Explains why functional programming is back and shows some features from Haskell that are being ported to other languages. Presented at ThoughtWorks Brazil Away Day 2011.

1. Functional
Programming
with Haskell
Adriano Bonat
abonat@thoughtworks.com
@tanob

2. Why FP?
• Source of new ideas
• Expressiveness
• Multi-core CPUs
• Different paradigm
New ideas:
Garbage collection (LISP)
Type inference (simply
typed lambda calculus)
Generics
Type classes
Expressiveness:
DSLs

3. What is it?
• Different programming paradigm
• OO
• Logic
• Procedural
• Functions are the main element in the
language

4. Function applications
“Functional programming is so called because a
program consists entirely of functions. [...]
Typically the main function is defined in terms of other
functions, which in turn are defined in terms of still
more functions, until at the bottom level the functions
are language primitives.”
John Hughes, 1989 -Why functional programming matters

5. Origin
Alonzo Church developed Lambda
Calculus as part of his
investigations on Math foundations
on 1936.

6. Lambda Calculus
• Variables
• Expressions (e1 e2)
• Lambda abstractions (λx. e)

7. Lambda Calculus (I)
• true = λxy. x
• false = λxy. y
• NOT a = (a)(false)(true)
• a AND b = (a)(b)(false)
• a OR b = (a)(true)(b)
• a XOR b = (a)((b)(false)(true))(b)

8. Haskell
• Academic origin
• Named in honor of Haskell Curry
• Defined by a committee
• First version released on 98 (Haskell 98)

9. Features
• Pureness
• Type Inference
• Algebraic datatypes (ADTs)
• Pattern Matching
• Lazyness
• High Order Functions
• Currification (aka Partial Application)
• Type Classes
• Monads

10. Pureness
• No side-effects
• A function call can have no effect other
than to compute its result
• Expressions can be evaluated at any time
• Programs are “referentially transparent”
Good for:
* reasoning
* compiler optimization
* concurrency

11. Type Inference
Let’s see the types for these declarations:
four = 4
add x y = x + y
emphasize x = x ++ “!”

12. Algebraic datatypes
Enumeration:
data Season = Summer | Winter | Autumn | Spring
Product:
data Pair = Pair Int Int
Sum:
data Shape = Circle Float | Rect Float Float
Polymorfic & Recursive:
data Tree a = Leaf a | Node (Tree a) (Tree a)

13. Algebraic datatypes (I)
data Maybe a = Nothing | Just a
data Either a b = Left a | Right b

14. Pattern Matching
Definition:
sum [] = 0
sum (elem:rest) = elem + sum rest
Application:
sum [1,2,3,10]

15. Pattern Matching (I)
area (Circle rad) = pi * rad ^ 2
area (Rect width height) = width * height
first (Pair value _) = value

16. High Order Functions
Functions which at least:
• Receive functions as parameters
• Return functions (aka curried functions)

17. High Order Functions (I)
map :: (a -> b) -> [a] -> [b]
map f [] = []
map f (x:xs) = f x : map f xs

18. Currification
add :: Int -> Int -> Int
add x y = x + y
inc :: Int -> Int
inc = add 1

19. Lazyness
• aka “call by need”
• Expressions can be evaluated when
necessary
• Allows the use of infinite lists
Being pure helps here

20. Lazyness (I)
Definition:
even_numbers :: [Int]
even_numbers = filter even [1..]
Application:
take 5 even_numbers

21. Lazyness (II)
fibs :: [Int]
fibs = 0 : 1 : zipWith (+) fibs (tail fibs)
From: http://en.wikipedia.org/wiki/Lazy_evaluation

22. Type Classes
• Created to solve the problem with numeric
operator overload and equality testing
• Some type classes defined by Haskell 98:
• Eq
• Read/Show

23. Type Classes (I)
class Eq a where
(==), (/=) :: a -> a -> Bool
x == y = not (x /= y)
x /= y = not (x == y) You can define what is
called a “minimal
implementation”.

24. Type Classes (II)
data User = User { name :: String }
instance Eq User where
user1 == user2 = name user1 == name user2
instance Show User where
show user = name user

25. Automatic Derivation
data Season = Summer | Winter | Autumn | Spring
deriving (Show, Eq)
show Summer
> “Summer”
Summer /=Winter
> True

26. Monads
• Adds to the type system a way to describe
actions
• The actions will happen in a certain order

27. Monads
• Common monads:
• IO
• State
• Reader
• Maybe

28. Monads
thing1 >>= x ->
func1 x >>= y ->
thing2 >>= _ ->
func2 y >>= z ->
return z
do
x <- thing1
y <- func1 x
thing2
z <- func2 y
return z
sugar no-sugar

29. Monads
class Monad m where
(>>=) :: m a -> (a -> m b) -> m b
(>>) :: m a -> m b -> m b
return :: a -> m a
“return” is a bad name, it
actually injects a value
into the monadic type.

30. Logger Monad
type Log = [String]
data Logger resultType = Logger (resultType, Log)
deriving Show
record x = Logger ((), [x])
instance Monad Logger where
return value = Logger (value, [])
prevLogger >>= nextAction =
let Logger (prevResult, prevLog) = prevLogger
Logger (newResult, newLog) = nextAction prevResult
in Logger (newResult, prevLog ++ newLog)

31. Testing?
• Go read about QuickCheck!

32. Want to learn more?
Freely available online:
http://book.realworldhaskell.org/

33. Your Knowledge Portfolio
"Learn at least one new language every year.
[...] Different languages solve the same
problems in different ways. By learning several
different approaches, you can help broaden
your thinking and avoid getting stuck in a
rut."
The Pragmatic Programmer

34. Functional
Programming
with Haskell
Adriano Bonat
abonat@thoughtworks.com
@tanob