RxJava 2.x implementation of extra sources, operators and components and ports of many 1.x companion libraries.
gradle
dependencies {
  compile "com.github.akarnokd:rxjava2-extensions:0.20.4"
}
Javadoc: https://akarnokd.github.com/RxJava2Extensions/javadoc/index.html
Maven search:
- Extra functional interfaces
- Mathematical operations over numerical sequences
- String operations
- Asynchronous jumpstarting a sequence
- Computational expressions
- Join patterns
- Debug support
- Custom Processors and Subjects
- FlowableProcessor utils
- Custom Schedulers
- Custom operators and transformers
- valve(), orderedMerge(), bufferWhile(),
- bufferUntil(), bufferSplit(), spanout(),
- mapFilter(), onBackpressureTimeout(), repeat(),
- repeatCallable(), every(), intervalBackpressure(),
- cacheLast(), timeoutLast(), timeoutLastAbsolute(),
- debounceFirst(), switchFlatMap(), flatMapSync(),
- flatMapAsync(), switchIfEmpty(),
- expand(), mapAsync(), filterAsync(),
- zipLatest(), coalesce(),
- windowWhile(), windowUntil(), windowSplit(),
- indexOf(), requestObserveOn(), requestSample()
- observeOnDrop(), observeOnLatest(), generateAsync(),
- partialCollect(), flatMapDrop(), flatMapLatest(),
- errorJump(), flatMap on signal type
- Custom parallel operators and transformers
- Special Publisher implementations
- Custom consumers
Support the join-patterns and async-util with functional interfaces of consumers with 3-9 type arguments
and have functional interfaces of functions without the throws Exception.
Supplier<T>-Callable<T>withoutthrows ExceptionConsumer3- 3 argumentConsumerConsumer4- 4 argumentConsumerConsumer5- 5 argumentConsumerConsumer6- 6 argumentConsumerConsumer7- 7 argumentConsumerConsumer8- 8 argumentConsumerConsumer9- 9 argumentConsumerPlainFunction-Functionwithoutthrows ExceptionPlainBiFunction-BiFunctionwithoutthrows ExceptionPlainFunction3-Function3withoutthrows ExceptionPlainFunction4-Function4withoutthrows ExceptionPlainFunction5-Function5withoutthrows ExceptionPlainFunction6-Function6withoutthrows ExceptionPlainFunction7-Function7withoutthrows ExceptionPlainFunction8-Function8withoutthrows ExceptionPlainFunction9-Function9withoutthrows Exception
Utility functions supporting these can be found in FunctionsEx class.
Although most of the operations can be performed with reduce, these operators have lower overhead
as they cut out the reboxing of primitive intermediate values.
The following operations are available in MathFlowable for Flowable sequences and MathObservable in Observable
sequences:
averageDouble()averageFloat()max()min()sumDouble()sumFloat()sumInt()sumLong()
Example
MathFlowable.averageDouble(Flowable.range(1, 10))
.test()
.assertResult(5.5);
Flowable.just(5, 1, 3, 2, 4)
.to(MathFlowable::min)
.test()
.assertResult(1);The StringFlowable and StringObservable support streaming the characters of a CharSequence:
StringFlowable.characters("Hello world")
.map(v -> Characters.toLower((char)v))
.subscribe(System.out::print, Throwable::printStackTrace, System.out::println);Splits an incoming sequence of Strings based on a Regex pattern within and between subsequent elements if necessary.
Flowable.just("abqw", "ercdqw", "eref")
.compose(StringFlowable.split("qwer"))
.test()
.assertResult("ab", "cd", "ef");
Flowable.just("ab", ":cde:" "fg")
.compose(StringFlowable.split(":"))
.test()
.assertResult("ab", "cde", "fg");Wrap functions and consumers into Flowables and Observables or into another layer of Functions.
Most of these can now be achieved via fromCallable and some function composition in plain RxJava (1.x and 2.x alike).
Run a function or action once on a background thread and cache its result.
AtomicInteger counter = new AtomicInteger();
Flowable<Integer> source = AsyncFlowable.start(() -> counter.incrementAndGet());
source.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(1);
source.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(1);Call a function (with parameters) to call a function inside a Flowable/Observable with the same
parameter and have the result emitted by that Flowable/Observable from a background thread.
Function<Integer, Flowable<String>> func = AsyncFlowable.toAsync(
param -> "[" + param + "]"
);
func.apply(1)
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult("[1]")
;Run a Callable that returns a Future to call blocking get() on to get the solo value or exception.
ExecutorService exec = Executors.newSingleThreadedScheduler();
AsyncFlowable.startFuture(() -> exec.submit(() -> 1))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(1);
exec.shutdown();Run a Callable that returns a Future to call blocking get() on to get a Publisher to stream back.
ExecutorService exec = Executors.newSingleThreadedScheduler();
AsyncFlowable.startFuture(() -> exec.submit(() -> Flowable.range(1, 5)))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(1, 2, 3, 4, 5);
exec.shutdown();Consume a Publisher and have Future that completes when the consumption ends with onComplete or onError.
Future<Object> f = AsyncFlowable.forEachFuture(Flowable.range(1, 100), System.out::println);
f.get();Allows emitting multiple values through a Processor mediator from a background thread and allows disposing the sequence externally.
AsyncFlowable.runAsync(Schedulers.single(),
UnicastProcessor.<Object>create(),
new BiConsumer<Subscriber<Object>, Disposable>() {
@Override
public void accept(Subscriber<? super Object> s, Disposable d) throws Exception {
s.onNext(1);
s.onNext(2);
s.onNext(3);
Thread.sleep(200);
s.onNext(4);
s.onNext(5);
s.onComplete();
}
}
).test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(1, 2, 3, 4, 5);The operators on StatementFlowable and StatementObservable allow taking different branches at subscription time:
Conditionally chose a source to subscribe to. This is similar to the imperative if statement but with reactive flows:
if ((System.currentTimeMillis() & 1) != 0) {
System.out.println("An odd millisecond");
} else {
System.out.println("An even millisecond");
}Flowable<String> source = StatementFlowable.ifThen(
() -> (System.currentTimeMillis() & 1) != 0,
Flowable.just("An odd millisecond"),
Flowable.just("An even millisecond")
);
source
.delay(1, TimeUnit.MILLISECONDS)
.repeat(1000)
.subscribe(System.out::println);Calculate a key and pick a source from a Map. This is similar to the imperative switch statement:
switch ((int)(System.currentTimeMillis() & 7)) {
case 1: System.out.println("one"); break;
case 2: System.out.println("two"); break;
case 3: System.out.println("three"); break;
default: System.out.println("Something else");
}Map<Integer, Flowable<String>> map = new HashMap<>();
map.put(1, Flowable.just("one"));
map.put(2, Flowable.just("two"));
map.put(3, Flowable.just("three"));
Flowable<String> source = StatementFlowable.switchCase(
() -> (int)(System.currentTimeMillis() & 7),
map,
Flowable.just("Something else")
);
source
.delay(1, TimeUnit.MILLISECONDS)
.repeat(1000)
.subscribe(System.out::println);Resubscribe if a condition is true after the last subscription completed normally. This is similar to the imperative do-while loop (executing the loop body at least once):
long start = System.currentTimeMillis();
do {
Thread.sleep(1);
System.out.println("Working...");
while (start + 100 > System.currentTimeMillis());long start = System.currentTimeMillis();
Flowable<String> source = StatementFlowable.doWhile(
Flowable.just("Working...").delay(1, TimeUnit.MILLISECONDS),
() -> start + 100 > System.currentTimeMillis()
);
source.subscribe(System.out::println);Subscribe and resubscribe if a condition is true. This is similar to the imperative while loop (where the loop body may not execute if the condition is false
to begin with):
while ((System.currentTimeMillis() & 1) != 0) {
System.out.println("What an odd millisecond!");
}Flowable<String> source = StatementFlowable.whileDo(
Flowable.just("What an odd millisecond!"),
() -> (System.currentTimeMillis() & 1) != 0
);
source.subscribe(System.out::println);(Conversion done)
TBD: examples
By default, RxJava 2's RxJavaPlugins only offers the ability to hook into the assembly process (i.e., when you apply an operator on a sequence or create one) unlike 1.x where there is an RxJavaHooks.enableAssemblyTracking() method. Since the standard format is of discussion there, 2.x doesn't have such feature built in but only
in this extension library.
You enable tracking via:
RxJavaAssemblyTracking.enable();and disable via:
RxJavaAssemblyTracking.disable();Note that this doesn't save or preserve the old hooks (named Assembly) you may have set as of now.
In debug mode, you can walk through the reference graph of Disposables and Subscriptions to find an FlowableOnAssemblyX named nodes (similar in the other base types) where there is an assembled field of type RxJavaAssemblyException. This has also a field named stacktrace that contains a pretty printed stacktrace string pointing to the assembly location:
RxJavaAssemblyException: assembled
at io.reactivex.Completable.error(Completable.java:280)
at hu.akarnokd.rxjava2.debug.RxJava2AssemblyTrackingTest.createCompletable(RxJava2AssemblyTrackingTest.java:78)
at hu.akarnokd.rxjava2.debug.RxJava2AssemblyTrackingTest.completable(RxJava2AssemblyTrackingTest.java:185)
This is a filtered list of stacktrace elements (skipping threading, unit test and self-related entries). Most modern IDEs should allow you to navigate to the locations when printed on (or pasted into) its console.
To avoid interference, the RxJavaAssemblyException is attached as the last cause to potential chain of the original exception that travels through each operator to the end consumer.
You can programmatically find this via:
RxJavaAssemblyException assembled = RxJavaAssemblyException.find(someThrowable);
if (assembled != null) {
System.err.println(assembled.stacktrace());
}Often, when a function throws or returns null, there is not enough information to
locate said function in the codebase. The FunctionTagging utility class offers
static wrappers for RxJava funtion types that when fail or return null, a custom
string tag is added or appended to the exception and allows locating that function
in your codebase. Since added logic has overhead, the tagging process has to be
enabled and can be disabled as necessary.
FunctionTagging.enable();
Function<Integer, Integer> tagged = FunctionTagging.tagFunction(v -> null, "F1");
FunctionTagging.disable();
Function<Integer, Integer> notTagged = FunctionTagging.tagFunction(v -> null, "F2");
assertNull(notTagged.apply(1));
try {
tagged.apply(1);
fail("Should have thrown");
} catch (NullPointerException ex) {
assertTrue(ex.getMessage().contains("F1"));
}To avoid lambda ambiguity, the methods are named tagX where X is the functional type name such as BiFunction, Function3 etc.
The wrappers check for null parameters and if the wrapped function returns a null and throw a NullPointerException containing the parameter
name (t1 .. t9) and the tag provided.
Custom operators and sources sometimes contain bugs that manifest themselves in odd sequence behavior or crashes
from within the standard operators. Since the revealing stacktraces is often missing or incomplete, diagnosing
such failures can be tiresome. Therefore, the hu.akarnokd.rxjava2.debug.validator.RxJavaProtocolValidator
class offers assembly hooks for the standard reactive base types.
The validation hooks can be enabled via RxJavaProtocolValidator.enable() and disabled via RxJavaProtocolValidator.disable().
The validator also supports chaining with existing hooks via enableAndChain() which returns a SavedHooks instance
to restore the original hooks specifically:
SavedHooks hooks = RxJavaProtocolValidator.enableAndChain();
// assemble and run flows
// ...
hooks.restore();By default, the violations, subclasses of ProtocolNonConformanceException, are reported to the RxJavaPlugins.onError
handler but can be overridden via RxJavaProtocolValidator.setOnViolationHandler.
RxJavaProtocolValidator.setOnViolationHandler(e -> e.printStackTrace());
RxJavaProtocolValidator.enable();
// ...The following error violations are detected:
| Exception | Violation description |
|---|---|
| MultipleTerminationsException | When multiple calls to onError or onComplete happened. |
| MultipleOnSubscribeCallsException | When multiple calls to onSubscribe happened |
| NullOnErrorParameterException | When the onError was called with a null Throwable. |
| NullOnNextParameterException | When the onNext was called with a null value. |
| NullOnSubscribeParameterException | When the onSubscribe was called with a null Disposable or Subscription. |
| NullOnSuccessParameterException | When the onSuccess was called with a null value. |
| OnNextAfterTerminationException | Wen the onNext was called after onError or onComplete. |
| OnSubscribeNotCalledException | When any of the onNext, onSuccess, onError or onComplete is invoked without invoking onSubscribe first. |
| OnSuccessAfterTerminationException | Wen the onSuccess was called after onError or onComplete. |
The standard RxJavaPlugins allows only one hook to be associated with each main intercept option.
If multiple hooks should be invoked, that option is not directly supported by RxJavaPlugins but
can be built upon the single hook scheme.
The hu.akarnokd.rxjava2.debug.multihook package offers hook managers that can work with multiple hooks
themselves.
The various multi-hook managers are built upon the generic MultiHandlerManager<H> class. The class offers the register
method to register a particular hook for which a Disposable is returned. This allows removing a particular
hook without the need to remember the hook instance or the manager class.
Offers multi-hook management for the RxJavaPlugins.setScheduleHandler and onSchedule hooks.
The enable() method will install the instance as the main hook, the disable() will restore the default no-hook.
The convenience append() will take any existing hook, register it with the manager and install the manager as the main hook.
These are the backpressure-aware, Reactive-Streams Processor-based implementations of the SingleSubject, MaybeSubject and CompletableSubject respectively. Their usage is quite similar.
PerhapsProcessor<Integer> ms = PerhapsProcessor.create();
TestSubscriber<Integer> to = ms.test();
ms.onNext(1);
ms.onComplete();
to.assertResult(1);Similarly with NonoProcessor, although calling onNext(null) will throw a NullPointerException to the caller.
NonoProcessor cs = NonoProcessor.create();
TestSubscriber<Void> to2 = cs.test();
cs.onComplete();
to2.assertResult();Finally
SoloProcessor<Integer> ss = SoloProcessor.create();
TestSubscriber<Integer> to3 = ss.test();
ss.onNext(1);
ss.onComplete();
to3.assertResult(1);Note that calling onComplete after onNext is optional with SoloProcessor but calling onComplete without calling onNext terminates the SoloProcessor with a NoSuchElementException.
Moved to RxJava as standard processor: io.reactivex.processors.MulticastProcessor.
A Subject variant that buffers items and allows one Observer to consume it at a time, but unlike UnicastSubject,
once the previous Observer disposes, a new Observer can subscribe and resume consuming the items.
UnicastWorkSubject<Integer> uws = UnicastWorkSubject.create();
uws.onNext(1);
uws.onNext(2);
uws.onNext(3);
uws.onNext(4);
uws.take(2).test().assertResult(1, 2);
uws.take(2).test().assertResult(3, 4);
uws.onComplete();
uws.test().assertResult();A Subject variant that buffers items and allows one or more Observers to exclusively consume one of the items in the buffer
asynchronously. If there are no Observers (or they all disposed), the DispatchWorkSubject will keep buffering and later
Observers can resume the consumption of the buffer.
DispatchWorkSubject<Integer> dws = DispatchWorkSubject.create();
Single<List<Integer>> asList = dws.toList();
TestObserver<List<Integer>> to = Single
.zip(asList, asList, (a, b) -> a.addAll(b))
.test();
Observable.range(1, 1000000).subscribe(dws);
to.awaitDone(5, TimeUnit.SECONDS)
.assertValueCount(1)
.assertComplete()
.assertNoErrors();
assertEquals(1000000, to.values().get().size());A FlowableProcessor variant that buffers items and allows one or more Subscribers to exclusively consume one of the items in the buffer
asynchronously. If there are no Subscribers (or they all disposed), the DispatchWorkSubscriber will keep buffering and later
Subscribers can resume the consumption of the buffer.
DispatchWorkProcessor<Integer> dwp = DispatchWorkProcessor.create();
Single<List<Integer>> asList = dwp.toList();
TestObserver<List<Integer>> to = Single
.zip(asList, asList, (a, b) -> a.addAll(b))
.test();
Flowable.range(1, 1000000).subscribe(dws);
to.awaitDone(5, TimeUnit.SECONDS)
.assertValueCount(1)
.assertComplete()
.assertNoErrors();
assertEquals(1000000, to.values().get().size());An utility class that helps working with Reactive-Streams Processor, FlowableProcessor
Subject instances via static methods.
Wraps an arbitrary Processor into a FlowableProcessor
Wraps a FlowableProcessor/Subject and makes sure if all subscribers/observer cancel/dispose
their subscriptions, the upstream's Subscription/Disposable gets cancelled/disposed as well.
This Scheduler implementation takes a Worker directly or from another Scheduler and shares it across its own Workers while
making sure disposing one of its own SharedWorker doesn't dispose any other SharedWorker or the underlying shared Worker.
This type of scheduler may help solve the problem when one has to return to the same thread/scheduler at different stages of the pipeline
but one doesn't want to or isn't able to use SingleScheduler or some other single-threaded thread-pool wrapped via Schedulers.from().
SharedScheduler shared = new SharedScheduler(Schedulers.io());
Flowable.just(1)
.subscribeOn(shared)
.map(v -> Thread.currentThread().getName())
.observeOn(Schedulers.computation())
.map(v -> v.toLowerCase())
.observeOn(shared)
.map(v -> v.equals(Thread.currentThread().getName().toLowerCase()))
.blockingForEach(System.out::println);It is similar to Schedulers.computation() but you can control the number of threads, the thread name prefix, the thread priority and to track each task sumbitted to its worker.
Tracking a task means that if one calls Worker.dispose(), all outstanding tasks is cancelled. However, certain use cases can get away with just preventing the execution of the task body and just run through all outstanding tasks yielding lower overhead.
The ParallelScheduler supports start and shutdown to start and stop the backing thread-pools. The non-ThreadFactory constructors create a daemon-thread backed set of single-threaded thread-pools.
Scheduler s = new ParallelScheduler(3);
try {
Flowable.range(1, 10)
.flatMap(v -> Flowable.just(1).subscribeOn(s).map(v -> v + 1))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertValueSet(Arrays.asList(2, 3, 4, 5, 6, 7, 8, 9, 10, 11))
.assertComplete()
.assertNoErrors();
} finally {
s.shutdown();
}This type of scheduler runs its execution loop on the "current thread", more specifically, the thread which invoked its execute() method. The method blocks until the shutdown() is invoked. This type of scheduler allows returning to the "main" thread from other threads.
public static void main(String[] args) {
BlockingScheduler scheduler = new BlockingScheduler();
scheduler.execute(() -> {
Flowable.range(1,10)
.subscribeOn(Schedulers.io())
.observeOn(scheduler)
.doAfterTerminate(() -> scheduler.shutdown())
.subscribe(v -> System.out.println(v + " on " + Thread.currentThread()));
});
System.out.println("BlockingScheduler finished");
}The custom transformers (to be applied with Flowable.compose for example), can be found in hu.akarnokd.rxjava2.operators.FlowableTransformers class. The custom source-like operators can be found in hu.akarnokd.rxjava2.operators.Flowables class. The operators and transformers for the other base
reactive classes (will) follow the usual naming scheme.
Pauses and resumes a main flow if the secondary flow signals false and true respectively.
Also available as ObservableTransformers.valve().
PublishProcessor<Boolean> valveSource = PublishProcessor.create();
Flowable.intervalRange(1, 20, 1, 1, TimeUnit.SECONDS)
.compose(FlowableTransformers.<Long>valve(valveSource))
.subscribe(System.out::println, Throwable::printStackTrace);
Thread.sleep(3100);
valveSource.onNext(false);
Thread.sleep(5000);
valveSource.onNext(true);
Thread.sleep(3000);
valveSource.onNext(false);
Thread.sleep(6000);
valveSource.onNext(true);
Thread.sleep(3000);Given a fixed number of input sources (which can be self-comparable or given a Comparator) merges them
into a single stream by repeatedly picking the smallest one from each source until all of them completes.
Flowables.orderedMerge(Flowable.just(1, 3, 5), Flowable.just(2, 4, 6))
.test()
.assertResult(1, 2, 3, 4, 5, 6);Buffers into a list/collection while the given predicate returns true for the current item, otherwise starts a new list/collection containing the given item (i.e., the "separator" ends up in the next list/collection).
Flowable.just("1", "2", "#", "3", "#", "4", "#")
.compose(FlowableTransformers.bufferWhile(v -> !"#".equals(v)))
.test()
.assertResult(
Arrays.asList("1", "2"),
Arrays.asList("#", "3"),
Arrays.asList("#", "4"),
Arrays.asList("#")
);Buffers into a list/collection until the given predicate returns true for the current item and starts an new empty list/collection (i.e., the "separator" ends up in the same list/collection).
Flowable.just("1", "2", "#", "3", "#", "4", "#")
.compose(FlowableTransformers.bufferUntil(v -> "#".equals(v)))
.test()
.assertResult(
Arrays.asList("1", "2", "#"),
Arrays.asList("3", "#"),
Arrays.asList("4", "#")
);Buffers into a list/collection while the predicate returns false. When it returns true, a new buffer is started and the particular item won't be in any of the buffers.
Flowable.just("1", "2", "#", "3", "#", "4", "#")
.compose(FlowableTransformers.bufferSplit(v -> "#".equals(v)))
.test()
.assertResult(
Arrays.asList("1", "2"),
Arrays.asList("3"),
Arrays.asList("4")
);Inserts a time delay between emissions from the upstream. For example, if the upstream emits 1, 2, 3 in a quick succession, a spanout(1, TimeUnit.SECONDS) will emit 1 immediately, 2 after a second and 3 after a second after 2. You can specify the initial delay, a custom scheduler and if an upstream error should be delayed after the normal items or not.
Flowable.range(1, 10)
.compose(FlowableTransformers.spanout(1, 1, TimeUnit.SECONDS))
.doOnNext(v -> System.out.println(System.currentTimeMillis() + ": " + v))
.test()
.awaitDone(20, TimeUnit.SECONDS)
.assertResult(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);A callback Consumer is called with the current upstream value and a BasicEmitter on which doXXX methods can be called
to transform a value, signal an error or stop a sequence. If none of the doXXX methods is called, the current value is dropped and another is requested from upstream. The operator is a pass-through for downstream requests otherwise.
Flowable.range(1, 10)
.compose(FlowableTransformers.mapFilter((v, e) -> {
if (v % 2 == 0) {
e.doNext(v * 2);
}
if (v == 5) {
e.doComplete();
}
}))
.test()
.assertResult(4, 8);Consumes the upstream in an unbounded manner and buffers elements until the downstream requests but each buffered element has an associated timeout after which it becomes unavailable. Note that this may create discontinuities in the stream. In addition, an overload allows specifying the maximum buffer size and an eviction action which gets triggered when the buffer reaches its capacity or elements time out.
Flowable.intervalRange(1, 5, 100, 100, TimeUnit.MILLISECONDS)
.compose(FlowableTransformers
.onBackpressureTimeout(2, 100, TimeUnit.MILLISECONDS,
Schedulers.single(), System.out::println))
.test(0)
.awaitDone(5, TimeUnit.SECONDS)
.assertResult();Repeats a scalar value indefinitely (until the downstream actually cancels), honoring backpressure and supporting synchronous fusion and/or conditional fusion.
Flowable.repeat("doesn't matter")
.map(v -> ThreadLocalRandom.current().nextDouble())
.take(100)
.all(v -> v < 1d)
.test()
.assertResult(true);Repeatedly calls a callable, indefinitely (until the downstream actually cancels) or if the callable throws or returns null (when it signals NullPointerException), honoring backpressure and supporting synchronous fusion and/or conditional fusion.
Flowable.repeatCallable(() -> ThreadLocalRandom.current().nextDouble())
.take(100)
.all(v -> v < 1d)
.test()
.assertResult(true);Relays every Nth item from upstream (skipping the in-between items).
Flowable.range(1, 5)
.compose(FlowableTransformers.<Integer>every(2))
.test()
.assertResult(2, 4)Emit an ever increasing series of long values, starting from 0L and "buffer" emissions in case the downstream can't keep up. The "buffering" is virtual and isn't accompanied by increased memory usage if it happens for a longer period of time.
Flowables.intervalBackpressure(1, TimeUnit.MILLISECONDS)
.observeOn(Schedulers.single())
.take(1000)
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertValueCount(1000)
.assertNoErrors()
.assertComplete();Caches the very last value of the upstream source and relays/replays it to Subscribers. The difference from replay(1) is that this operator is guaranteed
to hold onto exactly one value whereas replay(1) may keep a reference to the one before too due to continuity reasons.
Flowable<Integer> f = Flowable.range(1, 5)
.doOnSubscribe(s -> System.out.println("Subscribed!"))
.compose(FlowableTransformers.cacheLast());
// prints "Subscribed!"
f.test().assertResult(5);
// doesn't print anything else
f.test().assertResult(5);
f.test().assertResult(5);The operator consumes the upstream to get to the last value but completes if the sequence doesn't complete within the specified timeout. A use case is when the upstream generates estimates, each better than the previous but we'd like to receive the last of it and not wait for a potentially infinite series.
There are two variants: relative timeout and absolute timeout.
With relative timeout, the operator restarts the timeout after each upstream item, cancels the upstream and emits that latest item if the timeout happens:
Flowable.just(0, 50, 100, 400)
.flatMap(v -> Flowable.timer(v, TimeUnit.MILLISECONDS).map(w -> v))
.compose(FlowableTransformers.timeoutLast(200, TimeUnit.MILLISECONDS))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(100);With absolute timeout, the upstream operator is expected to complete within the specified amount of time and if it doesn't, the upstream gets cancelled and the latest item emitted.
Flowable.just(0, 50, 100, 150, 400)
.flatMap(v -> Flowable.timer(v, TimeUnit.MILLISECONDS).map(w -> v))
.compose(FlowableTransformers.timeoutLastAbsolute(200, TimeUnit.MILLISECONDS))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(150);Debounces the upstream by taking an item and dropping subsequent items until the specified amount of time elapses after the last item, after which the process repeats.
Flowable.just(0, 50, 100, 150, 400, 500, 550, 1000)
.flatMap(v -> Flowable.timer(v, TimeUnit.MILLISECONDS).map(w -> v))
.compose(FlowableTransformers.debounceFirst(200, TimeUnit.MILLISECONDS))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(0, 400, 1000);This is a combination of switchMap and a limited flatMap. It merges a maximum number of Publishers at once but if a new inner Publisher gets mapped in and the active count is at max, the oldest active Publisher is cancelled and the new inner Publisher gets flattened as well. Running with maxActive == 1 is equivalent to the plain switchMap.
Flowable.just(100, 300, 500)
.flatMap(v -> Flowable.timer(v, TimeUnit.MILLISECONDS).map(w -> v))
.compose(FlowableTransformers.switchFlatMap(v -> {
if (v == 100) {
return Flowable.intervalRange(1, 3, 75, 100, TimeUnit.MILLISECONDS)
.map(w -> "A" + w);
} else
if (v == 300) {
return Flowable.intervalRange(1, 3, 10, 100, TimeUnit.MILLISECONDS)
.map(w -> "B" + w);
}
return Flowable.intervalRange(1, 3, 20, 100, TimeUnit.MILLISECONDS)
.map(w -> "C" + w);
}, 2)
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult("A1", "A2", "B1", "A3", "B2", "C1", B3", "C2", "C3);A bounded-concurrency flatMap implementation optimized for mostly non-trivial, largely synchronous sources in mind and using different tracking method and configurable merging strategy: depth-first consumes each inner source as much as possible before switching to the next; breadth-first consumes one element from each source in a round-robin fashion. Overloads allow specifying the concurrency level (32 default), inner-prefetch (Flowable.bufferSize() default) and the merge strategy (depth-first default).
Flowable.range(1, 1000)
.compose(FlowableTransformers.flatMapSync(v -> Flowable.range(1, 1000)))
.test()
.assertValueCount(1_000_000)
.assertNoErrors()
.assertComplete();A bounded-concurrency flatMap implementation taking a scheduler which is used for collecting and emitting items from the active sources and freeing up the inner sources to keep producing. It also uses a different tracking method and configurable merging strategy: depth-first consumes each inner source as much as possible before switching to the next; breadth-first consumes one element from each source in a round-robin fashion. Overloads allow specifying the concurrency level (32 default), inner-prefetch (Flowable.bufferSize() default) and the merge strategy (depth-first default).
Flowable.range(1, 1000)
.compose(FlowableTransformers.flatMapAsync(v -> Flowable.range(1, 1000), Schedulers.single()))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertValueCount(1_000_000)
.assertNoErrors()
.assertComplete();Switches to the alternatives, one after the other if the main source or the previous alternative turns out to be empty.
Flowable.empty()
.compose(FlowableTransformers.switchIfEmpty(Arrays.asList(Flowable.empty(), Flowable.range(1, 5))))
.test()
.assertResult(1, 2, 3, 4, 5);
Flowable.empty()
.compose(FlowableTransformers.switchIfEmptyArray(Flowable.empty(), Flowable.range(1, 5)))
.test()
.assertResult(1, 2, 3, 4, 5);Streams values from the main source, maps each of them onto another Publisher and recursively streams those Publisher values until all Publishers terminate. Two recursing mode is available: breadth-first will stream the main source (level 1), then the Publishers generated by its items (level 2), then the Publishers generated by the level 2 and so on; depth-first will take an item from the main source, maps it to a Publisher then takes an item from this Publisher and maps it further.
Flowable.just(10)
.compose(FlowableTransformers.expand(v -> v == 0 ? Flowable.empty() : Flowable.just(v - 1)))
.test()
.assertResult(10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);Depth-first example:
Flowable.just(new File("."))
.compose(FlowableTransformers.expand(file -> {
if (file.isDirectory()) {
File[] files = file.listFiles();
if (files != null) {
return Flowable.fromArray(files);
}
}
return Flowable.empty();
}, ExpandStrategy.DEPTH_FIRST))
.subscribe(System.out::println);
// prints something like
// ~/git/RxJava2Extensions
// ~/git/RxJava2Extensions/src
// ~/git/RxJava2Extensions/src/main
// ~/git/RxJava2Extensions/src/main/java
// ~/git/RxJava2Extensions/src/main/java/hu
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2/operators
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2/operators/FlowableExpand.java
// ...
// ~/git/RxJava2Extensions/src/test
// ~/git/RxJava2Extensions/src/test/javaBreadth-first example:
Flowable.just(new File("."))
.compose(FlowableTransformers.expand(file -> {
if (file.isDirectory()) {
File[] files = file.listFiles();
if (files != null) {
return Flowable.fromArray(files);
}
}
return Flowable.empty();
}, ExpandStrategy.BREADTH_FIRST))
.subscribe(System.out::println);
// prints something like
// ~/git/RxJava2Extensions
// ~/git/RxJava2Extensions/src
// ~/git/RxJava2Extensions/build
// ~/git/RxJava2Extensions/gradle
// ~/git/RxJava2Extensions/HEADER
// ~/git/RxJava2Extensions/README.md
// ...
// ~/git/RxJava2Extensions/src/main
// ~/git/RxJava2Extensions/src/test
// ~/git/RxJava2Extensions/src/jmh
// ~/git/RxJava2Extensions/src/main/java
// ~/git/RxJava2Extensions/src/main/java/hu
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2/operators
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2/math
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2/async
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2/debug
// ...
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2/operators/FlowableExpand.java
// ~/git/RxJava2Extensions/src/main/java/hu/akarnokd/rxjava2/operators/FlowableTransformers.javaAlso available as ObservableTransformers.mapAsync().
This is an "asynchronous" version of the regular map() operator where an upstream value is mapped to a Publisher which
is expected to emit a single value to be the result itself or through a combiner function become the result. Only
one such Publisher is executed at once and the source order is kept. If the Publisher is empty, no value is emitted
and the sequence continues with the next upstream value. If the Publisher has more than one element, only the first
element is considered and the inner sequence gets cancelled after that first element.
Flowable.range(1, 5)
.compose(FlowableTransformers.mapAsync(v ->
Flowable.just(v + 1).delay(1, TimeUnit.SECONDS)))
.test()
.awaitDone(10, TimeUnit.SECONDS)
.assertResult(2, 3, 4, 5, 6);Example when using a combiner function to combine the original and the generated values:
Flowable.range(1, 5)
.compose(FlowableTransformers.mapAsync(v ->
Flowable.just(v + 1).delay(1, TimeUnit.SECONDS)), (v, w) -> v + "-" + w)
.test()
.awaitDone(10, TimeUnit.SECONDS)
.assertResult("1-2", "2-3", "3-4", "4-5", "5-6");Also available as ObservableTransformers.filterAsync().
This is an "asynchronous" version of the regular filter() operator where an upstream value is mapped to a Publisher
which is expected to emit a single true or false that indicates the original value should go through. An empty Publisher
is considered to be a false response. If the Publisher has more than one element, only the first
element is considered and the inner sequence gets cancelled after that first element.
Flowable.range(1, 10)
.compose(FlowableTransformers.filterAsync(v -> Flowable.just(v).delay(1, TimeUnit.SECONDS).filter(v % 2 == 0))
.test()
.awaitDone(15, TimeUnit.SECONDS)
.assertResult(2, 4, 6, 8, 10);*Moved to RxJava as standard operators: ConnectableObservable.refCount, ConnectableFlowable.refCount.
Zips the latest values from multiple sources and calls a combiner function for them.
If one of the sources is faster then the others, its unconsumed values will be overwritten by newer
values.
Unlike combineLatest, source items are participating in the combination at most once; i.e., the
operator emits only if all sources have produced an item.
The emission speed of this operator is determined by the slowest emitting source and the speed of the downstream consumer.
There are several overloads available: methods taking 2-4 sources and the respective combiner functions, a method taking a
varargs of sources and a method taking an Iterable of source Publishers.
The operator supports combining and scheduling the emission of the result via a custom Scheduler, thus
allows avoiding the buffering effects of the observeOn operator.
The operator terminates if any of the sources runs out of items and terminates by itself.
The operator works with asynchronous sources the best; synchronous sources may get consumed fully
in order they appear among the parameters and possibly never emit more than one combined result even
if the last source has more than one item.
TestScheduler scheduler = new TestScheduler();
TestSubscriber<String> ts = Flowables.zipLatest(toString,
Flowable.intervalRange(1, 6, 99, 100, TimeUnit.MILLISECONDS, scheduler),
Flowable.intervalRange(4, 3, 200, 200, TimeUnit.MILLISECONDS, scheduler)
)
.test();
scheduler.advanceTimeBy(200, TimeUnit.MILLISECONDS);
ts.assertValue("[2, 4]");
scheduler.advanceTimeBy(200, TimeUnit.MILLISECONDS);
ts.assertValues("[2, 4]", "[4, 5]");
scheduler.advanceTimeBy(200, TimeUnit.MILLISECONDS);
ts.assertResult("[2, 4]", "[4, 5]", "[6, 6]");Coalesces items from upstream into a container via a consumer and emits the container if
there is a downstream demand, otherwise it keeps coalescing into the same container. Note
that the operator keeps an internal unbounded buffer to collect up upstream values before
the coalescing happens and thus a computational heavy downstream hogging the emission thread
may lead to excessive memory usage. It is recommended to use observeOn in this case.
Flowable.range(1, 5)
.compose(FlowableTransformers.coalesce(ArrayList::new, (a, b) -> a.add(b))
.test(1)
.assertValue(Arrays.asList(1))
.requestMore(1)
.assertResult(Arrays.asList(1), Arrays.asList(2, 3, 4, 5));Emits elements into a Flowable window while the given predicate returns true. If the predicate returns false, a new Flowable window is emitted.
Flowable.just("1", "2", "#", "3", "#", "4", "#")
.compose(FlowableTransformers.windowWhile(v -> !"#".equals(v)))
.flatMapSingle(v -> v.toList())
.test()
.assertResult(
Arrays.asList("1", "2"),
Arrays.asList("#", "3"),
Arrays.asList("#", "4"),
Arrays.asList("#")
);Emits elements into a Flowable window until the given predicate returns true at which point a new Flowable window is emitted.
Flowable.just("1", "2", "#", "3", "#", "4", "#")
.compose(FlowableTransformers.windowUntil(v -> "#".equals(v)))
.flatMapSingle(v -> v.toList())
.test()
.assertResult(
Arrays.asList("1", "2", "#"),
Arrays.asList("3", "#"),
Arrays.asList("4", "#")
);Emits elements into a Flowable window until the given predicate returns true at which point a new Flowable window is emitted; the particular item will be dropped.
Flowable.just("1", "2", "#", "3", "#", "4", "#")
.compose(FlowableTransformers.windowSplit(v -> "#".equals(v)))
.flatMapSingle(v -> v.toList())
.test()
.assertResult(
Arrays.asList("1", "2"),
Arrays.asList("3"),
Arrays.asList("4")
);Returns the first index of an element that matches a predicate or -1L if no elements match.
(Also available for Observables as ObservableTransformers.indexOf().)
Flowable.range(1, 5)
.compose(FlowableTransformers.indexOf(v -> v == 5))
.test()
.assertResult(4);Requests items one-by-one from the upstream on the specified Scheduler and emits the received items from the
given Scheduler as well in a fashion that allows tasks to be interleaved on the target Scheduler (aka "fair" use)
on a much more granular basis than Flowable.observeOn.
Flowable.range(1, 5)
.compose(FlowableTransformers.requestObserveOn(Schedulers.single()))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertResult(1, 2, 3, 4, 5)
;Periodically (and after an optional initial delay) issues a single request(1) to the upstream and forwards the
items to a downstream that must be ready to receive them.
Flowables.repeatCallable(() -> 1)
.compose(FlowableTransformers.requestSample(1, TimeUnit.SECONDS, Schedulers.single()))
.take(5)
.test()
.awaitDone(7, TimeUnit.SECONDS)
.assertResult(1, 1, 1, 1, 1);The sampling can be of a more complex pattern by using another Publisher as the indicator when to request:
Flowables.repeatCallable(() -> 1)
.compose(FlowableTransformers.requestSample(
Flowable.fromArray(100, 500, 1000, 2000, 5000)
.concatMap(delay -> Flowable.timer(delay, TimeUnit.MILLISECONDS))
))
.take(5)
.test()
.awaitDone(10, TimeUnit.SECONDS)
.assertResult(1, 1, 1, 1, 1);Drop upstream items while the downstream is working on an item in its onNext method on an Scheduler.
This is similar to Flowable.onBackpressureDrop but instead dropping on a lack of requests, items
from upstream are dropped while a work indicator is active during the execution of the downstream's
onNext method.
Observable.range(1, 1000000)
.compose(ObservableTransformers.observeOnDrop(Schedulers.io()))
.doOnNext(v -> Thread.sleep(1))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertOf(to -> {
assertTrue(to.getValueCount() >= 1 && to.getValueCount() <= 1000000);
});Keeps the latest item from the upstream while the downstream working on the current item in its onNext
methdo on a Scheduler, so that when it finishes with the current item, it can continue immediately
with the latest item from the upstream.
This is similar to Flowable.onBackpressureLatest except that the latest item is always picked up, not just when
there is also a downstream demand for items.
Observable.range(1, 1000000)
.compose(ObservableTransformers.observeOnLatest(Schedulers.io()))
.doOnNext(v -> Thread.sleep(1))
.test()
.awaitDone(5, TimeUnit.SECONDS)
.assertOf(to -> {
assertTrue(to.getValueCount() >= 1 && to.getValueCount() <= 1000000);
});A source operator to bridge async APIs that can be repeatedly called to produce the next item (or terminate in some way) asynchronously and only call the API again once the result has been received and delivered to the downstream, while honoring the backpressure of the downstream. This means if the downstream stops requesting, the API won't be called until the latest result has been requested and consumed by the downstream.
Example APIs could be AsyncEnumerable style, coroutine style or async-await.
Let's assume there is an async API with the following interface definition:
interface AsyncAPI<T> extends AutoCloseable {
CompletableFuture<Void> nextValue(Consumer<? super T> onValue);
}When the call succeeds, the onValue is invoked with it. If there are no more items, the
CompletableFuture returned by the last nextValue is completed (with null ).
If there is an error, the same CompletableFuture is completed exceptionally. Each
nextValue invocation creates a fresh CompletableFuture which can be cancelled
if necesary. nextValue should not be invoked again until the onValue callback
has been notified.
An instance of this API can be obtained on demand, thus the state of this operator consists of the
AsyncAPI instance supplied for each individual {@code Subscriber}. The API can be transformed into
a Flowable as follows:
Flowable<Integer> source = Flowables.<Integer, AsyncAPI<Integer>>generateAsync(
// create a fresh API instance for each individual Subscriber
() -> new AsyncAPIImpl<Integer>(),
// this BiFunction will be called once the operator is ready to receive the next item
// and will invoke it again only when that item is delivered via emitter.onNext()
(state, emitter) -> {
// issue the async API call
CompletableFuture<Void> f = state.nextValue(
// handle the value received
value -> {
// we have the option to signal that item
if (value % 2 == 0) {
emitter.onNext(value);
} else if (value == 101) {
// or stop altogether, which will also trigger a cleanup
emitter.onComplete();
} else {
// or drop it and have the operator start a new call
emitter.onNothing();
}
}
);
// This API call may not produce further items or fail
f.whenComplete((done, error) -> {
// As per the CompletableFuture API, error != null is the error outcome,
// done is always null due to the Void type
if (error != null) {
emitter.onError(error);
} else {
emitter.onComplete();
}
});
// In case the downstream cancels, the current API call
// should be cancelled as well
emitter.replaceCancellable(() -> f.cancel(true));
// some sources may want to create a fresh state object
// after each invocation of this generator
return state;
},
// cleanup the state object
state -> { state.close(); }
);Allows converting upstream items into output objects where an upstream item may represent such output objects partially or may represent more than one output object.
For example, given a stream of {@code byte[]} where each array could contain part of a larger object, and thus more than one subsequent arrays are required to construct the output object. The same array could also contain more than one output items, therefore, it should be kept around in case the output is backpressured.
This example shows, given a flow of Strings with embedded separator |, how one
can split them along the separator and have invidiual items returned, even when
they span multiple subsequent items (cdefgh) or more than one is present in a source item (mno||pqr|s).
Flowable.just("ab|cdef", "gh|ijkl|", "mno||pqr|s", "|", "tuv|xy", "|z")
.compose(FlowableTransformers.partialCollect(new Consumer<PartialCollectEmitter<String, Integer, StringBuilder, String>>() {
@Override
public void accept(
PartialCollectEmitter<String, Integer, StringBuilder, String> emitter)
throws Exception {
Integer idx = emitter.getIndex();
if (idx == null) {
idx = 0;
}
StringBuilder sb = emitter.getAccumulator();
if (sb == null) {
sb = new StringBuilder();
emitter.setAccumulator(sb);
}
if (emitter.demand() != 0) {
boolean d = emitter.isComplete();
if (emitter.size() != 0) {
String str = emitter.getItem(0);
int j = str.indexOf('|', idx);
if (j >= 0) {
sb.append(str.substring(idx, j));
emitter.next(sb.toString());
sb.setLength(0);
idx = j + 1;
} else {
sb.append(str.substring(idx));
emitter.dropItems(1);
idx = 0;
}
} else if (d) {
if (sb.length() != 0) {
emitter.next(sb.toString());
}
emitter.complete();
return;
}
}
emitter.setIndex(idx);
}
}, Functions.emptyConsumer(), 128))
.test()
.assertResult(
"ab",
"cdefgh",
"ijkl",
"mno",
"",
"pqr",
"s",
"tuv",
"xy",
"z"
);Note that the resulting sequence completes only when the handler calls emitter.complete() because
the upstream's termination may not mean all output has been generated.
The operator allows generating more than one item per invocation of the handler, but the handler should
always check emitter.demand() if the downstream is ready to receive and emitter.size() to
see if there are more upstream items to produce. The operator will call the handler again if it
detects an item has been produced, therefore, there is no need to exhaustively process all source
items on one call (which may not be possible if only partial data is available).
The cleanup callback is called to release the source items if necessary (such as pooled ByteBuffers)
when it is dropped via emitter.dropItems() or when the operator gets cancelled.
The emitter.dropItems() has an additional function, indicate that the upstream can send more items
as the previous ones have been consumed by the handler. Note though that the operator uses a low
watermark algorithm to replenish items from upstream (also called stable prefetch), that is, when
more than 75% of the prefetch parameter has been consumed, that many items are requested from
the upstream. This reduces an overhead the one-by-one requesting would have.
FlatMap only one ObservableSource at a time and ignore upstream values until it terminates.
In the following example, click events are practically ignored while the requestData
is active.
Observable<ClickEvent> clicks = ...
clicks.compose(
ObservableTransformers.flatMapDrop(click ->
service.requestData()
.subscribeOn(Schedulers.io())
)
)
.observeOn(mainThread())
.subscribe(data -> { /* ... */ });FlatMap only one ObservableSource at a time and keep the latest upstream value until it terminates
and resume with the ObservableSource mapped for that latest upstream value.
Unlike flatMapDrop, this operator will resume with the latest upstream value and not wait for the upstream
to signal a fresh item.
Observable<ClickEvent> clicks = ...
clicks.compose(
ObservableTransformers.flatMapLatest(click ->
service.requestData()
.subscribeOn(Schedulers.io())
)
)
.observeOn(mainThread())
.subscribe(data -> { /* ... */ });Allows an upstream error to jump over an inner transformation and is
then re-applied once the inner transformation's returned Flowable terminates.
Flowable.range(1, 5)
.concatWith(Flowable.<Integer>error(new TestException()))
.compose(FlowableTransformers.errorJump(new FlowableTransformer<Integer, List<Integer>>() {
@Override
public Publisher<List<Integer>> apply(Flowable<Integer> v) {
return v.buffer(3);
}
}))
.test()
.assertFailure(TestException.class, Arrays.asList(1, 2, 3), Arrays.asList(4, 5));Available also: ObservableTransformers.errorJump()
Map the upstream signals onto some reactive type and relay its events to the downstream.
Availability:
SingleSingleTransformers.flatMap(use withSingle.compose())Singles.flatMapCompletable(use withSingle.as())Singles.flatMapMaybe(use withSingle.as())Singles.flatMapObservable(use withSingle.as())Singles.flatMapFlowable(use withSingle.as())
MaybeMaybeTransformers.flatMap(use withMaybe.compose())Maybes.flatMapCompletable(use withMaybe.as())Maybes.flatMapSingle(use withMaybe.as())Maybes.flatMapObservable(use withMaybe.as())Maybes.flatMapFlowable(use withMaybe.as())
CompletableCompletableTransformers.flatMap(use withCompletable.compose())Completables.flatMapSingle(use withCompletable.as())Completables.flatMapMaybe(use withCompletable.as())Completables.flatMapObservable(use withCompletable.as())Completables.flatMapFlowable(use withCompletable.as())
Note: same-type transformations for Flowable.flatMap, Observable.flatMap already exist in RxJava.
Sums the numerical values on each rail as integer, long or double.
Flowable.range(1, 5)
.parallel(1)
.compose(ParallelTransformers.<Integer>sumInteger())
.sequential()
.test()
.assertResult(15);
Flowable.range(1, 5)
.parallel(1)
.compose(ParallelTransformers.<Integer>sumLong())
.sequential()
.test()
.assertResult(15L);
Flowable.range(1, 5)
.parallel(1)
.compose(ParallelTransformers.<Integer>sumDouble())
.sequential()
.test()
.assertResult(15d);Merges the source ParallelFlowable rails in an ordered fashion picking the smallest of the available value from
them (determined by their natural order or via a Comparator). The operator supports delaying error and setting
the internal prefetch amount.
ParallelFlowable.fromArray(
Flowable.just(1, 3, 5, 7),
Flowable.just(0, 2, 4, 6, 8, 10)
)
.to(p -> ParallelTransformers.orderedMerge(p, (a, b) -> a.compareTo(b)))
.test()
.assertResult(0, 1, 2, 3, 4, 5, 6, 7, 8, 10);The Publisher-based sibling of the Completable type. The usage is practically the same as Completable with the exception that because Nono implements the Reactive-Streams Publisher, you can use it directly with operators of Flowable that accept Publisher in some form.
Examples:
Nono.fromAction(() -> System.out.println("Hello world!"))
.subscribe();
Nono.fromAction(() -> System.out.println("Hello world!"))
.delay(1, TimeUnit.SECONDS)
.blockingSubscribe();
Nono.complete()
.test()
.assertResult();
Nono.error(new IOException())
.test()
.assertFailure(IOException.class);
Flowable.range(1, 10)
.to(Nono::fromPublisher)
.test()
.assertResult();A hot, Reactive-Streams Processor implementation of Nono.
NonoProcessor np = NonoProcessor.create();
TestSubscriber<Void> ts = np.test();
np.onComplete();
ts.assertResult();The Publisher-based sibling of the Single type. The usage is practically the same as Single with the exception that because Solo implements the Reactive-Streams Publisher, you can use it directly with operators of Flowable that accept Publisher in some form.
Solo's emission protocol is a restriction over the general Publisher protocol: one either calls onNext followed by onComplete or just onError. Operators will and should never call onNext followed by onError or onComplete on its own. Note that some operators may react to onNext immediately not waiting for an onComplete but on their emission side, onComplete is always called after an onNext.
Examples:
Solo.fromCallable(() -> {
System.out.println("Hello world!");
return 1;
}).subscribe();
Solo.fromCallable(() -> "Hello world!")
.delay(1, TimeUnit.SECONDS)
.blockingSubscribe(System.out::println);
Flowable.concat(Solo.just(1), Solo.just(2))
.test()
.assertResult(1, 2);A hot, Reactive-Streams Processor implementation of Solo.
SoloProcessor<Integer> sp = SoloProcessor.create();
TestSubscriber<Integer> ts = sp.test();
sp.onNext(1);
sp.onComplete();
ts.assertResult(1);The Publisher-based sibling of the Maybe type. The usage is practically the same as Maybe with the exception that because Perhaps implements the Reactive-Streams Publisher, you can use it directly with operators of Flowable that accept Publisher in some form.
Perhaps's emission protocol is a restriction over the general Publisher protocol: one either calls onNext followed by onComplete, onComplete only or just onError. Operators will and should never call onNext followed by onError on its own. Note that some operators may react to onNext immediately not waiting for an onComplete but on their emission side, onComplete is always called after an onNext.
Examples:
Perhaps.fromCallable(() -> {
System.out.println("Hello world!");
return 1;
}).subscribe();
Perhaps.fromCallable(() -> "Hello world!")
.delay(1, TimeUnit.SECONDS)
.blockingSubscribe(System.out::println);
Flowable.concat(Perhaps.just(1), Perhaps.just(2))
.test()
.assertResult(1, 2);
Perhaps.fromCallable(() -> {
System.out.println("Hello world!");
return null; // null is considered to indicate an empty Perhaps
})
.test()
.assertResult();A hot, Reactive-Streams Processor implementation of Perhaps.
PerhapsProcessor<Integer> ph = PerhapsProcessor.create();
TestSubscriber<Integer> ts = ph.test();
ph.onNext(1);
ph.onComplete();
ts.assertResult(1);The utility classes can be found in hu.akarnokd.rxjava2.consumers package.
Wraps the given onXXX callbacks into a Disposable Subscriber,
adds it to the given CompositeDisposable and ensures, that if the upstream
completes or this particlular Disposable is disposed, the Subscriber is removed
from the given composite.
The Subscriber will be removed after the callback for the terminal event has been invoked.
CompositeDisposable composite = new CompositeDisposable();
Disposable d = FlowableConsumers.subscribeAutoRelease(
Flowable.just(1), composite,
System.out::println, Throwable::printStackTrace, () -> System.out.println("Done")
);
assertEquals(0, composite.size());
// --------------------------
Disposable d2 = FlowableConsumers.subscribeAutoRelease(
Flowable.never(), composite,
System.out::println, Throwable::printStackTrace, () -> System.out.println("Done")
);
assertEquals(1, composite.size());
d2.dispose();
assertEquals(0, composite.size());The Subscriber will be removed after the callback for the terminal event has been invoked.
subscribeAutoRelease: Similar toFlowableConsumers.subscribeAutoRelease()but targetingObservables andObservers-like consumers.
subscribeAutoRelease: Similar toFlowableConsumers.subscribeAutoRelease()but targetingSingles andSingleObservers-like consumers.
subscribeAutoRelease: Similar toFlowableConsumers.subscribeAutoRelease()but targetingMaybes andMaybeObservers-like consumers.
subscribeAutoRelease: Similar toFlowableConsumers.subscribeAutoRelease()but targetingCompletables andCompletableObservers-like consumers.