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Intro to functional programming - Confoo
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- 2. Call me Félix -Software Engineer - Independant - Code Java / Scala (mainly) - Build Distributed Systems - Organizer of Scala-Montreal Meetups
- 3. Agenda - Motivations - Definitionsand Examples
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- 6. “Controlling complexity is theessence of computer programming.” - Brian Kernighan
- 7. Complexity comes from inputand state. Possible Inputs Possible States Possible Outcomes X =
- 8. We should aimat reducing the input and state space. Possible Inputs Possible States Possible Outcomes X =
- 9. In Object-Oriented programming, everything*is an object.
- 10. Objects combine stateand behavior.
- 11. Objects are statemachines. A = x B = y A = x’ B = y’ A = x’ B = y A = x B = y’
- 12. Most objects arebadly designed state machines. A = x B = y A = x’ B = y’ A = x’ B = y A = x B = y’
- 13. Large state spaceare hard to reason about.
- 14. Concurrency adds new visiblestates. A = x B = y A = x’ B = y’ A = x’ B = y A = x B = y’
- 15. How can wewrite correct code if we can’t reason about it?
- 16. Let’s add moreunit tests!
- 17. Let’s add moreunit tests!
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- 19. Functional Programming imposes constraintsthat eliminate states and ease reasoning.
- 20. Functional Programming is aboutvalues, functions and types*.
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- 23. Example - Java finalString title = "functional programming"; final Person bob = new Person("bob", 34); class Person { private final String name; private final int age; private Person(String name, int age) { this.name = name; this.age = age; } ... }
- 24. Example - Scala valtitle = "functional programming" val bob = User("bob", 34) case class User(name: String, age: Int)
- 25. What about datastructures? Can they be values?
- 26. Persistent data structures createa new updated version. They are immutable.
- 27. Example - Java(1) // using functional java import fj.data.List; final List<String> canadianCities = List.list("Montreal", "Ottawa", "Toronto"); final List<String> americanCities = List.list("New York", "San Francisco"); final List<String> northAmericanCities = canadianCities.append(americanCities);
- 28. Example - Java(2) // using guava final ImmutableList<String> canadianCities = ImmutableList.of("Montreal", "Ottawa", "Toronto"); final ImmutableList<String> americanCities = ImmutableList.of("New York", "San Francisco"); final ImmutableList<String> northAmericanCities = ImmutableList.<String>builder().addAll(canadianCities) .addAll(americanCities).build(); // or final ImmutableList<String> northAmericanCities = Stream.concat(canadianCities.stream(), americanCities.stream()) .collect(GuavaCollectors.immutableList());
- 29. Example - Scala valcanadianCities = List("Montreal", "Ottawa", "Toronto") val americanCities = List("New York", "San Francisco") val northAmericanCities = canadianCities ++ americanCities
- 30. Immutability eliminates state andmeans you can safely share the reference.
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- 32. A function takesarguments and produces a result. (Pure) Functions have no side-effects.
- 33. Side-effects examples: - writeto disk - read from stdin - throw an exception - change a state
- 34. Example - Java //pure function public static String convert(String input) { return new StringBuilder(input).reverse() .toString().toUpperCase(); } // unpure function public static void printMessage(String message) { if(message == null) { throw new IllegalStateException("Should not be null"); } System.out.println(message); }
- 35. Example - Scala //pure function def convert(a: String): String = { a.reverse.toUpperCase } // unpure function def print(message: String): Unit = { if (message == null) { throw new IllegalStateException("Should not be null!") } println(message) }
- 36. Having no side-effect helpsreasoning about code.
- 37. Having no side-effect simplifiestesting.
- 38. Errors are handledby returning them as results.
- 39. Example - Java publicstatic Optional<Integer> parseInt(String s) {...} // using functional java public static Either<Exception, Integer> parse(String s) {...} // Use combinators to avoid branching final double discount = parseInt(ageAsString) .map(a -> a * 0.01).orElse(0.10); // Assume Optional<Integer> age1, age2; final Optional<Integer> sum = age1.flatMap(a1 -> age2.map(a2 -> a1 + a2));
- 40. Example - Scala defparseInt(s: String): Option[Int] = {...} val sum: Option[Int] = for { a <- parseInt("2") b <- parseInt("3") } yield a + b
- 41. Updating a stateis done by creating a new instance with the updated state.
- 42. For other necessaryside- effects, push them on the boundaries.
- 43. Looping can beimplemented with recursion*.
- 44. Example - Scala defsumAllFrom(value: Int): Int = { // always use the @tailrec annotation // tail recursive functions can be optimized to a loop @tailrec def internalSum(value: Int, currentSum: Int): Int = { if (value == 0) { currentSum } else { internalSum(value - 1, currentSum + value) } } internalSum(value, 0) }
- 45. A high-order functionstakes functions as parameters and/or produces a function.
- 46. Example - Java finalImmutableList<Integer> l = ImmutableList.of(1, 2, 3, 4); final ImmutableList<Integer> mapped = l.stream() .filter(e -> (e % 2) == 0) .map(e -> e * 2) .collect(GuavaCollectors.immutableList());
- 47. Example - Scala vall = List(1, 2, 3, 4) val mapped = l.filter(e => e % 2 == 0).map(_ * 2) val mappedStream = l.toStream .filter(e => e % 2 == 0) .map(_ * 2) .toList
- 48. Example - Java finalFunction<Integer, Integer> doubled = e -> e * 2; final Function<Integer, Integer> increment = e -> e + 1; final Function<Integer, Integer> doubleAndIncrement = doubled.andThen(increment);
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- 51. With types, thecompiler can verify some aspects of correctness.
- 52. Example - Scala caseclass User(name: String, city: String, phoneNumber: String) // ?! val user = User("Montreal", "John", "New York") // These types could include validation // so any object successfully created is valid. // Check value classes to avoid garbage trait Name {...} trait City {...} trait PhoneNumber {...} case class User(name: Name, city: City, phoneNumber:PhoneNumber)
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- 54. Object-Oriented is about semantic. FunctionalProgramming is about shape.
- 55. Example - Java interfaceProcess interface ProcessConfig interface ProcessFactory { Process createProcess(ProcessConfig config) } interface Machine interface MachineConfig interface MachineFactory { Machine createMachine(MachineConfig config) }
- 56. Example - Java interfaceProcess interface ProcessConfig interface Machine interface MachineConfig interface Factory<T, C> { T create(C config) } interface ProcessFactory extends Factory<Process, ProcessConfig> interface MachineFactory extends Factory<Machine, MachineConfig>
- 57. Example - Java interfaceProcess interface ProcessConfig interface Machine interface MachineConfig interface Factory<T, C> { T create(C config) } interface ProcessFactory extends Factory<Process, ProcessConfig> interface MachineFactory extends Factory<Machine, MachineConfig>
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- 59. Reuse by shapeis more powerful than reuse by semantic.
- 60. BUT understanding shapeis harder than understanding semantic.
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- 62. Summary - Functional programing:immutable values, pure functions and precise types*. - Benefits are: - Simple code - Concurrency and Parallelism for free - Easy to reason about - Reusable
- 63. Contact Félix-Étienne Trépanier Twitter -@felixtrepanier Github - @coderunner unconfoo session @ 2pm!
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