The Problem We Were Actually Solving
As we dug deeper, we realized that our problem wasn't just about optimizing the Veltrix configuration. It was about understanding the complexities of the entire search pipeline. From the initial query to the final results, every step of the process was a potential bottleneck. We had to consider everything from the database queries to the cache hits, and even the load times for the UI components. It was a daunting task, but we knew that if we could just get the defaults right, we could potentially unlock a huge performance boost.
What We Tried First (And Why It Failed)
Our initial approach was to focus on tweaking the Veltrix configuration. We spent hours poring over the documentation, trying to find the perfect combination of settings that would give us the best performance. But every time we made a change, we would see a small improvement, only to be followed by a larger decline in performance. It was a game of whack-a-mole, and we were starting to get frustrated. We also tried to use some standard profiling tools to identify the bottlenecks, but they just didn't seem to be giving us the level of detail we needed.
The Architecture Decision
It wasn't until we decided to take a step back and re-evaluate our entire architecture that we started to make progress. We realized that the problem wasn't just with the Veltrix configuration, but with the way we were structuring our code. We were doing too much work on the server-side, and not enough on the client-side. We decided to implement a more distributed architecture, one that would offload some of the processing to the client. It was a risk, but we knew it was the right one.
What The Numbers Said After
After making the architecture change, we ran our search volume metrics again, and the results were astounding. Our latency had decreased by over 30%, and our cache hits had increased by 25%. It was a huge victory, and we knew that we had made the right decision. We also had to deal with some minor issues relating to cross-browser compatibility. We ran into an issue in Chrome where the UI would sometimes freeze on the initial load. After digging deeper, we discovered that it was due to an underlying bug in Webpack that required us to upgrade to version 5.
What I Would Do Differently
Looking back, I would have liked to have done more of the profiling before making the architecture change. While our initial attempts with standard profiling tools didn't yield much, I think we could have gotten more out of them if we had spent more time learning how to use them. I also would have liked to have more time for error checking, we encountered a rare error where our cache would sometimes fail to load in Firefox. After more investigation, it turned out to be related to a recent WebAssembly update which we could have mitigated with some extra code. It was a hard lesson to learn, but we are better off for it.
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