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Multiple blocking calls wrapped in fromCallable in WebFlux

I'm using Feign Client in Reactive Java. The Feign client has an interceptor that sends a blocking request to get auth token and adds it as a header to the feign request.
the feign request is wrapped in Mono.FromCallable with Schedulers.boundedElastic().
my question is: does the inner call to get the auth token considered as a blocking call?
I get that both calls will be on a different thread from Schedulers.boundedElastic() but not sure is ok to execute them on the same thread or I should change it so they'll run on different threads.
Feign client:
#FeignClient(name = "remoteRestClient", url = "${remote.url}",
configuration = AuthConfiguration.class, decode404 = true)
#Profile({ "!test" })
public interface RemoteRestClient {
#GetMapping(value = "/getSomeData" )
Data getData();
}
interceptor:
public class ClientRequestInterceptor implements RequestInterceptor {
private IAPRequestBuilder iapRequestBuilder;
private String clientName;
public ClientRequestInterceptor(String clientName, String serviceAccount, String jwtClientId) {
this.iapRequestBuilder = new IAPRequestBuilder(serviceAccount, jwtClientId);
this.clientName = clientName;
}
#Override
public void apply(RequestTemplate template) {
try {
HttpRequest httpRequest = iapRequestBuilder.buildIapRequest(); <---- blocking call
template.header(HttpHeaders.AUTHORIZATION, httpRequest.getHeaders().getAuthorization());
} catch (IOException e) {
log.error("Building an IAP request has failed: {}", e.getMessage(), e);
throw new InterceptorException(String.format("failed to build IAP request for %s", clientName), e);
}
}
}
feign configuration:
public class AuthConfiguration {
#Value("${serviceAccount}")
private String serviceAccount;
#Value("${jwtClientId}")
private String jwtClientId;
#Bean
public ClientRequestInterceptor getClientRequestInterceptor() {
return new ClientRequestInterceptor("Entitlement", serviceAccount, jwtClientId);
}
}
and feign client call:
private Mono<Data> getData() {
return Mono.fromCallable(() -> RemoteRestClient.getData()
.publishOn(Schedulers.boundedElastic());
}

You can sort of tell that it is a blocking call since it returns a concrete class and not a Future (Mono or Flux). To be able to return a concrete class, the thread needs to wait until we have the response to return it.
So yes it is most likely a blocking call.
Reactor recommends that you use the subscribeOn operator when doing blocking calls, this will place that entire chain of operators on its own thread pool.
You have chosen to use the publishOn and it is worth pointing out the following from the docs:
affects where the subsequent operators execute
This in practice means that up until the publishOn operator all actions will be executed using any available anonymous thread.
But all calls after will be executed on the defined thread pool.
private Mono<Data> getData() {
return Mono.fromCallable(() -> RemoteRestClient.getData()
.publishOn(Schedulers.boundedElastic());
}
You have chosen to place it after so the thread pool switch will be done after the call to getData.
publishOns placing in the chain matters while subscribeOn affects the entire chain of operator which means it's placing does not matter.
So to answer your question again, yes it is most likely a blocking call (i can't confirm by 100% since i have not looked into the source code) and how you wish to solve it with either publishOn on subscribeOn is up to you.
Or look into if there is an reactive alternative library to use.

Concurrently call several Spring microservice URLs

I have a Spring Boot application that will call several microservice URLs using the GET method. These microservice URL endpoints are all implemented as #RestControllers. They don't return Flux or Mono.
I need my application to capture which URLs are not returning 2xx HTTP status.
I'm currently using the following code to do this:
List<String> failedServiceUrls = new ArrayList<>();
for (String serviceUrl : serviceUrls.getServiceUrls()) {
try {
ResponseEntity<String> response = rest.getForEntity(serviceUrl, String.class);
if (!response.getStatusCode().is2xxSuccessful()) {
failedServiceUrls.add(serviceUrl);
}
} catch (Exception e){
failedServiceUrls.add(serviceUrl);
}
}
// all checks are complete so send email with the failedServiceUrls.
mail.sendEmail("Service Check Complete", failedServiceUrls);
}
The problem is that each URL call is slow to respond and I have to wait for one URL call to complete prior to making the next one.
How can I change this to make the URLs calls be made concurrently? After all call have completed, I need to send an email with any URLs that have an error that should be collected in failedServiceUrls.
Update
I revised the above post to state that I just want the calls to be made concurrently. I don't care that rest.getForEntity call blocks.

Using the executor service in your code, you can call all microservices in parallel this way:
// synchronised it as per Maciej's comment:
failedServiceUrls = Collections.synchronizedList(failedServiceUrls);
ExecutorService executorService = Executors.newFixedThreadPool(serviceUrls.getServiceUrls().size());
List<Callable<String>> runnables = new ArrayList<>().stream().map(o -> new Callable<String>() {
#Override
public String call() throws Exception {
ResponseEntity<String> response = rest.getForEntity(serviceUrl, String.class);
// do something with the response
if (!response.getStatusCode().is2xxSuccessful()) {
failedServiceUrls.add(serviceUrl);
}
return response.getBody();
}
}).collect(toList());
List<Future<String>> result = executorService.invokeAll(runnables);
for(Future f : result) {
String resultFromService = f.get(); // blocker, it will wait until the execution is over
}

If you just want to make calls concurrently and you don't care about blocking threads you can:
wrap the blocking service call using Mono#fromCallable
transform serviceUrls.getServiceUrls() into a reactive stream using Flux#fromIterable
Concurrently call and filter failed services with Flux#filterWhen using Flux from 2. and asynchronous service call from 1.
Wait for all calls to complete using Flux#collectList and send email with invalid urls in subscribe
void sendFailedUrls() {
Flux.fromIterable(erviceUrls.getServiceUrls())
.filterWhen(url -> responseFailed(url))
.collectList()
.subscribe(failedURls -> mail.sendEmail("Service Check Complete", failedURls));
}
Mono<Boolean> responseFailed(String url) {
return Mono.fromCallable(() -> rest.getForEntity(url, String.class))
.map(response -> !response.getStatusCode().is2xxSuccessful())
.subscribeOn(Schedulers.boundedElastic());
}
Blocking calls with Reactor
Since the underlying service call is blocking it should be executed on a dedicated thread pool. Size of this thread pool should be equal to the number of concurrent calls if you want to achieve full concurrency. That's why we need .subscribeOn(Schedulers.boundedElastic())
See: https://projectreactor.io/docs/core/release/reference/#faq.wrap-blocking
Better solution using WebClient
Note however, that blocking calls should be avoided when using reactor and spring webflux. The correct way to do this would be to replace RestTemplate with WebClient from Spring 5 which is fully non-blocking.
See: https://docs.spring.io/spring-boot/docs/2.0.3.RELEASE/reference/html/boot-features-webclient.html

RSocket Channel with Spring Boot - Clients miss their own first message

Suppose I have a simple RSocket and Spring Boot Server. The server broadcasts all incoming client messages to all connected clients (including the sender). Client and server look like this:
Server:
public RSocketController() {
this.processor = DirectProcessor.<String>create().serialize();
this.sink = this.processor.sink();
}
#MessageMapping("channel")
Flux<String> channel(final Flux<String> messages) {
this.registerProducer(messages);
// breakpoint here
return processor
.doOnSubscribe(subscription -> logger.info("sub"))
.doOnNext(message -> logger.info("[Sent] " + message));
}
private Disposable registerProducer(Flux<String> flux) {
return flux
.doOnNext(message -> logger.info("[Received] " + message))
.map(String::toUpperCase)
// .delayElements(Duration.ofSeconds(1))
.subscribe(this.sink::next);
}
Client:
#ShellMethod("Connect to the server")
public void connect(String name) {
this.name = name;
this.rsocketRequester = rsocketRequesterBuilder
.rsocketStrategies(rsocketStrategies)
.connectTcp("localhost", 7000)
.block();
}
#ShellMethod("Establish a channel")
public void channel() {
this.rsocketRequester
.route("channel")
.data(this.fluxProcessor.doOnNext(message -> logger.info("[Sent] {}", message)))
.retrieveFlux(String.class)
.subscribe(message -> logger.info("[Received] {}", message));
}
#ShellMethod("Send a lower case message")
public void send(String message) {
this.fluxSink.next(message.toLowerCase());
}
The problem is: the first message a client sends is processed by the server, but does not reach the sender again. All subsequent messages are delivered without any problems. All other clients already connected will receive all messages.
What I noticed so far while debugging
when I call channel() in the client, retrieveFlux() and subscribe() are called. But on the server the breakpoint is not triggered in the corresponding method.
Only when the client sends the first message with send() is the breakpoint triggered on the server.
Using the .delayElements() on the server seems to "solve" the problem.
What am i doing wrong here?
And why does it need the send() first to trigger the servers breakpoint?
Thanks in advance!

A DirectProcessor does not have a buffer. If it does not have a subscriber, the message is dropped.
(Citing from its Javadoc: If there are no Subscribers, upstream items are dropped)
I think that when RSocketController.registerProducer() calls flux.[...].subscribe() it immediately starts processing the incoming messages from flux and passing them to the sink of the processor, but subscription to the processor has not happened yet. Thus the messages are dropped.
I guess that subscription to the processor is done by the framework, after returning from RSocketController.channel(...) method. -- I think that you are able to set a breakpoint in your processor.doOnSubscribe(..) method to see where it actually happens.
Thus maybe moving a registerProducer() call into a processor.doOnSubscribe() callback will solve your issue, like this:
#MessageMapping("channel")
Flux<String> channel(final Flux<String> messages) {
return processor
.doOnSubscribe(subscription -> this.registerProducer(messages))
.doOnSubscribe(subscription -> logger.info("sub"))
.doOnNext(message -> logger.info("[Sent] " + message));
}
But I think that personally I would prefer to replace a DirectProcessor with UnicastProcessor.create().onBackpressureBuffer().publish(). So that broadcasting to multiple subscribers is moved into a separate operation, so that there could be a buffer between the sink and subscribers, and late subscribers and backpressure could be handled in a better way.

What is the best practice for synchronizing parallel API requests to return the same result from a long running query without multiple executions?

I'm working on a backend Spring Boot project which is called by multiple clients. One of the functionalities is to merge data from two different databases and return the result, which may take up to 2 minutes.
I would like to be able to make concurrent calls to this endpoint wait for an already running request and return the same result without running the query again.
As shown below I've tried to setup a CompletableFuture field in the service singleton bean (which I know is a code smell since singleton service beans should be stateless).
//RestController
#Async
#GetMapping
public CompletableFuture<List<Foo>> getSyncedFoo() {
return service.syncFoo();
}
//ServiceImpl
private CompletableFuture<List<Foo>> syncTask;
#Override
#Async
#Transactional
public CompletableFuture<List<Foo>> syncFoo() {
if (this.syncTask == null || this.syncTask.isDone()) {
this.syncTask = CompletableFuture.supplyAsync(() -> {
// long running task
return new ArrayList<>();
});
}
return this.dbaseSyncTask;
}
I expected multiple frontend clients calling the api endpoint to receive the same response at roughly the same time, resulting in the backend performing the long running operation just once.
The operation was in fact executed just once but one of the clients received a 503 (Service Unavailable) while the other client received the expected response.
I suspect it's due to the shared use of the CompletableFuture, but I'm at a loss on what approach I should take. Could RxJava be of any use with the Observable strategy?

I've found a functional answer, for now.
#Service
public class FooServiceImpl implements FooService {
private CompletableFuture<List<Foo>> syncFuture;
private Observable<List<Foo>> syncObservable;
#Override
public Single<List<Foo>> syncFoo() {
if (syncFuture == null || syncFuture .isDone()) {
syncFuture = syncFooAsync();
syncObservable = Observable.fromFuture(syncFuture).share();
}
return Single.fromObservable(syncObservable);
}
private CompletableFuture<List<Foo>> syncFooAsync() {
return CompletableFuture.supplyAsync(() -> {
try {
Thread.sleep(10_000);
} catch (InterruptedException e) {
e.printStackTrace();
} finally {
return new ArrayList<>();
}
});
}
}
By using the RxJava library it s possible to multicast the created observable to multiple listeners using Observable::share method and the #RestController will happily work with the returned Single(s).
Sadly it still uses state in a singleton which is accessed concurrently by multiple threads so I fear situations where concurrency issues like the Observable completing while a new request is still in the process of creating a new subscription.
Hence I do not recommend this as a best practice so I'm not marking this as a final answer.

Equivalent of VertX CompositeFuture in RxJava

The VertX example for when you need to query multiple asynchronous resources and use them all in a single operation is:
Future<HttpServer> httpServerFuture = Future.future();
httpServer.listen(httpServerFuture.completer());
Future<NetServer> netServerFuture = Future.future();
netServer.listen(netServerFuture.completer());
CompositeFuture.all(httpServerFuture, netServerFuture).setHandler(ar -> {
if (ar.succeeded()) {
// All servers started
} else {
// At least one server failed
}
});
We need to query two different databases and then use the results in business logic, but the flow is equivalent.
What's the VertX/RxJava equivalent?
Currently people are doing this by nesting a new .flatMap() call every time they need a new variable. I'm left feeling there must be a better way...
We don't actually need the queries to be concurrent but we need to cache both results and pass them to the business logic at the same time some how.

there are many ways to do this, but i've tried to pick an approach that tacks closely to your sample:
#Override
public void start(Future<Void> startFuture) throws Exception {
final HttpServer httpServer = vertx.createHttpServer();
final Completable initializeHttpServer = httpServer.rxListen().toCompletable();
final NetServer netServer = vertx.createNetServer();
final Completable initializeNetServer = netServer.rxListen().toCompletable();
initializeHttpServer.andThen(initializeNetServer)
.subscribe(
() -> { /* All servers started */ },
error -> { /* At least one server failed */ }
);
}
the rxListen() invocations are converted into Completable instances, which are then run serially upon subscription.
the subscriber's onComplete callback will be invoked when both servers are done binding to their respective ports, or...
the onError callback will be invoked if an exception occurs
(also, fwiw, "nesting" flatMap operations for something as trivial as this shouldn't be necessary. "chaining" such operations, however, would be idiomatic usage).
hope that helps!
--UPDATE--
having read the question more carefully, i now see that you were actually asking about how to handle the results of two discrete asynchronous operations.
an alternative to flatMap'ing your way to combining the results would be to use the zip operator, like so:
#Override
public void start(Future<Void> startFuture) throws Exception {
final Single<String> dbQuery1 = Single.fromCallable(() -> { return "db-query-result-1"; });
final Single<String> dbQuery2 = Single.fromCallable(() -> { return "db-query-result-2"; });
Single.zip(dbQuery1, dbQuery2, (result1, result2) -> {
// handle the results from both db queries
// (with Pair being a standard tuple-like class)
return new Pair(result1, result2);
})
.subscribe(
pair -> {
// handle the results
},
error -> {
// something went wrong
}
);
}
per the docs, zip allows you to specify a series of reactive types (Single, Observable, etc) along with a function to transform all the results at once, with the central idea being that it will not emit anything until all the sources have emitted once (or more, depending on the reactive type).