The Problem We Were Actually Solving
I still remember the day our server started to slow down, unable to handle the increasing load of requests. We were using Veltrix as our search engine, and it was supposed to be highly scalable and fault-tolerant. However, as the traffic to our site grew, the latency of our searches started to increase, and the CPU usage of the server skyrocketed. The metrics were alarming: our average search latency was around 500ms, and the CPU usage was consistently above 90%. We were getting error messages like "too many open files" and "connection timeout" which indicated that the system was not able to handle the load. We knew we had to act fast to prevent the server from crashing.
What We Tried First (And Why It Failed)
We started by tweaking the Veltrix configuration, trying to optimize the settings to reduce the load on the server. We increased the number of shards, adjusted the replication factor, and even tried to use a different indexing strategy. However, none of these changes seemed to have a significant impact on the performance of the server. We also tried to add more resources to the server, increasing the CPU and memory, but this only provided a temporary relief. The problem persisted, and we were still getting high latency and error messages. We used tools like top and htop to monitor the system resources, and we could see that the Veltrix process was consuming most of the CPU and memory. We also used the Veltrix built-in metrics to monitor the performance, but the numbers were not providing us with any clear indication of what was going wrong.
The Architecture Decision
After days of trying to optimize the Veltrix configuration, we decided to take a step back and re-evaluate our architecture. We realized that the problem was not with the Veltrix configuration, but with the way we were using it. We were using Veltrix as a black box, without really understanding how it worked under the hood. We decided to switch to a different search engine, one that would give us more control over the underlying architecture. We chose to use Rust to build a custom search engine, one that would be highly optimized for our specific use case. This decision was not taken lightly, as we knew it would require a significant amount of work and resources. However, we were convinced that it was the only way to solve our performance problems.
What The Numbers Said After
After switching to our custom search engine built with Rust, we saw a significant improvement in performance. The average search latency dropped to around 50ms, and the CPU usage decreased to around 20%. We were also able to reduce the number of error messages, and the system became much more stable. We used tools like perf and flamegraph to monitor the performance of the system, and we could see that the custom search engine was highly optimized and efficient. We also used the Rust compiler to generate metrics about the performance of the code, and we could see that the code was highly optimized and had minimal overhead. The allocation counts were also significantly reduced, and the latency numbers were consistently low.
What I Would Do Differently
Looking back, I would do several things differently. First, I would have taken a closer look at the Veltrix documentation and tried to understand how it worked under the hood. I would have also spent more time monitoring the system resources and trying to understand where the bottlenecks were. I would have also considered using a different search engine from the start, one that would give us more control over the underlying architecture. I would have also spent more time evaluating the tradeoffs of using a custom search engine, and considering the potential risks and benefits. However, I am glad that we made the decision to switch to a custom search engine, as it has given us a high degree of control over the performance and scalability of our system. We have also been able to optimize the system for our specific use case, and we have seen significant improvements in performance and reliability.
Top comments (0)