Integration
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Integration

Integrating with Actors

For piping the elements of a stream as messages to an ordinary actor you can use the Sink.actorRef. Messages can be sent to a stream via the ActorRef that is materialized by Source.actorRef.

For more advanced use cases the ActorPublisher and ActorSubscriber traits are provided to support implementing Reactive Streams Publisher and Subscriber with an Actor.

These can be consumed by other Reactive Stream libraries or used as a Akka Streams Source or Sink.

Warning

AbstractActorPublisher and AbstractActorSubscriber cannot be used with remote actors, because if signals of the Reactive Streams protocol (e.g. request) are lost the the stream may deadlock.

Note

These Actors are designed to be implemented using Java 8 lambda expressions. In case you need to stay on a JVM prior to 8, Akka provides UntypedActorPublisher and UntypedActorSubscriber which can be used easily from any language level.

Source.actorRef

Messages sent to the actor that is materialized by Source.actorRef will be emitted to the stream if there is demand from downstream, otherwise they will be buffered until request for demand is received.

Depending on the defined OverflowStrategy it might drop elements if there is no space available in the buffer. The strategy OverflowStrategy.backpressure() is not supported for this Source type, you should consider using ActorPublisher if you want a backpressured actor interface.

The stream can be completed successfully by sending akka.actor.PoisonPill or akka.actor.Status.Success to the actor reference.

The stream can be completed with failure by sending akka.actor.Status.Failure to the actor reference.

The actor will be stopped when the stream is completed, failed or cancelled from downstream, i.e. you can watch it to get notified when that happens.

Sink.actorRef

The sink sends the elements of the stream to the given ActorRef. If the target actor terminates the stream will be cancelled. When the stream is completed successfully the given onCompleteMessage will be sent to the destination actor. When the stream is completed with failure a akka.actor.Status.Failure message will be sent to the destination actor.

Warning

There is no back-pressure signal from the destination actor, i.e. if the actor is not consuming the messages fast enough the mailbox of the actor will grow. For potentially slow consumer actors it is recommended to use a bounded mailbox with zero mailbox-push-timeout-time or use a rate limiting stage in front of this stage.

ActorPublisher

Extend akka.stream.actor.AbstractActorPublisher to implement a stream publisher that keeps track of the subscription life cycle and requested elements.

Here is an example of such an actor. It dispatches incoming jobs to the attached subscriber:

public static class JobManagerProtocol {
  final public static class Job {
    public final String payload;

    public Job(String payload) {
      this.payload = payload;
    }

  }

  public static class JobAcceptedMessage {
    @Override
    public String toString() {
      return "JobAccepted";
    }
  }
  public static final JobAcceptedMessage JobAccepted = new JobAcceptedMessage();

  public static class JobDeniedMessage {
    @Override
    public String toString() {
      return "JobDenied";
    }
  }
  public static final JobDeniedMessage JobDenied = new JobDeniedMessage();
}
public static class JobManager extends AbstractActorPublisher<JobManagerProtocol.Job> {

  public static Props props() { return Props.create(JobManager.class); }

  private final int MAX_BUFFER_SIZE = 100;
  private final List<JobManagerProtocol.Job> buf = new ArrayList<>();

  public JobManager() {
    receive(ReceiveBuilder.
      match(JobManagerProtocol.Job.class, job -> buf.size() == MAX_BUFFER_SIZE, job -> {
        sender().tell(JobManagerProtocol.JobDenied, self());
      }).
      match(JobManagerProtocol.Job.class, job -> {
        sender().tell(JobManagerProtocol.JobAccepted, self());

        if (buf.isEmpty() && totalDemand() > 0)
          onNext(job);
        else {
          buf.add(job);
          deliverBuf();
        }
      }).
      match(ActorPublisherMessage.Request.class, request -> deliverBuf()).
      match(ActorPublisherMessage.Cancel.class, cancel -> context().stop(self())).
      build());
  }

  void deliverBuf() {
    while (totalDemand() > 0) {
        /*
         * totalDemand is a Long and could be larger than
         * what buf.splitAt can accept
         */
      if (totalDemand() <= Integer.MAX_VALUE) {
        final List<JobManagerProtocol.Job> took =
          buf.subList(0, Math.min(buf.size(), (int) totalDemand()));
        took.forEach(this::onNext);
        buf.removeAll(took);
        break;
      } else {
        final List<JobManagerProtocol.Job> took =
          buf.subList(0, Math.min(buf.size(), Integer.MAX_VALUE));
        took.forEach(this::onNext);
        buf.removeAll(took);
      }
    }
  }
}

You send elements to the stream by calling onNext. You are allowed to send as many elements as have been requested by the stream subscriber. This amount can be inquired with totalDemand. It is only allowed to use onNext when isActive and totalDemand>0, otherwise onNext will throw IllegalStateException.

When the stream subscriber requests more elements the ActorPublisherMessage.Request message is delivered to this actor, and you can act on that event. The totalDemand is updated automatically.

When the stream subscriber cancels the subscription the ActorPublisherMessage.Cancel message is delivered to this actor. After that subsequent calls to onNext will be ignored.

You can complete the stream by calling onComplete. After that you are not allowed to call onNext, onError and onComplete.

You can terminate the stream with failure by calling onError. After that you are not allowed to call onNext, onError and onComplete.

If you suspect that this AbstractActorPublisher may never get subscribed to, you can override the subscriptionTimeout method to provide a timeout after which this Publisher should be considered canceled. The actor will be notified when the timeout triggers via an ActorPublisherMessage.SubscriptionTimeoutExceeded message and MUST then perform cleanup and stop itself.

If the actor is stopped the stream will be completed, unless it was not already terminated with failure, completed or canceled.

More detailed information can be found in the API documentation.

This is how it can be used as input Source to a Flow:

final Source<JobManagerProtocol.Job, ActorRef> jobManagerSource =
  Source.actorPublisher(JobManager.props());

final ActorRef ref = jobManagerSource
  .map(job -> job.payload.toUpperCase())
  .map(elem -> {
    System.out.println(elem);
    return elem;
  })
  .to(Sink.ignore())
  .run(mat);

ref.tell(new JobManagerProtocol.Job("a"), ActorRef.noSender());
ref.tell(new JobManagerProtocol.Job("b"), ActorRef.noSender());
ref.tell(new JobManagerProtocol.Job("c"), ActorRef.noSender());

You can only attach one subscriber to this publisher. Use a Broadcast-element or attach a Sink.asPublisher(true) to enable multiple subscribers.

ActorSubscriber

Extend akka.stream.actor.AbstractActorSubscriber to make your class a stream subscriber with full control of stream back pressure. It will receive ActorSubscriberMessage.OnNext, ActorSubscriberMessage.OnComplete and ActorSubscriberMessage.OnError messages from the stream. It can also receive other, non-stream messages, in the same way as any actor.

Here is an example of such an actor. It dispatches incoming jobs to child worker actors:

public static class WorkerPoolProtocol {

  public static class Msg {
    public final int id;
    public  final ActorRef replyTo;

    public Msg(int id, ActorRef replyTo) {
      this.id = id;
      this.replyTo = replyTo;
    }

    @Override
    public String toString() {
      return String.format("Msg(%s, %s)", id, replyTo);
    }
  }
  public static Msg msg(int id, ActorRef replyTo) {
    return new Msg(id, replyTo);
  }

  
  public static class Work {
    public final int id;
    public Work(int id) { this.id = id; }

    @Override
    public String toString() {
      return String.format("Work(%s)", id);
    }
  }
  public static Work work(int id) {
    return new Work(id);
  }
  
  
  public static class Reply {
    public final int id;
    public Reply(int id) { this.id = id; }

    @Override
    public String toString() {
      return String.format("Reply(%s)", id);
    }
  }
  public static Reply reply(int id) {
    return new Reply(id);
  }
  
  
  public static class Done {
    public final int id;
    public Done(int id) { this.id = id; }

    @Override
    public String toString() {
      return String.format("Done(%s)", id);
    }

    @Override
    public boolean equals(Object o) {
      if (this == o) {
        return true;
      }
      if (o == null || getClass() != o.getClass()) {
        return false;
      }

      Done done = (Done) o;

      if (id != done.id) {
        return false;
      }

      return true;
    }

    @Override
    public int hashCode() {
      return id;
    }
  }
  public static Done done(int id) {
    return new Done(id);
  }

}
  
public static class WorkerPool extends AbstractActorSubscriber {
  
  public static Props props() { return Props.create(WorkerPool.class); }
  
  final int MAX_QUEUE_SIZE = 10;
  final Map<Integer, ActorRef> queue = new HashMap<>();
  
  final Router router;
  
  @Override
  public RequestStrategy requestStrategy() {
    return new MaxInFlightRequestStrategy(MAX_QUEUE_SIZE) {
      @Override
      public int inFlightInternally() {
        return queue.size();
      }
    };
  }

  public WorkerPool() {
    final List<Routee> routees = new ArrayList<>();
    for (int i = 0; i < 3; i++)
      routees.add(new ActorRefRoutee(context().actorOf(Props.create(Worker.class))));
    router = new Router(new RoundRobinRoutingLogic(), routees);
    
    receive(ReceiveBuilder.
    match(ActorSubscriberMessage.OnNext.class, on -> on.element() instanceof WorkerPoolProtocol.Msg,
      onNext -> {
        WorkerPoolProtocol.Msg msg = (WorkerPoolProtocol.Msg) onNext.element();
        queue.put(msg.id, msg.replyTo);

        if (queue.size() > MAX_QUEUE_SIZE)
          throw new RuntimeException("queued too many: " + queue.size());
              
        router.route(WorkerPoolProtocol.work(msg.id), self());
    }).
    match(WorkerPoolProtocol.Reply.class, reply -> {
      int id = reply.id;
      queue.get(id).tell(WorkerPoolProtocol.done(id), self());
      queue.remove(id);
    }).
    build());
  }
}
  
static class Worker extends AbstractActor {
  public Worker() {
    receive(ReceiveBuilder.
      match(WorkerPoolProtocol.Work.class, work -> {
        // ...
        sender().tell(WorkerPoolProtocol.reply(work.id), self());
      }).build());
  }
}

Subclass must define the RequestStrategy to control stream back pressure. After each incoming message the AbstractActorSubscriber will automatically invoke the RequestStrategy.requestDemand and propagate the returned demand to the stream.

  • The provided WatermarkRequestStrategy is a good strategy if the actor performs work itself.
  • The provided MaxInFlightRequestStrategy is useful if messages are queued internally or delegated to other actors.
  • You can also implement a custom RequestStrategy or call request manually together with ZeroRequestStrategy or some other strategy. In that case you must also call request when the actor is started or when it is ready, otherwise it will not receive any elements.

More detailed information can be found in the API documentation.

This is how it can be used as output Sink to a Flow:

final int N = 117;
final List<Integer> data = new ArrayList<>(N);
for (int i = 0; i < N; i++) {
  data.add(i);
}

Source.from(data)
  .map(i -> WorkerPoolProtocol.msg(i, replyTo))
  .runWith(Sink.<WorkerPoolProtocol.Msg>actorSubscriber(WorkerPool.props()), mat);

Integrating with External Services

Stream transformations and side effects involving external non-stream based services can be performed with mapAsync or mapAsyncUnordered.

For example, sending emails to the authors of selected tweets using an external email service:

public Future<Email> send(Email email) {
  // ...
}

We start with the tweet stream of authors:

final Source<Author, BoxedUnit> authors = tweets
  .filter(t -> t.hashtags().contains(AKKA))
  .map(t -> t.author);

Assume that we can lookup their email address using:

public Future<Optional<String>> lookupEmail(String handle)

Transforming the stream of authors to a stream of email addresses by using the lookupEmail service can be done with mapAsync:

final Source<String, BoxedUnit> emailAddresses = authors
  .mapAsync(4, author -> addressSystem.lookupEmail(author.handle))
  .filter(o -> o.isPresent())
  .map(o -> o.get());

Finally, sending the emails:

final RunnableGraph<BoxedUnit> sendEmails = emailAddresses
  .mapAsync(4, address ->
    emailServer.send(new Email(address, "Akka", "I like your tweet")))
  .to(Sink.ignore());

sendEmails.run(mat);

mapAsync is applying the given function that is calling out to the external service to each of the elements as they pass through this processing step. The function returns a Future and the value of that future will be emitted downstreams. The number of Futures that shall run in parallel is given as the first argument to mapAsync. These Futures may complete in any order, but the elements that are emitted downstream are in the same order as received from upstream.

That means that back-pressure works as expected. For example if the emailServer.send is the bottleneck it will limit the rate at which incoming tweets are retrieved and email addresses looked up.

The final piece of this pipeline is to generate the demand that pulls the tweet authors information through the emailing pipeline: we attach a Sink.ignore which makes it all run. If our email process would return some interesting data for further transformation then we would of course not ignore it but send that result stream onwards for further processing or storage.

Note that mapAsync preserves the order of the stream elements. In this example the order is not important and then we can use the more efficient mapAsyncUnordered:

final Source<Author, BoxedUnit> authors =
  tweets
    .filter(t -> t.hashtags().contains(AKKA))
    .map(t -> t.author);

final Source<String, BoxedUnit> emailAddresses =
  authors
    .mapAsyncUnordered(4, author -> addressSystem.lookupEmail(author.handle))
    .filter(o -> o.isPresent())
    .map(o -> o.get());

final RunnableGraph<BoxedUnit> sendEmails =
  emailAddresses
    .mapAsyncUnordered(4, address ->
      emailServer.send(new Email(address, "Akka", "I like your tweet")))
    .to(Sink.ignore());

sendEmails.run(mat);

In the above example the services conveniently returned a Future of the result. If that is not the case you need to wrap the call in a Future. If the service call involves blocking you must also make sure that you run it on a dedicated execution context, to avoid starvation and disturbance of other tasks in the system.

final MessageDispatcher blockingEc = system.dispatchers().lookup("blocking-dispatcher");

final RunnableGraph<BoxedUnit> sendTextMessages =
  phoneNumbers
    .mapAsync(4, phoneNo  ->
      Futures.future(() ->
        smsServer.send(new TextMessage(phoneNo, "I like your tweet")),
        blockingEc)
    )
    .to(Sink.ignore());

sendTextMessages.run(mat);

The configuration of the "blocking-dispatcher" may look something like:

blocking-dispatcher {
  executor = "thread-pool-executor"
  thread-pool-executor {
    core-pool-size-min    = 10
    core-pool-size-max    = 10
  }
}

An alternative for blocking calls is to perform them in a map operation, still using a dedicated dispatcher for that operation.

final Flow<String, Boolean, BoxedUnit> send =
  Flow.of(String.class)
  .map(phoneNo -> smsServer.send(new TextMessage(phoneNo, "I like your tweet")))
  .withAttributes(ActorAttributes.dispatcher("blocking-dispatcher"));
final RunnableGraph<?> sendTextMessages =
  phoneNumbers.via(send).to(Sink.ignore());

sendTextMessages.run(mat);

However, that is not exactly the same as mapAsync, since the mapAsync may run several calls concurrently, but map performs them one at a time.

For a service that is exposed as an actor, or if an actor is used as a gateway in front of an external service, you can use ask:

final Source<Tweet, BoxedUnit> akkaTweets = tweets.filter(t -> t.hashtags().contains(AKKA));

final RunnableGraph<BoxedUnit> saveTweets =
  akkaTweets
    .mapAsync(4, tweet -> ask(database, new Save(tweet), 300))
    .to(Sink.ignore());

Note that if the ask is not completed within the given timeout the stream is completed with failure. If that is not desired outcome you can use recover on the ask Future.

Illustrating ordering and parallelism

Let us look at another example to get a better understanding of the ordering and parallelism characteristics of mapAsync and mapAsyncUnordered.

Several mapAsync and mapAsyncUnordered futures may run concurrently. The number of concurrent futures are limited by the downstream demand. For example, if 5 elements have been requested by downstream there will be at most 5 futures in progress.

mapAsync emits the future results in the same order as the input elements were received. That means that completed results are only emitted downstream when earlier results have been completed and emitted. One slow call will thereby delay the results of all successive calls, even though they are completed before the slow call.

mapAsyncUnordered emits the future results as soon as they are completed, i.e. it is possible that the elements are not emitted downstream in the same order as received from upstream. One slow call will thereby not delay the results of faster successive calls as long as there is downstream demand of several elements.

Here is a fictive service that we can use to illustrate these aspects.

static class SometimesSlowService {
  private final ExecutionContext ec;

  public SometimesSlowService(ExecutionContext ec) {
    this.ec = ec;
  }

  private final AtomicInteger runningCount = new AtomicInteger();

  public Future<String> convert(String s) {
    System.out.println("running: " + s + "(" + runningCount.incrementAndGet() + ")");
    return Futures.future(() -> {
      if (!s.isEmpty() && Character.isLowerCase(s.charAt(0)))
        Thread.sleep(500);
      else
        Thread.sleep(20);
      System.out.println("completed: " + s + "(" + runningCount.decrementAndGet() + ")");
      return s.toUpperCase();
    }, ec);
  }
}

Elements starting with a lower case character are simulated to take longer time to process.

Here is how we can use it with mapAsync:

final MessageDispatcher blockingEc = system.dispatchers().lookup("blocking-dispatcher");
final SometimesSlowService service = new SometimesSlowService(blockingEc);

final ActorMaterializer mat = ActorMaterializer.create(
  ActorMaterializerSettings.create(system).withInputBuffer(4, 4), system);

Source.from(Arrays.asList("a", "B", "C", "D", "e", "F", "g", "H", "i", "J"))
  .map(elem -> { System.out.println("before: " + elem); return elem; })
  .mapAsync(4, service::convert)
  .runForeach(elem -> System.out.println("after: " + elem), mat);

The output may look like this:

before: a
before: B
before: C
before: D
running: a (1)
running: B (2)
before: e
running: C (3)
before: F
running: D (4)
before: g
before: H
completed: C (3)
completed: B (2)
completed: D (1)
completed: a (0)
after: A
after: B
running: e (1)
after: C
after: D
running: F (2)
before: i
before: J
running: g (3)
running: H (4)
completed: H (2)
completed: F (3)
completed: e (1)
completed: g (0)
after: E
after: F
running: i (1)
after: G
after: H
running: J (2)
completed: J (1)
completed: i (0)
after: I
after: J

Note that after lines are in the same order as the before lines even though elements are completed in a different order. For example H is completed before g, but still emitted afterwards.

The numbers in parenthesis illustrates how many calls that are in progress at the same time. Here the downstream demand and thereby the number of concurrent calls are limited by the buffer size (4) of the ActorMaterializerSettings.

Here is how we can use the same service with mapAsyncUnordered:

final MessageDispatcher blockingEc = system.dispatchers().lookup("blocking-dispatcher");
final SometimesSlowService service = new SometimesSlowService(blockingEc);

final ActorMaterializer mat = ActorMaterializer.create(
  ActorMaterializerSettings.create(system).withInputBuffer(4, 4), system);

Source.from(Arrays.asList("a", "B", "C", "D", "e", "F", "g", "H", "i", "J"))
  .map(elem -> { System.out.println("before: " + elem); return elem; })
  .mapAsyncUnordered(4, service::convert)
  .runForeach(elem -> System.out.println("after: " + elem), mat);

The output may look like this:

before: a
before: B
before: C
before: D
running: a (1)
running: B (2)
before: e
running: C (3)
before: F
running: D (4)
before: g
before: H
completed: B (3)
completed: C (1)
completed: D (2)
after: B
after: D
running: e (2)
after: C
running: F (3)
before: i
before: J
completed: F (2)
after: F
running: g (3)
running: H (4)
completed: H (3)
after: H
completed: a (2)
after: A
running: i (3)
running: J (4)
completed: J (3)
after: J
completed: e (2)
after: E
completed: g (1)
after: G
completed: i (0)
after: I

Note that after lines are not in the same order as the before lines. For example H overtakes the slow G.

The numbers in parenthesis illustrates how many calls that are in progress at the same time. Here the downstream demand and thereby the number of concurrent calls are limited by the buffer size (4) of the ActorMaterializerSettings.

Integrating with Reactive Streams

Reactive Streams defines a standard for asynchronous stream processing with non-blocking back pressure. It makes it possible to plug together stream libraries that adhere to the standard. Akka Streams is one such library.

An incomplete list of other implementations:

The two most important interfaces in Reactive Streams are the Publisher and Subscriber.

import org.reactivestreams.Publisher;
import org.reactivestreams.Subscriber;
import org.reactivestreams.Processor;

Let us assume that a library provides a publisher of tweets:

Publisher<Tweet> tweets();

and another library knows how to store author handles in a database:

Subscriber<Author> storage();

Using an Akka Streams Flow we can transform the stream and connect those:

final Flow<Tweet, Author, BoxedUnit> authors = Flow.of(Tweet.class)
  .filter(t -> t.hashtags().contains(AKKA))
  .map(t -> t.author);

  Source.fromPublisher(rs.tweets())
    .via(authors)
    .to(Sink.fromSubscriber(rs.storage()));

The Publisher is used as an input Source to the flow and the Subscriber is used as an output Sink.

A Flow can also be also converted to a RunnableGraph[Processor[In, Out]] which materializes to a Processor when run() is called. run() itself can be called multiple times, resulting in a new Processor instance each time.

final Processor<Tweet, Author> processor =
  authors.toProcessor().run(mat);


rs.tweets().subscribe(processor);
processor.subscribe(rs.storage());

A publisher can be connected to a subscriber with the subscribe method.

It is also possible to expose a Source as a Publisher by using the Publisher-Sink:

final Publisher<Author> authorPublisher =
  Source.fromPublisher(rs.tweets()).via(authors).runWith(Sink.asPublisher(false), mat);

authorPublisher.subscribe(rs.storage());

A publisher that is created with Sink.asPublisher(false) supports only a single subscription. Additional subscription attempts will be rejected with an IllegalStateException.

A publisher that supports multiple subscribers using fan-out/broadcasting is created as follows:

Subscriber<Author> storage();
Subscriber<Author> alert();
final Publisher<Author> authorPublisher =
  Source.fromPublisher(rs.tweets())
    .via(authors)
    .runWith(Sink.asPublisher(true), mat);

authorPublisher.subscribe(rs.storage());
authorPublisher.subscribe(rs.alert());

The input buffer size of the stage controls how far apart the slowest subscriber can be from the fastest subscriber before slowing down the stream.

To make the picture complete, it is also possible to expose a Sink as a Subscriber by using the Subscriber-Source:

final Subscriber<Author> storage = rs.storage();

final Subscriber<Tweet> tweetSubscriber =
  authors
    .to(Sink.fromSubscriber(storage))
    .runWith(Source.asSubscriber(), mat);

rs.tweets().subscribe(tweetSubscriber);

It is also possible to use re-wrap Processor instances as a Flow by passing a factory function that will create the Processor instances:

// An example Processor factory
final Creator<Processor<Integer, Integer>> factory =
        new Creator<Processor<Integer, Integer>>() {
          public Processor<Integer, Integer> create() {
            return Flow.of(Integer.class).toProcessor().run(mat);
          }
        };

final Flow<Integer, Integer, BoxedUnit> flow = Flow.fromProcessor(factory);

Please note that a factory is necessary to achieve reusability of the resulting Flow.

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