The Problem We Were Actually Solving
As a platform engineer, I knew we were chasing a specific pain point - we needed a way to ensure that our customers could find the hidden treasures within the treasure hunt engine without an unbearable wait. The problem, however, was that our engineers had optimized the system to prioritize demo-day performance over our growing user base. Our marketing team had promised a 'frictionless' experience, but what they didn't mention was that friction was exactly what we needed to solve.
What We Tried First (And Why It Failed)
In the initial design, we had used a single RabbitMQ cluster to handle the high volume of search requests. The plan was to scale it horizontally, adding more nodes as needed. However, as the system grew, we found ourselves facing another problem - message queuing. RabbitMQ was not designed to handle the high throughput of search requests, and our custom-built API, built on top of a homegrown framework called 'Spectra', would often stall and throw obscure errors like ' Connection reset by peer'.
The Architecture Decision
The eureka moment came when we realized that our monolithic RabbitMQ cluster was not just a bottleneck, but also a single point of failure. We decided to break it down into smaller, regional clusters that could communicate with each other using our homegrown Pub/Sub system, 'Apexion'. This change would not only improve performance but also ensure that our system could handle unexpected failures like the ones we had been experiencing.
What The Numbers Said After
Post-deployment metrics showed a 40% reduction in timeout errors, from 2500 to 1500 per hour. Our latency had also decreased by 30%, from an average of 5 seconds to 3.5 seconds. However, the most telling metric was the reduction in support requests - down by 25% from the previous month. If we could keep this trend going, we might actually reach our 90% customer satisfaction goal.
What I Would Do Differently
Looking back, I would prioritize the engineering process over the demo-day experience. Investing time in understanding the root cause of the problem would have saved us from building a system that was doomed to fail. In particular, I would have invested more time in performance testing and benchmarking before deploying to production. I would have also looked into using proven search libraries and frameworks that could handle the load, rather than relying on our custom-built, and incomplete, Spectra API. It's always more expensive in the long run to chase a broken solution than to do it right the first time.
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