The problem we were actually solving, was not just optimizing the treasure hunt engine for Hytale, but also handling edge cases in the system that led to a massive number of crashes every week. We had over 50 Hytale servers running on our platform, and the crash rate was unacceptable. Every server had at least one instance of Treasure Hunt Engine (TBE), which was supposed to help players discover hidden treasures.
What we tried first (and why it failed), was a naive implementation of the TBE using a simple queue-based system. The idea was to have a single thread consume the queue and update the game state accordingly. Sounds simple, right? Well, it worked for a while, but soon we started noticing strange behavior - the TBE would get stuck in an infinite loop, or sometimes it would skip a few treasures altogether. We had no idea what was causing it.
The architecture decision, was to switch to a more robust implementation of TBE using a combination of actor model and event sourcing. Each server would spawn multiple actors, each responsible for a specific aspect of the treasure hunt - one for location, one for difficulty, and one for rewards. The actors would communicate with each other using events, which would be stored in an event store. This way, we could easily query and replay the history of events, and the system would be more fault-tolerant.
What the numbers said after, was that the crash rate decreased by 80% within the first week of deploying the new TBE implementation. We went from 200 crashes per day to just 40. The players were happy, and so were our servers.
What I would do differently, is to have included some sort of instrumentation and monitoring from the start. We spent weeks debugging and optimizing the new TBE, but we didn't have any metrics to back up our claims. We could have avoided a lot of pain if we had just added some simple metrics like throughput, latency, and error rates from the beginning. We also could have avoided some of the early mistakes if we had run some load tests before rolling out the new implementation.
Looking back, I realized that the key to success was not just the architecture decision itself, but also the attention to detail and the willingness to learn from our mistakes. We could have easily fallen into the trap of premature optimization, but instead, we took the time to understand the problem, and then designed a solution that met our needs. In the end, it was a combination of technical expertise, domain knowledge, and a bit of luck that saved the day.
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