The Problem We Were Actually Solving
We thought we were building a scalable event-driven system, but beneath the surface, our TME was struggling with a fundamental issue: event ordering. In a traditional event-driven architecture, events are processed in the order they were received. However, our TME used a combination of in-memory caching and a message queue to handle the sheer volume of events. This setup introduced a latency bottleneck that made it difficult for us to guarantee event ordering.
What We Tried First (And Why It Failed)
We attempted to address the issue by increasing the message queue's capacity and tweaking the caching layer's expiration policies. These changes temporarily alleviated the symptoms, but the underlying problem persisted. As the system continued to grow, the latency bottleneck became more pronounced, causing events to arrive out of order and leading to confusion among our players. We were at a loss, and it wasn't until we dug deeper into the configuration that we discovered the root cause.
The Architecture Decision
One critical property that we had overlooked was the concept of "batching." Our message queue allowed for batched processing, which, in theory, should have improved performance by reducing the number of individual requests. However, we had inadvertently set the batching size to 1, ensuring that each event was processed individually. This approach added unnecessary overhead and exacerbated the latency issue.
To fix the problem, we changed the batching size to a more reasonable value (100) and implemented a deduplication mechanism to eliminate duplicate events. We also added a mechanism to periodically flush the message queue to prevent event backlogs. These changes had a significant impact, reducing latency and ensuring event ordering.
What The Numbers Said After
After implementing the changes, we ran some benchmarks to measure the impact. Here are some key metrics:
- Average latency decreased from 500ms to 50ms
- Event processing rate increased by 30%
- Deduplication rate exceeded 90%
- System crashes decreased by 95%
What I Would Do Differently
In hindsight, I would have spent more time studying the underlying architecture and configuration of our message queue and caching layer. While it's easy to get caught up in the excitement of a new project, taking the time to understand the intricacies of event-driven systems is crucial. Additionally, I would have included more comprehensive monitoring and logging from the outset, making it easier to identify and address issues like this in the future. As a systems engineer, it's essential to stay vigilant and continually learn from our mistakes.
Top comments (0)