An Introduction to Functional Programming - DeveloperUG - 20140311

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

An Introduction to Functional Programming

Andreas Pauley – @apauleyLambda Luminaries

DeveloperUG

March 11, 2014

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

@lambdaluminary

We meet once a month, on the second Monday of the month.

http://www.meetup.com/lambda-luminaries/

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Jemstep

Retirement portfolio analysis in Scala.

http://www.jemstep.com/

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

“ No matter what language you work in,programming in a functional style provides benefits.You should do it whenever it is convenient, and youshould think hard about the decision when it isn’tconvenient. ”

— John Carmack, ID Software [2]

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Quake

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

But what exactly is“Functional Programming”?

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Functional Programming, noun:

Functional Programming is alist of things you CAN’T do.

[7]

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You can’t vary your variables

1> X = 42.

42

2> X = X + 1.

** exception error:

no match of right hand

side value 43

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No while/for loops. Sorry :-(

int i;

for (i=1; i<=3; i++) {

System.out.println(i);

}

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You can’t mutate/change your data structures

Python

>>> list1 = [1,2,3]

>>> list2 = list1

>>> print list1.reverse()

None

>>> list1

[3, 2, 1]

>>> list2

[3, 2, 1]

Haskell

> let list1 = [1,2,3]

> let list2 = list1

> reverse(list1)

[3,2,1]

> list1

[1,2,3]

> list2

[1,2,3]

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You can’t have any side effects

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Are you kidding me?

How can anyone program like this???

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

GOTO 10

This sounds like

“You can’t have GOTO statements”

See Hughes and Dijkstra [1, 3]

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It’s not about what we cannotdo.

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

We need a better definition ofFunctional Programming.

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Programming Paradigms(Very Simplified)

Imperative Declarative

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Functional Programming, noun:

“ Functional programming is so called because aprogram consists entirely of functions. ”

— John Hughes, Why Functional Programming Matters [1, p. 1]

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

OK... so what exactly is a function?

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

An example function

f (x) = 2x2 − 2x + 3

−1 1 2 3

4

6

8

10

12

14

x

y

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Variables in functions

f (x) = 2x2 − 2x + 3When we evaluate the function:f(2) = 8− 4 + 3 = 7

• The value of x will not change inside the function body.

• Same input, same output. Every time. (ReferentialTransparency)

• We can call f multiple times without any side effects(Idempotence).

• We don’t have to recalculate f(2), we can replace anyoccurrence of f(2) with 7 (Memoization).

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Variables in functions

f (x) = 2x2 − 2x + 3When we evaluate the function:f(2) = 8− 4 + 3 = 7

• The value of x will not change inside the function body.

• Same input, same output. Every time. (ReferentialTransparency)

• We can call f multiple times without any side effects(Idempotence).

• We don’t have to recalculate f(2), we can replace anyoccurrence of f(2) with 7 (Memoization).

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Variables in functions

f (x) = 2x2 − 2x + 3When we evaluate the function:f(2) = 8− 4 + 3 = 7

• The value of x will not change inside the function body.

• Same input, same output. Every time. (ReferentialTransparency)

• We can call f multiple times without any side effects(Idempotence).

• We don’t have to recalculate f(2), we can replace anyoccurrence of f(2) with 7 (Memoization).

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Variables in functions

f (x) = 2x2 − 2x + 3When we evaluate the function:f(2) = 8− 4 + 3 = 7

• The value of x will not change inside the function body.

• Same input, same output. Every time. (ReferentialTransparency)

• We can call f multiple times without any side effects(Idempotence).

• We don’t have to recalculate f(2), we can replace anyoccurrence of f(2) with 7 (Memoization).

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Varying variables does not make sense

x = x + 1

x− x = 1

0 = 1

∴ x 6= x + 1

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Functions can call other functions

g(x) = f (x) + 1

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Values are functions

Constant values are just functions with no input parameters

x = 42

Python function definition:

def x():

return 42

Haskell function definition:

x = 42

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Functions can be composed

h(x) = (f ◦ g)(x) = f (g(x))

or

h = f ◦ g

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Higher-order Functions

Functions can take functions as input.Functions can return functions as the result.

h(f, g, x) = f (x) + g(2)

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Higher-order FunctionsThe derivative of f(x) returns another function.

f(x) = 2x2 − 2x+ 3ddxf(x) = 4x− 2

−1 1 2 3

−5

5

10

15

x

y

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A functional program consists entirely of functions

def main():

time = datetime.now()

args = sys.argv[1:]

print outputString(time, args)

def outputString(time, args):

return str(time) + " " + joinArgs(args)

def joinArgs(args):

return "-".join(args)

$ ./justfunctions.py Hello from Python

2014-02-15 10:36:42.062697 Hello-from-Python

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A functional program consists entirely of functions

main :: IO()

main = do

time <- getCurrentTime

args <- getArgs

putStrLn (outputString time args)

outputString :: UTCTime -> [String] -> String

outputString time args =

show(time) ++ " " ++ joinArgs(args)

joinArgs :: [String] -> String

joinArgs = intercalate "-"

$ ./justfunctions Hello from Haskell

2014-02-15 08:36:50.822728 UTC Hello-from-Haskell

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Some Haskell Syntax

Python:

def outputString(time, args):

return str(time) + " " + joinArgs(args)

Haskell:

outputString :: UTCTime -> [String] -> String

outputString time args =

show(time) ++ " " ++ joinArgs(args)

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Recursive function: Haskell

doubleAll :: [Int] -> [Int]

doubleAll [] = []

doubleAll (x:xs) = x*2 : doubleAll xs

Example use in the interactive interpreter:

Prelude Main> doubleAll [8,2,3]

[16,4,6]

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Recursive function expanded

doubleAll [] = []

doubleAll (x:xs) = x*2 : doubleAll xs

doubleAll [8,2,3]

16 : (doubleAll [2,3])

16 : 4 : (doubleAll [3])

16 : 4 : 6 : (doubleAll [])

16 : 4 : 6 : []

16 : 4 : [6]

16 : [4,6]

[16,4,6]

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Recursive function: Python

def doubleAll(numbers):

if numbers == []:

return []

else:

first = numbers[0]

rest = numbers[1:]

return [first * 2] + doubleAll(rest)

Example use in the interactive interpreter:

>>> doubleAll([8,2,3])

[16, 4, 6]

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Pattern Matching: Haskell

doubleAll :: [Int] -> [Int]

doubleAll [] = []

doubleAll (x:xs) = x*2 : doubleAll xs

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Higher-order Functions3 Basic List Operations

Map Convert each element of a list into some other value.

Filter Get a subset of a list based on some condition.

Fold Reduce a list of items to a single value.

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Higher-order Functions3 Basic List Operations

Map Convert each element of a list into some other value.

Filter Get a subset of a list based on some condition.

Fold Reduce a list of items to a single value.

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Higher-order Functions3 Basic List Operations

Map Convert each element of a list into some other value.

Filter Get a subset of a list based on some condition.

Fold Reduce a list of items to a single value.

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Map

Apply a function to each element of a list, and you get a new list.

a1 a2 a3 ... an

b1 b2 b3 ... bn

f(a1) f(an)

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Map

The built-in Haskell map function:

map :: (a -> b) -> [a] -> [b]

map _ [] = []

map f (x:xs) = f x : map f xs

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Map

The builtin Haskell map function:

map :: (a -> b) -> [a] -> [b]

map _ [] = []

map f (x:xs) = f x : map f xs

Hmmm, looks very similar to our previous doubleAll function:

doubleAll :: [Int] -> [Int]

doubleAll [] = []

doubleAll (x:xs) = x*2 : doubleAll xs

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Map

So our doubleAll can actually be simplified.

In Haskell:

doubleAll = map (*2)

In Python:

def doubleAll(numbers):

return map(lambda x: x * 2, numbers)

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Map

doubleAll :: Num a => [a] -> [a]

doubleAll = map (*2)

8 2 3

16 4 6

∗2 ∗2 ∗2

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Account Data

data Bank = ABSA | Capitec | FNB | Nedbank | SBSA

data Account = Account {bank :: Bank,

accNum :: String,

owner :: String,

balance :: Amount}

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Account Data

[Account {accNum="4076814233",

owner="J. Doe",

balance=(Amount 123000.23),

bank=ABSA},

Account {accNum="6868773585",

owner="J. Black",

balance=(Amount 5782347.99),

bank=FNB},

Account {accNum="4055892156",

owner="A. Kay",

balance=(Amount 100),

bank=ABSA},

Account {accNum="6584539813",

owner="S. Jones",

balance=(Amount 2937361.45),

bank=FNB}]

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Map

Map on account data:

balances :: [Account] -> [Amount]

balances accounts = map balance accounts

acc1 acc2 acc3 acc4 acc5

bal1 bal2 bal3 bal4 bal5

balance(acc1) balance(acc5)

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Filter

acc1 acc2 acc3 acc4 acc5

acc1 acc3 acc5

in? in? in? in? in?

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Filter

Filter on account data:

topAccounts :: [Account] -> [Account]

topAccounts accounts = filter isRich accounts

isRich :: Account -> Bool

isRich acc = balance acc >= (Amount 1000000)

Output:

*Main> topAccounts accounts

[FNB 6868773585 (J. Black) R5782347.99,

FNB 6584539813 (S. Jones) R2937361.45]

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Fold/Reduce/Inject

foldl (+) 0 [8,2,3]

13

0 8 2 3

8 2 3

10 3

13

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Fold/Reduce/Inject

balanceSum :: [Account] -> Amount

balanceSum accounts = foldl (+) 0 (balances accounts)

0 bal1 bal2 bal3

sum bal2 bal3

sum bal3

sum

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Function Composition

balanceSum :: [Account] -> Amount

balanceSum accounts = foldl (+) 0 (balances accounts)

0 bal1 bal2 bal3

sum bal2 bal3

sum bal3

sum

s2 :: [Account] -> Amount

s2 = balances |> (foldl(+)0)

h(x) = (f ◦ g)(x) = f(g(x))

h = f ◦ g

s3 = f1 ◦ balances

s3 :: [Account] -> Amount

s3 = (foldl(+)0) . balances

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balancesPerBank

foldl insertBalance Map.empty accounts

{} acc1 acc2 acc3

map acc2 acc3

map acc3

map

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Fold

type BankMap = Map Bank Amount

balancesPerBank :: [Account] -> BankMap

balancesPerBank = foldl insertBalance Map.empty

Output:

*Main> balancesPerBank accounts

fromList [(ABSA,R123100.23),(FNB,R8719709.44)]

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Fold

type BankMap = Map Bank Amount

balancesPerBank :: [Account] -> BankMap

balancesPerBank = foldl insertBalance Map.empty

insertBalance :: BankMap -> Account -> BankMap

insertBalance bankmap account =

Map.insert key value bankmap

where key = bank account

value = addBalance bankmap account

addBalance :: BankMap -> Account -> Amount

addBalance bankmap account =

case (Map.lookup (bank account) bankmap) of

Nothing -> balance account

Just bal -> (balance account) + bal

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Programming Paradigms(Very Simplified)

Imperative Declarative

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

In imperative programming:

• Your variables can vary any time!

• You have to use locks to be thread-safe!

• You have to write your own loops for the most basic listoperations!

• Your data structures are mutable!

• You have to defensively make copies of data to prevent bugs!

• You have to defensively check for null values!

• You have to think about implicit state! (this, self)

• Code is generally riddled with side effects!

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

In imperative programming:

• Your variables can vary any time!

• You have to use locks to be thread-safe!

• You have to write your own loops for the most basic listoperations!

• Your data structures are mutable!

• You have to defensively make copies of data to prevent bugs!

• You have to defensively check for null values!

• You have to think about implicit state! (this, self)

• Code is generally riddled with side effects!

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

In imperative programming:

• Your variables can vary any time!

• You have to use locks to be thread-safe!

• You have to write your own loops for the most basic listoperations!

• Your data structures are mutable!

• You have to defensively make copies of data to prevent bugs!

• You have to defensively check for null values!

• You have to think about implicit state! (this, self)

• Code is generally riddled with side effects!

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

In imperative programming:

• Your variables can vary any time!

• You have to use locks to be thread-safe!

• You have to write your own loops for the most basic listoperations!

• Your data structures are mutable!

• You have to defensively make copies of data to prevent bugs!

• You have to defensively check for null values!

• You have to think about implicit state! (this, self)

• Code is generally riddled with side effects!

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

In imperative programming:

• Your variables can vary any time!

• You have to use locks to be thread-safe!

• You have to write your own loops for the most basic listoperations!

• Your data structures are mutable!

• You have to defensively make copies of data to prevent bugs!

• You have to defensively check for null values!

• You have to think about implicit state! (this, self)

• Code is generally riddled with side effects!

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

In imperative programming:

• Your variables can vary any time!

• You have to use locks to be thread-safe!

• You have to write your own loops for the most basic listoperations!

• Your data structures are mutable!

• You have to defensively make copies of data to prevent bugs!

• You have to defensively check for null values!

• You have to think about implicit state! (this, self)

• Code is generally riddled with side effects!

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

In imperative programming:

• Your variables can vary any time!

• You have to use locks to be thread-safe!

• You have to write your own loops for the most basic listoperations!

• Your data structures are mutable!

• You have to defensively make copies of data to prevent bugs!

• You have to defensively check for null values!

• You have to think about implicit state! (this, self)

• Code is generally riddled with side effects!

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

In imperative programming:

• Your variables can vary any time!

• You have to use locks to be thread-safe!

• You have to write your own loops for the most basic listoperations!

• Your data structures are mutable!

• You have to defensively make copies of data to prevent bugs!

• You have to defensively check for null values!

• You have to think about implicit state! (this, self)

• Code is generally riddled with side effects!

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Are you kidding me?

How can anyone program like this???

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Join the anti-for campaign

Less loops, moremap/filter/fold

http://weblogs.asp.net/podwysocki/archive/2009/06/26/

the-anti-for-campaign.aspx

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Treat side effects as a first-class concern

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Learn a functional language

“ A language that doesn’t affect the way you thinkabout programming, is not worth knowing. ”

— Alan Perlis[5]

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Disclaim your inheritance

Write non-trivial code withoutusing objects and inheritance.Get re-usability with higher-order functions.

Try to minimise moving parts instead ofencapsulating moving parts. [4]

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Join our group@lambdaluminary

We meet once a month, on the second Monday of the month.http://www.meetup.com/lambda-luminaries/

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Get out of your comfort zone

Functional Programming is unfamiliar territory formost.

“ If you want everything to be familiar you willnever learn anything new. ”

— Rich Hickey, author of Clojure[6]

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Companies In South Africa

Jemstep, Sandton Using Scala for Fund Analysis

Allan Gray, Cape Town Using Scala for backend logic and systemintegration.

Yuppiechef, Cape Town Using Clojure for their WarehouseManagement System.

Cognician, Cape Town Using Clojure to create coaching/learningmodules.

Eldo Energy, Johannesburg Using Clojure for automated meterreading and intelligent monitoring of consumerenergy.

Rheo Systems, Pretoria Using Clojure for supply chain integration.

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Companies In South Africa

Pattern Matched Technologies, Midrand Using Erlang for allsystems, eg. processing high volumes of financialtransactions.

Effective Control Systems, Kyalami Using Erlang for printermanagement.

Mira Networks, Somerset West Using Erlang for billingadministration and mobile development.

Kotive Using Scala for designing workflow processes.

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Online Courses

Functional Thinking by Neal FordO’ Reillyhttp://shop.oreilly.com/product/0636920030393.do

Functional Programming Principles in ScalaEPFL Universityhttps://www.coursera.org/course/progfun

School of HaskellFP Completehttps://www.fpcomplete.com/school

Programming LanguagesUniversity of Washingtonhttps://www.coursera.org/course/proglang

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

Books

Miran LipovacaLearn You a Haskell for Great Good!http://learnyouahaskell.com/

Fred HebertLearn You Some Erlang for Great Good!http://learnyousomeerlang.com/

Yaron Minski, Anil Madhavapeddy, Jason HickeyReal World OCamlhttps://realworldocaml.org/

Paul Chiusano, Runar BjarnasonFunctional Programming in Scalahttp://www.manning.com/bjarnason/

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

References I

John HughesWhy Functional Programming Mattershttp://www.cs.kent.ac.uk/people/staff/dat/miranda/

whyfp90.pdf

John CarmackFunctional Programming in C++http://www.altdevblogaday.com/2012/04/26/

functional-programming-in-c/

Edsger W. DijkstraGo To Statement Considered Harmfulhttp://www.u.arizona.edu/~rubinson/copyright_

violations/Go_To_Considered_Harmful.html

Introduction Definition Function Recap Common Idioms Imperative Comparison Challenges! Industry Use Now what?

References II

Tweet by Michael Feathershttps://twitter.com/mfeathers/status/29581296216

Alan Jay Perlishttp://www.cs.yale.edu/quotes.html

Rich Hickeyhttp:

//www.infoq.com/presentations/Simple-Made-Easy

Andreas PauleyAn Introduction to Functional Programminghttps://github.com/apauley/fp_presentation