Hi Everyone,
I want to add a large number of objects in C++ that are managed by `shared_ptr`
in a `vector`. However, when I push the limits of the amount that I want to
allocate the data in R becomes inconsistent.
I will first show the test script and then the c++ file that cause the error.
The expected results are shown at the bottom.
# test script
The test script permits to add an arbitrary number of objects to a vector.
require(Rcpp)
sourceCpp(file="~/tmp/example.cpp")
add_children <- function(number_of_children = 2) {
p <- 0
st <- initialize_storage(p, p)
for (i in 1 : number_of_children) {
st <- add_node(st, i, i)
}
return(st)
}
# example.cpp
The vector is stored in a object that is managed by R but the elements in the
vector are managed by `shared_ptr` and created with `make_shared`.
// [[Rcpp::plugins(cpp11)]]
#include <RcppCommon.h>
#include <memory>
#include <vector>
using namespace std;
struct Element : public std::enable_shared_from_this< Element > {
SEXP key_;
/* Simple constructor that assigns the key. */
Element(SEXP key) : key_(key) {}
/* Convert the object to a R object. */
operator SEXP() const;
};
typedef shared_ptr<Element> element_sp;
typedef vector<element_sp> element_sp_vec;
typedef shared_ptr<element_sp_vec> element_sp_vec_sp;
struct Storage {
/* Internal storage of nodes. */
element_sp_vec nodes_;
/* Empty constructor. */
Storage() {}
/* Add a node to the storage with its key set to key. */
void add_element(SEXP key) {
/* Since Element objects are managed by shared_ptr we create a new class
* with make_shared. */
element_sp e = make_shared<Element>(key);
/* Add the node to the internal storage. */
nodes_.push_back(e);
}
element_sp_vec_sp get_nodes() {
/* Create a shared pointer that will hold all the results. Although we
* could do this simpler it mimics the logic I implemented in my real
* program. There I need to swap elements in the list after the copy of the
* vector. */
element_sp_vec_sp res(new element_sp_vec());
/* Copy the data in the nodes vector to the result vector. */
*res = nodes_;
return res;
}
};
#include <Rcpp.h>
using namespace Rcpp;
/* Convert the Element object to a list with key set its internal member */
Element::operator SEXP() const
{
List serial;
serial["key"] = key_;
return serial;
}
typedef XPtr<Storage> st_xptr;
// [[Rcpp::export]]
SEXP
initialize_storage()
{
/* Create a new storage managed by R. */
Storage* st = new Storage();
st_xptr p(st, true);
return p;
}
// [[Rcpp::export]]
SEXP
add_element(SEXP st_sexp, SEXP key)
{
st_xptr st(st_sexp);
/* Add a new element to the internal storage. */
st->add_element(key);
return st;
}
// [[Rcpp::export]]
List
get_nodes(SEXP st_sexp)
{
st_xptr st(st_sexp);
/* Retrieve the elements in the internal storage. */
element_sp_vec_sp c_res = st->get_nodes();
/* Allocate a List to store all our results. */
List result(c_res->size());
int i = 0;
/* Iterate through the results and store the result in our list. */
for (auto it : *c_res) {
result[i] = wrap(*it);
++i;
}
return result;
}
Below follow a few test cases of the script with the behavior that I experience
on Mac OS Sierra with clang.
## n = 10
Everything works fine
n <- 10
a <- add_children(number_of_children = n)
res <- sapply(get_nodes(a), function(x) x[["key"]])
all(res == 0 : n)
# [1] TRUE
## n = 100
Everything works fine
n <- 100
a <- add_children(number_of_children = n)
res <- sapply(get_nodes(a), function(x) x[["key"]])
all(res == 0 : n)
# [1] TRUE
## n = 10000
Something goes wrong.
n <- 10000
a <- add_children(number_of_children = n)
res <- sapply(get_nodes(a), function(x) x[["key"]])
all(res == 0 : n)
# [1] FALSE
# There were 50 or more warnings (use warnings() to see the first 50)
Some further research shows that the warnings are:
warnings()
# Warning messages:
# 1: NAs introduced by coercion
# 2: NAs introduced by coercion
# 3: NAs introduced by coercion
# 4: NAs introduced by coercion
### etc.
a closer inspection into the content of `res` shows that it has a non numeric
value,
res[1000]
# [[1]]
# [1] "data"
which is surprising to me since the script only added numeric `SEXP` values to
the `vector`. My expected output for this value of `n` would be the same as the
cases above.
## gctorture
I reran the `n=10000` example with `gctorture(TRUE)` but did not receive any
warning but the data is corrupt. Two random elements in the `res` list:
# [[998]]
# <CHARSXP: "\"key\"">
#
# [[999]]
# [1] "srcref"
= Replication
I replicated these results on Mac OS X Sierra as well as Docker image based on
rocker.
sessionInfo()
# R version 3.3.1 (2016-06-21)
# Platform: x86_64-apple-darwin15.5.0 (64-bit)
# Running under: OS X 10.12 (Sierra)
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] Rcpp_0.12.7 setwidth_1.0-4 colorout_1.1-2
#
# loaded via a namespace (and not attached):
# [1] tools_3.3.1
### uname
uname -prsv
Darwin 16.0.0 Darwin Kernel Version 16.0.0: Mon Aug 29 17:56:20 PDT 2016;
root:xnu-3789.1.32~3/RELEASE_X86_64 i386
### clang
clang -v
Apple LLVM version 8.0.0 (clang-800.0.38)
Target: x86_64-apple-darwin16.0.0
Thread model: posix
InstalledDir:
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin
Any advise on what may be the problem here?
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