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大数据（9g）FlinkCEP-创新互联

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

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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

881.14.62.121.18.24org.apache.flinkflink-java${flink.version}org.apache.flinkflink-streaming-java_${scala.binary.version}${flink.version}org.apache.flinkflink-clients_${scala.binary.version}${flink.version}org.apache.flinkflink-runtime-web_${scala.binary.version}${flink.version}org.apache.flinkflink-cep_${scala.binary.version}${flink.version}
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
        SingleOutputStreamOperatord = env.fromElements("c", "a", "a", "a", "a", "b", "a", "a")
                .assignTimestampsAndWatermarks(WatermarkStrategy.forMonotonousTimestamps()
                        .withTimestampAssigner((element, recordTimestamp) ->1L));
        //定义模式
        Patternp = Pattern
                .begin("first")
                .where(new SimpleCondition() {@Override
                    public boolean filter(String value) {return value.equals("a");
                    }
                })
                .next("second")
                .where(new SimpleCondition() {@Override
                    public boolean filter(String value) {return value.equals("a");
                    }
                })
                .next("third")
                .where(new SimpleCondition() {@Override
                    public boolean filter(String value) {return value.equals("a");
                    }
                });
        //在流上匹配模型
        PatternStreampatternStream = CEP.pattern(d, p);
        //使用select方法将匹配到的事件流取出
        patternStream.select((PatternSelectFunction) 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>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.>forMonotonousTimestamps()
                        .withTimestampAssigner((element, recordTimestamp) ->element.f1));
        //定义模式
        Pattern, Tuple2>p = Pattern
                .>begin("=a")
                .where(new SimpleCondition>() {@Override
                    public boolean filter(Tuple2value) {return value.f0.equals("a");
                    }
                })
                .times(3)
                .consecutive(); //严格连续
        //在流上匹配模型
        PatternStream>patternStream = CEP.pattern(d, p);
        //使用select方法将匹配到的事件流取出
        patternStream.select((PatternSelectFunction, 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>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();
    }
}

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