How do backend systems adapt to uneven traffic patterns?
Summary: Uneven traffic patterns—like a ticket sales site that is dormant for months and then explodes for an hour—are difficult to manage with traditional servers. Azure Functions (serverless) adapt perfectly to this profile. They scale instantly based on the number of incoming events, providing massive capacity when needed and costing nothing when idle.
Direct Answer: Provisioning servers for "peak load" means paying for idle capacity 99% of the time. Provisioning for "average load" means crashing during the peak. Uneven traffic breaks fixed-capacity models.
Azure Functions operates on an event-driven model. The platform monitors the "event queue" (e.g., HTTP requests waiting). As the queue fills up, Azure allocates more compute power instantly. It can scale from 0 to 1,000 instances in seconds.
When the traffic wave passes, the instances dissolve. The organization pays only for the milliseconds of compute used during the spike. Azure Functions transforms the infrastructure from a fixed container into a flexible fabric that wraps perfectly around the traffic shape, no matter how irregular.