标题
- 1.主要目的
- 2.实现方式
- 3.开发一个MapReduce程序WeblogPreProcess
- 4.点击流模型PageViews表
- 5.点击流模型visit信息表
1.主要目的
数据清洗 —— 过滤“不合规”数据,清洗无意义的数据
2.实现方式
首先经过flume采集后的数据会有十个字段,每个字段都会由空格来分隔
3.开发一个MapReduce程序WeblogPreProcess
package cn.itcast.bigdata.weblog.pre;import java.io.IOException;
import java.net.URI;
import java.text.SimpleDateFormat;
import java.util.HashSet;
import java.util.Set;import cn.itcast.bigdata.weblog.utils.DateUtil;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import cn.itcast.bigdata.weblog.mrbean.WebLogBean;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;/*** 处理原始日志,过滤出真实pv请求 转换时间格式 对缺失字段填充默认值 对记录标记valid和invalid* */public class WeblogPreProcess extends Configured implements Tool {@Overridepublic int run(String[] args) throws Exception {//Configuration conf = new Configuration();Configuration conf = super.getConf();Job job = Job.getInstance(conf);/*String inputPath= "hdfs://node01:9000/weblog/"+DateUtil.getYestDate()+"/input";String outputPath="hdfs://node01:9000/weblog/"+DateUtil.getYestDate()+"/weblogPreOut";FileSystem fileSystem = FileSystem.get(new URI("hdfs://node01:9000"), conf);if (fileSystem.exists(new Path(outputPath))){fileSystem.delete(new Path(outputPath),true);}fileSystem.close();FileInputFormat.setInputPaths(job, new Path(inputPath));FileOutputFormat.setOutputPath(job, new Path(outputPath));job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);
*/FileInputFormat.addInputPath(job,new Path("file:Users/zhaozhuang/Desktop/8.大数据离线第八天/日志文件数据/input"));job.setInputFormatClass(TextInputFormat.class);FileOutputFormat.setOutputPath(job,new Path("file:Users/zhaozhuang/Desktop/8.大数据离线第八天/日志文件数据/weblogPreOut"));job.setOutputFormatClass(TextOutputFormat.class);job.setJarByClass(WeblogPreProcess.class);job.setMapperClass(WeblogPreProcessMapper.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(NullWritable.class);job.setNumReduceTasks(0);boolean res = job.waitForCompletion(true);return res?0:1;}static class WeblogPreProcessMapper extends Mapper<LongWritable, Text, Text, NullWritable> {// 用来存储网站url分类数据Set<String> pages = new HashSet<String>();Text k = new Text();NullWritable v = NullWritable.get();/*** 从外部配置文件中加载网站的有用url分类数据 存储到maptask的内存中,用来对日志数据进行过滤*/@Overrideprotected void setup(Context context) throws IOException, InterruptedException {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/");}@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String line = value.toString();WebLogBean webLogBean = WebLogParser.parser(line);if (webLogBean != null) {// 过滤js/图片/css等静态资源WebLogParser.filtStaticResource(webLogBean, pages);/* if (!webLogBean.isValid()) return; */k.set(webLogBean.toString());context.write(k, v);}}}public static void main(String[] args) throws Exception {Configuration configuration = new Configuration();int run = ToolRunner.run(configuration, new WeblogPreProcess(), args);System.exit(run);}
}
得到的数据:
4.点击流模型PageViews表
由于大量的指标统计从点击流模型中更容易得出,所以在预处理阶段,可以使用mr程序来生成点击流模型的数据。
有结构化数据转换为pageView模型的思路:
1.相同ip的数据放到一起按照时间排序,排序后打上标识
2.同一个ip的两条数据之间的时间差,如果大于30分,就不是同一个session,如果小于30分,就认为是同一个session
3.以ip作为key2,相同的数据发送到同一个reduce形成一个集合
package cn.itcast.bigdata.weblog.clickstream;import cn.itcast.bigdata.weblog.mrbean.WebLogBean;
import cn.itcast.bigdata.weblog.utils.DateUtil;
import org.apache.commons.beanutils.BeanUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;import java.io.IOException;
import java.net.URI;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.*;/*** * 将清洗之后的日志梳理出点击流pageviews模型数据* * 输入数据是清洗过后的结果数据* * 区分出每一次会话,给每一次visit(session)增加了session-id(随机uuid)* 梳理出每一次会话中所访问的每个页面(请求时间,url,停留时长,以及该页面在这次session中的序号)* 保留referral_url,body_bytes_send,useragent* * * @author* */
public class ClickStreamPageView extends Configured implements Tool {@Overridepublic int run(String[] args) throws Exception {Configuration conf = super.getConf();Job job = Job.getInstance(conf);/*String inputPath="hdfs://node01:9000/weblog/"+DateUtil.getYestDate()+"/weblogPreOut";String outputPath="hdfs://node01:9000/weblog/"+DateUtil.getYestDate()+"/pageViewOut";FileSystem fileSystem = FileSystem.get(new URI("hdfs://node01:9000"), conf);if (fileSystem.exists(new Path(outputPath))){fileSystem.delete(new Path(outputPath),true);}fileSystem.close();job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);FileInputFormat.setInputPaths(job, new Path(inputPath));FileOutputFormat.setOutputPath(job, new Path(outputPath));*/job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);TextInputFormat.addInputPath(job,new Path("file:Users/zhaozhuang/Desktop/8.大数据离线第八天/日志文件数据/weblogPreOut"));TextOutputFormat.setOutputPath(job,new Path("file:Users/zhaozhuang/Desktop/8.大数据离线第八天/日志文件数据/pageViewOut"));job.setJarByClass(ClickStreamPageView.class);job.setMapperClass(ClickStreamMapper.class);job.setReducerClass(ClickStreamReducer.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(WebLogBean.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);boolean b = job.waitForCompletion(true);return b?0:1;}static class ClickStreamMapper extends Mapper<LongWritable, Text, Text, WebLogBean> {Text k = new Text();WebLogBean v = new WebLogBean();@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String line = value.toString();String[] fields = line.split("\001");if (fields.length < 9) return;//将切分出来的各字段set到weblogbean中v.set("true".equals(fields[0]) ? true : false, fields[1], fields[2], fields[3], fields[4], fields[5], fields[6], fields[7], fields[8]);//只有有效记录才进入后续处理if (v.isValid()) {//此处用ip地址来标识用户k.set(v.getRemote_addr());context.write(k, v);}}}static class ClickStreamReducer extends Reducer<Text, WebLogBean, NullWritable, Text> {Text v = new Text();@Overrideprotected void reduce(Text key, Iterable<WebLogBean> values, Context context) throws IOException, InterruptedException {ArrayList<WebLogBean> beans = new ArrayList<WebLogBean>();// 先将一个用户的所有访问记录中的时间拿出来排序try {for (WebLogBean bean : values) {//为什么list集合当中不能直接添加循环出来的这个bean?//这里通过属性拷贝,每次new 一个对象,避免了bean的属性值每次覆盖WebLogBean webLogBean = new WebLogBean();try {BeanUtils.copyProperties(webLogBean, bean);} catch(Exception e) {e.printStackTrace();}beans.add(webLogBean);}//将bean按时间先后顺序排序Collections.sort(beans, new Comparator<WebLogBean>() {@Overridepublic int compare(WebLogBean o1, WebLogBean o2) {try {Date d1 = toDate(o1.getTime_local());Date d2 = toDate(o2.getTime_local());if (d1 == null || d2 == null)return 0;return d1.compareTo(d2);} catch (Exception e) {e.printStackTrace();return 0;}}});/*** 以下逻辑为:从有序bean中分辨出各次visit,并对一次visit中所访问的page按顺序标号step* 核心思想:* 就是比较相邻两条记录中的时间差,如果时间差<30分钟,则该两条记录属于同一个session* 否则,就属于不同的session* */int step = 1;String session = UUID.randomUUID().toString();for (int i = 0; i < beans.size(); i++) {WebLogBean bean = beans.get(i);// 如果仅有1条数据,则直接输出if (1 == beans.size()) {// 设置默认停留时长为60sv.set(session+"\001"+key.toString()+"\001"+bean.getRemote_user() + "\001" + bean.getTime_local() + "\001" + bean.getRequest() + "\001" + step + "\001" + (60) + "\001" + bean.getHttp_referer() + "\001" + bean.getHttp_user_agent() + "\001" + bean.getBody_bytes_sent() + "\001"+ bean.getStatus());context.write(NullWritable.get(), v);session = UUID.randomUUID().toString();break;}// 如果不止1条数据,则将第一条跳过不输出,遍历第二条时再输出if (i == 0) {continue;}// 求近两次时间差long timeDiff = timeDiff(toDate(bean.getTime_local()), toDate(beans.get(i - 1).getTime_local()));// 如果本次-上次时间差<30分钟,则输出前一次的页面访问信息if (timeDiff < 30 * 60 * 1000) {v.set(session+"\001"+key.toString()+"\001"+beans.get(i - 1).getRemote_user() + "\001" + beans.get(i - 1).getTime_local() + "\001" + beans.get(i - 1).getRequest() + "\001" + step + "\001" + (timeDiff / 1000) + "\001" + beans.get(i - 1).getHttp_referer() + "\001"+ beans.get(i - 1).getHttp_user_agent() + "\001" + beans.get(i - 1).getBody_bytes_sent() + "\001" + beans.get(i - 1).getStatus());context.write(NullWritable.get(), v);step++;} else {// 如果本次-上次时间差>30分钟,则输出前一次的页面访问信息且将step重置,以分隔为新的visitv.set(session+"\001"+key.toString()+"\001"+beans.get(i - 1).getRemote_user() + "\001" + beans.get(i - 1).getTime_local() + "\001" + beans.get(i - 1).getRequest() + "\001" + (step) + "\001" + (60) + "\001" + beans.get(i - 1).getHttp_referer() + "\001"+ beans.get(i - 1).getHttp_user_agent() + "\001" + beans.get(i - 1).getBody_bytes_sent() + "\001" + beans.get(i - 1).getStatus());context.write(NullWritable.get(), v);// 输出完上一条之后,重置step编号step = 1;session = UUID.randomUUID().toString();}// 如果此次遍历的是最后一条,则将本条直接输出if (i == beans.size() - 1) {// 设置默认停留市场为60sv.set(session+"\001"+key.toString()+"\001"+bean.getRemote_user() + "\001" + bean.getTime_local() + "\001" + bean.getRequest() + "\001" + step + "\001" + (60) + "\001" + bean.getHttp_referer() + "\001" + bean.getHttp_user_agent() + "\001" + bean.getBody_bytes_sent() + "\001" + bean.getStatus());context.write(NullWritable.get(), v);}}} catch (ParseException e) {e.printStackTrace();}}private String toStr(Date date) {SimpleDateFormat df = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss", Locale.US);return df.format(date);}private Date toDate(String timeStr) throws ParseException {SimpleDateFormat df = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss", Locale.US);return df.parse(timeStr);}private long timeDiff(String time1, String time2) throws ParseException {Date d1 = toDate(time1);Date d2 = toDate(time2);return d1.getTime() - d2.getTime();}private long timeDiff(Date time1, Date time2) throws ParseException {return time1.getTime() - time2.getTime();}}public static void main(String[] args) throws Exception {int run = ToolRunner.run(new Configuration(), new ClickStreamPageView(), args);System.exit(run);}
}
得到的数据
5.点击流模型visit信息表
注:“一次访问”=“N次连续请求”
直接从原始数据中用hql语法得出每个人的“次”访问信息比较困难,可先用mapreduce程序分析原始数据得出“次”信息数据,然后再用hql进行更多维度统计
package cn.itcast.bigdata.weblog.clickstream;import java.io.IOException;
import java.net.URI;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;import cn.itcast.bigdata.weblog.utils.DateUtil;
import org.apache.commons.beanutils.BeanUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import cn.itcast.bigdata.weblog.mrbean.PageViewsBean;
import cn.itcast.bigdata.weblog.mrbean.VisitBean;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;/*** 输入数据:pageviews模型结果数据* 从pageviews模型结果数据中进一步梳理出visit模型* sessionid start-time out-time start-page out-page pagecounts ......* * @author**/
public class ClickStreamVisit extends Configured implements Tool {@Overridepublic int run(String[] args) throws Exception {Configuration conf = super.getConf();Job job = Job.getInstance(conf);/*String inputPath = "hdfs://node01:9000/weblog/"+ DateUtil.getYestDate() + "/pageViewOut";String outPutPath="hdfs://node01:9000/weblog/"+ DateUtil.getYestDate() + "/clickStreamVisit";FileSystem fileSystem = FileSystem.get(new URI("hdfs://node01:9000"),conf);if (fileSystem.exists(new Path(outPutPath))){fileSystem.delete(new Path(outPutPath),true);}fileSystem.close();FileInputFormat.setInputPaths(job, new Path(inputPath));FileOutputFormat.setOutputPath(job, new Path(outPutPath));job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);*/job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);TextInputFormat.addInputPath(job,new Path("file:Users/zhaozhuang/Desktop/8.大数据离线第八天/日志文件数据/pageViewOut"));TextOutputFormat.setOutputPath(job,new Path("file:Users/zhaozhuang/Desktop/8.大数据离线第八天/日志文件数据/clickStreamVisit"));job.setJarByClass(ClickStreamVisit.class);job.setMapperClass(ClickStreamVisitMapper.class);job.setReducerClass(ClickStreamVisitReducer.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(PageViewsBean.class);job.setOutputKeyClass(NullWritable.class);job.setOutputValueClass(VisitBean.class);boolean res = job.waitForCompletion(true);return res?0:1;}// 以session作为key,发送数据到reducerstatic class ClickStreamVisitMapper extends Mapper<LongWritable, Text, Text, PageViewsBean> {PageViewsBean pvBean = new PageViewsBean();Text k = new Text();@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String line = value.toString();String[] fields = line.split("\001");int step = Integer.parseInt(fields[5]);//(String session, String remote_addr, String timestr, String request, int step, String staylong, String referal, String useragent, String bytes_send, String status)//299d6b78-9571-4fa9-bcc2-f2567c46df3472.46.128.140-2013-09-18 07:58:50/hadoop-zookeeper-intro/160"https://www.google.com/""Mozilla/5.0"14722200pvBean.set(fields[0], fields[1], fields[2], fields[3],fields[4], step, fields[6], fields[7], fields[8], fields[9]);k.set(pvBean.getSession());context.write(k, pvBean);}}static class ClickStreamVisitReducer extends Reducer<Text, PageViewsBean, NullWritable, VisitBean> {@Overrideprotected void reduce(Text session, Iterable<PageViewsBean> pvBeans, Context context) throws IOException, InterruptedException {// 将pvBeans按照step排序ArrayList<PageViewsBean> pvBeansList = new ArrayList<PageViewsBean>();for (PageViewsBean pvBean : pvBeans) {PageViewsBean bean = new PageViewsBean();try {BeanUtils.copyProperties(bean, pvBean);pvBeansList.add(bean);} catch (Exception e) {e.printStackTrace();}}Collections.sort(pvBeansList, new Comparator<PageViewsBean>() {@Overridepublic int compare(PageViewsBean o1, PageViewsBean o2) {return o1.getStep() > o2.getStep() ? 1 : -1;}});// 取这次visit的首尾pageview记录,将数据放入VisitBean中VisitBean visitBean = new VisitBean();// 取visit的首记录visitBean.setInPage(pvBeansList.get(0).getRequest());visitBean.setInTime(pvBeansList.get(0).getTimestr());// 取visit的尾记录visitBean.setOutPage(pvBeansList.get(pvBeansList.size() - 1).getRequest());visitBean.setOutTime(pvBeansList.get(pvBeansList.size() - 1).getTimestr());// visit访问的页面数visitBean.setPageVisits(pvBeansList.size());// 来访者的ipvisitBean.setRemote_addr(pvBeansList.get(0).getRemote_addr());// 本次visit的referalvisitBean.setReferal(pvBeansList.get(0).getReferal());visitBean.setSession(session.toString());context.write(NullWritable.get(), visitBean);}}public static void main(String[] args) throws Exception {ToolRunner.run(new Configuration(),new ClickStreamVisit(),args);}}
得到的数据