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A Taxonomy of Scala StrangeLoop 2012 Jamie Allen @jamie_allen http://github.com/jamie-allen/tax onomy-of-scala

Taxonomy of Scala

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Page 1: Taxonomy of Scala

A Taxonomy of ScalaStrangeLoop 2012

Jamie Allen@jamie_allen

http://github.com/jamie-allen/taxonomy-of-scala

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Agenda

• Goal• Object-Oriented Features• Pattern Matching• Functional Programming• Actors• Futures• Implicits• Type Theory• Macros• Category Theory

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Goal

Provide you with a reference point for many of the terms you hear in the Scala community

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How Programming in Scala Makes Me Feel

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How I Write Programs

• Pre-Scala:– Make it work– Make it work well– Make it work fast

• With Scala:– Make it work and work well– Make it work fast

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Object-Oriented Features

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Case Classes

case class Person(firstName: String = "Jamie", lastName: String = "Allen")

val jamieDoe = Person(lastName = "Doe") res0: Person = Person(Jamie,Doe)

• Data Transfer Objects (DTOs) done right• By default, class arguments are immutable & public• Should never be extended• Provide equals(), copy(), hashCode() and toString()

implementations• Don’t have to use new keyword to create instances• Named Parameters and Default arguments give us Builder pattern semantics

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Lazy Definitionslazy val calculatedValue = piToOneMillionDecimalPoints()

• Excellent for deferring expensive operations until they are needed

• Reducing initial footprint• Resolving ordering issues• Implemented with a guard field and

synchronization, ensuring it is created when necessary

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Importsimport scala.collection.immutable.Map

class Person(val fName: String, val lName: String) {import scala.collection.mutable.{Map => MMap}val cars: MMap[String, String] = MMap()...

}

• Can be anywhere in a class• Allow for selecting multiple classes from a package or using

wildcards• Aliasing• Order matters!

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Objectsobject Bootstrapper extends App { Person.createJamieAllen }

object Person { def createJamieAllen = new Person("Jamie", "Allen") def createJamieDoe = new Person("Jamie", "Doe") val aConstantValue = "A constant value”}

class Person(val firstName: String, val lastName: String)

• Singletons within a JVM process• No private constructor histrionics• Companion Objects, used for factories and constants

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The apply() methodArray(1, 2, 3)res0: Array[Int] = Array(1, 2, 3)

res0(1)res1: Int = 2

• In companion objects, it defines default behavior if no method is called on it

• In a class, it defines the same thing on an instance of the class

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Tuplesdef firstPerson = (1, Person(firstName = “Barbara”))val (num: Int, person: Person) = firstPerson

• Binds you to an implementation• Great way to group values without a DTO• How to return multiple values, but wrapped in

a single instance that you can bind to specific values

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Pattern Matching

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Pattern Matching Examplesname match { case "Lisa" => println("Found Lisa”) case Person("Bob") => println("Found Bob”) case "Karen" | "Michelle" => println("Found Karen or Michelle”) case Seq("Dave", "John") => println("Got Dave before John”) case Seq("Dave", "John", _*) => println("Got Dave before John”) case ("Susan", "Steve") => println("Got Susan and Steve”) case x: Int if x > 5 => println("got value greater than 5: " + x) case x => println("Got something that wasn't an Int: " + x) case _ => println("Not found”)}

• A gateway drug for Scala• Extremely powerful and readable• Not compiled down to lookup/table switch unless you

use the @switch annotation,

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Functional Programming

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Immutability

• Extends beyond marking instances final• You must not leak mutability

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Referential Transparency// Transparentval example1 = "jamie".reverseval example2 = example1.reverseprintln(example1 + example2) // eimajjamie

// Opaqueval example1 = new StringBuffer("Jamie").reverseval example2 = example1.reverseprintln(example1 append example2) // jamiejamie

• An expression is transparent if it can be replaced by its VALUE without changing the behavior of the program

• In math, all functions are referentially transparent

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Scala Collectionsval myMap = Map(1 -> "one", 2 -> "two", 3 -> "three")val mySet = Set(1, 4, 2, 8)val myList = List(1, 2, 8, 3, 3, 4)val myVector = Vector(1, 2, 3...)

• You have the choice of mutable or immutable collection instances, immutable by default

• Rich implementations, extremely flexible

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Rich Collection Functionalityval numbers = 1 to 20 // Range(1, 2, 3, ... 20)

numbers.head // Int = 1numbers.tail // Range(2, 3, 4, ... 20)numbers.take(5) // Range(1, 2, 3, 4, 5)numbers.drop(5) // Range(6, 7, 8, ... 20)

• There are many methods available to you in the Scala collections library

• Spend 5 minutes every day going over the ScalaDoc for one collection class

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Higher Order Functionsval names = List("Barb", "May", "Jon")

names map(_.toUpperCase)res0: List[java.lang.String] = List(BARB, MAY, JON)

• Really methods in Scala• Applying closures to collections

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Higher Order Functionsval names = List("Barb", "May", "Jon")

names map(_.toUpperCase)res0: List[java.lang.String] = List(BARB, MAY, JON)

names flatMap(_.toUpperCase)res1: List[Char] = List(B, A, R, B, M, A, Y, J, O, N)

names filter (_.contains("a"))res2: List[java.lang.String] = List(Barb, May)

val numbers = 1 to 20 // Range(1, 2, 3, ... 20)

numbers.groupBy(_ % 3)res3: Map[Int, IndexedSeq[Int]] = Map(1 -> Vector(1, 4, 7, 10, 13, 16, 19), 2 -> Vector(2, 5, 8, 11, 14, 17, 20), 0 -> Vector(3, 6, 9, 12, 15, 18))

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For Comprehensions

• Used for composing higher-order functions• As you chain higher-order functions, you may

find it easier to reason about them this way

val myNums = 1 to 20

for (i <- myNums) yield i + 1myNums map(_ + 1)

for { i <- myNums j <- 1 to i} yield i * jmyNums flatMap(i => 1 to i map (j => i * j))

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Parallel Collectionsscala> 1 to 1000000res0: scala.collection.immutable.Range.Inclusive = Range(1, 2, 3,...

scala> res0.parres1: s.c.parallel.immutable.ParRange = ParRange(1, 2, 3,...

scala> res1 map(_ + 1)res2: s.c.parallel.immutable.ParSeq[Int] = ParVector(2, 3, 4,...

scala> res2.seqres3: s.c.immutable.Range = Range(2, 3, 4,...

• You can easily parallelize the application of a function literal to your collection by calling the par() method on a collection instance

• Uses JSR166 under the covers to fork/join for you• Use the seq() method on the parallel collection to return to a non-parallel

instance

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Partial Functions

• A simple match without the match keyword• The receive block in Akka actors is an excellent example• Is characterized by what "isDefinedAt" in the case

statements

class MyActor extends Actor { def receive = { case s: String => println("Got a String: " + s) case i: Int => println("Got an Int: " + i) case x => println("Got something else: " + x) }}

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Curryingdef product(i: Int)(j: Int) = i * j val doubler = product(2)_doubler(3) // Int = 6doubler(4) // Int = 8

val tripler = product(3)_tripler(4) // Int = 12tripler(5) // Int = 15

• Take a function that takes n parameters as separate argument lists• “Curry” it to create a new function that only takes one parameter• Fix on a value and use it to apply a specific implementation of a

product with semantic value• Have to be defined explicitly as such in Scala• The _ is what explicitly marks this as curried

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Actors

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Actorsimport akka.actor._

class MyActor extends Actor { def receive = { case x => println(“Got value: “ + x) }}

• Based on concepts from Erlang/OTP• Akka is replacing the core language actors• Concurrency paradigm using networks of

independent objects that only communicate via messaging and mailboxes

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Futures

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Futuresimport scala.concurrent._

val costInDollars = Future { webServiceProxy.getCostInDollars.mapTo[Int]}

costInDollars map (myPurchase.setCostInDollars(_))

• Allows you to write asynchronous code, which can be more performant than blocking

• Are not typed, hence the mapTo call above

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Futures in Sequenceval customerPurchases = for ( costUSD <- Future{ proxy.getCostInDollars.mapTo[Int]} totalPurchase <- Future{ proxy.addToTotal(costUSD).mapTo[Int]}} yield ((customerId -> totalPurchase))

• Scala’s for comprehensions allow you to compose higher-order functions, including Futures

• By sequencing the expressions on multiple lines, you can order dependencies

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Futures in Parallelval costUSD = Future{proxy.getCostInUSD(cost).mapTo[Int]}val costCAD = Future{proxy.getCostInCAD(cost).mapTo[Int]}val combinedCosts = for { cUSD <- costUSD cCAD <- costCAD} yield (cUSD, cCAD)

val costs = for ( (costUSD, costCAD) <- Future{proxy.getCostInUSD(cost).mapTo[Int]} zip Future{proxy.getCostInCAD(cost).mapTo[Int]}} yield (costUSD, costCAD)

• Define the futures separately and then compose• Alternatively, the zip method allows you to parallelize

futures execution within a for comprehension

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Implicits

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Implicit Conversions

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Implicit Conversionscase class Person(firstName: String, lastName: String)implicit def PersonToInt(p: Person) = p.toString.head.toInt

val me = Person("Jamie", "Allen")

val weird = 1 + me res0: Int = 81

• Looks for definitions at compile time that will satisfy type incompatibilities

• Modern IDEs will warn you with an underline when they are in use

• Limit scope as much as possible (see Josh Suereth's NE Scala 2011)

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Implicit Parametersdef executeFutureWithTimeout(f: Future)(implicit t: Timeout)

implicit val t: Timeout = Timeout(20, TimeUnit.MILLISECONDS)executeFutureWithTimeout(Future {proxy.getCustomer(id)})

• Allow you to define default parameter values that are only overridden if you do so explicitly

• Handy to avoid code duplication

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Implicit Classesimplicit class Person(name: String)

class Person(name: String)implicit final def Person(name: String): Person = new Person(name)

• New to Scala 2.10• Create extension methods to existing types• Desugars at compile time into a class

definition with an implicit conversion

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Type Theory

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Type Inference

• Declaring a variable/value• Return types of methods/functions• See Daniel Spiewak's Philly ETE 2011 talk• Good idea to show types on public interfaces• Specify types when you want to type certainty

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Type Classes I

• Allow you to layer in varying implementations of behavior without changing an existing inheritance structure

case class Customer(id: Long, firstName: String, lastName: String)

trait CustomerOrderById extends Ordering[Customer] { def compare(x: Customer, y: Customer): Int = { ... }}implicit object CustomerIdSort extends CustomerOrderById

val customers = List(Customer(1, "Jamie", "Allen"), Customer(5, "John", "Doe"), Customer(2, "Jane", "Smith"))val sortedCustomers = customers.sorted(CustomerIdSort)sortedCustomers: List[Customer] = List(Customer(1,Jamie,Allen), Customer(2,Jane,Smith), Customer(5,John,Doe))

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Type Classes II

• Allows you to generalize types that are acceptable parameters for methods

case class Dog(name: String)case class Ferret(name: String)case class Cat(name: String)abstract class OkayPets[T]object OkayPets { implicit object OkayDog extends OkayPets[Dog] implicit object OkayFerret extends OkayPets[Ferret]}def getPet[T](t: T)(implicit p: OkayPets[T]) = t

val myDog = getPet(Dog("Sparky")) // Worksval myCat = getPet(Cat("Sneezy")) // Fails at compile time

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Higher Kinded TypesMap[A, B] // Type constructor, not a type!

val myMap = Map[Int, String]() // Now it’s a type!

• Use other types to construct a new type• Also called type constructors

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Algebraic Data Typessealed abstract class DayOfTheWeekcase object Sunday extends DayOfTheWeekcase object Monday extends DayOfTheWeek ...case object Saturday extends DayOfTheWeek

val nextDay(d: DayOfTheWeek): DayOfTheWeek = d match { case Sunday => Monday case Monday => Tuesday ... case Saturday => Sunday }}

• Allow you to model the world in finite terms, such as enumerations, but also define behavior around them, with all of the power of case classes

• A finite number of possible subtypes, enforced by the "sealed" keyword (must be defined in the same source file)

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Macros

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Macros• New to Scala 2.10• Macros are used for generating code at

compile time, similar to LISP macros• Does not have compiler pragmas such as #ifdef

• Are implemented as "hygenic" macros at the point you call reify() – identifiers cannot be closed over in a macro definition

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ScalaLogging Macrodef debug(message: String): Unit = macro LoggerMacros.debugprivate object LoggerMacros { def debug(c: LoggerContext)(message: c.Expr[String]) = c.universe.reify( if (c.prefix.splice.underlying.isDebugEnabled) c.prefix.splice.underlying.debug(message.splice) )}

import com.typesafe.scalalogging.Loggingclass MyClass extends Logging { logger.debug("This won't occur if debug is not defined")}

• Existing log libraries allow us to define logging statements and then determine whether they result in output at runtime

• ScalaLogging allows a user to use a logging facility but decide at compile time whether or not to include the logging statement based on log level.

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Category Theory

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Category Theory

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Concepts and Arrowsval myIntToStringArrow: Int => String = _.toString

myIntToStringArrow(1100)res0: String = 1100

• Concepts are types• Arrows are functions that convert one concept

to another

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Morphismval number = 1000val numericString = number.toString

• Morphisms change one value in a category to another in the same category, from one type to another where types are the category

• Simplified, it converts a type with one property to a type with another property

• Must be pure, not side-effecting

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Functorval numbers = List(1, 2, 3, 4)val numericStrings = numbers.map(_.toString)

• Functors are transformations from one category to another that preserve morphisms

• Simplified, converts a type from one to another while maintaining the conversion of a type with one property to a type with another property

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Monadval customerPurchases = for ( costUSD <- proxy.getCostInDollars totalPurchase <- proxy.addToTotal(costUSD)} yield ((customerId -> totalPurchase))

• Very ephemeral concept• Must meet the laws of a monad to be one• Combine functor applications because they can be bound

together, sequencing operations on the underlying types• flatMap() is the method the Scala compiler uses to

bind monads

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Thank You!