Mar_14.md

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---
title: Functional design: smart constructors
published: true
description:
tags: functional, typescript
series: Functional design
---

Sometimes you need guarantees about the values in your program beyond what can be accomplished with the usual type system checks. Smart constructors can be used for this purpose.

The Problem

interface Person {
  name: string
  age: number
}

function person(name: string, age: number): Person {
  return { name, age }
}

const p = person('', -1.2) // no error

As you can see, string and number are broad types. How can I define a non empty string? Or positive numbers? Or integers? Or positive integers?

More generally:

how can I define a refinement of a type T?

## The recipe

1. define a type R which represents the refinement
2. do not export a constructor for R
3. do export a function (the smart constructor) with the following signature

make: (t: T) => Option<R>

A possible implementation: branded types

A branded type is a type T intersected with a unique brand

type BrandedT = T & Brand

Let's implement NonEmptyString following the recipe above:

1. define a type NonEmptyString which represents the refinement

export interface NonEmptyStringBrand {
  readonly NonEmptyString: unique symbol // ensures uniqueness across modules / packages
}

export type NonEmptyString = string & NonEmptyStringBrand

2. do not export a constructor for NonEmptyString

// DON'T do this
export function nonEmptyString(s: string): NonEmptyString { ... }

3. do export a smart constructor make: (s: string) => Option<NonEmptyString>

import { Option, none, some } from 'fp-ts/lib/Option'

// runtime check implemented as a custom type guard
function isNonEmptyString(s: string): s is NonEmptyString {
  return s.length > 0
}

export function makeNonEmptyString(
  s: string
): Option<NonEmptyString> {
  return isNonEmptyString(s) ? some(s) : none
}

Let's do the same thing for the age field

export interface IntBrand {
  readonly Int: unique symbol
}

export type Int = number & IntBrand

function isInt(n: number): n is Int {
  return Number.isInteger(n) && n >= 0
}

export function makeInt(n: number): Option<Int> {
  return isInt(n) ? some(n) : none
}

Usage

interface Person {
  name: NonEmptyString
  age: Int
}

function person(name: NonEmptyString, age: Int): Person {
  return { name, age }
}

person('', -1.2) // static error

const goodName = makeNonEmptyString('Giulio')
const badName = makeNonEmptyString('')
const goodAge = makeInt(45)
const badAge = makeInt(-1.2)

goodName.chain(name =>
  goodAge.map(age => person(name, age))
) // some({ "name": "Giulio", "age": 45 })

badName.chain(name => goodAge.map(age => person(name, age))) // none

goodName.chain(name => badAge.map(age => person(name, age))) // none

Conclusion

This seems to just pushing the burden of the runtime check to the caller. That's fair, but the caller in turn might push this burden up to its caller, and so on until you reach the system boundary, where you should do input validation anyway.

For a library that makes easy to do runtime validation at the system boundary and supports branded types, check out io-ts