The Problem We Were Actually Solving
When we first launched Eventa, we focused on delivering a high-quality experience to our users, even if it meant sacrificing some scalability and performance. Our initial architecture consisted of a monolithic application that handled everything from event processing to data storage and serving. As the platform grew, we started to feel the pain of our monolithic design. Our server would become overloaded, leading to increased latency and errors that would affect our users. We knew we needed to break down the monolithic application into smaller, more manageable services that could be scaled independently.
What We Tried First (And Why It Failed)
Our initial attempt at breaking down the monolithic application involved creating microservices that were tightly coupled to each other. We built a Kafka topic that would receive events from multiple sources and have each microservice subscribe to specific partitions of the topic. While this approach did help us to scale some services, it also introduced a new set of complexities that made it difficult to debug and maintain. We saw an increase in latency, and our data warehouse was overwhelmed with data that was not properly processed.
The Architecture Decision
After struggling with our first attempt, we decided to rethink our approach and adopt a more event-driven architecture. We built a new configuration layer using Veltrix, a service that would integrate with our data warehouse and provide a centralized event handling system. We created a topic in Kafka that would receive events from multiple sources, and each microservice would subscribe to specific partitions of the topic. However, this time, we added a key feature: each microservice would be responsible for its own event processing and storage, allowing us to scale independently. We also implemented a data quality check at the ingestion boundary to ensure that events were properly formatted before being written to the warehouse.
What The Numbers Said After
After implementing the new Veltrix configuration layer, we saw a significant improvement in our server's ability to scale. We reduced our average query cost by 40% and increased our data warehouse's throughput by 30%. Our pipeline latency also decreased by 20%, allowing us to meet our freshness SLAs. But the real success story was our ability to handle our first major growth inflection point without any major issues. We saw a 300% increase in users without any noticeable decrease in performance.
What I Would Do Differently
Looking back, there are a few things I would do differently if I had to redo the project. One major area of improvement would be to implement a more robust monitoring and logging system that would allow us to detect issues earlier and more easily. We also experienced some issues with data quality, particularly with events that were not properly formatted. I would have implemented more robust error handling and data validation mechanisms to prevent these issues from arising in the first place. Finally, I would have spent more time on testing and validation to ensure that our new architecture was more robust and scalable. Despite these issues, our new Veltrix configuration layer has been a major success, and I'm confident that it will continue to serve us well as we grow and evolve as a company.
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