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Reactive Streams / Akka Streams - GeeCON Prague 2014

Konrad Malawski, profile picture
Konrad Malawski

This document contains the slides from a presentation on Akka Streams and Reactive Streams. It discusses key concepts like back pressure in Reactive Streams which allows asynchronous stream processing between different libraries. It also summarizes how Akka Streams works with linear flows, materialization, and integrating with actors to consume streams in an advanced way by treating grouped streams as individual streams processed by child actors.

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Konrad 'ktoso' Malawski GeeCON 2014 @ Kraków, PL Akka Streams Konrad `@ktosopl` Malawski OST: Tomas Dvorak ./‘ ./‘
hAkker @ Konrad `@ktosopl` Malawski Akka Team, Reactive Streams TCK
hAkker @ Konrad `@ktosopl` Malawski typesafe.com geecon.org Java.pl / KrakowScala.pl sckrk.com / meetup.com/Paper-Cup @ London GDGKrakow.pl meetup.com/Lambda-Lounge-Krakow
You? ?
Czech User Group? You?
You? ? z ?
You? ? z ? ?
You? ? z ? ? ?
Streams
Streams
Streams “You cannot enter the same river twice” ~ Heraclitus http://en.wikiquote.org/wiki/Heraclitus
Streams Real Time Stream Processing When you attach “late” to a Publisher, you may miss initial elements – it’s a river of data. http://en.wikiquote.org/wiki/Heraclitus
Reactive Streams
Reactive Streams Stream processing
Reactive Streams Back-pressured Stream processing
Reactive Streams Back-pressured Asynchronous Stream processing
Reactive Streams Back-pressured Asynchronous Stream processing Standardised (!)
Reactive Streams: Goals 1. Back-pressured Asynchronous Stream processing 2. Standard implemented by many libraries
Reactive Streams - Specification & TCK http://reactive-streams.org
Reactive Streams - Who? Kaazing Corp. rxJava @ Netflix, reactor @ Pivotal (SpringSource), vert.x @ Red Hat, Twitter, akka-streams @ Typesafe, spray @ Spray.io, Oracle, java (?) – Doug Lea - SUNY Oswego … http://reactive-streams.org
Reactive Streams - Inter-op We want to make different implementations co-operate with each other. http://reactive-streams.org
Reactive Streams - Inter-op The different implementations “talk to each other” using the Reactive Streams protocol. http://reactive-streams.org
Reactive Streams - Inter-op The Reactive Streams SPI is NOT meant to be user-api. You should use one of the implementing libraries. http://reactive-streams.org
What is back-pressure？
Back-pressure? Example Without Publisher[T] Subscriber[T]
Back-pressure? Example Without Fast Publisher Slow Subscriber
Back-pressure? “Why would I need that!?”
Back-pressure? Push + NACK model
Back-pressure? Push + NACK model Subscriber usually has some kind of buffer.
Back-pressure? Push + NACK model
Back-pressure? Push + NACK model
Back-pressure? Push + NACK model What if the buffer overflows?
Back-pressure? Push + NACK model (a) Use bounded buffer, drop messages + require re-sending
Back-pressure? Push + NACK model (a) Use bounded buffer, drop messages + require re-sending Kernel does this! Routers do this! (TCP)
Back-pressure? Push + NACK model (b) Increase buffer size… Well, while you have memory available!
Back-pressure? Push + NACK model (b)
Back-pressure? NACKing is NOT enough.
Negative ACKnowledgement
Back-pressure? Example NACKing Buffer overflow is imminent!
Back-pressure? Example NACKing Telling the Publisher to slow down / stop sending…
Back-pressure? Example NACKing NACK did not make it in time, because M was in-flight!
Back-pressure? speed(publisher) < speed(subscriber)
Back-pressure? Fast Subscriber, No Problem No problem!
Back-pressure? Reactive-Streams = “Dynamic Push/Pull”
Back-pressure? RS: Dynamic Push/Pull Just push – not safe when Slow Subscriber Just pull – too slow when Fast Subscriber
Back-pressure? RS: Dynamic Push/Pull Just push – not safe when Slow Subscriber Just pull – too slow when Fast Subscriber Solution: Dynamic adjustment
Back-pressure? RS: Dynamic Push/Pull Slow Subscriber sees it’s buffer can take 3 elements. Publisher will never blow up it’s buffer.
Back-pressure? RS: Dynamic Push/Pull Fast Publisher will send at-most 3 elements. This is pull-based-backpressure.
Back-pressure? RS: Dynamic Push/Pull Fast Subscriber can issue more Request(n), before more data arrives!
Back-pressure? RS: Dynamic Push/Pull Fast Subscriber can issue more Request(n), before more data arrives. Publisher can accumulate demand.
Back-pressure? RS: Accumulate demand Publisher accumulates total demand per subscriber.
Back-pressure? RS: Accumulate demand Total demand of elements is safe to publish. Subscriber’s buffer will not overflow.
Back-pressure? RS: Requesting “a lot” Fast Subscriber can issue arbitrary large requests, including “gimme all you got” (Long.MaxValue)
Back-pressure? RS: Dynamic Push/Pull MAX speed
Back-pressure? RS: Dynamic Push/Pull Easy MAX speed
How does fit all this?
Akka Akka has multiple modules: akka-actor: actors (concurrency abstraction) akka-camel: integration akka-remote: remote actors akka-cluster: clustering akka-persistence: CQRS / Event Sourcing akka-streams: stream processing …
Akka Akka is a high-performance concurrency library for Scala and Java. At it’s core it focuses on the Actor Model:
Akka Akka is a high-performance concurrency library for Scala and Java. At it’s core it focuses on the Actor Model: An Actor can only: • Send and receive messages • Create Actors • Change it’s behaviour
Akka class Player extends Actor { def receive = { case NextTurn => sender() ! decideOnMove() } def decideOnMove(): Move = ??? }
Akka Akka has multiple modules: akka-actor: actors (concurrency abstraction) akka-camel: integration akka-remote: remote actors akka-cluster: clustering akka-persistence: CQRS / Event Sourcing akka-streams: stream processing …
Akka Streams 0.9 early preview
Akka Streams – Linear Flow
Akka Streams – Linear Flow
Akka Streams – Linear Flow
Akka Streams – Linear Flow
Akka Streams – Linear Flow Flow[Double].map(_.toInt). [...] No Source attached yet. “Pipe ready to work with Doubles”.
Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") An ActorSystem is the world in which Actors live in. AkkaStreams uses Actors, so it needs ActorSystem.
Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() Contains logic on HOW to materialise the stream.
Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() A materialiser chooses HOW to materialise a Stream. The Flow’s AST is fully “lifted”. The Materialiser can choose to materialise the Flow in any way it sees fit. Our implementation uses Actors. But you could easily plug in an SparkMaterializer!
Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() You can configure it’s buffer sizes etc.
Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() val foreachSink = Sink.foreach[Int](println) val mf = Source(1 to 3).runWith(foreachSink)
Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() val foreachSink = Sink.foreach[Int](println) val mf = FlowFrom(1 to 3).runWith(foreachSink)(mat) Uses the implicit FlowMaterializer
Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() // sugar for runWith Source(1 to 3).foreach(println)
Akka Streams – Linear Flow val mf = Flow[Int]. map(_ * 2). runWith(Sink.foreach(println)) // is missing a Source, // can NOT run == won’t compile!
Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run!
Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run!
Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run!
Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run! f.connect(Source(1 to 10)).run()
Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run! f.connect(Source(1 to 10)).run() With a Source attached… it can run()
Akka Streams – Linear Flow Flow[Int]. map(_.toString). runWith(Source(1 to 10), Sink.ignore) Connects Source and Sink, then runs
Akka Streams – Flows are reusable f.withSource(IterableSource(1 to 10)).run() f.withSource(IterableSource(1 to 100)).run() f.withSource(IterableSource(1 to 1000)).run()
Akka Streams <-> Actors – Advanced val subscriber = ActorSubscriber( system.actorOf(Props[SubStreamParent], ”parent”)) Source(1 to 100). map(_.toString). filter(_.length == 2). drop(2). groupBy(_.last). runWith(subscriber)
Akka Streams <-> Actors – Advanced val subscriber = ActorSubscriber( system.actorOf(Props[SubStreamParent], ”parent”)) Source(1 to 100). map(_.toString). filter(_.length == 2). drop(2). groupBy(_.last). runWith(subscriber) Each “group” is a stream too! It’s a “Stream of Streams”.
Akka Streams <-> Actors – Advanced groupBy(_.last). GroupBy groups “11” to group “1”, “12” to group “2” etc.
Akka Streams <-> Actors – Advanced groupBy(_.last). Source It offers (groupKey, subStreamSource) to Subscriber
Akka Streams <-> Actors – Advanced groupBy(_.last). Source It can then start children, to handle the sub-flows!
Akka Streams <-> Actors – Advanced groupBy(_.last). Source For example, one child for each group.
Akka Streams <-> Actors – Advanced val subscriber = ActorSubscriber( system.actorOf(Props[SubStreamParent], ”parent”)) Source(1 to 100). map(_.toString). filter(_.length == 2). drop(2). groupBy(_.last). runWith(subscriber) The Actor, will consume SubStream offers.
Consuming streams with Actors (advanced)
Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber { override def requestStrategy = WatermarkRequestStrategy(highWatermark = 10) override def receive = { case OnNext(n: String) => println(s”n = $n”) } }
Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber { override def requestStrategy = WatermarkRequestStrategy(highWatermark = 10) override def receive = { case OnNext(n: String) => println(s”n = $n”) } }
Akka Streams – FlowGraph FlowGraph
Akka Streams – FlowGraph Linear Flows or non-akka pipelines Could be another RS implementation!
Akka Streams – GraphFlow Fan-out elements and Fan-in elements
Akka Streams – GraphFlow // first define some pipeline pieces val f1 = Flow[Input].map(_.toIntermediate) val f2 = Flow[Intermediate].map(_.enrich) val f3 = Flow[Enriched].filter(_.isImportant) val f4 = Flow[Intermediate].mapFuture(_.enrichAsync) // then add input and output placeholders val in = SubscriberSource[Input] val out = PublisherSink[Enriched]
Akka Streams – GraphFlow
Akka Streams – GraphFlow val b3 = Broadcast[Int]("b3") val b7 = Broadcast[Int]("b7") val b11 = Broadcast[Int]("b11") val m8 = Merge[Int]("m8") val m9 = Merge[Int]("m9") val m10 = Merge[Int]("m10") val m11 = Merge[Int]("m11") val in3 = Source(List(3)) val in5 = Source(List(5)) val in7 = Source(List(7))
Akka Streams – GraphFlow
Akka Streams – GraphFlow // First layer in7 ~> b7 b7 ~> m11 b7 ~> m8 in5 ~> m11 in3 ~> b3 b3 ~> m8 b3 ~> m10
Akka Streams – GraphFlow // Second layer m11 ~> b11 b11 ~> Flow[Int].grouped(1000) ~> resultFuture2 b11 ~> m9 b11 ~> m10 m8 ~> m9
Akka Streams – GraphFlow // Third layer m9 ~> Flow[Int].grouped(1000) ~> resultFuture9 m10 ~> Flow[Int].grouped(1000) ~> resultFuture10
Akka Streams – GraphFlow // Third layer m9 ~> Flow[Int].grouped(1000) ~> resultFuture9 m10 ~> Flow[Int].grouped(1000) ~> resultFuture10
Akka Streams – GraphFlow // Third layer m9 ~> Flow[Int].grouped(1000) ~> resultFuture9 m10 ~> Flow[Int].grouped(1000) ~> resultFuture10
Akka Streams – GraphFlow Sinks and Sources are “keys” which can be addressed within the graph val resultFuture2 = Sink.future[Seq[Int]] val resultFuture9 = Sink.future[Seq[Int]] val resultFuture10 = Sink.future[Seq[Int]] val g = FlowGraph { implicit b => // ... m10 ~> Flow[Int].grouped(1000) ~> resultFuture10 // ... }.run() Await.result(g.get(resultFuture2), 3.seconds).sorted should be(List(5, 7))
Akka Streams – GraphFlow Sinks and Sources are “keys” which can be addressed within the graph val resultFuture2 = Sink.future[Seq[Int]] val resultFuture9 = Sink.future[Seq[Int]] val resultFuture10 = Sink.future[Seq[Int]] val g = FlowGraph { implicit b => // ... m10 ~> Flow[Int].grouped(1000) ~> resultFuture10 // ... }.run() Await.result(g.get(resultFuture2), 3.seconds).sorted should be(List(5, 7))
Akka Streams – GraphFlow val g = FlowGraph {} FlowGraph is immutable and safe to share and re-use!
Available Elements 0.9 early preview
Available Sources • FutureSource • IterableSource • IteratorSource • PublisherSource • SubscriberSource • ThunkSource • TickSource (timer based) • … easy to add your own! 0.9 early preview
Available operations • drop / dropWithin • take / takeWithin • filter • groupBy • grouped • map • prefixAndTail • … easy to add your own! “Rate – detaching” operations: • buffer • collect • concat • conflate 0.9 early preview
Available Sinks • BlackHoleSink • FoldSink • ForeachSink • FutureSink • OnCompleteSink • PublisherSink / FanoutPublisherSink • SubscriberSink • … easy to add your own! 0.9 early preview
Available Junctions • Broadcast • Merge • FlexiMerge • Zip • Unzip • Concat • … easy to add your own! 0.9 early preview
Akka-Streams Coming soon!
Rough plans • 0.9 released • 0.10 in 2~3 weeks • 1.0 “soon” after… • Means “stabilised APIs” • Will not yet be performance tuned, though it’s already pretty good… we know where and how we can tune it for 1.x. 0.7 early preview
Java DSL • Partial Java DSL in 0.9 (released) • Full Java DSL in 0.10 (in 2~3 weeks) • as 1st class citizen (!) 0.7 early preview
Spray => Akka-Http && ReactiveStreams Spray is now merged into Akka, as Akka-Http Works on Reactive Streams Streaming end-to-end!
Links • http://akka.io • http://reactive-streams.org • https://groups.google.com/group/akka-user • 0.7 release http://akka.io/news/2014/09/12/akka-streams-0.7-released.html • javadsl https://github.com/akka/akka/pulls?q=is%3Apr+javadsl
Děkuji vám! Dzięki! ありがとう！ Ask questions, get Stickers! (for real!) http://akka.io ktoso @ typesafe.com twitter: ktosopl github: ktoso team blog: letitcrash.com
©Typesafe 2014 – All Rights Reserved

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Reactive Streams / Akka Streams - GeeCON Prague 2014

  • 1. Konrad 'ktoso' Malawski GeeCON 2014 @ Kraków, PL Akka Streams Konrad `@ktosopl` Malawski OST: Tomas Dvorak ./‘ ./‘
  • 2. hAkker @ Konrad `@ktosopl` Malawski Akka Team, Reactive Streams TCK
  • 3. hAkker @ Konrad `@ktosopl` Malawski typesafe.com geecon.org Java.pl / KrakowScala.pl sckrk.com / meetup.com/Paper-Cup @ London GDGKrakow.pl meetup.com/Lambda-Lounge-Krakow
  • 7. You? ? z ? ?
  • 8. You? ? z ? ? ?
  • 11. Streams “You cannot enter the same river twice” ~ Heraclitus http://en.wikiquote.org/wiki/Heraclitus
  • 12. Streams Real Time Stream Processing When you attach “late” to a Publisher, you may miss initial elements – it’s a river of data. http://en.wikiquote.org/wiki/Heraclitus
  • 15. Reactive Streams Back-pressured Stream processing
  • 16. Reactive Streams Back-pressured Asynchronous Stream processing
  • 17. Reactive Streams Back-pressured Asynchronous Stream processing Standardised (!)
  • 18. Reactive Streams: Goals 1. Back-pressured Asynchronous Stream processing 2. Standard implemented by many libraries
  • 19. Reactive Streams - Specification & TCK http://reactive-streams.org
  • 20. Reactive Streams - Who? Kaazing Corp. rxJava @ Netflix, reactor @ Pivotal (SpringSource), vert.x @ Red Hat, Twitter, akka-streams @ Typesafe, spray @ Spray.io, Oracle, java (?) – Doug Lea - SUNY Oswego … http://reactive-streams.org
  • 21. Reactive Streams - Inter-op We want to make different implementations co-operate with each other. http://reactive-streams.org
  • 22. Reactive Streams - Inter-op The different implementations “talk to each other” using the Reactive Streams protocol. http://reactive-streams.org
  • 23. Reactive Streams - Inter-op The Reactive Streams SPI is NOT meant to be user-api. You should use one of the implementing libraries. http://reactive-streams.org
  • 25. Back-pressure? Example Without Publisher[T] Subscriber[T]
  • 26. Back-pressure? Example Without Fast Publisher Slow Subscriber
  • 27. Back-pressure? “Why would I need that!?”
  • 28. Back-pressure? Push + NACK model
  • 29. Back-pressure? Push + NACK model Subscriber usually has some kind of buffer.
  • 30. Back-pressure? Push + NACK model
  • 31. Back-pressure? Push + NACK model
  • 32. Back-pressure? Push + NACK model What if the buffer overflows?
  • 33. Back-pressure? Push + NACK model (a) Use bounded buffer, drop messages + require re-sending
  • 34. Back-pressure? Push + NACK model (a) Use bounded buffer, drop messages + require re-sending Kernel does this! Routers do this! (TCP)
  • 35. Back-pressure? Push + NACK model (b) Increase buffer size… Well, while you have memory available!
  • 36. Back-pressure? Push + NACK model (b)
  • 39. Back-pressure? Example NACKing Buffer overflow is imminent!
  • 40. Back-pressure? Example NACKing Telling the Publisher to slow down / stop sending…
  • 41. Back-pressure? Example NACKing NACK did not make it in time, because M was in-flight!
  • 43. Back-pressure? Fast Subscriber, No Problem No problem!
  • 44. Back-pressure? Reactive-Streams = “Dynamic Push/Pull”
  • 45. Back-pressure? RS: Dynamic Push/Pull Just push – not safe when Slow Subscriber Just pull – too slow when Fast Subscriber
  • 46. Back-pressure? RS: Dynamic Push/Pull Just push – not safe when Slow Subscriber Just pull – too slow when Fast Subscriber Solution: Dynamic adjustment
  • 47. Back-pressure? RS: Dynamic Push/Pull Slow Subscriber sees it’s buffer can take 3 elements. Publisher will never blow up it’s buffer.
  • 48. Back-pressure? RS: Dynamic Push/Pull Fast Publisher will send at-most 3 elements. This is pull-based-backpressure.
  • 49. Back-pressure? RS: Dynamic Push/Pull Fast Subscriber can issue more Request(n), before more data arrives!
  • 50. Back-pressure? RS: Dynamic Push/Pull Fast Subscriber can issue more Request(n), before more data arrives. Publisher can accumulate demand.
  • 51. Back-pressure? RS: Accumulate demand Publisher accumulates total demand per subscriber.
  • 52. Back-pressure? RS: Accumulate demand Total demand of elements is safe to publish. Subscriber’s buffer will not overflow.
  • 53. Back-pressure? RS: Requesting “a lot” Fast Subscriber can issue arbitrary large requests, including “gimme all you got” (Long.MaxValue)
  • 54. Back-pressure? RS: Dynamic Push/Pull MAX speed
  • 55. Back-pressure? RS: Dynamic Push/Pull Easy MAX speed
  • 56. How does fit all this?
  • 57. Akka Akka has multiple modules: akka-actor: actors (concurrency abstraction) akka-camel: integration akka-remote: remote actors akka-cluster: clustering akka-persistence: CQRS / Event Sourcing akka-streams: stream processing …
  • 58. Akka Akka is a high-performance concurrency library for Scala and Java. At it’s core it focuses on the Actor Model:
  • 59. Akka Akka is a high-performance concurrency library for Scala and Java. At it’s core it focuses on the Actor Model: An Actor can only: • Send and receive messages • Create Actors • Change it’s behaviour
  • 60. Akka class Player extends Actor { def receive = { case NextTurn => sender() ! decideOnMove() } def decideOnMove(): Move = ??? }
  • 61. Akka Akka has multiple modules: akka-actor: actors (concurrency abstraction) akka-camel: integration akka-remote: remote actors akka-cluster: clustering akka-persistence: CQRS / Event Sourcing akka-streams: stream processing …
  • 62. Akka Streams 0.9 early preview
  • 63. Akka Streams – Linear Flow
  • 64. Akka Streams – Linear Flow
  • 65. Akka Streams – Linear Flow
  • 66. Akka Streams – Linear Flow
  • 67. Akka Streams – Linear Flow Flow[Double].map(_.toInt). [...] No Source attached yet. “Pipe ready to work with Doubles”.
  • 68. Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") An ActorSystem is the world in which Actors live in. AkkaStreams uses Actors, so it needs ActorSystem.
  • 69. Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() Contains logic on HOW to materialise the stream.
  • 70. Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() A materialiser chooses HOW to materialise a Stream. The Flow’s AST is fully “lifted”. The Materialiser can choose to materialise the Flow in any way it sees fit. Our implementation uses Actors. But you could easily plug in an SparkMaterializer!
  • 71. Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() You can configure it’s buffer sizes etc.
  • 72. Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() val foreachSink = Sink.foreach[Int](println) val mf = Source(1 to 3).runWith(foreachSink)
  • 73. Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() val foreachSink = Sink.foreach[Int](println) val mf = FlowFrom(1 to 3).runWith(foreachSink)(mat) Uses the implicit FlowMaterializer
  • 74. Akka Streams – Linear Flow implicit val sys = ActorSystem("tokyo-sys") implicit val mat = FlowMaterializer() // sugar for runWith Source(1 to 3).foreach(println)
  • 75. Akka Streams – Linear Flow val mf = Flow[Int]. map(_ * 2). runWith(Sink.foreach(println)) // is missing a Source, // can NOT run == won’t compile!
  • 76. Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run!
  • 77. Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run!
  • 78. Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run!
  • 79. Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run! f.connect(Source(1 to 10)).run()
  • 80. Akka Streams – Linear Flow val f = Flow[Int]. map(_ * 2). runWith(Sink.foreach(i => println(s"i = $i”))). // needs Source to run! f.connect(Source(1 to 10)).run() With a Source attached… it can run()
  • 81. Akka Streams – Linear Flow Flow[Int]. map(_.toString). runWith(Source(1 to 10), Sink.ignore) Connects Source and Sink, then runs
  • 82. Akka Streams – Flows are reusable f.withSource(IterableSource(1 to 10)).run() f.withSource(IterableSource(1 to 100)).run() f.withSource(IterableSource(1 to 1000)).run()
  • 83. Akka Streams <-> Actors – Advanced val subscriber = ActorSubscriber( system.actorOf(Props[SubStreamParent], ”parent”)) Source(1 to 100). map(_.toString). filter(_.length == 2). drop(2). groupBy(_.last). runWith(subscriber)
  • 84. Akka Streams <-> Actors – Advanced val subscriber = ActorSubscriber( system.actorOf(Props[SubStreamParent], ”parent”)) Source(1 to 100). map(_.toString). filter(_.length == 2). drop(2). groupBy(_.last). runWith(subscriber) Each “group” is a stream too! It’s a “Stream of Streams”.
  • 85. Akka Streams <-> Actors – Advanced groupBy(_.last). GroupBy groups “11” to group “1”, “12” to group “2” etc.
  • 86. Akka Streams <-> Actors – Advanced groupBy(_.last). Source It offers (groupKey, subStreamSource) to Subscriber
  • 87. Akka Streams <-> Actors – Advanced groupBy(_.last). Source It can then start children, to handle the sub-flows!
  • 88. Akka Streams <-> Actors – Advanced groupBy(_.last). Source For example, one child for each group.
  • 89. Akka Streams <-> Actors – Advanced val subscriber = ActorSubscriber( system.actorOf(Props[SubStreamParent], ”parent”)) Source(1 to 100). map(_.toString). filter(_.length == 2). drop(2). groupBy(_.last). runWith(subscriber) The Actor, will consume SubStream offers.
  • 90. Consuming streams with Actors (advanced)
  • 91. Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
  • 92. Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
  • 93. Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
  • 94. Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
  • 95. Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber with ImplicitFlowMaterializer with ActorLogging { override def requestStrategy = OneByOneRequestStrategy override def receive = { case OnNext((groupId: String, subStream: Source[String])) => val subSub = context.actorOf(Props[SubStreamSubscriber], s"sub-$groupId") subStream.runWith(Sink.subscriber(ActorSubscriber(subSub))) } }
  • 96. Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber { override def requestStrategy = WatermarkRequestStrategy(highWatermark = 10) override def receive = { case OnNext(n: String) => println(s”n = $n”) } }
  • 97. Akka Streams <-> Actors – Advanced class SubStreamParent extends ActorSubscriber { override def requestStrategy = WatermarkRequestStrategy(highWatermark = 10) override def receive = { case OnNext(n: String) => println(s”n = $n”) } }
  • 98. Akka Streams – FlowGraph FlowGraph
  • 99. Akka Streams – FlowGraph Linear Flows or non-akka pipelines Could be another RS implementation!
  • 100. Akka Streams – GraphFlow Fan-out elements and Fan-in elements
  • 101. Akka Streams – GraphFlow // first define some pipeline pieces val f1 = Flow[Input].map(_.toIntermediate) val f2 = Flow[Intermediate].map(_.enrich) val f3 = Flow[Enriched].filter(_.isImportant) val f4 = Flow[Intermediate].mapFuture(_.enrichAsync) // then add input and output placeholders val in = SubscriberSource[Input] val out = PublisherSink[Enriched]
  • 102. Akka Streams – GraphFlow
  • 103. Akka Streams – GraphFlow val b3 = Broadcast[Int]("b3") val b7 = Broadcast[Int]("b7") val b11 = Broadcast[Int]("b11") val m8 = Merge[Int]("m8") val m9 = Merge[Int]("m9") val m10 = Merge[Int]("m10") val m11 = Merge[Int]("m11") val in3 = Source(List(3)) val in5 = Source(List(5)) val in7 = Source(List(7))
  • 104. Akka Streams – GraphFlow
  • 105. Akka Streams – GraphFlow // First layer in7 ~> b7 b7 ~> m11 b7 ~> m8 in5 ~> m11 in3 ~> b3 b3 ~> m8 b3 ~> m10
  • 106. Akka Streams – GraphFlow // Second layer m11 ~> b11 b11 ~> Flow[Int].grouped(1000) ~> resultFuture2 b11 ~> m9 b11 ~> m10 m8 ~> m9
  • 107. Akka Streams – GraphFlow // Third layer m9 ~> Flow[Int].grouped(1000) ~> resultFuture9 m10 ~> Flow[Int].grouped(1000) ~> resultFuture10
  • 108. Akka Streams – GraphFlow // Third layer m9 ~> Flow[Int].grouped(1000) ~> resultFuture9 m10 ~> Flow[Int].grouped(1000) ~> resultFuture10
  • 109. Akka Streams – GraphFlow // Third layer m9 ~> Flow[Int].grouped(1000) ~> resultFuture9 m10 ~> Flow[Int].grouped(1000) ~> resultFuture10
  • 110. Akka Streams – GraphFlow Sinks and Sources are “keys” which can be addressed within the graph val resultFuture2 = Sink.future[Seq[Int]] val resultFuture9 = Sink.future[Seq[Int]] val resultFuture10 = Sink.future[Seq[Int]] val g = FlowGraph { implicit b => // ... m10 ~> Flow[Int].grouped(1000) ~> resultFuture10 // ... }.run() Await.result(g.get(resultFuture2), 3.seconds).sorted should be(List(5, 7))
  • 111. Akka Streams – GraphFlow Sinks and Sources are “keys” which can be addressed within the graph val resultFuture2 = Sink.future[Seq[Int]] val resultFuture9 = Sink.future[Seq[Int]] val resultFuture10 = Sink.future[Seq[Int]] val g = FlowGraph { implicit b => // ... m10 ~> Flow[Int].grouped(1000) ~> resultFuture10 // ... }.run() Await.result(g.get(resultFuture2), 3.seconds).sorted should be(List(5, 7))
  • 112. Akka Streams – GraphFlow val g = FlowGraph {} FlowGraph is immutable and safe to share and re-use!
  • 113. Available Elements 0.9 early preview
  • 114. Available Sources • FutureSource • IterableSource • IteratorSource • PublisherSource • SubscriberSource • ThunkSource • TickSource (timer based) • … easy to add your own! 0.9 early preview
  • 115. Available operations • drop / dropWithin • take / takeWithin • filter • groupBy • grouped • map • prefixAndTail • … easy to add your own! “Rate – detaching” operations: • buffer • collect • concat • conflate 0.9 early preview
  • 116. Available Sinks • BlackHoleSink • FoldSink • ForeachSink • FutureSink • OnCompleteSink • PublisherSink / FanoutPublisherSink • SubscriberSink • … easy to add your own! 0.9 early preview
  • 117. Available Junctions • Broadcast • Merge • FlexiMerge • Zip • Unzip • Concat • … easy to add your own! 0.9 early preview
  • 119. Rough plans • 0.9 released • 0.10 in 2~3 weeks • 1.0 “soon” after… • Means “stabilised APIs” • Will not yet be performance tuned, though it’s already pretty good… we know where and how we can tune it for 1.x. 0.7 early preview
  • 120. Java DSL • Partial Java DSL in 0.9 (released) • Full Java DSL in 0.10 (in 2~3 weeks) • as 1st class citizen (!) 0.7 early preview
  • 121. Spray => Akka-Http && ReactiveStreams Spray is now merged into Akka, as Akka-Http Works on Reactive Streams Streaming end-to-end!
  • 122. Links • http://akka.io • http://reactive-streams.org • https://groups.google.com/group/akka-user • 0.7 release http://akka.io/news/2014/09/12/akka-streams-0.7-released.html • javadsl https://github.com/akka/akka/pulls?q=is%3Apr+javadsl
  • 123. Děkuji vám! Dzięki! ありがとう！ Ask questions, get Stickers! (for real!) http://akka.io ktoso @ typesafe.com twitter: ktosopl github: ktoso team blog: letitcrash.com
  • 124. ©Typesafe 2014 – All Rights Reserved