[jira] [Created] (ARROW-7397) [C++] Json white space length detection error
Yibo Cai created ARROW-7397: --- Summary: [C++] Json white space length detection error Key: ARROW-7397 URL: https://issues.apache.org/jira/browse/ARROW-7397 Project: Apache Arrow Issue Type: Bug Components: C++ Reporter: Yibo Cai Commit 21ca13a5cd [1] introduces a bug in ConsumeWhitespace() function. When all chars in a string are white spaces, it should return string length. But current code returns 0. It's not noticed because x86 goes rapidjson simd code path which is okay. Arm64 now goes the buggy code path and triggers json unit test failure. [1] https://github.com/apache/arrow/commit/21ca13a5cd9c1478d64370732fcfae72d52350dd#diff-664e724274fbe0ff1e03745aa452b4d6R48 -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Created] (ARROW-7398) [Packaging][Python] Conda builds are failing on macOS
Krisztian Szucs created ARROW-7398:
--
Summary: [Packaging][Python] Conda builds are failing on macOS
Key: ARROW-7398
URL: https://issues.apache.org/jira/browse/ARROW-7398
Project: Apache Arrow
Issue Type: Improvement
Components: Packaging, Python
Reporter: Krisztian Szucs
Failing build:
https://dev.azure.com/ursa-labs/crossbow/_build/results?buildId=3636
Numpy has an import error:
{code}
CMake Error at cmake_modules/FindNumPy.cmake:62 (message):
NumPy import failure:
Traceback (most recent call last):
File
"/usr/local/miniconda/conda-bld/arrow-cpp_1576319182634/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla/lib/python3.8/site-packages/numpy/core/__init__.py",
line 40, in
from . import multiarray
File
"/usr/local/miniconda/conda-bld/arrow-cpp_1576319182634/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla/lib/python3.8/site-packages/numpy/core/multiarray.py",
line 13, in
from . import overrides
File
"/usr/local/miniconda/conda-bld/arrow-cpp_1576319182634/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla/lib/python3.8/site-packages/numpy/core/overrides.py",
line 6, in
from numpy.core._multiarray_umath import (
ImportError:
dlopen(/usr/local/miniconda/conda-bld/arrow-cpp_1576319182634/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla/lib/python3.8/site-packages/numpy/core/_multiarray_umath.cpython-38-darwin.so,
2): Library not loaded: @rpath/libomp.dylib
Referenced from:
/usr/local/miniconda/conda-bld/arrow-cpp_1576319182634/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla/lib/libcblas.3.dylib
Reason: image not found
{code}
This is probably relating the conda-forge/openblas-feedstock#90.
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[jira] [Created] (ARROW-7399) gandiva does not pick runtime cpu features
Pindikura Ravindra created ARROW-7399: - Summary: gandiva does not pick runtime cpu features Key: ARROW-7399 URL: https://issues.apache.org/jira/browse/ARROW-7399 Project: Apache Arrow Issue Type: Task Components: C++ - Gandiva Reporter: Pindikura Ravindra Assignee: Pindikura Ravindra [~yibo] reported that the IR code generated by gandiva is using 128-bit registers even though the test machine has cpu with avx2 feature. I was able to reproduce the same on a gce host. -- This message was sent by Atlassian Jira (v8.3.4#803005)
Re: [Gandiva] How to optimize per CPU feature
On Mon, Dec 16, 2019 at 7:55 AM Yibo Cai wrote: > On 12/13/19 7:45 PM, Ravindra Pindikura wrote: > > On Fri, Dec 13, 2019 at 3:41 PM Yibo Cai wrote: > > > >> Hi, > >> > >> Thanks to pravindra's patch [1], Gandiva loop vectorization is okay now. > >> > >> Will Gandiva detects CPU feature at runtime? My test CPU supports sse to > >> avx2, but I only > >> see "target-features"="+fxsr,+mmx,+sse,+sse2,+x87" in IR, and final code > >> doesn't leverage > >> registers longer than 128. > >> > > > > Can you please give some details about the hardware/OS-version you are > > running this on ? Also, are you building the binaries and running them on > > the same host ? > > > > I'm building and running on same host. > > Build: cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo -DARROW_BUILD_TESTS=ON > -DARROW_GANDIVA=ON .. > > OS: ubuntu 18.04 > > CPU: lscpu outputs below > > Architecture:x86_64 > CPU op-mode(s): 32-bit, 64-bit > Byte Order: Little Endian > CPU(s): 8 > On-line CPU(s) list: 0-7 > Thread(s) per core: 2 > Core(s) per socket: 4 > Socket(s): 1 > NUMA node(s):1 > Vendor ID: GenuineIntel > CPU family: 6 > Model: 60 > Model name: Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz > Stepping:3 > CPU MHz: 3591.845 > CPU max MHz: 4000. > CPU min MHz: 800. > BogoMIPS:7183.72 > Virtualization: VT-x > L1d cache: 32K > L1i cache: 32K > L2 cache:256K > L3 cache:8192K > NUMA node0 CPU(s): 0-7 > Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge > mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall > nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl > xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl > vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic > movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm > cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi > flexpriority ept vpid fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid > xsaveopt dtherm ida arat pln pts md_clear flush_l1d > > Thanks ! I've a PR to fix this https://github.com/apache/arrow/pull/6038 > > > >> [1] https://github.com/apache/arrow/pull/6019 > >> > > > > > -- Thanks and regards, Ravindra.
[jira] [Created] (ARROW-7400) [Java] Avoids the worst case for quick sort
Liya Fan created ARROW-7400: --- Summary: [Java] Avoids the worst case for quick sort Key: ARROW-7400 URL: https://issues.apache.org/jira/browse/ARROW-7400 Project: Apache Arrow Issue Type: Improvement Components: Java Reporter: Liya Fan Assignee: Liya Fan This issue is in response of a discussion in: https://github.com/apache/arrow/pull/5540#discussion_r329487232. The quick sort algorithm can degenerate to an O(n^2) algorithm, if the pivot is selected poorly. This is an important problem, as the worst case can happen, if the input vector is alrady sorted, which is frequently encountered in practice. After some investigation, we solve the problem with a simple but effective approach: take 3 samples and choose the median (with at most 3 comparisons) as the pivot. This sorts the vector which is already sorted in O(nlogn) time. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Created] (ARROW-7401) [Dev] "archery lint" outputs unapplicable patch file
Antoine Pitrou created ARROW-7401:
-
Summary: [Dev] "archery lint" outputs unapplicable patch file
Key: ARROW-7401
URL: https://issues.apache.org/jira/browse/ARROW-7401
Project: Apache Arrow
Issue Type: Bug
Components: Developer Tools
Reporter: Antoine Pitrou
{code}
INFO:archery:Running r linter
/home/antoine/arrow/dev/r/src/py-to-r.cpp had clang-format style issues
--- /home/antoine/arrow/dev/r/src/py-to-r.cpp
+++ /home/antoine/arrow/dev/r/src/py-to-r.cpp (after clang format)
@@ -21,16 +21,15 @@
// [[arrow::export]]
std::shared_ptr ImportArray(uintptr_t array, uintptr_t schema) {
- return VALUE_OR_STOP(arrow::ImportArray(
- reinterpret_cast(array),
- reinterpret_cast(schema)));
+ return VALUE_OR_STOP(arrow::ImportArray(reinterpret_cast(array),
+ reinterpret_cast(schema)));
}
// [[arrow::export]]
std::shared_ptr ImportRecordBatch(uintptr_t array,
uintptr_t schema) {
- return VALUE_OR_STOP(arrow::ImportRecordBatch(
- reinterpret_cast(array),
- reinterpret_cast(schema)));
+ return VALUE_OR_STOP(
+ arrow::ImportRecordBatch(reinterpret_cast(array),
+ reinterpret_cast(schema)));
}
// [[arrow::export]]
@@ -56,23 +55,22 @@
// [[arrow::export]]
void ExportSchema(const std::shared_ptr& schema, uintptr_t ptr)
{
- STOP_IF_NOT_OK(arrow::ExportSchema(*schema, reinterpret_cast(ptr)));
+ STOP_IF_NOT_OK(
+ arrow::ExportSchema(*schema, reinterpret_cast(ptr)));
}
// [[arrow::export]]
void ExportArray(const std::shared_ptr& array, uintptr_t ptr,
uintptr_t schema_ptr) {
- STOP_IF_NOT_OK(arrow::ExportArray(*array,
-reinterpret_cast(ptr),
+ STOP_IF_NOT_OK(arrow::ExportArray(*array, reinterpret_cast(ptr),
reinterpret_cast(schema_ptr)));
}
// [[arrow::export]]
-void ExportRecordBatch(const std::shared_ptr& batch,
- uintptr_t ptr, uintptr_t schema_ptr) {
+void ExportRecordBatch(const std::shared_ptr& batch,
uintptr_t ptr,
+ uintptr_t schema_ptr) {
STOP_IF_NOT_OK(
- arrow::ExportRecordBatch(*batch,
- reinterpret_cast(ptr),
+ arrow::ExportRecordBatch(*batch, reinterpret_cast(ptr),
reinterpret_cast(schema_ptr)));
}
{code}
If I try to apply the patch file using {{patch -p0}}:
{code}
Ignoring potentially dangerous file name
/home/antoine/arrow/dev/r/src/py-to-r.cpp
can't find file to patch at input line 3
Perhaps you used the wrong -p or --strip option?
The text leading up to this was:
--
|--- /home/antoine/arrow/dev/r/src/py-to-r.cpp
|+++ /home/antoine/arrow/dev/r/src/py-to-r.cpp (after clang format)
--
{code}
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[NIGHTLY] Arrow Build Report for Job nightly-2019-12-16-0
Arrow Build Report for Job nightly-2019-12-16-0 All tasks: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0 Failed Tasks: - centos-7: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-centos-7 - conda-osx-clang-py27: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-osx-clang-py27 - conda-osx-clang-py36: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-osx-clang-py36 - conda-osx-clang-py37: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-osx-clang-py37 - macos-r-autobrew: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-travis-macos-r-autobrew - test-ubuntu-16.04-cpp: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-ubuntu-16.04-cpp Succeeded Tasks: - centos-6: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-centos-6 - centos-8: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-centos-8 - conda-linux-gcc-py27: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-linux-gcc-py27 - conda-linux-gcc-py36: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-linux-gcc-py36 - conda-linux-gcc-py37: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-linux-gcc-py37 - conda-win-vs2015-py36: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-win-vs2015-py36 - conda-win-vs2015-py37: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-win-vs2015-py37 - debian-buster: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-debian-buster - debian-stretch: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-debian-stretch - gandiva-jar-osx: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-travis-gandiva-jar-osx - gandiva-jar-trusty: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-travis-gandiva-jar-trusty - homebrew-cpp: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-travis-homebrew-cpp - test-conda-cpp: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-cpp - test-conda-python-2.7-pandas-latest: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-2.7-pandas-latest - test-conda-python-2.7: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-2.7 - test-conda-python-3.6: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.6 - test-conda-python-3.7-dask-latest: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-dask-latest - test-conda-python-3.7-hdfs-2.9.2: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-hdfs-2.9.2 - test-conda-python-3.7-pandas-latest: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-pandas-latest - test-conda-python-3.7-pandas-master: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-pandas-master - test-conda-python-3.7-spark-master: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-spark-master - test-conda-python-3.7-turbodbc-latest: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-turbodbc-latest - test-conda-python-3.7-turbodbc-master: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-turbodbc-master - test-conda-python-3.7: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7 - test-conda-python-3.8-dask-master: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.8-dask-master - test-conda-python-3.8-pandas-latest: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.8-pandas-latest - test-conda-r-3.6: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-r-3.6 - test-debian-10-cpp: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-debian-10-cpp -
Re: [NIGHTLY] Arrow Build Report for Job nightly-2019-12-16-0
The Ubuntu 16.04 failure seems legitimate, is there a JIRA issue? https://circleci.com/gh/ursa-labs/crossbow/6109 On Mon, Dec 16, 2019 at 7:47 AM Crossbow wrote: > > > Arrow Build Report for Job nightly-2019-12-16-0 > > All tasks: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0 > > Failed Tasks: > - centos-7: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-centos-7 > - conda-osx-clang-py27: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-osx-clang-py27 > - conda-osx-clang-py36: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-osx-clang-py36 > - conda-osx-clang-py37: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-osx-clang-py37 > - macos-r-autobrew: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-travis-macos-r-autobrew > - test-ubuntu-16.04-cpp: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-ubuntu-16.04-cpp > > Succeeded Tasks: > - centos-6: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-centos-6 > - centos-8: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-centos-8 > - conda-linux-gcc-py27: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-linux-gcc-py27 > - conda-linux-gcc-py36: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-linux-gcc-py36 > - conda-linux-gcc-py37: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-linux-gcc-py37 > - conda-win-vs2015-py36: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-win-vs2015-py36 > - conda-win-vs2015-py37: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-conda-win-vs2015-py37 > - debian-buster: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-debian-buster > - debian-stretch: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-azure-debian-stretch > - gandiva-jar-osx: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-travis-gandiva-jar-osx > - gandiva-jar-trusty: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-travis-gandiva-jar-trusty > - homebrew-cpp: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-travis-homebrew-cpp > - test-conda-cpp: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-cpp > - test-conda-python-2.7-pandas-latest: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-2.7-pandas-latest > - test-conda-python-2.7: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-2.7 > - test-conda-python-3.6: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.6 > - test-conda-python-3.7-dask-latest: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-dask-latest > - test-conda-python-3.7-hdfs-2.9.2: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-hdfs-2.9.2 > - test-conda-python-3.7-pandas-latest: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-pandas-latest > - test-conda-python-3.7-pandas-master: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-pandas-master > - test-conda-python-3.7-spark-master: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-spark-master > - test-conda-python-3.7-turbodbc-latest: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-turbodbc-latest > - test-conda-python-3.7-turbodbc-master: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7-turbodbc-master > - test-conda-python-3.7: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.7 > - test-conda-python-3.8-dask-master: > URL: > https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-16-0-circle-test-conda-python-3.8-dask-master > - test-conda-python-3.8-pandas-latest: > URL: > https://github.
[jira] [Created] (ARROW-7402) [C++] Add more information on CUDA error
Kouhei Sutou created ARROW-7402: --- Summary: [C++] Add more information on CUDA error Key: ARROW-7402 URL: https://issues.apache.org/jira/browse/ARROW-7402 Project: Apache Arrow Issue Type: Improvement Components: C++, GPU Reporter: Kouhei Sutou Assignee: Kouhei Sutou -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Created] (ARROW-7403) [C++][JSON] Enable Rapidjson on Arm64 Neon
Yibo Cai created ARROW-7403: --- Summary: [C++][JSON] Enable Rapidjson on Arm64 Neon Key: ARROW-7403 URL: https://issues.apache.org/jira/browse/ARROW-7403 Project: Apache Arrow Issue Type: Improvement Components: C++ Reporter: Yibo Cai Assignee: Yibo Cai Rapidjson support Arm64 Neon, but it's not enabled in Arrow now. We need to define macro RAPIDJSON_NEON to build Rapidjson with Neon support. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Created] (ARROW-7404) [C++][Gandiva] Fix utf8 char length error on Arm64
Yibo Cai created ARROW-7404: --- Summary: [C++][Gandiva] Fix utf8 char length error on Arm64 Key: ARROW-7404 URL: https://issues.apache.org/jira/browse/ARROW-7404 Project: Apache Arrow Issue Type: Bug Components: C++ - Gandiva Reporter: Yibo Cai Assignee: Yibo Cai Current code checks if a UTF-8 eight-bit code unit is within 0x00~0x7F by "if (c >= 0)", where c is defined as "char". This checking assumes char is always signed, which is not true[1]. On Arm64, char is unsigned by default and causes some Gandiva unit tests fail. Fix it by casting to "signed char" explicitly. [1] Cited from https://en.cppreference.com/w/cpp/language/types The signedness of char depends on the compiler and the target platform: the defaults for ARM and PowerPC are typically unsigned, the defaults for x86 and x64 are typically signed. -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Created] (ARROW-7405) [Java] ListVector isEmpty API is incorrect
Ji Liu created ARROW-7405:
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Summary: [Java] ListVector isEmpty API is incorrect
Key: ARROW-7405
URL: https://issues.apache.org/jira/browse/ARROW-7405
Project: Apache Arrow
Issue Type: Bug
Components: Java
Reporter: Ji Liu
Assignee: Ji Liu
Currently {{isEmpty}} API is always return false in
{{BaseRepeatedValueVector}}, and its subclass {{ListVector}} did not overwrite
this method.
This will lead to incorrect result, for example, a {{ListVector}} with data
[1,2], null, [], [5,6] should get [false, false, true, false] with this API,
but now it would return [false, false, false, false].
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[jira] [Created] (ARROW-7406) [Java] NonNullableStructVector#hashCode should pass hasher to child vectors
Ji Liu created ARROW-7406:
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Summary: [Java] NonNullableStructVector#hashCode should pass
hasher to child vectors
Key: ARROW-7406
URL: https://issues.apache.org/jira/browse/ARROW-7406
Project: Apache Arrow
Issue Type: Bug
Components: Java
Reporter: Ji Liu
Assignee: Ji Liu
This was introduced by ARROW-6866 making parameter hasher useless in
hashCode(int index, {{ArrowBufHasher}} hasher), and the child vectors would
calculate hashCode using default hasher which is not correct.
This issue should be fixed by passing hasher to child vector when calculating
hashCode.
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[jira] [Created] (ARROW-7407) [Python] Failed to install pyarrow 0.15.1 on Python 3.8
Pavol Plaskoň created ARROW-7407:
Summary: [Python] Failed to install pyarrow 0.15.1 on Python 3.8
Key: ARROW-7407
URL: https://issues.apache.org/jira/browse/ARROW-7407
Project: Apache Arrow
Issue Type: Bug
Components: Python
Affects Versions: 0.15.1
Environment: Arch Linux (5.4.2), Python 3.8.0
Reporter: Pavol Plaskoň
Attachments: pyarrow-pip.log
Hi, I cannot install pyarrow 0.15.1
Steps:
{noformat}
$ python3 -m venv virtualenv
$ source virtualenv/bin/activate
$ pip install numpy
$ virtualenv/bin/python3 -c 'import numpy as n; print(n.__version__);
print(n.get_include());'
1.17.4
/tmp/virtualenv/lib/python3.8/site-packages/numpy/core/include
$ pip install pyarrow==0.15.1{noformat}
Log: see attached log
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