TypeScript Objects, Record Dictionaries, Classes, and Abstract Classes

TypeScript can describe not only variables, but also object structures, dictionary maps, and object-oriented models.

Object Types

const user: { name: string; age: number } = {
  name: "Alice",
  age: 25,
};

Complex object shapes are usually extracted into type or interface.

interface User {
  name: string;
  age: number;
}

Record Dictionaries

Record<K, V> describes key-value maps.

const userAges: Record<string, number> = {
  Alice: 25,
  Bob: 30,
};

You can also restrict the allowed keys.

type UserName = "Alice" | "Bob" | "Carol";
 
const scores: Record<UserName, number> = {
  Alice: 90,
  Bob: 88,
  Carol: 95,
};

Index Signatures

interface ScoreMap {
  [key: string]: number;
}

Index signatures are useful for dynamic keys, but they allow any string key.

Classes

class Person {
  constructor(
    public name: string,
    private age: number,
  ) {}
 
  greet() {
    return `Hello, ${this.name}`;
  }
}

Common access modifiers:

public: accessible outside
private: accessible only inside the class
protected: accessible inside the class and subclasses

Inheritance and Override

class Student extends Person {
  override greet() {
    return `Student: ${this.name}`;
  }
}

Abstract Classes

Abstract classes cannot be instantiated directly. They define requirements for subclasses and may include shared implementation.

abstract class Animal {
  abstract speak(): string;
}
 
class Dog extends Animal {
  speak() {
    return "woof";
  }
}

Use abstract classes when you need both shared behavior and strong subclass contracts.

Deeper Notes

When reviewing this topic, do not memorize names only. Focus on object types, Record dictionaries, index signatures, classes, inheritance, override, and abstract classes. 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.

Table of Contents