Motivations

R and S have a long history of interacting with compiled languages. In fact the original version of S written in the late 1970s was mainly a wrapper around FORTRAN routines. (History-of-S) Released in 2000, the cxx package was an early prototype of C++ bindings to R. Rcpp was first published to CRAN in 2008, and Rcpp11 in 2014. Of these Rcpp has by far the widest adoption, with over 2000 reverse dependencies as of 2020.

Rcpp has been a widely successful project, however over the years a number of issues and additional C++ features have arisen. Adding these features to Rcpp would require a great deal of work, or in some cases would be impossible without severely breaking backwards compatibility.

cpp11 is a ground up rewrite of C++ bindings to R with different design trade-offs and features.

Changes that motivated cpp11 include:

Copy-on-write semantics

R uses copy-on-write (also called copy-on-modify) semantics. Lets say you have two variables x and y that both point to the same underlying data.

x <- c(1, 2, 3)
y <- x

If you modify y, R will first copy the values of x to a new position, then point y to the new location and only after the copy modify y. This allows x to retain the original values.

y[[3]] <- 4
y
#> [1] 1 2 4

x
#> [1] 1 2 3

C++ does not have copy-on-write built into the language, however it has related concepts, copy-by-value and copy-by-reference. Copy-by-value works similarly to R, except that R only copies when something is changed, C++ always copies.

int x = 42;
int y = x;
y = 0;
// x is still == 42

Copy-by-reference does the opposite, both x and y always point to the same underlying value. In C++ you specify a reference with &.

int x = 42;
int &y = x;
y = 0;
// both x and y are now 0

Copy-by-reference is a valuable technique, as it avoids the overhead of copying the data. However it can also lead to errors when internal functions change their inputs unexpectedly. Rcpp uses copy-by-reference by default (even if you pass a Rcpp vector class by value). This gives Rcpp functions completely different semantics from normal R functions.

We can illustrate this by creating a Rcpp function that multiples its input vector by 2.

#include "Rcpp.h"
using namespace Rcpp;

// [[Rcpp::export]]
NumericVector times_two_rcpp(NumericVector x) {
  for (int i = 0; i < x.size(); ++i) {
    x[i] = x[i] * 2;
  }
  return x;
}

If you do this with regular R functions, you will see the value of y is x * 2, but the value of x is unchanged.

x <- c(1, 2, 3)
y <- x * 2
y
#> [1] 2 4 6

x
#> [1] 1 2 3

However if we now call our times_two_rcpp() function we get the right output value, but now x is also changed.

z <- times_two_rcpp(x)
z
#> [1] 2 4 6

x
#> [1] 2 4 6

cpp11 strives to make its functions behave similarly to normal R functions, while preserving the speed of Rcpp when read only access is needed. Each of the r_vector classes in cpp11 has a normal read only version that uses copy-by-reference, and a writable version which uses copy-by-value.

#include "cpp11/doubles.hpp"

[[cpp11::register]]
cpp11::doubles times_two_cpp11(cpp11::writable::doubles x) {
  for (int i = 0; i < x.size(); ++i) {
    x[i] = x[i] * 2;
  }
  return x;
}

Using cpp11::writable::doubles first copies the input vector, so when we do the multiplication we do not modify the original data.

x <- c(1, 2, 3)

z <- times_two_cpp11(x)
z
#> [1] 2 4 6

x
#> [1] 1 2 3

Improve safety

Internally R is written in C, not C++. In general C and C++ work well together, a large part of C++’s success is due to its high interoperability with C code. However one area in which C and C++ are generally not interoperable is error handling. In C++ the most common way to handle errors is with exceptions.

Exceptions provide a clean, safe way for objects to obtain and cleanup resources automatically even when errors occur.

C safety

The C language does not have support for exceptions, so error handling is done a variety of ways. These include error codes like errno, conditional statements, and in the R codebase the longjmp function.

longjmp, which stands for ‘long jump’ is a function that allows you to transfer the control flow of a program to another location elsewhere in the program. R uses long jumps extensively in its error handling routines. If an R function is executing and an error occurs, a long jump is called which ‘jumps’ the control flow into the error handling code.

Crucially long jumps are incompatible with C++ destructors. If a long jump occurs the destructors of any active C++ objects are not run, and therefore any resources (such as memory, file handles, etc.) managed by those objects will cause a resource leak.

For example, the following unsafe code would leak the memory allocated in the C++ std::vector x when the R API function Rf_allocVector() fails (since you can’t create a vector of -1 size).

std::vector<double> x({1., 2., 3.});

SEXP y = PROTECT(Rf_allocVector(REALSXP, -1));

cpp11 provides two mechanisms to make interfacing with Rs C API and C++ code safer. cpp11::unwind_protect() takes a functional object (a C++11 lamdba function or std::function) and converts any C long jumps encountered to C++ exceptions. Now instead of a C long jump happening when the Rf_allocVector() call fails, a C++ exception occurs, which does trigger the std::vector destructor, so that memory is automatically released.

std::vector<double> x({1., 2., 3.});

SEXP y;
unwind_protect([]() {
  y = Rf_allocVector(REALSXP, -1);
})

cpp11::safe() is a more concise way to wrap a particular R API function with unwind_protect().

std::vector<double> x({1., 2., 3.});

SEXP y = PROTECT(safe[Rf_allocVector](REALSXP, -1));

Again using cpp11::safe() converts the C long jump to a C++ exception, so the memory is automatically released.

cpp11 uses these mechanisms extensively internally when calling the R C API, which make cpp11 much safer against resource leaks than using Rcpp or calling Rs C API by hand.

C++ safety

In the inverse of C safety we also need to ensure that C++ exceptions do not reach the C call stack, as they will terminate R if that occurs. Like Rcpp, cpp11 automatically generates try / catch guards around registered functions to prevent this and also converts C++ exceptions into normal R errors. This is done without developer facing code changes.

With both C and C++ sides of the coin covered we can safely use R’s C API and C++ code together with C++ objects without leaking resources.

Altrep support

ALTREP which stands for ALTernative REPresntations is a feature introduced in R 3.5. ALTREP allows R internals and package authors to define alternative ways of representing data to R. One example of the use of altrep is the : operator.

Prior to R 3.5 : generated a full vector for the entire sequence. e.g. 1:1000 would require 1000 individual values. As of R 3.5 this sequence is instead represented by an ALTREP vector, so none of the values actually exist in memory. Instead each time R access a particular value in the sequence that value is computed on-the-fly. This saves memory and excution time, and allows users to use sequences which would otherwise be too big to fit in memory.

1:1e9
#>  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
#>  [ reached getOption("max.print") -- omitted 999999980 entries ]

Because Rcpp predates the introduction of ALTREP, it does not support the interfaces needed to access ALTREP objects. This means the objects must be converted to normal R objects as soon as they are used by Rcpp.

#include "Rcpp.h"

// [[Rcpp::export]]
Rcpp::IntegerVector identity_rcpp(Rcpp::IntegerVector x) {
  return x;
}
x <- identity_rcpp(1:100000)
lobstr::obj_size(x)
#> 400,728 B

Whereas cpp11 objects preserve the ALTREP object.

#include "cpp11/integers.hpp"

[[cpp11::register]]
cpp11::integers identity_cpp11(cpp11::integers x) {
  return x;
}
y <- identity_cpp11(1:100000)
lobstr::obj_size(y)
#> 680 B

Altrep benchmarks

In these benchmarks note that Rcpp allocates memory for the ALTREP vectors. This is because Rcpp implicitly converts them into normal R vectors. cpp11 retains them as ALTREP vectors, so no additional memory is needed.

foreach and accumulate both use iterators that take advantage of REAL_GET_REGION to buffer queries. This makes them faster than naive C-style for loops with ALTREP vectors.

The for2 case shows an optimization you can use if you know at compile-time that you won’t be dealing with ALTREP vectors. By specifying false to the second argument (is_altrep), you can disable the ALTREP support. This causes the ALTREP conditional code to be compiled out resulting in loop unrolling (and speeds) identical to that generated by Rcpp.

library(cpp11test)

cases <- expand.grid(
  len = 3e6,
  vector = c("normal", "altrep"),
  method = c("for", "foreach", "accumulate"),
  pkg = c("cpp11", "rcpp"),
  stringsAsFactors = FALSE
)

# Add special case
cases <- rbind(list(len = 3e6, vector = "normal", method = "for2", pkg = "cpp11"), cases)

b_sum <- bench::press(
  .grid = cases,
  {
    seq_real <- function(x) as.numeric(seq_len(x))
    funs <- c("normal" = rnorm, "altrep" = seq_real)
    x <- funs[[vector]](len)
    fun <- match.fun(sprintf("%ssum_dbl_%s_", ifelse(pkg == "cpp11", "", paste0(pkg, "_")), method))
    bench::mark(
      fun(x)
    )
  }
)[c("pkg", "method", "vector", "min", "median", "mem_alloc", "itr/sec", "n_gc")]

saveRDS(b_sum, "sum.Rds", version = 2)
knitr::kable(readRDS("sum.Rds"))
pkg method vector min median mem_alloc itr/sec n_gc
cpp11 for2 normal 0.003008334 0.003208736 0 302.9364 0
cpp11 for normal 0.002929285 0.003088721 0 319.9100 0
cpp11 for altrep 0.008089832 0.008442934 0 117.0562 0
cpp11 foreach normal 0.002966348 0.003361415 0 292.8306 0
cpp11 foreach altrep 0.004017410 0.004184819 0 236.2339 0
cpp11 accumulate normal 0.003033590 0.003241419 0 303.3408 0
cpp11 accumulate altrep 0.004067195 0.004311647 0 225.8066 0
rcpp for normal 0.002807784 0.003127156 0 311.3724 0
rcpp for altrep 0.002805196 0.003131571 24000048 311.6365 0
rcpp foreach normal 0.002928401 0.003462064 0 293.9831 0
rcpp foreach altrep 0.002810107 0.003068041 24000048 313.6250 0
rcpp accumulate normal 0.002803584 0.003006378 0 321.6647 0
rcpp accumulate altrep 0.002750792 0.003000370 24000048 322.9292 0

cpp11test/src/sum.cpp contains the code ran in these benchmarks.

UTF-8 everywhere

R has complicated support for Unicode strings and non-ASCII code pages, whose behavior often differs substantially on different operating systems, particularly Windows. Correctly dealing with this is challenging and often feels like whack a mole.

To combat this complexity cpp11 uses the UTF-8 everywhere philosophy. This means that whenever text data is converted from R data structures to C++ data structures by cpp11 the data is translated into UTF-8. Conversely any text data coming from C++ code is assumed to be UTF-8 and marked as such for R. Doing this universally avoids many locale specific issues when dealing with Unicode text.

Concretely cpp11 always uses Rf_translateCharUTF8() when obtaining const char* from CHRSXP objects and uses Rf_mkCharCE(, CE_UTF8) when creating new CHRSXP objects from const char* inputs.

C++11 features

C++11 provides a host of new features to the C++ language. cpp11 uses a number of these including

Simpler implementation

Rcpp is very ambitious, with a number of advanced features, including modules, sugar and extensive support for attributes. While these are useful features, many R packages do not use one or any of these advanced features. In addition the code needed to support these features is complex and can be challenging to maintain.

cpp11 takes a more limited scope, providing only the set of r_vector wrappers for R vector types, coercion methods to and from C++ and the limited attributes necessary to support use in R packages.

This limited scope allows the implementation to be much simpler, the headers in Rcpp 1.0.4 have 74,658 lines of code (excluding blank or commented lines) in 379 files. Some headers in Rcpp are automatically generated, removing these still gives you 25,249 lines of code in 357 files. In contrast the headers in cpp11 contain only 1,734 lines of code in 19 files.

This reduction in complexity should make cpp11 an easier project to maintain and ensure correctness, particularly around interactions with the R garbage collector.

Compilation speed

Rcpp always bundles all of its headers together, which causes slow compilation times and high peak memory usage when compiling. The headers in cpp11 are more easily decoupled, so you only can include only the particular headers you actually use in a source file. This can significantly improve the compilation speed and memory usage to compile your package.

Here are some real examples of the reduction in compile time and peak memory usage after converting packages to cpp11.

package Rcpp compile time cpp11 compile time Rcpp peak memory cpp11 peak memory Rcpp commit cpp11 commit
haven 17.42s 7.13s 428MB 204MB a3cf75a4 978cb034
readr 124.13s 81.08s 969MB 684MB ec0d8989 aa89ff72
roxygen2 17.34s 4.24s 371MB 109MB 6f081b75 e8e1e22d
tidyr 14.25s 3.34s 363MB 83MB 3899ed51 60f7c7d4

Header only

Rcpp has long been a mostly header only library, however is not a completely header only library. There have been cases when a package was first installed with version X of Rcpp, and then a newer version of Rcpp was later installed. Then when the original package X was loaded R would crash, because the Application Binary Interface of Rcpp had changed between the two versions.

Because cpp11 consists of exclusively headers this issue does not occur.

Vendoring

In the go community the concept of vendoring is widespread. Vendoring means that you copy the code for the dependencies into your project’s source tree. This ensures the dependency code is fixed and stable until it is updated. Because cpp11 is fully header only you can vendor the code in the same way. cpp11::vendor_cpp11() is provided to do this if you choose.

Vendoring has advantages and drawbacks however. The advantage is that changes to the cpp11 project could never break your existing code. The drawbacks are both minor, your package size is now slightly larger, and major, you no longer get bugfixes and new features until you explicitly update cpp11.

I think the majority of packages should use LinkingTo: cpp11 and not vendor the cpp11 dependency. However, vendoring can be appropriate for certain situations.

Protection

cpp11 uses a custom double linked list data structure to track objects it is managing. This struture is much more efficient for large numbers of objects than using R_PreserveObject() / R_ReleaseObjects() as is done in Rcpp.

library(cpp11test)
grid <- expand.grid(len = c(10 ^ (2:5), 2e5), pkg = c("cpp11", "rcpp"), stringsAsFactors = FALSE)
b_release <- bench::press(.grid = grid,
  {
    fun = match.fun(sprintf("%s_release_", pkg))
    bench::mark(
      fun(len),
      iterations = 1
    )
  }
)[c("len", "pkg", "min")]
saveRDS(b_release, "release.Rds", version = 2)

This plot shows the average time to protect and release a given object is essentially constant for cpp11. Whereas it is linear or worse with the number of objects being tracked for Rcpp.

len pkg min
1e+02 cpp11 26.28µs
1e+03 cpp11 127.51µs
1e+04 cpp11 1.36ms
1e+05 cpp11 14.89ms
2e+05 cpp11 35.62ms
1e+02 rcpp 6.7ms
1e+03 rcpp 1.62ms
1e+04 rcpp 340.38ms
1e+05 rcpp 24.79s
2e+05 rcpp 1.81m

Growing vectors

One major difference in Rcpp and cpp11 is how vectors are grown. Rcpp vectors have a push_back() method, but unlike std::vector() no additional space is reserved when pushing. This makes calling push_back() repeatably very expensive, as the entire vector has to be copied each call.

In contrast cpp11 vectors grow efficiently, reserving extra space. Because of this you can do ~10,000,000 vector appends with cpp11 in approximately the same amount of time that Rcpp does 10,000, as this benchmark demonstrates.

grid <- expand.grid(len = 10 ^ (0:7), pkg = "cpp11", stringsAsFactors = FALSE)
grid <- rbind(
  grid,
  expand.grid(len = 10 ^ (0:4), pkg = "rcpp", stringsAsFactors = FALSE)
)
b_grow <- bench::press(.grid = grid,
  {
    fun = match.fun(sprintf("%sgrow_", ifelse(pkg == "cpp11", "", paste0(pkg, "_"))))
    bench::mark(
      fun(len)
    )
  }
)[c("len", "pkg", "min", "mem_alloc", "n_itr", "n_gc")]
saveRDS(b_grow, "growth.Rds", version = 2)

len pkg min mem_alloc n_itr n_gc
1e+00 cpp11 3.3µs 0B 10000 0
1e+01 cpp11 6.05µs 0B 9999 1
1e+02 cpp11 8.49µs 1.89KB 10000 0
1e+03 cpp11 14.18µs 16.03KB 9999 1
1e+04 cpp11 63.77µs 256.22KB 3477 2
1e+05 cpp11 443.32µs 2MB 404 5
1e+06 cpp11 3.99ms 16MB 70 3
1e+07 cpp11 105.51ms 256MB 1 5
1e+00 rcpp 2.64µs 0B 10000 0
1e+01 rcpp 3.13µs 0B 9999 1
1e+02 rcpp 13.87µs 42.33KB 9997 3
1e+03 rcpp 440.77µs 3.86MB 319 1
1e+04 rcpp 54.13ms 381.96MB 2 2

Conclusion

Rcpp has been and will continue to be widely successful. cpp11 is a alternative implementation of C++ bindings to R that chooses different design trade-offs and features. Both packages can co-exist (even be used in the same package!) and continue to enrich the R community.