Serverless Architectures: Patterns and Pitfalls

Serverless computing helps teams run code without managing servers. You pay for actual compute time and the platform scales automatically. This is great for event-driven apps, APIs, and processing jobs that spike suddenly. But it also invites common patterns and familiar traps. A clear plan helps you move fast while staying reliable and affordable.

Patterns that help

  • Event-driven microservices: small, focused functions respond to events from queues, storage, or APIs. They stay simple and decoupled.
  • API composition: an API gateway sits in front of functions, handling authentication, routing, and rate limits.
  • Async processing: work is handed to a queue or publish/subscribe system, letting resources scale independently.
  • Orchestration: state machines or step logic coordinates multiple services without long-lived processes.
  • Backend for frontend: tailored endpoints reduce data transfer and improve user experience on different devices.

Pitfalls to avoid

  • Cold starts: initial latency can affect user experience. Mitigate with warm pools, provisioned concurrency, or careful sizing.
  • State and idempotency: functions are often stateless; design for safe retries and duplicate handling.
  • Observability gaps: distributed traces, metrics, and centralized logs are essential to diagnose failures.
  • Vendor lock-in: rely on several providers or portable patterns to keep options open.
  • Cost surprises: high fan-out or long executions can spike bills; set budgets and alerts.
  • Security drift: misconfigured IAM roles or overly broad permissions are common gaps.
  • Data locality: ensure data residency and latency meet your needs; avoid hidden egress fees.
  • Testing complexity: emulate cloud services locally or with emulators to catch integration issues early.

Practical tips

  • Build with idempotent operations and clear error handling.
  • Instrument everything: traces, metrics, and logs should be wired to a central dashboard.
  • Control costs: set reasonable memory, timeout, and concurrency limits; use budgets.
  • Test end-to-end: include integration tests that exercise event paths and failure scenarios.

Example scenario

A photo app uses an object storage event to trigger a function, which writes metadata to a database and queues a second task for resizing. A separate function handles user requests through an API gateway. If the queue backs up, an autoscale policy keeps the system responsive while adding a state machine to orchestrate retries.

Key Takeaways

  • Choose patterns that match workload and latency needs.
  • Plan for failures with retries, idempotency, and observability.
  • Monitor cost, security, and performance to stay reliable at scale.