Introduction to Functional Programming with fp-ts

Functional Programming (FP) is a paradigm that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data. In the TypeScript ecosystem, fp-ts is the gold standard for bringing these concepts to life.

Core Concepts of FP

1. Purity

A pure function's return value is determined only by its input values, without observable side effects. This makes code predictable and easy to test.

2. Immutability

Data is never modified after it is created. Instead of changing an object, you create a new one with the updated values.

3. Composition

Building complex logic by combining simple, reusable functions.

Enter fp-ts

fp-ts provides a set of data types and functions to manage complexity using FP patterns.

Option: Handling Missing Values

Instead of using null or undefined, use Option to explicitly represent the presence or absence of a value.

import * as O from 'fp-ts/Option'

const findUser = (id: number): O.Option<User> =>
  // ... logic returning some(user) or none

Either: Handling Errors

Either represents a value of one of two possible types. Use it for error handling where Left is the error and Right is the success.

import * as E from "fp-ts/Either";

const parseJson = (s: string): E.Either<Error, unknown> => {
  try {
    return E.right(JSON.parse(s));
  } catch (e) {
    return E.left(new Error("Invalid JSON"));
  }
};

TaskEither: Async Operations

Combine asynchronous computations with error handling using TaskEither.

Why use it?

While fp-ts has a steep learning curve, it rewards you with extremely robust, type-safe, and maintainable codebases. It forces you to handle edge cases explicitly, leading to fewer runtime bugs.

In the next post, we will dive deeper into Mastering fp-ts by building a real application.