I've found a lot of examples about it and doesn't know what's the 'right' implementation right there.
Basically I've got a object (let's call it NBAManager) and there's a method public Completable generateGame() for this object. The idea is that generateGame method gets called a lot of times and I want to generate games in a sequential way: I was thinking about concurrent queue. I came up with the following design: I'd create a singleton instance of NBAService: service for NBAManager and the body of generateGame() will look like this:
public Completable generateGame(RequestInfo info)
return service.generateGame(info);
So basically I'll pass up that Completable result. And inside of that NBAService object I'll have a queue (a concurrent one, because I want to have an opportunity to poll() and add(request) if there's a call of generateGame() while NBAManager was processing one of the earlier requests) of requests. I got stuck with this:
What's the right way to write such a job queue in Rx way? There're so many examples of it. Could you send me a link of a good implementation?
How do I handle the logic of queue execution? I believe we've to execute if there's one job only and if there're many then we just have to add it and that's it. How can I control it without runnable? I was thinking about using subjects.
Thanks!
There are multiple ways to implement this, you can choose how much RxJava should be invoked. The least involvement can use a single threaded ExecutorService as the "queue" and CompletableSubject for the delayed completion:
class NBAService {
static ExecutorService exec = Executors.newSingleThreadedExecutor();
public static Completable generateGame(RequestInfo info) {
CompletableSubject result = CompletableSubject.create();
exec.submit(() -> {
// do something with the RequestInfo instance
f(info).subscribe(result);
});
return result;
}
}
A more involved solution would be if you wanted to trigger the execution when the Completable is subscribed to. In this case, you can go with create() and subscribeOn():
class NBAService {
public static Completable generateGame(RequestInfo info) {
return Completable.create(emitter -> {
// do something with the RequestInfo instance
emitter.setDisposable(
f(info).subscribe(emitter::onComplete, emitter::onError)
);
})
.subscribeOn(Schedulers.single());
}
}
Related
I'm looking at the Simple RPC example from grpc.io's basic tutorial:
#Override
public void getFeature(Point request, StreamObserver<Feature> responseObserver) {
responseObserver.onNext(checkFeature(request));
responseObserver.onCompleted();
}
...
private Feature checkFeature(Point location) {
for (Feature feature : features) {
if (feature.getLocation().getLatitude() == location.getLatitude()
&& feature.getLocation().getLongitude() == location.getLongitude()) {
return feature;
}
}
// No feature was found, return an unnamed feature.
return Feature.newBuilder().setName("").setLocation(location).build();
}
Are there any caveats to interacting with the StreamObserver from other threads? For example, say checkFeature() asynchronously hits another service, returning a CompletableFuture:
#Override
public void getFeature(Point request, StreamObserver<Feature> responseObserver) {
checkFeature(request).
thenAccept(feature -> responseObserver.onNext(feature));
responseObserver.onCompleted();
}
Of course the above wouldn't work because the first thread would execute onCompleted() before the feature is returned. So let's fix that:
#Override
public void getFeature(Point request, StreamObserver<Feature> responseObserver) {
checkFeature(request).
thenAccept(feature -> {
responseObserver.onNext(feature);
responseObserver.onCompleted();
});
}
I think this should work, but I'm new to Java so I wonder what ramifications there are. For example,
Will Context.current() be consistent?
Will anything cause the StreamObserver to destruct or close prematurely besides onNext() for a unary calls and onError()?
Is there a better practice?
It would be great if someone could also step me through how they reasoned. I tried looking up actual implementations of StreamObserver but I wasn't sure what to look for.
Using thenAccept() to call onNext() and onCompleted() is fine, because the observer is not called concurrently from multiple threads.
The "broken" example that called onCompleted() separately was broken also because it could have called the observer from multiple threads without any form of synchronization. StreamObservers may not be called from multiple threads simultaneously.
Using thenAccept() isn't quite right though, as it doesn't handle the case where the future fails. So you need to receive the Throwable as well, which can be done with whenComplete():
#Override
public void getFeature(Point request, StreamObserver<Feature> responseObserver) {
checkFeature(request).
whenComplete((feature, t) -> {
if (t != null) {
responseObserver.onError(t);
} else {
responseObserver.onNext(feature);
responseObserver.onCompleted();
}
});
}
The Context could easily be "wrong" when processing that lambda. Typically we'd look for "architectural" solutions to make sure the context is propagated, like wrapping all application thread pools in Context.currentContextExecutor() when creating them, so individual call sites don't need to be concerned with propagation. I'm not familiar enough with CompletableFuture to provide strategy for it.
Will Context.current() be consistent?
Context.current() is using ThreadLocal. if you are accessing it on a different thread, it won't be consistent. You can propagate context between threads. You may find this post useful.
Will anything cause the StreamObserver to destruct or close prematurely besides onNext() for a unary calls and onError()?
Yes, Normal flow of StreamObserver ends with onError or onCompleted.
As StreamObserver javadoc states, "Since individual StreamObservers are not thread-safe, if multiple threads will be writing to a StreamObserver concurrently, the application must synchronize calls". If you are calling StreamObserver concurrently, you need to synchronize the calls. In other words, if you know for sure it won't be called concurrently even if you are using multiple threads, it should be fine.
If accessing the same StreamObserver on multiple threads, I would try to synchronize it unless the performance is critical since it is error prone. At least, it deserves a nice comment.
I have a spring-webflux API which, at a service layer, needs to read from an existing repository which uses JDBC.
Having done some reading on the subject, I would like to keep the execution of the blocking database call separate from the rest of my non-blocking async code.
I have defined a dedicated jdbcScheduler:
#Bean
public Scheduler jdbcScheduler() {
return Schedulers.fromExecutor(Executors.newFixedThreadPool(maxPoolSize));
}
And an AsyncWrapper utility to use it:
#Component
public class AsyncJdbcWrapper {
private final Scheduler jdbcScheduler;
#Autowired
public AsyncJdbcWrapper(Scheduler jdbcScheduler) {
this.jdbcScheduler = jdbcScheduler;
}
public <T> Mono<T> async(Callable<T> callable) {
return Mono.fromCallable(callable)
.subscribeOn(jdbcScheduler)
.publishOn(Schedulers.parallel());
}
}
Which is then used to wrap jdbc calls like so:
Mono<Integer> userIdMono = asyncWrapper.async(() -> userDao.getUserByUUID(request.getUserId()))
.map(userOption -> userOption.map(u -> u.getId())
.orElseThrow(() -> new IllegalArgumentException("Unable to find user with ID " + request.getUserId())));
I've got two questions:
1) Am I correctly pushing the execution of blocking calls to another set of threads? Being fairly new to this stuff I'm struggling with the intricacies of subscribeOn()/publishOn().
2) Say I want to make use of the resulting mono, e.g call an API with the result of the userIdMono, on which scheduler will that be executed? The one specifically created for the jdbc calls, or the main(?) thread that reactor usually operates within? e.g.
userIdMono.map(id -> someApiClient.call(id));
1) Use of subscribeOn is correctly putting the JDBC work on the jdbcScheduler
2) Neither, the results of the Callable - while computed on the jdbcScheduler, are publishOn the parallel Scheduler, so your map will be executed on a thread from the Schedulers.parallel() pool (rather than hogging the jdbcScheduler).
Background
I am using Realm within my app. When data is loaded it then undergoes intense processing therefore the processing occurs on a background thread.
The coding pattern in use is the Unit of Work pattern and Realm only exists within a repository under a DataManager. The idea here is that each repository can have a different database/file storage solution.
What I have tried
Below is an example of some similar code to what I have in my FooRespository class.
The idea here is that an instance of Realm is obtained, used to query the realm for objects of interest, return them and close the realm instance. Note that this is synchronous and at the end copies the objects from Realm to an unmanaged state.
public Observable<List<Foo>> getFoosById(List<String> fooIds) {
Realm realm = Realm.getInstance(fooRealmConfiguration);
RealmQuery<Foo> findFoosByIdQuery = realm.where(Foo.class);
for(String id : fooIds) {
findFoosByIdQuery.equalTo(Foo.FOO_ID_FIELD_NAME, id);
findFoosByIdQuery.or();
}
return findFoosByIdQuery
.findAll()
.asObservable()
.doOnUnsubscribe(realm::close)
.filter(RealmResults::isLoaded)
.flatMap(foos -> Observable.just(new ArrayList<>(realm.copyFromRealm(foos))));
}
This code is later used in conjunction with the heavy processing code via RxJava:
dataManager.getFoosById(foo)
.flatMap(this::processtheFoosInALongRunningProcess)
.subscribeOn(Schedulers.io()) //could be Schedulers.computation() etc
.subscribe(tileChannelSubscriber);
After reading the docs, my belief is that the above should work, as it is NOT asynchronous and therefore does not need a looper thread. I obtain the instance of realm within the same thread therefore it is not being passed between threads and neither are the objects.
The problem
When the above is executed I get
Realm access from incorrect thread. Realm objects can only be accessed
on the thread they were created.
This doesn't seem right. The only thing I can think of is that the pool of Realm instances is getting me an existing instance created from another process using the main thread.
Kay so
return findFoosByIdQuery
.findAll()
.asObservable()
This happens on UI thread, because that's where you're calling it from initially
.subscribeOn(Schedulers.io())
Aaaaand then you're tinkering with them on Schedulers.io().
Nope, that's not the same thread!
As much as I dislike the approach of copying from a zero-copy database, your current approach is riddled with issues due to misuse of realmResults.asObservable(), so here's a spoiler for what your code should be:
public Observable<List<Foo>> getFoosById(List<String> fooIds) {
return Observable.defer(() -> {
try(Realm realm = Realm.getInstance(fooRealmConfiguration)) { //try-finally also works
RealmQuery<Foo> findFoosByIdQuery = realm.where(Foo.class);
for(String id : fooIds) {
findFoosByIdQuery.equalTo(FooFields.ID, id);
findFoosByIdQuery.or(); // please guarantee this works?
}
RealmResults<Foo> results = findFoosByIdQuery.findAll();
return Observable.just(realm.copyFromRealm(results));
}
}).subscribeOn(Schedulers.io());
}
Note that you are creating the instance outside of all your RxJava processing pipeline. Thus on the main thread (or whichever thread you are on, when calling getFoosById().
Just because the method returns an Observable doesn't mean that it runs on another thread. Only the processing pipeline of the Observable created by the last statement of your getFoosById() method runs on the correct thread (the filter(), the flatMap() and all the processing done by the caller).
You thus have to ensure that the call of getFoosById()is already done on the thread used by Schedulers.io().
One way to achieve this is by using Observable.defer():
Observable.defer(() -> dataManager.getFoosById(foo))
.flatMap(this::processtheFoosInALongRunningProcess)
.subscribeOn(Schedulers.io()) //could be Schedulers.computation() etc
.subscribe(tileChannelSubscriber);
Is there a concept of using promises in java (just like ut is used in JavaScript) instead of using nested callbacks ?
If so, is there an example of how the callback is implemented in java and handlers are chained ?
Yep! Java 8 calls it CompletableFuture. It lets you implement stuff like this.
class MyCompletableFuture<T> extends CompletableFuture<T> {
static final Executor myExecutor = ...;
public MyCompletableFuture() { }
public <U> CompletableFuture<U> newIncompleteFuture() {
return new MyCompletableFuture<U>();
}
public Executor defaultExecutor() {
return myExecutor;
}
public void obtrudeValue(T value) {
throw new UnsupportedOperationException();
}
public void obtrudeException(Throwable ex) {
throw new UnsupportedOperationException();
}
}
The basic design is a semi-fluent API in which you can arrange:
(sequential or async)
(functions or actions)
triggered on completion of
i) ("then") ,or ii) ("andThen" and "orThen")
others. As in:
MyCompletableFuture<String> f = ...; g = ...
f.then((s -> aStringFunction(s)).thenAsync(s -> ...);
or
f.andThen(g, (s, t) -> combineStrings).or(CompletableFuture.async(()->...)....
UPDATE 7/20/17
I wanted to edit that there is also a Library called "ReactFX" which is supposed to be JavaFX as a reactive framework. There are many Reactive Java libraries from what I've seen, and since Play is based on the Reactive principal, I would assume that these Reactive libraries follow that same principal of non-blocking i/o, async calls from server to client and back while having communication be send by either end.
These libraries seem to be made for the client side of things, but there might be a Server reactive library as well, but I would assume that it would be wiser to use Play! with one of these client side reactive libraries.
You can take a look at https://www.playframework.com/
which implements this functionality here
https://www.playframework.com/documentation/2.2.0/api/java/play/libs/F.Promise.html
Additonal reading https://www.playframework.com/documentation/2.5.x/JavaAsync
Creating non-blocking actions
Because of the way Play works, action code must be as fast as possible, i.e., non-blocking. So what should we return from our action if we are not yet able to compute the result? We should return the promise of a result!
Java 8 provides a generic promise API called CompletionStage. A CompletionStage<Result> will eventually be redeemed with a value of type Result. By using a CompletionStage<Result> instead of a normal Result, we are able to return from our action quickly without blocking anything. Play will then serve the result as soon as the promise is redeemed.
The web client will be blocked while waiting for the response, but nothing will be blocked on the server, and server resources can be used to serve other clients.
How to create a CompletionStage
To create a CompletionStage<Result> we need another promise first: the promise that will give us the actual value we need to compute the result:
CompletionStage<Double> promiseOfPIValue = computePIAsynchronously();
CompletionStage<Result> promiseOfResult = promiseOfPIValue.thenApply(pi ->
ok("PI value computed: " + pi)
);
Play asynchronous API methods give you a CompletionStage. This is the case when you are calling an external web service using the play.libs.WS API, or if you are using Akka to schedule asynchronous tasks or to communicate with Actors using play.libs.Akka.
A simple way to execute a block of code asynchronously and to get a CompletionStage is to use the CompletableFuture.supplyAsync() helper:
CompletionStage<Integer> promiseOfInt = CompletableFuture.supplyAsync(() -> intensiveComputation());
Note: It’s important to understand which thread code runs on which promises. Here, the intensive computation will just be run on another thread.
You can’t magically turn synchronous IO into asynchronous by wrapping it in a CompletionStage. If you can’t change the application’s architecture to avoid blocking operations, at some point that operation will have to be executed, and that thread is going to block. So in addition to enclosing the operation in a CompletionStage, it’s necessary to configure it to run in a separate execution context that has been configured with enough threads to deal with the expected concurrency. See Understanding Play thread pools for more information.
It can also be helpful to use Actors for blocking operations. Actors provide a clean model for handling timeouts and failures, setting up blocking execution contexts, and managing any state that may be associated with the service. Also Actors provide patterns like ScatterGatherFirstCompletedRouter to address simultaneous cache and database requests and allow remote execution on a cluster of backend servers. But an Actor may be overkill depending on what you need.
Async results
We have been returning Result up until now. To send an asynchronous result our action needs to return a CompletionStage<Result>:
public CompletionStage<Result> index() {
return CompletableFuture.supplyAsync(() -> intensiveComputation())
.thenApply(i -> ok("Got result: " + i));
}
Actions are asynchronous by default
Play actions are asynchronous by default. For instance, in the controller code below, the returned Result is internally enclosed in a promise:
public Result index() {
return ok("Got request " + request() + "!");
}
Note: Whether the action code returns a Result or a CompletionStage<Result>, both kinds of returned object are handled internally in the same way. There is a single kind of Action, which is asynchronous, and not two kinds (a synchronous one and an asynchronous one). Returning a CompletionStage is a technique for writing non-blocking code.
Some info on CompletionStage
https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/CompletionStage.html
which is a subclass of the class mentioned in #Debosmit Ray's answer called CompletableFuture
This Youtube Video by LinkedIn dev Mr. Brikman explains a bit about Promises in
https://youtu.be/8z3h4Uv9YbE?t=15m46s
and
https://www.youtube.com/watch?v=4b1XLka0UIw
I believe the first video gives an example of a promise, the second video might also give some good info, I don't really recall which video had what content.
Either way the information here is very good, and worth looking into.
I personally do not use Play, but I have been looking at it for a long, long time, as it does a lot of really good stuff.
If you want to do Promise even before Java7, "java-promise" may be useful. (Of course it works with Java8)
You can easily control asynchronous operations like JavaScript's Promise.
https://github.com/riversun/java-promise
example
import org.riversun.promise.Promise;
public class Example {
public static void main(String[] args) {
Promise.resolve("foo")
.then(new Promise((action, data) -> {
new Thread(() -> {
String newData = data + "bar";
action.resolve(newData);
}).start();
}))
.then(new Promise((action, data) -> {
System.out.println(data);
action.resolve();
}))
.start();
System.out.println("Promise in Java");
}
}
result:
Promise in Java
foobar
This could get a little bit complicated and I'm not that experienced with Observables and the RX pattern so bear with me:
Suppose you've got some arbitrary SDK method which returns an Observable. You consume the method from a class which is - among other things - responsible for retrieving data and, while doing so, does some caching, so let's call it DataProvider. Then you've got another class which wants to access the data provided by DataProvider. Let's call it Consumer for now. So there we've got our setup.
Side note for all the pattern friends out there: I'm aware that this is not MVP, it's just an example for an analogous, but much more complex problem I'm facing in my application.
That being said, in Kotlin-like pseudo code the described situation would look like this:
class Consumer(val provider: DataProvider) {
fun logic() {
provider.getData().subscribe(...)
}
}
class DataProvider(val sdk: SDK) {
fun getData(): Consumer {
val observable = sdk.getData()
observable.subscribe(/*cache data as it passes through*/)
return observable
}
}
class SDK {
fun getData(): Observable {
return fetchDataFromNetwork()
}
}
The problem is, that upon calling sdk.subscribe() in the DataProvider I'm already triggering the Observable's subscribe() method which I don't want. I want the DataProvider to just silently listen - in this example the triggering should be done by the Consumer.
So what's the best RX compatible solution for this problem? The one outlined in the pseudo code above definitely isn't for various reasons one of which is the premature triggering of the network request before the Consumer has subscribed to the Observable. I've experimented with publish().autoComplete(2) before calling subscribe() in the DataProvider, but that doesn't seem to be the canonical way to do this kind of things. It just feels hacky.
Edit: Through SO's excellent "related" feature I've just stumbled across another question pointing in a different direction, but having a solution which could also be applicable here namely flatMap(). I knew that one before, but never actually had to use it. Seems like a viable way to me - what's your opinion regarding that?
If the caching step is not supposed to modify events in the chain, the doOnNext() operator can be used:
class DataProvider(val sdk: SDK) {
fun getData(): Observable<*> = sdk.getData().doOnNext(/*cache data as it passes through*/)
}
Yes, flatMap could be a solution. Moreover you could split your stream into chain of small Observables:
public class DataProvider {
private Api api;
private Parser parser;
private Cache cache;
public Observable<List<User>> getUsers() {
return api.getUsersFromNetwork()
.flatMap(parser::parseUsers)
.map(cache::cacheUsers);
}
}
public class Api {
public Observable<Response> getUsersFromNetwork() {
//makes https request or whatever
}
}
public class Parser {
public Observable<List<User>> parseUsers(Response response) {
//parse users
}
}
public class Cache {
public List<User> cacheUsers(List<User> users) {
//cache users
}
}
It's easy to test, maintain and replace implementations(with usage of interfaces). Also you could easily insert additional step into your stream(for instance log/convert/change data which you receive from server).
The other quite convenient operator is map. Basically instead of Observable<Data> it returns just Data. It could make your code even simpler.