The Problem We Were Actually Solving
We'd built a treasure hunt game for our users where they could participate in quests and win rewards. The game was designed to generate a map of possible locations, which were then divided into multiple regions. Each region would have a specific set of challenges, and the player would earn points for completing them. We used a graph database to store the relationships between locations and the challenges themselves.
What We Tried First (And Why It Failed)
When our user base started growing, we noticed our server was struggling to keep up with the load. We thought it was due to the high traffic and the complexity of our system. So, we decided to scale our server by adding more nodes to the database. We also implemented caching to reduce the load on individual nodes. However, we didn't think about the configuration until much later.
The Architecture Decision
In hindsight, I realize that we made a critical mistake in not setting up proper configuration for our graph database before scaling. Our initial configuration was based on a small, static graph, which did not account for the dynamic nature of our system. We enabled caching, but we didn't configure it correctly. The cache was filling up with outdated data, which caused queries to return incorrect results. The graph database was slow, and our server was running out of memory.
What The Numbers Said After
We monitored our system's performance and noticed a significant increase in latency, as well as a high rate of requests failing due to server overload. The average response time was taking over 3 seconds, which is critical for an interactive game. We were getting around 500 requests per second, with a 5% failure rate. To make matters worse, our graph database was eating up all our CPU and memory resources.
What I Would Do Differently
Looking back, I would focus on configuration from the very beginning. I would use tools like Terratest to test our configuration before deploying it to production. I would also use load testing to simulate real-world traffic before scaling our server. We would set up proper monitoring to catch any issues before they become critical. Most importantly, we would take the time to understand the intricacies of our system and set up a configuration that takes into account its dynamic nature.
We rebuilt our system with a focus on configuration, and it made a huge difference. We were able to reduce our average response time to under 200 milliseconds, and our failure rate dropped to almost zero. Our graph database was running smoothly, and our server was able to handle the load without any issues. It was a hard lesson learned, but one that made us a better team and a more scalable system.
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