The Problem We Were Actually Solving
The problem we were trying to solve was actually quite different from what it seemed. On the surface, it was just about creating a simple algorithm to generate a treasure map. But what we quickly discovered was that every small change in the algorithm had a ripple effect on the entire system. The latency would increase, the memory usage would skyrocket, and the whole thing would become unmanageable. We were trying to solve a problem of predictability and modularity.
What We Tried First (And Why It Failed)
We first tried to use a naive approach to solving this problem. We used a simple graph database to store the map nodes and edges, and a simple greedy algorithm to traverse the graph and find the shortest path. Sounds good on paper, but in reality, it turned out to be a disaster. The graph would get too large, and the algorithm would spend too much time finding the shortest path, causing significant latency issues. Not to mention the memory usage would go through the roof. We were getting error messages like " graph too large to handle" and "out of memory" on a regular basis.
The Architecture Decision
After weeks of trying to optimise the naive approach, we finally decided to switch to a more distributed architecture. We switched to using a combination of Redis and a custom-built service to handle the map generation. This allowed us to offload the computational intensive tasks to separate machines, reducing the latency and memory usage significantly. We also decided to use a consistency model of eventual consistency for the map nodes and edges, which allowed us to relax the consistency constraints and increase the availability of the system.
What The Numbers Said After
After switching to the new architecture, we saw a significant improvement in our metrics. Our latency dropped by 80%, our memory usage decreased by 60%, and our error rate decreased by 90%. We were also able to handle a much larger volume of requests without any issues. The numbers were telling us that our new architecture was working.
What I Would Do Differently
If I had to do it all over again, I would probably try to solve the problem of predictability and modularity from the start. I would have opted for a more modular architecture from the beginning, using a combination of services and APIs to handle different parts of the system. I would have also invested more time in testing and monitoring the system, to catch potential issues before they became major problems. But that's hindsight for you. At least we learned a valuable lesson about the high cost of premature optimisation.
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