functional-programming

name

functional-programming

description

Master functional programming principles and patterns including purity, immutability, composition, higher-order functions, algebraic data types, functors, monads, and effect management. Use when working with functional paradigms, pure functions, immutable data structures, function composition, type-safe error handling, or implementing functional patterns like map/filter/reduce, currying, partial application, recursion, lazy evaluation, or referential transparency.

Functional Programming Mastery

Core Principles

The Zen of Functional Programming

Purity is better than side effects Pure functions create predictable, testable code. Functions depend only on inputs and produce no side effects.

Immutability is better than mutation Data that cannot change eliminates entire categories of bugs. State cannot be modified; concurrent operations become naturally safe.

Composition is better than monoliths Small, focused functions combined thoughtfully solve complex problems elegantly.

Declarative is better than imperative Express what you want, not how to get it.

Referential transparency enables reasoning When expressions can be replaced by their values, code becomes mathematical.

Functions are first-class citizens Treat functions as data. Pass them, return them, compose them.

Explicit is better than hidden Make dependencies visible through function parameters. No global state, no hidden coupling.

Recursion is iteration without mutation When loops require changing variables, let recursion express repetition through function calls.

Type safety catches errors early Strong types are your allies. Let the compiler prove code correct before it runs.

Separate effects from logic Isolate impure operations from pure computation. Draw clear boundaries between the ideal and the real.

Statelessness enables concurrency Functions without shared state run safely in parallel.

Errors should be values, not exceptions Make failure explicit in types. Return Maybe, Either, Result.

Although practicality beats purity The real world requires side effects. Embrace them thoughtfully, manage them explicitly, isolate them carefully.

Fundamental Concepts

Pure Functions

Pure functions have two key properties:

1. Deterministic: Same input always produces same output
2. No side effects: No mutations, no I/O, no observable changes

// Pure
const add = (a: number, b: number): number => a + b;

// Impure - depends on external state
let total = 0;
const addToTotal = (n: number): void => { total += n; };

// Impure - has side effect
const logAndAdd = (a: number, b: number): number => {
  console.log('adding'); // Side effect
  return a + b;
};

Immutability

Never mutate data; always create new values.

// Mutable (avoid)
const addItem = (arr: number[], item: number): void => {
  arr.push(item);
};

// Immutable (prefer)
const addItem = (arr: readonly number[], item: number): readonly number[] => 
  [...arr, item];

// Deep immutability
type DeepReadonly<T> = {
  readonly [P in keyof T]: T[P] extends object ? DeepReadonly<T[P]> : T[P];
};

Function Composition

Build complex operations from simple functions.

// Basic composition
const compose = <A, B, C>(f: (b: B) => C, g: (a: A) => B) => 
  (a: A): C => f(g(a));

// Pipe (left-to-right composition)
const pipe = <A, B, C>(f: (a: A) => B, g: (b: B) => C) => 
  (a: A): C => g(f(a));

// Example
const double = (n: number): number => n * 2;
const increment = (n: number): number => n + 1;

const doubleThenIncrement = pipe(double, increment);
doubleThenIncrement(5); // 11

Higher-Order Functions

Functions that take or return functions.

// Takes function as argument
const map = <A, B>(f: (a: A) => B) => 
  (arr: readonly A[]): readonly B[] => 
    arr.map(f);

// Returns function
const add = (a: number) => (b: number): number => a + b;
const add5 = add(5);

// Both
const compose = <A, B, C>(f: (b: B) => C) => 
  (g: (a: A) => B) => 
    (a: A): C => f(g(a));

Currying and Partial Application

Transform multi-argument functions into chains of single-argument functions.

// Uncurried
const add = (a: number, b: number, c: number): number => a + b + c;

// Curried
const addCurried = (a: number) => (b: number) => (c: number): number => 
  a + b + c;

const add5 = addCurried(5);
const add5and3 = add5(3);
add5and3(2); // 10

// Auto-curry utility
type Curry<T extends any[], R> = 
  T extends [infer First, ...infer Rest]
    ? (arg: First) => Rest extends [] ? R : Curry<Rest, R>
    : R;

const curry = <T extends any[], R>(
  fn: (...args: T) => R
): <T, R> => {
   ( 
    args. >= fn. 
      ? (...args  T)
      : ((fn  ).(, ...args))
  )  ;
};

Algebraic Data Types

Sum Types (Union Types)

Represent "one of several" options.

// Option/Maybe - represents presence or absence
type Option<A> = 
  | { tag: 'Some'; value: A }
  | { tag: 'None' };

// Either - represents success or failure
type Either<E, A> = 
  | { tag: 'Left'; value: E }
  | { tag: 'Right'; value: A };

// Result - common alias for Either
type Result<E, A> = Either<E, A>;

// Custom sum type
type PaymentMethod =
  | { type: 'CreditCard'; cardNumber: string }
  | { type: 'PayPal'; email: string }
  | { type: 'BankTransfer'; accountNumber: string };

Product Types (Records/Tuples)

Combine multiple values.

// Record (named fields)
type Person = {
  readonly name: string;
  readonly age: number;
};

// Tuple (positional fields)
type Coordinate = readonly [number, number];

// Nested product types
type Address = {
  readonly street: string;
  readonly city: string;
  readonly zipCode: string;
};

type Customer = {
  readonly person: Person;
  readonly address: Address;
};

Functors

Containers that can be mapped over.

interface Functor<F> {
  map<A, B>(fa: F, f: (a: A) => B): F;
}

// Array is a Functor
const arrayMap = <A, B>(arr: readonly A[], f: (a: A) => B): readonly B[] => 
  arr.map(f);

// Option is a Functor
const optionMap = <A, B>(opt: Option<A>, f: (a: A) => B): Option<B> => 
  opt.tag === 'None' ? opt : { tag: 'Some', value: f(opt.value) };

// Functor laws:
// 1. Identity: map(id) = id
// 2. Composition: map(f . g) = map(f) . map(g)

Monads

Functors with additional structure for sequencing operations.

interface Monad<M> extends Functor<M> {
  of<A>(a: A): M;
  flatMap<A, B>(ma: M, f: (a: A) => M): M;
}

// Option Monad
const optionOf = <A>(value: A): Option<A> => 
  ({ tag: 'Some', value });

const optionFlatMap = <A, B>(
  opt: Option<A>, 
  f: (a: A) => Option<B>
): Option<B> => 
  opt.tag === 'None' ? opt : f(opt.value);

// Either Monad
const eitherOf = <E, A>(value: A): Either<E, A> => 
  ({ tag: 'Right', value });

const eitherFlatMap = <E, A, B>(
  either: Either<E, A>,
  f: (a: A) => Either<E, B>
): Either<E, B> => 
  either.tag === 'Left' ? either : f(either.value);

// Monad laws:
// 1. Left identity: flatMap(of(a), f) = f(a)
// 2. Right identity: flatMap(m, of) = m

Common Patterns

Pattern Matching

Safe exhaustive checking of sum types.

const match = <T extends { tag: string }, R>(
  value: T,
  patterns: { [K in T['tag']]: (val: Extract<T, { tag: K }>) => R }
): R => {
  return patterns[value.tag](value as any);
};

// Usage
const describeOption = <A>(opt: Option<A>): string =>
  match(opt, {
    Some: ({ value }) => `Value: ${value}`,
    None: () => 'No value'
  });

Recursion Patterns

Replace loops with recursive functions.

// Tail-recursive sum
const sum = (arr: readonly number[]): number => {
  const go = (i: number, acc: number): number =>
    i >= arr.length ? acc : go(i + 1, acc + arr[i]);
  return go(0, 0);
};

// Recursive tree traversal
type Tree<A> = 
  | { tag: 'Leaf'; value: A }
  | { tag: 'Branch'; left: Tree<A>; right: Tree<A> };

const mapTree = <A, B>(tree: Tree<A>, f: (a: A) => B): Tree<B> =>
  tree.tag === 'Leaf' 
    ? { tag: 'Leaf', value: f(tree.value) }
    : {
        tag: 'Branch',
        left: mapTree(tree.left, f),
        right: mapTree(tree., f)
      };

Trampolining

Optimize recursion to avoid stack overflow.

type Trampoline<A> = 
  | { tag: 'Done'; value: A }
  | { tag: 'Continue'; next: () => Trampoline<A> };

const run = <A>(trampoline: Trampoline<A>): A => {
  let current = trampoline;
  while (current.tag === 'Continue') {
    current = current.next();
  }
  return current.value;
};

// Tail-recursive factorial using trampoline
const factorial = (n: number, acc = 1): Trampoline<number> =>
  n <= 1
    ? { tag: 'Done', value: acc }
    : { tag: 'Continue', next: () => factorial(n - 1, n * acc) };

run(factorial(10000)); // No stack overflow

Lazy Evaluation

Defer computation until needed.

type Lazy<A> = () => A;

const lazy = <A>(compute: () => A): Lazy<A> => {
  let cached: { computed: boolean; value?: A } = { computed: false };
  return () => {
    if (!cached.computed) {
      cached.value = compute();
      cached.computed = true;
    }
    return cached.value!;
  };
};

// Infinite sequences
type Stream<A> = {
  head: () => A;
  tail: () => Stream<A>;
};

const naturals = (n = 0): Stream<number> => ({
  head: () => n,
  tail: () => naturals(n + 1)
});

const take = <A>(n: number, stream: Stream<A>): A[] => {
  const result: A[] = [];
  let current = stream;
  for (let i = 0; i < n; i++) {
    result.(current.());
    current = current.();
  }
   result;
};

Effect Management

IO Monad

Encapsulate side effects.

type IO<A> = () => A;

const ioOf = <A>(value: A): IO<A> => () => value;

const ioMap = <A, B>(io: IO<A>, f: (a: A) => B): IO<B> => 
  () => f(io());

const ioFlatMap = <A, B>(io: IO<A>, f: (a: A) => IO<B>): IO<B> => 
  () => f(io())();

// Example
const log = (message: string): IO<void> => 
  () => console.log(message);

const prompt = (question: string): IO<string> => 
  () => window.prompt(question) || '';

const program = ioFlatMap(
  prompt('What is your name?'),
   => ()
);


();

Reader Monad

Thread configuration through computations.

type Reader<R, A> = (env: R) => A;

const readerOf = <R, A>(value: A): Reader<R, A> => 
  () => value;

const readerMap = <R, A, B>(
  reader: Reader<R, A>,
  f: (a: A) => B
): Reader<R, B> => 
  env => f(reader(env));

const readerFlatMap = <R, A, B>(
  reader: Reader<R, A>,
  f: (a: A) => Reader<R, B>
): Reader<R, B> => 
  env => f(reader(env))(env);

const ask = <R>(): Reader<R, R> => env => env;

// Example
type Config = { apiUrl: string; timeout: number };

const getApiUrl: Reader<Config, string> = 
  config => config.apiUrl;

const fetchUser = (: ): <, <>> =>
   ().( r.());

Free Monad

Separate program description from interpretation.

type Free<F, A> =
  | { tag: 'Pure'; value: A }
  | { tag: 'Impure'; command: F; next: (x: any) => Free<F, A> };

const pure = <F, A>(value: A): Free<F, A> => 
  ({ tag: 'Pure', value });

const liftF = <F, A>(command: F): Free<F, A> => 
  ({ tag: 'Impure', command, next: pure });

const freeMap = <F, A, B>(
  free: Free<F, A>,
  f: (a: A) => B
): Free<F, B> =>
  free.tag === 'Pure'
    ? pure(f(free.value))
    : { ...free, next: x => freeMap(free.next(x), f) };

const freeFlatMap = <F, A, B>(
  free: Free<F, A>,
  f: (a: A) => Free<F, B>
): Free<F, B> =>
  free.tag === 'Pure'
    ? (free.)
    : { ...free, :  (free.(x), f) };

Performance Considerations

Memoization

Cache pure function results.

const memoize = <A extends any[], R>(
  fn: (...args: A) => R
): ((...args: A) => R) => {
  const cache = new Map<string, R>();
  return (...args: A): R => {
    const key = JSON.stringify(args);
    if (!cache.has(key)) {
      cache.set(key, fn(...args));
    }
    return cache.get(key)!;
  };
};

// Fibonacci with memoization
const fib = memoize((n: number): number =>
  n <= 1 ? n : fib(n - 1) + fib(n - 2)
);

Transducers

Compose data transformations without intermediate collections.

type Reducer<A, B> = (acc: B, value: A) => B;
type Transducer<A, B, C, D> = (reducer: Reducer<C, D>) => Reducer<A, B>;

const mapT = <A, B>(f: (a: A) => B): Transducer<A, any, B, any> =>
  reducer => (acc, value) => reducer(acc, f(value));

const filterT = <A>(pred: (a: A) => boolean): Transducer<A, any, A, any> =>
  reducer => (acc, value) => pred(value) ? reducer(acc, value) : acc;

const transduce = <A, B, C>(
  transducer: Transducer<A, B, A, C>,
  reducer: Reducer<A, C>,
  initial: C,
  collection: readonly A[]
): C => {
  const xf = transducer(reducer);
   collection.(xf, initial);
};

Testing Pure Functions

// Property-based testing pattern
type Gen<A> = () => A;

const genNumber: Gen<number> = () => Math.random() * 1000;
const genString: Gen<string> = () => Math.random().toString(36);

const property = <A>(
  name: string,
  gen: Gen<A>,
  pred: (a: A) => boolean,
  iterations = 100
): void => {
  for (let i = 0; i < iterations; i++) {
    const value = gen();
    if (!pred(value)) {
      throw new Error(`Property "${name}" failed for: ${value}`);
    }
  }
};

// Test pure function properties
property(
  'add is commutative',
  () => ({ a: genNumber(), : () }),
   (a, b) === (b, a)
);

(
  ,
   [, , , , ],
   (arr,  x) === arr
);

Best Practices

1. Start pure, end impure: Keep business logic pure; push effects to boundaries
2. Use readonly liberally: Make immutability explicit in types
3. Compose small functions: Build complex operations from simple, testable pieces
4. Make illegal states unrepresentable: Use types to prevent invalid data
5. Prefer expressions over statements: Every operation should return a value
6. Avoid null/undefined: Use Option/Maybe types instead
7. Make errors explicit: Use Either/Result instead of throwing exceptions
8. Use const over let: Prevent reassignment at language level
9. Leverage type inference: Let compiler deduce types where possible
10. Document with types: Types are better documentation than comments

Common Pitfalls

1. Forgetting to return in arrow functions: x => { x + 1 } returns undefined
2. Mutating in array methods: arr.sort() mutates; use [...arr].sort()
3. Shallow copying objects: {...obj} only copies top level; nested objects shared
4. Overusing composition: Sometimes direct implementation is clearer
5. Ignoring performance: Immutability and composition have costs
6. Fighting the language: TypeScript isn't Haskell; pragmatism matters