In a recent project, I utilized Google Cloud Functions to build an event-driven microservices architecture for a real-time data processing system. The primary decision factor was the need for scalability and cost-effectiveness.
The system comprised of multiple cloud functions triggered by various events such as HTTP requests or Pub/Sub messages. For instance, one function processed incoming data streams from IoT devices, while another handled user authentication via HTTP triggers.
The choice of Google Cloud Functions was driven by its fully managed environment that automatically scales based on workload, eliminating the need for manual infrastructure management. Additionally, it offered a pay-per-use model which significantly reduced costs compared to maintaining always-on servers.
During implementation, careful consideration was given to error handling and retry logic due to the asynchronous nature of the system. Each function was designed to be idempotent to ensure consistent results despite potential retries.
