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# Functional Programming Patterns for the Pragmatic Programmer

In this talk we will see a pragmatic approach to building a purely functional architecture that delivers cohesive functional components.
We will cover functional patterns such as Free Monads, Transformers, Kleisli arrows, dependently typed checked exceptions
and types as well as how they can be glued together to achieve pure functions that are composable, context free, dependently injectable and testable.

Dome project and code with instructions to run it can be found at:

https://github.com/47deg/func-architecture

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### Functional Programming Patterns for the Pragmatic Programmer

1. 1. Functional Programming Patterns (for the pragmatic programmer) ~ @raulraja CTO @47deg
2. 2. Acknowledgment • Scalaz • Rapture : Jon Pretty • Miles Sabin : Shapeless • Rúnar Bjarnason : Compositional Application Architecture With Reasonably Priced Monads • Noel Markham : A purely functional approach to building large applications • Jan Christopher Vogt : Tmaps
3. 3. Functions are ﬁrst class citizens in FP Architecture
4. 4. I want my main app services to strive for • Composability • Dependency Injection • Interpretation • Fault Tolerance
5. 5. Composability Composition gives us the power to easily mix simple functions to achieve more complex workﬂows.
6. 6. Composability We can achieve monadic function composition with Kleisli Arrows A M[B] In other words a function that for a given input it returns a type constructor… List[B], Option[B], Either[B], Task[B], Future[B]…
7. 7. Composability When the type constructor M[_] it's a Monad it can be composed and sequenced in for comprehensions val composed = for { a <- Kleisli((x : String) Option(x.toInt + 1)) b <- Kleisli((x : String) Option(x.toInt * 2)) } yield a + b
8. 8. Composability The deferred injection of the input parameter enables Dependency Injection val composed = for { a <- Kleisli((x : String) Option(x.toInt + 1)) b <- Kleisli((x : String) Option(x.toInt * 2)) } yield a + b composed.run("1")
9. 9. Composability : Kleisli What about when the args are not of the same type? val composed = for { a <- Kleisli((x : String) Option(x.toInt + 1)) b <- Kleisli((x : Int) Option(x * 2)) } yield a + b
10. 10. Composability : Kleisli By using Kleisli we just achieved • Composability • Dependency Injection • Interpretation • Fault Tolerance
11. 11. Interpretation : Free Monads What is a Free Monad? -- A monad on a custom ADT that can be run through an Interpreter
12. 12. Interpretation : Free Monads sealed trait Op[A] case class Ask[A](a: () A) extends Op[A] case class Async[A](a: () A) extends Op[A] case class Tell(a: () Unit) extends Op[Unit]
15. 15. Interpretation : Free Monads At this point a program like this is nothing but Data describing the sequence of execution but FREE of it's runtime interpretation. val program = for { a <- ask(1) b <- async(2) _ <- tell(println("log something")) } yield a + b
16. 16. Interpretation : Free Monads We isolate interpretations via Natural transformations AKA Interpreters. In other words with map over the outer type constructor Op object ProdInterpreter extends (Op ~> Task) { def apply[A](op: Op[A]) = op match { case Ask(a) Task(a()) case Async(a) Task.fork(Task.delay(a())) case Tell(a) Task.delay(a()) } }
17. 17. Interpretation : Free Monads We can have different interpreters for our production / test / experimental code. object TestInterpreter extends (Op ~> Id.Id) { def apply[A](op: Op[A]) = op match { case Ask(a) a() case Async(a) a() case Tell(a) a() } }
18. 18. Requirements • Composability • Dependency Injection • Interpretation • Fault Tolerance
19. 19. Fault Tolerance Most containers and patterns generalize to the most common super-type or simply Throwable loosing type information. val f = scala.concurrent.Future.failed(new NumberFormatException) val t = scala.util.Try(throw new NumberFormatException) val d = for { a <- 1.right[NumberFormatException] b <- (new RuntimeException).left[Int] } yield a + b
20. 20. Fault Tolerance We don't have to settle for Throwable!!! We could use instead… • Nested disjunctions • Coproducts • Delimited, Monadic, Dependently-typed, Accumulating Checked Exceptions
21. 21. Fault Tolerance : Dependently- typed Acc Exceptions Introducing rapture.core.Result
22. 22. Fault Tolerance : Dependently- typed Acc Exceptions Result is similar to / but has 3 possible outcomes (Answer, Errata, Unforeseen) val op = for { a <- Result.catching[NumberFormatException]("1".toInt) b <- Result.errata[Int, IllegalArgumentException]( new IllegalArgumentException("expected")) } yield a + b
23. 23. Fault Tolerance : Dependently- typed Acc Exceptions Result uses dependently typed monadic exception accumulation val op = for { a <- Result.catching[NumberFormatException]("1".toInt) b <- Result.errata[Int, IllegalArgumentException]( new IllegalArgumentException("expected")) } yield a + b
24. 24. Fault Tolerance : Dependently- typed Acc Exceptions You may recover by resolving errors to an Answer. op resolve ( each[IllegalArgumentException](_ 0), each[NumberFormatException](_ 0), each[IndexOutOfBoundsException](_ 0))
25. 25. Fault Tolerance : Dependently- typed Acc Exceptions Or reconcile exceptions into a new custom one. case class MyCustomException(e : Exception) extends Exception(e.getMessage) op reconcile ( each[IllegalArgumentException](MyCustomException(_)), each[NumberFormatException](MyCustomException(_)), each[IndexOutOfBoundsException](MyCustomException(_)))
26. 26. Requirements We have all the pieces we need Let's put them together! • Composability • Dependency Injection • Interpretation • Fault Tolerance
27. 27. Solving the Puzzle How do we assemble a type that is: Kleisli + Custom ADT + Result for { a <- Kleisli((x : String) ask(Result.catching[NumberFormatException](x.toInt))) b <- Kleisli((x : String) ask(Result.catching[IllegalArgumentException](x.toInt))) } yield a + b We want a and b to be seen as Int but this won't compile because there are 3 nested monads
28. 28. Solving the Puzzle : Monad Transformers Monad Transformers to the rescue! type ServiceDef[D, A, B <: Exception] = ResultT[({type λ[α] = ReaderT[OpMonad, D, α]})#λ, A, B]
29. 29. Solving the Puzzle : Services Two services with different dependencies case class Converter() { def convert(x: String): Int = x.toInt } case class Adder() { def add(x: Int): Int = x + 1 } case class Config(converter: Converter, adder: Adder) val system = Config(Converter(), Adder())
31. 31. Solving the Puzzle : Services Two services with different dependencies val composed = for { a <- service1("1").liftD[Config] b <- service2.liftD[Config] } yield a + b composed.exec(system)(TestInterpreter) composed.exec(system)(ProdInterpreter)
32. 32. Conclusion • Composability : Kleisli • Dependency Injection : Kleisli • Interpretation : Free monads • Fault Tolerance : Dependently typed checked exceptions
33. 33. Thanks! @raulraja @47deg http://github.com/47deg/func-architecture

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In this talk we will see a pragmatic approach to building a purely functional architecture that delivers cohesive functional components. We will cover functional patterns such as Free Monads, Transformers, Kleisli arrows, dependently typed checked exceptions and types as well as how they can be glued together to achieve pure functions that are composable, context free, dependently injectable and testable. Dome project and code with instructions to run it can be found at: https://github.com/47deg/func-architecture

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