Backend

Build reliable backend services with proper error handling, security, and observability.

Error Handling

• Never expose stack traces to clients—log internally, return generic message
• Structured error responses: code, message, request ID—enables debugging without leaking
• Fail fast on bad input—validate at entry point, not deep in business logic
• Unexpected errors: 500 + alert—expected errors: appropriate 4xx

Input Validation

• Validate everything from outside—query params, headers, body, path params
• Whitelist valid input, don't blacklist bad—reject unknown fields
• Validate early, before any processing—save resources, clearer errors
• Size limits on all inputs—prevent memory exhaustion attacks

Timeouts Everywhere

• Database queries: set timeout, typically 5-30s
• External HTTP calls: connect timeout + read timeout—don't wait forever
• Overall request timeout—gateway or middleware level
• Background jobs: max execution time—prevent zombie processes

Retry Patterns

• Exponential backoff: 1s, 2s, 4s, 8s...—prevents thundering herd
• Add jitter: randomize delay—prevents synchronized retries
• Idempotency keys for non-idempotent operations—safe to retry
• Circuit breaker for failing dependencies—stop hammering, fail fast

Database Practices

• Connection pooling: reuse connections—creating is expensive
• Transactions scoped minimal—hold locks briefly
• Read replicas for read-heavy workloads—separate read/write traffic
• Prepared statements always—SQL injection prevention, query plan cache

Caching Strategy

• Cache invalidation strategy decided upfront—TTL, event-based, or both
• Cache at right layer: query result, computed value, HTTP response
• Cache stampede prevention—lock or probabilistic early expiration
• Monitor hit rate—low hit rate = wasted resources

Rate Limiting

• Per-user/IP limits on expensive operations—login, signup, search
• Different limits for different operations—read vs write
• Return Retry-After header—tell clients when to retry
• Rate limit early in request pipeline—save resources

Health Checks

• Liveness: is process running—restart if fails
• Readiness: can handle traffic—remove from load balancer if fails
• Startup probe for slow-starting services—don't kill during init
• Health checks fast and cheap—don't hit database on every probe

Graceful Shutdown

• Stop accepting new requests first—drain load balancer
• Wait for in-flight requests to complete—with timeout
• Close database connections cleanly—prevent connection leaks
• SIGTERM handling: graceful; SIGKILL after timeout

Logging

• Structured logs (JSON)—parseable by log aggregators
• Request ID in every log—trace request across services
• Log level appropriate: debug for dev, info/error for prod
• Sensitive data never logged—passwords, tokens, PII

API Design

• Versioning strategy from day one—path (/v1/) or header
• Pagination for list endpoints—cursor or offset; include total count
• Consistent response format—same envelope everywhere
• Meaningful status codes—201 for create, 204 for delete, 404 for not found

Security Hygiene

• Secrets from environment or vault—never in code or config files
• Dependencies updated regularly—automated with Dependabot/Renovate
• Principle of least privilege—service accounts with minimal permissions
• Authentication and authorization separated—who you are vs what you can do

Observability

• Metrics: request count, latency percentiles, error rate—the RED method
• Distributed tracing for microservices—follow request across services
• Alerting on symptoms, not causes—high error rate, not CPU usage
• Dashboards for operational visibility—know normal to spot abnormal