The Problem We Were Actually Solving
At first glance, it looked like a typical event-driven system: we had a stream of events coming in from various sources, and a set of workers processing them in parallel. But upon closer inspection, we realized that our system was attempting to solve multiple problems at once: high-throughput event processing, low-latency decision-making, and efficient memory usage. The combination of these constraints revealed that our default configuration was not aligned with our actual requirements.
What We Tried First (And Why It Failed)
We initially followed the standard recipe for an event-driven system: a message broker (Apache Kafka in our case) acting as the central nervous system, and a cluster of workers processing events in parallel using a generic message handler library. The issue was that our event handlers were too chatty, with each handler waking up the entire worker process and causing it to re-acquire locks and re-read configuration, leading to a memory leak and increased latency. We were using the default configuration for the message handler library, which turned out to be the root cause of the problem.
The Architecture Decision
We decided to pivot and implement a producer-consumer architecture with a custom event processing framework. We chose Rust as our language of choice due to its excellent memory safety guarantees and the ability to write high-performance code. We also switched from Apache Kafka to a custom, latency-focused event transport layer. By doing so, we decoupled the event producers from the event consumers, allowing us to scale and optimize each component independently. We also implemented a more efficient event handling mechanism that used a shared memory pool to reduce memory garbage collection and latency.
What The Numbers Said After
After the architecture change, we ran a series of benchmarks to measure the impact of our changes. The results were striking: we were able to process 50% more events per second while reducing memory usage by 30%. The latency of our event processing system decreased from 100ms to 20ms under heavy load. Most importantly, we were able to scale our system to handle twice the number of concurrent events without any noticeable increase in latency or memory usage.
What I Would Do Differently
In retrospect, I would have explored more alternative architecture options before settling on the producer-consumer architecture. While it was the right choice in the end, it required significant rework and resource reallocation. I also would have prioritized profiling and benchmarking earlier in the development cycle to catch the memory leak and latency issues sooner. By doing so, we would have avoided significant delays and rework.
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