The Problem We Were Actually Solving
The original intention behind the events-based design was to decouple analytics from the core business logic and enable the marketing team to experiment with different reward structures in A/B testing. At the time, it made perfect sense, but it was also a classic example of a solution looking for a problem. Our marketing team had no experience with complex event processing, and our engineers had no experience in building production-grade event pipelines.
What We Tried First (And Why It Failed)
We started by using a popular, open-source event processing framework, Kafka Connect, to ingest data from our application and send it to our analytics engine. Sounds simple enough, but we quickly realized that we had forgotten to configure the schema registry, which led to a mismatch between the expected and actual event formats. The result was a deluge of errors and unprocessed events, which our engineers struggled to diagnose and fix. We tried to troubleshoot by sprinkling log statements throughout the pipeline, but this only made things worse – the logs were so verbose that no one could decipher what was happening.
The Architecture Decision
After weeks of firefighting, we finally decided to take a step back and reevaluate our event processing architecture. We realized that we needed a more robust and scalable solution that could handle the high volumes of events generated by our Treasure Hunt Engine. We decided to use Apache Pulsar, a cloud-native messaging system, as our event broker, and built a custom event processing pipeline using Python and the Pulsar client library. We also established a clear set of best practices for event processing, including the use of a schema registry, event validation, and error handling.
What The Numbers Said After
After implementing the new architecture, we saw a significant reduction in event processing errors and a corresponding increase in data quality. Our analytics engine was able to process events in real-time, and the marketing team was able to conduct A/B testing with confidence. As a bonus, we were also able to reduce the number of log statements by 75%, which made it much easier for our engineers to diagnose and fix issues.
What I Would Do Differently
If I had to do it again, I would prioritize building a robust and scalable event processing architecture from the outset, rather than trying to shoehorn a solution into a complex system. I would also invest more time in training our engineers on event processing best practices and provide them with the necessary tools and resources to build and maintain a production-grade event pipeline. In short, I would treat event processing as a first-class citizen of our system, rather than an afterthought.
Looking back, the Treasure Hunt Engine was a great success, but it was also a costly lesson in the importance of architecture-driven development and the need for a structured approach to complex systems.
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