Support arbitrary R objects about datastructures HOT 7 CLOSED

I generally prefer to avoid serialization in R because it is usually a performance bottleneck.

from datastructures.

Hey, I totally agree, this should be supported. I was planning to add this already, but to be honest I don't know the C API well enough yet to implement this. Unfortunately there are also not too many sources except the R documentation.

Let me quickly explain my problem. So, as far as I know I need to predefine C++ data types, because R dispatches dynamically. For instance, for a heap of int->double I need to predefine the following :

typedef binomial_heap<int, double> binomial_heap_id;

Now suppose I want a heap of int -> list. A list might look like this in R:

> list(a=1, b="a")
$a
[1] 1

$b
[1] "a"

If I look at the SEXP object on the backend, it has the following structure:

pryr::inspect(list(a=1, b="a"))

<VECSXP 0x7f90fc23b950>
  <REALSXP 0x7f90fc04baf8>
  <STRSXP 0x7f90fc04ba98>
    <CHARSXP 0x7f90fa814798>

attributes:
  <LISTSXP 0x7f90fbcbf440>
  tag:
    <SYMSXP 0x7f90fb80a940>
  car:
    <STRSXP 0x7f90fc23b8e0>
      [CHARSXP 0x7f90fa814798]
      <CHARSXP 0x7f90fb30d3f8>
  cdr:
    NULL

I haven't checked, how I could implement this for different data types. If I can just

typedef binomial_heap<int, SEXP> binomial_heap_id;

this would be easy.

As soon as I have more time, I'll have a look into this. Thanks for the feedback!

from datastructures.

SEXP is just a pointer, you just need to make sure R GC doesn't remove the memory by preserving it.
Check R_PreserveObject and R_ReleaseObject.

Update: However..

These functions need to be used with care: because R_ReleaseObject() will perform a recursive search for the object to protect, if many SEXPs are inserted and later removed in the same order, this can cause performance regressions.

from datastructures.

Another possibility is to serialize the R objects to raw vectors via serialize(x, NULL)

from datastructures.

Oh wow, that's great! Thanks!

from datastructures.

One more alternative, you could also store the R objects in a queue using a pairlist if you think R_PreserveObject/R_ReleaseObject is too heavy.

from datastructures.

Dear @randy3k,

the latest version of the package implements your suggestions. Thanks for the tips and feedback.
I will validate and test this for a couple of more days and then release to CRAN.

  hm <- hashmap("integer")
  keys <- 1:2
  values <- list(
    environment(),
    data.frame(A=rbeta(3, .5, .5), B=rgamma(3, 1)))
  hm[keys] <- values

  hm[1L]
  [[1]]
  <environment: R_GlobalEnv>

Please let me know if I can close this issue.

from datastructures.