文章目录
依赖接入
Flowable
Single
Maybe
BackpressureStrategy
线程切换
concat
例子1
依赖接入
implementation 'io.reactivex.rxjava3:rxandroid:3.0.0'implementation "io.reactivex.rxjava3:rxjava:3.0.4"
Flowable
//java 方式Flowable.just(1).subscribe(new Consumer<Integer>() {@Overridepublic void accept(Integer integer) throws Throwable {}}, new Consumer<Throwable>() {@Overridepublic void accept(Throwable throwable) throws Throwable {}});//或者用 Lambda 简写Flowable.just(1).subscribe( it -> {}, throwable -> {});
range 一组序列数据
Flowable.range(0, 4).subscribe(it -> {//结果 0 1 2 3}, throwable -> {});
Single
Single只发射单个数据或错误事件，即使发射多个数据，后面发射的数据也不会处理。
只有 onSuccess 和 onError事件，没有 onNext 、onComplete事件。
SingleEmitter
public interface SingleEmitter<@NonNull T> {void onSuccess(@NonNull T t);void onError(@NonNull Throwable t);void setDisposable(@Nullable Disposable d);void setCancellable(@Nullable Cancellable c);boolean isDisposed();boolean tryOnError(@NonNull Throwable t);}
示例1
Single.create(new SingleOnSubscribe<Integer>() {@Overridepublic void subscribe(@NonNull SingleEmitter<Integer> emitter) throws Throwable {emitter.onSuccess(1);}}).subscribe(integer -> {}, throwable -> {});
示例2
Single.just(1).subscribe(integer -> {}, throwable -> {});
Maybe
Maybe 是 RxJava2.x 之后才有的新类型，可以看成是Single和Completable的结合。
Maybe 也只能发射单个事件或错误事件，即使发射多个数据，后面发射的数据也不会处理。
只有 onSuccess 、 onError 、onComplete事件，没有 onNext 事件。
public interface MaybeEmitter<@NonNull T> {void onSuccess(@NonNull T t);void onError(@NonNull Throwable t);void onComplete();void setDisposable(@Nullable Disposable d);void setCancellable(@Nullable Cancellable c);boolean isDisposed();boolean tryOnError(@NonNull Throwable t);}
实例1
Maybe.create(new MaybeOnSubscribe<Integer>() {@Overridepublic void subscribe(@NonNull MaybeEmitter<Integer> emitter) throws Throwable {emitter.onSuccess(1);emitter.onComplete();}}).subscribe(integer -> {}, throwable -> {});
实例2
Maybe.just(1).subscribe(integer -> {}, throwable -> {});
BackpressureStrategy
背压策略
public enum BackpressureStrategy {/*** The {@code onNext} events are written without any buffering or dropping.* Downstream has to deal with any overflow.* <p>Useful when one applies one of the custom-parameter onBackpressureXXX operators.*/MISSING,/*** Signals a {@link io.reactivex.rxjava3.exceptions.MissingBackpressureException MissingBackpressureException}* in case the downstream can't keep up.*/ERROR,/*** Buffers <em>all</em> {@code onNext} values until the downstream consumes it.*/BUFFER,/*** Drops the most recent {@code onNext} value if the downstream can't keep up.*/DROP,/*** Keeps only the latest {@code onNext} value, overwriting any previous value if the* downstream can't keep up.*/LATEST}
MISSING 策略则表示通过 Create 方法创建的 Flowable 没有指定背压策略，不会对通过 OnNext 发射的数据做缓存或丢弃处理，需要下游通过背压操作符
BUFFER 策略则在还有数据未下发完成时就算上游调用onComplete或onError也会等待数据下发完成
LATEST 策略则当产生背压时仅会缓存最新的数据
DROP 策略为背压时丢弃背压数据
ERROR 策略是背压时抛出异常调用onError
Flowable.create(new FlowableOnSubscribe<Long>() {@Overridepublic void subscribe(@NonNull FlowableEmitter<Long> emitter) throws Throwable {emitter.onNext(1L);emitter.onNext(2L);emitter.onComplete();}}, BackpressureStrategy.DROP).subscribeOn(Schedulers.io()).observeOn(AndroidSchedulers.mainThread()).subscribe(it -> {}, throwable -> {});
线程切换
RxUtil
package com.example.streamimport io.reactivex.rxjava3.android.schedulers.AndroidSchedulersimport io.reactivex.rxjava3.core.FlowableTransformerimport io.reactivex.rxjava3.core.MaybeTransformerimport io.reactivex.rxjava3.core.ObservableTransformerimport io.reactivex.rxjava3.core.SingleTransformerimport io.reactivex.rxjava3.schedulers.Schedulers/*** @author yanjun.zhao* @time /6/12 8:39 PM* @desc*/object RxUtil {/*** 线程切换*/fun <T> maybeToMain(): MaybeTransformer<T, T> {return MaybeTransformer { upstream ->upstream.subscribeOn(Schedulers.io()).observeOn(AndroidSchedulers.mainThread())}}/*** 线程切换*/fun <T> singleToMain(): SingleTransformer<T, T> {return SingleTransformer { upstream ->upstream.subscribeOn(Schedulers.io()).observeOn(AndroidSchedulers.mainThread())}}/*** 线程切换*/fun <T> flowableToMain(): FlowableTransformer<T, T> {return FlowableTransformer { upstream ->upstream.subscribeOn(Schedulers.io()).observeOn(AndroidSchedulers.mainThread())}}fun <T> observableToMain(): ObservableTransformer<T, T> {return ObservableTransformer { upstream ->upstream.subscribeOn(Schedulers.io()).observeOn(AndroidSchedulers.mainThread())}}}
具体实现
package com.example.streamimport android.os.Bundleimport androidx.appcompat.app.AppCompatActivityimport io.reactivex.rxjava3.core.Flowableimport io.reactivex.rxjava3.core.Maybeimport io.reactivex.rxjava3.core.Observableimport io.reactivex.rxjava3.core.Singleclass MainActivity : AppCompatActivity() {override fun onCreate(savedInstanceState: Bundle?) {super.onCreate(savedInstanceState)setContentView(R.layout.activity_main)Single.just(1).map {//运行在子线程it}.compose(RxUtil.singleToMain()) //线程转换.subscribe({//运行在主线程},{it.printStackTrace()})Maybe.just(1).map {//运行在子线程it}.compose(RxUtil.maybeToMain()) //线程转换.subscribe({//运行在主线程},{it.printStackTrace()})Flowable.just(1).map {//运行在子线程it}.compose(RxUtil.flowableToMain()) //线程转换.subscribe({//运行在主线程},{it.printStackTrace()})Observable.just(1).map {//运行在子线程it}.compose(RxUtil.observableToMain()) //线程转换.subscribe({ it ->//运行在主线程},{it.printStackTrace()})}}
concat
Concat操作符连接多个Observable的输出，就好像它们是一个Observable，第一个Observable发射的所有数据在第二个Observable发射的任何数据前面，以此类推。
直到前面一个Observable终止，Concat才会订阅额外的一个Observable。注意：因此，如果你尝试连接一个"热"Observable（这种Observable在创建后立即开始发射数据，即使没有订阅者），Concat将不会看到也不会发射它之前发射的任何数据。例子1
private var ob1 = Observable.create<String> {Log.d("concat-数据源1", " ${Thread.currentThread().name} ")it.onNext("a1")it.onComplete()}private var ob2 = Observable.create<String> {Log.d("concat-数据源2", " ${Thread.currentThread().name} ")it.onNext("a2")it.onComplete()}private var ob3 = Observable.create<String> {Log.d("concat-数据源3", " ${Thread.currentThread().name} ")it.onNext("a3")it.onComplete()}Observable.concat<String>(ob1, ob2, ob3).subscribeOn(Schedulers.io()).subscribe{Log.d("concat-结果", " ${Thread.currentThread().name} " + it)}
结果是：
concat-数据源1: RxCachedThreadScheduler-1concat-结果: RxCachedThreadScheduler-1concat-数据源2: RxCachedThreadScheduler-1concat-结果: RxCachedThreadScheduler-1concat-数据源3: RxCachedThreadScheduler-1concat-结果: RxCachedThreadScheduler-1
结果分析：
concat 输出结果是有序的
concat 会使三个数据源都会执行
那么如果我要实现哪个数据源有数据，我就用哪个数据，一旦获取到想要的数据，后续数据源不再执行。其实很简单，用 firstElement() ，这个需求有点像图片加载流程 先从内存取，内存没有从本地文件取，本都文件没有就请求服务器。一旦哪个环节获取到了数据，立刻停止后面的流程
Observable.concat<String>(ob1, ob2, ob3).firstElement().subscribeOn(Schedulers.io()).subscribe {Log.d("concat-结果", " ${Thread.currentThread().name} ")}}
运行结果为：
concat-数据源1: RxCachedThreadScheduler-1concat-结果: RxCachedThreadScheduler-1
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版权声明：本文为CSDN博主「赵彦军」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：/zhaoyanjun6/article/details/10678
