Vert.x: How to wait for a future to complete - vert.x

Is there a way to wait for a future to complete without blocking the event loop?
An example of a use case with querying Mongo:
Future<Result> dbFut = Future.future();
mongo.findOne("myusers", myQuery, new JsonObject(), res -> {
if(res.succeeded()) {
...
dbFut.complete(res.result());
}
else {
...
dbFut.fail(res.cause());
}
}
});
// Here I need the result of the DB query
if(dbFut.succeeded()) {
doSomethingWith(dbFut.result());
}
else {
error();
}
I know the doSomethingWith(dbFut.result()); can be moved to the handler, yet if it's long, the code will get unreadable (Callback hell ?) It that the right solution ? Is that the omny solution without additional libraries ?
I'm aware that rxJava simplifies the code, but as I don't know it, learning Vert.x and rxJava is just too much.
I also wanted to give a try to vertx-sync. I put the dependency in the pom.xml; everything got downloaded fine but when I started my app, I got the following error
maurice#mickey> java \
-javaagent:~/.m2/repository/co/paralleluniverse/quasar-core/0.7.5/quasar-core-0.7.5-jdk8.jar \
-jar target/app-dev-0.1-fat.jar \
-conf conf/config.json
Error opening zip file or JAR manifest missing : ~/.m2/repository/co/paralleluniverse/quasar-core/0.7.5/quasar-core-0.7.5-jdk8.jar
Error occurred during initialization of VM
agent library failed to init: instrument
I know what the error means in general, but I don't know in that context... I tried to google for it but didn't find any clear explanation about which manifest to put where. And as previously, unless mandatory, I prefer to learn one thing at a time.
So, back to the question : is there a way with "basic" Vert.x to wait for a future without perturbation on the event loop ?

You can set a handler for the future to be executed upon completion or failure:
Future<Result> dbFut = Future.future();
mongo.findOne("myusers", myQuery, new JsonObject(), res -> {
if(res.succeeded()) {
...
dbFut.complete(res.result());
}
else {
...
dbFut.fail(res.cause());
}
}
});
dbFut.setHandler(asyncResult -> {
if(asyncResult.succeeded()) {
// your logic here
}
});
This is a pure Vert.x way that doesn't block the event loop

I agree that you should not block in the Vertx processing pipeline, but I make one exception to that rule: Start-up. By design, I want to block while my HTTP server is initialising.
This code might help you:
/**
* #return null when waiting on {#code Future<Void>}
*/
#Nullable
public static <T>
T awaitComplete(Future<T> f)
throws Throwable
{
final Object lock = new Object();
final AtomicReference<AsyncResult<T>> resultRef = new AtomicReference<>(null);
synchronized (lock)
{
// We *must* be locked before registering a callback.
// If result is ready, the callback is called immediately!
f.onComplete(
(AsyncResult<T> result) ->
{
resultRef.set(result);
synchronized (lock) {
lock.notify();
}
});
do {
// Nested sync on lock is fine. If we get a spurious wake-up before resultRef is set, we need to
// reacquire the lock, then wait again.
// Ref: https://stackoverflow.com/a/249907/257299
synchronized (lock)
{
// #Blocking
lock.wait();
}
}
while (null == resultRef.get());
}
final AsyncResult<T> result = resultRef.get();
#Nullable
final Throwable t = result.cause();
if (null != t) {
throw t;
}
#Nullable
final T x = result.result();
return x;
}

Related

Flutter Future timeouts not always working correctly

Hey I need some help here for How to use timeouts in flutter correctly. First of all to explain what the main goal is:
I want to recive data from my Firebase RealTime Database but need to secure this request api call with an time out of 15 sec. So after 15 sec my timeout should throw an exception that will return to the Users frontend the alert for reasons of time out.
So I used the simple way to call timeouts on future functions:
This functions should only check if on some firebase node an ID is existing or not:
Inside this class where I have declared this functions I also have an instance which called : timeoutControl this is a class which contains a duration and some reasons for the exceptions.
Future<bool> isUserCheckedIn(String oid, String maybeCheckedInUserIdentifier, String onGateId) async {
try {
databaseReference = _firebaseDatabase.ref("Boarding").child(oid).child(onGateId);
final snapshot = await databaseReference.get().timeout(Duration(seconds: timeoutControl.durationForTimeOutInSec), onTimeout: () => timeoutControl.onEppTimeoutForTask());
if(snapshot.hasChild(maybeCheckedInUserIdentifier)) {
return true;
}
else {
return false;
}
}
catch (exception) {
return false;
}
}
The TimeOutClass where the instance timeoutControl comes from:
class CustomTimeouts {
int durationForTimeOutInSec = 15; // The seconds for how long to try until we throw an timeout exception
CustomTimeouts();
// TODO: Implement the exception reasons here later ...
onEppTimeoutForUpload() {
throw Exception("Some reason ...");
}
onEppTimeoutForTask() {
throw Exception("Some reason ...");
}
onEppTimeoutForDownload() {
throw Exception("Some reason ...");
}
}
So as you can see for example I tried to use this implementation above. This works fine ... sometimes I need to fight with un explain able things -_-. Let me try to introduce what in somecases are the problem:
Inside the frontend class make this call:
bool isUserCheckedIn = await service.isUserCheckedIn(placeIdentifier, userId, gateId);
Map<String, dynamic> data = {"gateIdActive" : isUserCheckedIn};
/*
The response here is an Custom transaction handler which contains an error or an returned param
etc. so this isn't relevant for you ...
*/
_gateService.updateGate(placeIdentifier, gateId, data).then((response) {
if(response.hasError()) {
setState(() {
EppDialog.showErrorToast(response.getErrorMessage()); // Shows an error message
isSendButtonDiabled = false; /*Reset buttons state*/
});
}
else {
// Create an gate process here ...
createGateEntrys(); // <-- If the closures update was successful we also handle some
// other data inside the RTDB for other reasons here ...
}
});
IMPORTANT to know for you guys is that I am gonna use the returned "boolean" value from this function call to update some other data which will be pushed and uploaded into another RTDB other node location for other reasons. And if this was also successful the application is going on to update some entrys also inside the RTDB -->createGateEntrys()<-- This function is called as the last one and is also marked as an async function and called with its closures context and no await statement.
The Data inside my Firebase RTDB:
"GateCheckIns" / "4mrithabdaofgnL39238nH" (The place identifier) / "NFdxcfadaies45a" (The Gate Identifier)/ "nHz2mhagadzadzgadHjoeua334" : 1 (as top of the key some users id who is checked in)
So on real devices this works always without any problems... But the case of an real device or simulator could not be the reason why I'am faceing with this problem now. Sometimes inside the Simulator this Function returns always false no matter if the currentUsers Identifier is inside the this child nodes or not. Therefore I realized the timeout is always called immediately so right after 1-2 sec because the exception was always one of these I was calling from my CustomTimeouts class and the function which throws the exception inside the .timeout(duration, onTimeout: () => ...) call. I couldn't figure it out because as I said on real devices I was not faceing with this problem.
Hope I was able to explain the problem it's a little bit complicated I know but for me is important that someone could explain me for what should I pay attention to if I am useing timeouts in this style etc.
( This is my first question here on StackOverFlow :) )

Repeat Single based on onSuccess() value

I want to repeat a Single based on the single value emitted in onSuccess(). Here is a working example
import org.reactivestreams.Publisher;
import io.reactivex.Flowable;
import io.reactivex.Single;
import io.reactivex.functions.Function;
public class Temp {
void main() {
Job job = new Job();
Single.just(job)
.map(this::processJob)
.repeatWhen(new Function<Flowable<Object>, Publisher<?>>() {
#Override
public Publisher<?> apply(Flowable<Object> objectFlowable) throws Exception {
// TODO repeat when Single emits false
return null;
}
})
.subscribe();
}
/**
* returns true if process succeeded, false if failed
*/
boolean processJob(Job job) {
return true;
}
class Job {
}
}
I understand how repeatWhen works for Observables by relying on the "complete" notification. However since Single doesn't receive that notification I'm not sure what the Flowable<Object> is really giving me. Also why do I need to return a Publisher from this function?
Instead of relying on a boolean value, you could make your job throw an exception when it fails:
class Job {
var isSuccess: Boolean = false
}
fun processJob(job: Job): String {
if (job.isSuccess) {
return "job succeeds"
} else {
throw Exception("job failed")
}
}
val job = Job()
Single.just(job)
.map { processJob(it) }
.retry() // will resubscribe until your job succeeds
.subscribe(
{ value -> print(value) },
{ error -> print(error) }
)
i saw a small discrepancy in the latest docs and your code, so i did a little digging...
(side note - i think the semantics of retryWhen seem like the more appropriate operator for your case, so i've substituted it in for your usage of repeatWhen. but i think the root of your problem remains the same in either case).
the signature for retryWhen is:
retryWhen(Function<? super Flowable<Throwable>,? extends Publisher<?>> handler)
that parameter is a factory function whose input is a source that emits anytime onError is called upstream, giving you the ability to insert custom retry logic that may be influenced through interrogation of the underlying Throwable. this begins to answer your first question of "I'm not sure what the Flowable<Object> is really giving me" - it shouldn't be Flowable<Object> to begin with, it should be Flowable<Throwable> (for the reason i just described).
so where did Flowable<Object> come from? i managed to reproduce IntelliJ's generation of this code through it's auto-complete feature using RxJava version 2.1.17. upgrading to 2.2.0, however, produces the correct result of Flowable<Throwable>. so, see if upgrading to the latest version generates the correct result for you as well.
as for your second question of "Also why do I need to return a Publisher from this function?" - this is used to determine if re-subscription should happen. if the factory function returns a Publisher that emits a terminal state (ie calls onError() or onComplete()) re-subscription will not happen. however, if onNext() is called, it will. (this also explains why the Publisher isn't typed - the type doesn't matter. the only thing that does matter is what kind of notification it publishes).
another way to rewrite this, incorporating the above, might be as follows:
// just some type to use as a signal to retry
private class SpecialException extends RuntimeException {}
// job processing results in a Completable that either completes or
// doesn't (by way of an exception)
private Completable rxProcessJob(Job job) {
return Completable.complete();
// return Completable.error(new SpecialException());
}
...
rxProcessJob(new Job())
.retryWhen(errors -> {
return errors.flatMap(throwable -> {
if(throwable instanceof SpecialException) {
return PublishProcessor.just(1);
}
return PublishProcessor.error(throwable);
});
})
.subscribe(
() -> {
System.out.println("## onComplete()");
},
error -> {
System.out.println("## onError(" + error.getMessage() + ")");
}
);
i hope that helps!
The accepted answer would work, but is hackish. You don't need to throw an error; simply filter the output of processJob which converts the Single to a Maybe, and then use the repeatWhen handler to decide how many times, or with what delay, you may want to resubscribe. See Kotlin code below from a working example, you should be able to easily translate this to Java.
filter { it }
.repeatWhen { handler ->
handler.zipWith(1..3) { _, i -> i }
.flatMap { retryCount -> Flowable.timer(retryDelay.toDouble().pow(retryCount).toLong(), TimeUnit.SECONDS) }
.doOnNext { log.warn("Retrying...") }
}

How to do Async Http Call with Apache Beam (Java)?

Input PCollection is http requests, which is a bounded dataset. I want to make async http call (Java) in a ParDo , parse response and put results into output PCollection. My code is below. Getting exception as following.
I cound't figure out the reason. need a guide....
java.util.concurrent.CompletionException: java.lang.IllegalStateException: Can't add element ValueInGlobalWindow{value=streaming.mapserver.backfill.EnrichedPoint#2c59e, pane=PaneInfo.NO_FIRING} to committed bundle in PCollection Call Map Server With Rate Throttle/ParMultiDo(ProcessRequests).output [PCollection]
Code:
public class ProcessRequestsFn extends DoFn<PreparedRequest,EnrichedPoint> {
private static AsyncHttpClient _HttpClientAsync;
private static ExecutorService _ExecutorService;
static{
AsyncHttpClientConfig cg = config()
.setKeepAlive(true)
.setDisableHttpsEndpointIdentificationAlgorithm(true)
.setUseInsecureTrustManager(true)
.addRequestFilter(new RateLimitedThrottleRequestFilter(100,1000))
.build();
_HttpClientAsync = asyncHttpClient(cg);
_ExecutorService = Executors.newCachedThreadPool();
}
#DoFn.ProcessElement
public void processElement(ProcessContext c) {
PreparedRequest request = c.element();
if(request == null)
return;
_HttpClientAsync.prepareGet((request.getRequest()))
.execute()
.toCompletableFuture()
.thenApply(response -> { if(response.getStatusCode() == HttpStatusCodes.STATUS_CODE_OK){
return response.getResponseBody();
} return null; } )
.thenApply(responseBody->
{
List<EnrichedPoint> resList = new ArrayList<>();
/*some process logic here*/
System.out.printf("%d enriched points back\n", result.length());
}
return resList;
})
.thenAccept(resList -> {
for (EnrichedPoint enrichedPoint : resList) {
c.output(enrichedPoint);
}
})
.exceptionally(ex->{
System.out.println(ex);
return null;
});
}
}
The Scio library implements a DoFn which deals with asynchronous operations. The BaseAsyncDoFn might provide you the handling you need. Since you're dealing with CompletableFuture also take a look at the JavaAsyncDoFn.
Please note that you necessarily don't need to use the Scio library, but you can take the main idea of the BaseAsyncDoFn since it's independent of the rest of the Scio library.
The issue that your hitting is that your outputting outside the context of a processElement or finishBundle call.
You'll want to gather all your outputs in memory and output them eagerly during future processElement calls and at the end within finishBundle by blocking till all your calls finish.

RxJava: Merge multiple singles and complete after some have failed

I would like to merge two Single<MyData> such that if one of them fails but the other one succeeds then the error of the one that failed and the emission from the other one are reported, and then the resulting Single<MyData> (or Observable<MyData>) completes.
If both Single<MyData> fail then the result should also fail and also be marked as failed.
What I would like to have at the end is:
If both succeed then the emitted values and a producer marked as completed.
If one succeeds and the other fails, the emitted value, the thrown error and the producer marked as complete.
If all fail, the errors and the producer marked as failed.
It's like an 'OR' operation
This is not possible. There is only a single terminal event allowed. The contract for Single is success|error. If you need to receive a next event as well, you should consider to use Observable instead. The contract for Observable is next* complete|error, but you'll still not get a complete.
Observable.mergeDelayError(single1.toObservable(), single2.toObservable())
This can be accomplished with Single.create(SingleOnSubscribe). If your return type is Single<MyData> only one of the responses can be returned, but you could also modify this to instead return a Single<List<MyData>> or some other RxJava structure like Flowable<MyData> that supports multiple returns. In this example, the Single<MyData> returns whichever call returns last because that was the simplest to implement.
public Single<MyData> getCombinedSingle(List<Single<MyData>> singles) {
return Single.create(new SingleOnSubscribe<MyData> {
private boolean encounteredError = false;
private MyData myData;
#Override
public void subscribe(#NonNull Emitter<MyData> emitter) {
List<Disposable> disposables = new ArrayList<>();
Consumer<MyData> myDataConsumer = myData -> {
this.MyData = myData;
checkForFinish(emitter, disposables);
}
Consumer<Throwable> throwableConsumer = throwable -> {
throwable.printStackTrace();
encounteredError = true;
checkForFinish(emitter, disposables);
}
for (Single single: singles) {
disposables.put(single.subscribe(myDataConsumer, throwableConsumer);
}
}
private void checkForFinish(SingleEmitter<MyData> emitter, List<Disposable> disposables) {
if (disposables1.stream().allMatch(Disposable::isDisposed)) {
if (encounteredError) {
emitter.onError(new Throwable());
} else {
emitter.onSuccess(myData);
}
}
}
}
}
This could be modified to return a Throwable from the original Singles if needed.

Rxjava User-Retry observable with .cache operator?

i've an observable that I create with the following code.
Observable.create(new Observable.OnSubscribe<ReturnType>() {
#Override
public void call(Subscriber<? super ReturnType> subscriber) {
try {
if (!subscriber.isUnsubscribed()) {
subscriber.onNext(performRequest());
}
subscriber.onCompleted();
} catch (Exception e) {
subscriber.onError(e);
}
}
});
performRequest() will perform a long running task as you might expect.
Now, since i might be launching the same Observable twice or more in a very short amount of time, I decided to write such transformer:
protected Observable.Transformer<ReturnType, ReturnType> attachToRunningTaskIfAvailable() {
return origObservable -> {
synchronized (mapOfRunningTasks) {
// If not in maps
if ( ! mapOfRunningTasks.containsKey(getCacheKey()) ) {
Timber.d("Cache miss for %s", getCacheKey());
mapOfRunningTasks.put(
getCacheKey(),
origObservable
.doOnTerminate(() -> {
Timber.d("Removed from tasks %s", getCacheKey());
synchronized (mapOfRunningTasks) {
mapOfRunningTasks.remove(getCacheKey());
}
})
.cache()
);
} else {
Timber.d("Cache Hit for %s", getCacheKey());
}
return mapOfRunningTasks.get(getCacheKey());
}
};
}
Which basically puts the original .cache observable in a HashMap<String, Observable>.
This basically disallows multiple requests with the same getCacheKey() (Example login) to call performRequest() in parallel. Instead, if a second login request arrives while another is in progress, the second request observable gets "discarded" and the already-running will be used instead. => All the calls to onNext are going to be cached and sent to both subscribers actually hitting my backend only once.
Now, suppouse this code:
// Observable loginTask
public void doLogin(Observable<UserInfo> loginTask) {
loginTask.subscribe(
(userInfo) -> {},
(throwable) -> {
if (userWantsToRetry()) {
doLogin(loinTask);
}
}
);
}
Where loginTask was composed with the previous transformer. Well, when an error occurs (might be connectivity) and the userWantsToRetry() then i'll basically re-call the method with the same observable. Unfortunately that has been cached and I'll receive the same error without hitting performRequest() again since the sequence gets replayed.
Is there a way I could have both the "same requests grouping" behavior that the transformer provides me AND the retry button?
Your question has a lot going on and it's hard to put it into direct terms. I can make a couple recommendations though. Firstly your Observable.create can be simplified by using an Observable.defer(Func0<Observable<T>>). This will run the func every time a new subscriber is subscribed and catch and channel any exceptions to the subscriber's onError.
Observable.defer(() -> {
return Observable.just(performRequest());
});
Next, you can use observable.repeatWhen(Func1<Observable<Void>, Observable<?>>) to decide when you want to retry. Repeat operators will re-subscribe to the observable after an onComplete event. This particular overload will send an event to a subject when an onComplete event is received. The function you provide will receive this subject. Your function should call something like takeWhile(predicate) and onComplete when you do not want to retry again.
Observable.just(1,2,3).flatMap((Integer num) -> {
final AtomicInteger tryCount = new AtomicInteger(0);
return Observable.just(num)
.repeatWhen((Observable<? extends Void> notifications) ->
notifications.takeWhile((x) -> num == 2 && tryCount.incrementAndGet() != 3));
})
.subscribe(System.out::println);
Output:
1
2
2
2
3
The above example shows that retries are aloud when the event is not 2 and up to a max of 22 retries. If you switch to a repeatWhen then the flatMap would contain your decision as to use a cached observable or the realWork observable. Hope this helps!