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本篇文章我们来模拟一个真实的风险识别场景，模拟XX平台上可能出现盗号行为。
技术实现方案：
（1）通过将xxx平台用户登录时的登录日志发送到kafka（本文代码演示用的socket）；
（2）Flink CEP SQL规则引擎中定义好风控识别规则，接入kafka数据源，比如一个账号在5分钟内，在多个不同地区有登录行为，那我们认为该账号被盗；
（3）Flink CEP将识别到的风险数据可以进行下发，为数据应用层提供数据服务，如：风控系统，数据大屏，态势感知.....
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（1）我们先来定义一个数据生产者，模拟用户登录，产生登录日志：
package com.producers;
import java.io.BufferedWriter;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.net.ServerSocket;
import java.net.Socket;
import java.util.Random;
/**
* Created by lj on 2022-08-10.
*/
public class Socket_Producer1 {
public static void main(String[] args) throws IOException {
try {
ServerSocket ss = new ServerSocket(9999);
System.out.println("启动 server ....");
Socket s = ss.accept();
BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(s.getOutputStream()));
String response = "java,1,2";
//每 2s 发送一次消息
int i = 0;
Random r=new Random();
String[] userArr = {"user1","user2","user3","user4","user5","user6","user7","user8","user9"};
String[] loginIP = {"167.234.67.123","219.141.178.14","220.180.239.202","111.73.240.192","123.182.253.242"};
while(true){
Thread.sleep(2000);
response= userArr[r.nextInt(userArr.length)] + "," + loginIP[r.nextInt(loginIP.length)] +"\n";
System.out.println(response);
try{
bw.write(response);
bw.flush();
i++;
}catch (Exception ex){
System.out.println(ex.getMessage());
}
}
} catch (IOException | InterruptedException e) {
e.printStackTrace();
}
}
}
（2）在CEP中接入日志数据、定义风控规则
package com.examples;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableResult;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import java.time.LocalDateTime;
import static org.apache.flink.table.api.Expressions.$;
/**
* Created by lj on 2022-08-10.
*/
public class CEPSQLSocket1 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
DataStreamSource<String> streamSource = env.socketTextStream("127.0.0.1", 9999,"\n");
SingleOutputStreamOperator<UserLoginLog> userLoginLog = streamSource.map(new MapFunction<String, UserLoginLog>() {
@Override
public UserLoginLog map(String s) throws Exception {
String[] split = s.split(",");
return new UserLoginLog(split[0], split[1], LocalDateTime.now());
}
});
// 将流转化为表
Table table = tableEnv.fromDataStream(userLoginLog,
$("username"),
$("ip"),
$("rowtime1"), //.rowtime()
$("pt").proctime());
CEP_SQL(env,tableEnv,table);
env.execute();
}
private static void CEP_SQL(StreamExecutionEnvironment env,StreamTableEnvironment tEnv,Table table){
System.out.println("===============CEP_SQL=================");
tEnv.createTemporaryView("CEP_SQL", table);
String sql = "SELECT * " +
"FROM CEP_SQL " +
" MATCH_RECOGNIZE ( " +
" PARTITION BY username " +
" ORDER BY pt " + //在窗口内，对事件时间进行排序。
" MEASURES " + //定义如何根据匹配成功的输入事件构造输出事件
" e1.username as user1,"+
" First(e1.ip) as first_ip," +
" LAST(e2.ip) as last_ip," +
" e1.rowtime1 as rt," +
" LAST(e2.pt) as end_tstamp " + //最新的事件时间为end_timestamp
" ONE ROW PER MATCH " + //匹配成功输出一条
" AFTER MATCH skip to next row " + //匹配后跳转到下一行
" PATTERN ( e1 e2 ) WITHIN INTERVAL '5' MINUTE " +
" DEFINE " + //定义在PATTERN中出现的patternVariable的具体含义
" e1 AS " +
" e1.username <> '', " +
" e2 AS " +
" e1.username = e2.username AND e1.ip <> e2.ip " +
" ) MR";
TableResult res = tEnv.executeSql(sql);
// while (res.collect().hasNext()){
// Row next = res.collect().next();
// System.out.println(next);
// }
res.print();
tEnv.dropTemporaryView("CEP_SQL");
}
public static class UserLoginLog {
public String username;
public String ip;
public LocalDateTime rowtime1;
public UserLoginLog(){
}
public UserLoginLog(String username,String ip,LocalDateTime rowtime){
this.username = username;
this.ip = ip;
this.rowtime1 = rowtime;
}
}
}
（3）启动数据生产者，每2秒模拟一次用户登录行为
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（4）启动CEP规则引擎服务，实时显示出现异地登录的用户信息：
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