How do you architect a scalable, secure full-stack web app?

Long Answer

Designing a production-grade full-stack web application means balancing scalability, security, and maintainability without over-engineering. The blueprint below starts simple—then scales gracefully.

1) High-level shape: modular monolith → services

Begin with a modular monolith: one deployable back end split into domain modules (Accounts, Billing, Catalog, Orders). Each module has its own handlers, business logic, and data access layer with clear boundaries. Expose APIs via an API gateway (rate limits, auth, observability). When a module’s load, latency profile, or team ownership demands it, extract it into a service. This avoids premature microservices while preserving a migration path.

2) Front-end architecture

Choose an SSR/ISR framework (e.g., Next.js/Nuxt) for fast TTFB, SEO, and cacheability. Organize by feature folders (feature shell, UI components, hooks, tests). Use TypeScript, design tokens, and a component library with accessibility (ARIA) baked in. State: keep server state with data-fetch libraries (React Query/Apollo), and minimize global client state to UI concerns. Enforce security headers (CSP/COOP/CORP), sanitize user input, and avoid dangerous HTML. For scalability, ship route-level code-splitting, image/CDN optimization, and edge caching of public pages.

3) Back-end architecture

Use a typed language (TypeScript/Go/Java/Kotlin). Keep a hexagonal/clean architecture: controllers → services → repositories. Prefer REST for CRUD and GraphQL or gRPC where client aggregation or typed contracts help. All endpoints are behind the gateway with mTLS to services. Implement idempotency keys on write operations and outbox pattern for reliable events to the message bus (Kafka/Pub/Sub/SQS). Provide background workers for heavy jobs.

4) Data and caching

Adopt per-module schemas (same database initially) to prevent cross-domain coupling. Normalize transactional data; use read models (materialized views) for queries. Add Redis for hot keys, sessions (or tokens), rate limits, and locks. For search/analytics, use specialized stores (Elasticsearch/OpenSearch, ClickHouse/BigQuery). When traffic grows, split read/write, add replicas, and consider CQRS where it simplifies complexity.

5) Security fundamentals

Use OIDC (Auth0/Keycloak/Cognito) for authentication. Authorize with RBAC augmented by ABAC for fine-grained rules. Encrypt in transit (TLS 1.2+), encrypt at rest (KMS-managed keys), rotate secrets via a vault. Apply OWASP Top 10 controls: input validation, parameterized queries, CSRF tokens (if cookie auth), CSP + SRI, safe file uploads, strict cookies (SameSite/Lax|Strict, HttpOnly, Secure). Implement audit trails and privacy by design (data minimization, deletion APIs). Run SAST/DAST/Dependency scanning in CI.

6) Reliability & horizontal scalability

Scale reads via CDN and edge caching; scale compute using containers/serverless (Kubernetes/Cloud Run/App Service). Autoscale on RPS/latency; keep services stateless (sessions in Redis or JWTs). Use queues for spikes (orders, emails), with retry & DLQ; ensure handlers are idempotent. Version APIs, support gradual rollouts (canary/blue-green), and keep graceful shutdown & health probes to allow safe autoscaling.

7) Observability & SLOs

Emit OpenTelemetry traces, metrics, and structured logs. Correlate front-end and back-end via trace IDs. Define SLOs (availability, p95 latency) and alert on error-budget burn. Build dashboards per domain (errors, saturation, latency), and run synthetic checks from key geos. Record domain events for debugging user flows.

8) CI/CD & maintainability

Monorepo with workspaces (UI, API, shared types). Enforce contracts: OpenAPI/GraphQL schemas are the source of truth, generating clients/servers. Add pre-commit hooks (lint, typecheck), unit/integration tests, and contract tests for service boundaries. Keep IaC (Terraform/Pulumi/Bicep) and secrets in a vault. Introduce code owners and ADRs for major decisions. Document runbooks and an “onboarding map” for fast new-hire ramp-up.

9) Cost posture & evolution

Cache aggressively, right-size instances, and choose managed services to reduce ops toil. Start with a managed relational DB; add specialized stores as needs emerge. Extract services by pain (org scaling, independent deploy cadence, or perf isolation)—not fashion.

This gives a full-stack web application with clean front-end architecture, resilient back-end architecture, strong security, predictable scalability, and long-term maintainability.

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Table

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Common Mistakes

• Premature microservices; distributed monolith with chatty calls.
  
• No API versioning; breaking the front-end architecture on deploy.
  
• Mixing domain logic in controllers; no service boundaries.
  
• Global mutable state in the UI; complex, flaky behavior.
  
• Skipping CSP, CSRF, or input validation; basic security holes.
  
• One database for everything with cross-module joins and no ownership.
  
• No idempotency or outbox; duplicate orders and lost events.
  
• Synchronous heavy work; request timeouts under load.
  
• Logs without traces; outage forensics becomes guesswork.
  
• CI/CD without tests; config drift and slow rollbacks.
  
  

Sample Answers (Junior / Mid / Senior)

Junior:
“I’d use an SSR framework and a REST API. I’d store sessions in Redis, add input validation, and deploy with CI. Caching and a CDN help scalability.”

Mid:
“I’d keep a modular monolith with domain modules, expose OpenAPI, and use TypeScript end-to-end. OIDC for auth, RBAC for routes, CSP/CSRF, and parameterized queries. Redis for caching and queues for background jobs. OTEL traces and canary releases improve reliability.”

Senior:
“Architect UI with SSR/ISR, feature folders, and a11y. Back end follows hexagonal design, idempotent writes, outbox→bus, and per-module data. API gateway enforces auth/quotas; contracts generate clients. SLOs drive autoscale; blue-green deploys with IaC keep drift low. We start modular, then extract services only when throughput, latency, or team ownership requires it.”

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Evaluation Criteria

• Architecture fit: Modular monolith with a path to services; clear domain boundaries.
  
• Front end: SSR/ISR, feature organization, accessibility, CSP; minimal global state.
  
• Back end: Clean layers, contract-first APIs, idempotent writes, outbox/queue.
  
• Security: OIDC, RBAC/ABAC, OWASP controls, secrets management.
  
• Scalability: CDN, Redis, replicas, autoscaling, async jobs.
  
• Maintainability: Monorepo, typed codegen, tests, CI/CD, IaC, ADRs.
  
• Observability: Traces/logs/metrics, SLOs, burn-rate alerts.
  Red flags: Premature microservices, no contracts, shared DB with tight coupling, missing CSP/CSRF, synchronous heavy work, no rollbacks.
  
  

Preparation Tips

• Build a tiny reference app: Next.js UI + typed API (NestJS/FastAPI/Spring).
  
• Define OpenAPI/GraphQL and generate clients; enforce contract tests.
  
• Add OIDC, route-level RBAC, CSP, CSRF, and input validation.
  
• Implement Redis cache, idempotency keys, and outbox→queue for one flow.
  
• Create SLOs (99.9%, p95 latency), synthetic checks, and burn alerts.
  
• Add unit/integration/e2e tests and seed data fixtures.
  
• Containerize, provision DB/cache via IaC, and set up blue-green deploy.
  
• Document boundaries and an ADR for “when to extract a service.”
  
• Load test a hot endpoint and tune CDN/TTL, DB indexes, and autoscale.
  
  

Real-world Context

• E-commerce: Modular monolith, OpenAPI contracts, Redis cache, and outbox→Kafka. Checkout p95 dropped 35%; safe canaries reduced rollback time to minutes.
  
• Fintech: SSR front end with CSP, strict cookies, and OIDC; back end added idempotent ledger writes and audit trails. Zero duplicate transfers during spikes.
  
• SaaS analytics: Started monolith; extracted ingest and query as services when RPS >10k. Per-module schemas avoided data tangles; SLO burn alerts cut MTTR from 50→12 min.
  
• EdTech: Feature-folder UI and design tokens enabled rapid theming; accessibility reviews shrank production bugs and improved Lighthouse scores.
  
  

Key Takeaways

• Start with a modular monolith; extract services when justified.
  
• SSR/ISR UI with feature folders, a11y, CSP, and minimal client state.
  
• Clean back-end architecture with contracts, idempotency, and queues.
  
• Tight security: OIDC, RBAC/ABAC, OWASP, secrets vault, CSP/CSRF.
  
• Observe SLOs; ship via CI/CD, IaC, and progressive delivery.
  
  

Practice Exercise

Scenario:
You’re building a full-stack web application for a marketplace with public product pages, authenticated carts, and paid orders. Traffic spikes at launches; compliance requires audit logs and secure payments.

Tasks:

1. Choose SSR/ISR for public pages and justify cache keys/TTLs at the CDN. Organize the UI with feature folders and design tokens.
   
2. Define contract-first APIs (OpenAPI or GraphQL). Generate a typed client for the UI and a stub server for tests.
   
3. Sketch domain modules (Catalog, Accounts, Cart, Orders, Payments). Keep per-module schemas; list a couple of key DB indexes.
   
4. Implement idempotency keys for Checkout; use the outbox pattern to emit OrderCreated events to a queue.
   
5. Add Redis for sessions, rate limits, and hot reads. Describe eviction and TTLs.
   
6. Secure with OIDC, RBAC for admin endpoints, CSP + SRI, CSRF (if cookie auth), strict cookies, and file-upload hardening.
   
7. Observability: define SLOs (99.9% API availability; p95 < 250 ms), wire OTEL traces, and configure burn-rate alerts.
   
8. CI/CD: unit + integration tests, contract tests, and e2e for checkout; blue-green deploy with automated rollback and DB migration guardrails.
   
9. Write an ADR: “When to extract Payments into a separate service,” including triggers (throughput, latency isolation, PCI scope) and risks.
   
   

Deliverable:

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A 1–2 page architecture brief + diagram showing how the design meets scalability, security, and maintainability goals across the front-end architecture and back-end architecture