（6）Flink CEP SQL模拟账号短时间内异地登录风控预警

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.....

（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

（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：