Scaling Pacibook: Lessons Learned from Handling High-Volume Traffic
From a garage project to a global platform. We share the technical challenges and solutions in scaling Pacibook's infrastructure.
Scaling is a good problem to have, but it's still a problem. When Pacibook.com started gaining traction, we faced the classic challenges of rapid growth: database bottlenecks, latency spikes, and soaring cloud bills. Here is how we architected our infrastructure to handle the load.
Database Sharding: Breaking the Monolith
We initially started with a single database instance. As our user base grew, write operations became a bottleneck. We moved to a sharded architecture, distributing user data based on geographic regions. This reduced latency for users (as their data was closer to them) and improved write throughput by parallelizing operations across multiple nodes.
The Power of Serverless
We utilize serverless functions for event-driven tasks like image processing and notification dispatch. When a user uploads a photo, a lambda function spins up, resizes it, optimizes it for web, and shuts down. This allows us to pay only for what we use and scale to zero during quiet periods. It handles the "thundering herd" problem gracefully, scaling up instantly to meet demand.
Caching at the Edge
The fastest request is the one that never hits your origin server. We implemented aggressive caching strategies using a global CDN. Static assets, public profiles, and feed content are cached at the edge, ensuring that 90% of traffic is served from a server within 50 miles of the user.