Design & influences

This page is for readers who want to understand the library at a deeper level — the decisions behind the API, the structural patterns used throughout, and the prior work that shaped them. None of this is required to use the library effectively. Think of it as the author’s notes at the back of the book.

Where the ideas come from

Haskell

Most of the core types in this library descend, in some form, from Haskell. Maybe became Option, Either became Result, and the IO type — a lazy, composable wrapper around side effects — inspired Task. The naming convention of map, chain, and fold follows Haskell’s vocabulary (translated from fmap, >>=, and foldr into names that describe what they do rather than where they come from).

These comes from a Haskell library of the same name. It represents the inclusive-OR case: a value that can carry an error, a result, or both simultaneously — which neither Either nor a tuple cleanly expresses.

Elm

Elm deserves particular credit for two things. First, it took the Maybe and Result types from Haskell and gave them an API that felt natural to people without a Haskell background — friendly, named, and consistent. That accessibility is something this library tries to maintain.

Second, Elm is where RemoteData as a named pattern originated, in a package by Kris Jenkins. The insight — that a data fetch has exactly four states, that those states are mutually exclusive, and that encoding them as a union type eliminates a whole class of bugs — predates this library by several years. The name, the four variants, and the motivation in the guide are all drawn from that original work.

Rust

Rust brought Option<T> and Result<T, E> into the standard library of a mainstream systems language and proved that these types work outside academia. Seeing them become idiomatic in a performance-critical, widely-used language validated the premise that explicit absence and explicit failure are practical, not academic.

fp-ts

The most direct TypeScript ancestor is fp-ts by Giulio Canti — a comprehensive, rigorous encoding of functional programming in TypeScript that covered every major typeclass, used pipe as its composition primitive, and followed the data-last convention throughout. fp-ts is no longer actively developed — its author joined the Effect organisation, and that’s now where new development in this space happens.

This library borrows several things from fp-ts directly: the pipe and flow functions, the data-last convention, and the pattern of defining each type as a TypeScript type alias alongside a namespace of functions with the same name.

Optics

Lens and Optional belong to a tradition in functional programming of treating “a path through data” as a composable first-class value — a path you define once, name, pass around, and compose into deeper paths. The tradition originates primarily in Haskell, where Edward Kmett’s lens package is the canonical implementation.

The lens package defines a full optics hierarchy — Iso, Prism, Lens, Traversal, Fold, Getter, Setter — and unifies them using a profunctor encoding: an optic is a polymorphic function, and composition is ordinary function composition. This encoding is elegant, but it relies on higher-kinded types — and while TypeScript can approximate those through HKT encoding tricks (as fp-ts demonstrates), this library deliberately avoids that approach for the same reasons described in the typeclass section below.

This library uses a simpler “concrete” representation instead. Each optic is a plain record with get and set fields:

type Lens<S, A>     = { get: (s: S) => A;          set: (a: A) => (s: S) => S };
type Optional<S, A> = { get: (s: S) => Option<A>;  set: (a: A) => (s: S) => S };

The concrete form gives up uniform composition across the full hierarchy but gains implementation transparency — you can read the type and see exactly what it does — and introduces no encoding overhead whatsoever.

Structural principles

Tagged unions

Every core type in this library is a discriminated union — a union of object types, each distinguished by a literal kind field:

type Option<A> = { kind: "Some"; value: A } | { kind: "None" };

type Result<E, A> = { kind: "Ok"; value: A } | { kind: "Error"; error: E };

type RemoteData<E, A> =
  | { kind: "NotAsked" }
  | { kind: "Loading" }
  | { kind: "Failure"; error: E }
  | { kind: "Success"; value: A };

This representation has several properties that make it well-suited for TypeScript:

• Exhaustiveness checking: A switch or match over kind that handles every variant satisfies the compiler. If a new variant is added to the type, every existing match becomes a type error until the new case is handled.
• Transparency: The structure is plain data. You can inspect it with console.log, serialize it with JSON.stringify, and pattern-match it without any class machinery.
• No prototype chain: There’s nothing to inherit, override, or accidentally mutate. The operations live in separate namespace modules, not on the objects themselves.

The alternative — class-based encoding — would use instanceof for dispatch and method definitions for operations. This has appeal, but it couples operations to types (adding a method means touching the class), makes the types opaque (you can’t pattern-match without the class being in scope), and ties the library to a specific instantiation model.

InternalTypes

The four types in InternalTypes.ts are the structural vocabulary of the entire library:

type WithKind<K extends string> = { readonly kind: K };
type WithValue<T> = { readonly value: T };
type WithError<T> = { readonly error: T };
type WithErrors<T> = { readonly errors: NonEmptyList<T> };

These ensure that field names are consistent across every type in the library. The success payload is always named value. A single failure is always named error. Multiple accumulated failures are always named errors, and the type of errors is always NonEmptyList — guaranteeing at least one error exists when a type is in an invalid state.

This consistency matters at runtime too: Option.map and Result.map and RemoteData.map all look for .value to find the success payload. Sharing the field name is what makes this uniform without code duplication.

These is the deliberate exception. Its two payloads — TheseFirst, TheseSecond, and TheseBoth — use first and second as field names rather than value and error. These<A, B> makes no claim about which side is “good” and which is “bad”: it is a symmetric inclusive-OR, not a biased success/failure container. Importing the value/error convention would give it a directionality it doesn’t have.

The namespace pattern

Each type is defined as a pair: a TypeScript type alias and a namespace with the same name:

export type Option<A> = Some<A> | None;

export namespace Option {
  export const some = <A>(value: A): Some<A> => ({ kind: "Some", value });
  export const map  = <A, B>(f: (a: A) => B) => (data: Option<A>): Option<B> => ...
  export const fold = ...
}

A single import gives you both:

import { Option } from "@nlozgachev/pipelined/core";

const x: Option<number> = Option.some(42); // type and constructor from the same import

The namespace acts like a module — a flat collection of named functions. There’s no class, no prototype, no this. The functions are just functions; they happen to share a namespace prefix that signals they operate on the same type.

This pattern also means the operations are tree-shakeable. If your bundler can tell that Option.filter is never called, it can exclude it from the bundle. Method-on-class approaches don’t give bundlers the same opportunity because the method is attached to the prototype at definition time.

Data-last convention

Every operation in the library takes the data it operates on as the last argument. Comparing signatures:

// data-first (not used here)
map(option, f);

// data-last (used throughout)
map(f)(option);

With data-last, the function is curried: calling map(f) without the data returns a new function that accepts the data. This is what makes pipe and flow compose cleanly:

pipe(
  Option.some(5),
  Option.map((n) => n * 2), // map(n => n * 2) is already a function Option<number> → Option<number>
  Option.getOrElse(0),
);

Without data-last, each pipe step would need to be wrapped in an arrow function:

pipe(
  Option.some(5),
  (opt) => Option.map(opt, (n) => n * 2), // awkward — two arguments, data first
  (opt) => Option.getOrElse(opt, 0),
);

The convention is a direct import from fp-ts, which in turn took it from Haskell and OCaml.

Error type as the first type parameter

Result<E, A> puts the error type before the value type. Same for Validation<E, A> and RemoteData<E, A>. This is the opposite of many TypeScript APIs and feels counterintuitive at first glance.

The reason is about which type parameter map should transform. map transforms the success value — the A. For TypeScript to infer this correctly when you write Result.map(f), A needs to be the “last” type parameter in the sense that it’s the one that varies across a map operation. Putting E first keeps it stable while A changes — the same reason Haskell’s Either is Either e a with e first and a last.

In practice this rarely matters for reading type signatures: once you’ve seen Result<string, User> a few times, you read it as “can fail with string, succeeds with User” and the ordering is automatic.

NonEmptyList as a structural guarantee

Validation uses NonEmptyList<E> (defined as readonly [E, ...E[]]) for the errors field instead of E[]. This is a structural guarantee: when a value is Invalid, it always has at least one error. An Invalid with zero errors is a contradiction — it can’t be represented.

This matters for consumers of the invalid branch. If errors were E[], every handler would need to guard against the empty case even though it’s semantically impossible. With NonEmptyList, you can call errors[0] or errors.join(", ") without defensive checks.

What was deliberately left out

Typeclass names. The library contains implementations of what Haskell calls Functor (map), Monad (chain), and Applicative (ap). These names don’t appear in the API — the operations use names that describe what they do rather than the algebraic structure they belong to. The reasoning behind this is in Why this exists.

A typeclass system. fp-ts uses a HKT encoding to simulate higher-kinded types in TypeScript, which allows generic code over any type that implements a given typeclass. This library makes no attempt at that. The map on Option and the map on Result share a naming convention, not a shared interface. This is a real limitation — you can’t write a function that works generically over “any type with a map” — but the tradeoff is a much simpler type system with no encoding overhead.

Classes and extends. Every type is plain data. There’s no inheritance hierarchy and no instanceof checks in user-facing code.

Runtime overhead for brands. Brand<K, T> exists only as a compile-time phantom. At runtime, a branded value is exactly the underlying value — no wrapper object, no tag field, no extra allocation. The brand is erased entirely by the TypeScript compiler. Brand.wrap and Brand.unwrap are identity functions at runtime; their only job is to satisfy the type checker.

The full optics hierarchy. Lens and Optional cover two points in a much larger optics space. The most practically useful omissions are Prism — which focuses into one variant of a union type (e.g. the inner value of Some, or the Ok case of a Result) — and Traversal — which focuses on multiple values simultaneously, useful for updating all elements of a nested array in one composed path. Both were left out because the concrete { get, set } encoding doesn’t compose them uniformly with Lens and Optional without additional per-combination composition functions, adding complexity proportional to the square of the number of optic types. In practice, Lens and Optional cover the cases that arise most often in everyday TypeScript code.

Acknowledgements

The ideas behind this library were worked out over years of reading code written by people who thought carefully about these problems — the Haskell community’s decades of refining abstractions down to their most composable form; the Elm community’s insistence that good ideas should be approachable; the fp-ts contributors who did the genuinely hard work of encoding those ideas faithfully in TypeScript.

Particular thanks to Giulio Canti, whose fp-ts library is the clearest demonstration that typed functional programming is practical in TypeScript — and whose source code taught me more about the language than any tutorial. To Kris Jenkins, for naming RemoteData and making the case so clearly that the pattern spread beyond Elm. To Edward Kmett for the lens library and the optics tradition that Lens and Optional descend from. To the Haskell community more broadly — for These and for an enormous body of work that keeps the ecosystem moving forward. And to the TypeScript team, for building a type system expressive enough that most of these ideas can be encoded at all.

If you’ve read this far, thank you for your curiosity. Whether you found the library useful, built something interesting with it, spotted something wrong, or just wanted to understand how the pieces fit together — that kind of engagement is what makes writing software for others worthwhile.

Write good code. Make the impossible states unrepresentable.