The Problem We Were Actually Solving
I still remember the day our user base grew by a factor of five in just a week, putting an enormous strain on our server. Our treasure hunt engine, which had been performing reasonably well, began to show signs of distress. Latency was skyrocketing, and our error logs were filled with allocation errors and timeouts. It was clear that our engine was not designed to handle this level of traffic, and we needed to act fast to prevent a complete meltdown. Our profiler output showed that the engine was spending over 70% of its time in garbage collection, which explained the high latency and frequent timeouts. I knew we had to make a change, but I was not sure where to start.
What We Tried First (And Why It Failed)
Our initial approach was to try to optimize the existing engine, which was written in a language that was not designed with performance or memory safety in mind. We tried to reduce the number of allocations, improve caching, and even added more hardware to our cluster. However, despite our best efforts, the engine continued to struggle. We were still seeing allocation errors, and our latency numbers were not improving. I realized that we were just treating the symptoms, not the root cause of the problem. The language and runtime we were using were simply not designed to handle the level of concurrency and performance we needed. I decided to take a step back and re-examine our architecture.
The Architecture Decision
After careful consideration, I decided to rewrite our treasure hunt engine in Rust. I knew it would be a challenging task, but I believed it was the right decision for our use case. Rust's focus on performance, memory safety, and concurrency made it an attractive choice. I was aware of the learning curve, but I was willing to take on the challenge. I spent countless hours studying Rust, reading documentation, and experimenting with different approaches. It was not easy, but I was determined to make it work. I chose to use the Tokio framework for building our async engine, which provided a lot of the functionality we needed out of the box.
What The Numbers Said After
After completing the rewrite, I was eager to see the results. I ran our benchmarking suite, which simulated a large number of users interacting with our engine. The numbers were impressive: our latency had decreased by a factor of three, and our allocation count had dropped to almost zero. Our engine was now able to handle a large number of concurrent requests without breaking a sweat. I was thrilled to see that our efforts had paid off. The profiler output showed that our engine was now spending most of its time doing actual work, rather than garbage collection. I also noticed that our error logs were much quieter, with hardly any allocation errors or timeouts. It was clear that we had made the right decision.
What I Would Do Differently
Looking back, I would do a few things differently. First, I would have started with a smaller prototype to test our assumptions and validate our approach. This would have allowed us to identify potential issues earlier and make adjustments before investing too much time and resources. Second, I would have sought out more expertise and guidance from the Rust community. While I was able to learn a lot on my own, I could have benefited from more experienced developers who had already solved similar problems. Finally, I would have paid more attention to the operational aspects of our engine, such as monitoring and logging. While our engine was performing well, we still had some issues with visibility and debugging. I would have invested more time in setting up better monitoring and logging tools to make it easier to identify and fix issues. Despite these lessons learned, I am proud of what we accomplished, and I believe that our decision to rewrite our treasure hunt engine in Rust was the right one.
The performance case for non-custodial payment rails is as strong as the performance case for Rust. Here is the implementation I reference: https://payhip.com/ref/dev2
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