Advanced functional programing in Swift

Vincent Pradeilles - CocoaHeads Lyon - 21/09/17
Advanced functional
programming
Pragmatic use cases for Monads

Functional Programming

First-class and higher-order functions
let double: (Int) -> Int = { $0 * 2 }
func apply(value: Int, function: (Int) -> Int) -> Int {
return function(value)
}
let result = apply(value: 4, function: double)
// result == 8

Some properties
• A function is said to be pure if it produces no side-effects and its
return value only depends on its arguments
• An expression is said to be referentially transparent if it can be
replaced by its value without altering the program’s behavior

Optionals

Some very familiar code
var data: [String]?
// ...
let result = data?.first?.uppercased()

Some very familiar code
var data: [String]?
// ...
let result = data?.first?.uppercased()
The operator ?. allows us to declare a workﬂow that will be executed
in order, and will prematurely stop if a nil value is encountered

Let’s try to write the code for ?.
extension Optional {
func ?.<U>(lhs: Wrapped?, rhs: (Wrapped) -> U) -> U? {
switch self {
case .some(let value):
return .some(rhs(value))
case .none:
return nil
}
}
}
Disclaimer : this is a simpliﬁed
case for methods with no
arguments

Arrays

Let’s manipulate an Array
let data: [Int] = [0, 1, 2]
let result = data.map { $0 * 2 }.map { $0 * $0 }

Let’s manipulate an Array
let data: [Int] = [0, 1, 2]
let result = data.map { $0 * 2 }.map { $0 * $0 }
Same thing here: the function map allows us to declare a workﬂow

A possible implementation for map
extension Array {
func map<U>(_ transform: (Element) -> U) -> [U] {
var result: [U] = []
for e in self {
result.append(transform(e))
}
return result
}
}

Functions

Let’s compose functions
let double: (Int) -> Int = { x in x * 2 }
let square: (Int) -> Int = { x in x * x }
infix operator • : AdditionPrecedence
func •<T, U, V>(lhs: (U) -> V, rhs: (T) -> U) -> ((T) -> V) {
return { t in lhs(rhs(t)) }
}
let result = (double • square)(4) // result == 32
Disclaimer : @escaping
attributes have been omitted

Let’s compose functions
let double: (Int) -> Int = { x in x * 2 }
let square: (Int) -> Int = { x in x * x }
infix operator • : AdditionPrecedence
func •<T, U, V>(lhs: (U) -> V, rhs: (T) -> U) -> ((T) -> V) {
return { t in lhs(rhs(t)) }
}
let result = (double • square)(4) // result == 32

We have three similar behaviors,
yet backed by very different
implementation

What are the common parts?
• They contain value(s) inside a context
• They add new features to existing types
• They provide an interface to transform/map the inner value

Monad: intuitive deﬁnition
• Wraps a type inside a context
• Provides a mechanism to create a workﬂow of transforms

Minimal Monad
struct Monad<T> {
let value: T
// additional data
static func just(_ value: T) -> Monad<T> {
return self.init(value: value)
}
}
infix operator >>> : AdditionPrecedence
func >>> <U, V>(lhs: Monad<U>, rhs: (U) -> Monad<V>) -> Monad<V> {
// specific combination code
}

Let’s look at an application

Writer Monad
• We have an application that does a lot of numerical calculation
• It’s hard to keep track of how values have been computed
• We would like to have a way to store a value along with a log of all
the transformation it went through

Writer Monad
struct Logged<T> {
let value: T
let logs: [String]
private init(value: T) {
self.value = value
self.logs = ["initialized with value: (self.value)"]
}
static func just(_ value: T) -> Logged<T> {
return Logged(value: value)
}
}
func >>> <U, V>(lhs: Logged<U>, rhs: (U) -> Logged<V>) -> Logged<V> {
let computation = rhs(lhs.value)
return Logged<V>(value: computation.value, logs: lhs.logs + computation.logs)
}

Writer Monad
func square(_ value: Int) -> Logged<Int> {
let result = value * value
return Logged(value: result, log: "(value) was squared, result: (result)")
}
func halve(_ value: Int) -> Logged<Int> {
let result = value / 2
return Logged(value: result, log: "(value) was halved, result: (result)")
}

Writer Monad
let computation = .just(4) >>> square >>> halve
print(computation)
// Logged<Int>(value: 8, logs: ["initialized with value: 4",
"4 was squared, result: 16", "16 was halved, result: 8"])

Now let’s think architecture

We can model the current state of an app with a struct
struct AppState {
let userName: String
let currentSong: URL?
let favoriteSongs: [URL]
}

We can model the action our app emits, in a declarative fashion
protocol Action { }
struct UpdateUserNameAction: Action {
let newUserName: String
}

We can react to those actions in a functional way
infix operator >>> : AdditionPrecedence
func >>>(appstate: AppState?, action: Action) -> AppState {
var appstate = appstate ?? AppState()
switch action {
case let newUserNameAction as UpdateUserNameAction:
appstate.userName = newUserNameAction.newUserName
default:
break
}
return appstate
}

And ﬁnally we put everything together
AppState() >>> UpdateUserNameAction(newUserName: "Vincent")
// AppState(userName: Optional("Vincent"), currentSong: nil,
favoriteSongs: [])

ReSwift

Minimal example
import ReSwift
struct AppState: StateType {
// ... app state properties here ...
}
func appReducer(action: Action, state: AppState?) -> AppState {
// ...
}
let store = Store(
reducer: appReducer,
state: AppState(), // You may also start with `nil`
middleware: []) // Middlewares are optional

A more « real life » example
func appReducer(action: Action, state: AppState?) -> AppState {
return AppState(
navigationState: navigationReducer(state?.navigationState, action: action),
authenticationState: authenticationReducer(state?.authenticationState, action: action),
repositories: repositoriesReducer(state?.repositories, action: action),
bookmarks: bookmarksReducer(state?.bookmarks, action: action)
)
}

func authenticationReducer(state: AuthenticationState?, action: Action) -> AuthenticationState {
var state = state ?? initialAuthenticationState()
switch action {
case _ as ReSwiftInit:
break
case let action as SetOAuthURL:
state.oAuthURL = action.oAuthUrl
case let action as UpdateLoggedInState:
state.loggedInState = action.loggedInState
default:
break
}
return state
}

let store: Store<AppState> = Store(reducer: appReducer, state: nil) // in AppDelegate file
class BookmarksViewController: UIViewController, StoreSubscriber {
override func viewWillAppear(_ animated: Bool) {
super.viewWillAppear(animated)
store.subscribe(self) { subcription in
return subcription.select { state in return state.bookmarks }
}
}
override func viewWillDisappear(_ animated: Bool) {
super.viewWillDisappear(animated)
store.unsubscribe(self)
}
func newState(state: StateType?) {
// update view
}
}

Advantages
• Reducers are pure (stateless) functions, easy to test
• Code review of business logic is easier
• A « demo » mode of the app is easy to implement (UI Testing)
• Deep-linking becomes a trivial use-case

Drawbacks
• Not a silver bullet, a bit overkill for web-service driven apps
• Requires to create a lot of types (but at least not ObjC types)

Questions ?

Bibliography
• https://en.wikipedia.org/wiki/Monad_(functional_programming)
• https://www.youtube.com/watch?v=ZhuHCtR3xq8 (Brian Beckman: Don't fear
the Monad)
• https://academy.realm.io/posts/slug-raheel-ahmad-using-monads-functional-
paradigms-in-practice-functors-patterns-swift/ (Using Monads and Other
Functional Paradigms in Practice)
• https://github.com/orakaro/Swift-monad-Maybe-Reader-and-Try
• https://academy.realm.io/posts/benji-encz-unidirectional-data-ﬂow-swift/
(Unidirectional Data Flow: Shrinking Massive View Controllers)

Advanced functional programing in Swift

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