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我必须在给定时间内在 3 个具有相同相关 ID 的 kafka 源流中收集 3 个事件，并且如果它们迟到，则能够收集所有或部分这些事件。

我在 3 DataStream 和 CEP 模式上使用了联合。但我注意到，与模式匹配良好并因此在选择函数中收集的事件也会在超时函数中发送，一旦达到超时。

我不知道我在示例中做错了什么，或者我不明白什么，但我期待正匹配的事件也不会超时。

我得到的印象是存储了不相交的时间快照。

我正在使用 1.3.0 Flink 版本。

谢谢您的帮助。

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：
Key---0b3c116e-0703-43cb-8b3e-54b0b5e93948
Key---f969dd4d-47ff-445c-9182-0f95a569febb
Key---2ecbb89d-1463-4669-a657-555f73b6fb1d

超时事件：

第一次调用超时函数：
Key---f969dd4d-47ff-445c-9182-0f95a569febb
Key---0b3c116e-0703-43cb-8b3e-54b0b5e93948

第二次调用：
Key---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();
        }
    }
}
4

2 回答 2

3

让我们分析一下你的模式说了什么。您正在传递一个模式，例如：

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

它确实说，搜索三个在 5 秒内发生的任何事件的序列。现在 flink 开始在每个后续事件中搜索新的匹配项（目前正在进行引入新的工作，MatchingBehaviours请参阅FLINK-7169）。

所以举个简单的例子。如果你有一个A B C D E5 秒内的序列。CEP 库将返回结果：

  • 美国广播公司
  • BCD
  • CDE

和两个超时：

  • 德
  • D
于 2017-08-07T11:47:28.280 回答
0

你的程序....

在您的程序中按时间选择 文本 ，因此您将 PatterStream 对象传递给 BOTH Function.No 需要时间来选择字符串...您不要使用 PatternTimeOutFunction()。

见这里，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");
    }
}
于 2017-08-07T11:49:32.723 回答