Static linking does not really solve all problems in the Boost case as there are global variables that are picked up across different Boost versions. Thus if you link it statically it still has an effect on other dependencies. In the case of boost, you can get around this by using the bcp tool http://www.boost.org/doc/libs/1_66_0/tools/bcp/doc/html/index.html to rename your local boost to a different namespace to avoid collisions. We will also do thus in future with our wheel in Arrow, too. But we will not do this for conda-packages as there the assumption is that all artefacts will be linked against the same Boost version.
Uwe > Am 17.02.2018 um 16:55 schrieb Alex Samuel <a...@alexsamuel.net>: > > Yes, we do link our internal code with a different Boost version; we build > and (conda) package it ourselves. > > Why should this matter if Parquet links it statically? If static linking > won't allow us to use our own version, why bother linking statically at all? > > Thanks, > Alex > > > >> On 02/17/2018 10:52 AM, Uwe L. Korn wrote: >> Hi, >> The issue is here no that Boost is linked statically in one and dynamically >> in another but that you link against two different boost versions. The >> stacktrace shows links to Boost 1.55 whereas Arrow should be linked against >> 1.65 or 1.66 (the one coming from conda-forge). Arrow requires at least a >> Boost version of 1.60+ to work. The most likely guess from my side would be >> that your internal modules are linked against the system boost, not the >> conda-provided boost. >> Uwe >>> Am 17.02.2018 um 16:47 schrieb Alex Samuel <a...@alexsamuel.net>: >>> >>> Hi there, >>> >>> Sure, I'll append top of the stack. You can see our internal function >>> "asd::infra::util::start_of_date"; everything else is Boost, Python, or >>> libstdc++. >>> >>> My understanding (though I haven't demonstrated this conclusively) is that, >>> because Python loads extension modules RTLD_GLOBAL, an extension module can >>> pick up symbols from another or its dependencies, even if the former >>> "usually" satisfy relocations from their own shared lib dependencies. So, >>> one module linking Boost statically may interfere with another that links >>> it dynamically, by injecting its symbols. >>> >>> If necessary I can try to put together a minimal test case, but no >>> guarantee it will actually trigger the bug. But it might be worth testing >>> my theory above first, with gdb or by some other means. >>> >>> Thanks! >>> Alex >>> >>> >>> >>> #0 0x00007f6ddeba4c8b in std::basic_string<char, std::char_traits<char>, >>> std::allocator<char> >::basic_string(std::basic_string<char, >>> std::char_traits<char>, std::allocator<char> > const&) () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/numexpr/../../../libstdc++.so.6 >>> >>> #1 0x00007f6dd4f978d6 in >>> boost::re_detail::cpp_regex_traits_char_layer<char>::init() () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/../../../.././libboost_regex.so.1.55.0 >>> >>> #2 0x00007f6dd4fdbd88 in >>> boost::object_cache<boost::re_detail::cpp_regex_traits_base<char>, >>> boost::re_detail::cpp_regex_traits_implementation<char> >>> >::do_get(boost::re_detail::cpp_regex_traits_base<char> const&, unsigned >>> long) () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/../../../.././libboost_regex.so.1.55.0 >>> >>> #3 0x00007f6dd4fe5bb5 in boost::basic_regex<char, >>> boost::regex_traits<char, boost::cpp_regex_traits<char> > >::do_assign(char >>> const*, char const*, unsigned int) () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/../../../.././libboost_regex.so.1.55.0 >>> >>> #4 0x00007f6dd575a90e in >>> asd::infra::util::start_of_date(std::basic_string<char, >>> std::char_traits<char>, std::allocator<char> > const&, char const*) () >>> >>> at >>> /prod/sys/sysasd/opt/tudor-devtools/v1.3/Linux.el6.x86_64-corei7-avx-gcc4.83-anaconda2.0.1/include/boost/regex/v4/basic_regex.hpp:382 >>> >>> #5 0x00007f6dd2c36734 in >>> boost::python::objects::caller_py_function_impl<boost::python::detail::caller<unsigned >>> long (*)(std::basic_string<char, std::char_traits<char>, >>> std::allocator<char> > const&, char const*), >>> boost::python::default_call_policies, boost::mpl::vector3<unsigned long, >>> std::basic_string<char, std::char_traits<char>, std::allocator<char> > >>> const&, char const*> > >::operator()(_object*, _object*) () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/util.so >>> >>> #6 0x00007f6dd52ac71a in boost::python::objects::function::call(_object*, >>> _object*) const () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/../../../../libboost_python.so.1.55.0 >>> >>> #7 0x00007f6dd52aca68 in >>> boost::detail::function::void_function_ref_invoker0<boost::python::objects::(anonymous >>> namespace)::bind_return, >>> void>::invoke(boost::detail::function::function_buffer&) () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/../../../../libboost_python.so.1.55.0 >>> >>> #8 0x00007f6dd52b4cd3 in >>> boost::python::detail::exception_handler::operator()(boost::function0<void> >>> const&) const () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/../../../../libboost_python.so.1.55.0 >>> >>> #9 0x00007f6dd2c32c03 in >>> boost::detail::function::function_obj_invoker2<boost::_bi::bind_t<bool, >>> boost::python::detail::translate_exception<asd::infra::Exception, void >>> (*)(asd::infra::Exception const&)>, boost::_bi::list3<boost::arg<1>, >>> boost::arg<2>, boost::_bi::value<void (*)(asd::infra::Exception const&)> > >>> >, bool, boost::python::detail::exception_handler const&, >>> boost::function0<void> >>> const&>::invoke(boost::detail::function::function_buffer&, >>> boost::python::detail::exception_handler const&, boost::function0<void> >>> const&) () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/util.so >>> >>> #10 0x00007f6dd52b4a9d in >>> boost::python::handle_exception_impl(boost::function0<void>) () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/../../../../libboost_python.so.1.55.0 >>> >>> #11 0x00007f6dd52ab2b3 in function_call () >>> >>> from >>> /space/asd/conda/envs/rd-20180212-0/lib/python2.7/site-packages/asd/infra/../../../../libboost_python.so.1.55.0 >>> >>> #12 0x00007f6df2bc8e93 in PyObject_Call (func=0x2799850, arg=<value >>> optimized out>, >>> >>> kw=<value optimized out>) at Objects/abstract.c:2547 >>> >>> #13 0x00007f6df2c7b80d in do_call (f=<value optimized out>, >>> throwflag=<value optimized out>) >>> >>> at Python/ceval.c:4569 >>> >>> #14 call_function (f=<value optimized out>, throwflag=<value optimized >>> out>) at Python/ceval.c:4374 >>> >>> #15 PyEval_EvalFrameEx (f=<value optimized out>, throwflag=<value optimized >>> out>) at Python/ceval.c:2989 >>> >>> >>> >>>> On 02/17/2018 10:31 AM, Uwe L. Korn wrote: >>>> Hello, >>>> I am not sure why we are linking statically in the conda-forge packages, >>>> as a gut feeling we should link dynamically there. Wes, can you remember >>>> why? >>>> Alex, would it be possible for you to send us the part of the segmentation >>>> fault that is not private to your modules. That would be a good indicator >>>> for us what is going wrong. >>>> Typically it is best when you enable coredumps with `ulimit -c unlimited` >>>> and then run your program as usual. There should be no performance >>>> penalty. When ist segfaults, run `gdb python core` (note that the core >>>> file might also be postfixed with the PID but that depends on your >>>> system). In gdb type 'thread apply all bt full'. Post thd output pf that >>>> command and strip away the parts we should not see. Most relevant will be >>>> the stacktrace of the thread that segfaulted. >>>> Uwe >>>>> Am 16.02.2018 um 23:17 schrieb Alex Samuel <a...@alexsamuel.net>: >>>>> >>>>> Hello, >>>>> >>>>> I am having some troubles using the Continuum PyArrow conda package >>>>> dependencies in conjunction with internal C++ extension modules. >>>>> >>>>> Apparently, Arrow and Parquet link Boost statically. We have some >>>>> internal packages containing C++ code that linking Boost libs dynamicaly. >>>>> If we import Feather as well as our own extension modules into the same >>>>> Python process, we get random segfaults in Boost. I think what's >>>>> happening is that our extension modules are picking up Boost's symbols >>>>> from Arrow and Parquet already loaded into the process, rather than from >>>>> our own Boost shared libs. >>>>> >>>>> Could anyone explain the policy for linking Boost in binary >>>>> distributions, particularly conda packages? What is your expectation for >>>>> how other C++ extension modules should be built? >>>>> >>>>> Thanks in advance, >>>>> Alex >>>>>
