# ploeh blog danish software design

## Church-encoded payment types

*How to translate a domain-specific sum type into a Church-encoded C# API. An article for object-oriented developers.*

This article is part of a series of articles about Church encoding. In the previous articles, you've seen how to implement Boolean logic without Boolean primitives, as well as how to model natural numbers, how to implement a Maybe container, and how to implement an Either container. Common to all four examples is that they're based on sum types with exactly two mutually exclusive cases.

You may already have noticed that all three translate to `Match`

methods that take two arguments. The translation is so mechanical that you could automate it. Each case in a sum type becomes an argument in a `Match`

method. In this article, you'll see an example of a domain-specific sum type with three cases, translated to Church-encoding.

### A payment type model in F# #

In a previous article I described a particular business problem that was elegantly addressed with a discriminated union (sum type) in F#:

type PaymentService = { Name : string; Action : string } type PaymentType = | Individual of PaymentService | Parent of PaymentService | Child of originalTransactionKey : string * paymentService : PaymentService

In short, this model enables you to model various payments against a third-party payment service. An *individual* payment is, as the name implies, a single payment. A *parent* payment can be used to authorise a series of recurring, automated payments, for example to pay for a subscription. A *child* payment is one of those recurring payments; it must have a parent payment to authorise it, as automation means that no user interaction takes place.

One task that is easily addressed with the above `PaymentType`

discriminated union is that you can translate the data to JSON in a type-safe manner. The compiler will tell you whether or not you've handled all three cases.

### Auxiliary C# classes #

You can Church-encode `PaymentType`

just like Boolean values, natural numbers, Maybe, and Either. Before you do that, however, you need to define the input types involved in each case. These are normal classes, although I prefer to make them immutable:

public class PaymentService { public PaymentService(string name, string action) { this.Name = name; this.Action = action; } public string Name { get; } public string Action { get; } } public class ChildPaymentService { public ChildPaymentService( string originalTransactionKey, PaymentService paymentService) { this.OriginalTransactionKey = originalTransactionKey; this.PaymentService = paymentService; } public string OriginalTransactionKey { get; } public PaymentService PaymentService { get; } }

These are straightforward translations of the F# `PaymentService`

record type, and the tuple associated with the `Child`

case. In a real code base, I'd override `Equals`

for both classes in order to turn them into proper Value Objects, but in order to keep the size of the code down, I omitted doing that here.

### Church-encoded payment type #

You can now translate the `PaymentType`

F# discriminated union to a Church-encoded API in C#, starting with the interface:

public interface IPaymentType { T Match<T>( Func<PaymentService, T> individual, Func<PaymentService, T> parent, Func<ChildPaymentService, T> child); }

Since there's three cases in the sum type, that turns into a `Match`

method with three arguments, each corresponding to one of the cases. As was also the case for the previous articles' `INaturalNumber`

, `IMaybe<T>`

, and `IEither<L, R>`

interfaces, the data associated with each case is modelled as a function from the data to the generic return type `T`

.

Again, following the recipe implied by the previous examples, you should now add a concrete implementation of the `IPaymentType`

interface for each case. It's natural to start with the first argument to the `Match`

method, *individual:*

public class Individual : IPaymentType { private readonly PaymentService paymentService; public Individual(PaymentService paymentService) { this.paymentService = paymentService; } public T Match<T>( Func<PaymentService, T> individual, Func<PaymentService, T> parent, Func<ChildPaymentService, T> child) { return individual(paymentService); } }

The `Individual`

class adapts a `PaymentService`

value, which it passes as the argument to the `individual`

function argument when `Match`

is called. As you've seen in the previous articles, a particular implementation uses only one of the method arguments, so the two other arguments, `parent`

and `child`

, are simply ignored.

The *parent* implementation is almost identical:

public class Parent : IPaymentType { private readonly PaymentService paymentService; public Parent(PaymentService paymentService) { this.paymentService = paymentService; } public T Match<T>( Func<PaymentService, T> individual, Func<PaymentService, T> parent, Func<ChildPaymentService, T> child) { return parent(paymentService); } }

The `Parent`

class also adapts a `PaymentService`

value that it passes to a function when `Match`

is called. The only difference is that it calls the `parent`

function instead of the `individual`

function argument.

The third case is handled by the following `Child`

class:

public class Child : IPaymentType { private readonly ChildPaymentService childPaymentService; public Child(ChildPaymentService childPaymentService) { this.childPaymentService = childPaymentService; } public T Match<T>( Func<PaymentService, T> individual, Func<PaymentService, T> parent, Func<ChildPaymentService, T> child) { return child(childPaymentService); } }

While the two other classes both adapt a `PaymentService`

value, a `Child`

object instead composes a `ChildPaymentService`

value. When `Match`

is called, it calls the `child`

function argument with the composed value.

### Using the IPaymentType API #

One important feature that I originally had to implement was to translate a *payment type* value into a JSON document. For the purposes of this example, imagine that you can model the desired JSON document using this Data Transfer Object:

public class PaymentJsonModel { public string Name { get; set; } public string Action { get; set; } public IChurchBoolean StartRecurrent { get; set; } public IMaybe<string> TransactionKey { get; set; } }

This is a mutable object because most .NET serialisation APIs require that the class in question has a parameterless constructor and writeable properties. Notice, however, that in order to demonstrate that all this code still doesn't rely on any primitive Boolean operators and such, the class' properties are defined as `IChurchBoolean`

and `IMaybe<string>`

values, as well as regular `string`

values.

Writing a method that translates any `IPaymentType`

object into a `PaymentJsonModel`

object is now straightforward:

public static PaymentJsonModel ToJson(this IPaymentType payment) { return payment.Match( individual : ps => new PaymentJsonModel { Name = ps.Name, Action = ps.Action, StartRecurrent = new ChurchFalse(), TransactionKey = new Nothing<string>() }, parent : ps => new PaymentJsonModel { Name = ps.Name, Action = ps.Action, StartRecurrent = new ChurchTrue(), TransactionKey = new Nothing<string>() }, child : cps => new PaymentJsonModel { Name = cps.PaymentService.Name, Action = cps.PaymentService.Action, StartRecurrent = new ChurchFalse(), TransactionKey = new Just<string>(cps.OriginalTransactionKey) }); }

Because the `Match`

method takes three arguments, you have to supply a 'handler' function for each of them, and they all have to have the same return type. In this case they all return a new `PaymentJsonModel`

object, so that requirement is fulfilled. All three lambda expressions simply copy over `Name`

and `Action`

, but they differ in the values they assign to `StartRecurrent`

and `TransactionKey`

.

### Tests #

In order to show you that it all works, here's a few examples I wrote as xUnit.net tests:

[Fact] public void IndividualToJsonReturnsCorrectResult() { var sut = new Individual(new PaymentService("MasterCard", "Pay")); var actual = sut.ToJson(); Assert.Equal("MasterCard", actual.Name); Assert.Equal("Pay", actual.Action); Assert.False(actual.StartRecurrent.ToBool()); Assert.True(actual.TransactionKey.IsNothing().ToBool()); } [Fact] public void ParentToJsonReturnsCorrectResult() { var sut = new Parent(new PaymentService("MasterCard", "Pay")); var actual = sut.ToJson(); Assert.Equal("MasterCard", actual.Name); Assert.Equal("Pay", actual.Action); Assert.True(actual.StartRecurrent.ToBool()); Assert.True(actual.TransactionKey.IsNothing().ToBool()); } [Fact] public void ChildToJsonReturnsCorrectResult() { var sut = new Child( new ChildPaymentService( "12345", new PaymentService("MasterCard", "Pay"))); var actual = sut.ToJson(); Assert.Equal("MasterCard", actual.Name); Assert.Equal("Pay", actual.Action); Assert.False(actual.StartRecurrent.ToBool()); Assert.Equal("12345", actual.TransactionKey.Match("", x => x)); }

All three tests pass.

### Summary #

The major advantage of sum types in statically typed languages is that you can *make illegal states unrepresentable* (a maxim attributed to Yaron Minsky). Specifically, in the business example of payment types shown here, I need to be able to express that only three out of four combinations of *start recurrent* and *original transaction key* is legal. Specifically, I needed to express that the combination of *start recurrent = true* and the presence of a *transaction key* is illegal. Making such an illegal state unrepresentable is easy with a sum type, but as this article has shown, you can achieve the same goal in C#.

With the API shown here, there's only three possible states (`Individual`

, `Child`

, and `Parent`

). Notice that all three classes hide their data as `private`

class fields, so the only way to extract that data is to call `Match`

. The compiler will make sure that you handle all three cases, because you must supply a function for all three method arguments.

This article concludes the little series on how to use Church-encoding in C# to create sum types. You may, however, think that it doesn't really feel object-oriented, with its heavy reliance on function arguments (e.g. `Func<PaymentService, T>`

). It turns out, though, that with only a few refactorings, you'll come to the realisation that what you've seen here is isomorphic to a classic design pattern. Read on!

## Church-encoded Either

*Programming languages don't have to have a built-in notion of error handling. You can implement sane error handling from first principles. An introduction for object-oriented programmers.*

This article is part of a series of articles about Church encoding. In this series, you'll learn how to re-create various programming language features from first principles. In previous articles, you learned how to implement Boolean logic without Boolean primitives, how to model natural numbers, as well as how to implement Maybe (a type-safe alternative to null). Through these examples, you'll learn how to model sum types without explicit language support.

### Error handling without exceptions #

In a previous article, I've discussed how a language doesn't need to have built-in exceptions in order to support composable and type-safe error handling. In fact, exceptions are noting but glorified GOTO statements. A better approach is to use the *Either* abstraction, which enables you to model values that are either one or another thing.

In F#, this type is known as Result<'T, 'TError>, while in Haskell it's called Either. It enables you to model an outcome that is either something (like a success) or something else (such as an error).

Scott Wlaschin has already brilliantly described how this works in F#, but the `Either`

type can be used for error handling in Haskell in exactly the same way. When we use the terminology related to *either*, we distinguish between *left* and *right*. Typically, *right* is used to indicate success, via the pun that 'right' is 'correct'.

### Lambda calculus Either #

Church encoding is based on the lambda calculus, which defines a universal model of computation based entirely on functions (lambda expressions) and recursion. As far as I can tell, you can define *Either* in lambda calculus as an expression that takes two arguments, and where there's two fundamental 'implementations' of the contract:

left = λa.λl.λr.l a right = λb.λl.λr.r b

(I admit that I'm going out on a limb here, since I haven't found any source that puts either in the above form, so I'd appreciate feedback if I did it incorrectly.)

The contract is that, similar to *Maybe*, the `l`

function argument represents the *left* case, whereas the `r`

argument represents the *right* case. Contrary to *Maybe*, both *l* and *r* are used as functions. (Everything in lambda calculus is a function, but we don't always use the arguments as the function that they are.)

The `left`

function is a function that takes three arguments (`a`

, `l`

, and `r`

) and always returns `l a`

. Recall that in lambda calculus, everything is a function, which includes `l`

(and `r`

). In other words, `left`

unconditionally calls `l`

with `a`

, and that's the return value.

The `right`

function works like the `left`

function, with the only difference that it always returns `r b`

.

The idea, as usual, is that you can partially apply `left`

and `right`

, by, for instance calling `left three`

(where `three`

is the lambda calculus representation of the number 3, as described in the article on Church-encoded natural numbers). Such a partially applied function is a function that still takes the two arguments `l`

and `r`

.

The same is true if you partially apply `right`

with a value, like `right one`

.

In both cases, you have a function of the form `λl.λr.[...]`

. If you've been given such a function by an external source, you may not know if it's a `left`

or a `right`

expression, and that's the point. You must supply handlers (`l`

and `r`

) that cover all possible cases.

In the lambda calculus, expressions are always curried, so instead of viewing `left`

and `right`

as functions with three arguments, you can view them as functions that take a single element (`a`

or `b`

) and return functions that takes two arguments. This agrees with Haskell's `Left`

and `Right`

data constructors:

Prelude> :t Left Left :: a -> Either a b Prelude> :t Right Right :: b -> Either a b

Haskell tells us that `Left`

is a function that takes an `a`

value and returns an `Either a b`

value. Similarly, `Right`

is a function that takes a `b`

value as input, and returns an `Either a b`

### Church-encoded Either in C# #

Both lambda calculus and Haskell relies on currying and partial application to make the contract fit. In C#, as you've previously seen, you can instead define an interface and rely on class fields for the 'extra' function arguments. Since Church-encoded Either is represented by a function that takes two arguments, we'll once again define an interface with a single method that takes two arguments:

public interface IEither<L, R> { T Match<T>(Func<L, T> onLeft, Func<R, T> onRight); }

The `Match`

method takes two functions as arguments, one that handles the *left* case, and one that handles the *right* case. They correspond to the `l`

and `r`

variables in the above lambda expressions. The intent, as with other Church-encoded discriminated unions, is that when client code is given an `IEither<L, R>`

object, it can only interact with that object by telling the `Match`

method how to deal with both cases. Only one of the functions will be called, but at compile-time, you don't know which one. Both functions, however, must return a value of the generic type `T`

, and that's how you can translate an `IEither<L, R>`

object to a `T`

value.

Following the normal procedure for Church encoding, you must also supply two implementations of the `IEither<L, R>`

interface: one for each case.

public class Left<L, R> : IEither<L, R> { private readonly L left; public Left(L left) { this.left = left; } public T Match<T>(Func<L, T> onLeft, Func<R, T> onRight) { return onLeft(left); } }

The `Left<L, R>`

class is an Adapter of a value of the generic type `L`

, making it appear as an `IEither<L, R>`

object.

It always calls the `onLeft`

method argument with the adapted value `left`

, while it ignores the `onRight`

method argument. Since `onLeft`

returns a `T`

value, you can return the value produced by the function call.

The *right* case is implemented in a similar fashion:

public class Right<L, R> : IEither<L, R> { private readonly R right; public Right(R right) { this.right = right; } public T Match<T>(Func<L, T> onLeft, Func<R, T> onRight) { return onRight(right); } }

The `Right<L, R>`

class is the mirror image of `Left<L, R>`

. Instead of adapting an `L`

value, it adapts an `R`

value. It implements `Match`

by always calling `onRight`

with the `right`

value, which, again, produces a `T`

value that can be immediately returned.

Notice that for both implementations, the adapted values `left`

and `right`

are `private`

class fields not exposed as public members. The only way you, as a caller, can potentially extract these values is by calling `Match`

, and that forces you to explicitly deal with both cases.

Here's an example of using the API:

> IEither<string, int> e = new Right<string, int>(42); > e.Match(s => s.Length % 2 == 0, i => i % 2 == 0) true

I've deliberately declared `e`

as a an `IEither<string, int>`

in order to highlight the scenario where, as a client developer, you're often given a value of such a type, and you don't know if it's a *left* or a *right* value. Had I, instead, used the `var`

keyword, the compiler would have detected that `e`

is, really, a `Right<string, int>`

variable. You may consider this choice artificial, but the point I'm trying to get across is that, when writing client code, you're often given a polymorphic value, and you don't know the concrete type of the value. According to the Liskov Substitution Principle, your client code must be able to deal with any subtype without changing the correctness of the system. In the case of an Either value, the way you deal with all subtypes is by supplying handlers for both cases to the `Match`

method.

In the above example, the return value is `true`

because `42`

is an even number. If, instead, the `e`

object is a *left* case containing the string `"foo"`

, the return value is `false`

because the length of `"foo"`

is *3* - an odd number:

> IEither<string, int> e = new Left<string, int>("foo"); > e.Match(s => s.Length % 2 == 0, i => i % 2 == 0) false

Notice that the `e.Match`

method call is the same in both examples; the `onLeft`

and `onRight`

functions are the same in both cases. The results differ because the input values represent different cases.

If you've been following the overall series on Church encoding, you may think that it's cheating to use C#'s built-in `string`

and `int`

data types, but nothing prevents us from sticking to the data types we've built from scratch:

> IEither<IChurchBoolean, INaturalNumber> e; > e = new Right<IChurchBoolean, INaturalNumber>(NaturalNumber.Seven); > e.Match(b => new ChurchNot(b), n => n.IsEven()) ChurchFalse { } > e = new Left<IChurchBoolean, INaturalNumber>(new ChurchFalse()); > e.Match(b => new ChurchNot(b), n => n.IsEven()) ChurchNot(ChurchFalse)

For both the *left* and the *right* case, the `Match`

inverts the Boolean expression if it's a *left* case, and evaluates if the number is even if it's a *right* case. In the first example, the return value is a `ChurchFalse`

object because *7* is odd. In the second example, the return value is a `ChurchNot`

object containing a `ChurchFalse`

object (in other words, *true*), because the negation of *false* is *true*.

### Either instead of exceptions #

You can use Either to signal the success or failure of an operation. By convention, the *right* case is used to signal success, so, by elimination, *left* means failure. You can signal errors in numerous ways, e.g. by using `enum`

values, but another common strategy is to simply use string values.

Consider the following example. You receive a collection of values, where each element represents a vote for that element. For example, the list *Sandra, Zoey, Sandra* indicates two votes for *Sandra*, and one for *Zoey*. You need to write a method that returns the winner of a vote, but at least two distinct errors are possible: the input collection is empty, or there's a tie.

You can model the error cases with an `enum`

:

public enum VoteError { Empty = 0, Tie }

This enables you to write a method to find the winners, with an explicit Either return type:

public static IEither<VoteError, T> FindWinner<T>(IReadOnlyCollection<T> votes) { var countedVotes = from v in votes group v by v into g let count = g.Count() orderby count descending select new { Vote = g.Key, Count = count }; var c = countedVotes.Take(2).Count(); if (c == 0) return new Left<VoteError, T>(VoteError.Empty); var x0 = countedVotes.ElementAt(0); if (c == 1) return new Right<VoteError, T>(x0.Vote); var x1 = countedVotes.ElementAt(1); if (Equals(x0.Count, x1.Count)) return new Left<VoteError, T>(VoteError.Tie); return new Right<VoteError, T>(x0.Vote); }

Notice that the return type of the `FindWinner`

method is `IEither<VoteError, T>`

; either you get a `VoteError`

value, or you get a `T`

value, but any client code doesn't know which it'll be, so it must handle both cases.

The method uses a C# query expression to group, count, and order the votes. If there's no elements, the return value is a *left* value containing `VoteError.Empty`

. If there's only a single vote group (e.g. if the votes where all for *Sandra*), that value is returned in a *right* case. Otherwise, if the two highest ranked votes have the same count, a *left* value is returned containing `VoteError.Tie`

. Finally, in all other cases, the highest voted element is returned in a *right* case.

Here's some examples in *C# Interactive:*

> FindWinner<int>() Left<VoteError, int>(Empty) > FindWinner(1, 2, 3, 1, 4, 2) Left<VoteError, int>(Tie) > FindWinner("Sandra", "Zoey", "Sandra") Right<VoteError, string>("Sandra")

Instead of throwing two different types of exceptions on invalid input, the `FindWinner`

method handles invalid input as *left* cases, and valid input as the *right* case. You can do that consistently, and thereby eliminate the need for exceptions. Errors are, instead, reported as *left* values.

### Summary #

In this article, you saw how it's possible to define the *Either* container from first principles, using nothing but functions (and, for the C# examples, interfaces and classes in order to make the code easier to understand for object-oriented developers).

Like Maybe, you can also make Either a functor. This'll enable you to compose various error-producing functions in a sane manner.

Church-encoding enables you to model sum types as functions. So far in this article series, you've seen how to model Boolean values, natural numbers, Maybe, and Either. Common to all four examples is that the data type in question consists of two mutually exclusive cases. This is the reason they're all modelled as methods that take two arguments. What happens if, instead of two, you have *three* mutually exclusive cases? Read on.

**Next:** Church-encoded payment types.

## Church-encoded Maybe

*Programming languages don't have to have a built-in notion of null values. Missing or optional values can be created from first principles. An introduction for object-oriented programmers.*

This article is part of a series of articles about Church encoding. In this series, you'll learn how to re-create various programming language features from first principles. In previous articles, you learned how to implement Boolean logic without Boolean primitives, as well as how to model natural numbers. Through these examples, you'll learn how to model sum types without explicit language support.

### The billion-dollar mistake #

All mainstream programming languages have a built-in notion of *null*: a value that isn't there. There's nothing wrong with the concept; you often run into situations where you need to return a value, but in certain cases, you'll have nothing to return. Division by zero would be one example. Attempting to retrieve the first element from an empty collection would be another.

Unfortunately, for fifty years, we've been immersed in environments where null references have been the dominant way to model the absence of data. This, despite the fact that even Sir Antony Hoare, the inventor of null references, has publicly called it his *billion-dollar mistake*.

You can, however, model the potential absence of data in saner ways. Haskell, for example, has no built-in null support, but it does include a built-in Maybe type. In Haskell (as well as in F#, where it's called `option`

), `Maybe`

is defined as a sum type:

data Maybe a = Nothing | Just a deriving (Eq, Ord)

If you're not familiar with Haskell syntax, this is a type declaration that states that the parametrically polymorphic (AKA *generic*) data type `Maybe`

is inhabited by `Just`

values that contain other values, plus the constant `Nothing`

.

This article series, however, examines how to implement sum types with Church encoding.

### Lambda calculus maybe #

Church encoding is based on the lambda calculus, which defines a universal model of computation based entirely on functions (lambda expressions) and recursion. In lambda calculus, the contract of *Maybe* is defined as an expression that takes two arguments. There's two fundamental 'implementations' of the contract:

nothing = λn.λj.n just = λx.λn.λj.j x

The contract is that the first function argument (`n`

) represents the *nothing* case, whereas the second argument (`j`

) represents the `just`

case.

The `nothing`

function is a lambda expression that takes two arguments (`n`

and `j`

), and always returns the first, left-most argument (`n`

).

The `just`

function is a lambda expression that takes three arguments (`x`

, `n`

, and `j`

), and always returns `j x`

. Recall that in the lambda calculus, everything is a function, including `j`

, so `j x`

means that the function `j`

is called with the argument `x`

.

A few paragraphs above, I wrote that the contract of *maybe* is modelled as an expression that takes two arguments, yet `just`

takes three arguments. How does that fit?

In the lambda calculus, expressions are always curried, so instead of viewing `just`

as a function with three arguments, you can view it as a function that takes a single element (`x`

) and returns a function that takes two arguments. This agrees with Haskell's `Just`

data constructor:

Prelude> :t Just Just :: a -> Maybe a

Haskell tells us that `Just`

is a function that takes an `a`

value (corresponding to `x`

in the above `just`

lambda expression) and returns a `Maybe a`

value.

### Church-encoded Maybe in C# #

Both lambda calculus and Haskell rely on currying and partial application to make the contract fit. In C#, as you've previously seen, you can instead define an interface and rely on class fields for the 'extra' function arguments. Since Church-encoded Maybe is represented by a function that takes two arguments, we'll once again define an interface with a single method that takes two arguments:

public interface IMaybe<T> { TResult Match<TResult>(TResult nothing, Func<T, TResult> just); }

In the first article, about Church-encoded Boolean values, you saw how two mutually exclusive values could be modelled as a method that takes two arguments. Boolean values are simply constants (*true* and *false*), where the next example (natural numbers) included a case where one case (*successor*) contained data. In that example, however, the data was statically typed as another `INaturalNumber`

value. In the current `IMaybe<T>`

example, the data contained in the *just* case is generic (it's of the type `T`

).

Notice that there's two levels of generics in play. `IMaybe<T>`

itself is a container of the generic type `T`

, whereas `Match`

enables you to convert the container into the rank-2 polymorphic type `TResult`

.

Once more, the contract of `IMaybe<T>`

is that the first, left-hand argument represents the *nothing* case, whereas the second, right-hand argument represents the *just* case. The *nothing* implementation, then, is similar to the previous `ChurchTrue`

and `Zero`

classes:

public class Nothing<T> : IMaybe<T> { public TResult Match<TResult>(TResult nothing, Func<T, TResult> just) { return nothing; } }

Again, the implementation unconditionally returns `nothing`

while ignoring `just`

. You may, though, have noticed that, as is appropriate for Maybe, `Nothing<T>`

has a distinct type. In other words, `Nothing<string>`

doesn't have the same type as `Nothing<int>`

. This is not only 'by design', but is a fundamental result of how we define *Maybe*. The code simply wouldn't compile if you tried to remove the type argument from the class. This is in contrast to C# null, which has no type.

You implement the *just* case like this:

public class Just<T> : IMaybe<T> { private readonly T value; public Just(T value) { this.value = value; } public TResult Match<TResult>(TResult nothing, Func<T, TResult> just) { return just(value); } }

According to the contract, `Just<T>`

ignores `nothing`

and works exclusively with the `just`

function argument. Notice that the `value`

class field is `private`

and not exposed as a public member. The only way you, as a caller, can potentially extract the value is by calling `Match`

.

Here are some examples of using the API:

> new Nothing<Guid>().Match(nothing: "empty", just: g => g.ToString()) "empty" > new Just<int>(42).Match(nothing: "empty", just: i => i.ToString()) "42" > new Just<int>(1337).Match(nothing: 0, just: i => i) 1337

Notice that the third example shows how to extract the value contained in a `Nothing<int>`

object without changing the output type. All you have to do is to supply a 'fall-back' value that can be used in case the value is *nothing*.

### Maybe predicates #

You can easily implement the standard Maybe predicates `IsNothing`

and `IsJust`

:

public static IChurchBoolean IsNothing<T>(this IMaybe<T> m) { return m.Match<IChurchBoolean>( nothing : new ChurchTrue(), just : _ => new ChurchFalse()); } public static IChurchBoolean IsJust<T>(this IMaybe<T> m) { return m.Match<IChurchBoolean>( nothing : new ChurchFalse(), just : _ => new ChurchTrue()); }

Here, I arbitrarily chose to implement `IsJust`

'from scratch', but I could also have implemented it by negating the result of calling `IsNothing`

. Once again, notice that the predicates are expressed in terms of Church-encoded Boolean values, instead of the built-in `bool`

primitives.

### Functor #

From Haskell (and F#) we know that Maybe is a functor. In C#, you turn a container into a functor by implementing an appropriate `Select`

method. You can do this with `IMaybe<T>`

as well:

public static IMaybe<TResult> Select<T, TResult>( this IMaybe<T> source, Func<T, TResult> selector) { return source.Match<IMaybe<TResult>>( nothing: new Nothing<TResult>(), just: x => new Just<TResult>(selector(x))); }

Notice that this method turns an `IMaybe<T>`

object into an `IMaybe<TResult>`

object, using nothing but the `Match`

method. This is possible because `Match`

has a generic return type; thus, among other types of values, you can make it return `IMaybe<TResult>`

.

When `source`

is a `Nothing<T>`

object, `Match`

returns the object in the *nothing* case, which here becomes a new `Nothing<TResult>`

object.

When `source`

is a `Just<T>`

object, `Match`

invokes `selector`

with the value contained in the *just* object, packages the result in a new `Just<TResult>`

object, and returns it.

Because the `Select`

method has the correct signature, you can use it with query syntax, as well as with normal method call syntax:

IMaybe<int> m = new Just<int>(42); IMaybe<string> actual = from i in m select i.ToString();

This example simply creates a *just* value containing the number `42`

, and then maps it to a string. Another way to write the same expression would be with method call syntax:

IMaybe<int> m = new Just<int>(42); IMaybe<string> actual = m.Select(i => i.ToString());

In both cases, the result is a *just* case containing the string `"42"`

.

### Summary #

In this article, you saw how it's possible to define the *Maybe* container from first principles, using nothing but functions (and, for the C# examples, interfaces and classes in order to make the code easier to understand for object-oriented developers).

Church-encoding enables you to model sum types as functions. So far in this article series, you've seen how to model Boolean values, natural numbers, and Maybe. Common to all three examples is that the data type in question consists of two mutually exclusive cases. There's at least one more interesting variation on that pattern.

**Next:** Church-encoded Either.

## Church-encoded natural numbers

*Natural numbers don't have to be built into programming languages. An introduction for object-oriented programmers.*

This article is part of a series of articles about Church encoding. The previous article, about Church-encoding of Boolean values, concluded with the question: *how do you determine whether an integer is even or odd?*

That sounds easy, but turns out to be more complicated that you might think at first glance.

### Built-in options #

How would you normally check whether a number is even? In some languages, like Haskell, it's built into the base library:

Prelude> even 1337 False Prelude> even 42 True

In C#, surprisingly, I don't think it's built-in, but it's easy to implement a method to answer the question:

public static bool IsEven(this int i) { return i % 2 == 0; }

You could implement an `IsOdd`

method either by using the `!=`

operator instead of `==`

, but otherwise copy the implementation of `IsEven`

; or, alternatively, call `IsEven`

and negate the result.

This works fine in normal C# code, but in this article, the agenda is different. We're investigating how programming with the previous article's `IChurchBoolean`

API would look. The above built-in options use Boolean language primitives, so that's not really instructive.

### Boolean conversions #

It's easy to convert between Church-encoded Boolean values and built-in Boolean values. For reasons I'll explain shortly, I still don't think that's instructive in this particular context, but for good measure I'll cover how to do it.

A method like the above `IsEven`

returns `bool`

. If you, instead, want an `IChurchBoolean`

, you can use this simple conversion method:

public static IChurchBoolean ToChurchBoolean(this bool b) { if (b) return new ChurchTrue(); else return new ChurchFalse(); }

Alternatively, you can also use the ternary operator, but an ugly cast is necessary to make the C# compiler happy:

public static IChurchBoolean ToChurchBoolean(this bool b) { return b ? (IChurchBoolean)new ChurchTrue() : new ChurchFalse(); }

Regardless of which implementation you choose, you'd be able to interact with the result as an `IChurchBoolean`

values, as this small interactive session demonstrates:

> 42.IsEven().ToChurchBoolean().Match("Even", "Odd") "Even" > 1337.IsEven().ToChurchBoolean().Match("Even", "Odd") "Odd"

Still, converting from `bool`

to `IChurchBoolean`

doesn't address the underlying question: *is it possible to write programs without built-in Boolean primitives?*

The conversion function `ToChurchBoolean`

uses built-in Boolean values and functions, so it doesn't show whether or not it would be possible to make do without those.

Before we abandon that line of inquiry, however, I think it's only fair to share a conversion method that goes the other way:

public static bool ToBool(this IChurchBoolean b) { return b.Match(true, false); }

This function enables you to convert an `IChurchBoolean`

value into a primitive C# `bool`

, because when `b`

represents *true*, the first argument (i.e. `true`

) is returned, and when `b`

represents *false*, the second argument (i.e. `false`

) is returned.

### Peano numbers #

If we can't use built-in primitives or operators that return them (e.g. `==`

), we may not be able to move forward with built-in numbers, either. What we *can* do, however, is to follow the lambda calculus to implement natural numbers using Church encoding. This will enable us to determine if a natural number is even or odd.

Lambda calculus models natural numbers according to Peano's model. In short, a natural number is either zero (or one, depending on the specific interpretation), or a successor to another natural number. As an example, using the `Successor`

class that I'll develop later in this article, the number three can be represented as `new Successor(new Successor(new Successor(new Zero())))`

- it's the number after the number after the number after zero.

Like Church-encoded Boolean values, a Church-encoded natural number is a function that takes two arguments, corresponding to zero, and a successor function:

zero = λf.λx.x one = λf.λx.f x two = λf.λx.f (f x) three = λf.λx.f (f (f x)) ...

Each of these functions takes an initial value `x`

, as well as a function `f`

. In the lambda calculus, neither `x`

nor `f`

have any implied interpretation; it's the number of applications of `f`

that defines the number.

In most translations into programming languages that I've encountered, however, `x`

is usually interpreted as zero, and `f`

as the successor function. In Haskell, for example, a common way to model Peano numbers is to use a sum type:

data Peano = Zero | Succ Peano deriving (Eq, Show)

Basically, this means that a value of the `Peano`

type can either be the atom `Zero`

, or a `Succ`

value. Notice that `Succ`

contains another `Peano`

value; the data type is recursive.

You can write Haskell values like these:

*Peano> zero = Zero *Peano> one = Succ Zero *Peano> two = Succ (Succ Zero) *Peano> three = Succ (Succ (Succ Zero))

Alternatively, you can also define the numbers based on previous definitions:

*Peano> zero = Zero *Peano> one = Succ zero *Peano> two = Succ one *Peano> three = Succ two

This variation of Peano numbers uses an explicit sum type, but as the lambda calculus representation suggests, you can also use Church encoding to represent the two cases.

### Church-encoded natural numbers #

If you recall Church-encoded Boolean values, you may remember that they are functions that take two values: a value to be used in case of *true*, and a value to be used in the case of *false*. You can do something similar with natural numbers. `Zero`

is like *true* and *false*, in the sense that it's nothing but a label without any associated data. `Succ`

, on the other hand, contains another `Peano`

value. The way to do that is to turn the *successor* case into a function. Doing that, you'll arrive at an interface like this:

public interface INaturalNumber { T Match<T>(T zero, Func<INaturalNumber, T> succ); }

The first argument, on the left-hand side, is the case to use when an object represents *zero*. The second argument, on the right-hand side, is a function that will ultimately produce the value associated with a *successor*. The implied contract here is that the `INaturalNumber`

passed as input to `succ`

is the *predecessor* to 'the current value'. This may seem counter-intuitive, but hopefully becomes clearer when you see the `Successor`

class below. The crucial insight is that a successor value has no intrinsic value; it's entirely defined by how many predecessors it has.

The *zero* implementation is similar to how Church-encoding implements *true*:

public class Zero : INaturalNumber { public T Match<T>(T zero, Func<INaturalNumber, T> succ) { return zero; } }

Notice that the `Zero`

class implements `INaturalNumber`

by always returning `zero`

, and consequently always ignoring `succ`

.

Another class, `Successor`

, handles the right-hand side of the `Match`

method:

public class Successor : INaturalNumber { private readonly INaturalNumber predecessor; public Successor(INaturalNumber n) { this.predecessor = n; } public T Match<T>(T zero, Func<INaturalNumber, T> succ) { return succ(predecessor); } }

Notice that `Successor`

composes its `predecessor`

via Constructor Injection, and unconditionally calls `succ`

with its `predecessor`

when `Match`

is invoked.

### Working with natural numbers #

What can you do with this `INaturalNumber`

API, then?

Initially, you can define some numbers, like the above Haskell examples:

public static class NaturalNumber { public static INaturalNumber Zero = new Zero(); public static INaturalNumber One = new Successor(Zero); public static INaturalNumber Two = new Successor(One); public static INaturalNumber Three = new Successor(Two); public static INaturalNumber Four = new Successor(Three); public static INaturalNumber Five = new Successor(Four); public static INaturalNumber Six = new Successor(Five); public static INaturalNumber Seven = new Successor(Six); public static INaturalNumber Eight = new Successor(Seven); public static INaturalNumber Nine = new Successor(Eight); // More memmbers go here... }

Here, I arbitrarily chose to define the numbers from zero to nine, but you could go on for as long as you care.

You can also convert these Church-encoded numbers to primitive `int`

values, like this:

public static int Count(this INaturalNumber n) { return n.Match( 0, p => 1 + p.Count()); }

Here are some examples from a C# Interactive session:

> NaturalNumber.Zero.Count() 0 > NaturalNumber.One.Count() 1 > NaturalNumber.Seven.Count() 7

The implementation of `Count`

is recursive. When `n`

is a `Zero`

instance, it'll return the first argument (`0`

), but when it's a `Successor`

, it'll invoke the lambda expression `p => 1 + p.Count()`

. Notice that this lambda expression recursively calls `Count`

on `p`

, which is the `Successor`

's `predecessor`

. It'll keep doing that until it reaches a `Zero`

instance.

Recursion is a central part of the lambda calculus; you can't do anything useful without it. If you're a C# or Java programmer, you may be concerned, because recursion tends to be problematic in such languages. Deeply recursive functions will sooner or later crash because of a stack overflow.

You shouldn't, however, be concerned. First, I'm not trying to convince you to write all your future C# or Java code using Church-encoded numbers and Boolean values. The point of this article series is to investigate the fundamentals of computations, and to gain a better understanding of sum types. As such, the code examples presented here are only demonstrations of the underlying principles. Lambda calculus itself serves the same purpose: it's a universal model of computation; it wasn't intended to be a practical programming language - in fact, there were no programmable computers in 1936.

Furthermore, the problem with recursion causing stack overflow isn't universal. Languages like F# and Haskell support tail recursion, thereby enabling recursive functions to run to arbitrary depths.

### Pattern matching #

In the previous article, I hinted that there's a reason I decided to name the interface method `Match`

. This is because it looks a lot like pattern matching. In F#, you could write `count`

like this:

type Peano = Zero | Succ of Peano // Peano -> int let rec count n = match n with | Zero -> 0 | Succ p -> 1 + count p

This implementation, by the way, isn't tail-recursive, but you can easily refactor to a tail-recursive implementation like this:

// Peano -> int let count n = let rec countImp acc n = match n with | Zero -> acc | Succ p -> countImp (1 + acc) p countImp 0 n

Both variations use the `match`

keyword to handle both the `Zero`

and the `Succ`

case for any `Peano`

value `n`

. That's already close to the above C# code, but using the optional C# language feature of named arguments, you can rewrite the implementation of `Count`

to this:

public static int Count(this INaturalNumber n) { return n.Match( zero: 0, succ: p => 1 + p.Count()); }

This starts to look like pattern matching of sum types in F#. The argument names aren't required, but using them makes it clearer which cases the `Match`

method handles.

### Addition #

You can now start to add features and capabilities to the natural numbers API. An obvious next step is to implement addition:

public static INaturalNumber Add(this INaturalNumber x, INaturalNumber y) { return x.Match( zero: y, succ: p => new Successor(p.Add(y))); }

Again, the implementation is recursive. When `x`

is `zero`

, you simply return `y`

, because *zero + y* is *y*. When `x`

is a `Successor`

, you recursively add `y`

to its `predecessor`

, and put the result in a new `Successor`

. You can think of the predecessor `p`

as one less than the successor. By recursively subtracting one from any `Successor`

object, you'll eventually match the `zero`

case, which will then return `y`

. When the stack unrolls, each stack puts the previous result into a new `Successor`

. This happens exactly the correct number of times corresponding to the value of `x`

, because that's the size of the stack when `Add`

hits `zero`

.

Here are some examples:

> NaturalNumber.One.Add(NaturalNumber.Two).Count() 3 > NaturalNumber.Four.Add(NaturalNumber.Three).Count() 7 > NaturalNumber.Seven.Add(NaturalNumber.Six).Count() 13

You can also implement multiplication, but that's a bit more complicated, and not relevant to the topic of this article (which is how to determine if a number is even or odd).

### Testing for zero #

In addition to basic arithmetic, you can also define functions that tell you something about a natural number. We'll start gently with a function that tells us whether or not a number is zero:

public static IChurchBoolean IsZero(this INaturalNumber n) { return n.Match<IChurchBoolean>( zero: new ChurchTrue(), succ: _ => new ChurchFalse()); }

The `IsZero`

method simply returns a `ChurchTrue`

object when `n`

is a `Zero`

instance, and a `ChurchFalse`

object for all other numbers.

You can see that this works in this C# Interactive session:

> NaturalNumber.Two.IsZero() ChurchFalse { } > NaturalNumber.Zero.IsZero() ChurchTrue { } > NaturalNumber.Three.IsZero() ChurchFalse { }

You can also `Match`

on the returned Boolean value to return e.g. a string:

> NaturalNumber.Nine.IsZero().Match(trueCase: "Zero", falseCase: "Not zero") "Not zero" > NaturalNumber.Zero.IsZero().Match(trueCase: "Zero", falseCase: "Not zero") "Zero"

This already demonstrates that you can implement predicates and branching logic from first principles, without resorting to built-in Boolean primitives or operators.

### Detecting even numbers #

Testing whether a natural number is even or uneven requires a bit more work. It's probably easiest to understand if we first consider an F# implementation:

// Peano -> ChurchBoolean let rec isEven n = match n with | Zero -> ChurchTrue | Succ Zero -> ChurchFalse | Succ (Succ p) -> isEven p

Zero is even, so when `n`

matches `Zero`

, `isEven`

returns `ChurchTrue`

. Conversely, when the input is `Succ Zero`

(i.e. *one*), the return value is `ChurchFalse`

because *one* is odd.

The *zero* and *one* cases serve as exit cases for the recursive algorithm. Since we've handled `Zero`

and `Succ Zero`

(that is, *zero* and *one*), we know that any other case must be at least twice nested. This means that the `Succ (Succ p)`

pattern matches all other cases. You can think of `p`

as *n - 2*.

The algorithm proceeds to recursively call `isEven`

with `p`

(i.e. *n - 2*). Sooner or later, these recursive function calls will match either the `Zero`

or the `Succ Zero`

case, and exit with the appropriate return value.

C# doesn't have as sophisticated pattern matching features as F#, so we're going to have to figure out how implement this algorithm without relying on a nested pattern like `Succ (Succ p)`

. As an initial step, we can rewrite the function in F#, using two matches instead of one:

// Peano -> ChurchBoolean let rec isEven n = match n with | Zero -> ChurchTrue | Succ p1 -> match p1 with | Zero -> ChurchFalse | Succ p2 -> isEven p2

This isn't as elegant as the previous implementation, but on the other hand, it's straightforward to translate to C#:

public static IChurchBoolean IsEven(this INaturalNumber n) { return n.Match( zero: new ChurchTrue(), // 0 is even, so true succ: p1 => p1.Match( // Match previous zero: new ChurchFalse(), // If 0 then successor was 1 succ: p2 => p2.IsEven())); // Eval previous' previous }

Like in the F# example, when `n`

is a `Zero`

object, it'll return the value associated with the `zero`

case. Since zero is even, it returns a `ChurchTrue`

object.

In all other cases, a `Match`

on the predecessor `p1`

is required. If that nested match is `zero`

, then we know that `n`

must have been *one*, since the the predecessor turned out to be *zero*. In that case, then, return a `ChurchFalse`

object, because *one* isn't even.

The nested `Match`

considers the predecessor `p1`

. In the `succ`

case of the nested `Match`

, then, we can consider `p2`

; that is, the predecessor to the predecessor to `n`

- in other words: *n - 2*. The function recursively calls itself with *n - 2*, and it'll keep doing so until it matches either the *zero* or the *one* case.

The implementation works:

> NaturalNumber.Two.IsEven() ChurchTrue { } > NaturalNumber.Three.IsEven() ChurchFalse { }

`IsEven`

is implemented from first principles. The only language features we need are lambda expressions and recursion, although in order to make these examples slightly more idiomatic, I've also used interfaces and classes.

### Detecting odd numbers #

You could implement a corresponding `IsOdd`

method similarly to `IsEven`

, but it's easier to use the Boolean operators already in place from the previous article:

public static IChurchBoolean IsOdd(this INaturalNumber n) { return new ChurchNot(n.IsEven()); }

`IsOdd`

is simply the Boolean negation of `IsEven`

. Like `IsEven`

it also works correctly:

> NaturalNumber.Six.IsOdd().Match(trueCase: "Odd", falseCase: "Even") "Even" > NaturalNumber.Seven.IsOdd().Match(trueCase: "Odd", falseCase: "Even") "Odd"

You can implement other operators (like multiplication) and predicates from the building blocks shown here, but I'm not going to cover that here. I hope that this article gave you a sense of how a programming language can be designed from the low-level building blocks defined by the lambda calculus.

### Summary #

Giuseppe Peano described natural numbers as an initial number (zero) and successors to that number. Church formulated Peano numbers in the lambda calculus. Using Church encoding, you can translate this representation to various programming languages, including, as you've seen in this article, C#.

In the previous article, you saw how to model Boolean values as a set of functions with two arguments. In this article, you saw how to model natural numbers with another set of functions that take two arguments. In the next article, you'll see another data type modelled as a set of functions with two arguments. It looks like a patterns is starting to appear.

**Next:** Church-encoded Maybe.

## Church-encoded Boolean values

*Boolean values, and logical branching, don't have to be built into programming languages. An introduction for object-oriented programmers.*

This article is part of a series of articles about Church encoding.

Years ago, the so-called Anti-IF Campaign made the rounds on various social media (back then, IIRC, mostly known as 'the blogosphere'). The purpose of the campaign was never to eradicate every single use of `if`

statements or expressions in source code, but rather to educate people about alternatives to the Arrow anti-pattern.

One easy way to deal with arrow code is to Replace Nested Conditionals with Guard Clauses, but that's not always possible. Another way is to encapsulate some `if`

blocks in helper methods. Yet another way would be to use polymorphic dispatch, but how does that even work? Don't you, deep down, need at least a few `if`

keywords here and there?

It turns out that the answer, surprisingly, is *no*.

### Untyped Boolean functions #

`if/then/else`

expressions are based on Boolean values (*true* and *false*): if some Boolean value is true, then something happens; otherwise, something else happens. Most programming languages, including C, C++, Java, C#, and JavaScript, have a ternary operator, which in C# looks like this:

isEven ? "Probably not a prime." : "Could be a prime.";

You can think of an expression like that as a function that takes a Boolean value and two potential return values: one for the *true* case, and one for the *false* case.

In lambda calculus, the only primitive building blocks are functions. There's no built-in Boolean values, but you can define them with functions. Boolean values are functions that take two arguments. By conventions, the first argument (the one to the left) represents the *true* case, whereas the second argument (to the right) signifies the *false* case - just like the ternary operator. In the lambda calculus, functions are curried, but we know from uncurry isomorphisms that we can also represent a two-argument function as a function that takes a two-tuple (a *pair*) as a single argument. Furthermore, we know from function isomorphisms that we can represent a function as an instance method. Therefore, we can declare a Boolean value in C# to be an object that implements this interface:

public interface IChurchBoolean { object Match(object trueCase, object falseCase); }

You'll notice that I've chosen to call the method `Match`

, for reasons that should hopefully become clear as we go along.

The intent with such a Church-encoded Boolean is that any object that represents *true* should return the left argument (`trueCase`

), whereas an object that represents *false* should return the right argument (`falseCase`

).

In other words, *true* is an interface implementation:

public class ChurchTrue : IChurchBoolean { public object Match(object trueCase, object falseCase) { return trueCase; } }

Notice that this implementation always returns `trueCase`

while ignoring `falseCase`

. No explicit `if`

statement is required.

Likewise, *false* is implemented the same way:

public class ChurchFalse : IChurchBoolean { public object Match(object trueCase, object falseCase) { return falseCase; } }

So far, this doesn't offer much capability, but it does already give you the ability to choose between two values, as this little C# Interactive session demonstrates:

> var b = new ChurchTrue(); > b.Match("foo", "bar") "foo" > var b = new ChurchFalse(); > b.Match("foo", "bar") "bar"

When 'the Boolean value' is a `ChurchTrue`

instance, then the left argument is returned; otherwise, when `b`

is a `ChurchFalse`

object, the return value is the right-hand value - just like the ternary operator.

### Boolean And #

You can now define the standard Boolean operators *and*, *or*, and *not*. Starting with *and:*

public class ChurchAnd : IChurchBoolean { private readonly IChurchBoolean x; private readonly IChurchBoolean y; public ChurchAnd(IChurchBoolean x, IChurchBoolean y) { this.x = x; this.y = y; } public object Match(object trueCase, object falseCase) { return x.Match(y.Match(trueCase, falseCase), falseCase); } }

The `ChurchAnd`

class is an implementation of `IChurchBoolean`

that composes two other `IChurchBoolean`

values, `x`

and `y`

. You can use it like this:

var b = new ChurchAnd(new ChurchTrue(), new ChurchFalse());

In this case, `b`

represents *false*, because it'll always return the right-hand argument when `Match`

is invoked.

Notice that the implementation of `ChurchAnd.Match`

first matches on `x`

. Only if `x`

itself is *true* can the expression passed as the first argument be returned; otherwise, `falseCase`

will be returned. Thus, if `x`

is *true*, the expression `y.Match(trueCase, falseCase)`

will be returned, and only if that as well evaluates to *true* is the final result *true*. The `trueCase`

value is only returned if `y`

represents *true*, as well as `x`

.

In the lambda calculus, Boolean *and* is defined like this:

and = λx.λy.λt.λf.x (y t f) f

The way to read this is that Boolean *and* is a function that takes four arguments:

`x`

, a Boolean value`y`

, another Boolean value`t`

, the value to return if the expression is*true*; the`trueCase`

argument in the above C# implementation.`f`

, the value to return if the expression is*false*; the`falseCase`

argument in the above C# implementation.

`x`

and `y`

are functions. `and`

calls `x`

with two arguments. Since Boolean *and*requires both

`x`

and `y`

to be *true*, it passes

`f`

as the second argument to `x`

, because if `x`

represents *false*, it'll return its right-hand argument. Only if

`x`

represents *true*does it make sense to investigate the Boolean value of

`y`

, which is also a function that takes two arguments. Only if `y`

also represents *true*will

`t`

be returned.
This is exactly the same implementation as the above C# code.

Wait a minute, though, didn't I write that Boolean values are functions that take two arguments? And isn't `and`

a function that takes four arguments?

Yes, indeed. That's how currying works. You can view `and`

as a function that takes four arguments, but you can also view it as a function that takes two arguments (`x`

and `y`

) and returns another function that takes two arguments. This becomes clearer with partial application. When translating to C#, the 'contract' (that a Boolean value is a function that takes two arguments) is modelled as the interface `IChurchBoolean`

, while the 'extra arguments' `x`

and `y`

become class fields, injected via the class' constructor.

### Boolean Or #

In the lambda calculus, Boolean *or* is defined like this:

or = λx.λy.λt.λf.x t (y t f)

Translated to C#, this becomes:

public class ChurchOr : IChurchBoolean { private readonly IChurchBoolean x; private readonly IChurchBoolean y; public ChurchOr(IChurchBoolean x, IChurchBoolean y) { this.x = x; this.y = y; } public object Match(object trueCase, object falseCase) { return x.Match(trueCase, y.Match(trueCase, falseCase)); } }

You can see that this is another direct translation. Boolean *or* only requires (at least) one of the Boolean values to be *true*, so if `x`

is *true*, you can immediately return `trueCase`

. Otherwise, in the case where `x`

is *false*, there's still a chance that the entire expression could be *true*, so you'll have to evaluate `y`

as well. When `y`

represents *true*, you can still return `trueCase`

. Only when `y`

is also *false* should you return `falseCase`

.

You can use `ChurchOr`

like this:

var b = new ChurchOr(new ChurchTrue(), new ChurchFalse());

Here, `b`

is *true* because *true or false* is *true*.

### Boolean Not #

Finally, you can also define Boolean negation. In lambda calculus it's:

not = λx.λt.λf.x f t

Notice how this simply swaps the arguments passed to `x`

. In C#, this translates to:

public class ChurchNot : IChurchBoolean { private readonly IChurchBoolean b; public ChurchNot(IChurchBoolean b) { this.b = b; } public object Match(object trueCase, object falseCase) { return b.Match(falseCase, trueCase); } }

You can combine all the Boolean operators like this:

var b = new ChurchOr(new ChurchFalse(), new ChurchNot(new ChurchTrue()));

Here, `b`

is *false* because *false or (not true)* is *false*.

### Typed Boolean functions #

So far, the `IChurchBoolean`

interface has been untyped, in the sense that it took `object`

arguments and had an `object`

return type. You can, however, easily make the interface strongly typed, using generics:

public interface IChurchBoolean { T Match<T>(T trueCase, T falseCase); }

This doesn't really change the rest of the code you've seen in this article. The method signatures chance, but the implementations remain as shown.

### Semigroups and monoids #

The strongly typed signature accentuates that the `Match`

method is a binary operation; it takes two values of the type `T`

and returns a single `T`

value. Is it a monoid, then?

It's not a single monoid, but rather a collection of semigroups, some of which are monoids as well. The implementation of `ChurchTrue`

corresponds to the *first* semigroup, and `ChurchFalse`

to the *last* semigroup. You can make this explict in Haskell:

import Data.Semigroup churchTrue :: a -> a -> a churchTrue t f = getFirst (First t <> First f)

If you compare this implementation of `churchTrue`

to the Travis Whitaker's `true`

function, his is much simpler. I'm not suggesting that using `First`

is better; I'm only trying to illustrate the connection.

If you aren't familiar with how things are done in Haskell, `<>`

is the 'generic semigroup binary operator'. What it does depends on the type of expressions surrounding it. By wrapping both `t`

and `f`

in `First`

containers, the `<>`

operator becomes the operator that always returns the first argument (i.e. `First t`

). Since the result is a `First`

value, you have to unwrap it again by applying `getFirst`

.

Likewise, you can define *false:*

churchFalse :: a -> a -> a churchFalse t f = getLast (Last t <> Last f)

This still uses the `<>`

operator, but now with the `Last`

container, which gives it all the behaviour of the *last* semigroup.

The *any* and *all* monoids are implemented as compositions of these two fundamental semigroups. In the C# code in this article, they're implemented by `ChurchAnd`

and `ChurchOr`

, although in neither case have I defined an explicit identity value. This is, however, possible, so let's continue with the Haskell code to see what that would look like. First, you can define the 'naked' operations:

churchAnd x y t f = x (y t f) f churchOr x y t f = x t (y t f)

I have here omitted the type signatures on purpose, as I believe they might confuse rather than help. In both cases, the logic is the same as in the above `ChurchAnd`

and `ChurchOr`

classes, although, as you can see, Haskell code is much terser.

These two functions already work as desired, but we can easily turn both into their respective monoids. First, the *all* monoid:

newtype ChurchAll = ChurchAll { runAll :: forall a. a -> a -> a } instance Semigroup ChurchAll where ChurchAll x <> ChurchAll y = ChurchAll (churchAnd x y) instance Monoid ChurchAll where mempty = ChurchAll churchTrue mappend = (<>)

In order for this code to compile, you must enable the *RankNTypes* language extension, which I did by adding the `{-# LANGUAGE RankNTypes #-}`

pragma to the top of my code file. The `forall a`

declaration corresponds to the `<T>`

type annotation on the C# `Match`

method. You can think of this as that the type argument is scoped to the function instead of the type.

The `Semigroup`

instance simply delegates its behaviour to `churchAnd`

, and the `Monoid`

instance returns `churchTrue`

as the identity (`mempty`

).

Similarly, you can implement the *any* monoid:

newtype ChurchAny = ChurchAny { runAny :: forall a. a -> a -> a } instance Semigroup ChurchAny where ChurchAny x <> ChurchAny y = ChurchAny (churchOr x y) instance Monoid ChurchAny where mempty = ChurchAny churchFalse mappend = (<>)

As is also the case with `ChurchAll`

, the `ChurchAny`

instance of `Semigroup`

simply delegates to a 'naked' function (in this case `churchOr`

), and the `Monoid`

instance again delegates `mappend`

to `<>`

and returns `churchFalse`

as the identity.

The following brief GHCi session demonstrates that it all works as intended:

λ> runAny (ChurchAny churchTrue <> ChurchAny churchFalse) "foo" "bar" "foo" λ> runAny (ChurchAny churchFalse <> ChurchAny churchFalse) "foo" "bar" "bar" λ> runAll (ChurchAll churchFalse <> ChurchAll churchTrue) "foo" "bar" "bar" λ> runAll (ChurchAll churchTrue <> ChurchAll churchTrue) "foo" "bar" "foo"

Recall that a Church-encoded Boolean is a function that takes two values - in all the four above examples `"foo"`

and `"bar"`

. When the expression represents *true* it returns the left-hand value (`"foo"`

); otherwise, it returns the right-hand value (`"bar"`

).

In summary, the Church-encoded Boolean values *true* and *false* correspond to the *first* and *last* semigroups. You can compose the well-known monoids over Boolean values using these two basic building blocks.

### Summary #

You'd normally think of Boolean values as language primitives. *True* and *false* are built into most languages, as well as common operators like *and*, *or*, and *not*. While this is convenient, it doesn't *have* to be like this. Even in languages that already have built-in support for Boolean values, like Haskell or C#, you can define Church-encoded Boolean values from first principles.

In the lambda calculus, a Boolean value is function that takes two arguments and returns the left-hand argument when *true*, and the right-hand argument when *false*.

At this point, it may seem like you can't do much with the `IChurchBoolean`

API. How could you, for instance, determine whether an integer is even or odd?

This innocuous-looking question is harder to answer than you may think, so that's worthy of its own article.

## Church encoding

*Church encoding is a unified way to model data and functions. An introduction for object-oriented developers.*

This article series is part of an even larger series of articles about the relationship between design patterns and category theory.

When asked why I like functional programming so much, I often emphasise the superior modelling ability that I get from algebraic data types. Particularly, languages like F# and Haskell have sum types in addition to the product types that most statically typed languages seem to have.

In short, a *sum type* gives you the ability to declare, as part of the type system, that a particular data type must be exactly one of a *finite* list of mutually exclusive options. This differs from common object-oriented sub-typing because class inheritance or interface implementation offers conceptually infinite extensibility. Sometimes, unconstrained extensibility is exactly what you need, but in other cases, the ability to define a closed set of cases can be an effective modelling tool. If you need an easy-to-read introduction to algebraic data types, I recommend Tomas Petricek's fine article Power of mathematics: Reasoning about functional types.

Interestingly, TypeScript has sum types, so they don't have to belong exclusively in the realm of functional programming. In this article series, you'll see an alternative way to represent sum types in C# using *Church encoding*.

### Lambda calculus #

In the 1930s, several mathematicians were investigating the foundations of mathematics. One of them, Alonzo Church, developed lambda calculus as a universal model of computation. In a sense, you can think of lambda calculus as a sort of hypothetical programming language, although it was never designed to be a practical programming language. Even so, you can learn a lot from it.

In the untyped lambda calculus, the only primitive data type is a function. There are no primitive numbers, Boolean values, branching instructions, loops, or anything else you'd normally consider as parts of a programming language. Instead, there's only functions, written as *lambda expressions:*

λf.λx.f x

This looks opaque and mathematical, but most modern programmers should be familiar with lambda (λ) expressions. The above expression is an anonymous function that takes a single argument: `f`

. The body of the function is the return value; here, another lambda expression: `λx.f x`

. This lambda expression also takes a single argument: `x`

.

In the untyped lambda calculus, everything is a function, so that includes `f`

and `x`

. The return value of the entire expression is `f x`

, which means that the function `f`

is applied to the value (in fact: function) `x`

. The entire expression is therefore a higher-order function.

In C#, the corresponding lambda expression would be:

f => x => f(x)

This is a lambda expression that returns another lambda expression, which again returns the result of calling the function `f`

with the value `x`

.

In F#, it would be:

fun f -> fun x -> f x

and in Haskell, it would be:

\f -> \x -> f x

In both Haskell and F#, functions are already curried, so you can shorten that Haskell lambda expression to:

\f x -> f x

and the F# lambda expression to:

fun f x -> f x

This looks more like a function that takes two arguments, so alternatively, via uncurry isomorphisms, you can also write the C# representation like this:

(f, x) => f(x)

Those six lambda expressions, however, are statically typed, even though they're generic (or, as Haskellers would put it, parametric polymorphic). This means that they're not entirely equal to `λf.λx.f x`

, but it should give you a sense of what a lambda expression is.

It turns out that using nothing but lambda expressions, one can express any computation; lambda calculus is Turing-complete.

### Church encoding #

Since languages like C#, F#, Haskell, and others, include lambda expressions, you can reproduce as much of the lambda calculus as you'd like. In this article series, I'll mainly use it to show you how to represent sum types in C#. Later, you'll see how it relates to design patterns.

- Church-encoded Boolean values
- Church-encoded natural numbers
- Church-encoded Maybe
- Church-encoded Either
- Church-encoded payment types

These articles give you examples in C#. For Haskell examples, I found Travis Whitaker's article Scrap Your Constructors: Church Encoding Algebraic Types useful.

### Summary #

You can use lambda expressions to define all sorts of data types and computations. Because lambda calculus is a universal model of computation, you can learn about fundamental representations of computation. Particularly, lambda calculus offers a model of logical branching, which again teaches us how to model sum types.

## Comments

James

## Composite as a monoid - a business rules example

*Composites are monoids. An example in C#, F#, and Haskell.*

Towards the end of the first decade of the third millennium, I'd been writing object-oriented code for about ten years, and I'd started to notice some patterns in my code. I'd read Design Patterns 6-7 years earlier, but I noticed that I tended to use only a small subset of the patterns from the book - particularly Composite, Decorator, Chain of Responsibility, and a few others.

In particular, I noticed that modelling seemed to be easier, and the code better structured, when I could apply the Composite design pattern. It was also clear, however, that I couldn't always use the Composite pattern, so I started to speculate on what could be the distinguishing factors. In 2010, I made a first attempt at identifying when a Composite is possible, and when it isn't. Unfortunately, while it was a fine attempt (which I'll return to later), it didn't lead anywhere. Ultimately, I gave up on the subject and moved on to other things.

### A revelation #

One of my interests in the next decade became functional programming. One day in late 2016 I came across this Code Review question by Scott Nimrod. It was an solution to the Business Rules kata, which, briefly told, is about implementing changing business rules in a sustainable manner.

In my answer to the question, I gave an outline (repeated below) of how I would address the problem in F#. As a comment to my answer, Scott wrote:

"Feels like the Decorator Pattern..."

I responded,

"Not really; it's the Composite pattern..."

A few days later, as I was doing something else, it suddenly dawned on me that not only was a few lines of F# code equivalent to the Composite design pattern, but those lines of code were also manifestations of fundamental abstractions from category theory. Originally, I thought Composite was a combination of applicative functors and monoids, but as I investigated, I discovered that Composites are simply monoids.

This article shows a concrete example of that discovery, starting with my original F# code, subsequently translating it to C# to demonstrate that it's a Composite, and concluding with a translation to Haskell in order to demonstrate that it all fits with the formalisation of `Monoid`

there.

### Original F# solution outline #

The kata is about modelling volatile business rules in a sustainable manner. Particularly, you must implement various business rules associated with payments for products and services. Making a rough outline of a model, I started by introducing some types in F#:

type Membership = Basic | Gold type Good = | PhysicalProduct of string | Book of string | Video of string | Membership of Membership | Upgrade type Command = | Slip of string * (Good list) | Activate of Membership | Upgrade | PayAgent

This basically states that there's a closed hierarchy of goods, and a closed hierarchy of business commands, as well as a `Membership`

enumeration. A *good* can be a physical product with a name, a book with a name, a membership or upgrade, and so on. A *command* can be a packing slip, a membership activation, and so on.

Since I was only interested in a rough outline of a solution, I only sketched four business rules, all implemented as functions. The first creates a packing slip for certain goods:

// Good -> Command list let slipForShipping = function | PhysicalProduct name -> [Slip ("Shipping", [PhysicalProduct name])] | Book name -> [Slip ("Shipping", [Book name])] | Video name -> [Slip ("Shipping", [Video name])] | _ -> []

This function takes a `Good`

value as input and returns a list of `Command`

values as output. If the `Good`

is a `PhysicalProduct`

, `Book`

, or `Video`

, it returns a packing slip command; otherwise, it returns an empty list of commands.

The next business rule is a similar function:

// Good -> Command list let slipForRoyalty = function | Book name -> [Slip ("Royalty", [Book name])] | _ -> []

This business rule generates a royalty slip for any `Book`

, but does nothing for any other `Good`

.

The third business rule activates a membership:

// Good -> Command list let activate = function | Membership x -> [Activate x] | _ -> []

If the `Good`

is a `Membership`

, the `activate`

function returns a list containing a single `Activate`

command; otherwise, it returns an empty list.

Finally, the last rule upgrades a membership:

// Good -> Command list let upgrade = function | Good.Upgrade -> [Upgrade] | _ -> []

Similar to the previous functions, this one looks at the type of `Good`

, and returns an `Upgrade`

command when the input is an `Upgrade`

good, and an empty list otherwise.

Notice that all four functions share the same type: `Good -> Command list`

. I designed them like that on purpose, because this enables you to compose a list of business rules to a function that looks like a single rule:

// ('a -> 'b list) list -> 'a -> 'b list let handle rules good = List.collect (fun r -> r good) rules

This `handle`

function takes a list of business rules (`rules`

) and returns a new function with the type `Good -> Command list`

(or, actually, a function with the type `'a -> 'b list`

- once again I've fallen into the trap of using too descriptive names). Notice that this is the same type as the individual rules.

You can now compose the four specific business rules:

// Good -> Command list let handleAll = handle [slipForShipping; slipForRoyalty; activate; upgrade]

This function also has the type `Good -> Command list`

although it's a composition of four rules.

You can use it like this *F# Interactive* example:

> handleAll (Book "The Annotated Turing");; val it : Command list = [Slip ("Shipping",[Book "The Annotated Turing"]); Slip ("Royalty",[Book "The Annotated Turing"])]

(Yes, I like The Annotated Turing - read my review on Goodreads.)

Notice that while each of the business rules produces only zero or one `Command`

values, in this example `handleAll`

returns two `Command`

values.

This design, where a composition looks like the things it composes, sounds familiar.

### Business rules in C# #

You can translate the above F# model to an object-oriented model in C#. Translating discriminated unions like `Good`

and `Command`

to C# always involves compromises. In order to keep the example as simple as possible, I decided to translate each of those to a marker interface, although I loathe that 'pattern':

public interface IGood { }

While the interface doesn't afford any behaviour, various classes can still implement it, like, for example, `Book`

:

public class Book : IGood { public Book(string name) { Name = name; } public string Name { get; } }

Other `IGood`

'implementations' looks similar, and there's a comparable class hierarchy for `ICommand`

, which is another marker interface.

The above F# code used a shared function type of `Good -> Command list`

as a polymorphic type for a business rule. You can translate that to a C# interface:

public interface IRule { IReadOnlyCollection<ICommand> Handle(IGood good); }

The above `slipForShipping`

function becomes a class that implements the `IRule`

interface:

public class SlipForShippingRule : IRule { public IReadOnlyCollection<ICommand> Handle(IGood good) { if (good is PhysicalProduct || good is Book || good is Video) return new[] { new SlipCommand("Shipping", good) }; return new ICommand[0]; } }

Instead of pattern matching on a discriminated union, the `Handle`

method examines the subtype of `good`

and only returns a `SlipCommand`

if the `good`

is either a `PhysicalProduct`

, a `Book`

, or a `Video`

.

The other implementations are similar, so I'm not going to show all of them, but here's one more:

public class ActivateRule : IRule { public IReadOnlyCollection<ICommand> Handle(IGood good) { var m = good as MembershipGood; if (m != null) return new[] { new ActivateCommand(m.Membership) }; return new ICommand[0]; } }

Since 'all' members of `IRule`

return collections, which form monoids over concatenation, the interface itself gives rise to a monoid. This means that you can create a Composite:

public class CompositeRule : IRule { private readonly IRule[] rules; public CompositeRule(params IRule[] rules) { this.rules = rules; } public IReadOnlyCollection<ICommand> Handle(IGood good) { var commands = new List<ICommand>(); foreach (var rule in rules) commands.AddRange(rule.Handle(good)); return commands; } }

Notice how the implementation of `Handle`

follows the template for monoid accumulation. It starts with the *identity*, which, for the collection concatenation monoid is the empty collection. It then loops through all the composed `rules`

and updates the accumulator `commands`

in each iteration. Here, I used `AddRange`

, which mutates `commands`

instead of returning a new value, but the result is the same. Finally, the method returns the accumulator.

You can now compose all the business rules and use the composition as though it was a single object:

var rule = new CompositeRule( new SlipForShippingRule(), new SlipForRoyaltyRule(), new ActivateRule(), new UpgradeRule()); var book = new Book("The Annotated Turing"); var commands = rule.Handle(book);

When the method returns, `commands`

contains two `SlipCommand`

objects - a packing slip, and a royalty slip.

### Business rules in Haskell #

You can also port the F# code to Haskell, which is usually easy as long as the F# is written in a 'functional style'. Since Haskell has an explicit notion of monoids, you'll be able to see how the two above solutions are monoidal.

The data types are easy to translate to Haskell - you only have to adjust the syntax a bit:

data Membership = Basic | Gold deriving (Show, Eq, Enum, Bounded) data Good = PhysicalProduct String | Book String | Video String | Membership Membership | UpgradeGood deriving (Show, Eq) data Command = Slip String [Good] | Activate Membership | Upgrade | PayAgent deriving (Show, Eq)

The business rule functions are also easy to translate:

slipForShipping :: Good -> [Command] slipForShipping pp@(PhysicalProduct _) = [Slip "Shipping" [pp]] slipForShipping b@(Book _) = [Slip "Shipping" [b]] slipForShipping v@(Video _) = [Slip "Shipping" [v]] slipForShipping _ = [] slipForRoyalty :: Good -> [Command] slipForRoyalty b@(Book _) = [Slip "Royalty" [b]] slipForRoyalty _ = [] activate :: Good -> [Command] activate (Membership m) = [Activate m] activate _ = [] upgrade :: Good -> [Command] upgrade (UpgradeGood) = [Upgrade] upgrade _ = []

Notice that all four business rules share the same type: `Good -> [Command]`

. This is conceptually the same type as in the F# code; instead of writing `Command list`

, which is the F# syntax, the Haskell syntax for a list of `Command`

values is `[Command]`

.

All those functions are monoids because their return types form a monoid, so in Haskell, you can compose them without further ado:

handleAll :: Good -> [Command] handleAll = mconcat [slipForShipping, slipForRoyalty, activate, upgrade]

`mconcat`

is a built-in function that aggregates any list of monoidal values to a single value:

mconcat :: Monoid a => [a] -> a

Since all four functions are monoids, this just works out of the box. A Composite is just a monoid. Here's an example of using `handleAll`

from GHCi:

*BusinessRules> handleAll $ Book "The Annotated Turing" [Slip "Shipping" [Book "The Annotated Turing"],Slip "Royalty" [Book "The Annotated Turing"]]

The result is as you'd come to expect.

Notice that not only don't you have to write a `CompositeRule`

class, you don't even have to write a `handle`

helper function. Haskell already understands monoids, so composition happens automatically.

If you prefer, you could even skip the `handle`

function too:

*BusinessRules> mconcat [slipForShipping, slipForRoyalty, activate, upgrade] $ Book "Blindsight" [Slip "Shipping" [Book "Blindsight"],Slip "Royalty" [Book "Blindsight"]]

(Yes, you should also read Blindsight.)

It's not that composition as such is built into Haskell, but rather that the language is designed around a powerful ability to model abstractions, and one of the built-in abstractions just happens to be monoids. You could argue, however, that many of Haskell's fundamental abstractions are built from category theory, and one of the fundamental characteristics of a category is how morphisms compose.

### Summary #

Composite are monoids. This article shows an example, starting with a solution in F#. You can translate the F# code to object-oriented C# and model the composition of business rules as a Composite. You can also translate the F# code 'the other way', to the strictly functional language Haskell, and thereby demonstrate that the solution is based on a monoid.

*This article is a repost of a guest post on the NDC blog, reproduced here with kind permission.*

## Project Arbitraries with view patterns

*Write expressive property-based test with QuickCheck and view patterns.*

Recently, I was writing some QuickCheck-based tests of some business logic, and since the business logic in question involved a custom domain type called `Reservation`

, I had to write an `Arbitrary`

instance for it. Being a dutiful Haskell programmer, I wrapped it in a `newtype`

in order to prevent warnings about orphaned instances:

newtype ArbReservation = ArbReservation { getReservation :: Reservation } deriving (Show, Eq) instance Arbitrary ArbReservation where arbitrary = do (d, e, n, Positive q, b) <- arbitrary return $ ArbReservation $ Reservation d e n q b

This is all fine as long as you just need one `Reservation`

in a test, because in that case, you can simply pattern-match it out of `ArbReservation`

:

testProperty "tryAccept reservation in the past" $ \ (Positive capacity) (ArbReservation reservation) -> let stub (IsReservationInFuture _ next) = next False stub (ReadReservations _ next) = next [] stub (Create _ next) = next 0 actual = iter stub $ runMaybeT $ tryAccept capacity reservation in isNothing actual

Here, `reservation`

is a `Reservation`

value because it was pattern-matched out of `ArbReservation reservation`

. That's just like `capacity`

is an `Int`

, because it was pattern-matched out of `Positive capacity`

.

Incidentally, in the spirit of the previous article, I'm here using in-lined properties implemented as lambda expressions. The lambda expressions use non-idiomatic formatting in order to make the tests more readable (and to prevent horizontal scrolling), but the gist of the matter is that the entire expression has the type `Positive Int -> ArbReservation -> Bool`

. This is a `Testable`

property because all the input types have `Arbitrary`

instances.

### Discommodity creeps in #

That's fine for that test case, but for the next, I needed not only a single `Reservation`

value, but also a list of `Reservation`

values. Again, with QuickCheck, you can't write a property with a type like `Positive Int -> [Reservation] -> ArbReservation -> Bool`

, because there's no `Arbitrary`

instance for `[Reservation]`

. Instead, you'll need a property with the type `Positive Int -> [ArbReservation] -> ArbReservation -> Bool`

.

One way to do that is to write the property like this:

testProperty "tryAccept reservation when capacity is insufficient" $ \ (Positive i) reservations (ArbReservation reservation) -> let stub (IsReservationInFuture _ next) = next True stub (ReadReservations _ next) = next $ getReservation <$> reservations stub (Create _ next) = next 0 reservedSeats = sum $ reservationQuantity <$> getReservation <$> reservations capacity = reservedSeats + reservationQuantity reservation - i actual = iter stub $ runMaybeT $ tryAccept capacity reservation in isNothing actual

Here, `reservations`

has type type `[ArbReservation]`

, so every time the test needs to operate on the values, it first has to pull the `Reservation`

values out of it using `getReservation <$> reservations`

. That seems unnecessarily verbose and repetitive, so it'd be nice if a better option was available.

### View pattern #

Had I been writing F# code, I'd immediately be reaching for an active pattern, but this is Haskell. If there's one thing, though, I've learned about Haskell so far, it's that, if F# can do something, there's a very good chance Haskell can do it too - only, it may be called something else.

With a vague sense that I'd seen something similar in some Haskell code somewhere, I went looking, and about fifteen minutes later I'd found what I was looking for: a little language extension called *view patterns*. Just add the language extension to the top of the file where you want to use it:

`{-# LANGUAGE ViewPatterns #-}`

You can now change the property to pattern-match `reservations`

out of a function call, so to speak:

testProperty "tryAccept reservation when capacity is insufficient" $ \ (Positive i) (fmap getReservation -> reservations) (ArbReservation reservation) -> let stub (IsReservationInFuture _ next) = next True stub (ReadReservations _ next) = next reservations stub (Create _ next) = next 0 reservedSeats = sum $ reservationQuantity <$> reservations capacity = reservedSeats + reservationQuantity reservation - i actual = iter stub $ runMaybeT $ tryAccept capacity reservation in isNothing actual

The function `getReservation`

has the type `ArbReservation -> Reservation`

, so `fmap getReservation`

is a partially applied function with the type `[ArbReservation] -> [Reservation]`

. In order to be able to call the overall lambda expression, the caller must supply an `[ArbReservation]`

value to the view pattern, which means that the type of that argument must be `[ArbReservation]`

. The view pattern then immediately unpacks the result of the function and gives you `reservations`

, which is the return value of calling `fmap getReservation`

with the input value(s). Thus, `reservations`

has the type `[Reservation]`

.

The type of the entire property is now `Positive Int -> [ArbReservation] -> ArbReservation -> Bool`

.

This removes some noise from the body of the property, so I find that this is a useful trick in this particular situation.

### Summary #

You can use the *view patterns* GHC language extension when you need to write a function that takes an argument of a particular type, but you never care about the original input, but instead immediately wish to project it to a different value.

I haven't had much use for it before, but it seems to be useful in the context of QuickCheck properties.

## Comments

I've seen folks wrap up the view pattern in a pattern synonym:

```
pattern ArbReservations :: [Reservation] -> [ArbReservation]
pattern ArbReservations rs <- (coerce -> rs) where ArbReservations rs = coerce rs
foo :: [ArbReservation] -> IO ()
foo (ArbReservations rs) = traverse print rs
```

(`coerce`

is usually more efficient than `fmap`

.)

OTOH I don't think orphan instances of `Arbitrary`

are very harmful. It's unlikely that they'll get accidentally imported or overlap, because `Arbitrary`

is purely used for testing. So in this specific case I'd probably just stick with an orphan instance and turn off the warning for that file.

Benjamin, thank you for the *pattern synonyms* tip; I'll have to try that next time.

Regarding orphaned instances, your point is something I've been considering myself, but I'm still at the point of my Haskell journey where I'm trying to understand the subtleties of the ecosystem, so I wasn't sure whether or not it'd be a good idea to allow orphaned `Arbitrary`

instances.

When you suggest turning off the warning for a file, do you mean adding an `{-# OPTIONS_GHC -fno-warn-orphans #-}`

pragma, or did you have some other method in mind?

Yep I meant `OPTIONS_GHC`

.

Orphan instances are problematic because of the possibility that they'll be imported unintentionally or overlap with another orphan instance. If you import two modules which both define orphan instances for the same type then there's no way for GHC to know which one you meant when you attempt to use them. Since instances aren't named you can't even specify it manually, and the compiler can't check for this scenario ahead of time because of separate compilation. (Non-orphans are guaranteed unique by the fact that you can't import the parent type/class without importing the instance.)

In the case of `Arbitrary`

these problems typically don't apply because the class is intended to be used for testing. `Arbitrary`

instances are usually internal to your test project and not exported, so the potential for damage is small.

Benjamin, thank you for elaborating. That all makes sense to me.

## Inlined HUnit test lists

*An alternative way to organise tests lists with HUnit.*

In the previous article you saw how to write parametrised test with HUnit. While the tests themselves were elegant and readable (in my opinion), the composition of test lists left something to be desired. This article offers a different way to organise test lists.

### Duplication #

The main problem is one of duplication. Consider the `main`

function for the test library, as defined in the previous article:

main = defaultMain $ hUnitTestToTests $ TestList [ "adjustToBusinessHours returns correct result" ~: adjustToBusinessHoursReturnsCorrectResult, "adjustToDutchBankDay returns correct result" ~: adjustToDutchBankDayReturnsCorrectResult, "Composed adjust returns correct result" ~: composedAdjustReturnsCorrectResult ]

It annoys me that I have a function with a (somewhat) descriptive name, like `adjustToBusinessHoursReturnsCorrectResult`

, but then I also have to give the test a label - in this case `"adjustToBusinessHours returns correct result"`

. Not only is this duplication, but it also adds an extra maintenance overhead, because if I decide to rename the test, should I also rename the label?

Why do you even need the label? When you run the test, that label is printed during the test run, so that you can see what happens:

$ stack test --color never --ta "--plain" ZonedTimeAdjustment-0.1.0.0: test (suite: ZonedTimeAdjustment-test, args: --plain) :adjustToDutchBankDay returns correct result: : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] :adjustToBusinessHours returns correct result: : [OK] : [OK] : [OK] :Composed adjust returns correct result: : [OK] : [OK] : [OK] : [OK] : [OK] Test Cases Total Passed 20 20 Failed 0 0 Total 20 20

I considered it redundant to give each test case in the parametrised tests their own labels, but I could have done that too, if I'd wanted to.

What happens if you remove the labels?

main = defaultMain $ hUnitTestToTests $ TestList $ adjustToBusinessHoursReturnsCorrectResult ++ adjustToDutchBankDayReturnsCorrectResult ++ composedAdjustReturnsCorrectResult

That compiles, but produces output like this:

$ stack test --color never --ta "--plain" ZonedTimeAdjustment-0.1.0.0: test (suite: ZonedTimeAdjustment-test, args: --plain) : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] : [OK] Test Cases Total Passed 20 20 Failed 0 0 Total 20 20

If you don't care about the labels, then that's a fine solution. On the other hand, if you *do* care about the labels, then a different approach is warranted.

### Inlined test lists #

Looking at an expression like `"Composed adjust returns correct result" ~: composedAdjustReturnsCorrectResult`

, I find `"Composed adjust returns correct result"`

more readable than `composedAdjustReturnsCorrectResult`

, so if I want to reduce duplication, I want to go after a solution that names a test with a label, instead of a solution that names a test with a function name.

What is `composedAdjustReturnsCorrectResult`

? It's just the name of a pure function (because its type is `[Test]`

). Since it's referentially transparent, it means that in the test list in `main`

, I can replace the function with its body! I can do this with all three functions, although, in order to keep things simplified, I'm only going to show you two of them:

main :: IO () main = defaultMain $ hUnitTestToTests $ TestList [ "adjustToBusinessHours returns correct result" ~: do (dt, expected) <- [ (zt (2017, 10, 2) (6, 59, 4) 0, zt (2017, 10, 2) (9, 0, 0) 0), (zt (2017, 10, 2) (9, 42, 41) 0, zt (2017, 10, 2) (9, 42, 41) 0), (zt (2017, 10, 2) (19, 1, 32) 0, zt (2017, 10, 3) (9, 0, 0) 0) ] let actual = adjustToBusinessHours dt return $ ZT expected ~=? ZT actual , "Composed adjust returns correct result" ~: do (dt, expected) <- [ (zt (2017, 1, 31) ( 7, 45, 55) 2 , zt (2017, 2, 28) ( 7, 0, 0) 0), (zt (2017, 2, 6) (10, 3, 2) 1 , zt (2017, 3, 6) ( 9, 3, 2) 0), (zt (2017, 2, 9) ( 4, 20, 0) 0 , zt (2017, 3, 9) ( 9, 0, 0) 0), (zt (2017, 2, 12) (16, 2, 11) 0 , zt (2017, 3, 10) (16, 2, 11) 0), (zt (2017, 3, 14) (13, 48, 29) (-1), zt (2017, 4, 13) (14, 48, 29) 0) ] let adjustments = reverse [adjustToNextMonth, adjustToBusinessHours, adjustToDutchBankDay, adjustToUtc] let adjust = appEndo $ mconcat $ Endo <$> adjustments let actual = adjust dt return $ ZT expected ~=? ZT actual ]

In order to keep the code listing to a reasonable length, I didn't include the third test `"adjustToDutchBankDay returns correct result"`

, but it works in exactly the same way.

This is a list with two values. You can see that the values are separated by a `,`

, just like list elements normally are. What's unusual, however, is that each element in the list is defined with a multi-line `do`

block.

In C# and F#, I'm used to being able to just write new test functions, and they're automatically picked up by convention and executed by the test runner. I wouldn't be at all surprised if there was a mechanism using Template Haskell that enables something similar, but I find that there's something appealing about treating tests as first-class values all the way.

By inlining the tests, I can retain my F# and C# workflow. Just add a new test within the list, and it's automatically picked up by the `main`

function. Not only that, but it's no longer possible to write a test that compiles, but is never executed by the test runner because it has the wrong type. This occasionally happens to me in F#, but with the technique outlined here, if I accidentally give the test the wrong type, it's not going to compile.

### Conclusion #

Since HUnit tests are first-class values, you can define them inlined in test lists. For larger code bases, I'd assume that you'd want to spread your unit tests across multiple modules. In that case, I suppose that you could have each test module export a `[Test]`

value. In the test library's `main`

function, you'd need to manually concatenate all the exported test lists together, so a small maintenance burden remains. When you add a new test module, you'd have to add its exported tests to `main`

.

I wouldn't be surprised, however, if a clever reader could point out to me how to avoid that as well.

## Parametrised unit tests in Haskell

*Here's a way to write parametrised unit tests in Haskell.*

Sometimes you'd like to execute the same (unit) test for a number of test cases. The only thing that varies is the input values, and the expected outcome. The actual test code is the same for all test cases. Among object-oriented programmers, this is known as a parametrised test.

When I recently searched the web for how to do parametrised tests in Haskell, I could only find articles that talked about property-based testing, mostly with QuickCheck. I normally prefer property-based testing, but sometimes, I'd rather like to run a test with some deterministic test cases that are visible and readable in the code itself.

Here's one way I found that I could do that in Haskell.

### Testing date and time adjustments in C# #

In an earlier article, I discussed how to model date and time adjustments as a monoid. The example code was written in C#, and I used a few tests to demonstrate that the composition of adjustments work as intended:

[Theory] [InlineData("2017-01-31T07:45:55+2", "2017-02-28T07:00:00Z")] [InlineData("2017-02-06T10:03:02+1", "2017-03-06T09:03:02Z")] [InlineData("2017-02-09T04:20:00Z" , "2017-03-09T09:00:00Z")] [InlineData("2017-02-12T16:02:11Z" , "2017-03-10T16:02:11Z")] [InlineData("2017-03-14T13:48:29-1", "2017-04-13T14:48:29Z")] public void AccumulatedAdjustReturnsCorrectResult( string dtS, string expectedS) { var dt = DateTimeOffset.Parse(dtS); var sut = DateTimeOffsetAdjustment.Accumulate( new NextMonthAdjustment(), new BusinessHoursAdjustment(), new DutchBankDayAdjustment(), new UtcAdjustment()); var actual = sut.Adjust(dt); Assert.Equal(DateTimeOffset.Parse(expectedS), actual); }

The above parametrised test uses xUnit.net (particularly its `Theory`

feature) to execute the same test code for five test cases. Here's another example:

[Theory] [InlineData("2017-10-02T06:59:04Z", "2017-10-02T09:00:00Z")] [InlineData("2017-10-02T09:42:41Z", "2017-10-02T09:42:41Z")] [InlineData("2017-10-02T19:01:32Z", "2017-10-03T09:00:00Z")] public void AdjustReturnsCorrectResult(string dts, string expectedS) { var dt = DateTimeOffset.Parse(dts); var sut = new BusinessHoursAdjustment(); var actual = sut.Adjust(dt); Assert.Equal(DateTimeOffset.Parse(expectedS), actual); }

This one covers the three code paths through `BusinessHoursAdjustment.Adjust`

.

These tests are similar, so they share some good and bad qualities.

On the positive side, I like how *readable* such tests are. The test code is only a few lines of code, and the test cases (input, and expected output) are in close proximity to the code. Unless you're on a phone with a small screen, you can easily see all of it at once.

For a problem like this, I felt that I preferred examples rather than using property-based testing. If the date and time is this, then the adjusted result should be that, and so on. When we *read* code, we tend to prefer examples. Good documentation often contains examples, and for those of us who consider tests documentation, including examples in tests should advance that cause.

On the negative side, tests like these still contain noise. Most of it relates to the problem that xUnit.net tests aren't first-class values. These tests actually ought to take a `DateTimeOffset`

value as input, and compare it to another, expected `DateTimeOffset`

value. Unfortunately, `DateTimeOffset`

values aren't constants, so you can't use them in attributes, like the `[InlineData]`

attribute.

There are other workarounds than the one I ultimately chose, but none that are as simple (that I'm aware of). Strings are constants, so you can put formatted date and time strings in the `[InlineData]`

attributes, but the cost of doing this is two calls to `DateTimeOffset.Parse`

. Perhaps this isn't a big price, you think, but it does make me wish for something prettier.

### Comparing date and time #

In order to port the above tests to Haskell, I used Stack to create a new project with HUnit as the unit testing library.

The Haskell equivalent to `DateTimeOffset`

is called `ZonedTime`

. One problem with `ZonedTime`

values is that you can't readily compare them; the type isn't an `Eq`

instance. There are good reasons for that, but for the purposes of unit testing, I wanted to be able to compare them, so I defined this helper data type:

newtype ZonedTimeEq = ZT ZonedTime deriving (Show) instance Eq ZonedTimeEq where ZT (ZonedTime lt1 tz1) == ZT (ZonedTime lt2 tz2) = lt1 == lt2 && tz1 == tz2

This enables me to compare two `ZonedTimeEq`

values, which are only considered equal if they represent the same date, the same time, and the same time zone.

### Test Utility #

I also added a little function for creating `ZonedTime`

values:

zt (y, mth, d) (h, m, s) tz = ZonedTime (LocalTime (fromGregorian y mth d) (TimeOfDay h m s)) (hoursToTimeZone tz)

The motivation is simply that, as you can tell, creating a `ZonedTime`

value requires a verbose expression. Clearly, the `ZonedTime`

API is flexible, but in order to define some test cases, I found it advantageous to trade readability for flexibility. The `zt`

function enables me to compactly define some `ZonedTime`

values for my test cases.

### Testing business hours in Haskell #

In HUnit, a test is either a `Test`

, a list of `Test`

values, or an impure assertion. For a parametrised test, a `[Test]`

sounded promising. At the beginning, I struggled with finding a readable way to express the tests. I wanted to be able to start with a list of test cases (inputs and expected outputs), and then `fmap`

them to an executable test. At first, the readability goal seemed elusive, until I realised that I can also use `do`

notation with lists (since they're monads):

adjustToBusinessHoursReturnsCorrectResult :: [Test] adjustToBusinessHoursReturnsCorrectResult = do (dt, expected) <- [ (zt (2017, 10, 2) (6, 59, 4) 0, zt (2017, 10, 2) (9, 0, 0) 0), (zt (2017, 10, 2) (9, 42, 41) 0, zt (2017, 10, 2) (9, 42, 41) 0), (zt (2017, 10, 2) (19, 1, 32) 0, zt (2017, 10, 3) (9, 0, 0) 0) ] let actual = adjustToBusinessHours dt return $ ZT expected ~=? ZT actual

This is the same test as the above C# test named `AdjustReturnsCorrectResult`

, and it's about the same size as well. Since the test is written using `do`

notation, you can take a list of test cases and operate on each test case at a time. Although the test creates a list of tuples, the `<-`

arrow pulls each `(ZonedTime, ZonedTime)`

tuple out of the list and binds it to `(dt, expected)`

.

This test literally consists of only three expressions, so according to my normal heuristic for test formatting, I don't even need white space to indicate the three phases of the AAA pattern. The first expression sets up the test case `(dt, expected)`

.

The next expression exercises the System Under Test - in this case the `adjustToBusinessHours`

function. That's simply a function call.

The third expression verifies the result. It uses HUnit's `~=?`

operator to compare the expected and the actual values. Since `ZonedTime`

isn't an `Eq`

instance, both values are converted to `ZonedTimeEq`

values. The `~=?`

operator returns a `Test`

value, and since the entire test takes place inside a `do`

block, you must `return`

it. Since this particular `do`

block takes place inside the list monad, the type of `adjustToBusinessHoursReturnsCorrectResult`

is `[Test]`

. I added the type annotation for the benefit of you, dear reader, but technically, it's not required.

### Testing the composed function #

Translating the `AccumulatedAdjustReturnsCorrectResult`

C# test to Haskell follows the same recipe:

composedAdjustReturnsCorrectResult :: [Test] composedAdjustReturnsCorrectResult = do (dt, expected) <- [ (zt (2017, 1, 31) ( 7, 45, 55) 2 , zt (2017, 2, 28) ( 7, 0, 0) 0), (zt (2017, 2, 6) (10, 3, 2) 1 , zt (2017, 3, 6) ( 9, 3, 2) 0), (zt (2017, 2, 9) ( 4, 20, 0) 0 , zt (2017, 3, 9) ( 9, 0, 0) 0), (zt (2017, 2, 12) (16, 2, 11) 0 , zt (2017, 3, 10) (16, 2, 11) 0), (zt (2017, 3, 14) (13, 48, 29) (-1), zt (2017, 4, 13) (14, 48, 29) 0) ] let adjustments = reverse [adjustToNextMonth, adjustToBusinessHours, adjustToDutchBankDay, adjustToUtc] let adjust = appEndo $ mconcat $ Endo <$> adjustments let actual = adjust dt return $ ZT expected ~=? ZT actual

The only notable difference is that this unit test consists of five expressions, so according to my formatting heuristic, I inserted some blank lines in order to make it easier to distinguish the three AAA phases from each other.

### Running tests #

I also wrote a third test called `adjustToDutchBankDayReturnsCorrectResult`

, but that one is so similar to the two you've already seen that I see no point showing it here. In order to run all three tests, I define the tests' `main`

function as such:

main = defaultMain $ hUnitTestToTests $ TestList [ "adjustToBusinessHours returns correct result" ~: adjustToBusinessHoursReturnsCorrectResult, "adjustToDutchBankDay returns correct result" ~: adjustToDutchBankDayReturnsCorrectResult, "Composed adjust returns correct result" ~: composedAdjustReturnsCorrectResult ]

This uses `defaultMain`

from test-framework, and `hUnitTestToTests`

from test-framework-hunit.

I'm not happy about the duplication of text and test names, and the maintenance burden implied by having to explicitly add every test function to the test list. It's too easy to forget to add a test after you've written it. In the next article, I'll demonstrate an alternative way to compose the tests so that duplication is reduced.

### Conclusion #

Since HUnit tests are first-class values, you can manipulate and compose them just like any other value. That includes passing them around in lists and binding them with `do`

notation. Once you figure out how to write parametrised tests with HUnit, it's easy, readable, and elegant.

The overall configuration of the test runner, however, leaves a bit to be desired, so that's the topic for the next article.

**Next:** Inlined HUnit test lists.

## Comments