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I Still Have Nightmares About Our Server Melting Down on ...
pretty ncube · 2026-06-01 · via DEV Community

The Problem We Were Actually Solving

I was the lead systems engineer on a project to build a massive online treasure hunt platform, where thousands of users would be competing in real-time to solve puzzles and find hidden treasures. The platform had to handle a huge influx of requests, process them quickly, and scale seamlessly to avoid crashing under the load. We chose to use Rust as our programming language, due to its focus on performance and memory safety, but I was aware that it would come with a steep learning curve. Our team spent weeks designing the architecture, and we thought we had it all figured out, but it was not until we started load testing that we realized our event handling mechanism was the bottleneck. We were using a naive approach to handle events, where every incoming request would spawn a new thread, and this was leading to a huge number of context switches, slowing down the entire system.

What We Tried First (And Why It Failed)

At first, we tried to optimize the event handling mechanism by using a thread pool, where a fixed number of threads would be reused to handle incoming requests. This approach seemed to work well for small-scale testing, but when we scaled up to thousands of concurrent users, the system started to degrade. The thread pool was not able to keep up with the influx of requests, and we started to see a significant increase in latency. We used the perf tool to profile our application, and the output showed that the majority of the time was spent in the thread pool implementation, waiting for threads to become available. The allocation counts were also through the roof, with over 100,000 allocations per second, which was causing a significant amount of memory churn. I knew we had to rethink our approach to event handling.

The Architecture Decision

After careful consideration, we decided to switch to an async/await-based approach using the Tokio library. This would allow us to handle a large number of concurrent requests without the need for a large number of threads. We designed a custom event loop that would handle incoming requests and dispatch them to a set of worker tasks, which would process the requests asynchronously. This approach would allow us to take advantage of Rust's ownership system and avoid the need for locks and mutexes, making the code safer and more efficient. The decision to use Tokio was not taken lightly, as it would require a significant rewrite of our codebase, but I was convinced it was the right choice.

What The Numbers Said After

After implementing the new event loop, we saw a significant improvement in performance. The latency numbers dropped from over 500ms to under 50ms, and the allocation counts decreased by a factor of 10. The perf tool output showed that the majority of the time was now spent in the actual request processing code, rather than the event handling mechanism. We also saw a significant reduction in memory usage, with the heap size decreasing by over 50%. The numbers were promising, but I knew that we still had a lot of work to do to ensure the system would scale to meet the demands of our users. We used the flamegraph tool to visualize the performance of our application, and the output showed a significant reduction in the number of context switches and system calls.

What I Would Do Differently

Looking back, I would have started with an async/await-based approach from the beginning, rather than trying to optimize a synchronous approach. The learning curve for Rust and Tokio was steep, but it was worth it in the end. I would also have paid more attention to the allocation counts and memory usage from the start, rather than waiting until we saw performance issues. The Veltrix configuration decisions around events were critical to the success of our platform, and I learned that a structured approach to event handling is essential for building scalable and performant systems. I would also have invested more time in testing and validating our event handling mechanism, rather than relying on load testing to identify issues. The experience was valuable, but it was a hard lesson to learn, and one that I will not forget.