The Problem We Were Actually Solving
I was tasked with building a scalable treasure hunt engine for a popular online gaming platform, and I quickly realized that the default configuration of our system was not going to cut it. The engine needed to handle thousands of concurrent users, each with their own unique game state, and still provide sub-10ms latency. As I delved deeper into the problem, I discovered that our biggest constraint was not the hardware, but the language and runtime we were using. Our initial prototype was built using a high-level language that was easy to develop in, but it was not designed with performance or memory safety in mind. I spent countless hours optimizing the code, but no matter what I did, I could not get the latency below 50ms. It was then that I realized we needed to rethink our entire approach.
What We Tried First (And Why It Failed)
My first attempt at solving the problem was to try and optimize the existing codebase. I spent weeks poring over profiler output, looking for any signs of bottlenecks or inefficiencies. I used tools like Valgrind and Intel VTune Amplifier to analyze the memory usage and CPU cycles of our application. I even tried using a different garbage collector, hoping that it would reduce the pause times and improve overall performance. However, no matter what I did, I could not get the results I needed. The language and runtime were just not designed to handle the kind of workload we were throwing at them. I was seeing allocation counts in the millions, and latency numbers that were consistently above 50ms. It was clear that we needed a different approach.
The Architecture Decision
After much research and experimentation, I decided to rebuild the treasure hunt engine using Rust. I knew it would be a challenging task, as Rust has a steep learning curve, but I was convinced that it was the right choice for our use case. Rust's focus on memory safety and performance made it an ideal choice for building a high-performance, concurrent system. I spent several weeks learning the language and building a new prototype. It was not easy, but the results were well worth it. With Rust, I was able to achieve latency numbers below 10ms, and allocation counts that were a fraction of what they were before. I was using the Rust standard library, along with a few external crates like Tokio and async-std, to build a highly concurrent and asynchronous system.
What The Numbers Said After
The numbers spoke for themselves. With the new Rust-based engine, we were seeing average latency numbers of around 5ms, with a 99th percentile of 10ms. Allocation counts were down to almost zero, and CPU usage was consistently below 20%. We were handling thousands of concurrent users, and the system was still performing flawlessly. I was using tools like Prometheus and Grafana to monitor the system, and the metrics were all looking great. I was also using a tool called flamegraph to analyze the performance of the system, and it was showing me exactly where the bottlenecks were. It was clear that rebuilding the engine in Rust was the right decision.
What I Would Do Differently
Looking back, I would do a few things differently. First, I would have started with Rust from the beginning, rather than trying to optimize the existing codebase. I would have also spent more time learning the language and its ecosystem, rather than trying to rush into building a prototype. I would have also used more tools like Clippy and Rustfmt to help with code quality and formatting. Additionally, I would have written more tests and benchmarks to ensure that the system was performing as expected. Overall, however, I am happy with the decision to rebuild the engine in Rust. It was a challenging but rewarding experience, and it has given us a highly scalable and performant system that will serve us well for years to come. I am excited to see where this technology will take us, and I am already thinking about how we can use it to build even more complex and scalable systems in the future.
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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