                webLogBean.setHttp_user_agent(arr[11]);
            }
            if (Integer.parseInt(webLogBean.getStatus()) >= 400) {// 大于400，HTTP错误
                webLogBean.setValid(false);
            }
        } else {
            webLogBean.setValid(false);
        }
        return webLogBean;
    }

    public static String parseTime(String dt) {

        String timeString = "";
        try {
            Date parse = sd1.parse(dt);
            timeString = sd2.format(parse);

        } catch (ParseException e) {
            e.printStackTrace();
        }

        return timeString;
    }

    public static void main(String[] args) {
        WebLogParser wp = new WebLogParser();
        String parseTime = wp.parseTime("18/Sep/2013:06:49:48");
        System.out.println(parseTime);
    }

}

c) mapreduce程序该mapreduce程序是在本地进行mapreduce 程序

package com.lyz.bigdata.mr.weblogwash;

/**
 *@Author:759057893@qq.com Lyz
 *@Date: 2019/3/13 10:59
 *@Description:
 **/

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WeblogPreProcess {

    static class WeblogPreProcessMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
        Text k = new Text();
        NullWritable v = NullWritable.get();

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String line = value.toString();
            WebLogBean webLogBean = WebLogParser.parser(line);
            //可以插入一个静态资源过滤（.....）
            /*WebLogParser.filterStaticResource(webLogBean);*/
            if (!webLogBean.isValid())
                return;
            k.set(webLogBean.toString());
            context.write(k, v);

        }

    }

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //是否运行为本地模式，就是看这个参数值是否为local，默认就是local
        conf.set("mapreduce.framework","local");
        conf.set("fs.defaultFS","file:///");

        job.setJarByClass(WeblogPreProcess.class);

        job.setMapperClass(WeblogPreProcessMapper.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);


        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        job.waitForCompletion(true);

    }
}

上述代码写完之后，对本地进行配置：点击Edit Configurations

输入本地数据集的路径我的是在 F:/mapreduce/webdatawash/input

log.txt 数据集路径：

输出路径为： F:/mapreduce/webdatawash/output

上述的配置完成后，点击保存运行

运行结果如下：

清洗前的数据集部分如下：

清洗后的结果如下：

如果想打成jar包在虚拟机上跑，上面前两个类保持不变 mapreduce 代码如下：

package com.lyz.bigdata.mr.weblogwash;

/**
 *@Author:759057893@qq.com Lyz
 *@Date: 2019/3/13 10:59
 *@Description:
 **/

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WeblogPreProcess {

    static class WeblogPreProcessMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
        Text k = new Text();
        NullWritable v = NullWritable.get();

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String line = value.toString();
            WebLogBean webLogBean = WebLogParser.parser(line);
            //可以插入一个静态资源过滤（.....）
            /*WebLogParser.filterStaticResource(webLogBean);*/
            if (!webLogBean.isValid())
                return;
            k.set(webLogBean.toString());
            context.write(k, v);

        }

    }

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

  
        job.setJarByClass(WeblogPreProcess.class);

        job.setMapperClass(WeblogPreProcessMapper.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);


        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        job.waitForCompletion(true);

    }
}

然后打成jar包下面具体步骤请看我写的其他关于mapreduce的案例，操作步骤一样。