Hadoop 提取KPI 进行海量Web日志分析
Web日志包含着网站最重要的信息,通过日志分析,我们可以知道网站的访问量,哪个网页访问人数最多,哪个网页最有价值等。一般中型的网站(10W的PV以上),每天会产生1G以上Web日志文件。大型或超大型的网站,可能每小时就会产生10G的数据量。
- Web日志分析概述
- 需求分析:KPI指标设计
- 算法模型:Hadoop并行算法
- 架构设计:日志KPI系统架构
- 程序开发:MapReduce程序实现
1. Web日志分析概述
Web日志由Web服务器产生,可能是Nginx, Apache, Tomcat等。从Web日志中,我们可以获取网站每类页面的PV值(PageView,页面访问量)、独立IP数;稍微复杂一些的,可以计算得出用户所检索的关键词排行榜、用户停留时间最高的页面等;更复杂的,构建广告点击模型、分析用户行为特征等等。
在Web日志中,每条日志通常代表着用户的一次访问行为,例如下面就是一条nginx日志:
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"
拆解为以下8个变量
- 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”
注:要更多的信息,则要用其它手段去获取,通过js代码单独发送请求,使用cookies记录用户的访问信息。
利用这些日志信息,我们可以深入挖掘网站的秘密了。
少量数据的情况
少量数据的情况(10Mb,100Mb,10G),在单机处理尚能忍受的时候,我可以直接利用各种Unix/Linux工具,awk、grep、sort、join等都是日志分析的利器,再配合perl, python,正则表达工,基本就可以解决所有的问题。
例如,我们想从上面提到的nginx日志中得到访问量最高前10个IP,实现很简单:
~ 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
海量数据的情况
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
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
并行算法的设计:
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
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。
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2:MapReduce程序实现
开发流程:
- 对日志行的解析
- Map函数实现
- Reduce函数实现
- 启动程序实现
1). 对日志行的解析
新建文件:org.apache.hadoop.mr.kpi
整体代码
package org.apache.hadoop.mr.kpi;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.HashSet;
import java.util.Locale;
import java.util.Set;
public class KPI {
/**
* 20160512
* @author yue
*/
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和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; //用来记录从哪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
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錯誤
kpi.setValid(false);
}
}else{
kpi.setValid(false);
}
return kpi;
}
/**
* 按page的pv分类
* pageview:页面访问量统计
* @return
*/
public static KPI filterPVs(String line){
KPI kpi = parser(line);
Set<String> pages = new HashSet<String>();
pages.add("/about/");
pages.add("/black-ip-clustor/");
pages.add("/cassandra-clustor/");
pages.add("/finance-rhive-repurchase/");
pages.add("/hadoop-familiy-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;
}
/**
* 按page的独立IP分类
* @return
*/
public static KPI filterIPs(String line){
KPI kpi = parser(line);
Set<String> pages = new HashSet<String>();
pages.add("/about/");
pages.add("/black-ip-clustor/");
pages.add("/cassandra-clustor/");
pages.add("/finance-rhive-repurchase/");
pages.add("/hadoop-familiy-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;
}
/**
* PV按浏览器分类
* @return
*/
public static KPI filterBroswer(String line){
return parser(line);
}
/**
* PV按小时分类
* @return
*/
public static KPI filterTime(String line){
return parser(line);
}
/**
* Pv按访问域名分类
* @return
*/
public static KPI filterDomain(String line){
return parser(line);
}
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;
}
@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 super.toString();
}
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:
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.apache.hadoop.mr.kpi.KPIPV.java
- IP: org.apache.hadoop.mr.kpi.KPIIP.java
- Time: org.apache.hadoop.mr.kpi.KPITime.java
- Browser: org.apache.hadoop.mr.kpi.KPIBrowser.java
1). PV:org.apache.hadoop.mr.kpi.KPIPV.java
package org.apache.hadoop.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 {
/**
* @author yue
* 20160512
*/
public static class KPIPVMapper extends MapReduceBase implements Mapper<Object ,Text ,Text,IntWritable>{
private IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value,
OutputCollector<Text, IntWritable> 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>{
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> 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://192.168.37.134:9000/user/hdfs/log_kpi";
String output = "hdfs://192.168.37.134:9000/user/hdfs/log_kpi/pv";
JobConf conf = new JobConf(KPIPV.class);
conf.setJobName("KPIPV");
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());
我们运行一下KPIPV.java
用hadoop命令查看HDFS文件
~ 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
2). IP: org.apache.hadoop.mr.kpi.KPIIP.java
package org.apache.hadoop.mr.kpi;
import java.io.IOException;
import java.util.HashSet;
import java.util.Iterator;
import java.util.Set;
import org.apache.hadoop.fs.Path;
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.apache.hadoop.mr.kpi.KPIIP.KPIIPMapper.KPIIPReducer;
public class KPIIP {
/**
* @author yue
* 20160512
*/
public static class KPIIPMapper extends MapReduceBase implements Mapper<Object,Text,Text,Text>{
private Text word = new Text();
private Text ips = new Text();
public void map(Object key, Text value,
OutputCollector<Text, Text> output, Reporter reporter)
throws IOException {
KPI kpi = KPI.filterIPs(value.toString());
if(kpi.isValid()){
word.set(kpi.getRequest());
ips.set(kpi.getRemote_addr());
output.collect(word, ips);
}
}
public static class KPIIPReducer extends MapReduceBase implements Reducer<Text,Text,Text,Text>{
private Text result = new Text();
private Set<String>count = new HashSet<String>();
public void reduce(Text key, Iterator<Text> values,
OutputCollector<Text, Text> output, Reporter reporter)
throws IOException {
while(values.hasNext()){
count.add(values.next().toString());
}
result.set(String.valueOf(count.size()));
output.collect(key, result);
}
}
}
public static void main(String[] args) throws Exception{
String input = "hdfs://192.168.37.134:9000/user/hdfs/log_kpi";
String output = "hdfs://192.168.37.134:9000/user/hdfs/log_kpi/ip";
JobConf conf = new JobConf(KPIIP.class);
conf.setJobName("KPIIP");
conf.setMapOutputKeyClass(Text.class);
conf.setMapOutputValueClass(Text.class);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(Text.class);
conf.setMapperClass(KPIIPMapper.class);
conf.setCombinerClass(KPIIPReducer.class);
conf.setReducerClass(KPIIPReducer.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);
}
}
3). Time: org.apache.hadoop.mr.kpi.KPITime.java
package org.apache.hadoop.mr.kpi;
import java.io.IOException;
import java.text.ParseException;
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 KPITime {
/**
* @author yue 20160512
*/
public static class KPITimeMapper extends MapReduceBase implements
Mapper<Object, Text, Text, IntWritable> {
private IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
KPI kpi = KPI.filterTime(value.toString());
if(kpi.isValid()){
try {
word.set(kpi.getTime_local_Date_hour());
output.collect(word, one);
} catch (ParseException e) {
e.printStackTrace();
}
}
}
}
public static class KPITimeReducer extends MapReduceBase implements
Reducer<Text,IntWritable,Text,IntWritable>{
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> 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://192.168.37.134:9000/user/hdfs/log_kpi";
String output = "hdfs://192.168.37.134:9000/user/hdfs/log_kpi/time";
JobConf conf = new JobConf(KPITime.class);
conf.setJobName("KPITime");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(KPITimeMapper.class);
conf.setCombinerClass(KPITimeReducer.class);
conf.setReducerClass(KPITimeReducer.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);
}
}
4). Browser: org.apache.hadoop.mr.kpi.KPIBrowser.java
package org.apache.hadoop.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 KPIBrowser {
/**
* 20160512
* @author yue
*/
public static class KPIBrowserMapper extends MapReduceBase implements Mapper<Object,Text,Text,IntWritable>{
private IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key,Text value,OutputCollector<Text,IntWritable> output , Reporter reporter) throws IOException{
KPI kpi = KPI.filterBroswer(value.toString());
if(kpi.isValid()){
word.set(kpi.getHttp_user_agent());
output.collect(word, one);
}
}
}
public static class KPIBrowserReducer extends MapReduceBase implements Reducer<Text,IntWritable,Text,IntWritable>{
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterator<IntWritable> values,OutputCollector<Text, IntWritable> 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://192.168.37.134:9000/user/hdfs/log_kpi";
String output = "hdfs://192.168.37.134:9000/user/hdfs/log_kpi/browser";
JobConf conf = new JobConf(KPIBrowser.class);
conf.setJobName("KPIBrowser");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(KPIBrowserMapper.class);
conf.setCombinerClass(KPIBrowserReducer.class);
conf.setReducerClass(KPIBrowserReducer.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);
}
}