聊聊reactive streams的processors

时间 2019-12-05

标签聊聊 reactive streams processors 繁體版

原文原文链接

序

本文主要研究一下reactive streams的processorsjava

processors分类

processors既是Publisher也是Subscriber。在project reactor中processor有诸多实现，他们的分类大体以下：react

direct(DirectProcessor以及UnicastProcessor)
synchronous(EmitterProcessor及ReplayProcessor)
asynchronous(TopicProcessor及WorkQueueProcessor)

direct

DirectProcessor

它不支持backpressure特性，若是publisher发布了N个数据，若是其中一个subscriber请求数<N，则抛出IllegalStateException.缓存

@Test
    public void testDirectProcessor(){
        DirectProcessor<Integer> directProcessor = DirectProcessor.create();
        Flux<Integer> flux = directProcessor
                .filter(e -> e % 2 == 0)
                .map(e -> e +1);
        flux.subscribe(new Subscriber<Integer>() {
            private Subscription s;
            @Override
            public void onSubscribe(Subscription s) {
                this.s = s;
//                s.request(2);
            }

            @Override
            public void onNext(Integer integer) {
                LOGGER.info("subscribe:{}",integer);
            }

            @Override
            public void onError(Throwable t) {
                LOGGER.error(t.getMessage(),t);
            }

            @Override
            public void onComplete() {

            }
        });

        IntStream.range(1,20)
                .forEach(e -> {
                    directProcessor.onNext(e);
                });

        directProcessor.onComplete();
        directProcessor.blockLast();
    }

输出以下数据结构

16:00:11.201 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
16:00:11.216 [main] ERROR com.example.demo.ProcessorTest - Can't deliver value due to lack of requests
reactor.core.Exceptions$OverflowException: Can't deliver value due to lack of requests
    at reactor.core.Exceptions.failWithOverflow(Exceptions.java:215)
    at reactor.core.publisher.DirectProcessor$DirectInner.onNext(DirectProcessor.java:304)
    at reactor.core.publisher.DirectProcessor.onNext(DirectProcessor.java:106)
    at com.example.demo.ProcessorTest.lambda$testDirectProcessor$5(ProcessorTest.java:82)
    at java.util.stream.Streams$RangeIntSpliterator.forEachRemaining(Streams.java:110)
    at java.util.stream.IntPipeline$Head.forEach(IntPipeline.java:557)
    at com.example.demo.ProcessorTest.testDirectProcessor(ProcessorTest.java:81)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:50)
    at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12)
    at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:47)
    at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17)
    at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:325)
    at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:78)
    at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:57)
    at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290)
    at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71)
    at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288)
    at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58)
    at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268)
    at org.junit.runners.ParentRunner.run(ParentRunner.java:363)
    at org.junit.runner.JUnitCore.run(JUnitCore.java:137)
    at com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs(JUnit4IdeaTestRunner.java:69)
    at com.intellij.rt.execution.junit.JUnitStarter.prepareStreamsAndStart(JUnitStarter.java:234)
    at com.intellij.rt.execution.junit.JUnitStarter.main(JUnitStarter.java:74)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at com.intellij.rt.execution.application.AppMain.main(AppMain.java:144)

UnicastProcessor

支持backpressure特性，可是代价是至多只能有一个subscriber。默认是无界的，若是发布数据以后，subscriber还没来得及request，则它会把数据缓存下来。
若是设置了一个有界的queue，当buffer满并且subscriber没有发送足够多的request的时候，processor会拒绝推送数据。在这种场景下，processor内置了一个callback，每当一个element被rejected的时候会触发.

@Test
    public void testUnicastProcessor() throws InterruptedException {
        UnicastProcessor<Integer> unicastProcessor = UnicastProcessor.create(Queues.<Integer>get(8).get());
        Flux<Integer> flux = unicastProcessor
                .map(e -> e)
                .doOnError(e -> {
                    LOGGER.error(e.getMessage(),e);
                });

        IntStream.rangeClosed(1,12)
                .forEach(e -> {
                    LOGGER.info("emit:{}",e);
                    unicastProcessor.onNext(e);
                    try {
                        TimeUnit.SECONDS.sleep(1);
                    } catch (InterruptedException e1) {
                        e1.printStackTrace();
                    }
                });
        LOGGER.info("begin to sleep 7 seconds");
        TimeUnit.SECONDS.sleep(7);
        //UnicastProcessor allows only a single Subscriber
        flux.subscribe(e -> {
            LOGGER.info("flux subscriber:{}",e);
        });

        unicastProcessor.onComplete();
        TimeUnit.SECONDS.sleep(10);
//        unicastProcessor.blockLast(); //blockLast也是一个subscriber
    }

输出实例多线程

16:31:04.970 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
16:31:04.977 [main] INFO com.example.demo.ProcessorTest - emit:1
16:31:05.990 [main] INFO com.example.demo.ProcessorTest - emit:2
16:31:06.991 [main] INFO com.example.demo.ProcessorTest - emit:3
16:31:07.994 [main] INFO com.example.demo.ProcessorTest - emit:4
16:31:08.998 [main] INFO com.example.demo.ProcessorTest - emit:5
16:31:10.002 [main] INFO com.example.demo.ProcessorTest - emit:6
16:31:11.007 [main] INFO com.example.demo.ProcessorTest - emit:7
16:31:12.010 [main] INFO com.example.demo.ProcessorTest - emit:8
16:31:13.014 [main] INFO com.example.demo.ProcessorTest - emit:9
16:31:14.029 [main] INFO com.example.demo.ProcessorTest - emit:10
16:31:14.030 [main] DEBUG reactor.core.publisher.Operators - onNextDropped: 10
16:31:15.034 [main] INFO com.example.demo.ProcessorTest - emit:11
16:31:15.034 [main] DEBUG reactor.core.publisher.Operators - onNextDropped: 11
16:31:16.038 [main] INFO com.example.demo.ProcessorTest - emit:12
16:31:16.038 [main] DEBUG reactor.core.publisher.Operators - onNextDropped: 12
16:31:17.043 [main] INFO com.example.demo.ProcessorTest - begin to sleep 7 seconds
16:31:24.053 [main] INFO com.example.demo.ProcessorTest - flux subscriber:1
16:31:24.053 [main] INFO com.example.demo.ProcessorTest - flux subscriber:2
16:31:24.053 [main] INFO com.example.demo.ProcessorTest - flux subscriber:3
16:31:24.053 [main] INFO com.example.demo.ProcessorTest - flux subscriber:4
16:31:24.053 [main] INFO com.example.demo.ProcessorTest - flux subscriber:5
16:31:24.054 [main] INFO com.example.demo.ProcessorTest - flux subscriber:6
16:31:24.054 [main] INFO com.example.demo.ProcessorTest - flux subscriber:7
16:31:24.054 [main] INFO com.example.demo.ProcessorTest - flux subscriber:8
16:31:24.058 [main] ERROR com.example.demo.ProcessorTest - The receiver is overrun by more signals than expected (bounded queue...)
reactor.core.Exceptions$OverflowException: The receiver is overrun by more signals than expected (bounded queue...)
    at reactor.core.Exceptions.failWithOverflow(Exceptions.java:202)
    at reactor.core.publisher.UnicastProcessor.onNext(UnicastProcessor.java:330)
    at com.example.demo.ProcessorTest.lambda$testUnicastProcessor$8(ProcessorTest.java:108)
    at java.util.stream.Streams$RangeIntSpliterator.forEachRemaining(Streams.java:110)
    at java.util.stream.IntPipeline$Head.forEach(IntPipeline.java:557)

synchronous

EmitterProcessor

可以支持多个subscriber，同时还对每一个subscriber支持backpressure。它也能够订阅publisher，而后把数据同步重放。
它有一个bufferSize参数，用来在发布数据以后尚未订阅者期间的数据，onNext会一直阻塞直到数据被消费；当第一个订阅者订阅以后，它会接收到buffer里头的数据，然后续的订阅者就只能消费到自他们订阅那个时候起发布的数据。
当全部的subscriber都取消订阅以后，该processor会清空buffer，并中止接收新的订阅。

@Test
    public void testEmitterProcessor() throws InterruptedException {
        int bufferSize = 3; //小于8的会被重置为8
        FluxProcessor<Integer, Integer> processor = EmitterProcessor.create(bufferSize);
        Flux<Integer> flux1 = processor.map(e -> e);
        Flux<Integer> flux2 = processor.map(e -> e*10);

        IntStream.rangeClosed(1,8).forEach(e -> {
            LOGGER.info("emit:{}",e);
            processor.onNext(e); //若是发布的未消费数据超过bufferSize,则会阻塞在这里
        });

        flux1.subscribe(e -> {
            LOGGER.info("flux1 subscriber:{}",e);
        });

        IntStream.rangeClosed(9,10).forEach(e -> {
            LOGGER.info("emit:{}",e);
            processor.onNext(e);
            try {
                TimeUnit.SECONDS.sleep(1);
            } catch (InterruptedException e1) {
                e1.printStackTrace();
            }
        });

        //这个是后面添加的订阅,只能消费以后发布的数据
        flux2.subscribe(e -> {
            LOGGER.info("flux2 subscriber:{}",e);
        });

        processor.onNext(11);
        processor.onNext(12);

        processor.onComplete();
        processor.blockLast();
    }

输出实例并发

17:27:01.008 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
17:27:01.044 [main] INFO com.example.demo.ProcessorTest - emit:1
17:27:01.084 [main] INFO com.example.demo.ProcessorTest - emit:2
17:27:01.084 [main] INFO com.example.demo.ProcessorTest - emit:3
17:27:01.084 [main] INFO com.example.demo.ProcessorTest - emit:4
17:27:01.084 [main] INFO com.example.demo.ProcessorTest - emit:5
17:27:01.084 [main] INFO com.example.demo.ProcessorTest - emit:6
17:27:01.084 [main] INFO com.example.demo.ProcessorTest - emit:7
17:27:01.084 [main] INFO com.example.demo.ProcessorTest - emit:8
17:27:01.086 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:1
17:27:01.086 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:2
17:27:01.087 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:3
17:27:01.087 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:4
17:27:01.087 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:5
17:27:01.087 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:6
17:27:01.087 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:7
17:27:01.087 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:8
17:27:01.088 [main] INFO com.example.demo.ProcessorTest - emit:9
17:27:01.088 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:9
17:27:02.091 [main] INFO com.example.demo.ProcessorTest - emit:10
17:27:02.092 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:10
17:27:03.096 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:11
17:27:03.096 [main] INFO com.example.demo.ProcessorTest - flux2 subscriber:110
17:27:03.096 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:12
17:27:03.096 [main] INFO com.example.demo.ProcessorTest - flux2 subscriber:120

ReplayProcessor

能够缓存经过sink产生的数据或者订阅publisher的数据，而后重放给后来的订阅者。有以下四种配置app

cacheLast

只缓存最后一个数据

create(int)

缓存最后N个数据

createTimeout(Duration)

对每一个数据打上时间戳标签，只缓存age在指定ttl内的数据

createSizeOrTimeout(int,Duration)

对每一个数据打上时间戳标签，只缓存age在指定ttl内的N个数据

实例异步

@Test
    public void testReplayProcessor() throws InterruptedException {
        ReplayProcessor<Integer> replayProcessor = ReplayProcessor.create(3);
        Flux<Integer> flux1 = replayProcessor
                .map(e -> e);
        Flux<Integer> flux2 = replayProcessor
                .map(e -> e);

        flux1.subscribe(e -> {
            LOGGER.info("flux1 subscriber:{}",e);
        });


        IntStream.rangeClosed(1,5)
                .forEach(e -> {
                    replayProcessor.onNext(e);
                    try {
                        TimeUnit.SECONDS.sleep(1);
                    } catch (InterruptedException e1) {
                        e1.printStackTrace();
                    }
                });

        LOGGER.info("finish publish data");
        TimeUnit.SECONDS.sleep(3);

        LOGGER.info("begin to subscribe flux2");
        flux2.subscribe(e -> {
            LOGGER.info("flux2 subscriber:{}",e);
        });

        replayProcessor.onComplete();
        replayProcessor.blockLast();
    }

输出以下async

15:13:39.415 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
15:13:39.438 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:1
15:13:40.445 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:2
15:13:41.449 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:3
15:13:42.454 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:4
15:13:43.459 [main] INFO com.example.demo.ProcessorTest - flux1 subscriber:5
15:13:44.463 [main] INFO com.example.demo.ProcessorTest - finish publish data
15:13:47.466 [main] INFO com.example.demo.ProcessorTest - begin to subscribe flux2
15:13:47.467 [main] INFO com.example.demo.ProcessorTest - flux2 subscriber:3
15:13:47.467 [main] INFO com.example.demo.ProcessorTest - flux2 subscriber:4
15:13:47.468 [main] INFO com.example.demo.ProcessorTest - flux2 subscriber:5

asynchronous

TopicProcessor

TopicProcessor是一个异步的processor，当shared设置为true的时候，支持对多个publisher的并发重放。若是订阅的publisher是一个并发的stream或者是须要并发调用Topicrocessor的onNext,onCompleete,onError方法，则必须强制开启share。关闭share则是遵循reactive streams规范的processor，不容许并发调用。
TopicProcessor也支持把消息广播(fan-out)到多个subscriber，它给每一个subscriber绑定一个线程。可以支持的subscriber的最大个数由线程池executor限制。
TopicProcessor使用了RingBuffer数据结构来推送数据，每一个subscriber线程都在RingBuffer记录其消费的位置
TopicProcessor也支持autoCancel选项，默认为true，也就是当全部subscriber都取消订阅的时候，publisher也会被自动cannel

@Test
    public void testTopicProcessor() throws InterruptedException {
        TopicProcessor<Integer> topicProcessor = TopicProcessor.<Integer>builder()
                .share(true)
//                .executor(Executors.newSingleThreadExecutor())
                .build();
        Flux<Integer> flux1 = topicProcessor
                .map(e -> e);
        Flux<Integer> flux2 = topicProcessor
                .map(e -> e);
        Flux<Integer> flux3 = topicProcessor
                .map(e -> e);

        AtomicInteger count = new AtomicInteger(0);
        flux1.subscribe(e -> {
            LOGGER.info("flux1 subscriber:{}",e);
            count.incrementAndGet();
        });
        flux2.subscribe(e -> {
            LOGGER.info("flux2 subscriber:{}",e);
        });
        flux3.subscribe(e -> {
            LOGGER.info("flux3 subscriber:{}",e);
        });

        IntStream.rangeClosed(1,100)
                .parallel()
                .forEach(e -> {
//                    LOGGER.info("emit:{}",e);
                    topicProcessor.onNext(e);
                });

        topicProcessor.onComplete();
        topicProcessor.blockLast();

        TimeUnit.SECONDS.sleep(10);
        System.out.println(count.get());
    }

注意两个地方:ide

share背后设置的是EventLoopProcessor的multiproducers属性
reactor-core-3.1.2.RELEASE-sources.jar!/reactor/core/publisher/EventLoopProcessor.java

EventLoopProcessor(
            int bufferSize,
            @Nullable ThreadFactory threadFactory,
            @Nullable ExecutorService executor,
            ExecutorService requestExecutor,
            boolean autoCancel,
            boolean multiproducers,
            Supplier<Slot<IN>> factory,
            WaitStrategy strategy) {

        if (!Queues.isPowerOfTwo(bufferSize)) {
            throw new IllegalArgumentException("bufferSize must be a power of 2 : " + bufferSize);
        }

        if (bufferSize < 1){
            throw new IllegalArgumentException("bufferSize must be strictly positive, " +
                    "was: "+bufferSize);
        }

        this.autoCancel = autoCancel;

        contextClassLoader = new EventLoopContext(multiproducers);

        this.name = defaultName(threadFactory, getClass());

        this.requestTaskExecutor = Objects.requireNonNull(requestExecutor, "requestTaskExecutor");

        if (executor == null) {
            this.executor = Executors.newCachedThreadPool(threadFactory);
        }
        else {
            this.executor = executor;
        }

        if (multiproducers) {
            this.ringBuffer = RingBuffer.createMultiProducer(factory,
                    bufferSize,
                    strategy,
                    this);
        }
        else {
            this.ringBuffer = RingBuffer.createSingleProducer(factory,
                    bufferSize,
                    strategy,
                    this);
        }
    }

若是share为true,则建立的是createMultiProducer.
具体的表象就是若是有多线程调用processor的onNext方法,而没有开启share的话,会有并发问题,即数据会丢失.好比上面的代码,若是注释掉share(true),则最后count的大小就不必定是100,而开启share为true就能保证最后count的大小是100
若是设置executor(Executors.newSingleThreadExecutor()),则flux1,flux2,flux3的订阅者则是顺序执行,而不是并发的.

WorkQueueProcessor

WorkQueueProcessor也是一个异步的processor，当shared设置为true的时候，支持对多个publisher的并发重放。
WorkQueueProcessor使用了RingBuffer数据结构来推送数据。
WorkQueueProcessor不是每来一个subscriber就给其建立一个线程，所以比TopicProcessor的伸缩性更好一点。可以支持的subscriber的最大个数由线程池executor限制。可是值得注意的是最好不要给WorkQueueProcessor添加过多的subscriber，这样会增长processor的锁竞争。最好使用ThreadPoolExecutor或者ForkJoinPool，processor能够检测他们的容量而后再订阅者过多的时候抛出异常。
WorkQueueProcessor不遵循reactive streams的规范，所以比TopicProcessor所消耗的资源更少。做为trade-off，全部subscriber的request会累加在一块儿，而后WorkQueueProcessor每次只给一个subscriber重放数据，相比于TopicProcessorde fan-out广播模式，它相似于round-robin模式，可是公平的round-robin模式是不被保证的。

@Test
    public void testWorkQueueProcessor(){
        WorkQueueProcessor<Integer> workQueueProcessor = WorkQueueProcessor.create();
        Flux<Integer> flux1 = workQueueProcessor
                .map(e -> e);
        Flux<Integer> flux2 = workQueueProcessor
                .map(e -> e);
        Flux<Integer> flux3 = workQueueProcessor
                .map(e -> e);

        flux1.subscribe(e -> {
            LOGGER.info("flux1 subscriber:{}",e);
        });
        flux2.subscribe(e -> {
            LOGGER.info("flux2 subscriber:{}",e);
        });
        flux3.subscribe(e -> {
            LOGGER.info("flux3 subscriber:{}",e);
        });

        IntStream.range(1,20)
                .forEach(e -> {
                    workQueueProcessor.onNext(e);
                });

        workQueueProcessor.onComplete();
        workQueueProcessor.blockLast();
    }

输出实例

21:56:58.203 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
21:56:58.214 [main] DEBUG reactor.core.publisher.UnsafeSupport - Starting UnsafeSupport init in Java 1.8
21:56:58.215 [main] DEBUG reactor.core.publisher.UnsafeSupport - Unsafe is available
21:56:58.228 [WorkQueueProcessor-1] INFO com.example.demo.ProcessorTest - flux1 subscriber:1
21:56:58.228 [WorkQueueProcessor-3] INFO com.example.demo.ProcessorTest - flux3 subscriber:3
21:56:58.228 [WorkQueueProcessor-2] INFO com.example.demo.ProcessorTest - flux2 subscriber:2
21:56:58.229 [WorkQueueProcessor-1] INFO com.example.demo.ProcessorTest - flux1 subscriber:4
21:56:58.229 [WorkQueueProcessor-3] INFO com.example.demo.ProcessorTest - flux3 subscriber:5
21:56:58.229 [WorkQueueProcessor-2] INFO com.example.demo.ProcessorTest - flux2 subscriber:6
21:56:58.230 [WorkQueueProcessor-1] INFO com.example.demo.ProcessorTest - flux1 subscriber:7
21:56:58.230 [WorkQueueProcessor-3] INFO com.example.demo.ProcessorTest - flux3 subscriber:8
21:56:58.230 [WorkQueueProcessor-2] INFO com.example.demo.ProcessorTest - flux2 subscriber:9
21:56:58.230 [WorkQueueProcessor-1] INFO com.example.demo.ProcessorTest - flux1 subscriber:10
21:56:58.230 [WorkQueueProcessor-3] INFO com.example.demo.ProcessorTest - flux3 subscriber:11
21:56:58.230 [WorkQueueProcessor-2] INFO com.example.demo.ProcessorTest - flux2 subscriber:12
21:56:58.230 [WorkQueueProcessor-1] INFO com.example.demo.ProcessorTest - flux1 subscriber:13
21:56:58.230 [WorkQueueProcessor-3] INFO com.example.demo.ProcessorTest - flux3 subscriber:14
21:56:58.230 [WorkQueueProcessor-2] INFO com.example.demo.ProcessorTest - flux2 subscriber:15
21:56:58.230 [WorkQueueProcessor-3] INFO com.example.demo.ProcessorTest - flux3 subscriber:17
21:56:58.230 [WorkQueueProcessor-1] INFO com.example.demo.ProcessorTest - flux1 subscriber:16
21:56:58.230 [WorkQueueProcessor-3] INFO com.example.demo.ProcessorTest - flux3 subscriber:19
21:56:58.230 [WorkQueueProcessor-2] INFO com.example.demo.ProcessorTest - flux2 subscriber:18

能够看到WorkQueueProcessor的subscriber就相似kafka的同属于一个group的consumer,各自消费的消息总和就是publisher发布的总消息,不像TopicProcessor那种广播式的消息传递.