Mono.defualtEmpty() vs Mono.switchIfEmpty() - reactive-programming

when upper stream emit nullValue,
we can use 'Mono.defualtIfEmpty()' or 'Mono.switchIfEmpty()' to replace null value.
But switchIfEmpty() evaluate upper stream value eager. So we use Mono.defer() for lazy evaluation.
Is 'Mono.defualtIfEmpty()' eager evaluation too? like switchIfEmpty()?
How to change Mono.defualtIfEmpty() to do lazy evaluation?

Mono<T> defaultIfEmpty(T defaultV) use a precomputed value when the mono completes empty.
Mono<T> switchIfEmpty(Mono<? extends T> alternate) : If and only if the mono completes empty, then the mono passed as argument will be subscribed to.
With defaultIfEmpty, you must provide the fallback value on assembly, so it is necessarily eager.
As switchIfEmpty takes a Mono as argument, it makes it possible to use it both for eager and lazy evaluation.
Apart from cached Mono or Mono.just, most Mono objects/implementations are lazily evaluated.
In any case, the upstream Mono is not evaluated eagerly, whatever operator you choose.
The upstream mono is only evaluated upon subscription.
Example 1 : verify that upstream is not evaluated until subscription :
var upstream = Mono.fromCallable(() -> {
System.out.println("UPSTREAM EVALUATION");
return "upstream";
});
var defaultIfEmpty = upstream.defaultIfEmpty("default");
System.out.println("Nothing evaluated yet");
Thread.sleep(2000);
System.out.println("Still not evaluated");
defaultIfEmpty.block();
Output:
Nothing evaluated yet
Still not evaluated
UPSTREAM EVALUATION
Example 2 : Check that switchIfEmpty is triggered only after upstream evaluation
var upstream = Mono.fromRunnable(() -> System.out.println("UPSTREAM EVALUATION"));
var switchEmpty = upstream.switchIfEmpty(Mono.fromCallable(() -> {
System.out.println("SWITCH EVALUATED");
return "switch";
}));
System.out.println("Nothing evaluated yet");
Thread.sleep(2000);
System.out.println("Still not evaluated");
switchEmpty.block();
Output:
Nothing evaluated yet
Still not evaluated
UPSTREAM EVALUATION
SWITCH EVALUATED
Example 3 : SwitchIfEmpty is not evaluated if upstream sends a value :
var upstream = Mono.fromCallable(() -> {
System.out.println("UPSTREAM EVALUATION");
return "upstream";
});
var switchEmpty = upstream.switchIfEmpty(Mono.fromCallable(() -> {
System.out.println("SWITCH EVALUATED");
return "switch";
}));
System.out.println("Nothing evaluated yet");
Thread.sleep(2000);
System.out.println("Still not evaluated");
switchEmpty.block();
Output:
Nothing evaluated yet
Still not evaluated
UPSTREAM EVALUATION

Related

What would cause SingleEmitter.onSuccess() to generate a NoSuchElement exception?

I have a Single flow organized like this:
getSomething() // returns Single<>
.flatMap(something -> {
// various things
return Single.defer( () -> {
// various other things
return Single.<SomeType>create(emitter -> {
// some more stuff
someCallbackApi(result -> {
if (result.isError()) {
emitter.onError( result.getCause() );
} else {
// guaranteed non-null data
emitter.onSuccess( result.getData() ); // this generates NoSuchElement
}
});
});
})
.retryWhen( ... )
.flatMap( data -> handle(data) )
.retryWhen( ... );
})
.retryWhen( ... )
.onErrorResumeNext(error -> process(error))
.subscribe(data -> handleSuccess(data), error -> handleError(error));
In test cases, the callback api Single successfully retries a number of times (determined by the test case), and every time on the last retry, the call to emitter.onSuccess() generates the exception below. What is going on? I haven't been able to restructure or change the downstream operators or subscribers to avoid the problem.
java.util.NoSuchElementException: null
at io.reactivex.internal.operators.flowable.FlowableSingleSingle$SingleElementSubscriber.onComplete(FlowableSingleSingle.java:116)
at io.reactivex.subscribers.SerializedSubscriber.onComplete(SerializedSubscriber.java:168)
at io.reactivex.internal.operators.flowable.FlowableRepeatWhen$WhenReceiver.onComplete(FlowableRepeatWhen.java:118)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$MergeSubscriber.drainLoop(FlowableFlatMap.java:426)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$MergeSubscriber.drain(FlowableFlatMap.java:366)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$MergeSubscriber.onComplete(FlowableFlatMap.java:338)
at io.reactivex.internal.operators.flowable.FlowableZip$ZipCoordinator.drain(FlowableZip.java:210)
at io.reactivex.internal.operators.flowable.FlowableZip$ZipSubscriber.onNext(FlowableZip.java:381)
at io.reactivex.processors.UnicastProcessor.drainFused(UnicastProcessor.java:363)
at io.reactivex.processors.UnicastProcessor.drain(UnicastProcessor.java:396)
at io.reactivex.processors.UnicastProcessor.onNext(UnicastProcessor.java:458)
at io.reactivex.processors.SerializedProcessor.onNext(SerializedProcessor.java:103)
at io.reactivex.internal.operators.flowable.FlowableRepeatWhen$WhenSourceSubscriber.again(FlowableRepeatWhen.java:171)
at io.reactivex.internal.operators.flowable.FlowableRetryWhen$RetryWhenSubscriber.onError(FlowableRetryWhen.java:76)
at io.reactivex.internal.operators.single.SingleToFlowable$SingleToFlowableObserver.onError(SingleToFlowable.java:67)
at io.reactivex.internal.operators.single.SingleFlatMap$SingleFlatMapCallback$FlatMapSingleObserver.onError(SingleFlatMap.java:116)
at io.reactivex.internal.operators.flowable.FlowableSingleSingle$SingleElementSubscriber.onError(FlowableSingleSingle.java:97)
at io.reactivex.subscribers.SerializedSubscriber.onError(SerializedSubscriber.java:142)
at io.reactivex.internal.operators.flowable.FlowableRepeatWhen$WhenReceiver.onError(FlowableRepeatWhen.java:112)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$MergeSubscriber.checkTerminate(FlowableFlatMap.java:567)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$MergeSubscriber.drainLoop(FlowableFlatMap.java:374)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$MergeSubscriber.drain(FlowableFlatMap.java:366)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$MergeSubscriber.innerError(FlowableFlatMap.java:606)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$InnerSubscriber.onError(FlowableFlatMap.java:672)
at io.reactivex.internal.subscriptions.EmptySubscription.error(EmptySubscription.java:55)
at io.reactivex.internal.operators.flowable.FlowableError.subscribeActual(FlowableError.java:40)
at io.reactivex.Flowable.subscribe(Flowable.java:14918)
at io.reactivex.Flowable.subscribe(Flowable.java:14865)
at io.reactivex.internal.operators.flowable.FlowableFlatMap$MergeSubscriber.onNext(FlowableFlatMap.java:163)
at io.reactivex.internal.operators.flowable.FlowableZip$ZipCoordinator.drain(FlowableZip.java:249)
at io.reactivex.internal.operators.flowable.FlowableZip$ZipSubscriber.onNext(FlowableZip.java:381)
at io.reactivex.processors.UnicastProcessor.drainFused(UnicastProcessor.java:363)
at io.reactivex.processors.UnicastProcessor.drain(UnicastProcessor.java:396)
at io.reactivex.processors.UnicastProcessor.onNext(UnicastProcessor.java:458)
at io.reactivex.processors.SerializedProcessor.onNext(SerializedProcessor.java:103)
at io.reactivex.internal.operators.flowable.FlowableRepeatWhen$WhenSourceSubscriber.again(FlowableRepeatWhen.java:171)
at io.reactivex.internal.operators.flowable.FlowableRetryWhen$RetryWhenSubscriber.onError(FlowableRetryWhen.java:76)
at io.reactivex.internal.operators.single.SingleToFlowable$SingleToFlowableObserver.onError(SingleToFlowable.java:67)
at io.reactivex.internal.operators.single.SingleFlatMap$SingleFlatMapCallback$FlatMapSingleObserver.onError(SingleFlatMap.java:116)
at io.reactivex.internal.disposables.EmptyDisposable.error(EmptyDisposable.java:78)
at io.reactivex.internal.operators.single.SingleError.subscribeActual(SingleError.java:42)
at io.reactivex.Single.subscribe(Single.java:3603)
at io.reactivex.internal.operators.single.SingleFlatMap$SingleFlatMapCallback.onSuccess(SingleFlatMap.java:84)
at io.reactivex.internal.operators.flowable.FlowableSingleSingle$SingleElementSubscriber.onComplete(FlowableSingleSingle.java:114)
at io.reactivex.subscribers.SerializedSubscriber.onComplete(SerializedSubscriber.java:168)
at io.reactivex.internal.operators.flowable.FlowableRetryWhen$RetryWhenSubscriber.onComplete(FlowableRetryWhen.java:82)
at io.reactivex.internal.subscriptions.DeferredScalarSubscription.complete(DeferredScalarSubscription.java:134)
at io.reactivex.internal.operators.single.SingleToFlowable$SingleToFlowableObserver.onSuccess(SingleToFlowable.java:62)
at io.reactivex.internal.operators.single.SingleCreate$Emitter.onSuccess(SingleCreate.java:67)
Solved:
Many thanks to #dano for pointing out the retryWhen behavior when used with Single. In this case, the outermost retryWhen operator had a bad terminating condition, roughly like:
.retryWhen(errors -> errors.zipWith( Flowable.range(1, maxRetries), ...)
.flatMap( zipped -> {
if (zipped.retryCount() <= maxRetries) {
return Flowable.just(0L);
}
return Flowable.error( new Exception() );
})
...Flowable.range() will complete when it has generated the last number, which will cause the Single to emit NoSuchElement. Just bumping the count argument to Flowable.range() by one is enough to fix the problem:
.retryWhen(errors -> errors.zipWith( Flowable.range(1, maxRetries + 1), ...)
.flatMap( zipped -> {
if (zipped.retryCount() <= maxRetries) {
return Flowable.just(0L);
}
return Flowable.error( new Exception() );
})
This is happening because of the way you implemented the callback you passed to retryWhen. The retryWhen docuementation states (emphasis mine):
Re-subscribes to the current Single if and when the Publisher returned
by the handler function signals a value.
If the Publisher signals an onComplete, the resulting Single will
signal a NoSuchElementException.
One of the Flowable instances you're returning inside of the calls to retryWhen is emitting onComplete, which leads to the NoSuchElementException.
Here's a very simple example that produces the same error:
Single.error(new Exception("hey"))
.retryWhen(e -> Flowable.just(1))
.subscribe(System.out::println, e -> e.printStackTrace());
The stacktrace this produces starts with this, same as yours:
java.util.NoSuchElementException
at io.reactivex.internal.operators.flowable.FlowableSingleSingle$SingleElementSubscriber.onComplete(FlowableSingleSingle.java:116)
at io.reactivex.subscribers.SerializedSubscriber.onComplete(SerializedSubscriber.java:168)
at io.reactivex.internal.operators.flowable.FlowableRepeatWhen$WhenReceiver.onComplete(FlowableRepeatWhen.java:118)
You don't include any of your code from inside the retryWhen calls, so I can't say exactly what you did wrong, but generally you want to chain whatever you do to the Flowable that is passed in. So my example above would look like this, if we really wanted to retry forever:
Single.error(new Exception("hey"))
.retryWhen(e -> e.flatMap(ign -> Flowable.just(1)))
.subscribe(System.out::println, e -> e.printStackTrace());

WebFlux/Reactor: checking conditions before+after Flux execution with doOnComplete

I'm already querying some external resource with Flux.using(). Now I want to implement a kind of optimistic locking: read some state before query starts to execute and check if it was updated after query is finished. If so - throw some exception to break http request handling.
I've achieved this by using doOnComplete:
final AtomicReference<String> initialState = new AtomicReference<>();
return Flux.just("some", "constant", "data")
.doOnComplete(() -> initialState.set(getState()))
.concatWith(Flux.using(...)) //actual data query
.doOnComplete(() -> {if (!initialState.get().equals(getState())) throw new RuntimeException();})
.concatWithValues("another", "constant", "data")
My questions:
Is it correct? Is it guaranteed that 1st doOnComplete lambda would be finished before Flux.using() and is it guaranteed that 2nd doOnComplete lambda would be executed strictly after?
Does more elegant solution exists?
The first doOnComplete would be executed after Flux.just("some", "constant", "data") emits all elements and the second one after emitted Publisher defined in concatWith completes successfully. This is working because both publishers have a finite number of elements.
With the proposed approach, however the pre-/postconditions from a particular operation are handled outside of the operations at a higher level. In other words, the condition check belonging to the operation is leaking to the flux definition.
Suggestion, pushing the condition check down to the operation:
var otherElements = Flux.using( // actual data query
() -> "other",
x -> {
var initialState = getState();
return Flux.just(x).doOnComplete(() ->
{ if (!initialState.equals(getState())) throw new IllegalStateException(); }
);
},
x -> { }
);
Flux.just("some", "constant", "data")
.concatWith(otherElements)
.concatWith(Mono.just("another")) // "constant", "data" ...

Ensure sequential evaluation of scala stream

Consider this scala function that takes a server socket and turns it into a stream of sockets based on the accept method:
def accepting(ss: ServerSocket): Stream[Socket] =
try { Stream.cons(ss.accept(), accepting(ss)) }
catch { Stream.empty }
If this stream is evaluated left-right, it will function properly. But since the source of data is based on side-effects, the proper evaluation of an item depends on all previous items having been evaluated, and only previous items having been evaluated.
For example, if I were to skip the first 10 items in the stream and then take the next item, I would expect it to return the 11th socket to connect to this server socket, but it would actually return the first. So, what I need is a way to ensure that all previous items are evaluated before any given item is evaluated.
The best solution I've came up with is this:
def accepting(ss: ServerSocket): Stream[Socket] = {
def accepting(ss: ServerSocket, previous: Option[Socket]): Stream[Socket] = {
try {
lazy val current = ss.accept()
Stream.cons(current, accepting(ss, Some(current)))
} catch { Stream.empty }
}
accepting(ss, None)
}
But it's pretty ugly, and so my question is, is there a better way do achieve my goal?

Returning value from Scala future completion

Coming from a Java background, I have been trying to teach myself Scala for some time now. As part of that, I am doing a small pet project that exposes a HTTP endpoint that saves the registration numberof a vehicle against the owner and returns the status.
To give more context, I am using Slick as FRM which performs DB operations asynchronously and returns a Future.
Based on the output of this Future, I want to set the status variable to return back to the client.
Here, is the code
def addVehicleOwner(vehicle: Vehicle): String = {
var status = ""
val addFuture = db.run((vehicles returning vehicles.map(_.id)) += vehicle)
addFuture onComplete {
case Success(id) => {
BotLogger.info(LOGTAG, s"Vehicle registered at $id ")
status = String.format("Registration number - '%s' mapped to owner '%s' successfully", vehicle.registration,
vehicle.owner)
println(s"status inside success $status") //--------- (1)
}
case Failure(e: SQLException) if e.getMessage.contains("SQLITE_CONSTRAINT") => {
status = updateVehicleOwner(vehicle)
BotLogger.info(LOGTAG, s"Updated owner='${vehicle.owner}' for '${vehicle.registration}'")
}
case Failure(e) => {
BotLogger.error(LOGTAG, e)
status = "Sorry, unable to add now!"
}
}
exec(addFuture)
println(s"Status=$status") //--------- (2)
status
}
// Helper method for running a query in this example file:
def exec[T](sqlFuture: Future[T]):T = Await.result(sqlFuture, 1 seconds)
This was fairly simple in Java. With Scala, I am facing the following problems:
The expected value gets printed at (1), but (2) always prints empty string and same is what method returns. Can someone explain why?
I even tried marking the var status as #volatile var status, it still evaluates to empty string.
I know, that the above is not the functional way of doing things as I am muting state. What is the clean way of writing code for such cases.
Almost all the examples I could find described how to map the result of Success or handle Failure by doing a println. I want to do more than that.
What are some good references of small projects that I can refer to? Specially, that follow TDD.
Instead of relying on status to complete inside the closure, you can recover over the Future[T] which handle the exception if they occur, and always returns the result you want. This is taking advantage of the nature of expressions in Scala:
val addFuture =
db.run((vehicles returning vehicles.map(_.id)) += vehicle)
.recover {
case e: SQLException if e.getMessage.contains("SQLITE_CONSTRAINT") => {
val status = updateVehicleOwner(vehicle)
BotLogger.info(
LOGTAG,
s"Updated owner='${vehicle.owner}' for '${vehicle.registration}'"
)
status
}
case e => {
BotLogger.error(LOGTAG, e)
val status = "Sorry, unable to add now!"
status
}
}
val result: String = exec(addFuture)
println(s"Status = $result")
result
Note that Await.result should not be used in any production environment as it synchronously blocks on the Future, which is exactly the opposite of what you actually want. If you're already using a Future to delegate work, you want it to complete asynchronously. I'm assuming your exec method was simply for testing purposes.

Combining local result with possible (timeout/error) async web result

I have two methods that both return an IObservable
IObservable<Something[]> QueryLocal();
and
IObservable<Something[]> QueryWeb();
QueryLocal is always successful. QueryWeb is susceptible to both a timeout and possible web errors.
I wish to implement a QueryLocalAndWeb() that calls both and combines their results.
So far I have:
IObservable<Something[]> QueryLocalAndWeb()
{
var a = QueryLocal();
var b = QueryWeb();
var plan = a.And(b).Then((x, y) => x.Concat(y).ToArray());
return Observable.When(plan).Timeout(TimeSpan.FromSeconds(10), a);
}
However, I'm not sure that it handles the case where QueryWeb yields an error.
In the future I might have a QueryWeb2() that also needs to be taken into account.
So, how do I combine the results from a number of IObservables ignoring the ones that throw errors (or time out)?
I guess OnErrorResumeNext should be able to handle this scenario:
From MSDN:
Continues an observable sequence that is terminated normally or by an
exception with the next observable sequence.
IObservable<Something[]> QueryLocalAndWeb()
{
var a = QueryLocal();
var b = QueryWeb().Timeout(TimeSpan.FromSeconds(10));
return Observable.OnErrorResumeNext(b, a);
}
You can do concat of array by using Aggregation on the returned observable.
I am assuming that both local and web are cold observable i.e they start producing values only when someone subscribes to them.
How about:
var plan = a.And(b).Then((x, y) => x.Concat(y.Catch(Observable.Empty<Something[]>()).ToArray());