(1)RxJava2 的5大基类及本章学习结构图
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(1)Flowable --> 0..N flows, supporting Reactive-Streams and backpressure
(2)Observable --> 0..N flows, no backpressure,
(3)Single --> a flow of exactly 1 item or an error,
(4)Completable --> a flow without items but only a completion or error signal.
(5)Maybe --> a flow with no items, exactly one item or an error.
| Type | Class | Interface | Consumer |
|---|---|---|---|
| 0..N backpressured | Flowable | Publisher1 | Subscriber |
| 0..N unbounded | Observable | ObservableSource2 | Observer |
| 1 element or error | Single | SingleSource | SingleObserver |
| 0..1 element or error | Maybe | MaybeSource | MaybeObserver |
| 0 element or error | Completable | CompletableSource | CompletableObserver |
Observable(被观察者)/Observer（观察者）
Flowable(被观察者)/Subscriber(观察者)
(1)怎么解决背压问题？
Flowable.create(new FlowableOnSubscribe(), BackpressureStrategy.BUFFER)
BUFFER : 缓存池队列，下游进行消费数据，OOM
MISSING：不丢也不缓存，需要下游进行处理 Sample，ThrottleFirst, buffer（1，Seconds）...
ERROR：下游处理异常，MISSING
DROP：丢弃下游不能处理的数据
LATEST：保证写入最后一个数据，之前的可能会丢掉
(2)解决背压问题后带来的问题
解决背压问题是需要增加额外的处理逻辑，因此，Flowable的运行效率要比Observable 差一些
在不存在背压问题的场景下，尽量使用Observable
RxJava2.X中，Observeable用于订阅Observer，是不支持背压的，而Flowable用于订阅Subscriber，是支持背压(Backpressure)的。
//不支持背压，会导致内存暴增，最后导致oom
public void ClickOne(View view) {
//被观察者在主线程中，每1ms发送一个事件
Observable.interval(1, TimeUnit.MICROSECONDS)
//将观察者的工作放在新线程环境中
.observeOn(Schedulers.newThread())
//观察者处理每1000ms才处理一个事件
.subscribe(new Observer() {
@Override
public void onSubscribe(Disposable d) {
//这是新加入的方法，在订阅后发送数据之前，
//回首先调用这个方法，而Disposable可用于取消订阅
}
@Override
public void onNext(Long aLong) {
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
Log.w("TAG", "---->" + aLong);
}
@Override
public void onError(Throwable e) {
}
@Override
public void onComplete() {
}
});
}
//支持背压操作
Flowable.create(new FlowableOnSubscribe() {
@Override
public void subscribe(FlowableEmitter emitter) throws Exception {
for (int i = 0; i < 10000; i++) {
emitter.onNext(i);
}
emitter.onComplete();
}
}, BackpressureStrategy.DROP) //指定背压策略
.subscribeOn(Schedulers.computation())
.observeOn(Schedulers.newThread())
.subscribe(new Consumer() {
@Override
public void accept(Integer integer) throws Exception {
Log.d("TAG", integer.toString());
Thread.sleep(1000);
}
}, new Consumer() {
@Override
public void accept(Throwable throwable) throws Exception {
Log.d("TAG", throwable.toString());
}
});
其中还需要注意的一点在于，Flowable并不是订阅就开始发送数据，而是需等到执行Subscription#request才能开始发送数据。当然，使用简化subscribe订阅方法会默认指定Long.MAX_VALUE。
Flowable.range(1, 10)
.subscribe(new Subscriber() {
@Override
public void onSubscribe(Subscription s) {
Log.w("TAG", "onSubscribe start");
s.request(Integer.MAX_VALUE);
Log.w("TAG", "onSubscribe end");
}
@Override
public void onNext(Integer aLong) {
Log.w("TAG", "---->" + aLong);
}
@Override
public void onError(Throwable t) {
Log.w("TAG", "onError");
}
@Override
public void onComplete() {
Log.w("TAG", "onComplete");
}
});
}
Single、Completable、Maybe是简化的Observable，只是具有少部分功能.
Single：只能发射一条单一数据或者一条异常通知，不能发射完成通知，数据与通知只能发射一个，二选一。
//Single操作 单一操作
//只发射一条单一的数据，或者一条异常通知，不能发射完成通知，其中数据与通知只能发射一个。
public void ClickThree(View view) {
Single.create(new SingleOnSubscribe() {
@Override
public void subscribe(SingleEmitter emitter) throws Exception {
emitter.onSuccess("成功");
}
}).subscribe(new SingleObserver() {
@Override
public void onSubscribe(Disposable d) {
Log.e("TAG", "Disposable");
}
@Override
public void onSuccess(String s) {
Log.e("TAG", s);
}
@Override
public void onError(Throwable e) {
Log.e("TAG", e.toString());
}
});
}
Completable：只能发射一条完成通知或者一条异常通知，不能发射数据，要么发射完成通知要么发射异常通，二选一。
//Completable操作
//只发射一条完成通知，或者一条异常通知，不能发射数据，其中完成通知与异常通知只能发射一个
public void ClickFour(View view) {
Completable.create(new CompletableOnSubscribe() {
@Override
public void subscribe(CompletableEmitter emitter) throws Exception {
emitter.onComplete();
}
}).subscribe(new CompletableObserver() {
@Override
public void onSubscribe(Disposable d) {
Log.e("TAG", "onSubscribe");
}
@Override
public void onComplete() {
Log.e("TAG", "onComplete");
}
@Override
public void onError(Throwable e) {
Log.e("TAG", "onError" + e.getMessage());
}
});
}
Maybe：只能发射一条单一数据，和发射一条完成通知，或者一条异常通知，完成通知和异常通知二选一，只能在发射完成通知或异常通知之前发射数据，否则发射数据无效
//Maybe操作
//Maybe发射单一数据和完成通知
public void ClickFive(View view) {
Maybe.create(new MaybeOnSubscribe() {
@Override
public void subscribe(MaybeEmitter emitter) throws Exception {
emitter.onError(new Exception("异常测试"));
//emitter.onSuccess("111");
//emitter.onComplete();
}
}).subscribe(new MaybeObserver() {
@Override
public void onSubscribe(Disposable d) {
Log.e("TAG", "onSubscribe");
}
@Override
public void onSuccess(String s) {
Log.e("TAG", "onSuccess" + s);
}
@Override
public void onError(Throwable e) {
Log.e("TAG", "onError" + e.getMessage());
}
@Override
public void onComplete() {
Log.e("TAG", "onComplete");
}
});
}
二.线程调度
(1)什么是线程调度？
控制数据流和操作在不同线程之间进行切换
(2)怎么进行切换
SubscribeOn()
指定数据源所在的线程
.subscribeOn(Schedulers.newThread()) 指定数据源在新的线程里发射数据
ObserveOn()
指定观察者在哪个线程接收数据
.observeOn(Schedulers.newThread()) 指定观察者在新的线程里接收数据
(3)内置线程（Schedulers）
Schedulers.computation( )
多用于计算任务：工作线程数等于CPU数目
Schedulers.from(executor)
用户自定义线程池：根据需要进行自定义线程池执行任务
Schedulers.immediate()
当前线程中运行：无须指定
Schedulers.io( )
适用于IO操作：工作线程会被缓存一段时间，线程数可随需要增长
Schedulers.newThread( )
每次操作都会创建一个新的线程
Schedulers.single( )
只有一个工作线程，任务队列按照先进先出的顺序依次执行
Schedulers.trampoline( )
在当前线程执行，如果当前线程正在执行其他任务，则暂停，等待空闲执行
简单地说，subscribeOn() 指定的就是发射事件的线程，observerOn 指定的就是订阅者接收事件的线程。
多次指定发射事件的线程只有第一次指定的有效，也就是说多次调用 subscribeOn() 只有第一次的有效，其余的会被忽略。
但多次指定订阅者接收线程是可以的，也就是说每调用一次 observerOn()，下游的线程就会切换一次。
三.操作符(Operators)
Creating Observables 创建
Transforming Observables 变换
Filtering Observables 过滤
Combining Observables 组合
Error Handling Operators 错误处理
Observable Utility Operators 工具
Conditional and Boolean Operators 条件
Mathematical and Aggregate Operators 聚合
Backpressure Operators 背压
Connectable Observable Operators 可连接
Operators to Convert Observables 转换
操作符总结:
创建，变换，过滤，组合，统计，错误处理，背压处理 ，连接
source.operator1().operator2().operator3().subscribe(consumer);
(1)操作符太多我这边讲述几个常用的，如果有需要用到更多的操作符，大家都可以去对应官网看解释。http://reactivex.io/documentation/operators.html
1.创建 (Creating Observables)
Create — create an Observable from scratch by calling observer methods programmatically
Defer — do not create the Observable until the observer subscribes, and create a fresh Observable for each observer
Empty/Never/Throw — create Observables that have very precise and limited behavior
From — convert some other object or data structure into an Observable
Interval — create an Observable that emits a sequence of integers spaced by a particular time interval
Just — convert an object or a set of objects into an Observable that emits that or those objects
Range — create an Observable that emits a range of sequential integers
Repeat — create an Observable that emits a particular item or sequence of items repeatedly
Start — create an Observable that emits the return value of a function
Timer — create an Observable that emits a single item after a given delay
示例
Observable.range(1, 10).subscribe(new Consumer() {
@Override
public void accept(Integer t) throws Exception {
System.out.println(t);
}
});
Flowable.range(1, 10).subscribe(new Consumer() {
@Override
public void accept(Integer t) throws Exception {
System.out.println(t);
}
});
2.变换 (Transforming Observables)
Buffer — periodically gather items from an Observable into bundles and emit these bundles rather than emitting the items one at a time
FlatMap — transform the items emitted by an Observable into Observables, then flatten the emissions from those into a single Observable
GroupBy — divide an Observable into a set of Observables that each emit a different group of items from the original Observable, organized by key
Map — transform the items emitted by an Observable by applying a function to each item
Scan — apply a function to each item emitted by an Observable, sequentially, and emit each successive value
Window — periodically subdivide items from an Observable into Observable windows and emit these windows rather than emitting the items one at a time
示列
File folder = new File(FOLDER_PATH_P);
List namesList = new ArrayList<>();
Observable.fromArray(folder.listFiles()).flatMap(new Function>() {
@Override
public ObservableSource apply(File t) throws Exception {
return Observable.fromArray(t.listFiles());
}
}).map(new Function{
@Override
public String applay(File t) throws Exception {
return t.getName();
}
}).subscribe(new Consumer() {
@Override
public void accept(String t) throws Exception {
System.out.println("accept:" + t);
}
});
3.过滤 (Filtering Observables)
Debounce — only emit an item from an Observable if a particular timespan has passed without it emitting another item
Distinct — suppress duplicate items emitted by an Observable
ElementAt — emit only item n emitted by an Observable
Filter — emit only those items from an Observable that pass a predicate test
First — emit only the first item, or the first item that meets a condition, from an Observable
IgnoreElements — do not emit any items from an Observable but mirror its termination notification
Last — emit only the last item emitted by an Observable
Sample — emit the most recent item emitted by an Observable within periodic time intervals
Skip — suppress the first n items emitted by an Observable
SkipLast — suppress the last n items emitted by an Observable
Take — emit only the first n items emitted by an Observable
TakeLast — emit only the last n items emitted by an Observable
示列:
.filter(new Predicate() {
@Override
public boolean test(String t) throws Exception {
return t.endsWith("png");
}
})
Observable.just(1, 2, 3, 4).take(2).subscribe(new Consumer() {
@Override
public void accept(Integer t) throws Exception {
System.out.println(t);
}
});
4.组合 (Combining Observables)
And/Then/When — combine sets of items emitted by two or more Observables by means of Pattern and Plan intermediaries
CombineLatest — when an item is emitted by either of two Observables, combine the latest item emitted by each Observable via a specified function and emit items based on the results of this function
Join — combine items emitted by two Observables whenever an item from one Observable is emitted during a time window defined according to an item emitted by the other Observable
Merge — combine multiple Observables into one by merging their emissions
StartWith — emit a specified sequence of items before beginning to emit the items from the source Observable
Switch — convert an Observable that emits Observables into a single Observable that emits the items emitted by the most-recently-emitted of those Observables
Zip — combine the emissions of multiple Observables together via a specified function and emit single items for each combination based on the results of this function
示列:
Observable o1 = Observable.fromArray(folder1.listFiles());
Observable o2 = Observable.fromArray(folder2.listFiles());
o1.mergeWith(o2).map(new Function() {
@Override
public String apply(File t) throws Exception {
return t.getName();
}
}).subscribe(new Consumer() {
@Override
public void accept(String t) throws Exception {
System.out.println(t);
}
})
5.错误处理 (Error Handling Operators)
Catch — recover from an onError notification by continuing the sequence without error
Retry — if a source Observable sends an onError notification, resubscribe to it in the hopes that it will complete without error
示列:
.subscribe(new Consumer() {
@Override
public void accept(String t) throws Exception {
System.out.println("accept:" + t);
}
}, new Consumer() {
@Override
public void accept(Throwable t) throws Exception {
System.out.println("accept: error");
}
});
.retry(n) 出现异常的情况下，重试 n 次， 实际执行n+1次
6.工具 (Observable Utility Operators)
Delay — shift the emissions from an Observable forward in time by a particular amount
Do — register an action to take upon a variety of Observable lifecycle events
Materialize/Dematerialize — represent both the items emitted and the notifications sent as emitted items, or reverse this process
ObserveOn — specify the scheduler on which an observer will observe this Observable
Serialize — force an Observable to make serialized calls and to be well-behaved
Subscribe — operate upon the emissions and notifications from an Observable
SubscribeOn — specify the scheduler an Observable should use when it is subscribed to
TimeInterval — convert an Observable that emits items into one that emits indications of the amount of time elapsed between those emissions
Timeout — mirror the source Observable, but issue an error notification if a particular period of time elapses without any emitted items
Timestamp — attach a timestamp to each item emitted by an Observable
Using — create a disposable resource that has the same lifespan as the Observable
示列:
int total= 6;
Observable observable = Observable.intervalRange(0, total, 0, 1, TimeUnit.SECONDS);
从0开始增加，每一秒一次，执行6次
7.条件(Conditional and Boolean Operators）
All — determine whether all items emitted by an Observable meet some criteria
Amb — given two or more source Observables, emit all of the items from only the first of these Observables to emit an item
Contains — determine whether an Observable emits a particular item or not
DefaultIfEmpty — emit items from the source Observable, or a default item if the source Observable emits nothing
SequenceEqual — determine whether two Observables emit the same sequence of items
SkipUntil — discard items emitted by an Observable until a second Observable emits an item
SkipWhile — discard items emitted by an Observable until a specified condition becomes false
TakeUntil — discard items emitted by an Observable after a second Observable emits an item or terminates
TakeWhile — discard items emitted by an Observable after a specified condition becomes false
示列:
Observable observable = Observable.just(1, 2, 3, 4);
observable.contains(2).subscribe(new BiConsumer() {
@Override
public void accept(Boolean t1, Throwable t2) throws Exception {
System.out.println(t1);
}
});
= true
SkipUntil:
Observable observable1 = Observable.interval(1, TimeUnit.SECONDS).take(4);
Observable observable2 = Observable.just(1).delay(3, TimeUnit.SECONDS);
observable1.skipUntil(observable2).subscribe(new Consumer() {
@Override
public void accept(Long t) throws Exception {
System.out.println(t);
}
});
= 2 3
8.聚合(Mathematical and Aggregate Operators)
Average — calculates the average of numbers emitted by an Observable and emits this average
Concat — emit the emissions from two or more Observables without interleaving them
Count — count the number of items emitted by the source Observable and emit only this value
Max — determine, and emit, the maximum-valued item emitted by an Observable
Min — determine, and emit, the minimum-valued item emitted by an Observable
Reduce — apply a function to each item emitted by an Observable, sequentially, and emit the final value
Sum — calculate the sum of numbers emitted by an Observable and emit this sum
特殊计算需要使用：'io.reactivex:rxjava-math:1.0.0'
示列:
Observable.just(folder).flatMap(new Function>() {
@Override
public Observable apply(File t) throws Exception {
return Observable.fromArray(t.listFiles());
}
}).count().subscribe(new Consumer() {
@Override
public void accept(Long t) throws Exception {
System.out.println("count:" + t);
}
});
=文件数
9.可连接 (Connectable Observable Operators)
Connect — instruct a connectable Observable to begin emitting items to its subscribers
Publish — convert an ordinary Observable into a connectable Observable
RefCount — make a Connectable Observable behave like an ordinary Observable
Replay — ensure that all observers see the same sequence of emitted items, even if they subscribe after the Observable has begun emitting items
示列:
ConnectableObservable c1 = Observable.interval(1, TimeUnit.SECONDS).publish();
c1.subscribe(new Consumer() {
@Override
public void accept(Long t) throws Exception {
System.out.println(t);
}
});
c1.connect();
11.转换(Operators to Convert Observables)
To — convert an Observable into another object or data structure
示列:
Observable.just(1, 2).toList().subscribe(new Consumer>() {
@Override
public void accept(List t) throws Exception {
System.out.println(t);
}
});