The Problem We Were Actually Solving
I was the lead systems engineer on a project to build a treasure hunt engine, a highly concurrent and real-time system that had to handle thousands of players simultaneously. The engine was initially built using Elixir, a language I had grown fond of due to its concurrency model and ease of development. However, as our server grew and the number of concurrent players increased, we started to notice significant performance issues. Our latency numbers were rising, and our allocator was running out of memory. I spent countless hours profiling the system, using tools like erlang-vm-profiler and fprof, to understand where the bottlenecks were. The profiler output showed that our Elixir code was spending a significant amount of time in garbage collection, which was causing our latency to spike. The allocation counts were staggering, with over 10 million allocations per second, most of which were temporary and short-lived.
What We Tried First (And Why It Failed)
We tried to optimize our Elixir code, using techniques like reducing the number of allocations, using immutable data structures, and fine-tuning our garbage collection settings. We also tried using third-party libraries like Broadway and GenStage to improve our concurrency model. However, despite our best efforts, we were unable to get the performance we needed. Our latency numbers were still high, and our allocator was still running out of memory. I was frustrated and disappointed, as I had invested a lot of time and effort into learning Elixir and building our system around it. I felt like I was hitting a wall, and I didn't know how to overcome it.
The Architecture Decision
After months of struggling with performance issues, I made the difficult decision to rewrite our treasure hunt engine using Rust. I knew it would be a significant undertaking, but I believed it was necessary to achieve the performance and reliability we needed. Rust's ownership model and lack of garbage collection made it an attractive choice for systems programming. I was also drawn to Rust's emphasis on memory safety, which I believed would help us avoid the kind of memory-related issues we were experiencing with Elixir. The decision to switch to Rust was not taken lightly, as it would require a significant investment of time and resources. However, I believed it was the right decision for the long-term health and success of our project.
What The Numbers Said After
After rewriting our treasure hunt engine using Rust, we saw a significant improvement in performance. Our latency numbers dropped from an average of 50ms to less than 10ms, and our allocator was no longer running out of memory. The allocation counts were significantly reduced, with less than 1 million allocations per second. We also saw a significant reduction in memory usage, with our system using less than half the memory it was using before. I was thrilled with the results, as it showed that our decision to switch to Rust had been the right one. We used tools like perf and SystemTap to analyze our system's performance and identify areas for further optimization. We also used Rust's built-in profiling tools, such as the cargo bench command, to benchmark our code and identify performance bottlenecks.
What I Would Do Differently
In retrospect, I would have made the decision to switch to Rust earlier. I was too invested in Elixir and wanted to make it work, but in the end, it was holding us back. I would also have put more emphasis on measuring and analyzing our system's performance from the beginning, rather than trying to optimize it after the fact. I learned a valuable lesson about the importance of performance and memory safety in systems programming, and I will carry that with me for the rest of my career. I would also have sought out more expertise and guidance on Rust and systems programming, as it would have helped me to avoid some of the pitfalls and challenges we faced during the transition. Overall, I am happy with the decision we made, and I believe it has set us up for long-term success and scalability.
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