The Problem We Were Actually Solving
As I dug deeper into the requirements, I realized that we were trying to optimize for speed and responsiveness at the expense of something far more critical: configurability. Our initial design assumed that the rules engine would be fixed, and any changes would be manually implemented by a core team member. But in reality, our clients wanted to be able to tweak the engine in real-time, without requiring additional engineering work. The catch was that we'd already committed to a fixed architecture, which meant that minor changes would compound quickly and become showstoppers.
What We Tried First (And Why It Failed)
We tried to implement a bespoke, monolithic rules engine using a popular high-level language. The codebase grew rapidly, and performance began to tank. Our initial benchmarks showed a 50% increase in latency with just a few dozen users. The problem was that this language, despite its popularity, was not designed for systems engineering. It lacked the necessary abstractions for concurrent programming and had a garbage collector that incurred a 50ms pause every 10ms – the perfect storm of latency.
The Architecture Decision
