Your SlideShare is downloading. ×
  • Like
Introducing Akka
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.


Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Introducing Akka


Presented at Bangalore Java User Group on "Introducing Akka" on 28th June 2014

Presented at Bangalore Java User Group on "Introducing Akka" on 28th June 2014

Published in Engineering , Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads


Total Views
On SlideShare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. Introducing Akka Meetu Maltiar Cisco blog: twitter: @meetumaltiar 28th June 2014
  • 2. Agenda History of Akka Scala basics The use case of Akka Akka Actors Akka Supervision
  • 3. History Philipp Haller worked on Actor model and released it in Scala 2.1.7 in July 2006 Jonas Boner created Akka to bring highly concurrent, event driven to JVM Inspired by Erlang Actors, Jonas Boner began working on Akka early 2009 Jonas Boner as part of Scalable Solutions releases Akka version 0.5 in January 2010 Akka is now part of Typesafe Platform together with Play framework and Scala language
  • 4. Akka Who Uses It
  • 5. Scala Basics Scala is a JVM based strongly typed language Scala is hybrid: Functional as well as Object-Oriented Scala is compatible with Java Scala has support for currying, pattern matching, ADT’s, lazy evaluation, tail recursion etc Scala is compiled to Java byte-codes and run on Java Virtual Machine
  • 6. Scala Compared To Java Scala adds Scala removes pure object system static members operator overloading primitive types closures break and continue mixin composition with traits special treatment of interfaces existential types wildcards abstract types raw types pattern matching enums
  • 7. Scala Cheat Sheet(1) definitions Scala method definitions ! def fun(x: Int) = { result } ! def fun = result ! Scala variable definitions ! var x: Int = expression val x: String = expression Java method definitions ! Int fun(int x) { return result } ! (no parameterless methods) ! java variable definitions ! Int x = expression final String x = expression
  • 8. Scala Cheat Sheet(2) definitions Scala class and object ! class Sample(x: Int, p: Int) { def instMeth(y: Int): Int = x + y } ! object Sample { def staticMeth(x: Int, y: Int): Int = x * y } ! ! ! ! ! ! ! ! ! Java class ! class Sample { private final int x; public final int p; ! Sample(int x, int p) { this.x = x; this.p = p; } ! int instMeth(int y) { return x + y; } ! static int staticMeth(int x, int y) { return x *y; } }
  • 9. Scala: Pattern Matching All that is required to add a case keyword to each class that is to be pattern matchable ! Pattern match also returns a value ! Similar to switch except that Scala compares objects as expressions. Only one matcher is executed at a time. ! case class Employee(name: String) val employee = Employee(“john”) employee match { case Employee(“john”) => “Hello John!” case _ => “Hello there!” } ! res0: String = Hello John
  • 10. Akka The name comes from a goddess in Sami mythology that represented all wisdom and beauty in the world It is also the name of a beautiful mountain in Laponia in north part of Sweden Incidentally in India it means sister in Telugu!!
  • 11. The Problem It is way to hard to build => correct highly concurrent systems => Truly scalable systems => self-healing, fault-tolerant systems
  • 12. What is Akka? Right abstraction with actors for concurrent, fault-tolerant and scalable applications For Fault-Tolerance uses “Let It Crash” model Abstraction for transparent distribution of load We can Scale In and Scale Out
  • 13. Right Abstraction Never think in terms of shared state, state visibility, threads, locks, concurrent collections, thread notification etc Low level concurrency becomes Simple Workflow - we only think in terms of message flows in system We get high CPU utilisation, low latency, high throughput and scalability - for free as part of this model Proven and superior model for detecting and recovering from errors
  • 14. Actor Model Actor Model (1973): Carl Hewitt’s definition ! The fundamental unit of computation that embodies: - Processing - Storage - Communication ! Three Axioms - Create new Actors - Send messages to Actor it knows - Designate how it should handle the next message it receives
  • 15. Introducing Actors Actor is an entity encapsulating behaviour, state and a mailbox to receive messages For a message received by Actor a thread is allocated to it Then behaviour is applied to the message and potentially some state is changed or messages are passed to other Actors
  • 16. Introducing Actors.. There is elasticity between message processing and addition of new messages. New messages can be added while Actor execution is happening. When processing of messages is completed; the thread is deallocated from the Actor. It can again be reallocated a thread at a later time.
  • 17. Mailbox Actor having behaviour, state and a mailbox Messages are in mailbox No thread is allocated to the Actor
  • 18. Thread is allocated to the Actor It is now ready to read message and apply behaviour Mailbox
  • 19. Thread is allocated to the Actor It has read message and is applying behaviour Mailbox
  • 20. Mailbox Actor has handled message Thread is deallocated It will be allocated a thread later
  • 21. Create Actor System ActorSystem is a heavy-weight structure that will allocate 1…n threads. So,create one per logical application ! Top level actors are created from an ActorSystem ! This is so because first Actor is the child from ActorSystem. If we create another Actor from this first Actor: then second Actor will be child of the first Actor ! We therefore get a tree like structure and hence get automatic supervision ! val system = ActorSystem("myfirstApp")
  • 22. My First Actor import ! class MyFirstActor extends Actor { def receive = { case msg: String => println(msg) case _ => println("default") } } you extend an Actor ! receive method reads the message from mailbox ! receive is a partially applied function ! pattern match is applied on the message
  • 23. Create Actor package com.meetu.akka ! import ! object HelloWorldAkkaApplication extends App { val system = ActorSystem("myfirstApp") val myFirstActor: ActorRef = system.actorOf(Props[MyFirstActor]) …….. } Create an Actor System ! create actor from Actor System using actorOf method ! the actorOf method returns an ActorRef instead of Actor class type
  • 24. Create Actor when actorOf is called path is reserved ! A random UID is assigned to incarnation ! Actor instance is created ! preStart is called on instance
  • 25. Send Message package com.meetu.akka ! import ! object HelloWorldAkkaApplication extends App { val system = ActorSystem("myfirstApp") val myFirstActor: ActorRef = system.actorOf(Props[MyFirstActor]) myFirstActor ! "Hello World" myFirstActor.!("Hello World") } Scala version has a method named “!” ! This is asynchronous thread of execution continues after sending ! It accepts Any as a parameter ! In Scala we can skip a dot with a space: So it feels natural to use
  • 26. Ask Pattern package com.meetu.akka ! import import akka.pattern.ask import akka.util.Timeout import scala.concurrent.duration._ import scala.concurrent.Await import scala.concurrent.Future ! object AskPatternApp extends App { implicit val timeout = Timeout(500 millis) val system = ActorSystem("BlockingApp") val echoActor = system.actorOf(Props[EchoActor]) ! val future: Future[Any] = echoActor ? "Hello" val message = Await.result(future, timeout.duration).asInstanceOf[String] ! println(message) } ! class EchoActor extends Actor { def receive = { case msg => sender ! msg } } Ask pattern is blocking ! Thread of execution waits till response is reached
  • 27. Reply From Actor import ! class LongWorkingActor extends Actor { def receive = { case number: Int => sender ! ("Hi I received the " + number) } } Each Actor has been provided default sender ! Use “!” method to send back the message
  • 28. Routers RoundRobin ! Random ! SmallestMailBox ! Broadcast ! ScatterGatherFirstCompleted
  • 29. Round Robin Router import import akka.routing.RoundRobinPool import akka.routing.Broadcast ! object RouterApp extends App { val system = ActorSystem("routerApp") val router = system.actorOf(RoundRobinPool(5).props(Props[RouterWorkerActor]), "workers") router ! Broadcast("Hello") } ! class RouterWorkerActor extends Actor { def receive = { case msg => println(s"Message: $msg received in ${self.path}") } } A router sits on top of routees ! When messages are sent to Router, Routees get messages in Round Robin
  • 30. Failure: Typical Scenario There is a single thread of control ! If this Thread goes in failure we are doomed ! We therefore do explicit error handling on this thread ! Worse error do not propagate between threads. There is no way of knowing that something failed ! We therefore do defensive programming with: • Error handling tangled with business logic • Scattered all over code base ! We can do better than this
  • 31. Supervision Supervise means manage another Actor failures ! Error handling in Actors is handled by letting Actors monitor (supervise) each other of failure ! This means if Actor crashes a notification is sent to its supervisor (an Actor), who can react to failure ! This provides clean separation of processing and error handling
  • 32. …Let’s take a standard OO application
  • 33. Which components have critically important state and Explicit error handling
  • 34. Supervise Actor Every Actor exists in a Tree topology. Its parent provide automatic supervision ! Every Actor has a default Supervision strategy, which is usually sufficient ! supervision strategy can be overridden ! We have either One for One strategy. Here only the Actor that crashed is handled. ! Other one is All For One strategy. Here all children are restarted
  • 35. Supervision Actor class Supervisor extends Actor { override val supervisorStrategy = OneForOneStrategy(maxNrOfRetries = 10, withinTimeRange = 1 minute) { case _: ArithmeticException => Resume case _: NullPointerException => Restart case _: IllegalArgumentException => Stop case _: Exception => Escalate } ! def receive = { case p: Props => sender ! context.actorOf(p) } }
  • 36. Supervision: Child Actor class Child extends Actor { var state = 0 def receive = { case ex: Exception => throw ex case x: Int => state = x case "get" => sender ! state } }
  • 37. Supervision Application object SupervisionExampleApp extends App { implicit val timeout = Timeout(50000 milliseconds) val system = ActorSystem("supervisionExample") val supervisor = system.actorOf(Props[Supervisor], "supervisor") val future = supervisor ? Props[Child] val child = Await.result(future, timeout.duration).asInstanceOf[ActorRef] child ! 42 println("Normal response " + Await.result(child ? "get", timeout.duration).asInstanceOf[Int]) child ! new ArithmeticException println("Arithmetic Exception response " + Await.result(child ? "get", timeout.duration).asInstanceOf[Int]) child ! new NullPointerException println("Null Pointer response " + Await.result(child ? "get", timeout.duration).asInstanceOf[Int]) }
  • 38. Running Supervision Application
  • 39. Learning Resources Code examples at Github ! Akka Documentation ! Scala Documentation
  • 40. Thank You!!