You can also see some of the Gandiva python bindings in the tests in pyarrow: https://github.com/apache/arrow/blob/master/python/pyarrow/tests/test_gandiva.py
On Thu, Oct 31, 2019 at 10:26 AM Wes McKinney <wesmck...@gmail.com> wrote: > hi > > On Thu, Oct 31, 2019 at 12:11 AM Yibo Cai <yibo....@arm.com> wrote: > > > > Hi, > > > > Arrow cpp integrates Gandiva to provide low level operations on arrow > buffers. [1][2] > > I have some questions, any help is appreciated: > > - Arrow cpp already has a compute kernel[3], does it duplicate what > Gandiva provides? I see a Jira talk about it.[4] > > No. There are some cases of functional overlap but we are servicing a > spectrum of use cases beyond the scope of Gandiva. Additionally, it is > unclear to me that an LLVM JIT compilation step should be required to > evaluate simple expressions such as "a > 5" -- in addition to > introducing latency (due to the compilation step) it is also a heavy > dependency to require the LLVM runtime in all applications. > > Personally I'm interested in supporting a wide gamut of analytics > workloads, from data frame / data science type libraries to SQL-like > systems. Gandiva is designed for the needs of a SQL-based execution > engine where chunks of data are fed into Projection or Filter nodes in > a computation graph -- Gandiva generates a specialized kernel to > perform a unit of work inside those nodes. Realistically, I expect > many real world applications will contain a mixture of pre-compiled > analytic kernels and JIT-compiled kernels. > > Rome wasn't built in a day, so I'm expecting several years of work > ahead of us at the present rate. We need more help in this domain. > > > - Is Gandiva only for arrow cpp? What about other languages(go, rust, > ...)? > > It's being used in Java via JNI. The same approach could be applied > for the other languages as they have their own C FFI mechanisms. > > > - Gandiva leverages SIMD for vectorized operations[1], but I didn't see > any related code. Am I missing something? > > My understanding is that LLVM inserts many SIMD instructions > automatically based on the host CPU architecture version. Gandiva > developers may have some comments / pointers about this > > > > > [1] > https://www.dremio.com/announcing-gandiva-initiative-for-apache-arrow/ > > [2] https://github.com/apache/arrow/tree/master/cpp/src/gandiva > > [3] https://github.com/apache/arrow/tree/master/cpp/src/arrow/compute > > [4] https://issues.apache.org/jira/browse/ARROW-7017 > > > > Thanks, > > Yibo >
