hadoop 网站日志分析

一、项目要求

• 本文讨论的日志处理方法中的日志，仅指Web日志。其实并没有精确的定义，可能包括但不限于各种前端Web服务器——apache、lighttpd、nginx、tomcat等产生的用户访问日志，以及各种Web应用程序自己输出的日志。  

二、需求分析： KPI指标设计

 PV(PageView): 页面访问量统计
 IP: 页面独立IP的访问量统计
 Time: 用户每小时PV的统计
 Source: 用户来源域名的统计
 Browser: 用户的访问设备统计

下面我着重分析浏览器统计

三、分析过程

1、 日志的一条nginx记录内容

222.68.172.190  - - [18/Sep/2013:06:49:57 +0000] "GET /images/my.jpg HTTP/1.1" 200 19939 
"http://www.angularjs.cn/A00n" 
"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.66 Safari/537.36"

2、对上面的日志记录进行分析

remote_addr : 记录客户端的ip地址, 222.68.172.190
remote_user :  记录客户端用户名称, –
time_local:  记录访问时间与时区, [18/Sep/2013:06:49:57 +0000]
request: 记录请求的url与http协议, “GET /images/my.jpg HTTP/1.1″
status:  记录请求状态,成功是200, 200
body_bytes_sent:  记录发送给客户端文件主体内容大小, 19939
http_referer:  用来记录从那个页面链接访问过来的, “http://www.angularjs.cn/A00n”
http_user_agent:  记录客户浏览器的相关信息, “Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.66 Safari/537.36″  

3、java语言分析上面一条日志记录（使用空格切分）

String line = "222.68.172.190 - - [18/Sep/2013:06:49:57 +0000] \"GET /images/my.jpg HTTP/1.1\" 200 19939 \"http://www.angularjs.cn/A00n\" \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.66 Safari/537.36\"";
	    String[] elementList = line.split(" ");
	    for(int i=0;i<elementList.length;i++){
	    	System.out.println(i+" : "+elementList[i]);
	    }

测试结果：

0 : 222.68.172.190
1 : -
2 : -
3 : [18/Sep/2013:06:49:57
4 : +0000]
5 : "GET
6 : /images/my.jpg
7 : HTTP/1.1"
8 : 200
9 : 19939
10 : "http://www.angularjs.cn/A00n"
11 : "Mozilla/5.0
12 : (Windows
13 : NT
14 : 6.1)
15 : AppleWebKit/537.36
16 : (KHTML,
17 : like
18 : Gecko)
19 : Chrome/29.0.1547.66
20 : Safari/537.36"

4、实体Kpi类的代码：

public class Kpi {
	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 String method;//请求方法 get post
    private String http_version; //http版本
    
	public String getMethod() {
		return method;
	}
	public void setMethod(String method) {
		this.method = method;
	}
	public String getHttp_version() {
		return http_version;
	}
	public void setHttp_version(String http_version) {
		this.http_version = http_version;
	}
	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;
	}
	@Override
	public String toString() {
		return "Kpi [remote_addr=" + remote_addr + ", remote_user="
				+ remote_user + ", time_local=" + time_local + ", request="
				+ request + ", status=" + status + ", body_bytes_sent="
				+ body_bytes_sent + ", http_referer=" + http_referer
				+ ", http_user_agent=" + http_user_agent + ", method=" + method
				+ ", http_version=" + http_version + "]";
	}

	
    
}

5、kpi的工具类

package org.aaa.kpi;

public class KpiUtil {
	/***
	 * line记录转化成kpi对象
	 * @param line 日志的一条记录
	 * @author tianbx
	 * */
	public static Kpi transformLineKpi(String line){
		String[] elementList = line.split(" ");
		Kpi kpi = new Kpi();
	    kpi.setRemote_addr(elementList[0]);
	    kpi.setRemote_user(elementList[1]);
	    kpi.setTime_local(elementList[3].substring(1));
	    kpi.setMethod(elementList[5].substring(1));
	    kpi.setRequest(elementList[6]);
	    kpi.setHttp_version(elementList[7]);
	    kpi.setStatus(elementList[8]);
	    kpi.setBody_bytes_sent(elementList[9]);
	    kpi.setHttp_referer(elementList[10]);
	    kpi.setHttp_user_agent(elementList[11] + " " + elementList[12]);
		return kpi;
	}
}

6、算法模型: 并行算法 

Browser: 用户的访问设备统计
– Map: {key:$http_user_agent,value:1}
– Reduce: {key:$http_user_agent,value:求和(sum)} 
7、map-reduce分析代码

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.hmahout.kpi.entity.Kpi;
import org.hmahout.kpi.util.KpiUtil;

import cz.mallat.uasparser.UASparser;
import cz.mallat.uasparser.UserAgentInfo;

public class KpiBrowserSimpleV {

	public static class KpiBrowserSimpleMapper extends MapReduceBase 
		implements Mapper<Object, Text, Text, IntWritable> {
		UASparser parser = null;
		@Override
		public void map(Object key, Text value,
				OutputCollector<Text, IntWritable> out, Reporter reporter)
				throws IOException {
			Kpi kpi = KpiUtil.transformLineKpi(value.toString());

			if(kpi!=null && kpi.getHttP_user_agent_info()!=null){
				if(parser==null){
					parser = new UASparser();
				}
				UserAgentInfo info = 
				parser.parseBrowserOnly(kpi.getHttP_user_agent_info());
				if("unknown".equals(info.getUaName())){
					out.collect(new Text(info.getUaName()), new IntWritable(1));
				}else{
					out.collect(new Text(info.getUaFamily()), new IntWritable(1));
				}

			}
		}
	}

	public static class KpiBrowserSimpleReducer extends MapReduceBase implements
		Reducer<Text, IntWritable, Text, IntWritable>{

		@Override
		public void reduce(Text key, Iterator<IntWritable> value,
				OutputCollector<Text, IntWritable> out, Reporter reporter)
				throws IOException {
			IntWritable sum = new IntWritable(0);
			while(value.hasNext()){
				sum.set(sum.get()+value.next().get());
			}
			out.collect(key, sum);
		}
	}
	public static void main(String[] args) throws IOException {
		String input = "hdfs://127.0.0.1:9000/user/tianbx/log_kpi/input";
        String output ="hdfs://127.0.0.1:9000/user/tianbx/log_kpi/browerSimpleV";
        JobConf conf = new JobConf(KpiBrowserSimpleV.class);
        conf.setJobName("KpiBrowserSimpleV");
        String url = "classpath:";
        conf.addResource(url+"/hadoop/core-site.xml");
        conf.addResource(url+"/hadoop/hdfs-site.xml");
        conf.addResource(url+"/hadoop/mapred-site.xml");
        
        conf.setMapOutputKeyClass(Text.class);
        conf.setMapOutputValueClass(IntWritable.class);
        
        conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(IntWritable.class);
        
        conf.setMapperClass(KpiBrowserSimpleMapper.class);
        conf.setCombinerClass(KpiBrowserSimpleReducer.class);
        conf.setReducerClass(KpiBrowserSimpleReducer.class);

        conf.setInputFormat(TextInputFormat.class);
        conf.setOutputFormat(TextOutputFormat.class);

        FileInputFormat.setInputPaths(conf, new Path(input));
        FileOutputFormat.setOutputPath(conf, new Path(output));

        JobClient.runJob(conf);
        System.exit(0);
	}

}

8、输出文件log_kpi/browerSimpleV内容

AOL Explorer 1
Android Webkit 123
Chrome 4867
CoolNovo 23
Firefox 1700
Google App Engine 5
IE 1521
Jakarta Commons-HttpClient 3
Maxthon 27
Mobile Safari 273
Mozilla 130
Openwave Mobile Browser 2
Opera 2
Pale Moon 1
Python-urllib 4
Safari 246
Sogou Explorer 157
unknown 4685

8 R制作图片

data<-read.table(file="borwer.txt",header=FALSE,sep=",") 

 names(data)<-c("borwer","num")

 qplot(borwer,num,data=data,geom="bar")

解决问题

1、排除爬虫和程序点击，对抗作弊

解决办法：页面做个检测鼠标是否动。

2、浏览量 怎么排除图片

3、浏览量排除假点击？

4、哪一个搜索引擎访问的？

5、点击哪一个关键字访问的？

6、从哪一个地方访问的？

7、使用哪一个浏览器访问的？