Before I dive into the details, let me set the scene. Our company had just acquired a new feature: a treasure hunt engine that would allow users to create and share complex, real-time, multi-player hunts. The twist? We had to scale this monstrosity to tens of thousands of concurrent users within 12 weeks – or risk losing our new business unit to a competitor who'd done this before.
## What We Tried First (And Why It Failed)
Our initial approach was to throw a bunch of caching layers at the problem, thinking that if we could just keep the treasure hunt state in memory, we'd never have to worry about scaling. We deployed a Redis cluster, a memcached proxy, and even experimented with caching some of the hunts' state in the application itself. Sounds like a good idea, right? In theory, our approach made sense, but in practice, we quickly hit a wall.
One particular incident stood out. We launched the treasure hunt engine with a relatively simple hunt that had tens of concurrent players. Things seemed fine at first, but as the hunt progressed and our Redis cluster began to fill up with cached state, we started to see some weird performance issues. Turns out, that Redis cluster we set up was not only caching treasure hunt state, but also the entire hunt's logic – including the infamous "gold chest" mechanic, which, when triggered, would update the entire hunt's state for every player. Suddenly, our Redis cluster was serving up tens of thousands of redundant updates a second. We were hitting our Redis cluster's memory limits, causing page faults, and eventually, our entire system would grind to a halt.
