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.
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