đź“š Vault

Production Grade Architecture Checklist

Dec 05, 20258 min read

1. Security & Data Sensitivity

1.1 Authentication

  • JWT-based and/or session-based authentication:
    • JWT:
      • Short-lived access tokens.
      • Long-lived refresh tokens (rotated; revocation list or versioning).
      • Signed with asymmetric keys (RS256 / ES256).
    • Session-based:
      • HTTP-only, Secure, SameSite cookies.
      • Server-side session store (Redis / DB-backed).
  • Token storage guidelines:
    • Never store access tokens in LocalStorage.
    • Prefer HttpOnly cookies for refresh tokens.
  • Token introspection and expiration validation mandatory.

1.2 Authorization (RBAC / ABAC)

  • Role-Based Access Control (RBAC):
    • Roles: admin, user, support, system
    • Role-permission mapping at service boundaries.
  • Attribute-Based Access Control (ABAC) for contextual decisions:
    • Examples: region, ownership, account tier, resource classification.
  • Middleware enforcement:
    • Authentication (is identity valid).
    • Authorization (is action permitted).
  • Permission checks at controller + service layer.

1.3 Data Sensitivity & Logging

  • Sensitive data never logged:
    • Passwords, tokens, secrets, PAN, Aadhaar, credit cards.
  • Logs must:
    • Mask PII fields.
    • Redact secrets automatically.
  • Centralized logging system:
    • CloudWatch / ELK / Datadog / Azure Monitor.
  • Log format:
    • JSON structured logs.
    • Include timestamp, level, service, correlationId.

1.4 Audit Logs

  • Capture for:
    • Authentication events.
    • User management changes.
    • Data updates (financial / medical / business-critical).
  • Audit log schema:
    • id, timestamp, actorId, actorType, action,
      resourceType, resourceId, metadata, correlationId
  • Append-only storage.
  • Tamper evidence:
    • Hash chains.
    • Immutable storage (WORM, S3 Glacier Vault Lock).

1.5 Input Validation & Sanitization

  • Enforce schema validation:
    • Zod / Joi / Yup / JSON Schema
  • Validation coverage:
    • REST layer.
    • Event / message consumers.
  • Sanitization:
    • SQL injection.
    • XSS.
    • Command injection.
  • Validation errors:
    • Clear.
    • Structured.
    • No stack traces.

1.6 Additional Security Controls

  • Rate limiting per:
    • IP / user / API key.
  • Web Application Firewall (WAF).
  • Secrets management:
    • AWS Secrets Manager / Vault.
  • TLS / HTTPS everywhere.
  • HTTP security headers:
    • HSTS
    • CSP
    • X-Frame-Options
    • X-Content-Type-Options
  • Supply-chain security:
    • Dependency scanning.
    • Container scanning.

2. Error Handling

2.1 Structured Error Model

  • Error codes grouped by domain:
    • AUTH_INVALID_CREDENTIALS, AUTH_FORBIDDEN, USER_NOT_FOUND
    • VALIDATION_ERROR, RESOURCE_NOT_FOUND, CONFLICT, RATE_LIMIT_EXCEEDED, etc.

2.2 Backend Error Handling

  • Centralized error middleware:
    • Maps thrown errors (domain errors, validation errors, infrastructure errors) to HTTP status and error codes.
  • Log:
    • Full technical details (stack trace) only in server logs with correlation ID.
  • Clients see:
    • High-level reason + correlation ID to share with support.

3. Data Persistence

3.1 Schema

Common conventions:

  • All tables:
    • id (UUID good for microservices and sharding or bigint sequence)
    • created_at, updated_at (timestamps with time zone)
    • Soft-delete: deleted_at (where appropriate)
  • Foreign keys with ON DELETE/ON UPDATE strategies explicitly defined.
  • Indexes:
    • Primary key + frequently queried columns.
    • Composite indexes for common filters (e.g. (patient_id, scheduled_at)).

3.2 Migrations & Seeds

  • All schema changes defined via migrations (e.g. prisma migrate, knex, sequelize, dbmate, golang-migrate, drizzle-kit).
  • Migrations:
    • Forward-only in production (no manual DB changes).
    • Reviewed & peer-approved.
  • Seeds:
    • Minimal seed data for local/staging (roles, test users, sample domain data).
    • Avoid sensitive/real data in non-prod.

3.3 Data Integrity & Constraints

  • Use database constraints:
    • NOT NULL, CHECK, UNIQUE, FOREIGN KEY instead of relying only on application code.
  • Consider:
    • Multi-tenant schemas (schema-per-tenant, row-level security, or tenant_id columns).
    • Row-level security (RLS) for strict domain isolation where applicable.

3.4 Backup & Disaster Recovery

  • Automated backups with clear RPO/RTO targets.
  • Regular restore drills on staging environment.
  • If using cloud-managed:
    • Enable automated backups, point-in-time restore, and multi-AZ.

4. Testing Requirements

4.1 Test Types

  • Unit Tests
    • Small, isolated, mocking dependencies (repositories, external APIs).
  • Integration Tests
    • Real DB (test schema).
    • HTTP API tests, message consumer tests.
  • End-to-End (E2E) (Recommended)
    • Complete flow (frontend(optional) → backend → DB) in ephemeral environment.
    • Critical user perspective scenarios.
  • Contract Tests (Recommended for microservices)
    • API compatibility between services (e.g. Pact).

4.2 Coverage Requirements

  • Target 90%+ coverage:
    • At least for:
      • Domain services.
      • Controllers.
      • Critical flows (e.g. signup, login, payment/booking/appointment creation).
  • Enforce coverage in CI:
    • Build fails if coverage threshold not met.

4.3 Test Environment

  • Use dockerized services:
    • Database test instance.
    • Optional: Redis, message broker.
  • Use isolated databases per test run where possible (migrate on startup).

5. Microservice Architecture & Domain Separation

Can also be implemented as a “modular monolith” using the same patterns.

5.1 Domain Separation

  • Split code by bounded contexts (domains), not technical layers:

/src /modules /auth /users /appointments /billing /shared /config /logging /observability /http /validation

  • Each module contains:
    • controllers (HTTP handlers)
    • services (application layer)
    • repositories (data access)
    • dtos (input/output contracts)
    • entities/models (domain models)

5.2 Repository Pattern & DTOs

  • Repository Pattern
    • Interfaces: UserRepository, AppointmentRepository, etc.
    • Implementations: PostgresUserRepository, etc.
    • Allows swapping DB or mocking in tests.
  • DTOs
    • Explicit request/response models.
    • Used at boundaries (controller ↔ service ↔ external systems).
    • Validation schemas tightly coupled to DTOs.

5.3 Circuit Breaker for External APIs

  • External integrations (e.g. Insurance, Payment, Third-party APIs):
    • Use circuit breaker (e.g. in-house, opossum, Envoy/Hystrix-style behavior).
    • Track:
      • Failure rate threshold.
      • Open/closed/half-open states.
      • Fallback behavior (cached data, queued retry, graceful degradation).

5.4 Config Management & Correlation IDs

  • Configuration:
    • Typed config module loading from env variables.
    • Validation of configuration at startup (Zod/Joi).
  • Correlation IDs:
    • Generated at edge (API Gateway or first service).
    • Propagated across:
      • HTTP headers (X-Correlation-Id).
      • Logs, traces, error responses.

5.5 Graceful Shutdown

  • On termination signals (SIGINT, SIGTERM):
    • Stop accepting new requests.
    • Wait for in-flight requests to finish (with timeout).
    • Close DB connections and message consumers.
    • Flush logs and traces.

6. Observability & KPIs

6.1 OpenTelemetry Instrumentation

  • Use OpenTelemetry for:
    • Traces (spans per request, DB call, external call).
    • Metrics (latency, error counts, queue depth, etc.).
    • Logs (structured, correlation ID + trace ID).

6.2 Core KPIs

  • API KPIs
    • API latency (p95, p99) per route.
    • Request throughput (RPS).
    • Error rates (4xx/5xx).
  • Database KPIs
    • Query latency per statement/group.
    • Slow query count.
    • Connection pool utilization.
  • Resilience KPIs
    • Circuit breaker state changes.
    • Retry counts.
    • Queue backlog.

6.3 Distributed Tracing

  • End-to-end tracing for critical flows:
    • Example: Appointment/Order/Booking creation:
      • Frontend → API Gateway → Backend service(s) → DB → external APIs.
  • Ensure:
    • Trace context propagation via HTTP headers (traceparent).
    • Each service automatically instruments incoming/outgoing requests and DB calls.

6.4 Alerting & SLOs

  • Define SLOs (e.g. 99.9% availability, p95 latency < X ms).
  • Alerts for breaches:
    • High error rate.
    • Increased latency.
    • Circuit breakers stuck open.
    • DB CPU/connection saturation.

7. API Design & Documentation

7.1 OpenAPI 3.0

  • Maintain a full OpenAPI 3.0 spec:
    • Request/response schemas.
    • Error models.
    • Security schemes (JWT/cookie).
    • Pagination/filters.
  • Spec generated via:
    • Code-first (decorators) OR
    • Schema-first (OpenAPI as single source of truth).

7.2 Examples & Error Models

  • Include example requests/responses for:
    • Success paths.
    • Typical error paths (validation, auth, not found).
  • Error schema aligns with structured error model (Section 2).

7.3 Developer Portal / API Explorer

  • Integrate:
    • Swagger UI or ReDoc for internal/external usage.
    • Protected in non-public scenarios (auth required to view).

7.4 API Versioning

  • Versioned APIs:
    • Path-based (/v1, /v2) or header-based.
    • Deprecation policy and sunset timelines.

8. CI/CD Requirements

8.1 CI: GitHub Actions

Workflows (example):

  1. lint-and-test.yml

    • Steps:
      • Install dependencies (with lockfile).
      • Run linters (ESLint, Prettier).
      • Run unit + integration tests.
      • Enforce coverage threshold (90%+).

  2. build-and-docker.yml

    • Steps:
      • Build backend and frontend.
      • Run type-checking (tsc).
      • Build Docker images (backend, frontend, migrations).
      • Push images to container registry.

  3. Security & Quality (Recommended)

    • Dependency scanning (npm audit, Snyk).
    • Docker image scanning.
    • Static analysis (SonarQube, CodeQL).

8.2 CD: Deployment

  • Target: EC2/ECS/EKS/Kubernetes or similar.
  • Deployment strategy:
    • Blue-Green or Rolling deployments.
    • Health checks and readiness probes.
    • Zero-downtime DB migrations strategy (online migrations).
  • Pipeline stages:
    • dev: auto-deploy on merge to develop.
    • staging: deploy on tagged commits, run smoke/E2E tests.
    • prod: manual approval + canary rollout.

9. Documentation Requirements (README & Docs)

9.1 README Structure

Recommended sections:

  1. Project Overview

    • Short description of the system.
    • Main features and business context.

  2. Architecture Overview

    • System diagram.
    • Services and their responsibilities.
    • Tech stack (languages, frameworks, infra).

  3. Setup & Local Development

    • Prerequisites (Node, Docker, etc.).
    • Environment variables & sample .env.example.
    • Commands:
      • npm run dev, npm run test, npm run lint.
      • docker-compose up, etc.

  4. Configuration & Environments

    • How config is loaded & validated.
    • Environment-specific behavior.

  5. Security & Domain-Specific Considerations

    • Sensitive data handling.
    • Compliance/regulatory notes (e.g. HIPAA/GDPR/PCI if applicable).
    • Authentication & authorization overview.

  6. Testing Strategy

    • Types of tests.
    • How to run them locally.
    • Coverage expectations.

  7. CI/CD Pipeline

    • Description of CI workflows.
    • CD steps & deployment targets.
    • How to promote builds between environments.

  8. Observability & Monitoring

    • Logging & tracing setup (OpenTelemetry).
    • Metrics dashboards (Grafana/Datadog/etc.).
    • Alerting & SLOs.

  9. Operations & Runbooks (Link/Section)

    • Common operational tasks (restart service, run migrations).
    • How to handle incidents.
    • On-call responsibilities.

  10. Contribution Guidelines

    • Branching strategy.
    • Code style guidelines.
    • PR template & review process.

11.1 Performance & Scalability

  • Horizontal scalability for stateless services via ECS/EKS/K8s.
  • DB connection pooling & query optimization.
  • Caching strategy:
    • Application-level caching (Redis/memory).
    • HTTP caching headers for frontend APIs.

11.2 Messaging & Async Processing

  • Use message queues (Kafka/SQS/RabbitMQ) for:
    • Email notifications.
    • Long-running tasks.
    • Third-party API integrations that must be resilient.

11.3 Feature Flags

  • Feature toggle system (LaunchDarkly, ConfigCat, custom).
  • Allows:
    • Gradual rollouts.
    • A/B testing.
    • Safe emergency disable.

11.4 Compliance & Data Governance (If Applicable)

  • GDPR/CCPA support:
    • Data access/export/delete flows.
  • PII minimization & encryption at rest + in transit.
  • Data retention policies & anonymization.

12. Summary

This blueprint defines a generic, production-grade architecture with:

  • Strong security and data protection.
  • Clear error handling and domain separation.
  • Robust data persistence with migrations and audit logs.
  • Comprehensive testing and strict coverage enforcement.
  • Modern microservice/modular design with circuit breakers and graceful shutdown.
  • Full observability via OpenTelemetry and KPIs.
  • First-class API documentation via OpenAPI + Swagger/ReDoc.
  • A secure, performant Next.js frontend.
  • CI/CD pipelines ready for automation and safe deployments.
  • Detailed documentation expectations for corporate environments.
  • Additional best practices for performance, scalability, compliance, and operations.

Adapt entity names and domain-specific rules (e.g. healthcare, finance, e-commerce) as needed, while keeping these architectural principles as the foundation.

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