Imported from ComeOnOliver/skillshub (skills/aiskillstore/marketplace/curiouslearner/architecture-documenter/SKILL.md). Install upstream with npx skills add ComeOnOliver/skillshub --skill architecture-documenter. Copyright stays with the author.

Architecture Documenter Skill

Document system architecture and technical design decisions for effective team communication and knowledge sharing.

Instructions

You are a software architecture documentation expert. When invoked:

1. Analyze System Architecture:

   • Identify key components and services
   • Understand data flows and interactions
   • Map dependencies and integrations
   • Recognize architectural patterns
   • Assess scalability and reliability

2. Create Architecture Documentation:

   • System overview and context
   • Component diagrams and relationships
   • Data flow diagrams
   • Deployment architecture
   • Security architecture
   • Decision records (ADRs)

3. Document Technical Decisions:

   • What was decided
   • Why it was decided
   • Alternatives considered
   • Trade-offs made
   • Implementation details
   • Future considerations

4. Use Visual Diagrams:

   • System architecture diagrams
   • Sequence diagrams
   • Entity-relationship diagrams
   • Infrastructure diagrams
   • Network topology
   • State machines

5. Maintain Living Documentation:

   • Keep docs synchronized with code
   • Version architecture docs
   • Track evolution over time
   • Mark deprecated components
   • Update with lessons learned

Architecture Documentation Templates

System Architecture Document Template

# E-Commerce Platform - System Architecture

**Version**: 2.3
**Last Updated**: January 15, 2024
**Status**: Current
**Authors**: Engineering Team
**Reviewers**: Alice (EM), Bob (Tech Lead)

---

## Table of Contents

1. [Executive Summary](#executive-summary)
2. [System Context](#system-context)
3. [Architecture Overview](#architecture-overview)
4. [Core Components](#core-components)
5. [Data Architecture](#data-architecture)
6. [Infrastructure](#infrastructure)
7. [Security Architecture](#security-architecture)
8. [Scalability & Performance](#scalability--performance)
9. [Deployment](#deployment)
10. [Monitoring & Observability](#monitoring--observability)
11. [Future Considerations](#future-considerations)

---

## Executive Summary

### What This System Does

The E-Commerce Platform is a modern, cloud-native application that enables small to medium businesses to sell products online. It handles the complete e-commerce lifecycle from product catalog management to order fulfillment.

### Key Capabilities

- **Product Management**: Create, update, and manage product catalogs
- **Shopping Experience**: Browse products, search, filter, and compare
- **Checkout & Payments**: Secure checkout with multiple payment options
- **Order Management**: Track orders from placement to delivery
- **User Accounts**: Customer profiles, order history, preferences
- **Admin Dashboard**: Business analytics, inventory management

### System Scale

| Metric | Current | Target (6 months) |
|--------|---------|-------------------|
| Active Users | 5,000 businesses | 15,000 businesses |
| Products | 500,000 | 2,000,000 |
| Daily Orders | 10,000 | 50,000 |
| Monthly GMV | $2M | $10M |
| Peak RPS | 500 | 2,000 |
| Data Storage | 2 TB | 10 TB |

### Technology Stack Summary

- **Frontend**: React, TypeScript, Redux, Material-UI
- **Backend**: Node.js, Express, TypeScript
- **Database**: PostgreSQL (primary), Redis (cache)
- **Storage**: AWS S3
- **Hosting**: AWS (ECS, RDS, ElastiCache, CloudFront)
- **CI/CD**: GitHub Actions
- **Monitoring**: DataDog, Sentry

---

## System Context

### Business Context

**Problem We Solve**: Small businesses struggle with expensive, complex e-commerce solutions. Our platform provides an affordable, easy-to-use alternative.

**Target Users**:
- Small business owners (10-1000 products)
- Digital creators selling physical products
- Retail stores expanding online

**Business Model**: SaaS subscription ($29-$299/month) + transaction fees (2.9% + $0.30)

### System Boundary

┌─────────────────────────────────────────────────────┐ │ E-Commerce Platform │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────────┐ │ │ │ Customer │ │ Merchant │ │ Admin │ │ │ │ Web │ │Dashboard │ │ Portal │ │ │ └──────────┘ └──────────┘ └──────────────┘ │ │ │ │ ┌──────────────────────────────────────────────┐ │ │ │ Backend Services │ │ │ │ (Auth, Product, Order, Payment, etc.) │ │ │ └──────────────────────────────────────────────┘ │ │ │ │ ┌──────────────────────────────────────────────┐ │ │ │ Data & Storage Layer │ │ │ │ (PostgreSQL, Redis, S3) │ │ │ └──────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────┘ │ ┌───────────────┼───────────────┐ │ │ │ ┌─────────┐ ┌──────────┐ ┌──────────┐ │ Stripe │ │ SendGrid│ │ Shippo │ │ Payment │ │ Email │ │ Shipping │ └─────────┘ └──────────┘ └──────────┘

### External Dependencies

| Service | Purpose | SLA | Fallback Strategy |
|---------|---------|-----|-------------------|
| Stripe | Payment processing | 99.99% | Queue retries, manual processing |
| SendGrid | Email delivery | 99.95% | Alternative provider (AWS SES) |
| Shippo | Shipping labels | 99.9% | Manual label generation |
| AWS | Infrastructure | 99.99% | Multi-AZ deployment |
| Cloudflare | CDN/DNS | 99.99% | Direct origin access |

---

## Architecture Overview

### High-Level Architecture

                    Internet
                       │
                       ▼
                ┌──────────────┐
                │  Cloudflare  │ (CDN, DDoS protection)
                └──────┬───────┘
                       │
                       ▼
            ┌──────────────────────┐
            │   AWS CloudFront     │ (Static assets)
            └──────────────────────┘
                       │
    ┌──────────────────┼──────────────────┐
    │                  │                  │
    ▼                  ▼                  ▼

┌───────────────┐ ┌───────────────┐ ┌──────────────┐ │ React │ │ API Gateway │ │ Admin │ │ Frontend │ │ (Express) │ │ Portal │ │ (CloudFront) │ │ (ALB+ECS) │ │ │ └───────────────┘ └───────┬───────┘ └──────────────┘ │ ┌──────────────────┼──────────────────┐ │ │ │ ▼ ▼ ▼ ┌─────────┐ ┌──────────┐ ┌──────────┐ │ Auth │ │ Product │ │ Order │ │ Service │ │ Service │ │ Service │ └────┬────┘ └────┬─────┘ └────┬─────┘ │ │ │ └────────────────┼──────────────────┘ │ ┌────────────────┼────────────────┐ │ │ │ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌─────────┐ │PostgreSQL│ │ Redis │ │ S3 │ │ (RDS) │ │(ElastiCache) │(Images) │ └──────────┘ └─────────┘ └─────────┘

### Architecture Style

**Primary Pattern**: Modular Monolith (transitioning to Microservices)

**Rationale**:
- **Current**: Modular monolith provides simplicity while maintaining clear boundaries
- **Future**: Easy migration path to microservices as scale increases
- **Trade-off**: Accepts coupling cost for development velocity at current scale

### Key Architectural Principles

1. **Separation of Concerns**: Clear boundaries between modules
2. **API-First**: All features exposed via REST APIs
3. **Stateless Services**: No server-side session state (JWT-based auth)
4. **Caching Strategy**: Cache aggressively, invalidate carefully
5. **Eventual Consistency**: Accept eventual consistency for non-critical data
6. **Fail Fast**: Return errors quickly rather than retry indefinitely
7. **Observability**: Comprehensive logging, metrics, and tracing

---

## Core Components

### Frontend Application

**Technology**: React 18 + TypeScript + Redux Toolkit

**Structure**:

client/ ├── components/ # Reusable UI components ├── pages/ # Route-level pages ├── store/ # Redux state management ├── api/ # API client ├── hooks/ # Custom React hooks └── utils/ # Utility functions

**Key Features**:
- Server-side rendering (SSR) for SEO
- Code splitting by route
- Progressive Web App (PWA) capabilities
- Optimistic UI updates
- Offline support (service workers)

**State Management**:
- **Redux**: Global application state
- **React Query**: Server state caching
- **Local Storage**: User preferences, cart (guest users)

**Performance Targets**:
- First Contentful Paint: <1.5s
- Time to Interactive: <3s
- Lighthouse Score: >90

---

### API Gateway

**Technology**: Express.js + TypeScript

**Responsibilities**:
- Request routing
- Authentication/authorization
- Rate limiting
- Request/response transformation
- API versioning
- CORS handling

**Middleware Pipeline**:
```javascript
Request
  ↓
Logging (Morgan)
  ↓
Rate Limiting (express-rate-limit)
  ↓
CORS (cors)
  ↓
Authentication (JWT verification)
  ↓
Authorization (permission check)
  ↓
Request Validation (Joi)
  ↓
Route Handler
  ↓
Response Formatting
  ↓
Error Handling
  ↓
Response

API Versioning Strategy:

• URL versioning: /api/v1/products, /api/v2/products
• Maintain 2 versions simultaneously
• Deprecation warnings in headers
• 6-month sunset period for old versions

Service Modules

Authentication Service

Responsibilities:

• User registration and login
• JWT token generation and validation
• Password reset flow
• OAuth integration (Google, Facebook)
• Multi-factor authentication (MFA)

Database Schema:

users (
  id UUID PRIMARY KEY,
  email VARCHAR UNIQUE NOT NULL,
  password_hash VARCHAR NOT NULL,
  email_verified BOOLEAN DEFAULT FALSE,
  created_at TIMESTAMP,
  updated_at TIMESTAMP
)

sessions (
  id UUID PRIMARY KEY,
  user_id UUID REFERENCES users(id),
  token_hash VARCHAR NOT NULL,
  expires_at TIMESTAMP,
  created_at TIMESTAMP
)

oauth_accounts (
  id UUID PRIMARY KEY,
  user_id UUID REFERENCES users(id),
  provider VARCHAR NOT NULL, -- 'google', 'facebook'
  provider_user_id VARCHAR NOT NULL,
  access_token VARCHAR,
  refresh_token VARCHAR,
  UNIQUE(provider, provider_user_id)
)

Security Measures:

• Passwords hashed with Argon2id
• JWT tokens with 15-minute expiration
• Refresh tokens with 7-day expiration
• Rate limiting: 5 login attempts per 15 minutes
• Account lockout after 10 failed attempts
• MFA via TOTP (Google Authenticator)

Product Service

Responsibilities:

• Product CRUD operations
• Inventory management
• Search and filtering
• Product recommendations
• Category management

Database Schema:

products (
  id UUID PRIMARY KEY,
  merchant_id UUID REFERENCES users(id),
  name VARCHAR NOT NULL,
  description TEXT,
  price DECIMAL(10,2) NOT NULL,
  inventory_count INTEGER NOT NULL DEFAULT 0,
  category_id UUID REFERENCES categories(id),
  status VARCHAR DEFAULT 'draft', -- draft, active, archived
  created_at TIMESTAMP,
  updated_at TIMESTAMP
)

product_images (
  id UUID PRIMARY KEY,
  product_id UUID REFERENCES products(id) ON DELETE CASCADE,
  url VARCHAR NOT NULL,
  position INTEGER,
  created_at TIMESTAMP
)

categories (
  id UUID PRIMARY KEY,
  name VARCHAR NOT NULL,
  parent_id UUID REFERENCES categories(id),
  slug VARCHAR UNIQUE NOT NULL
)

Search Implementation:

• PostgreSQL full-text search with trigram indexes
• Elasticsearch for advanced features (planned)
• Caching: 5-minute TTL for product lists, 1-hour for individual products

Performance Optimizations:

• Database indexes on common query fields
• N+1 query prevention with eager loading
• Image CDN with automatic resizing
• Aggressive caching with Redis

Order Service

Responsibilities:

• Shopping cart management
• Order creation and processing
• Order status tracking
• Order history
• Invoice generation

Database Schema:

orders (
  id UUID PRIMARY KEY,
  customer_id UUID REFERENCES users(id),
  status VARCHAR NOT NULL, -- pending, paid, shipped, delivered, cancelled
  subtotal DECIMAL(10,2) NOT NULL,
  tax DECIMAL(10,2) NOT NULL,
  shipping DECIMAL(10,2) NOT NULL,
  total DECIMAL(10,2) NOT NULL,
  payment_id VARCHAR,
  shipping_address_id UUID REFERENCES addresses(id),
  created_at TIMESTAMP,
  updated_at TIMESTAMP
)

order_items (
  id UUID PRIMARY KEY,
  order_id UUID REFERENCES orders(id) ON DELETE CASCADE,
  product_id UUID REFERENCES products(id),
  quantity INTEGER NOT NULL,
  price DECIMAL(10,2) NOT NULL,
  product_snapshot JSONB -- Product details at time of purchase
)

order_events (
  id UUID PRIMARY KEY,
  order_id UUID REFERENCES orders(id),
  event_type VARCHAR NOT NULL, -- created, paid, shipped, etc.
  metadata JSONB,
  created_at TIMESTAMP
)

Order State Machine:

pending → paid → processing → shipped → delivered
   ↓       ↓         ↓           ↓
   └───────┴─────────┴───────────┴──→ cancelled

Transaction Handling:

• Database transactions for order creation
• Idempotency keys for payment processing
• Inventory reservation system
• Automatic rollback on payment failure

Payment Service

Responsibilities:

• Payment intent creation
• Payment processing (via Stripe)
• Refund handling
• Payment method management
• Transaction history

Integration with Stripe:

// Payment Intent Flow
1. Client requests payment intent
   ↓
2. Server creates Stripe PaymentIntent
   ↓
3. Client collects payment details
   ↓
4. Client confirms payment with Stripe
   ↓
5. Stripe webhook notifies server
   ↓
6. Server updates order status

Webhook Security:

• Stripe signature verification
• Idempotent webhook processing
• Async processing with job queue
• Retry logic for failed webhooks

Database Schema:

payments (
  id UUID PRIMARY KEY,
  order_id UUID REFERENCES orders(id),
  stripe_payment_intent_id VARCHAR UNIQUE,
  amount DECIMAL(10,2) NOT NULL,
  status VARCHAR NOT NULL, -- pending, succeeded, failed
  payment_method VARCHAR, -- card, bank_transfer
  metadata JSONB,
  created_at TIMESTAMP,
  updated_at TIMESTAMP
)

refunds (
  id UUID PRIMARY KEY,
  payment_id UUID REFERENCES payments(id),
  stripe_refund_id VARCHAR UNIQUE,
  amount DECIMAL(10,2) NOT NULL,
  reason VARCHAR,
  status VARCHAR NOT NULL,
  created_at TIMESTAMP
)

Data Architecture

Database Design

Primary Database: PostgreSQL 14

Schema Organization:

• public schema: Core application tables
• audit schema: Audit logs and event sourcing
• analytics schema: Denormalized data for reporting

Connection Pooling:

{
  max: 20,              // Max connections
  min: 5,               // Min connections
  idle: 10000,          // Close idle connections after 10s
  acquire: 30000,       // Max time to acquire connection
  evict: 1000           // Check for idle connections every 1s
}

Backup Strategy:

• Automated daily backups (RDS snapshots)
• Point-in-time recovery enabled (7-day window)
• Monthly backups retained for 1 year
• Backup tested quarterly

Caching Strategy

Redis Configuration:

• Deployment: AWS ElastiCache (Redis 7.0)
• Mode: Cluster mode enabled
• Nodes: 3 (primary + 2 replicas)
• Eviction policy: LRU (Least Recently Used)

Cache Patterns:

1. Cache-Aside (Read-heavy data):

async function getProduct(id) {
  // Try cache first
  let product = await cache.get(`product:${id}`);

  if (!product) {
    // Cache miss - fetch from database
    product = await db.products.findById(id);

    // Store in cache (1 hour TTL)
    await cache.set(`product:${id}`, product, 3600);
  }

  return product;
}

2. Write-Through (Critical data):

async function updateProduct(id, data) {
  // Update database
  const product = await db.products.update(id, data);

  // Update cache
  await cache.set(`product:${id}`, product, 3600);

  return product;
}

Cache Invalidation:

// Product updated
await cache.del(`product:${productId}`);
await cache.del(`products:merchant:${merchantId}`);
await cache.del(`products:category:${categoryId}`);

// Pattern-based invalidation
await cache.delPattern(`products:*`);

What We Cache:

Data Migration Strategy

Tools: Prisma Migrate (development), custom scripts (production)

Migration Process:

1. Create migration in development
2. Review SQL in PR
3. Test on staging (copy of production data)
4. Run on production during low-traffic window
5. Rollback plan documented

Zero-Downtime Migrations:

-- Example: Adding non-null column

-- Step 1: Add column as nullable
ALTER TABLE products ADD COLUMN new_field VARCHAR;

-- Step 2: Backfill data
UPDATE products SET new_field = 'default_value' WHERE new_field IS NULL;

-- Step 3: Add NOT NULL constraint
ALTER TABLE products ALTER COLUMN new_field SET NOT NULL;

Infrastructure

AWS Architecture

Regions: Primary: us-east-1, Disaster Recovery: us-west-2

VPC Design:

VPC (10.0.0.0/16)
├── Public Subnets (10.0.1.0/24, 10.0.2.0/24)
│   ├── NAT Gateways
│   └── Application Load Balancer
└── Private Subnets (10.0.10.0/24, 10.0.11.0/24)
    ├── ECS Tasks (Application)
    ├── RDS (Database)
    └── ElastiCache (Redis)

Compute:

• ECS Fargate: Serverless containers for application
• Auto-scaling: Target CPU 70%, min 2 tasks, max 10 tasks
• Task Definition:

  CPU: 1024 (1 vCPU)
  Memory: 2048 MB
  Container Port: 3000
  Environment: Production

Database:

• RDS PostgreSQL: db.r5.large (2 vCPU, 16 GB RAM)
• Multi-AZ: Yes (automatic failover)
• Read Replicas: 1 (for analytics queries)
• Storage: 500 GB GP3 (auto-scaling enabled)

Storage:

• S3 Bucket: product-images-prod
• Lifecycle Policy: Move to Glacier after 90 days
• CDN: CloudFront distribution for images
• Backup: Cross-region replication enabled

Networking:

• Load Balancer: Application Load Balancer (ALB)
• SSL/TLS: ACM certificates (auto-renewal)
• WAF: AWS WAF with OWASP rules
• DDoS Protection: AWS Shield Standard

Deployment Architecture

CI/CD Pipeline (GitHub Actions):

Code Push
  ↓
Automated Tests (Unit + Integration)
  ↓
Linting & Type Checking
  ↓
Build Docker Image
  ↓
Push to ECR (Elastic Container Registry)
  ↓
Deploy to Staging (Auto)
  ↓
Integration Tests (Staging)
  ↓
Manual Approval
  ↓
Deploy to Production (Canary)
  ↓
Monitor Metrics (15 minutes)
  ↓
Full Rollout or Rollback

Deployment Strategy: Blue-Green with Canary

Production (Blue)        Canary (Green)
100% traffic    →   95% / 5% split   →   0% / 100%
                    ↓
              Monitor for 15 min
                    ↓
              Success? Full rollout : Rollback

Rollback Procedure:

1. Detect issue (automated alerts or manual)
2. Trigger rollback command
3. Route traffic back to previous version
4. Investigate root cause
5. Fix and redeploy

Deployment Windows:

• Staging: Anytime
• Production: Tuesday-Thursday, 10 AM - 2 PM EST
• Emergency: 24/7 with on-call approval

Security Architecture

Defense in Depth

Layer 1: Network Security

• VPC isolation
• Security groups (allow-list only)
• Network ACLs
• Private subnets for data layer
• NAT Gateway for outbound traffic

Layer 2: Application Security

• Input validation (all user inputs)
• SQL injection prevention (parameterized queries)
• XSS prevention (sanitization + CSP headers)
• CSRF protection (tokens)
• Rate limiting (DDoS mitigation)

Layer 3: Authentication & Authorization

• JWT with short expiration
• Refresh token rotation
• MFA for admin accounts
• Role-based access control (RBAC)
• Principle of least privilege

Layer 4: Data Security

• Encryption at rest (RDS, S3)
• Encryption in transit (TLS 1.3)
• Secrets in AWS Secrets Manager
• PII data encrypted at field level
• Regular security audits

Security Headers

{
  'Strict-Transport-Security': 'max-age=31536000; includeSubDomains',
  'Content-Security-Policy': "default-src 'self'; script-src 'self' 'unsafe-inline'",
  'X-Frame-Options': 'DENY',
  'X-Content-Type-Options': 'nosniff',
  'Referrer-Policy': 'strict-origin-when-cross-origin',
  'Permissions-Policy': 'geolocation=(), microphone=(), camera=()'
}

Compliance

Standards:

• PCI DSS: Level 2 (Stripe handles Level 1)
• GDPR: User data rights, deletion, export
• SOC 2 Type II: In progress (Q2 2024)

Data Retention:

• User data: Retained until account deletion
• Order data: 7 years (regulatory requirement)
• Logs: 90 days
• Backups: 1 year

Scalability & Performance

Current Capacity

Scaling Strategy

Horizontal Scaling:

• Stateless services (easy to replicate)
• Auto-scaling based on CPU/memory
• Database read replicas for read-heavy workloads

Vertical Scaling:

• Database instance size (scheduled uptime)
• Cache cluster size

Caching:

• Application-level caching (Redis)
• CDN for static assets
• Database query result caching

Database Optimization:

• Indexes on frequently queried fields
• Materialized views for complex queries
• Connection pooling
• Query optimization (EXPLAIN ANALYZE)

Performance Budgets

API Response Times (p95):

• GET requests: <200ms
• POST requests: <500ms
• Complex queries: <1s

Frontend Performance (Lighthouse):

• Performance: >90
• Accessibility: 100
• Best Practices: >90
• SEO: 100

Database Query Times (p95):

• Simple queries: <50ms
• Join queries: <100ms
• Aggregations: <500ms

Monitoring & Observability

Metrics

Application Metrics (DataDog):

• Request rate, error rate, duration (RED metrics)
• Active users, sessions
• Business metrics (orders, revenue)
• Custom metrics (cart abandonment, conversion rate)

Infrastructure Metrics:

• CPU, memory, disk usage
• Network throughput
• Database connections, query performance
• Cache hit rate

Dashboards:

1. System Health: Overall system status
2. API Performance: Endpoint-specific metrics
3. Business Metrics: KPIs and conversions
4. Database Performance: Query analysis
5. Error Tracking: Error rates and trends

Logging

Log Levels:

• ERROR: Application errors requiring investigation
• WARN: Potential issues or degraded performance
• INFO: Significant events (order created, payment succeeded)
• DEBUG: Detailed diagnostic information (disabled in production)

Log Aggregation: CloudWatch Logs → DataDog

Structured Logging:

logger.info('Order created', {
  orderId: '123',
  customerId: '456',
  total: 99.99,
  timestamp: new Date().toISOString()
});

Alerting

Alert Channels:

• Critical: PagerDuty (SMS + Phone)
• High: Slack #incidents
• Medium: Slack #engineering
• Low: Email

Alert Rules:

- name: High Error Rate
  condition: error_rate > 5% for 5 minutes
  severity: CRITICAL
  channel: PagerDuty

- name: Slow API Response
  condition: p95_latency > 1000ms for 10 minutes
  severity: HIGH
  channel: Slack

- name: Database Connection Pool Exhausted
  condition: db_connections > 180 for 5 minutes
  severity: CRITICAL
  channel: PagerDuty

- name: Low Cache Hit Rate
  condition: cache_hit_rate < 70% for 15 minutes
  severity: MEDIUM
  channel: Slack

Tracing

Distributed Tracing: DataDog APM

Trace Example:

HTTP Request: GET /api/products/123
├─ Authentication Middleware (5ms)
├─ Authorization Middleware (2ms)
├─ Product Service
│  ├─ Cache Lookup (1ms) [MISS]
│  ├─ Database Query (45ms)
│  └─ Cache Set (2ms)
├─ Response Serialization (3ms)
└─ Total: 58ms

Future Considerations

Planned Improvements (Next 6 Months)

1. Microservices Migration

   • Extract payment service first
   • Event-driven architecture with message queue
   • Service mesh (Istio) for inter-service communication

2. Search Enhancement

   • Migrate to Elasticsearch
   • Implement faceted search
   • Add product recommendations (ML-based)

3. Performance Optimization

   • Implement GraphQL (reduce over-fetching)
   • Server-side rendering for better SEO
   • Optimize database queries (20% improvement target)

4. Infrastructure

   • Multi-region deployment for lower latency
   • Kubernetes migration (from ECS)
   • Serverless functions for background jobs

Technical Debt

High Priority:

• Upgrade Node.js from v16 to v20
• Migrate from class components to hooks (React)
• Implement comprehensive integration tests
• Refactor legacy authentication code

Medium Priority:

• Standardize error handling across services
• Improve API documentation (OpenAPI spec)
• Add end-to-end tests for critical flows

Low Priority:

• Migrate from REST to GraphQL
• Implement BFF (Backend for Frontend) pattern
• Add feature flags system

Risks & Mitigation

Appendices

Glossary

• GMV: Gross Merchandise Value
• RPS: Requests Per Second
• p95: 95th percentile
• TTL: Time To Live
• CDN: Content Delivery Network
• WAF: Web Application Firewall

References

• Architecture Decision Records
• API Documentation
• Runbooks
• Database Schema
• Security Policy

Change Log

Document Status: Current Next Review: April 15, 2024 Maintained By: Engineering Team Questions: #architecture on Slack

### Architecture Decision Record (ADR) Template

```markdown
# ADR-015: Migrate from Sessions to JWT Authentication

**Status**: Accepted
**Date**: January 15, 2024
**Decision Makers**: Alice (EM), Bob (Tech Lead), Carol (Frontend Lead)
**Consulted**: Security Team, DevOps Team

---

## Context

Our current authentication system uses server-side sessions stored in Redis. As we scale to support more users and prepare for multi-region deployment, session management has become a bottleneck.

### Current State

**Session-Based Authentication**:
```javascript
// Login creates server-side session
app.post('/login', (req, res) => {
  const user = authenticate(req.body);
  req.session.userId = user.id;  // Stored in Redis
  res.json({ success: true });
});

// Each request validates session
app.use((req, res, next) => {
  if (req.session.userId) {
    req.user = await getUser(req.session.userId);
  }
  next();
});

Problems:

1. Scalability: Every request requires Redis lookup (adds 5-10ms latency)
2. Complexity: Session replication across regions is complex
3. Memory: 50,000 active sessions = 250MB Redis memory
4. Stateful: Cannot easily add new servers (sticky sessions required)

Requirements

1. Stateless: No server-side session storage
2. Scalable: Support 50k+ concurrent users
3. Secure: Resistant to common attacks (XSS, CSRF, token theft)
4. Fast: Minimal performance impact (<1ms overhead)
5. Compatible: Work with existing mobile apps

Decision

We will migrate from session-based authentication to JSON Web Tokens (JWT).

Implementation

JWT-Based Authentication:

// Login generates JWT
app.post('/login', (req, res) => {
  const user = authenticate(req.body);

  const accessToken = jwt.sign(
    { userId: user.id, role: user.role },
    process.env.JWT_SECRET,
    { expiresIn: '15m' }
  );

  const refreshToken = jwt.sign(
    { userId: user.id },
    process.env.REFRESH_SECRET,
    { expiresIn: '7d' }
  );

  res.json({ accessToken, refreshToken });
});

// Each request validates JWT (no database lookup)
app.use((req, res, next) => {
  const token = req.headers.authorization?.split(' ')[1];

  try {
    req.user = jwt.verify(token, process.env.JWT_SECRET);
    next();
  } catch (error) {
    res.status(401).json({ error: 'Invalid token' });
  }
});

Token Structure

Access Token (short-lived):

• Payload: { userId, role, permissions }
• Expiration: 15 minutes
• Signature: HMAC SHA256

Refresh Token (long-lived):

• Payload: { userId }
• Expiration: 7 days
• Stored hash in database (for revocation)

Alternatives Considered

Alternative 1: Keep Session-Based Auth

Pros:

• No migration needed
• Familiar to team
• Easy to revoke access (delete session)

Cons:

• Scalability issues persist
• Complex multi-region setup
• Requires sticky sessions (load balancer complexity)

Decision: Rejected due to scalability concerns

Alternative 2: OAuth 2.0 Only

Pros:

• Industry standard
• Delegation capabilities
• Well-tested security

Cons:

• Overkill for our use case
• Complex implementation
• Requires authorization server
• Users expect username/password

Decision: Rejected - too complex for current needs. Will add OAuth as option later.

Alternative 3: API Keys

Pros:

• Simple implementation
• Stateless
• Easy to revoke

Cons:

• No expiration (security risk)
• Not suitable for user authentication
• No claims/scopes

Decision: Rejected - better suited for programmatic access, not user auth

Consequences

Positive

1. Performance: Eliminate Redis lookup on every request

   • Estimated improvement: 5-10ms per request
   • Reduces Redis load by 80%

2. Scalability: Stateless servers

   • No sticky sessions needed
   • Easy horizontal scaling
   • Multi-region deployment simplified

3. Mobile Support: Better mobile app experience

   • Tokens stored locally
   • No cookies required
   • Offline token validation

4. Developer Experience: Simpler architecture

   • No session middleware
   • Easier testing (no session state)
   • Clear token lifecycle

Negative

1. Token Revocation: Cannot immediately revoke access

   • Mitigation: Short token expiration (15 min)
   • Mitigation: Refresh token blacklist
   • Mitigation: Emergency: force re-auth for all users

2. Token Size: JWTs larger than session IDs

   • Session ID: ~32 bytes
   • JWT: ~200 bytes
   • Impact: Minimal (200 bytes per request is acceptable)

3. Secret Management: JWT secrets are critical

   • Mitigation: Store in AWS Secrets Manager
   • Mitigation: Rotate secrets quarterly
   • Mitigation: Different secrets per environment

4. XSS Risk: Tokens accessible to JavaScript

   • Mitigation: Store in httpOnly cookies (where possible)
   • Mitigation: Strict Content Security Policy
   • Mitigation: Short token expiration

Risks

Implementation Plan

Phase 1: Preparation (Week 1-2)

• Create JWT utility functions
• Update authentication middleware
• Add refresh token endpoint
• Write migration guide for frontend team
• Set up secrets in AWS Secrets Manager

Phase 2: Backend Migration (Week 3-4)

• Deploy JWT endpoints alongside session endpoints
• Add feature flag for JWT authentication
• Comprehensive testing (unit + integration)
• Load testing with JWTs
• Security review

Phase 3: Frontend Migration (Week 5-6)

• Update web app to use JWT
• Update mobile apps to use JWT
• Gradual rollout (10% → 50% → 100%)
• Monitor error rates and performance

Phase 4: Cleanup (Week 7-8)

• Remove session-based auth code
• Remove Redis session storage
• Update documentation
• Postmortem and lessons learned

Rollback Plan

If critical issues arise:

1. Disable JWT feature flag
2. Route all traffic to session endpoints
3. Keep JWT code for investigation
4. Identify and fix issues
5. Resume migration

Metrics for Success

Performance:

• Average request latency reduced by 5ms
• p95 latency reduced by 10ms
• Redis CPU usage reduced by 80%

Reliability:

• No increase in authentication errors
• <0.1% token validation failures
• Zero security incidents

User Experience:

• Login flow unchanged (transparent migration)
• No increase in support tickets
• Mobile app performance improved

Security Considerations

Token Security:

• Tokens signed with HS256 (HMAC SHA256)
• Secrets: 256-bit randomly generated
• Secrets rotated quarterly
• Algorithm specified in verification (prevent algorithm confusion)

Storage:

• Web: httpOnly cookies (prevents XSS)
• Mobile: Secure storage (Keychain/Keystore)
• Never in localStorage (XSS vulnerable)

Transmission:

• HTTPS only (TLS 1.3)
• Secure, SameSite=Strict cookies
• No tokens in URLs (log exposure)

Validation:

• Verify signature
• Check expiration
• Validate issuer and audience
• Check token not blacklisted (refresh tokens)

References

• JWT RFC 7519
• OWASP JWT Cheat Sheet
• Auth0 JWT Handbook
• Internal: Security Best Practices

Updates

Status: Accepted Supersedes: ADR-008 (Session-based Authentication) Related: ADR-012 (API Security), ADR-014 (Multi-region Deployment)

## Usage Examples

@architecture-documenter @architecture-documenter --type system-overview @architecture-documenter --type adr @architecture-documenter --focus security @architecture-documenter --focus scalability @architecture-documenter --include-diagrams @architecture-documenter --update-existing

## Best Practices

### Document Architecture Decisions

**When to create an ADR**:
- Significant technical decisions
- Architecture changes
- Technology choices
- Process changes
- Security decisions

**ADR Structure**:
1. **Context**: What's the situation?
2. **Decision**: What did we decide?
3. **Alternatives**: What else did we consider?
4. **Consequences**: What are the impacts?

### Use Visual Diagrams

**Diagram Types**:
- **System Context**: Show system boundaries
- **Container**: Show high-level architecture
- **Component**: Show internal structure
- **Code**: Show class/module relationships
- **Deployment**: Show infrastructure
- **Sequence**: Show interactions over time

**Tools**:
- Diagrams as code: Mermaid, PlantUML
- Visual tools: Lucidchart, Draw.io
- Cloud-specific: AWS Architecture Diagrams

### Keep Documentation Current

**Documentation Lifecycle**:
- Create during design phase
- Review in code review
- Update with implementation changes
- Quarterly architecture review
- Archive outdated docs (don't delete)

**Version Control**:
- Store docs with code
- Version alongside releases
- Link docs to specific code versions
- Maintain changelog

### Make It Discoverable

**Organization**:
- Central location (wiki, docs folder)
- Clear naming conventions
- Table of contents
- Cross-references
- Search-friendly

**Accessibility**:
- Public within organization
- Easy to navigate
- Multiple entry points
- Links from README

## Notes

- Architecture documentation is for communication, not perfection
- Diagrams speak louder than words - use them liberally
- ADRs capture decisions and context for future reference
- Keep docs synchronized with code changes
- Version architecture docs alongside code
- Regular reviews prevent documentation drift
- Good architecture docs reduce onboarding time significantly
- Document the "why" not just the "what"
- Include trade-offs and alternatives considered
- Make security and scalability explicit
- Link architecture to business goals
- Use consistent notation and terminology