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Flink CEP（三）pattern动态更新

news 2025/7/19 20:03:21

线上运行的CEP中肯定经常遇到规则变更的情况，如果每次变更时都将任务重启、重新发布是非常不优雅的。尤其在营销或者风控这种对实时性要求比较高的场景，如果规则窗口过长（一两个星期），状态过大，就会导致重启时间延长，期间就会造成一些想要处理的异常行为不能及时发现。

1.实现分析

外部加载：通常规则引擎会有专门的规则管理模块，提供用户去创建自己的规则，对于Flink任务来说需要到外部去加载规则
动态更新：需要提供定时去检测规则是否变更
历史状态清理：在模式匹配中是一系列NFAState 的不断变更，如果规则发生变更，需要清理历史状态
API：需要对外提供易用的API

2.代码实现

首先实现一个用户API。

package cep.functions;import java.io.Serializable;import org.apache.flink.api.common.functions.Function;import cep.pattern.Pattern;/*** @author StephenYou* Created on 2023-07-23* Description: 动态Pattern接口（用户调用API）不区分key*/
public interface DynamicPatternFunction<T> extends Function, Serializable {/**** 初始化* @throws Exception*/public void init() throws Exception;/*** 注入新的pattern* @return*/public Pattern<T,T> inject() throws Exception;/*** 一个扫描周期:ms* @return*/public long getPeriod() throws Exception;/*** 规则是否发生变更* @return*/public boolean isChanged() throws Exception;
}

希望上述API的调用方式如下。

//正常调用CEP.pattern(dataStream,pattern);//动态PatternCEP.injectionPattern(dataStream, new UserDynamicPatternFunction())

所以需要修改CEP-Lib源码

b.增加injectionPattern函数。

public class CEP {/**** Dynamic injection pattern function * @param input* @param dynamicPatternFunction* @return* @param <T>*/public static <T> PatternStream<T> injectionPattern throws Exception (DataStream<T> input,DynamicPatternFunction<T> dynamicPatternFunction){return new PatternStream<>(input, dynamicPatternFunction); }
}

增加PatternStream构造函数，因为需要动态更新，所以有必要传进去整个函数。

public class PatternStream<T> {PatternStream(final DataStream<T> inputStream, DynamicPatternFunction<T> dynamicPatternFunction) throws Exception {this(PatternStreamBuilder.forStreamAndPatternFunction(inputStream, dynamicPatternFunction));}
}

修改PatternStreamBuilder.build, 增加调用函数的过程。

        final CepOperator<IN, K, OUT> operator = null;if (patternFunction == null ) {operator = new CepOperator<>(inputSerializer,isProcessingTime,nfaFactory,comparator,pattern.getAfterMatchSkipStrategy(),processFunction,lateDataOutputTag);} else {operator = new CepOperator<>(inputSerializer,isProcessingTime,patternFunction,comparator,null,processFunction,lateDataOutputTag);}

增加对应的CepOperator构造函数。

    public CepOperator(final TypeSerializer<IN> inputSerializer,final boolean isProcessingTime,final DynamicPatternFunction patternFunction,@Nullable final EventComparator<IN> comparator,@Nullable final AfterMatchSkipStrategy afterMatchSkipStrategy,final PatternProcessFunction<IN, OUT> function,@Nullable final OutputTag<IN> lateDataOutputTag) {super(function);this.inputSerializer = Preconditions.checkNotNull(inputSerializer);this.patternFunction = patternFunction;this.isProcessingTime = isProcessingTime;this.comparator = comparator;this.lateDataOutputTag = lateDataOutputTag;if (afterMatchSkipStrategy == null) {this.afterMatchSkipStrategy = AfterMatchSkipStrategy.noSkip();} else {this.afterMatchSkipStrategy = afterMatchSkipStrategy;}this.nfaFactory = null;}

加载Pattern，构造NFA

    @Overridepublic void open() throws Exception {super.open();timerService =getInternalTimerService("watermark-callbacks", VoidNamespaceSerializer.INSTANCE, this);//初始化if (patternFunction != null) {patternFunction.init();Pattern pattern = patternFunction.inject();afterMatchSkipStrategy = pattern.getAfterMatchSkipStrategy();boolean timeoutHandling = getUserFunction() instanceof TimedOutPartialMatchHandler;nfaFactory = NFACompiler.compileFactory(pattern, timeoutHandling);long period = patternFunction.getPeriod();// 注册定时器检测规则是否变更if (period > 0) {getProcessingTimeService().registerTimer(timerService.currentProcessingTime() + period, this::onProcessingTime);}}nfa = nfaFactory.createNFA();nfa.open(cepRuntimeContext, new Configuration());context = new ContextFunctionImpl();collector = new TimestampedCollector<>(output);cepTimerService = new TimerServiceImpl();// metricsthis.numLateRecordsDropped = metrics.counter(LATE_ELEMENTS_DROPPED_METRIC_NAME);}

状态清理一共分为两块: 匹配状态数据清理、定时器清理；

进行状态清理:

    @Overridepublic void processElement(StreamRecord<IN> element) throws Exception {if (patternFunction != null) {// 规则版本更新if (needRefresh.value() < refreshVersion.get()) {//清除状态computationStates.clear();elementQueueState.clear();partialMatches.releaseCacheStatisticsTimer();//清除定时器Iterable<Long> registerTime = registerTimeState.get();if (registerTime != null) {Iterator<Long> iterator = registerTime.iterator();while (iterator.hasNext()) {Long l = iterator.next();//删除定时器timerService.deleteEventTimeTimer(VoidNamespace.INSTANCE, l);timerService.deleteProcessingTimeTimer(VoidNamespace.INSTANCE, l);//状态清理iterator.remove();}}//更新当前的版本needRefresh.update(refreshVersion.get());}}
}

上面是在处理每条数据时，清除状态和版本。接下来要进行状态和版本的初始化。

    @Overridepublic void initializeState(StateInitializationContext context) throws Exception {super.initializeState(context);//初始化状态if (patternFunction != null) {/*** 两个标识位状态*/refreshFlagState = context.getOperatorStateStore().getUnionListState(new ListStateDescriptor<Integer>("refreshFlagState", Integer.class));if (context.isRestored()) {if (refreshFlagState.get().iterator().hasNext()) {refreshVersion = new AtomicInteger(refreshFlagState.get().iterator().next());}} else {refreshVersion = new AtomicInteger(0);}needRefresh = context.getKeyedStateStore().getState(new ValueStateDescriptor<Integer>("needRefreshState", Integer.class, 0));}
}

3.测试验证

设置每10s变更一次Pattern。

 PatternStream patternStream = CEP.injectionPattern(source, new TestDynamicPatternFunction());patternStream.select(new PatternSelectFunction<Tuple3<String, Long, String>, Map>() {@Overridepublic Map select(Map map) throws Exception {map.put("processingTime", System.currentTimeMillis());return map;}}).print();env.execute("SyCep");}public static class TestDynamicPatternFunction implements DynamicPatternFunction<Tuple3<String, Long, String>> {public TestDynamicPatternFunction() {this.flag = true;}boolean flag;int time = 0;@Overridepublic void init() throws Exception {flag = true;}@Overridepublic Pattern<Tuple3<String, Long, String>, Tuple3<String, Long, String>> inject()throws Exception {// 2种patternif (flag) {Pattern pattern = Pattern.<Tuple3<String, Long, String>>begin("start").where(new IterativeCondition<Tuple3<String, Long, String>>() {@Overridepublic boolean filter(Tuple3<String, Long, String> value,Context<Tuple3<String, Long, String>> ctx) throws Exception {return value.f2.equals("success");}}).times(1).followedBy("middle").where(new IterativeCondition<Tuple3<String, Long, String>>() {@Overridepublic boolean filter(Tuple3<String, Long, String> value,Context<Tuple3<String, Long, String>> ctx) throws Exception {return value.f2.equals("fail");}}).times(1).next("end");return pattern;} else {Pattern pattern = Pattern.<Tuple3<String, Long, String>>begin("start2").where(new IterativeCondition<Tuple3<String, Long, String>>() {@Overridepublic boolean filter(Tuple3<String, Long, String> value,Context<Tuple3<String, Long, String>> ctx) throws Exception {return value.f2.equals("success2");}}).times(2).next("middle2").where(new IterativeCondition<Tuple3<String, Long, String>>() {@Overridepublic boolean filter(Tuple3<String, Long, String> value,Context<Tuple3<String, Long, String>> ctx) throws Exception {return value.f2.equals("fail2");}}).times(2).next("end2");return pattern;}}@Overridepublic long getPeriod() throws Exception {return 10000;}@Overridepublic boolean isChanged() throws Exception {flag = !flag ;time += getPeriod();System.out.println("change pattern : " + time);return true;}}

打印结果：符合预期