Scala IO 2019 - FP: The Good, The Bad and The Ugly

You are about to fall in love with Functional Programming, if not already. You are going to learn the good parts that are going to make your day-to-day life easier. But since nobody is perfect - not even FP -, you are also going to see its bad and ugly parts, and you'll discover how to deal with them: from learning challenges to performance issues on the JVM.

1. FP The Good, the Bad and the Ugly @DanielaSfregola Scala IO 2019
2. Hellooooo • Italian living in London UK • Former Java Developer • Scala Lover
3. • Rome, Cinecitta' 
 23 December 1966 • Director
 Sergio Leone • Music
 Ennio Morricone
4. Everything in Life • Good Parts
 the bits that we like • Bad Parts
 the traps to avoid • Ugly Parts
 the parts that we need to live with
5. FP: The Good
6. Increased Productivity
7. Immutability private var i: Int = 0 while (i < 10) { // do stuff here i = i + 1 } OOP
8. Immutability private var i: Int = 0 while (i < 10) { // do stuff here i = i + 1 } OOP (0 until 10).foreach { i => // do stuff here } FP
9. Immutability private var i: Int = 0 while (i < 10) { i = i * 2 println(i) i = i + 1 } OOP (0 until 10).foreach { i => println(i * 2) } FP
10. Purity def foo(s: String): Unit = { // implementation here } Impure
11. Purity def foo(s: String): Unit = { // implementation here } Impure def foo(s: String): Int = { // implementation here } Pure
12. Purity Composability
13. Puzzle Coding! from https://scalameta.org/metals/ Metals
14. FP: The Bad
15. Learning Curve
16. Functional Programming
 != 
 Categorical Programming
17. You do not need to know Category Theory to write good 
 Functional code
18. ...but Monads are EASY!
19. A Monad is just a Monoid in the Category of Endofunctors
20. Monads are like
 Burritos from https://twitter.com/monadburritos/status/915624691829587968
21. Is Lasagna a Monad?
22. The importance of talks and tutorials for Beginners
23. The Rock Star Developer Syndrome
24. The Rock Star Developer from https://twitter.com/honeypotio/status/689811823281991681
25. Be a Leader not a "Rock Star"
26. FP: The Ugly
27. Knowing when to break the rules
28. Case Class to Array def toArray(product: Product): Array[Any] = { product // Product .productIterator // Iterator[Any] .toArray // Array[Any] } FP
29. Case Class to Array def toArray(product: Product): Array[Any] = { product // Product .productIterator // Iterator[Any] .toArray // Array[Any] } def toArray(product: Product): Array[Any] = { val size = product.productArity val arr = new Array[Any](size) var i = 0 while (i < size) { arr(i) = product.productElement(i) i = i + 1 } arr } FP OOP
30. Benchmarking* import com.danielasfregola.randomdatagenerator.RandomDataGenerator._ def avgRunTime[A](f: => A, n: Int = 1000): Double = { val start = Instant.now.toEpochMilli (0 until n).foreach(_ => f) val end = Instant.now.toEpochMilli val diff = end - start diff / n.toDouble } case class BigExample(f1: String, f2: Int, f3: Long, f4: Char, f5: String, f6: String, f7: Int, f8: Long, f9: Char, f10: String, f11: String, f12: Int, f13: Long, f14: Char, f15: String, f16: String, f17: Int, f18: Long, f19: Char, f20: String, f21: String, f22: Int, f23: Long, f24: Char, f25: String, f26: String, f27: Int, f28: Long, f29: Char, f30: String) val big = random[BigExample] avgRunTime(toArrayFP(big)) avgRunTime(toArrayOOP(big)) *not a serious benchmarking, executed on my laptop
31. Benchmarking* import com.danielasfregola.randomdatagenerator.RandomDataGenerator._ def avgRunTime[A](f: => A, n: Int = 1000): Double = { val start = Instant.now.toEpochMilli (0 until n).foreach(_ => f) val end = Instant.now.toEpochMilli val diff = end - start diff / n.toDouble } case class BigExample(f1: String, f2: Int, f3: Long, f4: Char, f5: String, f6: String, f7: Int, f8: Long, f9: Char, f10: String, f11: String, f12: Int, f13: Long, f14: Char, f15: String, f16: String, f17: Int, f18: Long, f19: Char, f20: String, f21: String, f22: Int, f23: Long, f24: Char, f25: String, f26: String, f27: Int, f28: Long, f29: Char, f30: String) val big = random[BigExample] avgRunTime(toArrayFP(big)) avgRunTime(toArrayOOP(big)) *not a serious benchmarking, executed on my laptop toArrayOOP avg execution: 0.002 ms toArrayFP avg execution: 0.028 ms
32. JVM and FP do not always go along... OOP version is much faster than the FP one!
33. My ideal implementation of map function for List[A] final override def map[B](f: A => B): List[B] = { @tailrec def loop(l: List[A], res: List[B]): List[B] = { l match { case head :: tail => loop(tail, res :+ f(head)) case Nil => res } } loop(this, res = Nil) }
34. The real implementation of map function for List[A]* final override def map[B](f: A => B): List[B] = { if (this eq Nil) Nil else { val h = new ::[B](f(head), Nil) var t: ::[B] = h var rest = tail while (rest ne Nil) { val nx = new ::(f(rest.head), Nil) t.next = nx t = nx rest = rest.tail } releaseFence() h } } *scala 2.13.0
35. If you have a really good reason, break the rules! ...but keep the ugly bits isolated
36. Scala FP OOP
37. Scala is Perfect
38. Scala is Perfect not
39. Not even Scala is perfect... • Slow Compiler • pipelining compilation by Jason Zaugg • zinc by Eugene Yokota • Tooling • bloop by Jorge Vicente Cantero • metals by Ólafur Páll Geirsson
40. Thank You! •Twitter: @DanielaSfregola •Blog: danielasfregola.com •Book: Get Programming with Scala
 by Manning