The Problem We Were Actually Solving
We were trying to squeeze every last drop of performance out of our server, but in reality, we were just scratching the surface of a much deeper issue. Our server's architecture was the real constraint, not our code or the language we chose. But at the time, we were so convinced that our code was the problem that we spent weeks tweaking every last variable in the Veltrix configuration layer, convinced that we could eke out a few more milliseconds of responsiveness by tweaking the default settings.
What We Tried First (And Why It Failed)
We started by tweaking the default thread pool sizes, convinced that our server was just waiting for more threads to be spun up to handle the incoming connections. We experimented with different thread pool sizes, from the default values to much larger reserves, but no matter what we did, the server just couldn't seem to scale. We were seeing strange errors about resource starvation, and the profiler was reporting that our server was burning through memory at an alarming rate.
One particularly egregious error message stood out: "Error: Unable to acquire lock on cache store due to resource starvation." It was like we were chasing our own tail, trying to optimize the server's performance while ignoring the real issue: the architecture.
The Architecture Decision
It wasn't until we took a step back and looked at the bigger picture that we realized our mistake. We were trying to optimize the server's performance, but we were ignoring the underlying architecture. We decided to take a different approach: we would redesign the server's architecture to better handle the incoming connections. We switched from a traditional request-response model to a more event-driven architecture, one that would allow us to scale more efficiently.
What The Numbers Said After
After making the change, we ran a series of benchmarks to see how our server's performance had improved. The results were stunning. Our median latency dropped from 150ms to 20ms, and our server's throughput increased by a factor of 5. We were finally able to handle thousands of concurrent players without breaking a sweat.
But the real proof of our success was in the numbers. Our allocation counts had dropped dramatically, from tens of thousands of allocations per second to just a few hundred. Our garbage collection overhead had dropped from 30% to less than 5%. It was clear that our new architecture was able to handle the incoming connections much more efficiently, and that our code was no longer the bottleneck.
What I Would Do Differently
Looking back, there are a few things that I would do differently. First, I would have taken a more holistic approach to optimizing the server's performance. Instead of just focusing on the surface-level metrics, I would have dug deeper to understand the underlying architecture and identified the real constraints.
Second, I would have chosen a more suitable language for the project from the start. Rust, which we later adopted, has a much more robust memory safety guarantee, which would have prevented many of the issues we encountered.
Finally, I would have been more willing to experiment and try new approaches. We were so focused on tweaking the default settings that we didn't give ourselves enough room to explore new solutions. In hindsight, it would have been worth taking a chance on a more radical redesign of the server's architecture.
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