java flink（二十六） 实战之电商黑名单过滤 Flink CEP编程实现、什么是CEP、CEP组合模式demo、CEP循环模式demo

java flink（二十六） 实战之电商黑名单过滤 Flink CEP编程实现、什么是CEP、CEP组合模式demo、CEP循环模式demo

什么是CEP：

1、复杂事件处理

2、Flink中实现复杂事件处理库

3、CEP允许在无休止的事件中检测事件模式，让我们有机会掌握数据中的重要部分

4、一个或多个由简单事件构成的事件通过一定的规则匹配，然后输出用户想要的数据。

CEP API:

begin 第一个事件定义 

where 条件

next 后一个紧跟着事件定义

subtype 子类型判断

followedBy 后边的时间 不一定紧跟着

CEP 个体模式 ：

CEP 模式序列 

 

CEP 超时处理 

CEP组合模式demo 

题目：根据上篇的黑名单过滤功能，我们进行优化，如果两条失败之间穿插了一条乱序的成功登录，那么这两条失败不会被检测。

我们利用CEP，首先第一个事件是检测第一条失败登录，然后第二个事件是检测第二个失败登录，进行筛选打印报警信息。

1、引入jar包：

2、代码展示

package Project;
 
import Beans.LoginEvent;
import Beans.LoginFailWarning;
import org.apache.flink.cep.CEP;
import org.apache.flink.cep.PatternSelectFunction;
import org.apache.flink.cep.PatternStream;
import org.apache.flink.cep.pattern.Pattern;
import org.apache.flink.cep.pattern.conditions.SimpleCondition;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
 
import java.net.URL;
import java.util.List;
import java.util.Map;
 
public class LoginFailWithCep {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
 
        //读取数据
        URL resource = LoginFailWithCep.class.getResource("/LoginLog.csv");
        DataStream<LoginEvent> logEventStream = env.readTextFile(resource.getPath())
                .map(line -> {
                    String[] fields = line.split(",");
                    return new LoginEvent(new Long(fields[0]), fields[1], fields[2], new Long(fields[3]));
                })
                .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<LoginEvent>(Time.seconds(3)) {
                    @Override
                    public long extractTimestamp(LoginEvent loginEvent) {
                        return loginEvent.getTimestamp() * 1000;
                    }
                });
        //定义匹配模式
        //匹配第一次失败与第二次失败在2s内
        Pattern<LoginEvent, LoginEvent> loginFailPattern = Pattern.<LoginEvent>begin("firstFail").where(new SimpleCondition<LoginEvent>() {
            @Override
            public boolean filter(LoginEvent loginEvent) throws Exception {
                return "fail".equals(loginEvent.getLoginState());
            }
        }).next("secondFail").where(new SimpleCondition<LoginEvent>() {
            @Override
            public boolean filter(LoginEvent loginEvent) throws Exception {
                return "fail".equals(loginEvent.getLoginState());
            }
        }).within(Time.seconds(2)); //2s内
        //将匹配模式应用到数据流上
        PatternStream<LoginEvent> patternStream = CEP.pattern(logEventStream.keyBy(LoginEvent::getUserId), loginFailPattern);
        //检出符合匹配条件的复杂事件 进行转换处理 得到报警信息
        SingleOutputStreamOperator<LoginFailWarning> warningStream = patternStream.select(new LoginFailMatchDetectWarning());
        warningStream.print();
        env.execute("login fail detect with cep job");
    }
    //实现自定义的pattern select function
    public static class LoginFailMatchDetectWarning implements PatternSelectFunction<LoginEvent, LoginFailWarning>{
        @Override
        public LoginFailWarning select(Map<String, List<LoginEvent>> map) throws Exception {
            LoginEvent firstFailEvent = map.get("firstFail").get(0);
            LoginEvent secondFailEvent = map.get("secondFail").get(0);
            return new LoginFailWarning(firstFailEvent.getUserId(),firstFailEvent.getTimestamp(),secondFailEvent.getTimestamp(),"login fail 2 times");
        }
    }
}

题目二：如果是检测连续三条失败，那么我们还要继续编写组合模式的条件，这样比较麻烦，我们利用CEP循环模式进行实现

package Project;
 
import Beans.LoginEvent;
import Beans.LoginFailWarning;
import org.apache.flink.cep.CEP;
import org.apache.flink.cep.PatternSelectFunction;
import org.apache.flink.cep.PatternStream;
import org.apache.flink.cep.pattern.Pattern;
import org.apache.flink.cep.pattern.conditions.SimpleCondition;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
 
import java.net.URL;
import java.util.List;
import java.util.Map;
 
public class LoginFailWithCep {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
 
        //读取数据
        URL resource = LoginFailWithCep.class.getResource("/LoginLog.csv");
        DataStream<LoginEvent> logEventStream = env.readTextFile(resource.getPath())
                .map(line -> {
                    String[] fields = line.split(",");
                    return new LoginEvent(new Long(fields[0]), fields[1], fields[2], new Long(fields[3]));
                })
                .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<LoginEvent>(Time.seconds(3)) {
                    @Override
                    public long extractTimestamp(LoginEvent loginEvent) {
                        return loginEvent.getTimestamp() * 1000;
                    }
                });
        //定义匹配模式
        //匹配第一次失败与第二次失败在2s内
        Pattern<LoginEvent, LoginEvent> loginFailPattern = Pattern.<LoginEvent>begin("failEvents")
                .where(new SimpleCondition<LoginEvent>() {
            @Override
            public boolean filter(LoginEvent loginEvent) throws Exception {
                return "fail".equals(loginEvent.getLoginState());
            }})
                //.times(3) //连续三次  非严格近邻（只要后边有就算）
                .times(3).consecutive() //必须严格近邻
                .within(Time.seconds(5));
 
        //将匹配模式应用到数据流上
        PatternStream<LoginEvent> patternStream = CEP.pattern(logEventStream.keyBy(LoginEvent::getUserId), loginFailPattern);
        //检出符合匹配条件的复杂事件 进行转换处理 得到报警信息
        SingleOutputStreamOperator<LoginFailWarning> warningStream = patternStream.select(new LoginFailMatchDetectWarning());
        warningStream.print();
        env.execute("login fail detect with cep job");
    }
    //实现自定义的pattern select function
    public static class LoginFailMatchDetectWarning implements PatternSelectFunction<LoginEvent, LoginFailWarning>{
        @Override
        public LoginFailWarning select(Map<String, List<LoginEvent>> map) throws Exception {
//            LoginEvent firstFailEvent = map.get("firstFail").get(0);
//            LoginEvent secondFailEvent = map.get("secondFail").get(0);
//            return new LoginFailWarning(firstFailEvent.getUserId(),firstFailEvent.getTimestamp(),secondFailEvent.getTimestamp(),"login fail 2 times");
            LoginEvent firstFailEvent = map.get("failEvents").get(0);
            LoginEvent lastFailEvent = map.get("failEvents").get(map.size()-1);
            return new LoginFailWarning(firstFailEvent.getUserId(),firstFailEvent.getTimestamp(),lastFailEvent.getTimestamp(),"login fail 3 times");
        }
    }
}

 

原文链接：https://blog.csdn.net/qq_40771567/article/details/117109794