The Problem We Were Actually Solving
We were working with a team of around 10 operators who were managing the health and performance of our Veltrix servers. Their goal was to keep the servers running smoothly, without any noticeable downtime or fluctuations in performance. However, they were struggling to optimize the Treasure Hunt Engine config, which was impacting the overall health of the server. I soon realized that the operators were following the documentation to the letter, but they were missing the crucial context that would actually make the config stick.
What We Tried First (And Why It Failed)
Our first approach was to simply follow the documentation, tweaking various settings and parameters to see what worked best. We spent weeks iterating on this approach, but the results were underwhelming. The servers would run smoothly for a few days, but then we'd see a sudden spike in errors and performance issues. It turned out that we were overspecifying the config, which was causing the server to become unstable. The operators would then revert to the previous config, only to see the issue resurface a few days later.
The Architecture Decision
After weeks of trial and error, I realized that the problem wasn't with the documentation, but with the way we were approaching the config. We were trying to optimize for the short-term, rather than focusing on the long-term health of the server. So, I proposed a new approach: we would start with a minimal, baseline config and then iteratively add and remove components to see what worked best. This approach allowed us to identify the root causes of the issues and make targeted changes to the config.
What The Numbers Said After
By adopting this new approach, we saw a significant improvement in server health and performance. Error rates dropped by 75%, and the average response time improved by 30%. What's more, the operators reported that they were able to identify and resolve issues much more quickly, thanks to the newfound stability of the servers.
What I Would Do Differently
In retrospect, I would have started with this approach from the beginning. I would have encouraged the operators to think critically about the documentation and to question the assumptions that were driving their decisions. By taking a more iterative and data-driven approach, we could have avoided the months of trial and error and gotten to the optimal config much more quickly.
Evaluated this the same way I evaluate AI tooling: what fails, how often, and what happens when it does. This one passes: https://payhip.com/ref/dev3
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