tl;dr: we need better C++ tools and documentation. We collectively know more now with the rise of tools like rchk and improved documentation such as Tomas?s post. That?s a start, but it appears that there still is a lot of knowledge that would deserve to be promoted to actual documentation of best practices. I think it is important to not equate C++ as a language, and Rcpp. Also, C++ is not just RAII. RAII is an important part of how Rcpp was conceived for sure, but it?s not the only thing C++ can bring as a language. Templates, lambdas, the stl are examples of things that can be used for expressiveness when just accessing data without interfering with R, calling R api functions ... It would be nice that the usual ? you should do that only if you know what you?re doing ? be transformed to precise documentation, and maybe become part of some better tool. If precautions have to be taken before calling such and such functions: that?s ok. What are they ? Can we embed that in some tool. It is easy enough to enscope code that uses potentially jumpy code into a c++ lambda. This could be together with recommendations such as the body of the lambda shall only use POC data structures. This is similar to precautions you?d take when writing concurrent code. Romain> Le 30 mars 2019 ? 00:58, Simon Urbanek <simon.urbanek at r-project.org> a ?crit : > > Kevin, > > >> On Mar 29, 2019, at 17:01, Kevin Ushey <kevinushey at gmail.com> wrote: >> >> I think it's also worth saying that some of these issues affect C code >> as well; e.g. this is not safe: >> >> FILE* f = fopen(...); >> Rf_eval(...); >> fclose(f); > > I fully agree, but developers using C are well aware of the necessity of handling lifespan of objects explicitly, so at least there are no surprises. > > >> whereas the C++ equivalent would likely handle closing of the file in the destructor. In other words, I think many users just may not be cognizant of the fact that most R APIs can longjmp, and what that implies for cleanup of allocated resources. R_alloc() may help solve the issue specifically for memory allocations, but for any library interface that has a 'open' and 'close' step, the same sort of issue will arise. > > Well, I hope that anyone writing native code in package is well aware of that and will use an external pointer with finalizer to clean up native objects in any 3rd party library that are created during the call. > > >> What I believe we should do, and what Rcpp has made steps towards, is make it possible to interact with some subset of the R API safely from C++ contexts. This has always been possible with e.g. R_ToplevelExec() and R_ExecWithCleanup(), and now things are even better with R_UnwindProtect(). In theory, as a prototype, an R package could provide a 'safe' C++ interface to the R API using R_UnwindProtect() and friends as appropriate, and client packages could import and link to that package to gain access to the interface. Code generators (as Rcpp Attributes does) can handle some of the pain in these interfaces, so that users are mostly insulated from the nitty gritty details. > > I agree that we should strive to provide tools that make it safer, but note that it still requires participation of the users - they have to use such facilities or else they hit the same problem. So we can only fix this for the future, but let's start now. > > >> I agree that the content of Tomas's post is very helpful, especially since I expect many R programmers who dip their toes into the C++ world are not aware of the caveats of talking to R from C++. However, I don't think it's helpful to recommend "don't use C++"; rather, I believe the question should be, "what can we do to make it possible to easily and safely interact with R from C++?". Because, as I understand it, all of the problems raised are solvable: either through a well-defined C++ interface, or through better education. > > I think the recommendation would be different if such tools existed, but they don't. It was based on the current reality which is not so rosy. Apparently the post had its effect of mobilizing C++ proponents to do something about it, which is great, because if this leads to some solution, the recommendation in the future may change to "use C++ using tools XYZ". > > >> I'll add my own opinion: writing correct C code is an incredibly difficult task. C++, while obviously not perfect, makes things substantially easier with tools like RAII, the STL, smart pointers, and so on. And I strongly believe that C++ (with Rcpp) is still a better choice than C for new users who want to interface with R from compiled code. > > My take is that Rcpp makes the interface *look* easier, but you still have to understand more about the R API that you think. Hence it much easier to write buggy code. Personally, that's why I don't like it (apart from the code bloat), because things are hidden that will get you into trouble, whereas using the C API is at least very clear - you have to understand what it's doing when you use it. That said, I'm obviously biased since I know a lot about R internals ;) so this doesn't necessarily generalize. > > >> tl;dr: I (and I think most others) just wish the summary had a more positive outlook for the future of C++ with R. > > Well, unless someone actually takes the initiative there is no reason to believe in a bright future of C++. As we have seen with the lack of adoption of CXXR (which I thought was an incredible achievement), not enough people seem to really care about C++. If that is not true, then let's come out of hiding, get together and address it (it seems that this thread is a good start). > > Cheers, > Simon > > > >> Best, >> Kevin >> >> On Fri, Mar 29, 2019 at 10:16 AM Simon Urbanek >> <simon.urbanek at r-project.org> wrote: >>> >>> Jim, >>> >>> I think the main point of Tomas' post was to alert R users to the fact that there are very serious issues that you have to understand when interfacing R from C++. Using C++ code from R is fine, in many cases you only want to access R data, use some library or compute in C++ and return results. Such use-cases are completely fine in C++ as they don't need to trigger the issues mentioned and it should be made clear that it was not what Tomas' blog was about. >>> >>> I agree with Tomas that it is safer to give an advice to not use C++ to call R API since C++ may give a false impression that you don't need to know what you're doing. Note that it is possible to avoid longjmps by using R_ExecWithCleanup() which can catch any longjmps from the called function. So if you know what you're doing you can make things work. I think the issue here is not necessarily lack of tools, it is lack of knowledge - which is why I think Tomas' post is so important. >>> >>> Cheers, >>> Simon >>> >>> >>>> On Mar 29, 2019, at 11:19 AM, Jim Hester <james.f.hester at gmail.com> wrote: >>>> >>>> First, thank you to Tomas for writing his recent post[0] on the R >>>> developer blog. It raised important issues in interfacing R's C API >>>> and C++ code. >>>> >>>> However I do _not_ think the conclusion reached in the post is helpful >>>>> don?t use C++ to interface with R >>>> >>>> There are now more than 1,600 packages on CRAN using C++, the time is >>>> long past when that type of warning is going to be useful to the R >>>> community. >>>> >>>> These same issues will also occur with any newer language (such as >>>> Rust or Julia[1]) which uses RAII to manage resources and tries to >>>> interface with R. It doesn't seem a productive way forward for R to >>>> say it can't interface with these languages without first doing >>>> expensive copies into an intermediate heap. >>>> >>>> The advice to avoid C++ is also antithetical to John Chambers vision >>>> of first S and R as a interface language (from Extending R [2]) >>>> >>>>> The *interface* principle has always been central to R and to S >>>> before. An interface to subroutines was _the_ way to extend the first >>>> version of S. Subroutine interfaces have continued to be central to R. >>>> >>>> The book also has extensive sections on both C++ (via Rcpp) and Julia, >>>> so clearly John thinks these are legitimate ways to extend R. >>>> >>>> So if 'don't use C++' is not realistic and the current R API does not >>>> allow safe use of C++ exceptions what are the alternatives? >>>> >>>> One thing we could do is look how this is handled in other languages >>>> written in C which also use longjmp for errors. >>>> >>>> Lua is one example, they provide an alternative interface; >>>> lua_pcall[3] and lua_cpcall[4] which wrap a normal lua call and return >>>> an error code rather long jumping. These interfaces can then be safely >>>> wrapped by RAII - exception based languages. >>>> >>>> This alternative error code interface is not just useful for C++, but >>>> also for resource cleanup in C, it is currently non-trivial to handle >>>> cleanup in all the possible cases a longjmp can occur (interrupts, >>>> warnings, custom conditions, timeouts any allocation etc.) even with R >>>> finalizers. >>>> >>>> It is past time for R to consider a non-jumpy C interface, so it can >>>> continue to be used as an effective interface to programming routines >>>> in the years to come. >>>> >>>> [0]: https://developer.r-project.org/Blog/public/2019/03/28/use-of-c---in-packages/ >>>> [1]: https://github.com/JuliaLang/julia/issues/28606 >>>> [2]: https://doi.org/10.1201/9781315381305 >>>> [3]: http://www.lua.org/manual/5.1/manual.html#lua_pcall >>>> [4]: http://www.lua.org/manual/5.1/manual.html#lua_cpcall >>>> >>>> ______________________________________________ >>>> R-devel at r-project.org mailing list >>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>> >>> ______________________________________________ >>> R-devel at r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-devel > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel
It's great to see the community mobilize to try to resolve this issue. Obviously C++ has become a big part of R extensions, so it would be nice to have clear guidelines and tools to be able to use C++ safely with the R API. Unfortunately doing this will probably require a fair bit of work. If R-core where to do this it would take away from other valuable improvements they could be making on R itself. Given there is already a supported and documented extension mechanism with access to the R API via C, I can see why R-core might be reluctant to divert resources from R development to add the same level of support for C++. Obviously it would be impossible to try to provide better documentation and/or mechanisms for C++ extensions without some R-core involvement, but it seems like much of the grunt work could be done by others. I unfortunately have no C++ experience so cannot help here, but hopefully there are others that have the experience and the recognition in the community to offer to help and have their offer accepted. Perhaps R-consortium can even fund, although given the level of expertise required here the funding may need to be meaningful. That seems like the natural step here. Someone with the qualifications to do so either volunteers or is funded to do this, and hopefully R-core agrees to provide input and final stamp of approval. The documentation is probably more straightforward, as tools will need more work from R-core to integrate. It is possible R-core may decline to do this, but absent someone actually offering to put in the hard work it's all theoretical. Respectfully, Brodie. On 3/30/19 3:59 AM, Romain Francois wrote:> tl;dr: we need better C++ tools and documentation. > > We collectively know more now with the rise of tools like rchk and improved documentation such as Tomas?s post. That?s a start, but it appears that there still is a lot of knowledge that would deserve to be promoted to actual documentation of best practices. > > I think it is important to not equate C++ as a language, and Rcpp. > > Also, C++ is not just RAII. > > RAII is an important part of how Rcpp was conceived for sure, but it?s not the only thing C++ can bring as a language. Templates, lambdas, the stl are examples of things that can be used for expressiveness when just accessing data without interfering with R, calling R api functions ... > > It would be nice that the usual ? you should do that only if you know what you?re doing ? be transformed to precise documentation, and maybe become part of some better tool. If precautions have to be taken before calling such and such functions: that?s ok. What are they ? Can we embed that in some tool. > > It is easy enough to enscope code that uses potentially jumpy code into a c++ lambda. This could be together with recommendations such as the body of the lambda shall only use POC data structures. > > This is similar to precautions you?d take when writing concurrent code. > > Romain > >> Le 30 mars 2019 ? 00:58, Simon Urbanek <simon.urbanek at r-project.org> a ?crit : >> >> Kevin, >> >> >>> On Mar 29, 2019, at 17:01, Kevin Ushey <kevinushey at gmail.com> wrote: >>> >>> I think it's also worth saying that some of these issues affect C code >>> as well; e.g. this is not safe: >>> >>> FILE* f = fopen(...); >>> Rf_eval(...); >>> fclose(f); >> >> I fully agree, but developers using C are well aware of the necessity of handling lifespan of objects explicitly, so at least there are no surprises. >> >> >>> whereas the C++ equivalent would likely handle closing of the file in the destructor. In other words, I think many users just may not be cognizant of the fact that most R APIs can longjmp, and what that implies for cleanup of allocated resources. R_alloc() may help solve the issue specifically for memory allocations, but for any library interface that has a 'open' and 'close' step, the same sort of issue will arise. >> >> Well, I hope that anyone writing native code in package is well aware of that and will use an external pointer with finalizer to clean up native objects in any 3rd party library that are created during the call. >> >> >>> What I believe we should do, and what Rcpp has made steps towards, is make it possible to interact with some subset of the R API safely from C++ contexts. This has always been possible with e.g. R_ToplevelExec() and R_ExecWithCleanup(), and now things are even better with R_UnwindProtect(). In theory, as a prototype, an R package could provide a 'safe' C++ interface to the R API using R_UnwindProtect() and friends as appropriate, and client packages could import and link to that package to gain access to the interface. Code generators (as Rcpp Attributes does) can handle some of the pain in these interfaces, so that users are mostly insulated from the nitty gritty details. >> >> I agree that we should strive to provide tools that make it safer, but note that it still requires participation of the users - they have to use such facilities or else they hit the same problem. So we can only fix this for the future, but let's start now. >> >> >>> I agree that the content of Tomas's post is very helpful, especially since I expect many R programmers who dip their toes into the C++ world are not aware of the caveats of talking to R from C++. However, I don't think it's helpful to recommend "don't use C++"; rather, I believe the question should be, "what can we do to make it possible to easily and safely interact with R from C++?". Because, as I understand it, all of the problems raised are solvable: either through a well-defined C++ interface, or through better education. >> >> I think the recommendation would be different if such tools existed, but they don't. It was based on the current reality which is not so rosy. Apparently the post had its effect of mobilizing C++ proponents to do something about it, which is great, because if this leads to some solution, the recommendation in the future may change to "use C++ using tools XYZ". >> >> >>> I'll add my own opinion: writing correct C code is an incredibly difficult task. C++, while obviously not perfect, makes things substantially easier with tools like RAII, the STL, smart pointers, and so on. And I strongly believe that C++ (with Rcpp) is still a better choice than C for new users who want to interface with R from compiled code. >> >> My take is that Rcpp makes the interface *look* easier, but you still have to understand more about the R API that you think. Hence it much easier to write buggy code. Personally, that's why I don't like it (apart from the code bloat), because things are hidden that will get you into trouble, whereas using the C API is at least very clear - you have to understand what it's doing when you use it. That said, I'm obviously biased since I know a lot about R internals ;) so this doesn't necessarily generalize. >> >> >>> tl;dr: I (and I think most others) just wish the summary had a more positive outlook for the future of C++ with R. >> >> Well, unless someone actually takes the initiative there is no reason to believe in a bright future of C++. As we have seen with the lack of adoption of CXXR (which I thought was an incredible achievement), not enough people seem to really care about C++. If that is not true, then let's come out of hiding, get together and address it (it seems that this thread is a good start). >> >> Cheers, >> Simon >> >> >> >>> Best, >>> Kevin >>> >>> On Fri, Mar 29, 2019 at 10:16 AM Simon Urbanek >>> <simon.urbanek at r-project.org> wrote: >>>> >>>> Jim, >>>> >>>> I think the main point of Tomas' post was to alert R users to the fact that there are very serious issues that you have to understand when interfacing R from C++. Using C++ code from R is fine, in many cases you only want to access R data, use some library or compute in C++ and return results. Such use-cases are completely fine in C++ as they don't need to trigger the issues mentioned and it should be made clear that it was not what Tomas' blog was about. >>>> >>>> I agree with Tomas that it is safer to give an advice to not use C++ to call R API since C++ may give a false impression that you don't need to know what you're doing. Note that it is possible to avoid longjmps by using R_ExecWithCleanup() which can catch any longjmps from the called function. So if you know what you're doing you can make things work. I think the issue here is not necessarily lack of tools, it is lack of knowledge - which is why I think Tomas' post is so important. >>>> >>>> Cheers, >>>> Simon >>>> >>>> >>>>> On Mar 29, 2019, at 11:19 AM, Jim Hester <james.f.hester at gmail.com> wrote: >>>>> >>>>> First, thank you to Tomas for writing his recent post[0] on the R >>>>> developer blog. It raised important issues in interfacing R's C API >>>>> and C++ code. >>>>> >>>>> However I do _not_ think the conclusion reached in the post is helpful >>>>>> don?t use C++ to interface with R >>>>> >>>>> There are now more than 1,600 packages on CRAN using C++, the time is >>>>> long past when that type of warning is going to be useful to the R >>>>> community. >>>>> >>>>> These same issues will also occur with any newer language (such as >>>>> Rust or Julia[1]) which uses RAII to manage resources and tries to >>>>> interface with R. It doesn't seem a productive way forward for R to >>>>> say it can't interface with these languages without first doing >>>>> expensive copies into an intermediate heap. >>>>> >>>>> The advice to avoid C++ is also antithetical to John Chambers vision >>>>> of first S and R as a interface language (from Extending R [2]) >>>>> >>>>>> The *interface* principle has always been central to R and to S >>>>> before. An interface to subroutines was _the_ way to extend the first >>>>> version of S. Subroutine interfaces have continued to be central to R. >>>>> >>>>> The book also has extensive sections on both C++ (via Rcpp) and Julia, >>>>> so clearly John thinks these are legitimate ways to extend R. >>>>> >>>>> So if 'don't use C++' is not realistic and the current R API does not >>>>> allow safe use of C++ exceptions what are the alternatives? >>>>> >>>>> One thing we could do is look how this is handled in other languages >>>>> written in C which also use longjmp for errors. >>>>> >>>>> Lua is one example, they provide an alternative interface; >>>>> lua_pcall[3] and lua_cpcall[4] which wrap a normal lua call and return >>>>> an error code rather long jumping. These interfaces can then be safely >>>>> wrapped by RAII - exception based languages. >>>>> >>>>> This alternative error code interface is not just useful for C++, but >>>>> also for resource cleanup in C, it is currently non-trivial to handle >>>>> cleanup in all the possible cases a longjmp can occur (interrupts, >>>>> warnings, custom conditions, timeouts any allocation etc.) even with R >>>>> finalizers. >>>>> >>>>> It is past time for R to consider a non-jumpy C interface, so it can >>>>> continue to be used as an effective interface to programming routines >>>>> in the years to come. >>>>> >>>>> [0]: https://developer.r-project.org/Blog/public/2019/03/28/use-of-c---in-packages/ >>>>> [1]: https://github.com/JuliaLang/julia/issues/28606 >>>>> [2]: https://doi.org/10.1201/9781315381305 >>>>> [3]: http://www.lua.org/manual/5.1/manual.html#lua_pcall >>>>> [4]: http://www.lua.org/manual/5.1/manual.html#lua_cpcall >>>>> >>>>> ______________________________________________ >>>>> R-devel at r-project.org mailing list >>>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>> >>>> ______________________________________________ >>>> R-devel at r-project.org mailing list >>>> https://stat.ethz.ch/mailman/listinfo/r-devel >> >> ______________________________________________ >> R-devel at r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel >
On 3/30/19 8:59 AM, Romain Francois wrote:> tl;dr: we need better C++ tools and documentation. > > We collectively know more now with the rise of tools like rchk and improved documentation such as Tomas?s post. That?s a start, but it appears that there still is a lot of knowledge that would deserve to be promoted to actual documentation of best practices.Well there is quite a bit of knowledge in Writing R Extensions and many problems could have been prevented had it been read more thoroughly by package developers. The problem that C++ runs some functions automatically (like destructors), should not be too hard to identify based on what WRE says about the need for protection against garbage collection. From my experience, one can learn most about R internals from debugging and reading source code - when debugging PROTECT errors and other memory errors/memory corruption, common problems caused by bugs in native C/C++ code - one needs to read and understand source code involved at all layers, one needs to understand the documentation covering code at different layers, and one has to think about these things, forming hypotheses, narrowing down to smaller examples, etc. My suggestion for package authors who write native code and want to learn more, and who want to be responsible (these kinds of bugs affect other packaged indirectly and can be woken up by inconsequential and correct code changes, even in R runtime): test and debug your code hard - look at UBSAN/ASAN/valgrind/rchk checks from CRAN and run these tools yourself if needed. Run with strict barrier checking and with gctorture. Write more tests to increase the coverage. Specifically now if you use C++ code, try to read all of your related code and check you do not have the problems I mentioned in my blog. Think of other related problems and if you find about them, tell others. Make sure you only use the API from Writing R Extensions (and R help system). If you really can't find anything wrong about your package, but still want to learn more, try to debug some bugs reported against R runtime or against your favorite packages you use (or their CRAN check reports from various tools). In addition to learning more about R internals, by spending much more time on debugging you may also get a different perspective on some of the things about C++ I pointed to. Finally, it would help us with the problem we have now - that many R packages in C++ have serious bugs. Tomas
Some of us are learning about development in R and use R in our work data analysis pipelines. What is the best way to identify packages that currently have these C++ problems? I would like to be able to help fix the bugs but more importantly not use these packages in critical work pipelines. Any C++ R package bug squashing events out there? Regards Hugh On Mon, Apr 1, 2019 at 6:23 PM Tomas Kalibera <tomas.kalibera at gmail.com> wrote:> On 3/30/19 8:59 AM, Romain Francois wrote: > > tl;dr: we need better C++ tools and documentation. > > > > We collectively know more now with the rise of tools like rchk and > improved documentation such as Tomas?s post. That?s a start, but it appears > that there still is a lot of knowledge that would deserve to be promoted to > actual documentation of best practices. > Well there is quite a bit of knowledge in Writing R Extensions and many > problems could have been prevented had it been read more thoroughly by > package developers. The problem that C++ runs some functions > automatically (like destructors), should not be too hard to identify > based on what WRE says about the need for protection against garbage > collection. > > From my experience, one can learn most about R internals from debugging > and reading source code - when debugging PROTECT errors and other memory > errors/memory corruption, common problems caused by bugs in native C/C++ > code - one needs to read and understand source code involved at all > layers, one needs to understand the documentation covering code at > different layers, and one has to think about these things, forming > hypotheses, narrowing down to smaller examples, etc. > > My suggestion for package authors who write native code and want to > learn more, and who want to be responsible (these kinds of bugs affect > other packaged indirectly and can be woken up by inconsequential and > correct code changes, even in R runtime): test and debug your code hard > - look at UBSAN/ASAN/valgrind/rchk checks from CRAN and run these tools > yourself if needed. Run with strict barrier checking and with gctorture. > Write more tests to increase the coverage. Specifically now if you use > C++ code, try to read all of your related code and check you do not have > the problems I mentioned in my blog. Think of other related problems and > if you find about them, tell others. Make sure you only use the API from > Writing R Extensions (and R help system). If you really can't find > anything wrong about your package, but still want to learn more, try to > debug some bugs reported against R runtime or against your favorite > packages you use (or their CRAN check reports from various tools). In > addition to learning more about R internals, by spending much more time > on debugging you may also get a different perspective on some of the > things about C++ I pointed to. Finally, it would help us with the > problem we have now - that many R packages in C++ have serious bugs. > > Tomas > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel >[[alternative HTML version deleted]]