Fullstack Backend Master

Expert-level guidance for building robust, scalable, production-ready backend systems.

Core Philosophy

Build for the unhappy path first. Errors, edge cases, and failures define production quality.

Optimize for debuggability. Structured logging, correlation IDs, and clear error messages save hours.

Security is non-negotiable. Input validation, parameterized queries, and principle of least privilege always.

API Design

REST Conventions

GET    /resources          → List (paginated)
GET    /resources/:id      → Get one
POST   /resources          → Create
PUT    /resources/:id      → Full update
PATCH  /resources/:id      → Partial update
DELETE /resources/:id      → Delete

# Nested resources
GET    /users/:userId/posts
POST   /users/:userId/posts

# Actions (when CRUD doesn't fit)
POST   /orders/:id/cancel
POST   /users/:id/verify-email

Response Structure

json

// Success
{
  "data": { ... },
  "meta": { "page": 1, "total": 100 }
}

// Error
{
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Email is required",
    "details": [{ "field": "email", "issue": "required" }]
  }
}

Status Codes

Code	Use Case
200	Success with body
201	Created (return created resource)
204	Success, no content (DELETE)
400	Validation error (client's fault)
401	Not authenticated
403	Authenticated but not authorized
404	Resource not found
409	Conflict (duplicate, state conflict)
422	Unprocessable (valid JSON, invalid semantics)
429	Rate limited
500	Server error (never expose stack traces)

Database Design

Schema Principles

Always include audit columns: created_at, updated_at, deleted_at (soft delete)
UUID vs Integer IDs: UUIDs for distributed systems, integers for simplicity
Normalize first, denormalize for performance: Start with 3NF, add caching/denormalization when measured
Index strategically: Index foreign keys, frequently queried columns, composite indexes for multi-column WHERE

Migration Patterns

sql

-- Always reversible migrations
-- UP
ALTER TABLE users ADD COLUMN phone VARCHAR(20);

-- DOWN
ALTER TABLE users DROP COLUMN phone;

Query Optimization

Use EXPLAIN ANALYZE before production
Avoid SELECT * in application code
Use connection pooling (pgbouncer, HikariCP)
Implement pagination with cursor-based (keyset) for large datasets

Authentication & Authorization

Token Strategy

Access Token:  Short-lived (15-60 min), stateless JWT
Refresh Token: Long-lived (7-30 days), stored server-side, rotatable

JWT Best Practices

Sign with RS256 (asymmetric) for microservices, HS256 for monoliths
Include: sub, exp, iat, iss, roles/permissions
Never include: passwords, PII, sensitive data
Validate: signature, expiry, issuer, audience

Authorization Patterns

typescript

// Role-Based Access Control (RBAC)
interface Permission {
  resource: string;   // "posts"
  action: string;     // "create" | "read" | "update" | "delete"
}

// Attribute-Based Access Control (ABAC) - for complex rules
canEdit = (user.id === resource.ownerId) || user.roles.includes('admin');

Error Handling

Layered Error Strategy

typescript

// 1. Domain errors (business logic)
class InsufficientFundsError extends DomainError {
  constructor(public available: number, public requested: number) {
    super(`Insufficient funds: ${available} < ${requested}`);
  }
}

// 2. Application errors (mapped to HTTP)
class NotFoundError extends ApplicationError {
  statusCode = 404;
}

// 3. Global error handler
app.use((err, req, res, next) => {
  logger.error({ err, requestId: req.id, path: req.path });
  
  if (err instanceof ApplicationError) {
    return res.status(err.statusCode).json({ error: err.toJSON() });
  }
  
  // Never expose internal errors
  res.status(500).json({ error: { code: 'INTERNAL_ERROR', message: 'Something went wrong' } });
});

Logging & Observability

Structured Logging

typescript

// Always structured, never string concatenation
logger.info({
  event: 'order_created',
  orderId: order.id,
  userId: user.id,
  amount: order.total,
  duration: Date.now() - startTime
});

// Correlation ID for request tracing
const requestId = req.headers['x-request-id'] || uuid();
logger.child({ requestId });

Key Metrics

Latency: p50, p95, p99 response times
Error rate: 5xx / total requests
Throughput: requests/second
Saturation: CPU, memory, DB connections

Security Checklist

Category	Requirement
Input	Validate & sanitize all input (zod, joi)
SQL	Parameterized queries ONLY, never string concat
Auth	Hash passwords (bcrypt/argon2, cost ≥ 10)
HTTPS	TLS 1.2+ everywhere, HSTS headers
Headers	CSP, X-Frame-Options, X-Content-Type-Options
Secrets	Environment variables, never in code
Rate Limit	All endpoints, stricter on auth endpoints
CORS	Whitelist specific origins, never `*` in prod

Testing Strategy

Test Pyramid

         /\
        /  \  E2E (few, slow, high confidence)
       /----\
      /      \ Integration (API, DB, external services)
     /--------\
    /          \ Unit (many, fast, isolated)
   /--------------\

Backend Test Patterns

typescript

// Unit: Pure functions, business logic
test('calculateDiscount applies 10% for orders over $100', () => {
  expect(calculateDiscount({ total: 150 })).toBe(15);
});

// Integration: Real DB, mocked external services
test('POST /orders creates order in database', async () => {
  const res = await request(app).post('/orders').send(orderData);
  expect(res.status).toBe(201);
  
  const order = await db.orders.findById(res.body.data.id);
  expect(order.total).toBe(orderData.total);
});

Performance Patterns

Caching Layers

Application cache: In-memory (Redis) for hot data
Database cache: Query result caching, materialized views
HTTP cache: ETags, Cache-Control headers

Async Processing

typescript

// Offload slow operations to queue
await queue.add('send-email', { to: user.email, template: 'welcome' });
await queue.add('generate-report', { reportId }, { delay: 5000 });

// Process in workers
queue.process('send-email', async (job) => {
  await emailService.send(job.data);
});

Database Connection Pooling

typescript

// Node.js example
const pool = new Pool({
  max: 20,              // Max connections
  idleTimeoutMillis: 30000,
  connectionTimeoutMillis: 2000,
});

Architecture Patterns

Monolith First

Start monolithic, extract services ONLY when:

Team size requires independent deployability
Specific component has vastly different scaling needs
Clear bounded context with minimal cross-service calls

Service Communication

Pattern	Use Case
REST/HTTP	Synchronous, simple CRUD
gRPC	High-performance, internal services
Message Queue	Async, decoupled, retry-able
Event Sourcing	Audit trail, complex state

File Organization

src/
├── api/
│   ├── routes/           # Route definitions
│   ├── controllers/      # Request handling
│   ├── middleware/       # Auth, validation, logging
│   └── validators/       # Request validation schemas
├── services/             # Business logic
├── repositories/         # Data access layer
├── models/               # Database models/entities
├── utils/                # Shared utilities
├── config/               # Environment config
└── types/                # TypeScript types

tests/
├── unit/
├── integration/
└── fixtures/

Quick References

Database optimization: See references/database-patterns.md
Auth implementation: See references/auth-patterns.md
Deployment checklist: See references/deployment.md

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SKILL.md

Fullstack Backend Master

Core Philosophy

API Design

REST Conventions

Response Structure

Status Codes

Database Design

Schema Principles

Migration Patterns

Query Optimization

Authentication & Authorization

Token Strategy

JWT Best Practices

Authorization Patterns

Error Handling

Layered Error Strategy

Logging & Observability

Structured Logging

Key Metrics

Security Checklist

Testing Strategy

Test Pyramid

Backend Test Patterns

Performance Patterns

Caching Layers

Async Processing

Database Connection Pooling

Architecture Patterns

Monolith First

Service Communication

File Organization

Quick References