为什么patternstream的相同事件可以同时发送到patternselectfunction和patterntimeoutfunction？

qvsjd97n  于 2021-06-25  发布在  Flink
关注(0)|答案(2)|浏览(366)

在给定的时间内，我必须在3个具有相同correlationid的kafka源流中收集3个事件，如果这些事件延迟到达，则能够收集全部或部分事件。
我在3数据流上使用了union和cep模式。但我注意到，与模式匹配良好的事件，因此在select函数中收集的事件，在达到超时时也会在timeout函数中发送。
我不知道在我的例子中我做错了什么，或者我不明白什么，但是我期望正匹配的事件也不会超时。
我得到的印象是不相交的时间快照存储。
我使用的是1.3.0版本的flink。
谢谢你的帮助。
控制台输出，我们可以看到3个相关事件中的2个被选中并超时：
匹配事件：
钥匙---0b3c116e-0703-43cb-8b3e-54b0b5e93948
钥匙---f969dd4d-47ff-445c-9182-0f95a569febb
钥匙---2ecbb89d-1463-4669-a657-555f73b6fb1d
超时事件：
第一次调用超时函数：
钥匙---f969dd4d-47ff-445c-9182-0f95a569febb
钥匙---0b3c116e-0703-43cb-8b3e-54b0b5e93948
第二个电话：
钥匙---f969dd4d-47ff-445c-9182-0f95a569febb

11:01:44,677 INFO  com.bnpp.pe.cep.Main                                          - Matching events:
11:01:44,678 INFO  com.bnpp.pe.cep.Main                                          - SctRequestProcessStep2Event(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---0b3c116e-0703-43cb-8b3e-54b0b5e93948, debtorIban=BE42063929068055, creditorIban=BE42063929068056, amount=100.0, communication=test), succeeded=false)
11:01:44,678 INFO  com.bnpp.pe.cep.Main                                          - SctRequestProcessStep1Event(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---2ecbb89d-1463-4669-a657-555f73b6fb1d, debtorIban=BE42063929068055, creditorIban=BE42063929068056, amount=100.0, communication=test), succeeded=false)
11:01:44,678 INFO  com.bnpp.pe.cep.Main                                          - SctRequestProcessStep3Event(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---f969dd4d-47ff-445c-9182-0f95a569febb, debtorIban=BE42063929068055, creditorIban=BE42063929068056, amount=100.0, communication=test), succeeded=false)
Right(SctRequestFinalEvent(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---2196fdb0-01e8-4cc6-af4b-04bcf9dc67a2, debtorIban=null, creditorIban=null, amount=null, communication=null), state=SUCCESS))
11:01:49,635 INFO  com.bnpp.pe.cep.Main                                          - Timed out events:
11:01:49,636 INFO  com.bnpp.pe.cep.Main                                          - SctRequestProcessStep3Event(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---f969dd4d-47ff-445c-9182-0f95a569febb, debtorIban=BE42063929068055, creditorIban=BE42063929068056, amount=100.0, communication=test), succeeded=false)
11:01:49,636 INFO  com.bnpp.pe.cep.Main                                          - SctRequestProcessStep2Event(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---0b3c116e-0703-43cb-8b3e-54b0b5e93948, debtorIban=BE42063929068055, creditorIban=BE42063929068056, amount=100.0, communication=test), succeeded=false)
11:01:49,636 INFO  com.bnpp.pe.cep.Main                                          - Timed out events:
11:01:49,636 INFO  com.bnpp.pe.cep.Main                                          - SctRequestProcessStep3Event(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---f969dd4d-47ff-445c-9182-0f95a569febb, debtorIban=BE42063929068055, creditorIban=BE42063929068056, amount=100.0, communication=test), succeeded=false)
Left(SctRequestFinalEvent(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---aa437bcf-ecaa-4561-9f4e-08a902f0e248, debtorIban=null, creditorIban=null, amount=null, communication=null), state=FAILED))
Left(SctRequestFinalEvent(super=SctRequestEvent(correlationId=cId---a14a4e23-56c5-4242-9c43-d465d2b84454, key=Key---5420eb41-2723-42ac-83fd-d203d6bf2526, debtorIban=null, creditorIban=null, amount=null, communication=null), state=FAILED))

我的测试代码：

package com.bnpp.pe.cep;

import com.bnpp.pe.event.Event;
import com.bnpp.pe.event.SctRequestFinalEvent;
import com.bnpp.pe.util.EventHelper;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.cep.CEP;
import org.apache.flink.cep.PatternSelectFunction;
import org.apache.flink.cep.PatternStream;
import org.apache.flink.cep.PatternTimeoutFunction;
import org.apache.flink.cep.pattern.Pattern;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import org.apache.flink.streaming.util.serialization.DeserializationSchema;

import java.io.Serializable;
import java.util.List;
import java.util.Map;
import java.util.Properties;

/**
 * Created by Laurent Bauchau on 2/08/2017.
 */
@Slf4j
public class Main implements Serializable {

    public static void main(String... args) {
        new Main();
    }

    public static final String step1Topic = "sctinst-step1";
    public static final String step2Topic = "sctinst-step2";
    public static final String step3Topic = "sctinst-step3";

    private static final String PATTERN_NAME = "the_3_correlated_events_pattern";

    private final FlinkKafkaConsumer010<Event> kafkaSource1;
    private final DeserializationSchema<Event> deserializationSchema1;

    private final FlinkKafkaConsumer010<Event> kafkaSource2;
    private final DeserializationSchema<Event> deserializationSchema2;

    private final FlinkKafkaConsumer010<Event> kafkaSource3;
    private final DeserializationSchema<Event> deserializationSchema3;

    private Main() {

        // Kafka init
        Properties kafkaProperties = new Properties();
        kafkaProperties.setProperty("bootstrap.servers", "localhost:9092");
        kafkaProperties.setProperty("zookeeper.connect", "localhost:2180");
        kafkaProperties.setProperty("group.id", "sct-validation-cgroup1");

        deserializationSchema1 = new SctRequestProcessStep1EventDeserializer();
        kafkaSource1 = new FlinkKafkaConsumer010<>(step1Topic, deserializationSchema1, kafkaProperties);

        deserializationSchema2 = new SctRequestProcessStep2EventDeserializer();
        kafkaSource2 = new FlinkKafkaConsumer010<>(step2Topic, deserializationSchema2, kafkaProperties);

        deserializationSchema3 = new SctRequestProcessStep3EventDeserializer();
        kafkaSource3 = new FlinkKafkaConsumer010<>(step3Topic, deserializationSchema3, kafkaProperties);

        try {
            StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
            env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);

            DataStream<Event> s1 = env.addSource(kafkaSource1);
            DataStream<Event> s2 = env.addSource(kafkaSource2);
            DataStream<Event> s3 = env.addSource(kafkaSource3);

            DataStream<Event> unionStream = s1.union(s2, s3);

            Pattern successPattern = Pattern.<Event>begin(PATTERN_NAME)
                    .times(3)
                    .within(Time.seconds(5));

            PatternStream<Event> matchingStream = CEP.pattern(
                    unionStream.keyBy(new CIDKeySelector()),
                    successPattern);

            matchingStream.select(new MyPatternTimeoutFunction(), new MyPatternSelectFunction())
                    .print()
                    .setParallelism(1);

            env.execute();

        } catch (Exception e) {
            log.error(e.getMessage(), e);
        }
    }

    private static class MyPatternTimeoutFunction implements PatternTimeoutFunction<Event, SctRequestFinalEvent> {

        @Override
        public SctRequestFinalEvent timeout(Map<String, List<Event>> pattern, long timeoutTimestamp) throws Exception {

            List<Event> events = pattern.get(PATTERN_NAME);
            log.info("Timed out events:");
            events.forEach(e -> log.info(e.toString()));

            // Resulting event creation
            SctRequestFinalEvent event = new SctRequestFinalEvent();
            EventHelper.correlate(events.get(0), event);
            EventHelper.injectKey(event);
            event.setState(SctRequestFinalEvent.State.FAILED);

            return event;
        }
    }

    private static class MyPatternSelectFunction
            implements PatternSelectFunction<Event, SctRequestFinalEvent> {

        @Override
        public SctRequestFinalEvent select(Map<String, List<Event>> pattern) throws Exception {

            List<Event> events = pattern.get(PATTERN_NAME);
            log.info("Matching events:");
            events.forEach(e -> log.info(e.toString()));

            // Resulting event creation
            SctRequestFinalEvent event = new SctRequestFinalEvent();
            EventHelper.correlate(events.get(0), event);
            EventHelper.injectKey(event);
            event.setState(SctRequestFinalEvent.State.SUCCESS);

            return event;
        }
    }

    private static class CIDKeySelector implements KeySelector<Event, String> {
        @Override
        public String getKey(Event event) throws Exception {
            return event.getCorrelationId();
        }
    }
}
apache-flinkflink-cep

2条答案

按热度按时间
ma8fv8wu

ma8fv8wu1#

你的程序。。。。
在您的程序中，按时间选择文本，这样您就可以将patterstream对象传递给这两个函数。不需要时间来选择字符串…您不需要使用patterntimeoutfunction（）。
看这里，没有时间因素。

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.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;

import java.util.Map;

public class FlinkCEP {

    public static void main(String[] args) throws Exception {

        // set up the execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        DataStream<String> text = env.socketTextStream("localhost", 1111)
                .flatMap(new LineTokenizer());

        text.print();

        Pattern<String, String> pattern =
                Pattern.<String>begin("start").where(txt -> txt.equals("a"))
                       .next("middle").where(txt -> txt.equals("b"))
                       .followedBy("end").where(txt -> txt.equals("c")).within(Time.seconds(1));

        PatternStream<String> patternStream = CEP.pattern(text, pattern);

        DataStream<String> alerts = patternStream.select(new PatternSelectFunction<String, String>() {
            @Override
            public String select(Map<String, String> matches) throws Exception {
                return "Found: " +
                        matches.get("start") + "->" +
                        matches.get("middle") + "->" +
                        matches.get("end");
            }
        });

        // emit result
        alerts.print();

        // execute program
        env.execute("WordCount Example");
    }
}
赞(0）分享  回复(0）举报  2021-06-25
zvokhttg

zvokhttg2#

让我们来分析一下你的模式是怎么说的。您传递的模式如下：

Pattern.<Event>begin(PATTERN_NAME)
    .times(3)
    .within(Time.seconds(5));

也就是说，搜索5秒内发生的任何事件的三个序列。现在，Flink开始寻找新的匹配每个后续事件（有正在进行的工作，以引进新的） MatchingBehaviours 见flink-7169）。
举个简单的例子。如果你有这样的序列 A B C D E 5秒内。cep库将返回结果：
a、b、c
b、c、d
c d e公司
两个时间间隔：
东德
d

赞(0）分享  回复(0）举报  2021-06-25
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