That (memory or object type growth over time) is what it feels like as well to me. I've been trying to figure out how to get visibility into that, so I created a plumber endpoint for debugging that I can call to log things like memory.profile() or gc() on demand so I can see how that compares between good/bad performing times.
Not on Docker, just an EC2 directly running Plumber. From an OS perspective the R process doesn't really appear to be growing in memory size.
Anyone else have ideas on performance metrics to look (like memory.profile() or gc()) at from within a long running R process like Plumber that might degrade over time.