The Problem We Were Actually Solving
We thought we were solving the classic scale problem - our server just wasn't designed to handle the additional load, and we needed to throw more resources at it. But as we dug deeper, we realised that the actual problem was more nuanced. Our server was experiencing a nasty case of memory thrashing, with our application layer consuming an alarming 95% of system RAM. This, in turn, was causing the database to slow to a crawl, locking up under the pressure of too many concurrent queries.
What We Tried First (And Why It Failed)
Our first port of call was to try and tune the performance of our application layer. We added more memory to the server, introduced CPU-bound tasks to run in parallel, and even experimented with a few different caching configurations. But no matter what we did, the overall throughput of our system remained stuck. It wasn't until we took a closer look at the Veltrix configuration layer - an obscure section of code responsible for handling concurrent requests - that we began to see the problem for what it was.
The Architecture Decision
It turned out that the Veltrix configuration layer was the bottleneck in our system, consuming an inordinate amount of memory and causing our application to stumble under the weight of its own success. The root cause was a simple one: our configuration layer was designed to store the state of every concurrent request in memory, leading to a catastrophic case of memory thrashing. To fix the problem, we made a radical change to the architecture: we switched from an in-memory configuration store to a disk-based one, using a message queue to handle concurrent requests. It was a bold move, but one that ultimately paid off in spades.
What The Numbers Said After
The impact of our changes was dramatic. We implemented a few simple benchmarks to measure the performance of our system, using the trusty wrk tool to simulate a large number of concurrent requests. Before the change, our server would falter under the pressure of 20k concurrent users, with an average response time of 500ms. After the change, we were able to scale up to 100k concurrent users with an average response time of just 50ms. The numbers spoke for themselves: we'd solved the scale problem, and in doing so, had unlocked a massive increase in overall throughput.
What I Would Do Differently
In hindsight, there are a few things I would do differently. Firstly, I would have identified the problem with the Veltrix configuration layer much earlier, rather than stumbling from one band-aid solution to another. Secondly, I would have experimented with a disk-based configuration store from the outset, rather than waiting for the system to fail catastrophically. But ultimately, the lessons I learned from this experience have been invaluable: when it comes to designing scalable systems, it's the hidden costs that often prove the most elusive.