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- 1. Scala. From Zero to Hero. Monads. k.v.kozlov@gmail.com, 2016
- 2. Functions as Objects What about functions? In fact function values are treated as objects in Scala. The function type A => B is just an abbreviation for the class scala.Function1[A, B], which is deﬁned as follows. package scala trait Function1[A, B] { def apply(x: A): B } So functions are objects with apply methods.
- 3. Functions as Objects An anonymous function such as (x: Int) => x * x is expanded to: { class AnonFun extends Function1[Int, Int] { def apply(x: Int) = x * x } new AnonFun } or, shorter, using anonymous class syntax: new Function1[Int, Int] { def apply(x: Int) = x * x }
- 4. Functions as Objects A function call, such as f(a, b), where f is a value of some class type, is expanded to f.apply(a, b) So the OO-translation of val f = (x: Int) => x * x f(7) would be val f = new Function1[Int, Int] { def apply(x: Int) = x * x } f.apply(7)
- 5. Case Classes A case class deﬁnition is similar to a normal class deﬁnition, except that it is preceded by the modiﬁer case. For example: trait Expr case class Number(n: Int) extends Expr case class Sum(e1: Expr, e2: Expr) extends Expr Like before, this deﬁnes a trait Expr, and two concrete subclasses Number and Sum
- 6. Case Classes It also implicitly deﬁnes companion objects with apply methods. object Number { def apply(n: Int) = new Number(n) } object Sum { def apply(e1: Expr, e2: Expr) = new Sum(e1, e2) } so you can write Number(1) instead of new Number(1).
- 7. Pattern matching Pattern matching is a generalization of switch from C/Java to class hierarchies. It’s expressed in Scala using the keyword match. Example eval(Sum(Number(1), Number(2))) def eval(e: Expr): Int = e match { case Number(n) => n case Sum(e1, e2) => eval(e1) + eval(e2) }
- 8. Lists The list is a fundamental data stucture in functional programming. A list having x1, ...,xn as elements is written List(x1, ...,xn) Example val fruit = List(«apples», «oranges», «pears») val nums = List(1, 2, 3, 4) val diag3 = List(List(1, 0, 0), List(0, 1, 0), List(0, 0, 1)) val empty = List() There are two important differences between lists and arrays. ● List are imuttable - the elements of list cannot be changed ● Lists are recursive, while arrays are flat
- 9. Constructors of Lists All lists are constructed from: ● The empty list Nil, and ● The constructor operation :: (pronounces cons): x :: xs gives a new list with the first element x, followed by the element of xs Example fruit = «apples» :: («oranges» :: («pears» :: Nil)) nums = 1 :: (2 :: (3 :: (4 :: Nil))) empty = Nil
- 10. Map A simple way to define map is as follows: abstract class List[T] { ... def map[U](f: T => U): List[U] = this match { case Nil => Nil case x :: xs => f(x) :: xs.map(f) } Example def scaleList(xs: List[Double], factor: Double) = xs map (x => x * factor)
- 11. flatMap abstract class List[T] { def flatMap[U](f: T => List[U]): List[U] = this match { case x :: xs => f(x) ++ xs.flatMap(f) case Nil => Nil } }
- 12. Filter This pattern is generalized by the method filter of the List class: abstract class List[T] { ... def filter(p: T => Boolean): List[T] = this match { case Nil => Nil case x :: xs => if (p(x)) x :: xs.filter(p) else xs.filter(p) } } Example def posElems(xs: List[Int]): List[Int] = xs filter (x => x > 0)
- 13. For-Expression Higher-order functions such as map, flatMap or filter provide powerful constructs for manipulating lists. But sometimes the level of abstraction required by these function make the program difficult to understand. In this case, Scala's for expression notation can help.
- 14. For-Expression Example Let person be a list of elements of class Person, with fields name and age. case class Person(name: String, age: Int) To obtain the names of person over 20 years old, you can write: for ( p <- persons if p.age > 20 ) yield p.name Which is equivalent to: persons filter (p => p.age > 20) map (p => p.name) The for-expression is similar to loops in imperative languages, except that is builds a list of the result of all iterations.
- 15. Syntax of For A for-expression is of the form: for ( s ) yield e where s is a sequence of generators and filters, and e is an expression whose value is returned by an iteration. ● A generator is of the form p <- e, where p is a pattern and e an expression whose value is a collection. ● A filter is of the form if f where f is a boolean expression. ● The sequence must start with generator. ● If there are several generators in the sequence, the last generators vary faster than the first. Example for { i <- 1 until n j <- 1 until i if isPrime(i + j) } yield (i, j)
- 16. Queries with for case class Book(title: String, authors: List[String]) val books: List[Book] = List( Book(title = ”Structure and Interpretation of Computer Programs”, authors = List(”Abelson, Harald”, ”Sussman, Gerald J.”)), Book(title = ”Introduction to Functional Programming”, authors = List(”Bird, Richard”, ”Wadler, Phil”)), Book(title = ”Effective Java”, authors = List(”Bloch, Joshua”)), Book(title = ”Java Puzzlers”, authors = List(”Bloch, Joshua”, ”Gafter, Neal”)), Book(title = ”Programming in Scala”, authors = List(”Odersky, Martin”, ”Spoon, Lex”, ”Venners, Bill”))) To find the names of all authors who have written at least two books present in the database: for { b1 <- books b2 <- books if b1.title < b2.title a1 <- b1.authors a2 <- b2.authors if a1 == a2 } yield a1
- 17. For-Expressions and Higher-Order Functions The syntax of for is closely related to the higher-order functions map, flatMap and filter. First of all, these functions can all be defined in terms of for: def map[T, U](xs: List[T], f: T => U): List[U] = for (x <- xs) yield f(x) def flatMap[T, U](xs: List[T], f: T => Iterable[U]): List[U] = for (x <- xs; y <- f(x)) yield y def filter[T](xs: List[T], p: T => Boolean): List[T] = for (x <- xs if p(x)) yield x And vice versa
- 18. Translation of For In reality, the Scala compiler expresses for- expression in terms of map, flatMap and a lazy variant of filter. 1. A simple for expression for (x <- e1) yield e2 is translated to e1.map(x => e2)
- 19. Translation of For 2: A for-expression for (x <- e1 if f; s) yield e2 where f is a ﬁlter and s is a (potentially empty) sequence of generators and ﬁlters, is translated to for (x <- e1.withFilter(x => f); s) yield e2 (and the translation continues with the new expression) You can think of withFilter as a variant of filter that does not produce an intermediate list, but instead ﬁlters the following map or flatMap function application.
- 20. Translation of For 3: A for-expression for (x <- e1; y <- e2; s) yield e3 where s is a (potentially empty) sequence of generators and ﬁlters, is translated into e1.flatMap(x => for (y <- e2; s) yield e3) (and the translation continues with the new expression)
- 21. Example Take the for-expression that computed pairs whose sum is prime: for { i <- 1 until n j <- 1 until i if isPrime(i + j) } yield (i, j) Applying the translation scheme to this expression gives: (1 until n).flatMap(i => (1 until i).withFilter(j => isPrime(i+j)) .map(j => (i, j)))
- 22. For and Databases For example, books might not be a list, but a database stored on some server. As long as the client interface to the database deﬁnes the methods map, flatMap and withFilter, we can use the for syntax for querying the database. This is the basis of the Scala data base connection frameworks ScalaQuery and Slick. Similar ideas underly Microsoft’s LINQ (for (c < -coffees; if c.sales > 999 )yield c.nam e).run select"CO F_NAM E" from "CO FFEES" w here "SALES" > 999
- 23. As soon as you understand Monads, you will understand that this is a Monad, too. {{alt: What if someone broke out of a hypothetical situation in your room right now?}}
- 24. Monads Data structures with map and flatMap seem to be quite common. In fact there’s a name that describes this class of a data structures together with some algebraic laws that they should have. They are called monads.
- 25. What is a Monad? A monad M is a parametric type M[T] with two operations, flatMap and unit, that have to satisfy some laws. trait M[T] { def flatMap[U](f: T => M[U]): M[U] } def unit[T](x: T): M[T] In the literature, flatMap is more commonly called bind.
- 26. Examples of Monads – List is a monad with unit(x) = List(x) – Set is monad with unit(x) = Set(x) – Option is a monad with unit(x) = Some(x) – Generator is a monad with unit(x) = single(x) – …...... flatMap is an operation on each of these types, whereas unit in Scala is di erent forﬀ each monad.
- 27. Monads and map map can be deﬁned for every monad as a combination of flatMap and unit: m map f == m flatMap (x => unit(f(x))) == m flatMap (f andThen unit)
- 28. Monad Laws To qualify as a monad, a type has to satisfy three laws: Associativity m flatMap f flatMap g == m flatMap (x => f(x) flatMap g) Left unit unit(x) flatMap f == f(x) Right unit m flatMap unit == m
- 29. The Option monad sealed trait Option[A] { def map[B](f: A => B): Option[B] def flatMap[B](f: A => Option[B]): Option[B] } case class Some[A](a: A) extends Option[A] case class None[A] extends Option[A] The Option monad makes the possibility of missing data explicit in the type system, while hiding the boilerplate «if non-null» logic
- 30. Checking Monad Laws Let’s check the monad laws for Option. Here’s flatMap for Option: abstract class Option[+T] { def flatMap[U](f: T => Option[U]): Option[U] = this match { case Some(x) => f(x) case None => None } }
- 31. Try Try resembles Option, but instead of Some/None there is a Success case with a value and a Failure case that contains an exception: abstract class Try[+T] case class Success[T](x: T) extends Try[T] case class Failure(ex: Exception) extends Try[Nothing] Try is used to pass results of computations that can fail with an exception between threads and computers
- 32. Creating a Try You can wrap up an arbitrary computation in a Try. Try(expr) // gives Success(someValue) or Failure(someException) Here’s an implementation of Try: object Try { def apply[T](expr: => T): Try[T] = try Success(expr) catch { case NonFatal(ex) => Failure(ex) } }
- 33. Composing Try Just like with Option, Try-valued computations can be composed in for expresssions. for { x <- computeX y <- computeY } yield f(x, y) If computeX and computeY succeed with results Success(x) and Success(y), this will return Success(f(x, y)). If either computation fails with an exception ex, this will return Failure(ex).
- 34. Deﬁnition of flatMap and map on Try abstract class Try[T] { def flatMap[U](f: T => Try[U]): Try[U] = this match { case Success(x) => try f(x) catch { case NonFatal(ex) => Failure(ex) } case fail: Failure => fail } def map[U](f: T => U): Try[U] = this match { case Success(x) => Try(f(x)) case fail: Failure => fail } } The Try monad makes the possibility of errors explicit in the type system, while hiding the boilerplate «try/catch» logic
- 35. We have seen that for-expressions are useful not only for collections. Many other types also deﬁne map,flatMap, and withFilter operations and with them for-expressions. Examples: Generator, Option, Try. Many of the types deﬁning flatMap are monads. (If they also deﬁne withFilter, they are called “monads with zero”). The three monad laws give useful guidance in the design of library APIs.
- 36. Lets play a simple game: trait Adventure { def collectCoins(): List[Coin] def buyTreasure(coins: List[Coin]): Treasure } val adventure = Adventure() val coins = adventure.collectCoins() val treasure = adventure.buyTreasure(coins)
- 37. Actions may fail def collectCoins(): List[Coin] = { if (eatenByMonster(this)) throw new GameOverException(“Ooops”) List(Gold, Gold, Silver) } def buyTreasure(coins: List[Coin]): Treasure = { if (coins.sumBy(_.value) < treasureCost) throw new GameOverException(“Nice try!”) Diamond } val adventure = Adventure() val coins = adventure.collectCoins() val treasure = adventure.buyTreasure(coins)
- 38. Sequential composition of actions that may fail val adventure = Adventure() val coins = adventure.collectCoins() // block until coins are collected // only continue if there is no exception val treasure = adventure.buyTreasure(coins) // block until treasure is bought // only continue if there is no exception
- 39. Expose possibility of failure in the types, honestly T => S T => Try[S]
- 40. Making failure evident intypes import scala.util.{Try, Success, Failure} abstract class Try[T] case class Success[T](elem: T) extends Try[T] case class Failure(t: Throwable) extends Try[Nothing] object Try { def apply[T](r: =>T): Try[T] = { try { Success(r) } catch { case t => Failure(t) } } trait Adventure { def collectCoins(): Try[List[Coin]] def buyTreasure(coins: List[Coin]): Try[Treasure] }
- 41. Dealing with failure explicitly val adventure = Adventure() val coins: Try[List[Coin]] = adventure.collectCoins() val treasure: Try[Treasure] = coins match { case Success(cs) adventure.buyTreasure(cs)⇒ case failure @ Failure(t) failure⇒ }
- 42. Noise reduction val adventure = Adventure() val treasure: Try[Treasure] = adventure.collectCoins().flatMap(coins {⇒ adventure.buyTreasure(coins) }) val treasure: Try[Treasure] = for { coins <- adventure.collectCoins() treasure <- buyTreasure(coins) } yield treasure
- 43. Amonad that handles exceptions. Try[T] The Try monad makes the possibility of errors explicit in the type system, while hiding the boilerplate «try/catch» logic
- 44. trait Socket { def readFromMemory(): Array[Byte] def sendToEurope(packet: Array[Byte]): Array[Byte] } val socket = Socket() val packet = socket.readFromMemory() val confirmation = socket.sendToEurope(packet)
- 45. Timings for various operations on a typical PC
- 46. val socket = Socket() val packet = socket.readFromMemory() // block for 50,000 ns // only continue if there is no exception val confirmation = socket.sendToEurope(packet) // block for 150,000,000 ns // only continue if there is no exception Lets translate this into human terms. 1 nanosecond → 1 second val socket = Socket() val packet = socket.readFromMemory() // block for 3 days // only continue if there is no exception val confirmation = socket.sendToEurope(packet) // block for 5 years // only continue if there is no exception
- 47. Futures asynchronously notify consumers Future[T] A monad that handles exceptions and latency import scala.concurrent._ import scala.concurrent.ExecutionContext.Implicits.global trait Future[T] { def onComplete(callback: Try[T] Unit)⇒ (implicit executor: ExecutionContext): Unit }
- 48. Futures asynchronously notify consumers import scala.concurrent._ trait Future[T] { def onComplete(callback: Try[T] Unit)⇒ (implicit executor: ExecutionContext): Unit } trait Socket { def readFromMemory(): Future[Array[Byte]] def sendToEurope(packet: Array[Byte]): Future[Array[Byte]] }
- 49. Sendpackets using futures val socket = Socket() val packet: Future[Array[Byte]] = socket.readFromMemory() packet onComplete { case Success(p) {⇒ val confirmation: Future[Array[Byte]] = socket.sendToEurope(p) } case Failure(t) => … }
- 50. Creating Futures // Starts an asynchronous computation // and returns a future object to which you // can subscribe to be notified when the // future completes object Future { def apply(body: T)⇒ (implicit context: ExecutionContext): Future[T] }
- 51. Creating Futures import scala.concurrent.ExecutionContext.Implicits.global import akka.serializer._ val memory = Queue[EMailMessage]( EMailMessage(from = “Erik”, to = “Roland”), EMailMessage(from = “Martin”, to = “Erik”), EMailMessage(from = “Roland”, to = “Martin”)) def readFromMemory(): Future[Array[Byte]] = Future { val email = queue.dequeue() val serializer = serialization.findSerializerFor(email) serializer.toBinary(email) }
- 52. Some Other Useful Monads ● The List monad makes nondeterminism explicit in the type system, while hiding the boilerplate of nested for-loops. ● The Promise monad makes asynchronous computation explicit in the type system, while hiding the boilerplate of threading logic ● The Transaction monad (non-standard) makes transactionality explicit in the type system, while hiding the boilerplate of invoking rollbacks ● … and more. Use monads wherever possible to keep your code clean!
- 53. Future[T] and Try[T] are dual trait Future[T] { def OnComplete[U](func: Try[T] U)⇒ (implicit ex: ExecutionContext): Unit } Lets simplify: (Try[T] Unit) Unit⇒ ⇒
- 54. Future[T] and Try[T] are dual (Try[T] Unit) Unit⇒ ⇒ Reverse: Unit (Unit Try[T])⇒ ⇒ Simplify: () (() Try[T])⇒ ⇒ ≈ Try[T]
- 55. Future[T] and Try[T] are dual Receive result of type Try[T] by passing callback (Try[T] Unit)⇒ to method def asynchronous(): Future[T] = { … } Receive result of type Try[T] by blocking until method returns def synchronous(): Try[T] = { … }
- 56. Iterable[T] trait Iterable[T] { def iterator(): Iterator[T] } trait Iterator[T] { def hasNext: Boolean def next(): T } () (() Try[Option[T]])⇒ ⇒
- 57. Iterable[T] vs Observables[T] () (() Try[Option[T]])⇒ ⇒ Reverse: (Try[Option[T]] Unit) Unit⇒ ⇒ Simplify: ( T Unit⇒ , Throwable Unit⇒ , () Unit⇒ ) Unit⇒
- 58. ( T Unit,⇒ , Throwable Unit⇒ , () Unit⇒ ) Unit⇒ trait Observable[T] { def Subscribe(observer: Observer[T]): Subscription } trait Observer[T] { def onNext(value: T): Unit def onError(error: Throwable): Unit def onCompleted(): Unit } trait Subscription { def unsubscribe(): Unit }
- 59. Hello Observables
- 60. More INFO ● Functional Programming Principles in Scala https://www.coursera.org/course/progfun ● Principles of Reactive Programming https://www.coursera.org/course/reactive ● Programming Languages https://www.coursera.org/course/proglang ● Paradigms of Computer Programming https://www.edx.org/course/louvainx/louvainx-louv1-01x-paradigms-compu ter-1203 ● Functional Reactive Programming & ClojureScript https://www.youtube.com/watch?v=R4sTvHXkToQ&list=PL6JL99ajzlXU bXUY0nwWr2imcrhJHT3Z7 ● An Introduction to Functional Reactive Programming https://www.youtube.com/watch?v=ZOCCzDNsAtI ● The reactive manifesto http://www.reactivemanifesto.org/

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