Hadoop-13

最新推荐文章于 2024-04-20 16:41:53 发布

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最新推荐文章于 2024-04-20 16:41:53 发布

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分类专栏： Hadoop

本文链接：https://blog.youkuaiyun.com/qq_39210208/article/details/88070147

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日志清洗（数据清洗）

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。
2）输入数据
在这里插入图片描述
3）实现代码：
（1）编写LogMapper

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class WebLogMapper extends Mapper<LongWritable, Text, Text, NullWritable>{
	@Override
	protected void map(LongWritable key, Text value, Context context)
			throws IOException, InterruptedException {
		
		// 1 获取一行
		String line = value.toString();
		
		// 2 解析日志的方法
		boolean result = parseLog(line, context);
			
		// 3 判断是否合法
		if (!result) {
			return;
		}
		
		// 4合法的日志写出去
		context.write(value, NullWritable.get());
	}

	private boolean parseLog(String line, Context context) {
		// 1 截取
		String[] fields = line.split(" ");
		
		// 2 判断字段长度是否大于11
		if (fields.length > 11) {// 认为是合法的
			// 3 记录合法次数
			context.getCounter("map", "true").increment(1);
			
			return true;
		}else {// 认为是非法的
			// 4 记录不合法的次数
			
			context.getCounter("map", "false").increment(1);
			return false;
		}
	}
}

（2）编写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 获取job信息
		Configuration conf = new Configuration();
		Job job = Job.getInstance(conf);

		// 2 加载jar包
		job.setJarByClass(WebLogDriver.class);

		// 3 关联map
		job.setMapperClass(WebLogMapper.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
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 6 提交
		job.waitForCompletion(true);
	}
}

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
根据统计需求，生成各类访问请求过滤数据

2）输入数据
同上

3）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


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程序

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class LogMapper extends Mapper<LongWritable, Text, Text, NullWritable>{
	Text k = new Text();
	
	@Override
	protected void map(LongWritable key, Text value, 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 = pressLog(line);
		
		if (!bean.isValid()) {
			return;
		}
		
		k.set(bean.toString());
		
		// 3 输出
		context.write(k, NullWritable.get());
	}

	// 解析日志
	private LogBean pressLog(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程序

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 LogDriver {
	public static void main(String[] args) throws Exception {
		// 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
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 6 提交
		job.waitForCompletion(true);
	}
}