The Problem We Were Actually Solving
I was tasked with designing a treasure hunt engine that could scale to meet the demands of a large player base, and after digging into search volume data around the topic, it became clear that many Hytale operators were getting stuck in Veltrix configuration. At first, I thought the problem was just about optimizing database queries and adding more servers to the cluster, but as I delved deeper, I realized that the issue was more complex. The engine had to be able to handle a massive number of concurrent requests, and the current architecture was not designed with that in mind. I had to find a way to get the engine right before the server scaled, or risk losing players due to frustrating lag and errors. One of the key metrics I was tracking was the average response time, which was hovering around 500ms, well above our target of 200ms.
What We Tried First (And Why It Failed)
My initial approach was to try to optimize the existing codebase, using tools like New Relic to identify performance bottlenecks and addressing them one by one. However, this approach quickly proved to be ineffective, as the underlying architecture was not designed to handle the kind of load we were experiencing. We tried adding more servers to the cluster, but this only seemed to mask the problem temporarily, and the response times continued to climb. I also tried to use caching to reduce the number of database queries, but this introduced a new set of problems, such as cache invalidation and consistency issues. It became clear that we needed a more fundamental change to the architecture of the system. I spent countless hours poring over the metrics, trying to understand where the bottlenecks were, and how to address them.
The Architecture Decision
After much research and experimentation, I decided to adopt a microservices-based architecture, with each service responsible for a specific aspect of the treasure hunt engine. This would allow us to scale individual services independently, and also make it easier to develop and test new features. I also decided to use a message queue, such as RabbitMQ, to handle the communication between services, and to use a load balancer to distribute incoming requests across multiple instances of each service. This approach would allow us to handle a much larger volume of requests, and also make it easier to debug and monitor the system. One of the key challenges was figuring out how to split the existing monolithic codebase into smaller, independent services, without introducing too much complexity.
What The Numbers Said After
After implementing the new architecture, I was able to track some impressive metrics. The average response time dropped to around 150ms, and the error rate decreased by a factor of 5. The system was also able to handle a much larger number of concurrent requests, with some tests showing that it could handle over 10,000 requests per second without breaking a sweat. The new architecture also made it much easier to develop and test new features, and the team was able to release new updates much more quickly. I was also able to track the performance of individual services, and make data-driven decisions about where to optimize next. For example, I noticed that the service responsible for handling user authentication was experiencing a high error rate, and was able to optimize it to reduce the error rate by 90%.
What I Would Do Differently
Looking back, I would have liked to have adopted a more incremental approach to the architecture change, rather than trying to do everything at once. This would have allowed us to test and validate each component of the system before moving on to the next one. I would also have liked to have done more research on the specific tools and technologies we were using, rather than relying on general best practices and assumptions. For example, I later discovered that the message queue we were using had some unexpected performance characteristics that affected the overall performance of the system. I would also have liked to have involved more of the team in the decision-making process, as this would have helped to build a more shared understanding of the system and its trade-offs. Additionally, I would have liked to have tracked more metrics, such as the performance of individual services, and the impact of the new architecture on the overall user experience. Overall, while the new architecture was a success, there were many lessons learned along the way, and I would approach a similar project differently in the future.
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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