Akka Reactive Toolkit

2. Reactive Systems Toolkit - Akka • Microservice Systems • Clusters within clusters • System Orchestration Clustering Streams Actors Cluster Features • Reactive Services • Akka & Play Services • Lagom Microservices • Membership • Sharding • Event Sourcing & CQRS • Publish & Subscribe • Distributed Data • Reactive Streams • Akka Streams • Alpakka • Actor Model • Supervision • Routing

3. Akka History Akka - Initially developed by Jonas Bonér in 2009 First public release 2009 Inspired by Carl Hewitt’s early 70’s work and the Erlang runtime system A distributed, highly concurrent, and event driven implementation of the Actor Model on the JVM - Java & Scala

5. Actors Actor Model Actor Model - breaking free from imperative, synchronous, thread heavy systems development

6. Actors Actor Messaging

15. Actors Actor Model

19. Actors Actor Model Failure is NOT unexpected or unwanted. It’s just a fact of life. Failure handing is an architectural feature not an afterthought.

20. Actors Supervision

25. Actors Routing & Concurrency

34. Actors Summary • Actors are message driven, stateful building blocks • Messages are passed asynchronously • Actors may create other actors • Actors form supervision hierarchies • Actors are lightweight and do not hold threads

36. Streams Reactive Streams Reactive Streams - standard for asynchronous stream processing with non-blocking back pressure

37. Streams Reactive Streams Java 9 Reactive Streams

38. Streams Akka Streams

39. Streams Akka Streams

40. Streams Akka Streams != drop log scan ## dropWhile map scanAsync + dropWithin mapAsync shape ++ ensuring mapAsyncUnordered sliding -> eq mapConcat splitAfter == equals mapError splitWhen Closed expand mapMaterializedValue statefulMapConcat ClosedMat filter merge synchronized Repr filterNot mergeMat take ReprMat flatMapConcat mergeSorted takeWhile Shape flatMapMerge mergeSortedMat takeWithin addAttributes fold monitor throttle alsoTo foldAsync named to alsoToMat formatted ne toMat asInstanceOf getClass notify toString asJava groupBy notifyAll traversalBuilder async grouped orElse via backpressureTimeout groupedWeightedWithin orElseMat viaMat batch groupedWithin prefixAndTail wait batchWeighted hashCode prepend watchTermination buffer idleTimeout prependMat withAttributes collect initialDelay recover zip combine initialTimeout recoverWith zipMat completionTimeout interleave recoverWithRetries zipWith concat interleaveMat reduce zipWithIndex concatMat intersperse runFold zipWithMat conflate isInstanceOf runFoldAsync ! conflateWithSeed keepAlive runForeach delay limit runReduce detach limitWeighted runWith Rich set of flow processing functions

41. Streams Akka Streams import GraphDSL.Implicits._ RunnableGraph.fromGraph(GraphDSL.create() { implicit builder => val A: Outlet[Int] = builder.add(Source.single(0)).out val B: UniformFanOutShape[Int, Int] = builder.add(Broadcast[Int](2)) val C: UniformFanInShape[Int, Int] = builder.add(Merge[Int](2)) val D: FlowShape[Int, Int] = builder.add(Flow[Int].map(_ + 1)) val E: UniformFanOutShape[Int, Int] = builder.add(Balance[Int](2)) val F: UniformFanInShape[Int, Int] = builder.add(Merge[Int](2)) val G: Inlet[Any] = builder.add(Sink.foreach(println)).in C <~ F A ~> B ~> C ~> F B ~> D ~> E ~> F E ~> G ClosedShape })

42. Streams Akka Streams RunnableGraph.fromGraph( GraphDSL.create(builder -> { final Outlet<Integer> A = builder.add(Source.single(0)).out(); final UniformFanOutShape<Integer, Integer> B = builder.add(Broadcast.create(2)); final UniformFanInShape<Integer, Integer> C = builder.add(Merge.create(2)); final FlowShape<Integer, Integer> D = builder.add(Flow.of(Integer.class).map(i -> i + 1)); final UniformFanOutShape<Integer, Integer> E = builder.add(Balance.create(2)); final UniformFanInShape<Integer, Integer> F = builder.add(Merge.create(2)); final Inlet<Integer> G = builder.add(Sink.<Integer> foreach(System.out::println)).in(); builder.from(F).toFanIn(C); builder.from(A).viaFanOut(B).viaFanIn(C).toFanIn(F); builder.from(B).via(D).viaFanOut(E).toFanIn(F); builder.from(E).toInlet(G); return ClosedShape.getInstance(); }));

43. Streams Akka HTTP

44. Streams Akka HTTP

45. Streams Alpakka Connectors • AMQP • Apache Geode • AWS DynamoDB • AWS Kinesis • AWS Lambda • AWS S3 • AWS SNS • AWS SQS • Azure Storage Queue • Cassandra • File • FTP • Google Cloud Pub/Sub • HBbase • IronMq • JMS • MQTT • Server-sent Events (SSE) Alpakka - Akka Streams alternative to Apache Camel

46. Streams Alpakka // Read huge file with Wikipedia content Source<WikipediaEntry, CompletionStage<IOResult>> wikipediaEntries = FileIO.fromPath(Paths.get("/tmp", "wiki")) .via(parseWikiEntries()); // Enrich the data by fetching matching image from a // web service with HTTP Source<RichWikipediaEntry, CompletionStage<IOResult>> enrichedData = wikipediaEntries .via(enrichWithImageData); // Store content in Kafka // and corresponding image in AWS S3 enrichedData .alsoTo(s3ImageStorage()) .to(kafkaTopic) .run(materializer);

47. Streams Summary • Akka Streams is a Reactive Streams implementation • Flow control of data via demand based back-pressure • Provides a rich set of flow processing transformations

49. Cluster Features Cluster Features - actor systems that span clusters of multiple JVMs

50. Cluster Features Cluster Membership

51. Cluster Features Cluster Membership State Diagram of Akka Cluster Member States

52. Cluster Features Cluster Sharding Send messages to actors distributed across a cluster via logical identifiers

53. Cluster Features Cluster Sharding

64. Cluster Features Event Sourcing & CQRS Akka Persistence Akka Persistence Query Cluster Sharding Actor implementation of Event Sourcing & CQRS (Command Query Responsibility Segregation)

65. Cluster Features Event Sourcing & CQRS Akka Persistence Akka Persistence Query Cluster Sharding

69. Cluster Features Publish and Subscribe Distributed Publish & Subscribe

70. Cluster Features Publish and Subscribe

73. Cluster Features Distributed Data Share data between nodes in an Akka Cluster Conflict Free Replicated Data Types (CRDTs)

74. Cluster Features Distributed Data

80. Cluster Features Summary • Groups of collaborating actors abstract functionality • Cluster aware actors react to cluster state changes • Use out-of-the-box features or create custom features

82. Clustering Clustering - strength in numbers

83. Clustering Reactive Services

84. Clustering Akka & Play Services

85. Clustering Lagom Microservices

86. Clustering Lagom Microservices

87. Clustering Lagom Microservices Microservice Microservice Microservice

88. Clustering Summary • Clusters dynamically react to node topology changes • Reactive microservices with Akka, Play, and Lagom

90. Reactive Systems Application landscape is now a city of services

91. Reactive Systems Monoliths, Microliths, Microservices, and Lambdas Built with Akka and Java or Scala

92. Reactive Systems Clusters within a cluster Towns within a city

93. Reactive Systems System Orchestration Application Management Network Partition Split Brain Resolution

94. Reactive Systems System Orchestration Application Management Network Partition Split Brain Resolution

95. Reactive Systems System Orchestration Akka Telemetry and Monitoring Diagnostic RecorderConfiguration Checker

96. Reactive Systems Reactive, Cognitive, and Real-time Data-driven Applications Fast Data Platform

100. Reactive Systems Summary Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications for Java and Scala

102. Akka Customers

103. Reactive Systems Toolkit Reactive Programming Toolkit

106. Upgrade your grey matter!  Get the free O’Reilly book by Hugh McKee,   Developer Advocate at Lightbend https://www.lightbend.com/resources/e-books

Akka Reactive Toolkit

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