Introduction to functional programming

1. Intro to functional
programming

2. whoami

3. Hello!
I am Omar Abdelhafith from Jordan
I live in Dublin
I write blogs on http://nsomar.com
I work with computers @Zendesk
I write mainly Swift, Haskell and Elixir
On twitter @ifnottrue
On github @oarrabi
I organize Swift and Elixir meetups (in Dublin)

4. We are hiring
http://bit.ly/zendesk-ios

5. Intro to functional
programming

6. Intro to functional programming
• What topics to discuss?
• lambda calculus
• practical category theory
• functors, monad, monoids
• promises, futures, next
• frp, reactive
• fp langs

7. Functional
programming
First item: Intro to functional
programming

8. To start: Intro to functional
programming
• History
• Definition
• Some Examples
• PS: This is an interactive session

9. Note: Languages used
• Java
• Swift
• Haskell
• Pseudo language

10. Let's start

11. Functional programming
Functional Programming (FP):
Is a declarative programming paradigm, where programs are
built around mathematical functions and immutable data
transformation.

12. ^ That is my
definition

13. FP is a very wide field
The definition can vary from
lenient to rigorous

14. Lenient Definition
Graham Hutton:
Functional programming is style of programming in which the
basic method of computation is the application of functions to
arguments;
• No mention of immutability

15. In-the-middle Definition
From Haskell wiki:
In functional programming, programs are executed by
evaluating expressions, ... Functional programming typically
avoids using mutable state.
... (it) requires that functions are first-class, ...(to end of quote)
• Mathematical functions is implied

16. Super Rigorus Definition
Well, Not really a definition. But,
Standarized ladder of functional programming (Lambda Conf)

19. Some history

21. Lambda calculus
• Alonzo Church In 1930 invented λ-calculus
• Calculation is done by lambda abstraction (definition)
and binding (application)
• There are no assignments of variables

25. • John McCarthy created LISP in the 1950s
• Inspired from lambda calculus
• LISP is the first FP lang
• Just a bit after Fortran
• Introduced lots of concepts (macros, tree ds)
• Still alive today (Common Lisp, Dialects, etc..)

27. • Created by an International comittee in 1987
• Pure functional
• lazy
• in 90s they implemented ideas from Category Theory
• functors, monads, monoids

28. Today

30. Today
• JVM: Scala, Clojure
• Web: Clojure Script, ELM
• BEAM VM: Erlang, Elixir
• LLVM: Swift
• General purpose: Idris

31. Recap?
Is a declarative programming paradigm, where programs are
built around mathematical functions and immutable data
transformation.

32. Decompose
• Declarative programming paradigm
• Mathematical functions
• Immutable data

33. Decompose
• Declarative programming paradigm
• Mathematical functions
• Immutable data
• Everything is an expression
• Functions are data

34. Programming paradigms
• Imperative (C, C++, Java, Go):
• state and control (if, for, while)
• How to do
• Declarative (Haskell, SQL)
• logic but no control
• What to do

35. Imperative Programming
Paradigm
• Tell the computer how to do things
• Change state
• Based on statment

36. Imperative Programming
Paradigm
• Procedural (Go, C):
• functions and data
• Object Oriented Programming (Java, C++, C#):
• objects with behaviour and state
• Tell dont ask

37. Declarative Programming
Paradigm
• Tell the computer what to do
• High level
• Less control

38. Declarative Programming
Paradigm
• Data base query
• Regex
• DSLs
• Functional programming

39. Examples

40. SQL

41. Regex

43. More complex regex

45. DSL

47. Functional

49. Another FP example

52. Decompose
• Declarative programming paradigm ✅
• Mathematical functions
• Immutable data
• Everything is an expression
• Functions are data

53. Mathematical functions
• They are pure
• They are Total
• They are composable

55. Pure functions
• Given same input, return same output
• They dont cause side effects

56. Pure functions: Another
definition
Functions that you can memoize (memoization)
-- Bartosz Milewski

57. A bit offtopic - Memoization
• Technique to speedup programms by storing results
• Similar to caching but low level
• Tabulate

58. Memoization Example: Fibonacci
Without memoization

59. • fib(n: 10) -> 88 times
• fib(n: 11) -> 143 times

60. Memoization Example: Fibonacci
With memoization

62. • fib(n: 10) -> 9 times
• fib(n: 11) -> 10 times

64. Pure functions:
functions that can be
memoized

65. Pure functions
• sin(x) ❓
• length(s) ❓
• add(n1,n2) ❓
• random() ❓
• printf("Hello World") ❓

66. Pure functions
• sin(x) ✅
• length(s) ❓
• add(n1,n2) ❓
• random() ❓
• printf("Hello World") ❓

67. Pure functions
• sin(x) ✅
• length(s) ✅
• add(n1,n2) ❓
• random() ❓
• printf("Hello World") ❓

68. Pure functions
• sin(x) ✅
• length(s) ✅
• add(n1,n2) ✅
• random() ❓
• printf("Hello World") ❓

69. Pure functions
• sin(x) ✅
• length(s) ✅
• add(n1,n2) ✅
• random() ❌
• printf("Hello World") ❓

70. Pure functions
• sin(x) ✅
• length(s) ✅
• add(n1,n2) ✅
• random() ❌ but random(seed) ✅
• printf("Hello World") ❓

71. Pure functions
• sin(x) ✅
• length(s) ✅
• add(n1,n2) ✅
• random() ❌ but random(seed) ✅
• printf("Hello World") ❌

72. Mathematical functions
• They are pure ✅
• They are Total
• They are composable

73. Total functions
• For all set of inputs return always an output
• add :: (Int, Int) -> Int

74. Examples
• add(int, int), sub(int, int) ❓
• mul(int, int), div(int, int) ❓
• is_even(int) ❓
• int parseInt(String s) throws
NumberFormatException ❓
• FILE *fopen(const char *filename, ...) ❓

75. Examples
• add(int, int), sub(int, int) ✅
• mul(int, int), div(int, int) ❓
• int parseInt(String s) throws
NumberFormatException ❓
• FILE *fopen(const char *filename, ...) ❓

76. Examples
• add(int, int), sub(int, int) ✅
• mul(int, int) ✅, div(int, int) ❌
• int parseInt(String s) throws
NumberFormatException ❓
• FILE *fopen(const char *filename, ...) ❓

77. Examples
• add(int, int), sub(int, int) ✅
• mul(int, int) ✅, div(int, int) ❌
• int parseInt(String s) throws
NumberFormatException ❌
• FILE *fopen(const char *filename, ...) ❓

78. Examples
• add(int, int), sub(int, int) ✅
• mul(int, int) ✅, div(int, int) ❌
• int parseInt(String s) throws
NumberFormatException ❌
• FILE *fopen(const char *filename, ...) ❌

79. Mathematical functions
• They are pure ✅
• They are Total ✅
• They are composable

80. Function composition
• f :: a -> b
• g :: b -> c
• g(f(a))
• f o g :: a -> c

81. Function composition
• multiplyBy2 :: Int -> Int
• convertToString :: Int -> String
• x = multiplyBy2(2)
• convertToString(x)
• convertToString(multiplyBy2(2))
• (convertToString . multiplyBy2)(2)

82. Example in FP language
Let's delay it after high order functions

83. Decompose
• Declarative programming paradigm ✅
• Mathematical functions ✅
• Immutable data
• Everything is an expression
• Functions are data

84. Immutable data
• Data does not change
• it gets copied when passed
• In FP, all data is immutable

87. What about
performance
No problem with "Copy on write"

88. Decompose
• Declarative programming paradigm ✅
• Mathematical functions ✅
• Immutable data ✅
• Everything is an expression
• Functions are data

89. Everything is an expression
• Statments: tell how to do
• Expression: return things

90. Statements

91. Expressions

92. Everything is an expression
• Statments: tell how to do
• Expression: return things
• In FP: There is no void
• In Haskell ---> absurd :: Void -> a

93. Decompose
• Declarative programming paradigm ✅
• Mathematical functions ✅
• Immutable data ✅
• Everything is an expression ✅
• Functions are data

94. Functions are data
• In FP functions are data
• Higher-order function
• They can be passed to functions
• They can be returned from functions

95. They can be passed as
params

99. They can be returned

103. Composed: Passed
and returned

106. Decompose
• Declarative programming paradigm ✅
• Mathematical functions ✅
• Immutable data ✅
• Everything is an expression ✅
• Functions are data ✅

107. Recap
• Declarative: What to do and not how to do it
• Mathematical functions: Pure, Total, Composable
• Immutable data: Dont change the state
• Everything is an expression: and they are composable
• Functions are data: Can be passed and returned from
functions

108. Why are we seeing a rise in FP
• Multi core and paralellism
• Determinism, testability
• They align well with cloud computing and microservices

109. Questions

110. Thanks