RxJava之背压策略

作者: 103style | 来源:发表于2019-07-24 15:10 被阅读8次

转载请以链接形式标明出处：
本文出自:103style的博客

本文基于 RxJava 2.x 版本

RxJava背压策略简介

Backpressure is when in an Flowable processing pipeline, some asynchronous stages can't process the values fast enough and need a way to tell the upstream producer to slow down.
背压是在Flowable处理事件流中，某些异步阶段无法足够快地处理这些值，并且需要一种方法来告诉上游生产商减速。

所以RxJava的背压策略(Backpressure)是指处理上述上游流速过快现象的一种策略。类似 Java中的线程池中的饱和策略RejectedExecutionHandler。

Observable背压导致崩溃的原因

我们先使用 Observable看看是什么情况:

Observable
        .create(new ObservableOnSubscribe<Integer>() {
            @Override
            public void subscribe(ObservableEmitter<Integer> emitter) throws Exception {
                for (int i = 0; ; i++) {
                    emitter.onNext(i);
                }
            }
        })
        .subscribeOn(Schedulers.computation())
        .observeOn(AndroidSchedulers.mainThread())
        .subscribe(new Consumer<Integer>() {
            @Override
            public void accept(Integer integer) throws Exception {
                try {
                    Thread.sleep(3000);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
                System.out.println(integer);
            }
        });

image.png

输出：

I/art: Background partial concurrent mark sweep GC freed 7(224B) AllocSpace objects, 0(0B) LOS objects, 27% free, 43MB/59MB, paused 528us total 106.928ms
I/System.out: 0
I/art: Background partial concurrent mark sweep GC freed 8(256B) AllocSpace objects, 0(0B) LOS objects, 20% free, 62MB/78MB, paused 1.065ms total 327.346ms
I/System.out: 1
I/art: Background partial concurrent mark sweep GC freed 8(256B) AllocSpace objects, 0(0B) LOS objects, 16% free, 82MB/98MB, paused 1.345ms total 299.700ms
I/art: Background partial concurrent mark sweep GC freed 8(256B) AllocSpace objects, 0(0B) LOS objects, 13% free, 103MB/119MB, paused 1.609ms total 377.432ms
I/System.out: 2
I/art: Background sticky concurrent mark sweep GC freed 29(800B) AllocSpace objects, 0(0B) LOS objects, 0% free, 120MB/120MB, paused 1.280ms total 105.749ms
I/art: Background partial concurrent mark sweep GC freed 22(640B) AllocSpace objects, 0(0B) LOS objects, 11% free, 126MB/142MB, paused 1.818ms total 679.398ms
I/System.out: 3
I/art: Background partial concurrent mark sweep GC freed 9(288B) AllocSpace objects, 0(0B) LOS objects, 9% free, 148MB/164MB, paused 1.946ms total 555.619ms
I/System.out: 4
I/art: Background sticky concurrent mark sweep GC freed 29(800B) AllocSpace objects, 0(0B) LOS objects, 0% free, 165MB/165MB, paused 1.253ms total 107.036ms
I/art: Background partial concurrent mark sweep GC freed 8(256B) AllocSpace objects, 0(0B) LOS objects, 8% free, 172MB/188MB, paused 2.355ms total 570.029ms
I/art: Background sticky concurrent mark sweep GC freed 28(768B) AllocSpace objects, 0(0B) LOS objects, 0% free, 188MB/188MB, paused 11.474ms total 82.399ms
I/System.out: 5
I/art: Background partial concurrent mark sweep GC freed 23(672B) AllocSpace objects, 0(0B) LOS objects, 7% free, 197MB/213MB, paused 2.355ms total 631.635ms
I/art: Background partial concurrent mark sweep GC freed 22(640B) AllocSpace objects, 0(0B) LOS objects, 6% free, 226MB/242MB, paused 3.091ms total 908.581ms
I/System.out: 6
I/art: Background sticky concurrent mark sweep GC freed 29(800B) AllocSpace objects, 0(0B) LOS objects, 0% free, 242MB/242MB, paused 1.672ms total 102.676ms
I/art: Waiting for a blocking GC Alloc
I/art: Clamp target GC heap from 267MB to 256MB
I/art: Alloc sticky concurrent mark sweep GC freed 0(0B) AllocSpace objects, 0(0B) LOS objects, 1% free, 252MB/256MB, paused 1.581ms total 10.336ms
I/art: WaitForGcToComplete blocked for 12.447ms for cause Alloc
I/art: Starting a blocking GC Alloc
I/art: Starting a blocking GC Alloc
I/System.out: 9
I/art: Waiting for a blocking GC Alloc
I/art: Waiting for a blocking GC Alloc
I/art: Clamp target GC heap from 268MB to 256MB
I/art: Alloc concurrent mark sweep GC freed 0(0B) AllocSpace objects, 0(0B) LOS objects, 1% free, 252MB/256MB, paused 1.574ms total 818.037ms
I/art: WaitForGcToComplete blocked for 2.539s for cause Alloc
I/art: Starting a blocking GC Alloc
I/art: Waiting for a blocking GC Alloc
W/art: Throwing OutOfMemoryError "Failed to allocate a 12 byte allocation with 4109520 free bytes and 3MB until OOM; failed due to fragmentation (required continguous free 4096 bytes for a new buffer where largest contiguous free 0 bytes)"

我们可以从上图中看到，内存在逐步上升，在一定的时间后，到达256M之后会触发GC，最后抛出OutOfMemoryError。因为上游的事件发送太快而下游的消费者消耗的比较慢。

那导致内存暴增的源头是什么呢？

我们对上面的代码做一点点修改，注释了observeOn(AndroidSchedulers.mainThread())，会发现内存显示很正常，不会存在上述问题。

    Observable
            .create(new ObservableOnSubscribe<Integer>() {
                @Override
                public void subscribe(ObservableEmitter<Integer> emitter) throws Exception {
                    for (int i = 0; ; i++) {
                        emitter.onNext(i);
                    }
                }
            })
            .subscribeOn(Schedulers.computation())
//          .observeOn(AndroidSchedulers.mainThread())
            .subscribe(new Consumer<Integer>() {
                @Override
                public void accept(Integer integer) throws Exception {
                    try {
                        Thread.sleep(3000);
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                    System.out.println(integer);
                }
            });

注释了observeOn

所以内存暴增的源头就在 observeOn(AndroidSchedulers.mainThread()).

我们来看看 observeOn的源码，通过 RxJava subscribeOn和observeOn源码介绍，我们知道在 ObservableObserveOn.ObserveOnObserver的 onSubscribe中构建了一个容量默认为128的SpscLinkedArrayQueue。

queue = new SpscLinkedArrayQueue<T>(bufferSize);

上游每发送一个事件都会通过queue.offer(t)保存到SpscLinkedArrayQueue中。

public void onNext(T t) {
    if (done) {
        return;
    }
    if (sourceMode != QueueDisposable.ASYNC) {
        queue.offer(t);
    }
    schedule();
}

我们可以写个测试代码来看看，因为生产比消费快的多，相当于一直添加元素，如下：

private void test(){
    SpscLinkedArrayQueue<Integer> queue = new SpscLinkedArrayQueue<>(128);
    for (int i = 0; ; i++) {
        queue.offer(i);
    }
}

运行会发现内存变化和Observable一样迅速暴增。

测试代码内存变化

SpscLinkedArrayQueue的详细介绍后面再说。现在可以大致理解为 一直狂吃，然后最后撑破肚皮，然后裂开。

Flowable的用法

我们来看看 Flowable的用法：

Flowable.create(FlowableOnSubscribe<T> source, BackpressureStrategy mode)

BackpressureStrategy 包含五种模式：MISSING、ERROR、BUFFER、DROP、LATEST。

下面对这五种BackpressureStrategy 分别介绍其用法以及 发送事件速度 > 接收事件速度 时的处理方式：

BackpressureStrategy.MISSING
处理方式：抛出异常MissingBackpressureException，并提示 缓存区满了
代码示例：

Flowable
        .create(new FlowableOnSubscribe<Object>() {
            @Override
            public void subscribe(FlowableEmitter<Object> emitter) throws Exception {
                for (int i = 0; i < Flowable.bufferSize() * 2; i++) {
                    emitter.onNext(i);
                }
                emitter.onComplete();
            }
        }, BackpressureStrategy.MISSING)
        .subscribeOn(Schedulers.computation())
        .observeOn(AndroidSchedulers.mainThread())
        .subscribe(new Subscriber<Object>() {
            @Override
            public void onSubscribe(Subscription s) {
                s.request(Integer.MAX_VALUE);
            }

            @Override
            public void onNext(Object o) {
                System.out.println("onNext: " + o);
                try {
                    Thread.sleep(100);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
            }

            @Override
            public void onError(Throwable t) {
                t.printStackTrace();
            }

            @Override
            public void onComplete() {
                System.out.println("onComplete");
            }
        });

输出结果：

System.out: onNext: 0
System.err: io.reactivex.exceptions.MissingBackpressureException: Queue is full?!

BackpressureStrategy.ERROR
处理方式：直接抛出异常MissingBackpressureException
修改上述代码的 BackpressureStrategy.MISSING为BackpressureStrategy.ERROR：

Flowable
        .create(new FlowableOnSubscribe<Object>() {
            ...
        }, BackpressureStrategy.ERROR)
        ...

输出结果：

System.out: onNext: 0
System.err: io.reactivex.exceptions.MissingBackpressureException: create: could not emit value due to lack of requests

BackpressureStrategy.BUFFER
处理方式：类似Observable一样扩充缓存区大小
修改上述代码的 BackpressureStrategy.MISSING为BackpressureStrategy.BUFFER：

Flowable
        .create(new FlowableOnSubscribe<Object>() {
            ...
        }, BackpressureStrategy.BUFFER)
        ...

输出结果：

System.out: onNext: 0
System.out: onNext: 1
System.out: onNext: 2
System.out: onNext: 3
System.out: onNext: 4
System.out: onNext: 5
System.out: onNext: 6
...
System.out: onNext: 247
System.out: onNext: 248
System.out: onNext: 249
System.out: onNext: 250
System.out: onNext: 251
System.out: onNext: 252
System.out: onNext: 253
System.out: onNext: 254
System.out: onNext: 255
System.out: onComplete

BackpressureStrategy.DROP
处理方式：丢弃缓存区满后处理缓冲区数据期间发送过来的事件
示例代码：

  Flowable
        .create(new FlowableOnSubscribe<Object>() {
            @Override
            public void subscribe(FlowableEmitter<Object> emitter) throws Exception {
                for (int i = 0; ; i++) {
                    emitter.onNext(i);
                }
            }
        }, BackpressureStrategy.DROP)
        .subscribeOn(Schedulers.computation())
        .observeOn(AndroidSchedulers.mainThread())
        .subscribe(new Subscriber<Object>() {
            @Override
            public void onSubscribe(Subscription s) {
                s.request(Integer.MAX_VALUE);
            }

            @Override
            public void onNext(Object o) {
                System.out.println("onNext: " + o);
                try {
                    Thread.sleep(10);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }

            }

            @Override
            public void onError(Throwable t) {
                t.printStackTrace();
            }

            @Override
            public void onComplete() {
                System.out.println("onComplete");
            }
        });

输出结果：

System.out: onNext: 0
System.out: onNext: 1
System.out: onNext: 2
System.out: onNext: 3
...
System.out: onNext: 124
System.out: onNext: 125
System.out: onNext: 126
System.out: onNext: 127
System.out: onNext: 1070801
System.out: onNext: 1070802
System.out: onNext: 1070803
System.out: onNext: 1070804
System.out: onNext: 1070805
...

BackpressureStrategy.LATEST
处理方式：丢弃缓存区满后处理缓冲区数据期间发送过来的非最后一个事件。下面示例代码输出了 129 个事件，下面的源码分析会介绍。
示例代码：

Flowable
        .create(new FlowableOnSubscribe<Object>() {
            @Override
            public void subscribe(FlowableEmitter<Object> emitter) throws Exception {
                for (int i = 0; i < Flowable.bufferSize() * 2; i++) {
                    emitter.onNext(i);
                }
                emitter.onComplete();
            }
        }, BackpressureStrategy.LATEST)
        .subscribeOn(Schedulers.computation())
        .observeOn(AndroidSchedulers.mainThread())
        .subscribe(new Subscriber<Object>() {
            @Override
            public void onSubscribe(Subscription s) {
                s.request(Integer.MAX_VALUE);
            }

            @Override
            public void onNext(Object o) {
                System.out.println("onNext: " + o);
                try {
                    Thread.sleep(10);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }

            }

            @Override
            public void onError(Throwable t) {
                t.printStackTrace();
            }

            @Override
            public void onComplete() {
                System.out.println("onComplete");
            }
        });

输出结果：

System.out: onNext: 0
System.out: onNext: 1
System.out: onNext: 2
System.out: onNext: 3
...
System.out: onNext: 124
System.out: onNext: 125
System.out: onNext: 126
System.out: onNext: 127
System.out: onNext: 255
System.out: onComplete

五种背压策略源码分析

通知之前 RxJava之create操作符源码解析的介绍。我们知道Flowable.create(new FlowableOnSubscribe<Object>(){...}, BackpressureStrategy.LATEST) 返回的是一个FlowableCreate对象。

分别对不同的背压策略创建了不同的Emitter.

public final class FlowableCreate<T> extends Flowable<T> {
    //...
    public FlowableCreate(FlowableOnSubscribe<T> source, BackpressureStrategy backpressure) {
        this.source = source;
        this.backpressure = backpressure;
    }
    public void subscribeActual(Subscriber<? super T> t) {
        BaseEmitter<T> emitter;
        switch (backpressure) {
            case MISSING: {
                emitter = new MissingEmitter<T>(t);
                break;
            }
            case ERROR: {
                emitter = new ErrorAsyncEmitter<T>(t);
                break;
            }
            case DROP: {
                emitter = new DropAsyncEmitter<T>(t);
                break;
            }
            case LATEST: {
                emitter = new LatestAsyncEmitter<T>(t);
                break;
            }
            default: {
                emitter = new BufferAsyncEmitter<T>(t, bufferSize());
                break;
            }
        }
        t.onSubscribe(emitter);
        try {
            source.subscribe(emitter);
        } catch (Throwable ex) {
            Exceptions.throwIfFatal(ex);
            emitter.onError(ex);
        }
    }
    //...
}

MissingEmitter

static final class MissingEmitter<T> extends BaseEmitter<T> {
    MissingEmitter(Subscriber<? super T> downstream) {
        super(downstream);
    }
    @Override
    public void onNext(T t) {
        if (isCancelled()) {
            return;
        }
        if (t != null) {
            downstream.onNext(t);
        } else {
            onError(new NullPointerException("onNext called with null. Null values are generally not allowed in 2.x operators and sources."));
            return;
        }
        for (;;) {
            long r = get();
            if (r == 0L || compareAndSet(r, r - 1)) {
                return;
            }
        }
    }

}

通过上面的代码我们可以看到MissingEmitter基本上没做什么操作，所以BackpressureStrategy.MISSING示例中的代码实际上是调用了ObserveOn中返回对象的FlowableObserveOn.ObserveOnSubscriber的 onNext：

public final void onNext(T t) {
    if (done) {
        return;
    }
    if (sourceMode == ASYNC) {
        trySchedule();
        return;
    }
    if (!queue.offer(t)) {
        upstream.cancel();
        error = new MissingBackpressureException("Queue is full?!");
        done = true;
    }
    trySchedule();
}

上面代码中我们看到了背压情况下出现的报错信息，出现的前提是queue.offer(t)返回false。这里的queue是onSubscribe中构造的容量为Flowable.bufferSize()的SpscArrayQueue.

public void onSubscribe(Subscription s) {
    if (SubscriptionHelper.validate(this.upstream, s)) {
        this.upstream = s;
        //...
        queue = new SpscArrayQueue<T>(prefetch);
        downstream.onSubscribe(this);
        s.request(prefetch);
    }
}

SpscArrayQueue的offer方法，我们可以看到当SpscArrayQueue数据 “满了” 的时候即返回false.

public boolean offer(E e) {
    //...
    final int mask = this.mask;
    final long index = producerIndex.get();
    final int offset = calcElementOffset(index, mask);
    if (index >= producerLookAhead) {
        int step = lookAheadStep;
        if (null == lvElement(calcElementOffset(index + step, mask))) { // LoadLoad
            producerLookAhead = index + step;
        } else if (null != lvElement(offset)) {
            return false;
        }
    }
    soElement(offset, e); // StoreStore
    soProducerIndex(index + 1); // ordered store -> atomic and ordered for size()
    return true;
}

所以BackpressureStrategy.MISSING在缓冲区满了之后再发射事件即会抛出 message 为 "Queue is full?!" 的 MissingBackpressureException.

ErrorAsyncEmitter

abstract static class BaseEmitter<T>
extends AtomicLong
implements FlowableEmitter<T>, Subscription {
    //...
    @Override
    public final void request(long n) {
        if (SubscriptionHelper.validate(n)) {
            BackpressureHelper.add(this, n);
            onRequested();
        }
    }
    //...
}
abstract static class NoOverflowBaseAsyncEmitter<T> extends BaseEmitter<T> {
    NoOverflowBaseAsyncEmitter(Subscriber<? super T> downstream) {
        super(downstream);
    }
    @Override
    public final void onNext(T t) {
        //...
        if (get() != 0) {
            downstream.onNext(t);
            BackpressureHelper.produced(this, 1);
        } else {
            onOverflow();
        }
    }
    abstract void onOverflow();
}
static final class ErrorAsyncEmitter<T> extends NoOverflowBaseAsyncEmitter<T> {
    ErrorAsyncEmitter(Subscriber<? super T> downstream) {
        super(downstream);
    }
    @Override
    void onOverflow() {
        onError(new MissingBackpressureException("create: could not emit value due to lack of requests"));
    }
}

通过在onSubscribe中调用request(Flowable.bufferSize())设置当前AtomicLong的value值。然后 onNext 中每传递一个事件就通过BackpressureHelper.produced(this, 1)将value 减 1. 当发送了Flowable.bufferSize()个事件，get() != 0不成立，调用onOverflow()方法抛出 MissingBackpressureException异常。

DropAsyncEmitter

static final class DropAsyncEmitter<T> extends NoOverflowBaseAsyncEmitter<T> {

    private static final long serialVersionUID = 8360058422307496563L;

    DropAsyncEmitter(Subscriber<? super T> downstream) {
        super(downstream);
    }

    @Override
    void onOverflow() {
        // nothing to do
    }

}

和 ErrorAsyncEmitter 类似，只不过当发送超过超过Flowable.bufferSize()的事件时，啥也没做，即实现丢弃的功能。

LatestAsyncEmitter

static final class LatestAsyncEmitter<T> extends BaseEmitter<T> {
    final AtomicReference<T> queue;
    //...
    LatestAsyncEmitter(Subscriber<? super T> downstream) {
        super(downstream);
        this.queue = new AtomicReference<T>();
        //...
    }

    @Override
    public void onNext(T t) {
        //...
        queue.set(t);
        drain();
    }
    //...
}

我们可以看到每次调用onNext都会更新传过来的值到queue中，所以queue中保存了最新的值。

接着来看drain方法：
上面我们知道在onSubscribe中调用request()设置当前AtomicLong的value值。

void drain() {
    //...
    for (;;) {
        long r = get();
        long e = 0L;
        while (e != r) {
            //...
            boolean d = done;
            T o = q.getAndSet(null);
            boolean empty = o == null;
            if (d && empty) {
                //...
                return;
            }
            if (empty) {
                break;
            }
            a.onNext(o);
            e++;
        }
        if (e == r) {
            //...
            boolean d = done;
            boolean empty = q.get() == null;
            if (d && empty) {
                //...
                return;
            }
        }
        if (e != 0) {
            BackpressureHelper.produced(this, e);
        }
        //...
    }
}

在for (;;)里面通过get()获取当前AtomicLong的值。然后通过a.onNext(o);传递给下游，然后e++，在通过BackpressureHelper.produced(this, e);减掉AtomicLong的值。
当调用Flowable.bufferSize()次 onNext之后，get()返回的值为0，所以e != r不成立，在e == r的判断中，在从onNext过来时empty为false，所以直接跳出 for循环。
通过上面我们知道当传递超过Flowable.bufferSize()的事件过来，只会更新queue中的值为最新的事件，其他啥也没做。那最后一个事件时怎么发出的呢？，继续往下看。

最后一个事件时怎么发出的？
我们在上面的drain()中调用a.onNext(o)最终是调用observeOn构建对象中的ObserveOnSubscriber的onNext，即调用runAsync();。

public final void onNext(T t) {
    //...
    trySchedule();
}
final void trySchedule() {
    //...
    worker.schedule(this);
}
@Override
public final void run() {
    if (outputFused) {
        runBackfused();
    } else if (sourceMode == SYNC) {
        runSync();
    } else {
        runAsync();
    }
}

runAsync()：

void runAsync() {
    //...
    for (;;) {
        long r = requested.get();
        while (e != r) {
            boolean d = done;
            T v;
            try {
                v = q.poll();
            } catch (Throwable ex) {
                //...
                return;
            }
            //...
            a.onNext(v);
            e++;
            if (e == limit) {
                if (r != Long.MAX_VALUE) {
                    r = requested.addAndGet(-e);
                }
                upstream.request(e);
                e = 0L;
            }
        }
        //...
    }
}

我们可以看到在for循环中通过q.poll()去获取缓存队列SpscArrayQueue中的事件。然后通过a.onNext(v);去执行我们示例代码中的耗时操作。
然后当e == limit是，回去调用LatestAsyncEmitter的request(e)，而limit是在构造函数中初始化的，值为缓存队列容量Flowable.bufferSize()的 3/4。所以当队列中的事件消耗了容量的3/4之后，会再去请求上游发送事件。
```
BaseObserveOnSubscriber(
        Worker worker,
        boolean delayError,
        int prefetch) {
    //...
    this.limit = prefetch - (prefetch >> 2);
}
```

request方法：

@Override
public final void request(long n) {
    if (SubscriptionHelper.validate(n)) {
        System.out.println("n = " + n);
        BackpressureHelper.add(this, n);
        onRequested();
    }
}
@Override
void onRequested() {
    drain();
}

即继续执行drain()方法，因为queue中还保存最新的值事件。所以会通过a.onNext(o)发送这个最新的事件。

如果在执行完等待队列3/4的事件之后，上游的事件还没发送结束，下游即会再次缓存上游发送过来的容量的3/4个事件。
示例代码：

Flowable.create(new FlowableOnSubscribe<Object>() {
    @Override
    public void subscribe(FlowableEmitter<Object> emitter) throws Exception {
        for (int i = 0; i < Flowable.bufferSize() * 2; i++) {
            emitter.onNext(i);
        }
        Thread.sleep(10 * Flowable.bufferSize());
        for (int i = 0; i < Flowable.bufferSize() * 2; i++) {
            emitter.onNext(Flowable.bufferSize() * 2 + i);
        }
        emitter.onComplete();
    }
}, BackpressureStrategy.LATEST)
        .subscribeOn(Schedulers.computation())
        .observeOn(AndroidSchedulers.mainThread())
        .subscribe(new Subscriber<Object>() {
            @Override
            public void onSubscribe(Subscription s) {
                s.request(Integer.MAX_VALUE);
            }
            @Override
            public void onNext(Object o) {
                System.out.println("onNext: " + o);
                try {
                    Thread.sleep(10);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
            }
            @Override
            public void onError(Throwable t) {
                t.printStackTrace();
            }
            @Override
            public void onComplete() {
                System.out.println("onComplete");
            }
        });

输出结果：

System.out: onNext: 0
System.out: onNext: 1
System.out: onNext: 2
System.out: onNext: 3
//....
System.out: onNext: 125
System.out: onNext: 126
System.out: onNext: 127
System.out: onNext: 255
System.out: onNext: 256
System.out: onNext: 257
//...
System.out: onNext: 349
System.out: onNext: 350
System.out: onNext: 511
System.out: onComplete

可以看到输出结果中255-350即为容量128 的 3/4个元素。

BufferAsyncEmitter

我们可以看到内部有一个SpscLinkedArrayQueue的缓存队列，每次调用onNext都会先保存到缓存队列，然后通过drain()方法一直去遍历当前的缓存队列。然后和LatestAsyncEmitter一样，当下游的缓存队列满了之后，即不再放下游发送事件，只是把上游的事件保存在SpscLinkedArrayQueue中，等待下游处理了容量的3/4的事件之后，上游在发送容量的3/4的事件过去。知道上游的事件消耗完，或者异常退出。即和Observable的效果类似，只不过缓存队列一个在上游一个在下游。

static final class BufferAsyncEmitter<T> extends BaseEmitter<T> {
    final SpscLinkedArrayQueue<T> queue;
    //...

    BufferAsyncEmitter(Subscriber<? super T> actual, int capacityHint) {
        super(actual);
        this.queue = new SpscLinkedArrayQueue<T>(capacityHint);
        this.wip = new AtomicInteger();
    }
    @Override
    public void onNext(T t) {
        //...
        queue.offer(t);
        drain();
    }
    void drain() {
        //...
        final SpscLinkedArrayQueue<T> q = queue;
        for (;;) {
            long r = get();
            long e = 0L;
            while (e != r) {
                //...
                boolean d = done;
                T o = q.poll();
                boolean empty = o == null;
                if (d && empty) {
                    //...
                    return;
                }
                if (empty) {
                    break;
                }
                a.onNext(o);
                e++;
            }
            if (e == r) {
                //...
                boolean d = done;
                boolean empty = q.isEmpty();
                if (d && empty) {
                    //...
                    return;
                }
            }
            if (e != 0) {
                BackpressureHelper.produced(this, e);
            }
            missed = wip.addAndGet(-missed);
            if (missed == 0) {
                break;
            }
        }
    }
}

小结

我们知道了Observable出现背压的原因是上游发送的超多事件缓存在observeOn返回对象的缓存队列中，事件的增加导致了内存的增加。
我们介绍了Flowable的使用和五种背压策略的具体实现。
- MISSING：超过observeOn配置的bufferSize则抛出异常MissingBackpressureException并提示Queue is full?!。
- ERROR：超过observeOn配置的bufferSize则直接抛出异常MissingBackpressureException。
- BUFFER：超过observeOn配置的bufferSize则缓存到上游的缓冲队列，等待下游消耗了容量的3/4的事件之后，在继续发送上游缓存的事件给下游。
- DROP：超过observeOn配置的bufferSize则丢弃。
- LATEST：超过observeOn配置的bufferSize则丢弃并保存最新的值到queue，如果在下游消耗了容量的3/4的事件之后，上游还有事件在发送，则继续往下游发送事件，当没有事件的时候，再发送queue中保存的最新的那个事件。

参考文章

以上

RxJava之背压策略

目录

RxJava背压策略简介

Observable背压导致崩溃的原因

Flowable的用法

五种背压策略源码分析

小结

参考文章

相关文章

网友评论

延伸阅读

深度阅读

栏目导航

热点阅读

Android开发经验谈

Android开发

Android RxJava

原理-Android 综合

框架【库】