The Problem We Were Actually Solving
At its core, our Treasure Hunt Engine is a web-based game that generates treasure maps for users to solve. It's a simple concept, but one that relies heavily on caching, load balancing, and database performance. As our user base grew, so too did the pressure on our system. But the problem wasn't just about scaling up the infrastructure - it was about understanding the long-term costs of our design decisions.
What We Tried First (And Why It Failed)
Initially, we tried to tackle the problem by throwing more hardware at it. We upgraded our servers, added more load balancers, and even hired a team of developers to work on optimizing the code. But despite our best efforts, the system continued to struggle. The root of the issue lay in the way we were caching data. Our current caching strategy was based on a simple LRU (Least Recently Used) policy, which worked well for small datasets but began to fail miserably as the size of our user base grew.
The Architecture Decision
