The Problem We Were Actually Solving
As I investigated further, I realized that the default config was actually optimizing for the development workflow, where we could rapidly iterate and deploy new features without worrying about event delivery. However, this approach was translating poorly to production, where event reliability was paramount. The system was designed to handle events from various sources, including user interactions, game updates, and external APIs. However, the default config was treating all these events as equal, without accounting for their varying priority, volume, and reliability.
What We Tried First (And Why It Failed)
Initially, we tried to brute-force the issue by increasing the event queue size and adjusting the retry settings. We hoped that this would buy us some time and allow the system to catch up on the missed events. However, this approach only masked the symptoms, as the underlying architecture was still flawed. The event queue was overflowing, causing the system to slow down and eventually fail. We also noticed that the event retries were causing more problems than they were solving, as they were introducing latency and amplifying the effects of the underlying issues.
The Architecture Decision
After some investigation and discussion with the team, we decided to adopt a distributed event handling system, using Apache Kafka as the message broker and ZooKeeper for service discovery. We also implemented a priority-based event routing system, where events were classified into different tiers based on their priority and reliability requirements. This allowed us to optimize the event handling for each tier, reducing the latency and increasing the overall throughput. We also implemented a dead-letter queue (DLQ) to catch any events that failed to process, allowing us to identify and resolve the underlying issues.
What The Numbers Said After
After implementing the new architecture, we saw a significant reduction in event failures, from 20% to less than 1%. The system was able to handle the event volume with a much lower latency, and the users started receiving their rewards in a timely manner. We also saw a reduction in the number of pages received by the operators, from multiple times a night to almost zero. The system was no longer hogging the CPU resources, and the overall performance improved dramatically.
What I Would Do Differently
Looking back, I would have taken a more structured approach to event handling from the beginning. I would have worked with the development team to design the event handling system with production requirements in mind, rather than relying on the default config. I would have also implemented monitoring and logging from the start, to detect potential issues earlier. Additionally, I would have created a more detailed documentation for the event handling system, to ensure that future teams can understand and maintain it smoothly.
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