How to Build an Ecommerce Website Step by Step
From scoping the actual requirements to inventory, checkout, and order states — the realistic build order for a real store.
REST and GraphQL solve the same fundamental problem — letting a client fetch data from a server — with very different trade-offs. Neither is universally "better"; the right choice depends on your actual client needs.
GET /api/posts/1 → returns the full Post object, every field
GET /api/posts/1/comments → returns all comments for that post
Each endpoint returns a fixed shape. A mobile app that only needs a post's title and author still receives every field the server defines — simple to cache (HTTP caching works natively per-URL) but often over-fetches data the client doesn't need, or under-fetches, requiring several round trips for related data.
query {
post(id: 1) {
title
author { name }
comments(limit: 5) { body author { name } }
}
}
One request, exactly the fields asked for, including nested relationships — no over-fetching, no separate round trip for comments. The trade-off: HTTP caching doesn't work the normal way (every request is a POST to the same /graphql URL), so caching needs its own dedicated layer (Apollo Client's cache, or a server-side solution like a persisted-query cache).
// Naive GraphQL resolver — fires one query PER post for its author
const resolvers = {
Post: {
author: (post) => db.users.findById(post.authorId), // N+1!
},
};
GraphQL's flexible nested queries make N+1 easy to introduce accidentally. The standard fix is DataLoader — it batches all the individual author lookups requested within one tick into a single WHERE id IN (...) query.
For most small-to-medium applications with one primary client, a well-designed REST API (with actual API Resources controlling exactly what's returned, as covered in the Laravel API guide) is simpler to build, cache, and reason about. GraphQL earns its added complexity specifically when multiple diverse clients are querying a genuinely complex data graph.
From scoping the actual requirements to inventory, checkout, and order states — the realistic build order for a real store.
A production-grade schema for products, variants, orders, and carts — and the one rule that prevents the worst ecommerce data bugs.
Cache-aside, TTLs, invalidation, and using Redis for rate limiting and sessions — practical caching patterns that hold up in production.