![#4: Recursion
• Recursion is almost always better than a loop.
• Simple fallback: Tail-recursive functions
• Guaranteed to be efficient
@tailrec
def loop(xs: List[T], ys: List[U]): Boolean =
if (xs.isEmpty) ys.isEmpty
else ys.nonEmpty && loop(xs.tail, ys.tail)
23](https://image.slidesharecdn.com/flatmap-140512133815-phpapp02/85/flatMap-Oslo-presentation-slides-23-320.jpg)
![Why CanBuildFrom?
• Why not define it like this?
class Functor[F[_], T] {
def map[U](f: T => U): F[U]
}
• Does not work for arrays, since we need a class-tag to
build a new array.
• More generally, does not work in any case where we
need some additional information to build a new
collection.
• This is precisely what’s achieved by CanBuildFrom.
28](https://image.slidesharecdn.com/flatmap-140512133815-phpapp02/85/flatMap-Oslo-presentation-slides-28-320.jpg)
: F[U]
does not accommodate this type class.
So the “obvious” type signature for `map` is inadequate.
Again, CanBuildFrom fixes this.
30](https://image.slidesharecdn.com/flatmap-140512133815-phpapp02/85/flatMap-Oslo-presentation-slides-30-320.jpg)
![Why Not Use a Monad?
The fundamentalist functional approach would mandate that
typer state is represented as a monad.
Instead of now:
def typed(tree: untpd.Tree, expected: Type): tpd.Tree
def isSubType(tp1: Type, tp2: Type): Boolean
we’d write:
def typed(tree: untpd.Tree, expected: Type):
TyperState[tps.Tree]
def isSubType(tp1: Type, tp2: Type):
TyperState[Boolean]
33](https://image.slidesharecdn.com/flatmap-140512133815-phpapp02/85/flatMap-Oslo-presentation-slides-33-320.jpg)
![#6 Parameterized Types
class List[+T]
class Set[T]
class Function1[-T, +R]
List[Number]
Set[String]
Function1[String, Int]
Variance expressed by +/- annotations
A good way to explain variance is by mapping to abstract
types.
38](https://image.slidesharecdn.com/flatmap-140512133815-phpapp02/85/flatMap-Oslo-presentation-slides-38-320.jpg)
![Modelling Parameterized Types
class Set[T] { ... } class Set { type $T }
Set[String] Set { type $T = String }
class List[+T] { ... } class List { type $T }List[Number]
List { type $T <: Number }
Parameters Abstract members
Arguments Refinements](https://image.slidesharecdn.com/flatmap-140512133815-phpapp02/85/flatMap-Oslo-presentation-slides-39-320.jpg)
![#7 Implicit Parameters
Implicit parameters are a simple concept
But they are surprisingly versatile.
Can represent a typeclass:
def min(x: A, b: A)(implicit cmp: Ordering[A]): A
40](https://image.slidesharecdn.com/flatmap-140512133815-phpapp02/85/flatMap-Oslo-presentation-slides-40-320.jpg)
![#7 Implicit Parameters
Can represent a context
def typed(tree: untpd.Tree, expected:
Type)(implicit ctx: Context): Type
def compile(cmdLine: String)
(implicit defaultOptions: List[String]): Unit
Can represent a capability:
def accessProfile(id: CustomerId)
(implicit admin: AdminRights): Info
41](https://image.slidesharecdn.com/flatmap-140512133815-phpapp02/85/flatMap-Oslo-presentation-slides-41-320.jpg)
flatMap Oslo presentation slides
- 1. Scala - The Simple Parts Martin Odersky Typesafe and EPFL
- 2. 10 Years of Scala
- 3. Grown Up? Scala’s user community is pretty large for its age group. And, despite it coming out of academia it is amazingly successful in practice. But Scala is also discussed more controversially than usual for a language at its stage of adoption. Why? 3
- 4. Controversies Internal controversies: Different communities not agreeing what programming in Scala should be. External complaints: “Scala is too academic” “Scala has sold out to industry” “Scala’s types are too hard” “Scala’s types are not strict enough” “Scala is everything and the kitchen sink” Signs that we have not made clear enough what the essence of programming in Scala is. 4
- 5. 1-5 The Picture So Far Agile, with lightweight syntax Object-Oriented Functional Safe and performant, with strong static tpying = scalable
- 6. What is “Scalable”? • 1st meaning: “Growable” – can be molded into new languages by adding libraries (domain specific or general) See: “Growing a language” (Guy Steele, 1998) • 2nd meaning: “Enabling Growth” – can be used for small as well as large systems – allows for smooth growth from small to large. 6
- 7. A Growable Language • Flexible Syntax • Flexible Types • User-definable operators • Higher-order functions • Implicits ... • Make it relatively easy to build new DSLs on top of Scala • And where this fails, we can always use the macro system (even though so far it’s labeled experimental) 7
- 9. Growable = Good? In fact, it’s a double edged sword. • DSLs can fraction the user community (“The Lisp curse”) • Besides, no language is liked by everyone, no matter whether its a DSL or general purpose. • Host languages get the blame for the DSLs they embed. Growable is great for experimentation. But it demands conformity and discipline for large scale production use. 9
- 10. A Language For Growth • Can start with a one-liner. • Can experiment quickly. • Can grow without fearing to fall off the cliff. • Scala deployments now go into the millions of lines of code. – Language works for very large programs – Tools are challenged (build times!) but are catching up. “A large system is one where you do not know that some of its components even exist” 10
- 11. What Enables Growth? • Unique combination of Object/Oriented and Functional • Large systems rely on both. Object-Oriented Functional • Unfortunately, there’s no established term for this object/functional? 11
- 12. 12 Would prefer it like this OOPFP
- 13. But unfortunately it’s often more like this OOP FP And that’s where we are How many OOP people see FP How many FP people see OOP
- 14. 1-14 A Modular Language! Large Systems Object-Oriented Functional Small Scripts = modular
- 15. Modular Programming • Systems should be composed from modules. • Modules should be simple parts that can be combined in many ways to give interesting results. (Simple: encapsulates one functionality) But that’s old hat! – Should we go back to Modula-2? – Modula-2 was limited by the Von-Neumann Bottleneck (see John Backus’ Turing award lecture). – Today’s systems need richer types. 15
- 16. FP is Essential for Modular Programming Read: “Why Functional Programming Matters” (John Hughes, 1985). Paraphrasing: “Functional Programming is good because it leads to modules that can be combined freely.” 16
- 17. Functions and Modules • Functional does not always imply Modular. • Non-modular concepts in functional languages: – Haskell’s type classes – OCaml’s records and variants – Dynamic typing 17
- 18. Objects and Modules • Object-oriented languages are in a sense the successors of classical modular languages. • But Object-Oriented does not always imply Modular either. • Non-modular concepts in OOP languages: – Smalltalk’s virtual image. – Monkey-patching – Mutable state makes transformations hard. – Weak composition facilities require external DI frameworks. – Weak decomposition facilities encourage mixing applications with domain logic. 18
- 19. Scala – The Simple Parts Before discussing library modules, let’s start with the simple parts in the language itself. Here are seven simple building blocks that can be combined in many ways. Together, they cover much of Scala’s programming in the small. Warning: This is pretty boring. 19
- 20. #1 Expressions Everything is an expression if (age >= 18) "grownup" else "minor" val result = try { val people = file.iterator people.map(_.age).max } catch { case ex: IOException => -1 } 20
- 21. #2: Scopes • Everything can be nested. • Static scoping discipline. • Two name spaces: Terms and Types. • Same rules for each. 21
- 22. #3: Patterns and Case Classes trait Expr case class Number(n: Int) extends Expr case class Plus(l: Expr, r: Expr) extends Expr def eval(e: Expr): Int = e match { case Number(n) => n case Plus(l, r) => eval(l) + eval(r) } Simple & flexible, even if a bit verbose. 22
- 23. #4: Recursion • Recursion is almost always better than a loop. • Simple fallback: Tail-recursive functions • Guaranteed to be efficient @tailrec def loop(xs: List[T], ys: List[U]): Boolean = if (xs.isEmpty) ys.isEmpty else ys.nonEmpty && loop(xs.tail, ys.tail) 23
- 24. #5: Function Values • Functions are values • Can be named or anonymous • Scope rules are correct. def isMinor(p: Person) = p.age < 18 val (minors, adults) = people.partition(isMinor) val infants = minors.filter(_.age <= 3) • (this one is pretty standard by now) 24
- 25. #6 Collections • Extensible set of collection types • Uniform operations • Transforms instead of CRUD • Very simple to use 25
- 26. Collection Objection “The type of map is ugly / a lie” 26
- 27. Collection Objection “The type of map is ugly / a lie” 27
- 28. Why CanBuildFrom? • Why not define it like this? class Functor[F[_], T] { def map[U](f: T => U): F[U] } • Does not work for arrays, since we need a class-tag to build a new array. • More generally, does not work in any case where we need some additional information to build a new collection. • This is precisely what’s achieved by CanBuildFrom. 28
- 29. Collection Objection “Set is not a Functor” WAT? Let’s check Wikipedia: (in fact, Set is in some sense the canonical functor!) 29
- 30. Who’s right, Mathematics or Haskell? Crucial difference: – In mathematics, a type comes with an equality – In programming, a single conceptual value might have more than one representation, so equality has to be given explicitly. – If we construct a set, we need an equality operation to avoid duplicates. – In Haskell this is given by a typeclass Eq. – But the naive functor type def map[U](f: T => U): F[U] does not accommodate this type class. So the “obvious” type signature for `map` is inadequate. Again, CanBuildFrom fixes this. 30
- 31. #7 Vars • Are vars not anti-modular? • Indeed, global state often leads to hidden dependencies. • But used-wisely, mutable state can cut down on annoying boilerplate and increase clarity. 31
- 32. Comparing Compilers scalac: Mixed imperative/functional – Started from a Java codebase. – Mutable state is over-used. – Now we know better. dotc: Mostly functional architecture. State is used for – caching lazy vals, memoized functions, interned names, LRU caches. – persisting once a value is stable, store it in an object. – copy on write avoid copying untpd.Tree to tpd.Tree. – fresh values fresh names, unique ids – typer state current constraint & current diagnostics (versioned, explorable). 32
- 33. Why Not Use a Monad? The fundamentalist functional approach would mandate that typer state is represented as a monad. Instead of now: def typed(tree: untpd.Tree, expected: Type): tpd.Tree def isSubType(tp1: Type, tp2: Type): Boolean we’d write: def typed(tree: untpd.Tree, expected: Type): TyperState[tps.Tree] def isSubType(tp1: Type, tp2: Type): TyperState[Boolean] 33
- 34. Why Not Use a Monad? Instead of now: if (isSubType(t1, t2) && isSubType(t2, t3)) result we’d write: for { c1 <- isSubType(t1, t2) c2 <- isSubType(t2, t3) if c1 && c2 } yield result Why would this be better? 34
- 35. Scala’s Modular Roots Modula-2 First language I programmed intensively First language for which I wrote a compiler. Modula-3 Introduced universal subtyping Haskell Type classes Implicits SML modules Object ≅ Structure Class ≅ Functor Trait ≅ Signature Abstract Type ≅ Abstract Type Refinement ≅ Sharing Constraint 35
- 36. Features Supporting Modular Programming 1. Rich types with functional semantics. – gives us the domains of discourse 2. Static typing. – Gives us the means to guarantee encapsulation – Read “On the Criteria for Decomposing Systems into Modules” (David Parnas, 1972) 3. Objects 4. Classes and Traits 36
- 37. #5 Abstract Types trait Food class Grass extends Food trait Animal { type Diet <: Food def eat(food: Diet) } class Cow extends Animal with Food { type Diet = Grass def eat(food: Grass) = ??? } class Lion extends Animal { type Diet = Cow def eat(food: Cow) = ??? } 37
- 38. #6 Parameterized Types class List[+T] class Set[T] class Function1[-T, +R] List[Number] Set[String] Function1[String, Int] Variance expressed by +/- annotations A good way to explain variance is by mapping to abstract types. 38
- 39. Modelling Parameterized Types class Set[T] { ... } class Set { type $T } Set[String] Set { type $T = String } class List[+T] { ... } class List { type $T }List[Number] List { type $T <: Number } Parameters Abstract members Arguments Refinements
- 40. #7 Implicit Parameters Implicit parameters are a simple concept But they are surprisingly versatile. Can represent a typeclass: def min(x: A, b: A)(implicit cmp: Ordering[A]): A 40
- 41. #7 Implicit Parameters Can represent a context def typed(tree: untpd.Tree, expected: Type)(implicit ctx: Context): Type def compile(cmdLine: String) (implicit defaultOptions: List[String]): Unit Can represent a capability: def accessProfile(id: CustomerId) (implicit admin: AdminRights): Info 41
- 42. Simple Parts Summary Language Expressions Scopes and Nesting Case Classes and Patterns Recursion Function Values Collections Vars A fairly modest and boring set of parts that can be combined in many ways. (Boring is good!) 42 Library Static Types Objects Classes Traits Abstract Types Type Parameters Implicit Parameters
- 43. Thank You Follow us on twitter: @typesafe