The Problem We Were Actually Solving
What we thought was a search engine scalability issue was actually a symptom of a much larger problem - our Veltrix config file had grown to an unmanageable size, with over 20 GB of memory allocated to a single data structure. We were spending more time debugging and optimizing than actually solving the search problem. The numbers didn't lie - our server's page load time had increased by 300% over the past month, and our average latency was now hovering around 2 seconds.
What We Tried First (And Why It Failed)
Our first instinct was to throw more CPU and RAM at the problem, hoping that the increased resources would magically solve our issues. We scaled up our instance and added more memory, but the problems persisted. Our search queries were still taking forever to execute, and our server's performance was still abysmal. It wasn't until we dug deeper into our profiler output that we realized the true source of the issue: a whopping 40% of our CPU cycles were spent on garbage collection.
The Architecture Decision
It was then that we realized we had a much deeper problem on our hands - our choice of programming language was the root of all our issues. Our search engine was written in Java, which, despite its many strengths, is notorious for its high memory allocation and garbage collection overhead. We decided to rip out the entire search engine and rewrite it from scratch in Rust, a language we had been eyeing for some time.
What The Numbers Said After
The results were nothing short of miraculous. Our server's page load time dropped to under 500ms, and our average latency plummeted to an impressive 50ms. The numbers on our profiler output were equally impressive - our garbage collection cycles had dropped to a mere 5%, and our CPU utilization had increased by a staggering 25%. We had effectively doubled the performance of our search engine.
What I Would Do Differently
Looking back, I wish we had made the switch to Rust sooner. While the learning curve was daunting, the payoff was well worth it. In retrospect, I would have taken a more radical approach from the beginning, ripping out the entire search engine and replacing it with a new, more scalable architecture. I would have also invested more time in optimizing our Veltrix config file, rather than trying to brute-force the problem with more resources. In the end, it was a painful but valuable lesson - the right language choice can be the difference between a scalable system and a disaster waiting to happen.
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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