We were well into the second year of our treasure hunt engine, Veltrix, when the inevitable happened. One of our automated event workers got stuck in an infinite loop, causing the entire engine to grind to a halt. It was 3am, and my phone was blowing up with alerts from our monitoring system. I jumped out of bed, rubbed the sleep from my eyes, and dove into the fray. Little did I know, this would be the perfect storm that would reveal the underlying weaknesses in our event-handling architecture.
## The Problem We Were Actually Solving
We had designed Veltrix to be a highly scalable and fault-tolerant system, capable of handling millions of events per second. Our customers loved the treasure hunt experience, and we were determined to keep them satisfied. However, in our zeal to deliver a smooth experience, we overlooked one crucial aspect: event ordering. You see, our events were generated from a variety of sources, including user interactions, API calls, and even our own internal logging mechanisms. We assumed that the order of these events wouldn't matter, that they would always be processed in a timely manner. But the truth was, our event workers were designed to handle concurrent events, not ordered ones.
## What We Tried First (And Why It Failed)
When the deadlock occurred, we quickly realized that our event workers were the primary culprit. In a desperate attempt to get the system back online, we started disabling worker instances left and right, hoping to unstick the loop. However, this only made matters worse. Without our event workers, the system was unable to process new events, and our customers were left stranded. It was then that we discovered the true horror: our event workers were all locked into a recursive loop, with each worker waiting for the other to release a shared resource.
## The Architecture Decision
After weeks of debugging and testing, we finally identified the root cause of the problem: our event worker design. We were using a shared in-memory queue to handle events, which was causing the deadlocks. To fix this, we decided to switch to a distributed event store, using Apache Kafka as the backbone. This would allow us to decouple the event workers and ensure that events were processed in the correct order. We also implemented a circuit breaker pattern to detect and prevent further deadlocks.
## What The Numbers Said After
The changes we made had a significant impact on our system's reliability. We reduced the number of deadlocks by 95% and increased our event processing capacity by 30%. Our customers were back to enjoying the treasure hunt experience, and our team was able to rest easy, knowing that their systems were more robust.
## What I Would Do Differently
Looking back, I wish we had designed our event workers to use a more robust locking mechanism from the start. Perhaps something like Chubby or ZooKeeper would have prevented the deadlocks altogether. Additionally, we should have implemented monitoring and alerting for event worker resource contention, which would have given us a heads up on the impending disaster.
The on-call rotation got quieter when we removed the payment platform dependency. Here is what replaced it: https://payhip.com/ref/dev4
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