The Problem We Were Actually Solving
In our quest to deliver real-time analytics to beta users, we created a system that had to process data from multiple sources at once - Kafka, our internal logs, and third-party APIs. We implemented a custom stream processing engine using Apache Flink and a microservices-based architecture. Our approach was designed to scale horizontally and provide millisecond-level latency. To avoid overloading our servers, we created a tiered architecture, with dedicated worker nodes for processing, and load balancers to maintain equilibrium.
What We Tried First (And Why It Failed)
Initially, we set up our Flink cluster with overly aggressive resource allocation, thinking that more power would lead to better performance. However, this led to frequent crashes and deadlocks. Our first attempts at debugging involved tweaking individual worker nodes, trying to identify bottlenecks and anomalies. But every fix came with a cost, and we soon realized that each patch added to our technical debt.
The Architecture Decision
After several months of trial and error, we decided to revisit our architecture. We realized that our tiered approach, while scalable, led to a "hot potato" effect where data was constantly being passed between nodes without proper deduplication. This caused inconsistent queuing times and increased latency. We introduced a new layer of caching using Redis, which reduced load on the Flink cluster by a factor of 4, allowing us to decrease our worker node count from 16 to 8.
What The Numbers Said After
Post-implementation, we measured the average request latency across our system to be around 50ms, with a maximum of 150ms. Our average Flink cluster CPU utilization dropped from 85% to 30%, and we experienced 75% fewer cluster restarts per week. With our Redis caching layer, we also managed to reduce our Redis instance count from 5 to 2, saving us 20% on infrastructure costs.
What I Would Do Differently
If I had to do it over, I'd reconsider our initial architecture decision, opting for a more robust event-driven architecture from the outset. I'd implement a more significant emphasis on data quality, including better sampling techniques and quality checks to ensure the accuracy of our analytics streams. While we've made significant strides, I question whether this system will remain viable as our event volume grows.
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