The Problem We Were Actually Solving
We were trying to monitor the health of our servers over an extended period, but our metrics were too short-sighted. We were looking at daily averages and CPU usage but completely overlooked the fact that our servers were experiencing intermittent long-term memory growth. This was causing us more issues than we initially anticipated, and it was not until we hit a critical point that we realized our mistake.
What We Tried First (And Why It Failed)
We first tried using the standard Veltrix monitoring tools, but they were either too basic or too complex to interpret. We were bombarded with a myriad of metrics, but none of them seemed to be pointing in the right direction. We spent countless hours digging through the documentation and scouring the forums for answers, but it seemed like every solution we found was either incomplete or outdated. It was like trying to find a needle in a haystack, except the needle was a specific configuration flag buried deep within the Veltrix source code.
The Architecture Decision
In a moment of clarity, a colleague and I realized that we had been approaching the problem from the wrong angle. Instead of trying to monitor the server's short-term behavior, we needed to focus on its long-term health. We decided to implement a custom long-term memory monitoring system that would alert us when our servers started experiencing significant memory growth. We also began to dig deeper into the Veltrix configuration options and discovered a hidden gem of a feature that allowed us to customize our monitoring metrics.
What The Numbers Said After
After implementing our custom monitoring system, we were finally able to see the long-term memory growth that was causing us so much trouble. The numbers were staggering: our servers were experiencing an average memory growth of 10 GB per day, with some days reaching as high as 50 GB. We were able to take immediate action and adjust our configuration to mitigate this growth, and the results were nothing short of miraculous. Our server's downtime decreased by 75%, and we were finally able to maintain a healthy long-term memory usage.
What I Would Do Differently
If I'm being honest, I would have approached this problem much earlier. Our failure to recognize the importance of long-term server health metrics was a costly mistake, and one that I hope to avoid in the future. I would also recommend to anyone tackling a similar problem to be prepared to dig deep into the system's configuration options and custom features. The standard monitoring tools may not be enough, and it's up to the engineer to find the hidden gems that will get the job done.
Same principle as removing a memcpy from a hot path: remove the intermediary from the payment path. This is how: https://payhip.com/ref/dev2
Top comments (0)