Node.js + TypeScript: How We Build Reliable Backends

Why Node.js for Backend Development

Node.js is not the only choice, but for the majority of web and mobile backends we build, it is the right one. The reasons are practical:

• Non-blocking I/O: Node's event loop handles thousands of concurrent connections efficiently — ideal for the API-heavy, I/O-bound workloads typical in web and mobile backends
• Shared language: Our frontend and backend teams both write TypeScript. Shared types, shared utilities, shared validation schemas — no translation layer between teams
• Ecosystem: npm has a library for almost everything. Payment gateways, SMS providers, mapping APIs, push notifications — mature, well-maintained packages exist for every integration we encounter
• Real-time support: WebSocket and Server-Sent Events work naturally within the Node runtime, making features like live order tracking or chat straightforward to implement

TypeScript: Non-Negotiable

We do not write backend JavaScript without TypeScript. The discipline it enforces pays dividends throughout the project lifecycle:

• API contracts become self-documenting — the type of a request body or response is explicit in the code
• Refactoring is safe — the compiler catches every callsite affected by a change
• Onboarding new developers is faster — types serve as inline documentation
• Shared types between frontend and backend mean the API contract is enforced at compile time, not discovered at runtime

Our API Layer: Fastify

We use Fastify over Express as our HTTP framework. Fastify is schema-first — routes declare their request/response shapes using JSON Schema or TypeBox, and Fastify validates inputs and serialises outputs automatically. This gives us runtime validation that complements TypeScript's compile-time safety, and the serialisation performance is significantly faster than Express. Fastify plugins handle authentication, rate limiting, CORS, and logging as first-class concerns.

Database: PostgreSQL as the Foundation

For relational data — users, orders, products, transactions — PostgreSQL is our first choice. It is rock-solid, ACID-compliant, and its support for JSONB columns gives us document-style flexibility when we need it without sacrificing relational integrity. We use Drizzle ORM for its TypeScript-native query builder that generates fully typed results without the overhead and magic of heavier ORMs like Prisma.

• Migrations managed with Drizzle Kit — version-controlled schema changes
• Row-level security for multi-tenant applications
• PostgreSQL's full-text search for simple search features without needing Elasticsearch
• Connection pooling with PgBouncer for high-concurrency production deployments

When We Reach for MongoDB

PostgreSQL is our default, but MongoDB earns its place in specific scenarios: unstructured or highly variable document schemas, event logging and audit trails where write throughput is critical, and projects that use MongoDB's Atlas Search for vector search or full-text search at scale. We do not treat either as universally superior — the data model dictates the database choice.

Structure: How We Organise a Node.js Project

Consistency across projects matters for maintainability and team mobility. Our standard structure separates concerns cleanly:

• routes/ — HTTP layer, input validation, response shaping
• services/ — business logic, completely decoupled from HTTP
• db/ — database schema, queries, migrations
• lib/ — shared utilities, third-party client wrappers
• types/ — shared TypeScript interfaces exported for frontend consumption

Deployment and Infrastructure

Node.js backends are containerised with Docker and deployed on AWS (EC2 with Application Load Balancer, or ECS for container orchestration at scale). Environment configuration is managed via AWS Secrets Manager, never hardcoded or committed to version control. GitHub Actions handles CI/CD — running tests, building Docker images, and deploying on merge to main.