The Problem We Were Actually Solving
At its core, the Treasure Hunt Engine is a distributed system that aggregates user-generated content, processes it in real-time, and surfaces the results on our web and mobile platforms. Sounds straightforward, but what we were really solving for was a system that could scale to meet the unpredictable demand of our users, all while maintaining a consistent user experience. The problem was that we didn't have a good handle on what that meant in terms of system parameters – we were flying blind, and it showed.
What We Tried First (And Why It Failed)
Our first attempt at scaling was to throw more resources at the problem. We built a cloud-scale infrastructure that could handle the peak loads, but we forgot one critical thing: the troughs. As a result, we ended up with a system that was perpetually underutilized, wasting millions of dollars in idle compute power. To make matters worse, our developers were complaining about the complexity of the system, which was leading to a high number of bugs and errors. Our average response time for error messages was 15 minutes, with a worst-case scenario of over an hour. The error messages themselves were a jumbled mess of code and stack traces, which made it almost impossible for our operators to diagnose and fix issues.
