TypeScript Functions, Default Parameters, Rest Parameters, and async/await

Functions are one of the most common places to use TypeScript types. Parameter types, return types, and async return types should usually be explicit.

Basic Function Types

function calculateDiscount(price: number, rate: number): number {
  return price * rate;
}

Return types can often be inferred, but public or complex functions are clearer with explicit return types.

Default Parameters

function calculateDiscount(price: number, rate: number = 0.5): number {
  return price * rate;
}
 
calculateDiscount(1000);
calculateDiscount(1000, 0.3);

Default parameters reduce repeated configuration.

Rest Parameters

function joinNames(prefix: string, ...names: string[]): string {
  return `${prefix}: ${names.join(", ")}`;
}

...names means the function accepts any number of string arguments.

Promise Basics

Async functions usually return Promise<T>.

async function fetchUser(): Promise<{ id: string; name: string }> {
  return { id: "1", name: "Alice" };
}

async automatically wraps returned values in a Promise.

await

await waits for a Promise to resolve.

async function main() {
  try {
    const user = await fetchUser();
    console.log(user.name);
  } catch (error) {
    console.error("Failed to fetch user", error);
  }
}

Practical Advice

For async service functions, make return values explicit.

async function pullContacts(args: {
  limit?: number;
  page?: number;
}): Promise<ContactList> {
  return { contacts: [] };
}

This gives callers better type hints and catches interface changes earlier.

Deeper Notes

When reviewing this topic, do not memorize names only. Focus on function types, default parameters, rest parameters, Promise, async/await, and error handling. 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.

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