Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microservices Architectures
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Microservices. Streaming data. Event Sourcing and CQRS. Concurrency, routing, self-healing, persistence, clustering… You get the picture. The Akka toolkit makes all of this simple for Java and Scala developers at Amazon, LinkedIn, Starbucks, Verizon and others. So how does Akka provide all these features out of the box? Join Hugh McKee, Akka expert and Developer Advocate at Lightbend, on an illustrated journey that goes deep into how Akka works–from individual Akka actors to fully distributed clusters across multiple datacenters.
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Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microservices Architectures
- 1. TODO!
- 2. Upgrade your grey matter! Get the free O’Reilly book and Lightbend white paper on Actors and Akka https://www.lightbend.com/learn
- 3. Quick Intro to Lightbend 2017 Acquired OpsClarity 6m+ OSS Downloads/month Launched Fast Data Platform Investment from IBM Launched Lagom Framework 2016 2015 Spark Support 500k+ OSS Downloads/ month Acquired Play Framework Founded by Martin Odersky, Scala creator & Jonas Bonér, Akka creator 2012 20132011 2014 Launched Reactive Platform Published the Reactive Manifesto
- 4. 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
- 5. 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
- 6. 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
- 7. Actors Actor Model Actor Model - breaking free from imperative, synchronous, thread heavy systems development
- 21. 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.
- 36. 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
- 37. 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
- 38. Streams Reactive Streams Reactive Streams - standard for asynchronous stream processing with non-blocking back pressure
- 39. Streams Reactive Streams Java 9 Reactive Streams
- 42. 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
- 43. 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 })
- 44. 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(); }));
- 47. 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
- 48. 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);
- 49. 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
- 50. 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
- 51. Cluster Features Cluster Features - actor systems that span clusters of multiple JVMs
- 53. Cluster Features Cluster Membership State Diagram of Akka Cluster Member States
- 54. Cluster Features Cluster Sharding Send messages to actors distributed across a cluster via logical identifiers
- 66. Cluster Features Event Sourcing & CQRS Akka Persistence Akka Persistence Query Cluster Sharding Actor implementation of Event Sourcing & CQRS (Command Query Responsibility Segregation)
- 67. Cluster Features Event Sourcing & CQRS Akka Persistence Akka Persistence Query Cluster Sharding
- 68. Cluster Features Event Sourcing & CQRS Akka Persistence Akka Persistence Query Cluster Sharding
- 69. Cluster Features Event Sourcing & CQRS Akka Persistence Akka Persistence Query Cluster Sharding
- 70. Cluster Features Event Sourcing & CQRS Akka Persistence Akka Persistence Query Cluster Sharding
- 71. Cluster Features Publish and Subscribe Distributed Publish & Subscribe
- 72. Cluster Features Publish and Subscribe
- 73. Cluster Features Publish and Subscribe
- 74. Cluster Features Publish and Subscribe
- 75. Cluster Features Distributed Data Share data between nodes in an Akka Cluster Conflict Free Replicated Data Types (CRDTs)
- 82. 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
- 83. 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
- 84. Clustering Clustering - strength in numbers
- 86. Clustering Akka & Play Services
- 90. Clustering Summary • Clusters dynamically react to node topology changes • Reactive microservices with Akka, Play, and Lagom
- 91. 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
- 92. Reactive Systems Application landscape is now a city of services
- 93. Reactive Systems Monoliths, Microliths, Microservices, and Lambdas Built with Akka and Java or Scala
- 94. Reactive Systems Clusters within a cluster Towns within a city
- 95. Reactive Systems System Orchestration Application Management Network Partition Split Brain Resolution
- 96. Reactive Systems System Orchestration Application Management Network Partition Split Brain Resolution
- 97. Reactive Systems System Orchestration Akka Telemetry and Monitoring Diagnostic RecorderConfiguration Checker
- 99. Reactive Systems Reactive, Cognitive, and Real-time Data-driven Applications Fast Data Platform
- 100. Reactive Systems Reactive, Cognitive, and Real-time Data-driven Applications Fast Data Platform
- 101. Reactive Systems Reactive, Cognitive, and Real-time Data-driven Applications Fast Data Platform
- 102. Reactive Systems Reactive, Cognitive, and Real-time Data-driven Applications Fast Data Platform
- 103. Reactive Systems Summary Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications for Java and Scala
- 104. 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
- 105. Reactive Systems Toolkit Reactive Programming Toolkit
- 106. 106 Some of the world’s most admired brands trust Lightbend