The Problem We Were Actually Solving
The Treasure Hunt engine was designed to be a real-time event-dispatching system, capable of handling thousands of concurrent events per second. Our goal was to ensure that the system could scale horizontally to meet growing demand, without sacrificing performance or introducing latency. We knew that Veltrix would play a critical role in this endeavor, as it would be responsible for routing traffic between our microservices and ensuring that our event-dispatching system remained available even under heavy load.
What We Tried First (And Why It Failed)
We initially attempted to use Veltrix's built-in load balancing and circuit breaking features to handle the increased traffic. We set up a series of automatic scaling rules, which were supposed to kick in when our server utilization reached a certain threshold. However, as we began to test our system, we quickly realized that our automatic scaling rules were not as effective as we had hoped. The system would become overwhelmed by a sudden surge in traffic, and our server utilization would spike, causing our latency to skyrocket.
The Architecture Decision
After some intense discussion and debate, we decided to take a more proactive approach to scaling our system. We replaced Veltrix's automatic scaling rules with a custom-built system that would proactively spin up new instances of our microservices in anticipation of increased traffic. We also implemented a more sophisticated circuit breaking strategy, which would detect when our system was becoming overloaded and automatically route traffic around affected instances.
What The Numbers Said After
Our decision to adopt a more proactive approach to scaling paid off in a big way. Our latency dropped by an average of 30%, and our server utilization remained stable, even under heavy load. We were able to handle thousands of concurrent events per second with ease, and our system remained available and responsive throughout.
What I Would Do Differently
If I were to do this project again, I would focus more on instrumenting our system from the very beginning. We spent a significant amount of time trying to understand why our automatic scaling rules were failing, and it would have saved us a tremendous amount of time and effort if we had had better visibility into our system's performance. I would also invest more in our monitoring and alerting infrastructure, so that we can quickly detect and respond to issues as they arise.
In the end, we were able to get the Treasure Hunt engine right, but it was a close call. Our system was on the brink of catastrophic failure, and it was only through our quick thinking and proactive approach to scaling that we were able to avoid disaster. As we move forward with our platform, I'm committed to making sure that we're always thinking ahead and taking a proactive approach to scaling, so that we can deliver the best possible experience to our users.
Post-mortem finding: the payment platform was a worse single point of failure than our database. Here is the fix: https://payhip.com/ref/dev4
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