Either, Some or None: An introduction to monadic structures and functional programming

We will discuss and understand the monadic structures, going through the most important monads, it's operations and main advantages of the functional programming using Scala, Kotlin, Swift, a little bit of Clojure and why not JavaScript.

During the talk we should also realize why concepts as laziness and immutable data are the cornerstone of high throughput threaded systems and how its guarantee reliability and performance.

By the end of the day you should know by heart the main monads and how to use them solve different day-by-day problems and deeply in love with functional programming.

Transcript

1. Either, Some or None
   An introduction to monadic structures and
   functional programming
   September 29th, São Paulo, Brazil
   #monads #functional_programming #category_theory_applied
   

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2. Edson Hilios
   MSc. Transp. Engineering & BSc. Engineering
   Software Engineer at _____
   

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3. What I would like to know about
   monadic structures and monads
   

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4. Why talk about monads?
   ● Implemented in major modern languages:
   ● Provides simple ways of express complex computation;
   ● They are hard to read about because of specific vocabulary;
   

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5. Goals
   ● A soft theory of Monad
   ● Monodic structures and operations
   ● Some common monads
   ● Real use cases
   ● Recap
   

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6. WTF is a Monad?
   

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7. If this than that
   Monads express decisions about how and which conditions a software
   should execute.
   It allows you to handle and route different inputs/outputs without
   repeating yourself.
   

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8. It's a design pattern to describe and
   manipulate flows of data
   

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9. Useful monad properties
   ● Composable
   ● Polymorphic
   ● Immutable
   ● Lazily evaluated
   

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10. Interacting with the Monads
    ● Map
    ● Filter
    ● Aggregate
    ● Group
    

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11. The 3 Monadic laws
    trait Monoid[A] {
    // The identity
    def get[A]: A
    // The functor
    def map[B](f: A => B): Monad[B]
    // The bind
    def flatMap[B](f: A => Monad[B]): Monad[B]
    }
    Scala
    

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12. Show me the Monads
    

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13. The optional pattern
    val aString: String = "Hello, world!"
    val len: Int = aString.length
    Scala
    

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14. The optional pattern
    val aString: String = null
    val len: Int = aString.length
    //=> java.lang.NullPointerException
    Scala
    

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15. The optional pattern
    val maybeString: Option[String] = Some("Hello, world!")
    val maybeLen: Int = maybeString.map(_.length).getOrElse(-1)
    Scala
    

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16. The optional pattern
    val maybeString: String? = "Hello, world!"
    val maybeLen = maybeString?.length ?: -1
    Kotlin
    

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17. The optional pattern
    let maybeString: String? = "Hello, world!"
    let maybeLen = maybeString.length ?? -1
    Swift
    

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18. The continuation monad
    (some-> (maybe-a-result from-db)
    (then-do-something)
    (then-another)
    (and-finishes))
    Clojure
    Execute if last result is not nil
    It will return the value or nil
    Pass the result to the next fn
    

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19. Call me Maybe
    s::String -> Maybe String
    s "" = Nothing
    s x = Just x
    Haskell
    

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20. The identity monad
    val query: Option[String] = Option(myNullableOutput)
    query match {
    case Some(q) => myTable.filter(_.name == q)
    case None => myTable.all()
    }
    Scala
    

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21. Exception handling
    val maybeAnOperation: Try[Any] = Try {
    potentiallyHarmfulOperation()
    } match {
    case Success(s: Any) => ???
    case Failure(e: ArithmeticException) => ???
    case Failure(e: Exception) => ???
    }
    Scala
    

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22. The functor
    def firstFunctor(x: Any): Try[String] = ???
    def secondFunctor(y: String): Try[Int] = ???
    def thirdFunctor(z: Int): Try[MyType] = ???
    maybeAnException
    .map(firstFunctor)
    .map(secondFunctor)
    .map(thirdFunctor)
    Try[Any]
    Try[Try[String]]
    Try[Try[Try[Int]]]
    Try[Try[Try[Try[MyType]]]]
    Scala
    

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23. Bind operations
    def firstFunctor(x: Any): Try[String] = ???
    def secondFunctor(y: String): Try[Int] = ???
    def thirdFunctor(z: Int): Try[MyType] = ???
    maybeAnException
    .flatMap(firstFunctor)
    .flatMap(secondFunctor)
    .flatMap(thirdFunctor)
    Try[Any]
    Try[String]
    Try[Int]
    Try[MyType]
    Scala
    

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24. Either this or that
    val json: Either[JsError, JsSuccess] = parse("""{"foo": "bar"}""")
    json match {
    case JsSuccess(o) => println("OK")
    case JsError(err) => println(err.list)
    }
    Scala
    

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25. Applicative functor
    def nonEmpty(s: Option[String]): Validated[Invalid, Valid]
    val isValid = (
    nonEmpty("Some string") |@|
    nonEmpty("") |@|
    nonEmpty("Other str") |@|
    nonEmpty(null)
    ).fold(left => false, rigth => true)
    Cats
    

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26. Valid
    Invalid("Cannot be blank")
    Valid
    Invalid("Cannot be null")
    Holomorphism
    {
    "1": "Some string",
    "2": "",
    "3": "Other str",
    "4": null
    }
    JSON
    

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27. Right or left identity
    def httpRequest(input: Either[Valid, Invalid]): HttpResponse =
    input.fold(
    left => HttpResponse(StatusCodes.BadRequest),
    right => HttpResponse(StatusCodes.OK))
    Scala
    

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28. Future pattern
    (def eventualString
    (future (Thread/sleep 1000)
    "Hello, async!")
    @eventualString
    ;; => Returns Hello, async after 1s
    Clojure
    

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29. Asynchronous operations
    val eventualLength: Task[String] = Task {
    Thread.sleep(1000)
    "Hello, async!"
    } map { result =>
    result.length
    }
    eventualLength.run() // => Eventually returns 13 after 1s
    Scalaz
    

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30. Sequential or concurrent
    firstTask
    .flatMap(secondTask)
    .flatMap(thirdTask)
    .run()
    // => Task[T]
    Task.gatherUnordered(
    firstTask,
    secondTask,
    thirdTask
    ).run()
    // => Task[List(f, s, t)]
    Scalaz
    Scalaz
    

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31. Assigning callbacks
    firstTask.flatMap(secondTask).flatMap(thirdTask)
    .runAsync {
    case Success(s) => // Perform side effects
    case Failure(e) => // Do something on failure
    }
    Scalaz
    

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32. It's all about Promises
    let eventualResponse = new Promise(function() {
    setTimeout(() => resolve("Hello, world!"), 2000)
    })
    eventualResponse
    .then(applyFunctor)
    .then(msg => console.log("Apply side-effects!"))
    ES2016
    

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33. The observable pattern
    val observer = new Observer[Any] {
    def onNext(elem: Any): Future[Ack] =
    Continue
    def onError(ex: Throwable): Unit =
    ex.printStackTrace()
    def onComplete(): Unit =
    println("Completed")
    }
    Monix
    

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34. Observable operations
    ● Group by batches or time intervals;
    ● Fork and merge streams;
    ● Handle errors and apply retry strategies;
    ● Delay execution and back-pressure.
    

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35. Becoming reactive
    Rx.Observable.webSocket('ws://localhost:8000')
    .retryWhen((errors) => {
    return Rx.Observable.timer(5000) // retry in 5 secs
    })
    .groupBy((msg) => msg.category)
    .throttle(1000) // One message per sec and discard the rest
    .subscribe((msgs) => console.log(msgs))
    ES2016
    

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36. Recapping the monads
    ● Maybe is useful to handle failures scenarios;
    ● Either for validations and data deserialization;
    ● Future lets you create asynchronous operations;
    

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37. Monads FTW
    ● SwiftZ and Runes for Swift
    ● Kathegory for Kotlin
    ● Scalaz, Cats and Monix for Scala
    ● ReactiveX (aka Rx): Scala, C#, C++, Kotlin, Swift and many others
    

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38. Homework
    ● Reader and Writer Monads
    ● Monad Transformers
    ● Free Monad and Free Applicative
    ● Lenses, Kleisli, and others;
    

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39. Twitter: @hilios
    Site: http://edson.hilios.com.br
    Talk: https://speakerdeck.com/hilios
    GitHub: https://github.com/hilios
    Thank you!
    

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