I'd be open to this simplification of the data structure, but I'll see what others think
On Wed, Apr 21, 2021 at 9:26 AM Niranda Perera <niranda.per...@gmail.com> wrote: > > @Wes McKinney On a separate note, why would there be 2 pointers in the Buffer > class, const uint8_t* data_ & uint8_t* mutable_data_? Can't we have 1 > pointer and then use const_cast<> in mutable_data() method (or vise versa)? > > On Wed, Apr 21, 2021 at 10:09 AM Niranda Perera <niranda.per...@gmail.com> > wrote: >> >> Sorry, that's exactly what you've mentioned in the jira. :-) Please ignore! >> >> On Wed, Apr 21, 2021 at 10:07 AM Niranda Perera <niranda.per...@gmail.com> >> wrote: >>> >>> @Wes, @Antoine, >>> As @Weston pointed out, it seems like the issue is here. >>> https://github.com/apache/arrow/blob/37c27d1eaf0fa61281ad103c08a0251bb6883ec4/cpp/src/arrow/python/numpy_convert.cc#L51 >>> When the numpy buffer's is_mutable_ marked as true, ideally, *mutable_data_ >>> should have also been set IMO. >>> >>> On Wed, Apr 21, 2021 at 10:00 AM Wes McKinney <wesmck...@gmail.com> wrote: >>>> >>>> Definitely a bug. I just opened >>>> >>>> https://issues.apache.org/jira/browse/ARROW-12495 >>>> >>>> If there's a chance to get this into 4.0.0 this would be a nice one >>>> but I suspect the next RC is already under way (it need not block >>>> since this bug has been present a long time) >>>> >>>> On Wed, Apr 21, 2021 at 3:31 AM Antoine Pitrou <anto...@python.org> wrote: >>>> > >>>> > >>>> > It sounds like a bug if is_mutable_ is true but mutable_data_ is nullptr. >>>> > >>>> > Regards >>>> > >>>> > Antoine. >>>> > >>>> > >>>> > Le 21/04/2021 à 03:17, Weston Pace a écrit : >>>> > > If it comes from pandas (and is eligible for zero-copy) then the >>>> > > buffer implementation will be `NumPyBuffer`. Printing one in GDB >>>> > > yields... >>>> > > >>>> > > ``` >>>> > > $12 = {_vptr.Buffer = 0x7f0b66e147f8 <vtable for >>>> > > arrow::py::NumPyBuffer+16>, is_mutable_ = true, is_cpu_ = true, data_ >>>> > > = 0x55b71f901a70 "\001", mutable_data_ = 0x0, size_ = 16, capacity_ = >>>> > > 16, >>>> > > parent_ = {<std::__shared_ptr<arrow::Buffer, >>>> > > (__gnu_cxx::_Lock_policy)2>> = >>>> > > {<std::__shared_ptr_access<arrow::Buffer, (__gnu_cxx::_Lock_policy)2, >>>> > > false, false>> = {<No data fields>}, _M_ptr = 0x0, >>>> > > _M_refcount = {_M_pi = 0x0}}, <No data fields>}, >>>> > > memory_manager_ = {<std::__shared_ptr<arrow::MemoryManager, >>>> > > (__gnu_cxx::_Lock_policy)2>> = >>>> > > {<std::__shared_ptr_access<arrow::MemoryManager, >>>> > > (__gnu_cxx::_Lock_policy)2, false, false>> = {<No data fields>}, >>>> > > _M_ptr = 0x55b71fdca4e0, _M_refcount = {_M_pi = >>>> > > 0x55b71fb90640}}, <No data fields>}} >>>> > > ``` >>>> > > >>>> > > Notice that `is_cpu_` and `is_mutable_` are both `true`. It's maybe a >>>> > > bug that `is_mutable_` is true. Although maybe not as it appears to >>>> > > be telling whether the underlying numpy buffer itself is mutable or >>>> > > not... >>>> > > >>>> > > ``` >>>> > > if (PyArray_FLAGS(ndarray) & NPY_ARRAY_WRITEABLE) { >>>> > > is_mutable_ = true; >>>> > > } >>>> > > ``` >>>> > > >>>> > > >>>> > > On Tue, Apr 20, 2021 at 2:15 PM Niranda Perera >>>> > > <niranda.per...@gmail.com> wrote: >>>> > >> >>>> > >> Hi all, >>>> > >> >>>> > >> We have been using Arrow v2.0.0 and we encountered the following >>>> > >> issue. >>>> > >> >>>> > >> I was reading a table with numeric data using pandas.read_csv and then >>>> > >> converting it into pyarrow table. In our application (Cylon >>>> > >> <https://github.com/cylondata/cylon>), we are accessing this pyarrow >>>> > >> table >>>> > >> from c++. We want to access the mutable data of the arrays in the >>>> > >> pyarrow >>>> > >> table. >>>> > >> >>>> > >> But the following returns a nullptr. >>>> > >> T *mutable_data = array->data()->GetMutableValues<T>(1); // returns >>>> > >> nullptr >>>> > >> >>>> > >> Interestingly, >>>> > >> array->data()->buffers[1]->IsMutable(); // returns true >>>> > >> array->data()->buffers[1]->IsCpu(); // returns true >>>> > >> >>>> > >> This only happens when I use pandas df to create a pyarrow table. It >>>> > >> wouldn't happen when I use pyarrow.read_csv. So, I am guessing >>>> > >> there's some >>>> > >> issue in the buffer creation from pandas df. >>>> > >> >>>> > >> Is this an expected behavior? or has this been resolved in v2.0< >>>> > >> releases? >>>> > >> >>>> > >> Best >>>> > >> -- >>>> > >> Niranda Perera >>>> > >> https://niranda.dev/ >>>> > >> @n1r44 <https://twitter.com/N1R44> >>> >>> >>> >>> -- >>> Niranda Perera >>> https://niranda.dev/ >>> @n1r44 >>> >> >> >> -- >> Niranda Perera >> https://niranda.dev/ >> @n1r44 >> > > > -- > Niranda Perera > https://niranda.dev/ > @n1r44 >
