TypeScript's type system lets us describe data more clearly. Common categories include primitive types, arrays, tuples, enums, union types, and literal types.
Primitive Types
let title: string = "TypeScript";
let count: number = 10;
let enabled: boolean = true;
let big: bigint = 9007199254740991n;string represents text, number represents both integers and floating-point numbers, and boolean represents true or false values.
Arrays
Arrays can be declared in two common ways.
let numbers: number[] = [1, 2, 3];
let names: Array<string> = ["Alice", "Bob"];When elements should share a type, make that element type explicit.
Tuples
Tuples describe fixed-position data.
let user: [string, number] = ["Alice", 25];
let option: [string, boolean?] = ["dark"];
let list: [number, ...string[]] = [1, "a", "b"];Enums
Enums define named constants.
enum Color {
Red,
Green,
Blue,
}
const favorite: Color = Color.Green;In many projects, union literals are also a practical alternative.
type Direction = "up" | "down" | "left" | "right";Union Types
Union types allow a value to be one of several types.
let id: string | number;
id = "u_1";
id = 1001;They are useful for API parameters, form inputs, and state values.
Literal Types
Literal types restrict values to exact options.
type Gender = "male" | "female";
type Status = "idle" | "loading" | "success" | "error";This makes state modeling safer and avoids many magic-string mistakes.
Deeper Notes
When reviewing this topic, do not memorize names only. Focus on primitive types, arrays, tuples, enums, unions, literal types, and type modeling. If this stays at the definition level, it becomes hard to explain in interviews or apply in projects. A stronger way to study it is to place it in a concrete scenario: who calls it, where the input comes from, what happens on failure, and whether data or state can be processed twice.
- TypeScript moves uncertainty from runtime to development time, but it does not replace runtime validation.
- Type design should model business constraints first instead of showing off complex generics.
- For third-party input, JSON, forms, and API responses, combine types with narrowing or schema validation.
In a real project, use it as a decision framework: identify inputs, constraints, failure modes, and observability before choosing a specific tool or pattern. If a solution looks simple, keep asking whether it still works when scale grows, permissions change, recovery matters, and more people collaborate on it.
Practical Checklist
- Identify where this concept sits in the system: development-time constraint, runtime behavior, infrastructure capability, or collaboration workflow.
- Write one minimal working example and one failure example; only knowing the happy path is usually not enough.
- Record common misuses: edge cases, permission assumptions, performance assumptions, sync/async differences, or environment differences.
- Connect the concept to a project experience so that an interview answer can be grounded in real tradeoffs.
- End with one sentence about tradeoff: what it gives up and what it buys.
Self-Check Questions
- What core problem does this topic solve?
- What alternatives exist, and what are their costs?
- Where are the most likely edge cases?
- How would code, tests, or monitoring prove that it is reliable?
Applied Scenario
A practical scenario is a project with shared frontend/backend types. An API returns JSON; the frontend validates it with a schema, then passes the validated data into TypeScript types. Types improve editor feedback and compile-time checking, but they cannot guarantee that network data is valid. Good type design expresses business constraints such as roles, states, permissions, request parameters, and response shapes instead of turning everything into string or any.
Common Pitfalls:
- Treating TypeScript types as runtime validation.
- Using any for convenience and pushing errors into production.
- Making generics so complex that callers cannot understand them.