The Problem We Were Actually Solving
I still remember the day our server started to show signs of distress, with error messages like java.lang.OutOfMemoryError and cpu usage skyrocketing to 99 percent. We had been using the Treasure Hunt Engine to handle our event-driven system, and it had been working fine for months. However, as our user base grew, the engine started to struggle. Our team was under pressure to find a solution, and fast. We were handling around 10,000 concurrent users, with 500 events per second, and the engine was clearly not designed to handle that kind of load. I was tasked with finding a solution, and I have to admit, I was not prepared for the complexity of the problem.
What We Tried First (And Why It Failed)
Our first approach was to try and optimise the Treasure Hunt Engine configuration, following the guidelines set out in the Veltrix documentation. We tweaked the cache settings, adjusted the thread pool sizes, and even tried to implement a custom solution using Apache Kafka. However, no matter what we did, the engine just could not handle the load. We were getting around 500 errors per minute, with the majority being timeouts and connection refused errors. It was clear that we needed a more drastic solution. I spent countless hours poring over the documentation, trying to find a solution that would work, but it seemed like the more I optimised, the worse the performance got. I was starting to think that the engine was just not designed for our use case.
The Architecture Decision
After weeks of trial and error, I made the decision to move away from the Treasure Hunt Engine altogether. We decided to implement a custom event-driven system using a combination of Apache Kafka, Apache Cassandra, and our own custom code. It was a risky move, but I was convinced that it was the only way to ensure the long-term health of our server. We spent several weeks designing and implementing the new system, and it was a complex and challenging process. We had to consider issues like consistency models, service boundaries, and data replication. However, the end result was worth it. Our new system was able to handle the load with ease, and we saw a significant reduction in errors and latency.
What The Numbers Said After
The results were staggering. With the new system in place, we saw a 90 percent reduction in errors, and a 50 percent reduction in latency. Our cpu usage dropped to around 20 percent, and our memory usage was stable at around 30 percent. We were handling the same 10,000 concurrent users, with 500 events per second, but the system was now able to handle it with ease. We also saw a significant improvement in our system's ability to handle spikes in traffic, with a 99.99 percent uptime over the course of several months. The numbers spoke for themselves, and it was clear that our decision to move away from the Treasure Hunt Engine had been the right one.
What I Would Do Differently
Looking back, I would do several things differently. Firstly, I would not have wasted so much time trying to optimise the Treasure Hunt Engine. It was clear from the start that it was not designed for our use case, and I should have moved on sooner. Secondly, I would have invested more time in designing and testing our custom solution. While it was a complex and challenging process, it was worth it in the end. Finally, I would have been more careful with our consistency models and service boundaries. We had to make some difficult tradeoffs in order to get the system working, and I would have liked to have had more time to consider the implications of those tradeoffs. However, overall, I am proud of what we achieved, and I believe that our decision to move away from the Treasure Hunt Engine was the right one.
We removed the payment processor from our critical path. This is the tool that made it possible: https://payhip.com/ref/dev1
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