Building a Full-Stack E-commerce Platform with NestJS and Next.js

1. Choosing the Right Tech Stack for E-commerce

When planning an e-commerce platform—which inherently contains complex business logic dealing with users, products, orders, payments, and reviews—selecting the right tech stack required deep consideration.

In this post, I will share the architecture we chose and the "reasons" behind it to ensure stable service operation and efficient team collaboration.

2. Frontend: Next.js for SEO and Performance

For the frontend, we decided to build upon React due to its gentle learning curve and overwhelming ecosystem. Specifically, we chose the Next.js framework for the following reasons:

Crucial SEO (SSR & SSG): Search Engine Optimization (SEO) for product detail pages directly impacts revenue. Server-Side Rendering (SSR) and Static Site Generation (SSG) allow search engine bots to perfectly index page content.
Image Optimization: The next/image component automatically converts numerous product images to modern formats (like WebP) and resizes them appropriately for various device resolutions, drastically improving loading performance (LCP).

3. Backend: Why NestJS Instead of Spring?

When deciding on a backend framework, we debated heavily between the Java/Spring ecosystem and the Node.js ecosystem. Ultimately, we chose NestJS.

When the Freedom of Express Becomes Poison

Express, the most famous framework in the Node.js ecosystem, offers high freedom. However, as projects grow, architectures tend to crumble into 'spaghetti code'. NestJS, on the other hand, enforces a modular, controller-service-based architecture, allowing us to manage complexity systematically.

Team Collaboration and Code Consistency

NestJS provides clear design patterns such as Dependency Injection (DI) and decorators. This ensures that regardless of who is developing, similar code structures are written. This greatly facilitates code reviews and enhances maintainability.

Minimizing Context Switching Costs (Why Not Spring?)

The Power of a Single Language: Moring team members—who are already familiar with frontend development—to learn Java, Spring, JVM, and Gradle would impose an excessive learning curve. Building a full stack with TypeScript alone drastically reduces the cognitive load (context switching) between frontend and backend.
Fast Feedback Loop: For prototype development, speed is life. NestJS has significantly faster restart and build times compared to Spring, resulting in a short feedback loop. The vast NPM ecosystem allows for the immediate adoption of necessary functionalities.

Preparing for Performance Issues (Fastify Migration)

NestJS runs on Express by default but provides the flexibility to switch its underlying engine to Fastify with minimal configuration changes, which boasts more than double the benchmark performance of Express.

4. Database: PostgreSQL and Prisma, the Heart of E-commerce

The Need for RDBMS and ACID Transactions

In an order system, processes like stock deduction and payment authorization must be bundled into a single atomic unit (transaction). If one fails, everything must be rolled back to maintain data integrity. PostgreSQL perfectly supports ACID transactions and is highly optimized for complex JOIN queries.

Powerful JSONB Utilization in PostgreSQL

To flexibly handle unstructured data like special product options (colors, sizes, custom attributes), we introduced the JSONB type. You can store options like {"color": "blue", "size": "L"} in a JSONB column and index them efficiently—a much more robust tool than MySQL's JSON type.

The Ultimate Type Sharing: Prisma ORM

Prisma automatically generates perfect TypeScript types based on the DB schema. By sharing these types generated on the backend with the frontend (Next.js), we could dramatically lower the probability of runtime errors.

5. Infrastructure and Architecture Configuration

Monorepo: We adopted pnpm workspaces and Turborepo. (Dev Note: During the initial monorepo setup, we experienced and fixed issues where the distinction between dependencies and devDependencies in common packages affected the TS server's speed and behavior.)
Infrastructure: We used Docker and Docker Compose to prevent local environment fragmentation and fully automated our CI/CD pipeline via GitHub Actions.
Git Hooks & Logging: We applied husky to enforce linting/testing before commits and integrated Winston to build a robust logging system capable of file storage and formatting.

In conclusion, this technology stack is the optimal combination that brings the rock-solid architectural enterprise benefits of Spring while concurrently capturing the incredibly rapid development speed and flexibility of the TypeScript ecosystem.