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Functional TypeScript with fp-ts

December 12, 2020 – 10 minute read

I have a confession to make.

I actually enjoy JavaScript.

(this was originally posted on https://www.bekk.christmas/post/2020/12/functional-typescript-with-fp-ts)

I know, I know, I’m not really supposed to feel this way. It’s supposed to be this weird language full of flaws that never follows established rules and conventions, and we’re all supposed to not like it. But that’s just not the case for me – despite all the quirks and unusual behavior I still enjoy it.

There are, of course, sides of it I enjoy less. The two things I dislike the most are lack of strong, static typing, and a well-built standard library. The standard library is still growing, and the newer parts of it are not too bad – the older parts, though, are all over the place: they mutate, and lack consistency.

Not too long ago I came across this library named fp-ts, that together with TypeScript made my whole JavaScript experience a lot better. This blog post aims to give you a short introduction to this library, and show you some of its strenghts. To keep this blog post short, I will assume you know both JavaScript and TypeScript. You will probably still understand most of it even if you are not fluid in any of them, but consider yourself warned.


fp-ts introduces many functional concepts. If you come from Java or Kotlin, you can compare it to Vavr or Arrow, respectively. It provides several well known data types, type classes, a consistent library of functions, and several other functional abstractions.

Wading through every feature of fp-ts would be an enourmous task, and one way too overkill for this blog. Instead, I will take you through some of the simpler concepts that anyone can benefit from. My goal is to show you exactly how to make use of some of these concepts, so you can take use of them right after.

Let’s get started!

The data types

Let’s start with two data types I use more or less daily: Option and Either. If you are completely new to functional programming, I suggest starting with these as they encourage a coding style that is safe, and can help you understand other aspects of functional programming later on.

An Option type represents an optional value. Something you either have, or don’t have. This is useful when lacking a value is valid in your domain, or when a function may or may not return a value. Let’s see some code.

import { Option, some, none, map } from "fp-ts/Option"

type User = {
  id: number,
  username: string,
  expiration: Date

function getUserById(id: number): Option<User> { .. }

const userOne = getUserById(1) // -> some({ id: 1, name: "user-name", expiration: "2099-01-01T00:00:00Z" })
const userTwo = getUserById(2) // -> none

const getUserName = (u: User): string => u.username

const usernameOne = map(getUserName)(userOne) // -> some("user-name")
const usernameTwo = map(getUserName)(userTwo) // -> none

Before we go through the code, I’d just like to point out the use of partial application in the two last lines. Lots of functions in fp-ts are curried by default, as is often common in functional languages. This pattern is really convenient when you want to bind some, but not all, parameters of a function.

So, an Option wraps a value, and allows operations to be performed through functions such as map, filter, fold and others. This example demonstrates a really nice property of the Optional: your business code can describe the "happy path" – error handling is abstracted into the Option itself. We never have to check for null values before getting the username from the user, because the function getUserName is run in a safe context. map runs the provided function on an Option only if it is a some, and not a none. The same is true for other functions on the Option.

But what if you wanted to display, or use, the username? You can’t just extract the value from inside an Option, as you don’t know whether it is a some or a none. To get the actual value from the Option, you need to specify what to do both when it is a none, and a some. Let’s take a look at two safe ways of extracting your value from the Option.

import { fold, getOrElse } from "fp-ts/Option";

// Let’s calculate the length of the username
const usernameLength = fold(
  () => 0,
  (username: string) => username.length

const usernameOneLength: number = usernameLength(usernameOne); // -> 9
const usernameTwoLength: number = usernameLength(usernameTwo); // -> 0

const getOrEmpty = getOrElse(() => "");

const usernameOneValue: string = getOrEmpty(usernameOne); // -> "user-name"
const usernameTwoValue: string = getOrEmpty(usernameTwo); // -> ""

With both fold and getOrElse, the type system forces us to handle both the missing and the non-missing state. You now have a safe way of handling missing values, and even a safe way of getting them out as well – no more checking for null all over the place!

Let’s modify the function getUserById from the first example a bit. Instead of just returning a none, we would like to know why it was not returned. An Option can’t help you with this. Instead, you need something like the Either. Where an Option is either a none or a some, the Either is either a left or a right. It holds a value in both cases. The Either is often used to model situations where an operation can either fail or succeed. By convention, the left case represents the failure, and the right case represents success.

Aaaaaanyway. As stated, let’s change our getUserById function to also tell us why it was unsuccessful.

import { Either, right, left, map } from "fp-ts/Either"

type UserError = "UserNotFound" | "UserExpired"

function getUserById(id: number): Either<UserError, User> { .. }

// Pretending that a user with ID 1 exists, but not with 2
const userOne = getUserById(1) // -> right({id: 1, name: "user-name", expiration: "2099-01-01TT00:00:00Z'})
const userTwo = getUserById(2) // -> left("UserNotFound")

const usernameOne = map(getUserName)(userOne) // -> right("user-name")
const usernameTwo = map(getUserName)(userTwo) // -> left("UserNotFound")

This is not too far from the first example with the Option, with the added value that we now also know why it failed. It was either not found, or it was expired. Just as with the Option, Either is also a wrapper around your value(s), abstracting away the error case until you need the actual value. Either has its own version of fold, among others, which can be used to extract the value. I’ll leave you with the task of implementing this – if you need a hint, I can tell you it’s more or less the same as with Option!

So, which type should you use? It’s the usual, booring answer: it all the depends. It all depends on how the operation might fail, and what it would result in. It also boils down to semantics – is the lack of a value valid in your domain, or is it an error? In the former case, and Option is more suitable. In the latter, an Either might be better. As always: if you are unsure, just try one of them – you will soon find out if it was right or wrong.

Pipes and Flows

Function composition is a central concept in functional programming. It is the act of combining simple functions to build more complicated ones. Smaller and simpler functions are easier to reason about and test, but can’t perform complex operations by themselves.

You could of course just call your simple functions in succession in a larger function. Either by saving the result of each step, or wrapping your functions inside each other. Both of these gets more and more tedious the more functions you need to call, and hides the important details: the actual logic and transformation. Let’s take a look at two functions called pipe and flow, which both make composition easier. They are quite alike, but have different use cases.

import { pipe, flow } from "fp-ts/function";

const square = (x: number) => x * x;
const timesTen = (x: number) => x * 10;

const result = pipe(3, square, timesTen); // -> 90

const squareAndMultiply = flow(square, timesTen);

const result2 = squareAndMultiply(3); // -> 90

result and result2 have the same value, but are computed differently. pipe gives us the ability to pipe a value through a list of functions, and produce an output. This is nice for those one-off situations where you need to combine a few functions to produce a result. flow is more suited for those situations where you want to compose functions and create a new function permanently. In both cases, everything needs to typecheck – the input to one function needs to be of the same type as the output from the previous, all the way through.

Extended built-ins

As I said in the beginning of this post, JavaScripts standard library is in a bit of a weird position. If we take Array as an example, there is a distinction between functions that mutates in place, and functions that instead returns a new value. Things are moving to a better place, but we still have these old, mutating, functions that we have to live with. fp-ts fixes this by providing a consistent library even for JavaScript built-ins such as Array and Map. It’s not only consistent on the different types themselves, but also across the types thanks to extensive use of type classes 1. Every class that adheres to the Functor type class supports the map function, and every class that adheres to the Filterable type class can be filtered and partitioned. If this is greek to you, just ignore the lingo and appreciate the fact that most types has map, filter, reduce and loads of other functions implemented on them. You can even implement them on types you create yourself as well!

... and so much more

We have only really scratched the surface here. These concepts should give you enough to get you started, and hopefully see the value in this library. When you’re ready, there are tons of other concepts to dive into, which can make your code even more readable and safe. I haven’t had the time to wade through it all myself, so I still keep finding small gems which makes my day just a bit easier.

If you want to know more, the learning resources section of the documentation is actually quite good. As the author states, fp-ts does not really aim to teach functional programming from the ground up, but the resources are still good and manages to convince at least me quite well.

I also recommend reading the source code. It is surprisingly readable, even to me – a person who is neither fluent in advanced typescript or an FP zealot.

1 These are not «real» type classes, they are type classes implementet with interfaces. You can’t use the same map function on all Functors, but all Functors has a map function. You still need to use the type specific implementation of map, but at least it encourages the same pattern.