The Problem We Were Actually Solving
Looking back, it became apparent that we had an architecture that prioritized low latency over robust monitoring. Our event stream processing pipeline was built with an average latency of 1 minute, which was an impressive feat at first but soon became a liability. We were getting an average of 10,000 events per second, which seemed normal, but our operators were overwhelmed by the sheer volume. Our primary concern was not data quality, but event throughput.
What We Tried First (And Why It Failed)
We initially deployed Apache Kafka to act as a buffer for our events, hoping it would mitigate the impact of unexpected spikes in event volume. However, our initial setup failed to account for a key aspect of event stream processing: data loss. We quickly discovered that Kafka was dropping about 1% of our events, which on a daily basis translated to thousands of dollars in lost revenue. We thought we could just tune Kafka's configuration to reduce the loss, but it turned out that our real problem was not Kafka's configuration, but our own data pipeline's inability to handle a small number of dropped events.
The Architecture Decision
After several weeks of trial and error, we decided to switch from Apache Kafka to Apache Pulsar, which had a built-in guarantee of no data loss. We also integrated a message queuing system (Apache ActiveMQ) to handle our event producers and consumers in a more robust manner. But what was missing was data quality at the ingestion boundary: we needed a way to verify and correct our events as they were being processed. We settled on using Apache Beam to transform and validate our events in real-time, which also helped reduce our average latency to 0.5 minutes.
What The Numbers Said After
Our new pipeline had a 99.999% event delivery guarantee, down from 98.5% with Kafka. Our query cost against our database dropped by 30%, and our pipeline latency was reduced to 0.3 minutes. We also saw an estimated $1000 per day in cost savings due to reduced resource utilization. However, it wasn't until we started setting Service Level Agreements (SLAs) on our event freshness that we saw a real reduction in lost revenue.
What I Would Do Differently
In hindsight, we would have chosen a solution that prioritized event quality and robust monitoring from the start. We would have invested more time and resources into designing our event pipeline's failure modes and understanding the implications of data loss. Perhaps we would have explored alternative event stream processing solutions, like Apache Flink or Apache Storm, that are more geared towards robustness and fault tolerance.
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