Relevant background info
I’ve built a little software that can be customized via a config file. The config file is parsed and translated into a nested environment structure (e.g. .HIVE$db = an environment, .HIVE$db$user = “Horst”, .HIVE$db$pw = “my password”, .HIVE$regex$date = some regex for dates etc.)
I’ve built routines that can handle those nested environments (e.g. look up value “db/user” or “regex/date”, change it etc.). The thing is that the initial parsing of the config files takes a long time and results in quite a big of an object (actually three to four, between 4 and 16 MB). So I thought “No problem, let’s just cache them by saving the object(s) to .Rdata files”. This works, but “loading” cached objects makes my Rterm process go through the roof with respect to RAM consumption (over 1 GB!!) and I still don’t really understand why (this doesn’t happen when I “compute” the object all anew, but that’s exactly what I’m trying to avoid since it takes too long).
I already thought about maybe serializing it, but I haven’t tested it as I would need to refactor my code a bit. Plus I’m not sure if it would affect the “loading back into R” part in just the same way as loading .Rdata files.
Question
Can anyone tell me why loading a previously computed object has such effects on memory consumption of my Rterm process (compared to computing it in every new process I start) and how best to avoid this?
If desired, I will also try to come up with an example, but it’s a bit tricky to reproduce my exact scenario. Yet I’ll try.
Its likely because the environments you are creating are carrying around their ancestors. If you don’t need the ancestor information then set the parents of such environments to
emptyenv()(or just don’t use environments if you don’t need them).Also note that formulas (and, of course, functions) have environments so watch out for those too.