Introduction to Scala

Slides from a talk given at Brunel University and Enear in Portugal

Transcript

1. An Introduction to Scala Nigel.Warren@UnderscoreConsulting.com
2. Dept of Computer Science Centre for Intelligent Data Analysis
3. Scala Introduction Proposition … Reuse - Java Libraries and runtime JVM Based Consise - Less Code that does more Parallel Ready - Builtin Support via Parallel Libs Expressive - Combines OO and FP Fewer Bugs - Static Inferred Type System
4. A Little History Created by Prof Martin Odersky at EPFL Lausanne 1996-1997 - Pizza 1998-2000 - GJ, Java Generics, javac 2003-2006 - Scala as an academic ‘Experiment’ 2006-Now - Scala in production 2014 - Ten years of Scala
5. Scala in Use
6. Clearing Connectivity Layer Services
7. Web Backend Systems
8. Sphonic - Realtime Fraud Detection and KYC System
9. Content Management and Delivery System
10. Reuse Principle - Don’t reinvent underlying core abstractions and environment Use any underlying Java Library direct from Scala Scala can extend underlying Java types with new features Scala compiles to Java .class files and hence uses the JVM runtime environment (JIT)
11. Java Virtual Machine Host Hardware Scala Class Files Java Class Files Scala Class Files Java Source Files Scala Source Files
12. Rich Scala Libraries Collections - Implausibly powerful and productive collections for iterative and parallel operations Async - Future (impl Promise) based asynchronous model DSL - Internal and External Domain Specific Languages via Parser Combinators
13. Consise Java … public class Person { public final String name; public final int age; Person(String name, int age) { this.name = name; this.age = age; } } Scala … case class Person(val name : String, val age : Int)
14. Parallel Ready Exploits immutability to ‘automate’ parallel operations val movie : List[Image] = loadImages movie.map( img => img computeHash ) // and in parallel movie.par.map( img => img computeHash )
15. Parallel Collections Split work by number of available processors On completion of tasks threads steal work from other queues
16. Expressive Object Oriented Scala Agile LightWeight Syntax Safe - Strong Static Typing Functional Programming
17. Adoption Experience Reduction in Lines of Code (Counting problem) Aim to increase readability Works seamlessly* with Java Selectively Migrate project/libraries Common entry point is testing framework Programmer Joy <==> Programer Productivity The ‘well of confusion’ - idiom overload
18. Fewer Bugs Use Types to drive out bugs at compile time. e.g. Options to exclude NullPointerExceptions Immutability and Pure functions Collapses concurrent update problems Easy to test Scala Test and Scala Check More time coding Less time in debugger
19. Scala Libraries Akka - Actors and Distribution Tools Play / Spray - Web Framework (REST services) JSON and XML parsers Testing - ScalaTest and ScalaCheck TypeLevel - Shapeless, Spire, Scalaz, Scalaz-Stream
20. Scala Research EPFL - Programming Methods Laboratory (LAMP) Type simplicity via dependent types - ‘Dotty’ ‘The Trouble with Types’ - InfoQ.com Stanford - Pervasive Parallel Lab (PPL) DSL based parallisation - “Delite” UC Berkeley - AMP Labs Spark, GraphX, ML Lib ( now Apache )
21. Q & A
22. Thanks Nigel.Warren@UnderscoreConsulting.com