Situation: On a pull request for our caching layer, a principal engineer suggested using an in-memory local Node.js cache (node-cache) rather than our shared distributed Redis instance to avoid network latency.
Task: I was concerned that in-memory caching across our horizontally auto-scaling container fleet would cause severe cache inconsistency and memory leaks, but I needed to challenge the senior engineer's suggestion respectfully.
Action: Rather than arguing seniority or making blunt declarations, I framed my comments around inquiries and objective edge cases on the PR: 'Could you help me understand how this in-memory cache handles consistency when users update their profile on instance A, but their subsequent read hits instance B?'. I shared a lightweight benchmark showing our Redis network latency was sub-millisecond, and linked to our memory utilization graphs showing our containers were already operating at 80% RAM capacity.
Result: The principal engineer reviewed the memory metrics and agreed that horizontal container scaling made distributed Redis significantly safer for our consistency model. He commended my respectful, metric-driven review, and we documented the decision in our engineering wiki.