StreamRefs - Reactive Streams over the network

Dependency

To use Akka Streams, add the module to your project:

sbt
libraryDependencies += "com.typesafe.akka" %% "akka-stream" % "2.5.16"
Maven
<dependency>
  <groupId>com.typesafe.akka</groupId>
  <artifactId>akka-stream_2.11</artifactId>
  <version>2.5.16</version>
</dependency>
Gradle
dependencies {
  compile group: 'com.typesafe.akka', name: 'akka-stream_2.11', version: '2.5.16'
}

Introduction

Warning

This module is currently marked as may change in the sense of being the subject of active research. This means that API or semantics can change without warning or deprecation period and it is not recommended to use this module in production just yet—you have been warned.

Stream references, or “stream refs” for short, allow running Akka Streams across multiple nodes within an Akka Cluster.

Unlike heavier “streaming data processing” frameworks, Akka Streams are not “deployed” nor automatically distributed. Akka stream refs are, as the name implies, references to existing parts of a stream, and can be used to create a distributed processing framework or introduce such capabilities in specific parts of your application.

Stream refs are trivial to make use of in existing clustered Akka applications, and require no additional configuration or setup. They automatically maintain flow-control / back-pressure over the network, and employ Akka’s failure detection mechanisms to fail-fast (“let it crash!”) in the case of failures of remote nodes. They can be seen as an implementation of the Work Pulling Pattern, which one would otherwise implement manually.

Note

A useful way to think about stream refs is: “like an ActorRef, but for Akka Streams’s Source and Sink”.

Stream refs refer to an already existing, possibly remote, Sink or Source. This is not to be mistaken with deploying streams remotely, which this feature is not intended for.

Stream References

The prime use case for stream refs is to replace raw actor or HTTP messaging between systems where a long running stream of data is expected between two entities. Often times, they can be used to effectively achieve point to point streaming without the need of setting up additional message brokers or similar secondary clusters.

Stream refs are well suited for any system in which you need to send messages between nodes and need to do so in a flow-controlled fashion. Typical examples include sending work requests to worker nodes, as fast as possible, but not faster than the worker node can process them, or sending data elements which the downstream may be slow at processing. It is recommended to mix and introduce stream refs in Actor messaging based systems, where the actor messaging is used to orchestrate and prepare such message flows, and later the stream refs are used to do the flow-controlled message transfer.

Stream refs are not persistent, however it is simple to build a resume-able stream by introducing such protocol on the actor messaging layer. Stream refs are absolutely expected to be sent over Akka remoting to other nodes within a cluster, and as such, complement and do not compete with plain Actor messaging. Actors would usually be used to establish the stream, by means of some initial message saying “I want to offer you many log elements (the stream ref)”, or alternatively in the opposite way “If you need to send me much data, here is the stream ref you can use to do so”.

Since the two sides (“local” and “remote”) of each reference may be confusing to refer to as “remote” and “local” – since either side can be seen as “local” or “remote” depending how we look at it – we propose to use the terminology “origin” and “target”, which is defined by where the stream ref was created. For SourceRefs, the “origin” is the side which has the data that it is going to stream out. For SinkRefs the “origin” side is the actor system that is ready to receive the data and has allocated the ref. Those two may be seen as duals of each other, however to explain patterns about sharing references, we found this wording to be rather useful.

Source Refs - offering streaming data to a remote system

A SourceRefSourceRef can be offered to a remote actor system in order for it to consume some source of data that we have prepared locally.

In order to share a Source with a remote endpoint you need to materialize it by running it into the Sink.sourceRef. That sink materializes the SourceRef that you can then send to other nodes. Please note that it materializes into a Future so you will have to use the pipeTo

Scala
import akka.stream.SourceRef
import akka.pattern.pipe

case class RequestLogs(streamId: Int)
case class LogsOffer(streamId: Int, sourceRef: SourceRef[String])

class DataSource extends Actor {
  import context.dispatcher
  implicit val mat = ActorMaterializer()(context)

  def receive = {
    case RequestLogs(streamId) ⇒
      // obtain the source you want to offer:
      val source: Source[String, NotUsed] = streamLogs(streamId)

      // materialize the SourceRef:
      val ref: Future[SourceRef[String]] = source.runWith(StreamRefs.sourceRef())

      // wrap the SourceRef in some domain message, such that the sender knows what source it is
      val reply: Future[LogsOffer] = ref.map(LogsOffer(streamId, _))

      // reply to sender
      reply pipeTo sender()
  }

  def streamLogs(streamId: Long): Source[String, NotUsed] = ???
}
Full source at GitHub
Java
static class RequestLogs {
  public final long streamId;

  public RequestLogs(long streamId) {
    this.streamId = streamId;
  }
}

static class LogsOffer {
  final SourceRef<String> sourceRef;

  public LogsOffer(SourceRef<String> sourceRef) {
    this.sourceRef = sourceRef;
  }
}

static class DataSource extends AbstractActor {
  @Override
  public Receive createReceive() {
    return receiveBuilder()
        .match(RequestLogs.class, this::handleRequestLogs)
        .build();
  }

  private void handleRequestLogs(RequestLogs requestLogs) {
    Source<String, NotUsed> logs = streamLogs(requestLogs.streamId);
    CompletionStage<SourceRef<String>> logsRef = logs.runWith(StreamRefs.sourceRef(), mat);

    PatternsCS.pipe(logsRef.thenApply(ref -> new LogsOffer(ref)), context().dispatcher())
        .to(sender());
  }

  private Source<String, NotUsed> streamLogs(long streamId) {
    return Source.repeat("[INFO] some interesting logs here (for id: " + streamId + ")");
  }
}
Full source at GitHub

The origin actor which creates and owns the Source could also perform some validation or additional setup when preparing the source. Once it has handed out the SourceRef the remote side can run it like this:

Scala
val sourceActor = system.actorOf(Props[DataSource], "dataSource")

sourceActor ! RequestLogs(1337)
val offer = expectMsgType[LogsOffer]

// implicitly converted to a Source:
offer.sourceRef.runWith(Sink.foreach(println))
// alternatively explicitly obtain Source from SourceRef:
// offer.sourceRef.source.runWith(Sink.foreach(println))
Full source at GitHub
Java
ActorRef sourceActor = system.actorOf(Props.create(DataSource.class), "dataSource");

sourceActor.tell(new RequestLogs(1337), getTestActor());
LogsOffer offer = expectMsgClass(LogsOffer.class);

offer.sourceRef.getSource()
    .runWith(Sink.foreach(log -> System.out.println(log)), mat);
Full source at GitHub

The process of preparing and running a SourceRef powered distributed stream is shown by the animation below:

SourceRef usage animation

Warning

A SourceRef is by design “single-shot”. i.e. it may only be materialized once. This is in order to not complicate the mental model of what materialization means.

Multicast can be mimicked by starting a BroadcastHub operator once then attaching multiple new streams to it, each emitting a new stream ref. This way materialization of the BroadcastHubs Source creates a unique single-shot stream ref, however they can all be powered using a single Source – located before the BroadcastHub operator.

Sink Refs - offering to receive streaming data from a remote system

The dual of SourceRefSourceRefs.

They can be used to offer the other side the capability to send to the origin side data in a streaming, flow-controlled fashion. The origin here allocates a Sink, which could be as simple as a Sink.foreach or as advanced as a complex sink which streams the incoming data into various other systems (e.g. any of the Alpakka provided Sinks).

Note

To form a good mental model of SinkRefs, you can think of them as being similar to “passive mode” in FTP.

Scala
import akka.pattern.pipe
import akka.stream.SinkRef

case class PrepareUpload(id: String)
case class MeasurementsSinkReady(id: String, sinkRef: SinkRef[String])

class DataReceiver extends Actor {

  import context.dispatcher
  implicit val mat = ActorMaterializer()(context)

  def receive = {
    case PrepareUpload(nodeId) ⇒
      // obtain the source you want to offer:
      val sink: Sink[String, NotUsed] = logsSinkFor(nodeId)

      // materialize the SinkRef (the remote is like a source of data for us):
      val ref: Future[SinkRef[String]] = StreamRefs.sinkRef[String]().to(sink).run()

      // wrap the SinkRef in some domain message, such that the sender knows what source it is
      val reply: Future[MeasurementsSinkReady] = ref.map(MeasurementsSinkReady(nodeId, _))

      // reply to sender
      reply pipeTo sender()
  }

  def logsSinkFor(nodeId: String): Sink[String, NotUsed] = ???
}
Full source at GitHub
Java
static class PrepareUpload {
  final String id;

  public PrepareUpload(String id) {
    this.id = id;
  }
}
static class MeasurementsSinkReady {
  final String id;
  final SinkRef<String> sinkRef;

  public MeasurementsSinkReady(String id, SinkRef<String> ref) {
    this.id = id;
    this.sinkRef = ref;
  }
}

static class DataReceiver extends AbstractActor {
  @Override
  public Receive createReceive() {
    return receiveBuilder()
        .match(PrepareUpload.class, prepare -> {
          Sink<String, NotUsed> sink = logsSinkFor(prepare.id);
          CompletionStage<SinkRef<String>> sinkRef = StreamRefs.<String>sinkRef().to(sink).run(mat);

          PatternsCS.pipe(sinkRef.thenApply(ref -> new MeasurementsSinkReady(prepare.id, ref)), context().dispatcher())
              .to(sender());
        })
        .build();
  }

  private Sink<String, NotUsed> logsSinkFor(String id) {
    return Sink.<String>ignore().mapMaterializedValue(done -> NotUsed.getInstance());
  }
}
Full source at GitHub

Using the offered SinkRef to send data to the origin of the Sink is also simple, as we can treat the SinkRef just as any other Sink and directly runWith or run with it.

Scala
val receiver = system.actorOf(Props[DataReceiver], "receiver")

receiver ! PrepareUpload("system-42-tmp")
val ready = expectMsgType[MeasurementsSinkReady]

// stream local metrics to Sink's origin:
localMetrics().runWith(ready.sinkRef)
Full source at GitHub
Java
ActorRef receiver = system.actorOf(Props.create(DataReceiver.class), "dataReceiver");

receiver.tell(new PrepareUpload("system-42-tmp"), getTestActor());
MeasurementsSinkReady ready = expectMsgClass(MeasurementsSinkReady.class);

Source.repeat("hello")
    .runWith(ready.sinkRef.getSink(), mat);
Full source at GitHub

The process of preparing and running a SinkRef powered distributed stream is shown by the animation below:

SourceRef usage animation

Warning

A SinkRef is by design “single-shot”. i.e. it may only be materialized once. This is in order to not complicate the mental model what materializing such value would mean.

If you have an use case for building a fan-in operation accepting writes from multiple remote nodes, you can build your Sink and prepend it with a MergeHub operator, each time materializing a new SinkRef targeting that MergeHub. This has the added benefit of giving you full control how to merge these streams (i.e. by using “merge preferred” or any other variation of the fan-in operators).

Delivery guarantees

Stream refs utilise normal actor messaging for their trainsport, and as such provide the same level of basic delivery guarantees. Stream refs do extend the semantics somewhat by demand re-delivery and sequence fault detection; in other words:

  • messages are sent over actor remoting
    • which relies on TCP (classic remoting, or artery tcp) or Aeron UDP for basic redelivery mechanisms
  • messages are guaranteed to to be in-order
  • messages can be lost, however:
    • a dropped demand signal will be re-delivered automatically (similar to system messages)
    • a dropped element signal* will cause the stream to fail

Bulk Stream References

Warning

Bulk stream references are not implemented yet. See ticket Bulk Transfer Stream Refs #24276 to track progress or signal demand for this feature.

Bulk stream refs can be used to create simple to use side-channels to transfer humongous amounts of data such as huge log files, messages or even media, with as much ease as if it was a trivial local stream.

Configuration

Stream reference subscription timeouts

All stream references have a subscription timeout, which is intended to prevent resource leaks in situations in which a remote node would requests the allocation of many streams yet never actually run them. In order to prevent this, each stream reference has a default timeout (of 30 seconds), after which the origin will abort the stream offer if the target has not materialized the stream ref in time. After the timeout has triggered, materialization of the target side will fail pointing out that the origin is missing.

Since these timeouts are often very different based on the kind of stream offered, and there can be many different kinds of them in the same application, it is possible to not only configure this setting globally (akka.stream.materializer.stream-ref.subscription-timeout), but also via attributes:

Scala
// configure the timeout for source
import scala.concurrent.duration._
import akka.stream.StreamRefAttributes

// configuring Sink.sourceRef (notice that we apply the attributes to the Sink!):
Source.repeat("hello")
  .runWith(StreamRefs.sourceRef().addAttributes(StreamRefAttributes.subscriptionTimeout(5.seconds)))

// configuring SinkRef.source:
StreamRefs.sinkRef().addAttributes(StreamRefAttributes.subscriptionTimeout(5.seconds))
  .runWith(Sink.ignore) // not very interesting Sink, just an example
Full source at GitHub
Java
FiniteDuration timeout = FiniteDuration.create(5, TimeUnit.SECONDS);
Attributes timeoutAttributes = StreamRefAttributes.subscriptionTimeout(timeout);

// configuring Sink.sourceRef (notice that we apply the attributes to the Sink!):
Source.repeat("hello")
    .runWith(StreamRefs.<String>sourceRef().addAttributes(timeoutAttributes), mat);

// configuring SinkRef.source:
StreamRefs.<String>sinkRef().addAttributes(timeoutAttributes)
    .runWith(Sink.<String>ignore(), mat); // not very interesting sink, just an example
Full source at GitHub

General configuration

Other settings can be set globally, in your application.conf, by overriding any of the following values in the akka.stream.materializer.stream-ref.* keyspace:

# configure defaults for SourceRef and SinkRef
stream-ref {
  # Buffer of a SinkRef that is used to batch Request elements from the other side of the stream ref
  #
  # The buffer will be attempted to be filled eagerly even while the local stage did not request elements,
  # because the delay of requesting over network boundaries is much higher.
  buffer-capacity = 32

  # Demand is signalled by sending a cumulative demand message ("requesting messages until the n-th sequence number)
  # Using a cumulative demand model allows us to re-deliver the demand message in case of message loss (which should
  # be very rare in any case, yet possible -- mostly under connection break-down and re-establishment).
  #
  # The semantics of handling and updating the demand however are in-line with what Reactive Streams dictates.
  #
  # In normal operation, demand is signalled in response to arriving elements, however if no new elements arrive
  # within `demand-redelivery-interval` a re-delivery of the demand will be triggered, assuming that it may have gotten lost.
  demand-redelivery-interval = 1 second

  # Subscription timeout, during which the "remote side" MUST subscribe (materialize) the handed out stream ref.
  # This timeout does not have to be very low in normal situations, since the remote side may also need to
  # prepare things before it is ready to materialize the reference. However the timeout is needed to avoid leaking
  # in-active streams which are never subscribed to.
  subscription-timeout = 30 seconds
}
Full source at GitHub
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