[Rd] object.size vs lobstr::obj_size

Tomas Kalibera tom@@@k@||ber@ @end|ng |rom gm@||@com
Fri Mar 27 15:01:11 CET 2020

On 2/19/20 3:55 AM, Stefan Schreiber wrote:
> I have posted this question on R-help where it was suggested to me
> that I might get a better response on R-devel. So far I have gotten no
> response. The post I am talking about is here:
> https://stat.ethz.ch/pipermail/r-help/2020-February/465700.html
> My apologies for cross-posting, which I am aware is impolite and I
> should have posted on R-devel in the first place - but I wasn't sure.
> Here is my question again:
> I am currently working through Advanced R by H. Wickham and came
> across the `lobstr::obj_size` function which appears to calculate the
> size of an object by taking into account whether the same object has
> been referenced multiple times, e.g.
> x <- runif(1e6)
> y <- list(x, x, x)
> lobstr::obj_size(y)
> # 8,000,128 B
> # versus:
> object.size(y)
> # 24000224 bytes
> Reading through `?object.size` in the "Details" it reads: [...] but
> does not detect if elements of a list are shared [...].
> My questions are:
> (1) is the result of `obj_size()` the "correct" one when it comes to
> actual size used in memory?
> (2) And if yes, why wouldn't `object.size()` be updated to reflect the
> more precise calculation of an object in question similar to
> `obj_size()`?

Please keep in mind that "actual size used in memory" is an elusive 
concept, particularly in managed languages such as R. Even in native 
languages, you have on-demand paging (not all data in physical memory, 
some may be imputed (all zeros), some may be swapped out, some may be 
stored in files (code), etc). Also you have internal and external 
fragmentation caused by the "C library" memory allocator, overhead of 
object headers and allocator meta-data. On top of that you have the 
managed heap: more of internal and external fragmentation, more headers. 
Moreover, memory representation may change invisibly and sometimes in 
surprising ways (in R it is copy-on-write, so the sharing, but also 
compact objects via ALTREP, e.g. sequences). R has the symbol table, 
string cache (strings are interned, as in some other language runtimes, 
so the price is paid only once for each string). In principle, managed 
runtimes could do much more, including say compression of objects with 
adaptive decompression, some systems internally split representation of 
large objects depending on their size with additional overheads, systems 
could have some transparent de-duplication (not only for strings), some 
choices could be adaptive based on memory pressure. Then in R, packages 
often can maintain memory related to specific R objects, linked say via 
external pointers, and again there may be no meaningful way to map that 
usage to individual objects.

Not only that what is a size of an object tree is not easy to define. 
That information is in addition not very useful, either, because 
innocuous changes may change it in arbitrary ways out of control of the 
user: there is no good intuition how much that size will change from 
intended application-level modifications of the tree. Users of the 
system could hardly create a reliable mental model of the memory usage, 
because it depends on internal design of the virtual machine, which in 
addition can change over time.

As the concept is elusive, the best advice would be don't ask for the 
object size, find some other solutions to your problem. In some cases, 
it makes sense to ask for object size in some application-specific way, 
and then implement object size methods for specific application classes 
(e.g. structures holding strings would sum up number of characters in 
the strings, etc). Such application-specific way may be inspired by some 
particular (perhaps trivial) serialization format.

I've used object.size() myself only for profiling when quickly 
identifying objects that are probably very large from objects of trivial 
size, where these nuances did not matter, but for that I knew roughly 
what the objects were (e.g. that they were not hiding things in 

Intuitively, the choices made by object.size() in R are conservative, 
they provide an over-approximation that somewhat intuitively makes sense 
at user level, and they reduce surprises of significant size expansion 
due to minimal updates. The choices and their limitations are 
documented. I think this at least no worse than than say taking into 
account sharing, looking at current "size" of compact objects, etc. One 
could provide more options to object.size(), but I don't think that it 
would be useful.


> There are probably valid reasons for this and any insight would be
> greatly appreciated.
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