I’ve been deep inside one of the most complex parts of DocBeacon, the tracking and analytics engine.
We aggregate user behavior data on three levels:
Share-level (each document share link)
Document-level (per uploaded file)
User-level (overall engagement footprint)
The challenge:
Every time a new event is logged (a view, scroll, or dwell), DocBeacon performs a hierarchical aggregation to update summary stats across all three levels. For users with large historical data, this can become very expensive, both computationally and in terms of API overhead.
The logical solution seems simple: reduce the frequency of aggregation.
But that’s where things got tricky.
Months ago, I noticed rare cases where the aggregation would silently fail to trigger. That meant the top-level summaries occasionally drifted out of sync, sometimes off by just a few views, other times more dramatically. The bug was elusive: hard to reproduce, impossible to ignore.
During this refactor, I’ve been dissecting the entire chain of event handling, from event queue → aggregation trigger → rollup storage. The logic is being restructured to make trigger conditions more deterministic and fault-tolerant.
So far, progress has been solid, but reproducing the original edge case remains challenging. Debugging aggregation bugs is like chasing ghosts, you only see their footprints.
Once this refactor ships, the analytics engine will be leaner, more predictable, and less resource-hungry. It’s the kind of work no one sees on the surface, but it’s what makes real-time analytics trustworthy.
Still testing. Hoping to roll out next week.
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