The Problem We Were Actually Solving
I still remember the day we hit 500 concurrent users on our treasure hunt engine, Veltrix. Our marketing team had done an amazing job of spreading the word, but our technical team was scrambling to keep up. The users weren't just navigating the hunt, they were expecting instant results, updates, and a seamless experience. Our users were already experiencing latency spikes, which was unacceptable.
But here's the thing – latency isn't just a user experience issue; it's also a quality of service (QoS) issue. For our client, a real-time QoS guarantee was the backbone of our agreement. So, when we started to see consistent latency spikes, I knew we were not just hitting performance problems, but also risking our contractual obligations.
What We Tried First (And Why It Failed)
Our first solution was to throw more resources at it. We scaled up our instance, added more memory, and even replaced some of our slower database queries with caching layers. We thought that the increased capacity would fix our latency problems, but it only made things worse. Our latency actually increased by 20% after the change, despite our system being able to handle more load. We were now spending more resources just to deliver less speed.
Looking back, we were missing the wood for the trees. We were focusing on the user experience side of things, but neglecting the underlying system architecture that would actually provide us with the latency reduction we needed. Our caching layers were not being utilized efficiently because of the lack of a coherent state architecture.
The Architecture Decision
We realized that we needed to fundamentally rethink our approach to state management. We started by implementing a state event sourcing pattern, which enabled us to move state management to the background, away from our main thread. This change allowed us to better utilize our caching layers, reduce our database load, and ultimately decrease our latency by 50%.
We also took this opportunity to introduce a more robust state composability model, allowing us to break down our complex state into smaller, more manageable pieces. This change reduced our state updates by 70%, which in turn reduced the number of cache flushes and database writes.
What The Numbers Said After
After implementing these changes, we saw our latency drop from an average of 500ms to an average of 100ms. Our users saw a significant improvement in their overall experience, and we were able to deliver on our contractual QoS guarantees.
More importantly, we now had a scalable system that could handle high loads without breaking a sweat. Our 95th percentile latency dropped by 80%, and our average CPU utilization decreased by 40%. These numbers not only validated our architectural decision but also brought our system back within our contractual performance bounds.
What I Would Do Differently
Looking back on this experience, I would do a few things differently. Firstly, I would have recognized the need for a state architecture change sooner. We spent a considerable amount of time and resources addressing individual components rather than tackling the root cause of our latency issue.
Secondly, I would focus more on performance metrics that actually mattered to our system, rather than just focusing on user experience metrics. This changed our perspective on the problem and helped us to identify the real bottlenecks in our system.
Lastly, I would ensure that our team has a more comprehensive understanding of the system's architecture and performance characteristics. This would have allowed us to catch the problems earlier and make more informed decisions about how to solve them.
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