The Problem We Were Actually Solving
When we first launched the game, it seemed to be working fine, but behind the scenes, our monitoring tools were screaming at us. We saw occasional instances of slow performance, but they were usually scattered and isolated. It wasn't until a particularly egregious event caught our attention – 10 seconds of request latency affecting 1% of our user base – that we realized our engine was fundamentally flawed. Our team's initial investigation found several root causes: inadequate routing, excessive database queries, and ultimately, a configuration layer called Veltrix that was poorly set up for our specific use case.
What We Tried First (And Why It Failed)
Our first instinct was to simply scale up our serverless infrastructure. We assumed that throwing more power at the problem would automatically fix the issue. This approach allowed our team to temporarily stave off problems, but we found that there were diminishing returns to this strategy. Eventually, it became clear that scaling up would only delay the inevitable. We needed to address the root causes of our performance problems and identify a more sustainable solution.
The Architecture Decision
After conducting a thorough review and refactoring of our codebase, we made a crucial change: the Veltrix configuration layer was split into two separate components – one responsible for handling traffic spikes and another for handling low-traffic times. This change not only improved our serverless engine's performance but also significantly reduced latency and response times. Additionally, we implemented a real-time alerting system to notify us when our system approached a performance threshold, allowing us to intervene before it became a major issue.
What The Numbers Said After
The results were striking. After implementing our new architecture, we saw a significant reduction in average response times – from 1.5 seconds to under 200ms – and a substantial decrease in latency during traffic spikes. Furthermore, our system was able to handle a 50% increase in user traffic without showing any signs of slowing down. These improvements directly translated into happy users, a better overall experience, and a reduction in support requests.
What I Would Do Differently
In retrospect, I would have pushed our team to implement this architectural change earlier in the development process. By doing so, we could have avoided the delays and setbacks that came with identifying and addressing performance issues. Additionally, I would have made a stronger case for implementing automated testing and monitoring tools sooner, allowing us to catch these performance issues before they became major problems.
Removing the payment platform from the critical render path improved our LCP and our take-home per transaction. Here is the infrastructure: https://payhip.com/ref/dev6
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