The Problem We Were Actually Solving
When we started, our primary goal was to ensure our system could scale to handle thousands of concurrent users. We wanted to provide real-time updates to the leaderboard, without a significant delay. Our core metric for success was the time it took for the leaderboard to update after each participant submitted their solution.
To ensure this, we designed the treasure hunt engine around a publish-subscribe system where each update was a published event that subscribed clients would then receive. This allowed us to decouple the client update logic from the server-side processing of the new solution, making it possible to handle multiple client requests concurrently. We then deployed this system across a cluster of 16 machines.
What We Tried First (And Why It Failed)
Initially, we focused on minimizing the latency between the client's update request and the server's response. We optimized our database queries, minimized network latency, and even used a custom-built RPC library to compress and encrypt data in transit. However, we soon realized that our initial approach was overkill.
While we managed to shave off a few milliseconds from our average response time, we didn't account for the increased complexity of our system. This led to an average of 12% solution processing failures due to our RPC library's inability to handle long-running operations. Moreover, our database queries, although optimized, were still causing noticeable delays during peak hours.
The Architecture Decision
We took a step back and assessed the situation. We realized that our primary concern should be the overall system's reliability rather than just minimizing latency. We decided to implement a solution that would not only optimize the processing time but also ensure data consistency across our servers. We opted to use a distributed cache to store critical data, which significantly reduced the load on our database and decreased solution processing time by 50%.
Furthermore, we rewrote our RPC library to allow for short-running operations to bypass the cache, maintaining responsiveness while ensuring data consistency. This change ensured that our system could handle failures without cascading errors.
What The Numbers Said After
After deploying the updated system, we observed a significant decrease in solution processing failures (from 12% to 0.5%) and a notable improvement in our leaderboard update time (from 400 milliseconds to 150 milliseconds). The reduced latency allowed us to onboard over 20 new users per day without any noticeable system degradation.
What I Would Do Differently
If I were to do this project again, I would prioritize building a smaller, more controlled environment before scaling up. This would have allowed us to identify the root cause of our solution processing failures earlier and avoid the time-consuming optimization process.
Additionally, I would have considered alternative caching solutions that would have taken less time to implement and fewer resources to manage. Our distributed cache solution, although effective, required significant development and testing efforts.
In conclusion, building a real-world treasure hunt engine is a daunting task that requires careful planning, attention to detail, and a willingness to learn from failures. The key takeaway from our experience is that minimizing latency is just one aspect of building a reliable and scalable system. It's essential to focus on overall system reliability, ensuring that every component works together seamlessly to deliver a smooth user experience.
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