Extract KPI statistical indicators with Hadoop for massive web log analysis

Others 2022-05-18 00:22:58 views: 0

Hadoop family series of articles , mainly introduce Hadoop family products, commonly used projects include Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa, newly added projects include, YARN, Hcatalog, Oozie, Cassandra, Hama , Whirr, Flume, Bigtop, Crunch, Hue, etc.

Since 2011, China has entered an era of surging big data, and family software represented by Hadoop has occupied a vast area of ​​big data processing. Open source industry and manufacturers, all data software, all move closer to Hadoop. Hadoop has also changed from a niche field to a standard for big data development. On the basis of the original Hadoop technology, the Hadoop family of products has emerged, and the concept of "big data" has been continuously innovated and technological progress has been introduced.

As developers in the IT industry, we must also keep up with the rhythm, seize the opportunity, and rise with Hadoop!

 

hadoop-kpi-logo

foreword

Web logs contain the most important information of the website. Through log analysis, we can know the number of visits to the website, which page has the most visitors, and which page is the most valuable. Generally, a medium-sized website (PV of 10W or more) will generate more than 1G of Web log files every day. Large or very large websites may generate 10G of data per hour.

For log data of this scale, Hadoop is the most suitable for log analysis.

content

  1. Overview of Web Log Analysis
  2. Demand analysis: KPI indicator design
  3. Algorithm Model: Hadoop Parallel Algorithm
  4. Architecture Design: Log KPI System Architecture
  5. Program Development 1: Building a Hadoop Project with Maven
  6. Program development 2: MapReduce program implementation

1. Overview of Web Log Analysis

Web logs are generated by web servers, which may be Nginx, Apache, Tomcat, etc. From the web log, we can obtain the PV value (PageView, page views) and the number of independent IPs of each type of page of the website; a little more complicated, we can calculate the keyword rankings retrieved by the user and the one with the highest user stay time. Pages, etc.; more complex, building ad click models, analyzing user behavior characteristics, and so on.

In web logs, each log usually represents a user's access behavior. For example, the following is an nginx log:


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"

Disassembled into the following 8 variables

  • remote_addr: record the client's ip address, 222.68.172.190
  • remote_user: record client user name, –
  • time_local: record access time and time zone, [18/Sep/2013:06:49:57 +0000]
  • request: record the requested url and http protocol, "GET /images/my.jpg HTTP/1.1"
  • status: record request status, success is 200, 200
  • body_bytes_sent: record the content size of the file body sent to the client, 19939
  • http_referer: used to record visits from that page link, "http://www.angularjs.cn/A00n"
  • http_user_agent: record information about client browser, “Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.66 Safari/537.36”

Note: If you want more information, you need to use other means to obtain it, send a separate request through js code, and use cookies to record the user's access information.

Using these log information, we can dig deeper into the secrets of the website.

In the case of a small amount of data

In the case of a small amount of data (10Mb, 100Mb, 10G), when the stand-alone processing is still bearable, I can directly use various Unix/Linux tools, awk, grep, sort, join, etc. are all powerful tools for log analysis, and then cooperate with perl , python, regular expression workers, can basically solve all problems.

For example, we want to get the top 10 most visited IPs from the nginx log mentioned above, the implementation is very simple:


~ cat access.log.10 | awk '{a[$1]++} END {for(b in a) print b"\t"a[b]}' | sort -k2 -r | head -n 10
163.177.71.12   972
101.226.68.137  972
183.195.232.138 971
50.116.27.194   97
14.17.29.86     96
61.135.216.104  94
61.135.216.105  91
61.186.190.41   9
59.39.192.108   9
220.181.51.212  9

The case of massive data

当数据量每天以10G、100G增长的时候，单机处理能力已经不能满足需求。我们就需要增加系统的复杂性，用计算机集群，存储阵列来解决。在Hadoop出现之前，海量数据存储，和海量日志分析都是非常困难的。只有少数一些公司，掌握着高效的并行计算，分步式计算，分步式存储的核心技术。

Hadoop的出现，大幅度的降低了海量数据处理的门槛，让小公司甚至是个人都能力，搞定海量数据。并且，Hadoop非常适用于日志分析系统。

2.需求分析：KPI指标设计

下面我们将从一个公司案例出发来全面的解释，如何用进行海量Web日志分析，提取KPI数据。

案例介绍
某电子商务网站，在线团购业务。每日PV数100w，独立IP数5w。用户通常在工作日上午10:00-12:00和下午15:00-18:00访问量最大。日间主要是通过PC端浏览器访问，休息日及夜间通过移动设备访问较多。网站搜索浏量占整个网站的80%，PC用户不足1%的用户会消费，移动用户有5%会消费。

通过简短的描述，我们可以粗略地看出，这家电商网站的经营状况，并认识到愿意消费的用户从哪里来，有哪些潜在的用户可以挖掘，网站是否存在倒闭风险等。

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

注：商业保密限制，无法提供电商网站的日志。
下面的内容，将以我的个人网站为例提取数据进行分析。

百度统计，对我个人网站做的统计！http://www.fens.me

基本统计指标：
hadoop-kpi-baidu

用户的访问设备统计指标：
hadoop-kpi-baidu2

从商业的角度，个人网站的特征与电商网站不太一样，没有转化率，同时跳出率也比较高。从技术的角度，同样都关注KPI指标设计。

3.算法模型：Hadoop并行算法

hadoop-kpi-log

并行算法的设计：
注：找到第一节有定义的8个变量

PV(PageView): 页面访问量统计

  • Map过程{key:$request,value:1}
  • Reduce过程{key:$request,value:求和(sum)}

IP: 页面独立IP的访问量统计

  • Map: {key:$request,value:$remote_addr}
  • Reduce: {key:$request,value:去重再求和(sum(unique))}

Time: 用户每小时PV的统计

  • Map: {key:$time_local,value:1}
  • Reduce: {key:$time_local,value:求和(sum)}

Source: 用户来源域名的统计

  • Map: {key:$http_referer,value:1}
  • Reduce: {key:$http_referer,value:求和(sum)}

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)}

4.架构设计：日志KPI系统架构

hadoop-kpi-architect

上图中，左边是Application业务系统，右边是Hadoop的HDFS, MapReduce。

  1. 日志是由业务系统产生的，我们可以设置web服务器每天产生一个新的目录，目录下面会产生多个日志文件，每个日志文件64M。
  2. 设置系统定时器CRON，夜间在0点后，向HDFS导入昨天的日志文件。
  3. 完成导入后，设置系统定时器，启动MapReduce程序，提取并计算统计指标。
  4. 完成计算后，设置系统定时器，从HDFS导出统计指标数据到数据库，方便以后的即使查询。

hadoop-kpi-process

上面这幅图，我们可以看得更清楚，数据是如何流动的。蓝色背景的部分是在Hadoop中的，接下来我们的任务就是完成MapReduce的程序实现。

5.程序开发1：用Maven构建Hadoop项目

请参考文章：用Maven构建Hadoop项目

win7的开发环境 和 Hadoop的运行环境 ，在上面文章中已经介绍过了。

我们需要放日志文件，上传的HDFS里/user/hdfs/log_kpi/目录，参考下面的命令操作


~ hadoop fs -mkdir /user/hdfs/log_kpi
~ hadoop fs -copyFromLocal /home/conan/datafiles/access.log.10 /user/hdfs/log_kpi/

我已经把整个MapReduce的实现都放到了github上面：

https://github.com/bsspirit/maven_hadoop_template/releases/tag/kpi_v1

6.程序开发2：MapReduce程序实现

开发流程：

  1. 对日志行的解析
  2. Map函数实现
  3. Reduce函数实现
  4. 启动程序实现

1). 对日志行的解析
新建文件：org.conan.myhadoop.mr.kpi.KPI.java


package org.conan.myhadoop.mr.kpi;

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

/*
 * KPI Object
 */
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 boolean valid = true;// 判断数据是否合法
    
    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append("valid:" + this.valid);
        sb.append("\nremote_addr:" + this.remote_addr);
        sb.append("\nremote_user:" + this.remote_user);
        sb.append("\ntime_local:" + this.time_local);
        sb.append("\nrequest:" + this.request);
        sb.append("\nstatus:" + this.status);
        sb.append("\nbody_bytes_sent:" + this.body_bytes_sent);
        sb.append("\nhttp_referer:" + this.http_referer);
        sb.append("\nhttp_user_agent:" + this.http_user_agent);
        return sb.toString();
    }

    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 Date getTime_local_Date() throws ParseException {
        SimpleDateFormat df = new SimpleDateFormat("dd/MMM/yyyy:HH:mm:ss", Locale.US);
        return df.parse(this.time_local);
    }
    
    public String getTime_local_Date_hour() throws ParseException{
        SimpleDateFormat df = new SimpleDateFormat("yyyyMMddHH");
        return df.format(this.getTime_local_Date());
    }

    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 String getHttp_referer_domain(){
        if(http_referer.length()<8){ 
            return http_referer;
        }
        
        String str=this.http_referer.replace("\"", "").replace("http://", "").replace("https://", "");
        return str.indexOf("/")>0?str.substring(0, str.indexOf("/")):str;
    }

    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;
    }

    public static void main(String args[]) {
        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\"";
        System.out.println(line);
        KPI kpi = new KPI();
        String[] arr = line.split(" ");

        kpi.setRemote_addr(arr[0]);
        kpi.setRemote_user(arr[1]);
        kpi.setTime_local(arr[3].substring(1));
        kpi.setRequest(arr[6]);
        kpi.setStatus(arr[8]);
        kpi.setBody_bytes_sent(arr[9]);
        kpi.setHttp_referer(arr[10]);
        kpi.setHttp_user_agent(arr[11] + " " + arr[12]);
        System.out.println(kpi);

        try {
            SimpleDateFormat df = new SimpleDateFormat("yyyy.MM.dd:HH:mm:ss", Locale.US);
            System.out.println(df.format(kpi.getTime_local_Date()));
            System.out.println(kpi.getTime_local_Date_hour());
            System.out.println(kpi.getHttp_referer_domain());
        } catch (ParseException e) {
            e.printStackTrace();
        }
    }

}

从日志文件中，取一行通过main函数写一个简单的解析测试。

控制台输出：


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"
valid:true
remote_addr:222.68.172.190
remote_user:-
time_local:18/Sep/2013:06:49:57
request:/images/my.jpg
status:200
body_bytes_sent:19939
http_referer:"http://www.angularjs.cn/A00n"
http_user_agent:"Mozilla/5.0 (Windows
2013.09.18:06:49:57
2013091806
www.angularjs.cn

我们看到日志行，被正确的解析成了kpi对象的属性。我们把解析过程，单独封装成一个方法。


    private static KPI parser(String line) {
        System.out.println(line);
        KPI kpi = new KPI();
        String[] arr = line.split(" ");
        if (arr.length > 11) {
            kpi.setRemote_addr(arr[0]);
            kpi.setRemote_user(arr[1]);
            kpi.setTime_local(arr[3].substring(1));
            kpi.setRequest(arr[6]);
            kpi.setStatus(arr[8]);
            kpi.setBody_bytes_sent(arr[9]);
            kpi.setHttp_referer(arr[10]);
            
            if (arr.length > 12) {
                kpi.setHttp_user_agent(arr[11] + " " + arr[12]);
            } else {
                kpi.setHttp_user_agent(arr[11]);
            }

            if (Integer.parseInt(kpi.getStatus()) >= 400) {// 大于400，HTTP错误
                kpi.setValid(false);
            }
        } else {
            kpi.setValid(false);
        }
        return kpi;
    }

对map方法，reduce方法，启动方法，我们单独写一个类来实现

下面将分别介绍MapReduce的实现类：

  • PV:org.conan.myhadoop.mr.kpi.KPIPV.java
  • IP: org.conan.myhadoop.mr.kpi.KPIIP.java
  • Time: org.conan.myhadoop.mr.kpi.KPITime.java
  • Browser: org.conan.myhadoop.mr.kpi.KPIBrowser.java

1). PV:org.conan.myhadoop.mr.kpi.KPIPV.java


package org.conan.myhadoop.mr.kpi;

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;

public class KPIPV { 

    public static class KPIPVMapper extends MapReduceBase implements Mapper<object, text,="" intwritable="" style="margin: 0pt; padding: 0pt;"> {
        private IntWritable one = new IntWritable(1);
        private Text word = new Text();

        @Override
        public void map(Object key, Text value, OutputCollector<text, intwritable="" style="margin: 0pt; padding: 0pt;"> output, Reporter reporter) throws IOException {
            KPI kpi = KPI.filterPVs(value.toString());
            if (kpi.isValid()) {
                word.set(kpi.getRequest());
                output.collect(word, one);
            }
        }
    }

    public static class KPIPVReducer extends MapReduceBase implements Reducer<text, intwritable,="" text,="" intwritable="" style="margin: 0pt; padding: 0pt;"> {
        private IntWritable result = new IntWritable();

        @Override
        public void reduce(Text key, Iterator values, OutputCollector<text, intwritable="" style="margin: 0pt; padding: 0pt;"> output, Reporter reporter) throws IOException {
            int sum = 0;
            while (values.hasNext()) {
                sum += values.next().get();
            }
            result.set(sum);
            output.collect(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        String input = "hdfs://192.168.1.210:9000/user/hdfs/log_kpi/";
        String output = "hdfs://192.168.1.210:9000/user/hdfs/log_kpi/pv";

        JobConf conf = new JobConf(KPIPV.class);
        conf.setJobName("KPIPV");
        conf.addResource("classpath:/hadoop/core-site.xml");
        conf.addResource("classpath:/hadoop/hdfs-site.xml");
        conf.addResource("classpath:/hadoop/mapred-site.xml");

        conf.setMapOutputKeyClass(Text.class);
        conf.setMapOutputValueClass(IntWritable.class);

        conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(IntWritable.class);

        conf.setMapperClass(KPIPVMapper.class);
        conf.setCombinerClass(KPIPVReducer.class);
        conf.setReducerClass(KPIPVReducer.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);
    }
}

在程序中会调用KPI类的方法

KPI kpi = KPI.filterPVs(value.toString());

通过filterPVs方法，我们可以实现对PV，更多的控制。

在KPK.java中，增加filterPVs方法


    /**
     * 按page的pv分类
     */
    public static KPI filterPVs(String line) {
        KPI kpi = parser(line);
        Set pages = new HashSet();
        pages.add("/about");
        pages.add("/black-ip-list/");
        pages.add("/cassandra-clustor/");
        pages.add("/finance-rhive-repurchase/");
        pages.add("/hadoop-family-roadmap/");
        pages.add("/hadoop-hive-intro/");
        pages.add("/hadoop-zookeeper-intro/");
        pages.add("/hadoop-mahout-roadmap/");

        if (!pages.contains(kpi.getRequest())) {
            kpi.setValid(false);
        }
        return kpi;
    }

在filterPVs方法，我们定义了一个pages的过滤，就是只对这个页面进行PV统计。

我们运行一下KPIPV.java


2013-10-9 11:53:28 org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush
信息: Starting flush of map output
2013-10-9 11:53:28 org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill
信息: Finished spill 0
2013-10-9 11:53:28 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
2013-10-9 11:53:30 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: hdfs://192.168.1.210:9000/user/hdfs/log_kpi/access.log.10:0+3025757
2013-10-9 11:53:30 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: hdfs://192.168.1.210:9000/user/hdfs/log_kpi/access.log.10:0+3025757
2013-10-9 11:53:30 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_m_000000_0' done.
2013-10-9 11:53:30 org.apache.hadoop.mapred.Task initialize
信息:  Using ResourceCalculatorPlugin : null
2013-10-9 11:53:30 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: 
2013-10-9 11:53:30 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Merging 1 sorted segments
2013-10-9 11:53:30 org.apache.hadoop.mapred.Merger$MergeQueue merge
信息: Down to the last merge-pass, with 1 segments left of total size: 213 bytes
2013-10-9 11:53:30 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: 
2013-10-9 11:53:30 org.apache.hadoop.mapred.Task done
信息: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
2013-10-9 11:53:30 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: 
2013-10-9 11:53:30 org.apache.hadoop.mapred.Task commit
信息: Task attempt_local_0001_r_000000_0 is allowed to commit now
2013-10-9 11:53:30 org.apache.hadoop.mapred.FileOutputCommitter commitTask
信息: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://192.168.1.210:9000/user/hdfs/log_kpi/pv
2013-10-9 11:53:31 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息:  map 100% reduce 0%
2013-10-9 11:53:33 org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate
信息: reduce > reduce
2013-10-9 11:53:33 org.apache.hadoop.mapred.Task sendDone
信息: Task 'attempt_local_0001_r_000000_0' done.
2013-10-9 11:53:34 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息:  map 100% reduce 100%
2013-10-9 11:53:34 org.apache.hadoop.mapred.JobClient monitorAndPrintJob
信息: Job complete: job_local_0001
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息: Counters: 20
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:   File Input Format Counters 
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Bytes Read=3025757
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:   File Output Format Counters 
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Bytes Written=183
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:   FileSystemCounters
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     FILE_BYTES_READ=545
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     HDFS_BYTES_READ=6051514
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     FILE_BYTES_WRITTEN=83472
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     HDFS_BYTES_WRITTEN=183
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:   Map-Reduce Framework
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Map output materialized bytes=217
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Map input records=14619
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Reduce shuffle bytes=0
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Spilled Records=16
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Map output bytes=2004
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Total committed heap usage (bytes)=376569856
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Map input bytes=3025757
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     SPLIT_RAW_BYTES=110
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Combine input records=76
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Reduce input records=8
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Reduce input groups=8
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Combine output records=8
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Reduce output records=8
2013-10-9 11:53:34 org.apache.hadoop.mapred.Counters log
信息:     Map output records=76

View HDFS files with hadoop command


~ hadoop fs -cat /user/hdfs/log_kpi/pv/part-00000

/about  5
/black-ip-list/ 2
/cassandra-clustor/     3
/finance-rhive-repurchase/      13
/hadoop-family-roadmap/ 13
/hadoop-hive-intro/     14
/hadoop-mahout-roadmap/ 20
/hadoop-zookeeper-intro/        6

In this way, we get the PV value of the specified page in the log file just now.

Specifying a page, like a site map of a website, if not specified, all access links will be found. Through the specification of the "site map", we can more easily find the information we need.

Later, the extraction ideas of other statistical indicators are similar to the implementation process of PV. You can directly download the source code and run it to see the results! !

 

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