大数据学习——日志分析

有两个海量日志文件存储在hdfs上， 
其中登陆日志格式：user，ip，time，oper（枚举值：1为上线，2为下线）；
访问之日格式为：ip，time，url，假设登陆日志中上下线信息完整，切同一上下线时间段内是用的ip唯一，
计算访问日志中独立user数量最多的前10个url，用MapReduce实现。

提示：
1、要统计前10，需要两个步骤，第一个步骤实现join，统计出每个url对应的独立用户数，第二步骤求出top10
2、两个大表join，用同一job多输入
3、要根据ip字段join，所以要根据ip分区
4、求top10

 数据：

login.log

tom,192.168.1.11,2017-11-20 10:00,1
tom,192.168.1.11,2017-11-20 11:00,2
sua,192.168.1.12,2017-11-20 10:01,1
sua,192.168.1.12,2017-11-20 10:30,2
lala,192.168.1.11,2017-11-20 11:01,1
lala,192.168.1.11,2017-11-20 11:30,2
tom,192.168.1.14,2017-11-20 11:01,1
tom,192.168.1.14,2017-11-20 11:40,2
jer,192.168.1.15,2017-11-20 10:00,1
jer,192.168.1.15,2017-11-20 10:40,2
sua,192.168.1.16,2017-11-20 11:00,1
sua,192.168.1.16,2017-11-20 12:00,2

visit.log

192.168.1.11,2017-11-20 10:02,url1
192.168.1.11,2017-11-20 10:04,url1
192.168.1.14,2017-11-20 11:02,url1
192.168.1.12,2017-11-20 10:02,url1
192.168.1.11,2017-11-20 11:02,url1
192.168.1.15,2017-11-20 10:02,url1
192.168.1.16,2017-11-20 11:02,url1
192.168.1.11,2017-11-20 10:03,url2
192.168.1.14,2017-11-20 11:03,url2
192.168.1.12,2017-11-20 10:03,url2
192.168.1.15,2017-11-20 10:03,url2
192.168.1.16,2017-11-20 11:03,url2
192.168.1.12,2017-11-20 10:15,url3
192.168.1.15,2017-11-20 10:16,url3
192.168.1.16,2017-11-20 11:02,url3

实现代码：

 

 

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">

  <modelVersion>4.0.0</modelVersion>

  <groupId>com.cyf</groupId>
  <artifactId>TwoLog</artifactId>
  <packaging>jar</packaging>
  <version>1.0-SNAPSHOT</version>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
  </properties>
  <dependencies>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>2.6.4</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-hdfs</artifactId>
      <version>2.6.4</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.6.4</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>2.6.4</version>
    </dependency>
  </dependencies>

  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-jar-plugin</artifactId>
        <version>2.4</version>
        <configuration>
          <archive>
            <manifest>
              <addClasspath>true</addClasspath>
              <classpathPrefix>lib/</classpathPrefix>
              <mainClass>com.cyf.LoginlogFormatMP</mainClass>
            </manifest>
          </archive>
        </configuration>
      </plugin>
    </plugins>
  </build>

</project>

IpJoinBean.java

package log;

import org.apache.hadoop.io.WritableComparable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

/**
 * Created by mac on 17/11/24.
 *  根据IPjoin登陆表和访问表的输出值
 */
public class IpJoinBean implements WritableComparable<IpJoinBean>{
     private  String user = "";
     private String ip = "";
     private String loginTime = "";
     private String logoutTime = "";
     private String type = "";
     private String url = "";
     private String visitTime = "";

    public String getUser() {
        return user;
    }

    public void setUser(String user) {
        this.user = user;
    }
    public String getIp() {
        return ip;
    }

    public void setIp(String ip) {
        this.ip = ip;
    }

    public String getLoginTime() {
        return loginTime;
    }

    public void setLoginTime(String loginTime) {
        this.loginTime = loginTime;
    }

    public String getLogoutTime() {
        return logoutTime;
    }

    public void setLogoutTime(String logoutTime) {
        this.logoutTime = logoutTime;
    }

    public String getType() {
        return type;
    }

    public void setType(String type) {
        this.type = type;
    }

    public String getUrl() {
        return url;
    }

    public void setUrl(String url) {
        this.url = url;
    }

    public String getVisitTime() {
        return visitTime;
    }

    public void setVisitTime(String visitTime) {
        this.visitTime = visitTime;
    }



    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeUTF(type);
        dataOutput.writeUTF(ip);
        dataOutput.writeUTF(user);
        dataOutput.writeUTF(url);
        dataOutput.writeUTF(loginTime);
        dataOutput.writeUTF(logoutTime);
        dataOutput.writeUTF(visitTime);

    }

    public void readFields(DataInput dataInput) throws IOException {
        this.type = dataInput.readUTF();
        this.ip = dataInput.readUTF();
        this.user = dataInput.readUTF();
        this.url = dataInput.readUTF();
        this.loginTime = dataInput.readUTF();
        this.logoutTime = dataInput.readUTF();
        this.visitTime = dataInput.readUTF();

    }
    public boolean visitMatchLogin(IpJoinBean login){
        if(this.visitTime.compareTo(login.loginTime)>=0&&this.visitTime.compareTo(login.logoutTime)<=0){
            return true;
        }
        return false;
    }

    public int compareTo(IpJoinBean o) {
        return this.ip.compareTo(o.ip)>=0?1:-1;
    }

    @Override
    public String toString() {
        return "ip:"+ip+" user:"+user+" url:"+url+" loginTime:"+loginTime+" logouttime:"+logoutTime+" visitTime:" +visitTime;
    }
    public IpJoinBean(String ip,String user,String url,String loginTime,String logoutTime,String visitTime){
        this.ip = ip;
        this.user = user;
        this.url = url;
        this.loginTime = loginTime;
        this.logoutTime = logoutTime;
        this.visitTime = visitTime;
    }
    public IpJoinBean(){
        super();
    }
}

ReversBean.java

package log;

import org.apache.hadoop.io.WritableComparable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

/**
 * Created by mac on 17/11/27.
 */
public class ReversBean implements WritableComparable<ReversBean>{
    private String count;

    public int compareTo(ReversBean o) {
        return o.count.compareTo(this.count);
    }

    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeUTF(count);
    }

    public void readFields(DataInput dataInput) throws IOException {
        this.count=dataInput.readUTF();
    }

    public ReversBean(String count) {
        this.count = count;
    }

    public ReversBean() {
    }

    public String getCount() {
        return count;
    }

    public void setCount(String count) {
        this.count = count;
    }
}

AllToOneGroupingComparator.java

package log;

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

/**
 * Created by mac on 17/11/25.
 */
public class AllToOneGroupingComparator extends WritableComparator {
    protected  AllToOneGroupingComparator(){
        super(ReversBean.class,true);
    }
    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        return 0;
    }
}

LoginlogFormatMP.java

package com.cyf;

import org.apache.hadoop.conf.Configuration;
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 java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

/**
 * Created by mac on 17/11/24.
 * 将用户登陆信息整理为 ip－user－loginTime－logoutTime格式文件
 */
public class LoginlogFormatMP {
    public static class readFilesMapper extends Mapper<LongWritable,Text,Text,Text>{
        Text outKey = new Text();
        Text outValue = new Text();
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String[] line = value.toString().split(",");
            String user = line[0];
            String ip = line[1];
            String time = line[2];
            String opr = line[3];
            System.out.print("map=============================line"+user+ip+time+opr);
            outKey.set(user+"_"+ip);
            outValue.set(time+"_"+opr);
            context.write(outKey,outValue);
        }
    }

    public static class timeConcatReducer extends Reducer<Text,Text,Text,NullWritable> {
        List loginTimes = new ArrayList();
        List logoutTimes = new ArrayList();
        @Override
        protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            for(Text value :values){
                String time = value.toString().split("_")[0];
                String opr = value.toString().split("_")[1];
                if(opr.equals("1")){
                    loginTimes.add(time);
                }else if(opr.equals("2")){
                    logoutTimes.add(time);
                }
            }
            Collections.sort(loginTimes);
            Collections.sort(logoutTimes);
            for(int i = 0;i<loginTimes.size();i++){
                System.out.print(key+"_"+loginTimes.get(i)+"_"+logoutTimes.get(i));
                //假设登陆和登出信息完整
                context.write(new Text(key+"_"+loginTimes.get(i)+"_"+logoutTimes.get(i)),NullWritable.get());
            }
            loginTimes.clear();
            logoutTimes.clear();
        }

    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(LoginlogFormatMP.class);
        job.setMapperClass(readFilesMapper.class);
        job.setReducerClass(timeConcatReducer.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setNumReduceTasks(1);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputKeyClass(Text.class);
        FileInputFormat.addInputPath(job,new Path("/logs/input/login.log"));

        Path outPath = new Path("/logs/output/out");
        FileOutputFormat.setOutputPath(job,outPath);

        job.waitForCompletion(true);
    }

}

JoinWithIpMp.java

package com.cyf;


import log.IpJoinBean;
import org.apache.hadoop.conf.Configuration;
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.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;


/**
 * Created by mac on 17/11/25.
 *  通过IP将处理后的登陆日志与访问日志关联
 */
public class JoinWithIpMp {
    public static class readFilesMapper extends Mapper<LongWritable,Text,Text,IpJoinBean> {
        private String inputPath ;
        private String fileName;

        private IpJoinBean outPutValue = new IpJoinBean();

        @ Override
        protected void setup(Context context) throws IOException, InterruptedException {
            // 每个文件传进来时获得文件中属性前缀
            FileSplit input = (FileSplit) context.getInputSplit();

            try {
                //获得文件名
                fileName = input.getPath().getName();;
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
        @Override
        protected void map(LongWritable key,Text value,Context context) throws IOException, InterruptedException {
            //将Ip作为key，把两份日志中的统一Ip分到同一分区分组下，在reduce中进行join
            if(fileName.startsWith("visit")){

                String[] parms = value.toString().split(",");
                String ip = parms[0];
                String time = parms[1];
                String url = parms[2];
                outPutValue.setIp(ip);
                outPutValue.setVisitTime(time);
                outPutValue.setUrl(url);
                outPutValue.setType("visit");

            }else{
                String[] parms = value.toString().split("_");
                String user = parms[0];
                String ip = parms[1];
                String loginTime = parms[2];
                String logoutTime = parms[3];
                outPutValue.setUser(user);
                outPutValue.setIp(ip);
                outPutValue.setType("login");
                outPutValue.setLoginTime(loginTime);
                outPutValue.setLogoutTime(logoutTime);

            }

            context.write(new Text(outPutValue.getIp()),outPutValue);
        }
    }
    public static class countClicksReducer extends Reducer<Text,IpJoinBean,Text,NullWritable> {
        private List<IpJoinBean> loginData = new ArrayList<IpJoinBean>();
        private List<IpJoinBean> visitData = new ArrayList<IpJoinBean>();
        @Override
        protected void reduce(Text key,Iterable<IpJoinBean> values,Context context) throws IOException,InterruptedException{

            for(IpJoinBean value:values){
                System.out.println("value============================  "+value);
                if(value.getType().equals("visit")){
                    visitData.add(new IpJoinBean(value.getIp(),value.getUser(),value.getUrl(),value.getLoginTime(),value.getLogoutTime(),value.getVisitTime()));
                }else if(value.getType().equals("login")){
                    loginData.add(new IpJoinBean(value.getIp(),value.getUser(),value.getUrl(),value.getLoginTime(),value.getLogoutTime(),value.getVisitTime()));
                }
            }
             //统计出能够关联到登陆信息的访问时间和ip，与用户关联
            for(IpJoinBean visit:visitData){
                for(IpJoinBean login:loginData){
                    if(visit.visitMatchLogin(login)){
                        context.write(new Text(visit.getIp()+"_"+login.getUser()+"_"+visit.getUrl()),NullWritable.get());
                    }
                }
            }
            loginData.clear();
            visitData.clear();

        }

    }
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(JoinWithIpMp.class);
        job.setMapperClass(readFilesMapper.class);
        job.setReducerClass(countClicksReducer.class);
        //job.setInputFormatClass(TextInputFormat.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IpJoinBean.class);
        job.setNumReduceTasks(1);

        Path[] inPaths = new Path[2];
        inPaths[0] = new Path("/logs/output/out/");
        inPaths[1] = new Path("/logs/input/visit.log");
        FileInputFormat.setInputPaths(job,inPaths);

        Path outPath = new Path("/logs/joinOut");
        FileOutputFormat.setOutputPath(job,outPath);

        job.waitForCompletion(true);

    }

}

CountUVMP.java

package com.cyf;

import org.apache.hadoop.conf.Configuration;
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 java.io.IOException;
import java.util.*;

/**
 * Created by mac on 17/11/25.
 * 统计每个url的独立访问数
 */
public class CountUVMP {
    public static class readFilesMapper extends Mapper<LongWritable,Text,Text,Text> {
        Text outKey = new Text();
        Text outValue = new Text();
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] line = value.toString().split("_");

            String user = line[1];
            String url = line[2];
            context.write(new Text(url),new Text(user));

        }
    }

    public static class timeConcatReducer extends Reducer<Text,Text,Text,NullWritable> {

        @Override
        protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            int i = 0;
            List<String> users = new ArrayList<String>();
            for(Text value : values){
                if( users.contains(value.toString())){
                    continue;
                }
                users.add(value.toString());
            }
            context.write(new Text(key+"_"+users.size()),NullWritable.get());
        }

    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(CountUVMP.class);
        job.setMapperClass(readFilesMapper.class);
        job.setReducerClass(timeConcatReducer.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setNumReduceTasks(1);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputKeyClass(Text.class);
        FileInputFormat.addInputPath(job,new Path("/logs/joinOut"));

        Path outPath = new Path("/logs/uvResult");
        FileOutputFormat.setOutputPath(job,outPath);

        job.waitForCompletion(true);
    }
}

Top10MP.java

package com.cyf;

import java.io.IOException;


import log.AllToOneGroupingComparator;
import log.ReversBean;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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.output.FileOutputFormat;

/**
 * Created by mac on 17/11/25.
 * 求出UV前10的url
 */

public class Top10MP {
    public static class TopOneMap extends Mapper<LongWritable, Text, ReversBean, Text> {

        int i = 0 ;

        @Override
        protected void map(LongWritable key, Text value,Context context)
                throws IOException, InterruptedException {
            //以url的访客数作为key，实现倒序排序
             String[] values = value.toString().split("_");
             String url = values[0];
             String count = values[1];
             context.write(new ReversBean(count),value);

        }
    }
    public static class TopReduce extends Reducer<ReversBean, Text, Text,IntWritable> {
        int count = 0;
        int top = 0;

        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            Configuration conf = context.getConfiguration();
            top = Integer.parseInt(conf.get("top"));
        }
        //每个分区下的所有key分为一个组，按照uv数从大到小排序，每组取出前10即可
        @Override
        protected void reduce(ReversBean key, Iterable<Text> values,Context context)
                throws IOException, InterruptedException {
            for (Text value : values ){
                if(count >= top){
                    return;
                }else{
                    count++;
                    String[] countresults = value.toString().split("_");
                    String url = countresults[0];
                    String count = countresults[1];
                    context.write(new Text(url),new IntWritable((Integer.parseInt(count))));
                }

            }

        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        conf.set("top", "2");

        /*FileSystem fileSystem = FileSystem.get(conf);
        Path dr = new Path("/Users/mac/Test/uvTop10");
        if(fileSystem.exists(dr)){
            fileSystem.delete(dr);
        }*/

        Job job = Job.getInstance(conf);
        job.setJarByClass(Top10MP.class);

        job.setMapperClass(TopOneMap.class);
        job.setReducerClass(TopReduce.class);

        job.setMapOutputKeyClass(ReversBean.class);
        job.setMapOutputValueClass(Text.class);

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

        job.setGroupingComparatorClass(AllToOneGroupingComparator.class);

        FileInputFormat.setInputPaths(job,new Path("/logs/uvResult/"));
        FileOutputFormat.setOutputPath(job, new Path("/logs/uvTop10"));

        //根据数据量决定，数据量太大可以先分两步，第一步骤分区数尽量大，第二步骤分区数唯一实现全局topN
        job.setNumReduceTasks(1);

        boolean b = job.waitForCompletion(true);
        System.exit(b?0:1);
    }
}

 

修改pom.xml
<mainClass>com.cyf.LoginlogFormatMP</mainClass>
<mainClass>com.cyf.JoinWithIpMp</mainClass>
<mainClass>com.cyf.CountUVMP</mainClass>
<mainClass>com.cyf.Top10MP</mainClass>

运行命令
hadoop jar TwoLog-1.0.jar com.cyf.LoginlogFormatMP
hadoop jar TwoLog-2.0.jar com.cyf.JoinWithIpMp
hadoop jar TwoLog-3.0.jar com.cyf.CountUVMP
hadoop jar TwoLog-4.0.jar com.cyf.Top10MP