The Problem We Were Actually Solving
I was tasked with optimizing the performance of our Treasure Hunt Engine, a system that handled thousands of concurrent users and generated a massive amount of data. As a Veltrix operator, my primary goal was to ensure the engine could handle the load without significant latency or errors. However, despite my best efforts, the engine was consistently failing to meet our performance targets. The system would often become unresponsive, and users would experience significant delays. I knew that I had to dig deeper to identify the root cause of the problem.
What We Tried First (And Why It Failed)
Initially, I focused on tweaking the engine's configuration parameters, hoping to find a sweet spot that would improve performance. I spent countless hours poring over the documentation, trying different combinations of settings, and monitoring the system's behavior. However, no matter what I did, the engine's performance remained subpar. I was frustrated and confused, as the documentation seemed to suggest that the default settings should be sufficient for our use case. It wasn't until I started to analyze the system's metrics, such as allocation counts and latency numbers, that I began to understand the true nature of the problem. The profiler output showed a significant amount of time being spent in garbage collection, which was causing the system to pause and become unresponsive.
The Architecture Decision
It was at this point that I realized the Treasure Hunt Engine's underlying architecture was the main constraint. The engine was built on top of a runtime that was not designed to handle the level of concurrency and data generation that our system required. I made the decision to migrate the engine to a new runtime, one that was designed with performance and memory safety in mind. Specifically, I chose to use Rust, a language that I had experience with and knew could provide the necessary performance and reliability. However, I was also aware of the potential downsides of using Rust, including the steep learning curve and the potential for increased development time.
What The Numbers Said After
After migrating the engine to the new runtime, I saw a significant improvement in performance. The allocation counts decreased by over 50%, and the latency numbers dropped by an average of 30%. The system was able to handle the same load with much less memory usage, and the users experienced a much more responsive interface. The profiler output showed that the time spent in garbage collection had decreased dramatically, and the system was now able to run for extended periods without significant pauses. One specific metric that stood out was the reduction in average latency from 250ms to 170ms, which was a significant improvement. Additionally, the 99th percentile latency decreased from 500ms to 350ms, which indicated that the system was much more consistent in its performance.
What I Would Do Differently
In hindsight, I would have liked to have taken a more data-driven approach from the beginning. Instead of relying on the documentation and my own intuition, I would have focused on gathering more metrics and analyzing the system's behavior. This would have allowed me to identify the root cause of the problem more quickly and make more informed decisions about the architecture and configuration of the system. Additionally, I would have liked to have had more experience with the new runtime and language before embarking on the migration project. While I had some experience with Rust, I still encountered some challenges and pitfalls during the migration process. Despite these challenges, I am confident that the decision to migrate to a new runtime was the correct one, and I am excited to see how the system will continue to evolve and improve over time. One specific thing I would do differently is to use a tool like perf to get a more detailed understanding of the system's performance characteristics, which would have allowed me to make even more targeted optimizations.
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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