Functional Programming Patterns (BuildStuff '14)

1. Functional Design Patterns @ScottWlaschin fsharpforfunandprofit.com

2. Functional Design Patterns @ScottWlaschin fsharpforfunandprofit.com X

3. Functional Design Patterns @ScottWlaschin fsharpforfunandprofit.com

4. Functional patterns •Apomorphisms •Dynamorphisms •Chronomorphisms •Zygohistomorphic prepromorphisms

5. Functional patterns •Core Principles of FP design –Functions, types, composition •Functions as parameters –Abstraction, Dependency injection –Partial application, Continuations, Folds, •Chaining functions –Error handling, Async –Monads •Dealing with wrapped data –Lifting, Functors –Validation with Applicatives •Aggregating data and operations –Monoids

6. This talk

7. Functional programming is scary

8. Functional programming is scary

9. Object oriented programming is scary

10. •Single Responsibility Principle •Open/Closed principle •Dependency Inversion Principle •Interface Segregation Principle •Factory pattern •Strategy pattern •Decorator pattern •Visitor pattern •Functions •Functions •Functions, also •Functions •Yes, functions •Oh my, functions again! •Functions •Functions  OO pattern/principle FP pattern/principle Seriously, FP patterns are different

11. A short and mostly wrong history of programming paradigms

12. •What we envisioned: –Smalltalk •What we got: –C++ –Object oriented Cobol –PHP –Java “Worse is better” Object-Oriented programming

13. •What we envisioned: –Haskell, OCaml, F#, etc •What we got: –?? Functional programming Please don’t let this happen to FP!

14. CORE PRINCIPLES OF FP DESIGN Important to understand!

15. Core principles of FP design Steal from mathematics Types are not classes Functions are things Composition everywhere Function

16. Core principle: Steal from mathematics “Programming is one of the most difficult branches of applied mathematics” - E. W. Dijkstra

17. Why mathematics Dijkstra said: •Mathematical assertions tend to be unusually precise. •Mathematical assertions tend to be general. They are applicable to a large (often infinite) class of instances. •Mathematics embodies a discipline of reasoning allowing assertions to be made with an unusually high confidence level.

18. “Object-oriented programming is an exceptionally bad idea which could only have originated in California.” E. W. Dijkstra

19. Mathematical functions Function add1(x) input x maps to x+1 … -1 0 1 2 3 … Domain (int) Codomain (int) … 0 1 2 3 4 … let add1 x = x + 1 val add1 : int -> int

20. Function add1(x) input x maps to x+1 … -1 0 1 2 3 … Domain (int) Codomain (int) … 0 1 2 3 4 … Mathematical functions int add1(int input) { switch (input) { case 0: return 1; case 1: return 2; case 2: return 3; case 3: return 4; etc ad infinitum } } •Input and output values already exist •A function is not a calculation, just a mapping •Input and output values are unchanged (immutable)

21. Mathematical functions •A (mathematical) function always gives the same output value for a given input value •A (mathematical) function has no side effects Function add1(x) input x maps to x+1 … -1 0 1 2 3 … Domain (int) Codomain (int) … 0 1 2 3 4 …

22. Functions don't have to be about arithmetic Function CustomerName(x) input Customer maps to PersonalName … Cust1 Cust2 Cust3 Cust3 … Customer (domain) Name (codomain) … Alice Bob Sue John Pam… Name CustomerName(Customer input) { switch (input) { case Cust1: return “Alice”; case Cust2: return “Bob”; case Cust3: return “Sue”; etc } }

23. Functions can work on functions Function List.map int->int maps to int list->int list … add1 times2 subtract3 add42 … int->int int list -> int list … eachAdd1 eachTimes2 eachSubtract3 eachAdd42 …

24. Guideline: Strive for purity

25. The practical benefits of pure functions customer.SetName(newName); let newCustomer = setCustomerName(aCustomer, newName) Pure functions are easy to reason about var name = customer.GetName(); let name,newCustomer = getCustomerName(aCustomer) Reasoning about code that might not be pure: Reasoning about code that is pure: The customer is being changed.

26. The practical benefits of pure functions let x = doSomething() let y = doSomethingElse(x) return y + 1 Pure functions are easy to refactor

27. The practical benefits of pure functions Pure functions are easy to refactor let x = doSomething() let y = doSomethingElse(x) return y + 1

28. The practical benefits of pure functions Pure functions are easy to refactor let helper() = let x = doSomething() let y = doSomethingElse(x) return y return helper() + 1

29. More practical benefits of pure functions •Laziness –only evaluate when I need the output •Cacheable results –same answer every time –"memoization" •No order dependencies –I can evaluate them in any order I like •Parallelizable –"embarrassingly parallel"

30. How to design a pure function Pure Awesomeness!

31. How to design a pure function •Haskell –very pure •F#, OCaml, Clojure, Scala –easy to be pure •C#, Java, JavaScript –have to make an effort

32. Core principle: Types are not classes

34. Types are not classes Set of valid inputs Set of valid outputs So what is a type?

35. Types separate data from behavior Lists Lists List.map List.collect List.filter List.etc

36. Core principle: Functions are things Function

37. Functions as things The Tunnel of Transformation Function apple -> banana A function is a standalone thing, not attached to a class

38. Functions as things let z = 1 int-> int->int 1 let add x y = x + y

39. Functions as inputs and outputs let useFn f = f() + 1 let add x = (fun y -> x + y) int->(int->int) int->int int int int->int let transformInt f x = (f x) + 1 int->int int int Function as output Function as input Function as parameter (int->int)->int int->int

40. Core principle: Composition everywhere

41. Function composition Function 1 apple -> banana Function 2 banana -> cherry

42. Function composition >> Function 1 apple -> banana Function 2 banana -> cherry

43. Function composition New Function apple -> cherry Can't tell it was built from smaller functions!

44. Types can be composed too “algebraic types"

45. Product types × = Alice, Jan 12th Bob, Feb 2nd Carol, Mar 3rd Set of people Set of dates type Birthday = Person * Date

46. Sum types Set of Cash values Set of Cheque values Set of CreditCard values + + type PaymentMethod = | Cash | Cheque of ChequeNumber | Card of CardType * CardNumber

47. DDD & DOMAIN MODELLING

48. Domain modelling pattern: Use types to represent constraints

49. Types represent constraints on input and output type Suit = Club | Diamond | Spade | Heart type String50 = // non-null, not more than 50 chars type EmailAddress = // non-null, must contain ‘@’ type StringTransformer = string -> string type GetCustomer = CustomerId -> Customer option Instant mockability

50. Domain modelling pattern: Types are cheap

52. Design principle: Strive for totality

53. twelveDividedBy(x) input x maps to 12/x … 3 2 1 0 … Domain (int) Codomain (int) … 4 6 12 … Totality int TwelveDividedBy(int input) { switch (input) { case 3: return 4; case 2: return 6; case 1: return 12; case 0: return ??; } } What happens here?

54. twelveDividedBy(x) input x maps to 12/x … 3 2 1 -1 … NonZeroInteger int … 4 6 12 … Totality int TwelveDividedBy(int input) { switch (input) { case 3: return 4; case 2: return 6; case 1: return 12; case -1: return -12; } } Constrain the input 0 is doesn’t have to be handled NonZeroInteger -> int 0 is missing Types are documentation

55. twelveDividedBy(x) input x maps to 12/x … 3 2 1 0 -1 … int Option<Int> … Some 4 Some 6 Some 12 None … Totality int TwelveDividedBy(int input) { switch (input) { case 3: return Some 4; case 2: return Some 6; case 1: return Some 12; case 0: return None; } } Extend the output 0 is valid input int -> int option Types are documentation

56. Design principle: Use types to indicate errors

57. Output types as error codes LoadCustomer: CustomerId -> Customer LoadCustomer: CustomerId -> SuccessOrFailure<Customer> ParseInt: string -> int ParseInt: string -> int option FetchPage: Uri -> String FetchPage: Uri -> SuccessOrFailure<String> No nulls No exceptions Use the signature, Luke!

58. Domain modelling principle: “Make illegal states unrepresentable”

59. Types can represent business rules type EmailContactInfo = | Unverified of EmailAddress | Verified of VerifiedEmailAddress type ContactInfo = | EmailOnly of EmailContactInfo | AddrOnly of PostalContactInfo | EmailAndAddr of EmailContactInfo * PostalContactInfo

60. Domain modelling principle: Use sum-types instead of inheritance

61. Using sum vs. inheritance interface IPaymentMethod {..} class Cash : IPaymentMethod {..} class Cheque : IPaymentMethod {..} class Card : IPaymentMethod {..} type PaymentMethod = | Cash | Cheque of ChequeNumber | Card of CardType * CardNumber class Evil : IPaymentMethod {..} Definition is scattered around many locations What goes in here? What is the common behaviour? OO version:

62. Domain modelling principle: Use sum-types for state machines

63. type ShoppingCart = | EmptyCartState | ActiveCartState of ActiveCartData | PaidCartState of PaidCartData Empty Cart Active Cart Paid Cart Add Item Remove Item Pay Add Item Remove Item

64. Domain modelling principle: It’s ok to expose public data

65. It’s ok to expose public data type PersonalName = { FirstName: String50 MiddleInitial: String1 option LastName: String50 } Immutable Can’t create invalid values

66. Domain modelling principle: Types are executable documentation

67. Types are executable documentation type Suit = Club | Diamond | Spade | Heart type Rank = Two | Three | Four | Five | Six | Seven | Eight | Nine | Ten | Jack | Queen | King | Ace type Card = Suit * Rank type Hand = Card list type Deck = Card list type Player = {Name:string; Hand:Hand} type Game = {Deck:Deck; Players: Player list} type Deal = Deck –› (Deck * Card) type PickupCard = (Hand * Card) –› Hand

68. Types are executable documentation type CardType = Visa | Mastercard type CardNumber = CardNumber of string type ChequeNumber = ChequeNumber of int type PaymentMethod = | Cash | Cheque of ChequeNumber | Card of CardType * CardNumber

69. More on DDD and designing with types at fsharpforfunandprofit.com/ddd Static types only! Sorry Clojure and JS developers 

70. HIGH-LEVEL DESIGN

71. Design paradigm: Functions all the way down

72. “Functions in the small, objects in the large”

73. Low-level operation ToUpper Service Domain logic High-level use-case AddressValidator VerifyEmailAddress UpdateProfileData “Service” is the new “microservice” string string AddressResult EmailVerification Saga Http Response Address Email Http Request

74. Design paradigm: Transformation-oriented programming

75. Interacting with the outside world type ContactDTO = { FirstName: string MiddleInitial: string LastName: string EmailAddress: string IsEmailVerified: bool } type EmailAddress = ... type VerifiedEmail = VerifiedEmail of EmailAddress type EmailContactInfo = | Unverified of EmailAddress | Verified of VerifiedEmail type PersonalName = { FirstName: String50 MiddleInitial: String1 option LastName: String50 } type Contact = { Name: PersonalName Email: EmailContactInfo }

76. Transformation oriented programming Input Function Output

77. Transformation oriented programming Input Transformation to internal model Internal Model Output Transformation from internal model validation and wrapping happens here unwrapping happens here

78. Outbound tranformation Flow of control in a FP use case Inbound tranformation Customer Domain Validator Update Request DTO Domain Type Send ToDTO Response DTO Works well with domain events, FRP, etc

79. Flow of control in a OO use case Application Services Customer Domain App Service App Service Validation Value Entity Infrastructure Entity Customer Repo. Value Email Message Value Database Service SMTP Service

80. Interacting with the outside world Nasty, unclean outside world Nasty, unclean outside world Nasty, unclean outside world Beautiful, clean, internal model Gate with filters Gate with filters Gate with filters

81. Interacting with the other domains Subdomain/ bounded context Gate with filters Subdomain/ bounded context Gate with filters

82. Interacting with the other domains Bounded context Bounded context Bounded context

83. FUNCTIONS AS PARAMETERS

84. Guideline: Parameterize all the things

85. Parameterize all the things let printList() = for i in [1..10] do printfn "the number is %i" i

86. Parameterize all the things It's second nature to parameterize the data input: let printList aList = for i in aList do printfn "the number is %i" i

87. Parameterize all the things let printList anAction aList = for i in aList do anAction i FPers would parameterize the action as well: We've decoupled the behavior from the data

88. Parameterize all the things public static int Product(int n) { int product = 1; for (int i = 1; i <= n; i++) { product *= i; } return product; } public static int Sum(int n) { int sum = 0; for (int i = 1; i <= n; i++) { sum += i; } return sum; }

89. public static int Product(int n) { int product = 1; for (int i = 1; i <= n; i++) { product *= i; } return product; } public static int Sum(int n) { int sum = 0; for (int i = 1; i <= n; i++) { sum += i; } return sum; } Parameterize all the things

90. Parameterize all the things let product n = let initialValue = 1 let action productSoFar x = productSoFar * x [1..n] |> List.fold action initialValue let sum n = let initialValue = 0 let action sumSoFar x = sumSoFar+x [1..n] |> List.fold action initialValue Lots of collection functions like this: "fold", "map", "reduce", "collect", etc.

91. Guideline: Be as generic as possible

92. Generic code let printList anAction aList = for i in aList do anAction i // val printList : // ('a -> unit) -> seq<'a> -> unit Any kind of collection, any kind of action! F# and other functional languages make code generic automatically

93. Generic code int -> int How many ways are there to implement this function? 'a -> 'a How many ways are there to this function?

94. Generic code int list -> int list How many ways are there to implement this function? 'a list -> 'a list How many ways are there to this function?

95. Generic code ('a -> 'b) -> 'a list -> 'b list How many ways are there to implement this function?

96. Tip: Function types are "interfaces"

97. Function types are interfaces interface IBunchOfStuff { int DoSomething(int x); string DoSomethingElse(int x); void DoAThirdThing(string x); } Let's take the Single Responsibility Principle and the Interface Segregation Principle to the extreme... Every interface should have only one method!

98. Function types are interfaces interface IBunchOfStuff { int DoSomething(int x); } An interface with one method is a just a function type type IBunchOfStuff: int -> int Any function with that type is compatible with it let add2 x = x + 2 // int -> int let times3 x = x * 2 // int -> int

99. Strategy pattern is trivial in FP class MyClassinterface IBunchOfStuff { public MyClass(IBunchOfStuff strategy) {..} int DoSomethingWithStuff(int x) { return _strategy.DoSomething(x) } } Object-oriented strategy pattern: Functional equivalent: let DoSomethingWithStuff strategy x = strategy x

100. Decorator pattern in FP Functional equivalent of decorator pattern let add1 x = x + 1 // int -> int

101. Decorator pattern in FP Functional equivalent of decorator pattern let add1 x = x + 1 // int -> int let logged f x = printfn "input is %A" x let result = f x printfn "output is %A" result result

102. Decorator pattern in FP Functional equivalent of decorator pattern let add1 x = x + 1 // int -> int let logged f x = printfn "input is %A" x let result = f x printfn "output is %A" result result let add1Decorated = // int -> int logged add1 [1..5] |> List.map add1 [1..5] |> List.map add1Decorated

103. Tip Every function is a one parameter function

104. Writing functions in different ways let add x y = x + y let add = (fun x y -> x + y) let add x = (fun y -> x + y) int-> int->int int-> int->int int-> (int->int)

105. let three = 1 + 2 let add1 = (+) 1 let three = (+) 1 2 let add1ToEach = List.map add1

106. Pattern: Partial application

107. let names = ["Alice"; "Bob"; "Scott"] Names |> List.iter hello let name = "Scott" printfn "Hello, my name is %s" name let name = "Scott" (printfn "Hello, my name is %s") name let name = "Scott" let hello = (printfn "Hello, my name is %s") hello name

108. Pattern: Use partial application to do dependency injection

109. type GetCustomer = CustomerId -> Customer let getCustomerFromDatabase connection (customerId:CustomerId) = // from connection // select customer // where customerId = customerId type of getCustomerFromDatabase = DbConnection -> CustomerId -> Customer let getCustomer1 = getCustomerFromDatabase myConnection // getCustomer1 : CustomerId -> Customer Persistence agnostic

110. type GetCustomer = CustomerId -> Customer let getCustomerFromMemory map (customerId:CustomerId) = map |> Map.find customerId type of getCustomerFromMemory = Map<Id,Customer> -> CustomerId -> Customer let getCustomer2 = getCustomerFromMemory inMemoryMap // getCustomer2 : CustomerId -> Customer

111. Pattern: The Hollywood principle: continuations

112. Continuations int Divide(int top, int bottom) { if (bottom == 0) { throw new InvalidOperationException("div by 0"); } else { return top/bottom; } } Method has decided to throw an exception

113. Continuations void Divide(int top, int bottom, Action ifZero, Action<int> ifSuccess) { if (bottom == 0) { ifZero(); } else { ifSuccess( top/bottom ); } } Let the caller decide what happens what happens next?

114. Continuations let divide ifZero ifSuccess top bottom = if (bottom=0) then ifZero() else ifSuccess (top/bottom) F# version Four parameters is a lot though!

115. Continuations let divide ifZero ifSuccess top bottom = if (bottom=0) then ifZero() else ifSuccess (top/bottom) let ifZero1 () = printfn "bad" let ifSuccess1 x = printfn "good %i" x let divide1 = divide ifZero1 ifSuccess1 //test let good1 = divide1 6 3 let bad1 = divide1 6 0 setup the functions to print a message Partially apply the continuations Use it like a normal function – only two parameters

116. Continuations let divide ifZero ifSuccess top bottom = if (bottom=0) then ifZero() else ifSuccess (top/bottom) let ifZero2() = None let ifSuccess2 x = Some x let divide2 = divide ifZero2 ifSuccess2 //test let good2 = divide2 6 3 let bad2 = divide2 6 0 setup the functions to return an Option Use it like a normal function – only two parameters Partially apply the continuations

117. Continuations let divide ifZero ifSuccess top bottom = if (bottom=0) then ifZero() else ifSuccess (top/bottom) let ifZero3() = failwith "div by 0" let ifSuccess3 x = x let divide3 = divide ifZero3 ifSuccess3 //test let good3 = divide3 6 3 let bad3 = divide3 6 0 setup the functions to throw an exception Use it like a normal function – only two parameters Partially apply the continuations

118. MONADS

119. Pyramid of doom: null testing example let example input = let x = doSomething input if x <> null then let y = doSomethingElse x if y <> null then let z = doAThirdThing y if z <> null then let result = z result else null else null else null I know you could do early returns, but bear with me...

120. Pyramid of doom: async example let taskExample input = let taskX = startTask input taskX.WhenFinished (fun x -> let taskY = startAnotherTask x taskY.WhenFinished (fun y -> let taskZ = startThirdTask y taskZ.WhenFinished (fun z -> z // final result

121. Pyramid of doom: null example let example input = let x = doSomething input if x <> null then let y = doSomethingElse x if y <> null then let z = doAThirdThing y if z <> null then let result = z result else null else null else null Nulls are a code smell: replace with Option!

122. Pyramid of doom: option example let example input = let x = doSomething input if x.IsSome then let y = doSomethingElse (x.Value) if y.IsSome then let z = doAThirdThing (y.Value) if z.IsSome then let result = z.Value Some result else None else None else None Much more elegant, yes? No! This is fugly! Let’s do a cut & paste refactoring

123. Pyramid of doom: option example let example input = let x = doSomething input if x.IsSome then let y = doSomethingElse (x.Value) if y.IsSome then let z = doAThirdThing (y.Value) if z.IsSome then let result = z.Value Some result else None else None else None

124. Pyramid of doom: option example let doWithX x = let y = doSomethingElse x if y.IsSome then let z = doAThirdThing (y.Value) if z.IsSome then let result = z.Value Some result else None else None let example input = let x = doSomething input if x.IsSome then doWithX x else None

125. Pyramid of doom: option example let doWithX x = let y = doSomethingElse x if y.IsSome then let z = doAThirdThing (y.Value) if z.IsSome then let result = z.Value Some result else None else None let example input = let x = doSomething input if x.IsSome then doWithX x else None

126. Pyramid of doom: option example let doWithY y = let z = doAThirdThing y if z.IsSome then let result = z.Value Some result else None let doWithX x = let y = doSomethingElse x if y.IsSome then doWithY y else None let example input = let x = doSomething input if x.IsSome then doWithX x else None

127. Pyramid of doom: option example refactored let doWithZ z = let result = z Some result let doWithY y = let z = doAThirdThing y if z.IsSome then doWithZ z.Value else None let doWithX x = let y = doSomethingElse x if y.IsSome then doWithY y.Value else None let optionExample input = let x = doSomething input if x.IsSome then doWithX x.Value else None Three small pyramids instead of one big one! This is still ugly! But the code has a pattern...

128. Pyramid of doom: option example refactored let doWithZ z = let result = z Some result let doWithY y = let z = doAThirdThing y if z.IsSome then doWithZ z.Value else None let doWithX x = let y = doSomethingElse x if y.IsSome then doWithY y.Value else None let optionExample input = let x = doSomething input if x.IsSome then doWithX x.Value else None But the code has a pattern...

129. let doWithY y = let z = doAThirdThing y if z.IsSome then doWithZ z.Value else None

130. let doWithY y = let z = doAThirdThing y z |> ifSomeDo doWithZ let ifSomeDo f x = if x.IsSome then f x.Value else None

131. let doWithY y = y |> doAThirdThing |> ifSomeDo doWithZ let ifSomeDo f x = if x.IsSome then f x.Value else None

132. let example input = doSomething input |> ifSomeDo doSomethingElse |> ifSomeDo doAThirdThing |> ifSomeDo (fun z -> Some z) let ifSomeDo f x = if x.IsSome then f x.Value else None

133. A switch analogy Some None Input ->

134. Connecting switches on Some Bypass on None

135. Connecting switches

136. Connecting switches

137. Composing switches >> >> Composing one-track functions is fine...

138. Composing switches >> >> ... and composing two-track functions is fine...

139. Composing switches   ... but composing switches is not allowed!

140. Composing switches Two-track input Two-track input One-track input Two-track input  

141. Building an adapter block Two-track input Slot for switch function Two-track output

142. Building an adapter block Two-track input Two-track output

143. let bind nextFunction optionInput = match optionInput with | Some s -> nextFunction s | None -> None Building an adapter block Two-track input Two-track output

147. Pattern: Use bind to chain options

148. Pyramid of doom: using bind let bind f opt = match opt with | Some v -> f v | None -> None let example input = let x = doSomething input if x.IsSome then let y = doSomethingElse (x.Value) if y.IsSome then let z = doAThirdThing (y.Value) if z.IsSome then let result = z.Value Some result else None else None else None

149. let example input = doSomething input |> bind doSomethingElse |> bind doAThirdThing |> bind (fun z -> Some z) Pyramid of doom: using bind let bind f opt = match opt with | Some v -> f v | None -> None No pyramids! Code is linear and clear. This pattern is called “monadic bind”

150. Pattern: Use bind to chain tasks

151. Connecting tasks When task completes Wait Wait

152. Pyramid of doom: using bind for tasks let taskBind f task = task.WhenFinished (fun taskResult -> f taskResult) let taskExample input = startTask input |> taskBind startAnotherTask |> taskBind startThirdTask |> taskBind (fun z -> z) a.k.a “promise” “future” This pattern is also a “monadic bind”

153. Computation expressions let example input = maybe { let! x = doSomething input let! y = doSomethingElse x let! z = doAThirdThing y return z } let taskExample input = task { let! x = startTask input let! y = startAnotherTask x let! z = startThirdTask z return z } Computation expression Computation expression

154. Pattern: Use bind to chain error handlers

155. Example use case Name is blank Email not valid Receive request Validate and canonicalize request Update existing user record Send verification email Return result to user User not found Db error Authorization error Timeout "As a user I want to update my name and email address" type Request = { userId: int; name: string; email: string } - and see sensible error messages when something goes wrong!

156. Use case without error handling string UpdateCustomerWithErrorHandling() { var request = receiveRequest(); validateRequest(request); canonicalizeEmail(request); db.updateDbFromRequest(request); smtpServer.sendEmail(request.Email) return "OK"; }

157. Use case with error handling string UpdateCustomerWithErrorHandling() { var request = receiveRequest(); var isValidated = validateRequest(request); if (!isValidated) { return "Request is not valid" } canonicalizeEmail(request); db.updateDbFromRequest(request); smtpServer.sendEmail(request.Email) return "OK"; }

158. Use case with error handling string UpdateCustomerWithErrorHandling() { var request = receiveRequest(); var isValidated = validateRequest(request); if (!isValidated) { return "Request is not valid" } canonicalizeEmail(request); var result = db.updateDbFromRequest(request); if (!result) { return "Customer record not found" } smtpServer.sendEmail(request.Email) return "OK"; }

159. Use case with error handling string UpdateCustomerWithErrorHandling() { var request = receiveRequest(); var isValidated = validateRequest(request); if (!isValidated) { return "Request is not valid" } canonicalizeEmail(request); try { var result = db.updateDbFromRequest(request); if (!result) { return "Customer record not found" } } catch { return "DB error: Customer record not updated" } smtpServer.sendEmail(request.Email) return "OK"; }

160. Use case with error handling string UpdateCustomerWithErrorHandling() { var request = receiveRequest(); var isValidated = validateRequest(request); if (!isValidated) { return "Request is not valid" } canonicalizeEmail(request); try { var result = db.updateDbFromRequest(request); if (!result) { return "Customer record not found" } } catch { return "DB error: Customer record not updated" } if (!smtpServer.sendEmail(request.Email)) { log.Error "Customer email not sent" } return "OK"; }

161. Use case with error handling string UpdateCustomerWithErrorHandling() { var request = receiveRequest(); var isValidated = validateRequest(request); if (!isValidated) { return "Request is not valid" } canonicalizeEmail(request); try { var result = db.updateDbFromRequest(request); if (!result) { return "Customer record not found" } } catch { return "DB error: Customer record not updated" } if (!smtpServer.sendEmail(request.Email)) { log.Error "Customer email not sent" } return "OK"; }

162. A structure for managing errors Request Success Validate Failure let validateInput input = if input.name = "" then Failure "Name must not be blank" else if input.email = "" then Failure "Email must not be blank" else Success input // happy path type TwoTrack<'TEntity> = | Success of 'TEntity | Failure of string

163. name50 Bind example let nameNotBlank input = if input.name = "" then Failure "Name must not be blank" else Success input let name50 input = if input.name.Length > 50 then Failure "Name must not be longer than 50 chars" else Success input let emailNotBlank input = if input.email = "" then Failure "Email must not be blank" else Success input nameNotBlank emailNotBlank

164. Switches again Success! Failure Input ->

165. Connecting switches Validate UpdateDb on success bypass

166. Connecting switches Validate UpdateDb

167. Connecting switches Validate UpdateDb SendEmail

168. Connecting switches Validate UpdateDb SendEmail

171. MAPS AND APPLICATIVES

172. World of normal values int string bool World of options int option string option bool option

173. World of options World of normal values int string bool int option string option bool option 

174. World of options World of normal values int string bool int option string option bool option 

175. How not to code with options Let’s say you have an int wrapped in an Option, and you want to add 42 to it: let add42 x = x + 42 let add42ToOption opt = if opt.IsSome then let newVal = add42 opt.Value Some newVal else None 

176. World of options World of normal values add42

177. World of options World of normal values add42

178. Lifting World of options World of normal values 'a -> 'b 'a option -> 'b option Option.map

179. The right way to code with options Let’s say you have an int wrapped in an Option, and you want to add 42 to it: let add42 x = x + 42 let add42ToOption = Option.map add42 Some 1 |> add42ToOption Some 1 |> Option.map add42 

180. Pattern: Use “map” to lift functions

181. Lifting to lists World of lists World of normal values 'a -> 'b 'a list-> 'b list List.map

182. Lifting to async World of async World of normal values 'a -> 'b 'a async > 'b async Async.map

183. The right way to code with wrapped types Most wrapped types provide a “map” let add42 x = x + 42 Some 1 |> Option.map add42 [1;2;3] |> List.map add42

184. Guideline: If you create a wrapped generic type, create a “map” for it.

185. Maps type TwoTrack<'TEntity> = | Success of 'TEntity | Failure of string let mapTT f x = match x with | Success entity -> Success (f entity) | Failure s -> Failure s

186. Tip: Use applicatives for validation

187. Series validation name50 emailNotBlank Problem: Validation done in series. So only one error at a time is returned

188. Parallel validation name50 emailNotBlank Split input Combine output Now we do get all errors at once! But how to combine?

189. Creating a valid data structure type Request= { UserId: UserId; Name: String50; Email: EmailAddress} type RequestDTO= { UserId: int; Name: string; Email: string}

190. How not to do validation // do the validation of the DTO let userIdOrError = validateUserId dto.userId let nameOrError = validateName dto.name let emailOrError = validateEmail dto.email if userIdOrError.IsSuccess && nameOrError.IsSuccess && emailOrError.IsSuccess then { userId = userIdOrError.SuccessValue name = nameOrError.SuccessValue email = emailOrError.SuccessValue } else if userIdOrError.IsFailure && nameOrError.IsSuccess && emailOrError.IsSuccess then userIdOrError.Errors else ... 

191. Lifting to TwoTracks World of two-tracks World of normal values createRequest userId name email createRequestTT userIdOrError nameOrError emailOrError lift 3 parameter function

192. The right way let createRequest userId name email = { userId = userIdOrError.SuccessValue name = nameOrError.SuccessValue email = emailOrError.SuccessValue } let createRequestTT = lift3 createRequest

193. The right way let createRequestTT = lift3 createRequest let userIdOrError = validateUserId dto.userId let nameOrError = validateName dto.name let emailOrError = validateEmail dto.email let requestOrError = createRequestTT userIdOrError nameOrError emailOrError 

194. The right way let userIdOrError = validateUserId dto.userId let nameOrError = validateName dto.name let emailOrError = validateEmail dto.email let requestOrError = createRequest <!> userIdOrError <*> nameOrError <*> emailOrError 

195. Guideline: If you use a wrapped generic type, look for a set of “lifts” associated with it

196. Guideline: If you create a wrapped generic type, also create a set of “lifts” for clients to use with it But I’m not going explain how right now!

197. MONOIDS

198. Mathematics Ahead

199. Thinking like a mathematician 1 + 2 = 3 1 + (2 + 3) = (1 + 2) + 3 1 + 0 = 1 0 + 1 = 1

200. 1 + 2 = 3 Some things A way of combining them

201. 1 x 2 = 3 Some things A way of combining them

202. max(1,2) = 2 Some things A way of combining them

203. "a" + "b" = "ab" Some things A way of combining them

204. concat([a],[b]) = [a; b] Some things A way of combining them

205. 1 + 2 1 + 2 + 3 1 + 2 + 3 + 4 Is an integer Is an integer A pairwise operation has become an operation that works on lists!

206. 1 + (2 + 3) = (1 + 2) + 3 Order of combining doesn’t matter 1 + 2 + 3 + 4 (1 + 2) + (3 + 4) ((1 + 2) + 3) + 4 All the same

207. 1 - (2 - 3) = (1 - 2) - 3 Order of combining does matter

208. 1 + 0 = 1 0 + 1 = 1 A special kind of thing that when you combine it with something, just gives you back the original something

209. 42 * 1 = 42 1 * 42 = 42 A special kind of thing that when you combine it with something, just gives you back the original something

210. "" + "hello" = "hello" "hello" + "" = "hello" “Zero” for strings

211. The generalization •You start with a bunch of things, and some way of combining them two at a time. •Rule 1 (Closure): The result of combining two things is always another one of the things. •Rule 2 (Associativity): When combining more than two things, which pairwise combination you do first doesn't matter. •Rule 3 (Identity element): There is a special thing called "zero" such that when you combine any thing with "zero" you get the original thing back. A monoid!

212. •Rule 1 (Closure): The result of combining two things is always another one of the things. •Benefit: converts pairwise operations into operations that work on lists. 1 + 2 + 3 + 4 [ 1; 2; 3; 4 ] |> List.reduce (+)

213. 1 * 2 * 3 * 4 [ 1; 2; 3; 4 ] |> List.reduce (*) •Rule 1 (Closure): The result of combining two things is always another one of the things. •Benefit: converts pairwise operations into operations that work on lists.

214. "a" + "b" + "c" + "d" [ "a"; "b"; "c"; "d" ] |> List.reduce (+) •Rule 1 (Closure): The result of combining two things is always another one of the things. •Benefit: converts pairwise operations into operations that work on lists.

215. •Rule 2 (Associativity): When combining more than two things, which pairwise combination you do first doesn't matter. •Benefit: Divide and conquer, parallelization, and incremental accumulation. 1 + 2 + 3 + 4

216. •Rule 2 (Associativity): When combining more than two things, which pairwise combination you do first doesn't matter. •Benefit: Divide and conquer, parallelization, and incremental accumulation. (1 + 2) (3 + 4) 3 + 7

217. •Rule 2 (Associativity): When combining more than two things, which pairwise combination you do first doesn't matter. •Benefit: Divide and conquer, parallelization, and incremental accumulation. (1 + 2 + 3)

218. •Rule 2 (Associativity): When combining more than two things, which pairwise combination you do first doesn't matter. •Benefit: Divide and conquer, parallelization, and incremental accumulation. (1 + 2 + 3) + 4

219. •Rule 2 (Associativity): When combining more than two things, which pairwise combination you do first doesn't matter. •Benefit: Divide and conquer, parallelization, and incremental accumulation. (6) + 4

220. Issues with reduce •How can I use reduce on an empty list? •In a divide and conquer algorithm, what should I do if one of the "divide" steps has nothing in it? •When using an incremental algorithm, what value should I start with when I have no data?

221. •Rule 3 (Identity element): There is a special thing called "zero" such that when you combine any thing with "zero" you get the original thing back. •Benefit: Initial value for empty or missing data

222. Pattern: Simplifying aggregation code with monoids

223. type OrderLine = {Qty:int; Total:float} let orderLines = [ {Qty=2; Total=19.98} {Qty=1; Total= 1.99} {Qty=3; Total= 3.99} ] let addLine line1 line2 = let newQty = line1.Qty + line2.Qty let newTotal = line1.Total + line2.Total {Qty=newQty; Total=newTotal} orderLines |> List.reduce addLine Any combination of monoids is also a monoid

224. Pattern: Convert non-monoids to monoids

225. Customer + Customer + Customer Customer Stats + Customer Stats + Customer Stats Reduce Map Not a monoid A monoid Summary Stats

226. Hadoop make me a sandwich https://twitter.com/daviottenheimer/status/532661754820829185

227. Guideline: Convert expensive monoids to cheap monoids

228. Log file (Mon) + Log File (Tue) + Log File (Wed) = Really big file Summary (Mon) + Summary (Tue) + Summary (Wed) Map Strings are monoids A monoid Much more efficient for incremental updates “Monoid homomorphism”

229. Pattern: Seeing monoids everywhere

230. Monoids in the real world Metrics guideline: Use counters rather than rates Alternative metrics guideline: Make sure your metrics are monoids • incremental updates • can handle missing data

231. Is function composition a monoid? >> Function 1 apple -> banana Function 2 banana -> cherry New Function apple -> cherry Not the same thing. Not a monoid 

232. Is function composition a monoid? >> Function 1 apple -> apple Same thing Function 2 apple -> apple Function 3 apple -> apple A monoid! 

233. Is function composition a monoid? “Functions with same type of input and output” Functions where the input and output are the same type are monoids! What shall we call these kinds of functions?

234. Is function composition a monoid? “Functions with same type of input and output” “Endomorphisms” Functions where the input and output are the same type are monoids! What shall we call these kinds of functions? All endomorphisms are monoids

235. Endomorphism example let plus1 x = x + 1 // int->int let times2 x = x * 2 // int->int let subtract42 x = x – 42 // int->int let functions = [ plus1 times2 subtract42 ] let newFunction = // int->int functions |> List.reduce (>>) newFunction 20 // => 0 Endomorphisms Put them in a list Reduce them Another endomorphism

236. Event sourcing Any function containing an endomorphism can be converted into a monoid. For example: Event sourcing Is an endomorphism Event -> State -> State

237. Event sourcing example let applyFns = [ apply event1 // State -> State apply event2 // State -> State apply event3] // State -> State let applyAll = // State -> State applyFns |> List.reduce (>>) let newState = applyAll oldState • incremental updates • can handle missing events Partial application of event A function that can apply all the events in one step

238. Bind Any function containing an endomorphism can be converted into a monoid. For example: Option.Bind Is an endomorphism (fn param) -> Option -> Option

239. Event sourcing example let bindFns = [ Option.bind (fun x-> if x > 1 then Some (x*2) else None) Option.bind (fun x-> if x < 10 then Some x else None) ] let bindAll = // Option->Option bindFns |> List.reduce (>>) Some 4 |> bindAll // Some 8 Some 5 |> bindAll // None Partial application of Bind

240. Predicates as monoids type Predicate<'A> = 'A -> bool let predAnd pred1 pred2 x = if pred1 x then pred2 x else false let predicates = [ isMoreThan10Chars // string -> bool isMixedCase // string -> bool isNotDictionaryWord // string -> bool ] let combinedPredicate = // string -> bool predicates |> List.reduce (predAnd) Not an endomorphism But can still be combined

241. Pattern: Monads are monoids

242. Series combination = >> Result is same kind of thing (Closure) Order not important (Associative) Monoid!

243. Parallel combination = + Same thing (Closure) Order not important (Associative) Monoid!

244. Monad laws •The Monad laws are just the monoid definitions in diguise –Closure, Associativity, Identity •What happens if you break the monad laws? –You lose monoid benefits such as aggregation

245. A monad is just a monoid in the category of endofunctors!

246. THANKS!

https://twitter.com	549
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http://intellignet.blogspot.com	3
http://www.hackernews.im	3
http://intellignet.blogspot.co.il	2
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