similar to: Objectsize function visiting every element for alt-rep strings

On Mon, 21 Jan 2019, Martin Maechler wrote: >>>>>> Travers Ching >>>>>> on Tue, 15 Jan 2019 12:50:45 -0800 writes: > > > I have a toy alt-rep string package that generates > > randomly seeded strings. example: library(altstringisode) > > x <- altrandomStrings(1e8) head(x) [1] > >

On 1/22/19 6:17 PM, Kevin Ushey wrote: > I think that object.size() is most commonly used to answer the question, > "what R objects are consuming the most memory currently in my R session?" > and for that reason I think returning the size of the internal > representations of objects (for e.g. ALTREP objects; unevaluated promises) > is the right default behavior. I

Below is a toy alt-rep string example, that generates N random strings: https://gist.github.com/traversc/a48a504eb062554f2d6ff8043ca16f9c example: `x <- altrandomStrings(1e8)` `head(x)` [1] "2PN0bdwPY7CA8M06zVKEkhHgZVgtV1" "5PN2qmWqBlQ9wQj99nsQzldVI5ZuGX" ... `object.size(1e8)` Object.size will call the `set_altstring_Elt_method` for every single element, materializing

>>>>> Travers Ching >>>>> on Tue, 15 Jan 2019 12:50:45 -0800 writes: > I have a toy alt-rep string package that generates > randomly seeded strings. example: library(altstringisode) > x <- altrandomStrings(1e8) head(x) [1] > "2PN0bdwPY7CA8M06zVKEkhHgZVgtV1" > "5PN2qmWqBlQ9wQj99nsQzldVI5ZuGX" ... etc

I think that object.size() is most commonly used to answer the question, "what R objects are consuming the most memory currently in my R session?" and for that reason I think returning the size of the internal representations of objects (for e.g. ALTREP objects; unevaluated promises) is the right default behavior. I also agree it would be worth considering adding arguments that control

Travers, Great to hear you're trying out the ALTREP stuff, good on you :). Did you mean the get_altstring_Elt_method? I see the code in size.c within utils that grabs each element, but I don't see any setting (and the setters are noops currently anyway they just do things the old way). One thing we have to decide is what object.size means for an altrep. I tend to think it should mean

Hello, I?m experimenting with ALTREP and was wondering if there is a preferred way to create an ALTREP wrapper vector without using .Internal(wrap_meta(?)), which R CMD check doesn?t like since it uses an .Internal() function. I was trying to create a factor that used an ALTREP integer, but attempting to set the class and levels attributes always ended up duplicating and materializing the

Hello, I have encountered two bugs when using ALTREP APIs. 1. STDVEC_DATAPTR >From RInternal.h file it has a comment: /* ALTREP support */ > void *(STDVEC_DATAPTR)(SEXP x); However, this comment might not be true, the easiest way to verify it is to define a C++ function: void C_testFunc(SEXP a) > { > STDVEC_DATAPTR(a); > } and call it in R via > a=1:10 > >

Hello Luke and Gabriel, Thank you very much for your quick responses. The explanation of STDVEC is very helpful and I appreciate it! For the wrapper, I have a few new questions. 1. Like Luke said a mutable object is not possible. However, I noticed that there is one extra argument *deep* in the function duplicate. I've googled all the available documentation for ALTREP but I did not find

Hi Jiefei and Kylie, Great to see people engaging with the ALTREP framework and identifying places we may need more tooling. Comments inline. On Thu, Jul 18, 2019 at 12:22 PM King Jiefei <szwjf08 at gmail.com> wrote: > > If that is the case and you are 100% sure the reference number should be 1 > for your variable *y*, my solution is to call *SET_NAMED *in C++ to reset > the

Hi all, The coercion function defined for the ALTREP object will not be called by R when an assignment operation implicitly introduces coercion for a large ALTREP object. For example, If I create a vector of length 10, the ALTREP coercion function seems to work fine. ``` > x <- 1:10 > y <- wrap_altrep(x) > .Internal(inspect(y)) @0x000000001f9271c0 13 INTSXP g0c0 [REF(2)] I am

>>>>> Martin Maechler >>>>> on Tue, 8 Sep 2020 10:40:24 +0200 writes: >>>>> Hugh Parsonage >>>>> on Tue, 8 Sep 2020 18:08:11 +1000 writes: >> I can only reproduce on Windows, but reliably (both 4.0.0 and 4.0.2): >> $> R --vanilla >> x <- c(0L, -2e9:2e9) >> # > Segmentation

I can only reproduce on Windows, but reliably (both 4.0.0 and 4.0.2): $> R --vanilla x <- c(0L, -2e9:2e9) # > Segmentation fault Tried to reproduce on Linux but the above worked as expected. Not an issue merely with the length of the vector; for example, x <- rep_len(1:10, 1e10) works, though the altrep vector must be long to reproduce: x <- c(0L, -1e9:1e9) #ok Segmentation

Hi Gabriel, here is a simple package for reproducing the problem. https://github.com/Jiefei-Wang/testPkg Best, Jiefei On Thu, Oct 8, 2020 at 5:04 AM Gabriel Becker <gabembecker at gmail.com> wrote: > Jiefei, > > Where does the code for your altrep class live? > > Thanks, > ~G > > On Wed, Oct 7, 2020 at 4:25 AM Jiefei Wang <szwjf08 at gmail.com> wrote: >

On Tue, 8 Sep 2020, Hugh Parsonage wrote: > Thanks Martin. On further testing, it seems that the segmentation > fault can only occur when the amount of obtainable memory is > sufficiently high. On my machine (admittedly with other processes > running): > > $ R --vanilla --max-mem-size=30G -e "x <- c(0L, -2e9:2e9)" > Segmentation fault > > $ R --vanilla

You should really take this up with RStudio. Calling object.size on every top level assignment as they appear to do is a bad idea, even without ALTREP. object.size is only a cheap operation for simple atomic vectors. For anything with recursive sturcture it needs to walk the object, so the effort is proprtional to object size: > x <- rep("A", 1e8) > system.time(object.size(x))

>>>>> luke-tierney >>>>> on Tue, 8 Sep 2020 09:42:43 -0500 (CDT) writes: > On Tue, 8 Sep 2020, Martin Maechler wrote: >>>>>>> Martin Maechler >>>>>>> on Tue, 8 Sep 2020 10:40:24 +0200 writes: >> >>>>>>> Hugh Parsonage >>>>>>> on Tue, 8 Sep 2020

I ran into strange behavior when removing names. Two ways of removing names: i <- rep(1:4, length.out=20000) k <- c(a=1, b=2, c=3, d=4) x1 <- unname(k[i]) x2 <- k[i] x2 <- unname(x2) Are they identical? identical(x1,x2) # TRUE but no identical(serialize(x1,NULL),serialize(x2,NULL)) # FALSE But problem is with serialization type 3, cause:

My understanding is that R will not make copies of lists if there is only one reference to the object. However, I've encountered a case where R does make copies, even though (I think) there should be only one reference to the object. I hope that someone could shed some light on why this is happening. I'll start with a simple example. Below, x is a list with one element, and changing that

I'm confused by this: > identical(function() {}, function() {}) [1] FALSE Yet, after loading the Matrix package (which redefines det), the following is checked (in library.checkConflicts): > identical(get("det", baseenv()), get("det", asNamespace("Matrix")), ignore.environment=T) [1] TRUE I've looked at the code in identical.c and for closures it