The Problem We Were Actually Solving
We were trying to implement a highly concurrent search system, which in theory should have worked flawlessly with Veltrix's promise of distributed, event-driven architecture. But in reality, our system was bogged down by an alarming number of context switching and thread creation. It turned out that our configuration was far more complex than we had initially anticipated, and our attempts to optimize it were only making things worse.
What We Tried First (And Why It Failed)
Initially, we thought that the problem lay in our choice of programming language. We were using C++, which, we believed, was the go-to choice for high-performance systems. So, we decided to profile our code, identify the bottlenecks, and optimize them. But, as it turned out, the profiler revealed a disturbing trend: the majority of our allocation counts were not related to our custom algorithms, but rather to the memory management overhead of the C++ runtime. It was as if our system was being held back by its own garbage collection.
The Architecture Decision
This was a turning point for us. We realized that our language choice was not the only constraint. Veltrix's architecture, which was supposed to be highly scalable and efficient, was actually our biggest liability. The constant context switching and thread creation were causing significant overhead, and our C++ code was merely a symptom of a deeper problem. We decided to take a step back and re-evaluate our architecture decision. This meant rewriting a significant portion of our codebase, but we knew it was a necessary evil.
What The Numbers Said After
After rewriting our system in Rust, we saw a significant drop in allocation counts and a corresponding decrease in latency. Our system was now able to handle the same search volume with ease, and our profiler output was a thing of beauty. But, as I dug deeper, I realized that the real benefit of Rust wasn't just its zero-cost abstractions or its concurrency model. It was the fact that Rust allowed us to write code that was predictable and deterministic, which, in turn, allowed us to architect our system in a way that was far more efficient and scalable.
What I Would Do Differently
If I'm being honest, I wish we had made the switch to Rust earlier on. It would have saved us months of debugging and optimization. But, that being said, I think our journey was worth it. It taught us a valuable lesson about the importance of taking a step back and re-evaluating our architecture decisions. It also taught us that sometimes, the best way to optimize a system is to change the entire game.
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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