大数据（9g）FlinkCEP

Posted 2022-12-15 小基基o_O

文章目录

• 概述
• 示例代码
• • 环境和依赖
  • Java代码
  • • 上面代码可改成下面

概述

• CEP
  Complex Event Processing：复合事件处理
  通过分析事件间的关系，从事件流中查询出符合要求的事件序列
• 例如【切菜=>洗菜=>炒菜】3个事件按时间序串联，是正常的事件流
  当发现【切菜=>炒菜】忽略洗菜的事件流，可认为是异常事件

示例代码

环境和依赖

WIN10+JDK1.8+IDEA2021+Maven3.6.3
CEP额外依赖为flink-cep

<properties>
    <maven.compiler.source>8</maven.compiler.source>
    <maven.compiler.target>8</maven.compiler.target>
    <flink.version>1.14.6</flink.version>
    <scala.binary.version>2.12</scala.binary.version>
    <lombok.version>1.18.24</lombok.version>
</properties>
<!-- https://mvnrepository.com/ -->
<dependencies>
    <!-- Flink -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-java</artifactId>
        <version>$flink.version</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_$scala.binary.version</artifactId>
        <version>$flink.version</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-clients_$scala.binary.version</artifactId>
        <version>$flink.version</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-runtime-web_$scala.binary.version</artifactId>
        <version>$flink.version</version>
    </dependency>
    <!-- FlinkCEP -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-cep_$scala.binary.version</artifactId>
        <version>$flink.version</version>
    </dependency>
</dependencies>

Java代码

监测 严格近邻的连续三次a的事件流

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
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.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class CepPractice 
    public static void main(String[] args) throws Exception 
        //创建环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment().setParallelism(1);
        //添加数据源，确定水位线策略
        SingleOutputStreamOperator<String> d = env.fromElements("c", "a", "a", "a", "a", "b", "a", "a")
                .assignTimestampsAndWatermarks(WatermarkStrategy.<String>forMonotonousTimestamps()
                        .withTimestampAssigner((element, recordTimestamp) -> 1L));
        //定义模式
        Pattern<String, String> p = Pattern
                .<String>begin("first")
                .where(new SimpleCondition<String>() 
                    @Override
                    public boolean filter(String value) 
                        return value.equals("a");
                    
                )
                .next("second")
                .where(new SimpleCondition<String>() 
                    @Override
                    public boolean filter(String value) 
                        return value.equals("a");
                    
                )
                .next("third")
                .where(new SimpleCondition<String>() 
                    @Override
                    public boolean filter(String value) 
                        return value.equals("a");
                    
                );
        //在流上匹配模型
        PatternStream<String> patternStream = CEP.pattern(d, p);
        //使用select方法将匹配到的事件流取出
        patternStream.select((PatternSelectFunction<String, String>) map -> 
            //Map的key是事件名称（上面的first、second和third）
            //Map的key对应的value是列表，储存匹配到的事件
            String first = map.get("first").toString();
            String second = map.get("second").toString();
            String third = map.get("third").toString();
            return first + "->" + second + "->" + third;
        ).print();
        //执行
        env.execute();
    

打印结果

[a]->[a]->[a]
[a]->[a]->[a]

上面代码可改成下面

留意.times(3).consecutive()

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.java.tuple.Tuple2;
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.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import java.util.List;

public class CepPractice2 
    public static void main(String[] args) throws Exception 
        //创建环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment().setParallelism(1);
        //添加数据源，确定水位线策略
        SingleOutputStreamOperator<Tuple2<String, Long>> d = env.fromElements(
                Tuple2.of("a", 1000L), Tuple2.of("a", 2000L), Tuple2.of("a", 3000L),
                Tuple2.of("a", 4000L), Tuple2.of("b", 5000L), Tuple2.of("a", 6000L))
                .assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple2<String, Long>>forMonotonousTimestamps()
                        .withTimestampAssigner((element, recordTimestamp) -> element.f1));
        //定义模式
        Pattern<Tuple2<String, Long>, Tuple2<String, Long>> p = Pattern
                .<Tuple2<String, Long>>begin("=a")
                .where(new SimpleCondition<Tuple2<String, Long>>() 
                    @Override
                    public boolean filter(Tuple2<String, Long> value) 
                        return value.f0.equals("a");
                    
                )
                .times(3)
                .consecutive(); //严格连续
        //在流上匹配模型
        PatternStream<Tuple2<String, Long>> patternStream = CEP.pattern(d, p);
        //使用select方法将匹配到的事件流取出
        patternStream.select((PatternSelectFunction<Tuple2<String, Long>, String>) map -> 
            //Map的key是事件名称（上面的first、second和third）
            //Map的key对应的value是列表，储存匹配到的事件
            List<Tuple2<String, Long>> ls = map.get("=a");
            String first = ls.get(0).f0;
            String second = ls.get(1).f0;
            String third = ls.get(2).f0;
            return String.join("=>", first, second, third);
        ).print();
        //执行
        env.execute();