I found something that looks a bit odd to me:
The memory consumption of reference classes seems to increase dramatically
(roughly 10x in the example below) with serialization/deserialization
(saveRDS() / readRDS()).
Maybe creating instances of the same class has a synergy effect (references?)
which is lost after a saveRDS()/readRDS() cycle? Class definitions spring to
mind but I was unable to track
down any size difference along these lines.
Any ideas?
Best
Jan
# start new R session, empty workspace
untouched=memory.size();
MySmallClass = setRefClass("MySmallClass",
fields = list(
myField = "numeric"
),
methods = list(
initialize = function(f) {
myField<<- f;
})
);
# Generate 10K instances
o = lapply(rnorm(10000), MySmallClass$new)
withobj=memory.size()
print(paste("Initial Mem:", untouched, "With objects:", withobj, "Difference:",
round(withobj-untouched, 2)));
#[1] "Initial Mem: 14.06 With objects: 29.14 Difference: 15.08"
# Save and read back those instances
saveRDS(o, "test.rds")
untouched=memory.size();
p=readRDS("test.rds")
withobj=memory.size()
print(paste("Initial Mem:", untouched, "With objects:", withobj, "Difference:",
round(withobj-untouched, 2)));
#[1] "Initial Mem: 31.2 With objects: 200.71 Difference: 169.51"
# Platform info:
# R version 2.15.1
# Platform: i386-pc-mingw32/i386 (32-bit)
# Binary package from CRAN
# Windows 7 32 Bit
# (I observed a similar behavior on Linux 64 Bit, same R version)
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