[jira] [Created] (ARROW-7394) [C++] Implement zero-copy optimizations when performing Filter on ChunkedArray

Wes McKinney created ARROW-7394: --- Summary: [C++] Implement zero-copy optimizations when performing Filter on ChunkedArray Key: ARROW-7394 URL: https://issues.apache.org/jira/browse/ARROW-7394 Project:

Re: Planned Support for ORC Dataset?

hi Jacques, I agree with you, it's worth distinguishing between ORC in two different forms: * The raw binary files * The "dataset format" that is maintained by the Hive libraries. For example, I don't think there is any practical way for us to handle this in C++ It may be that without the 2nd

Re: Planned Support for ORC Dataset?

To clarify, I don't really question the value. That was the wrong word. I question the benefit/value tradeoff. You've got two options it seems: - Support Orc without acid (solves a much smaller set of usecases/users) - Support Orc with acid (a magnitude more implementation work) On Fri, Dec 13,

Re: Planned Support for ORC Dataset?

I question the value of adding the Orc format. The format is fragmented with the main tool writing it (hive) writing a version of the format (acid v2) that can't be consumed by systems that only use the Orc libraries (since they don't support acid). If you want to consume that data, you have to

Re: [Discuss][FlightRPC] Extensions to Flight: "DoBidirectional"

I support moving forward with the current proposal. On Thu, Dec 12, 2019 at 12:20 PM David Li wrote: > Just following up here again, any other thoughts? > > I think we do have justifications for potentially separate streams in > a call, but that's more of an orthogonal question - it doesn't

[jira] [Created] (ARROW-7393) Fix plasma build for Kava

Donatien Criaud created ARROW-7393: -- Summary: Fix plasma build for Kava Key: ARROW-7393 URL: https://issues.apache.org/jira/browse/ARROW-7393 Project: Apache Arrow Issue Type: Bug

[jira] [Created] (ARROW-7392) [Packaging] Add conda packaging tasks for python 3.8

Krisztian Szucs created ARROW-7392: -- Summary: [Packaging] Add conda packaging tasks for python 3.8 Key: ARROW-7392 URL: https://issues.apache.org/jira/browse/ARROW-7392 Project: Apache Arrow

[jira] [Created] (ARROW-7391) [Python] Remove unnecessary classes from the binding layer

Ben Kietzman created ARROW-7391: --- Summary: [Python] Remove unnecessary classes from the binding layer Key: ARROW-7391 URL: https://issues.apache.org/jira/browse/ARROW-7391 Project: Apache Arrow

[jira] [Created] (ARROW-7390) [C++][Dataset] Concurrency race in Projector::Project

Francois Saint-Jacques created ARROW-7390: - Summary: [C++][Dataset] Concurrency race in Projector::Project Key: ARROW-7390 URL: https://issues.apache.org/jira/browse/ARROW-7390 Project:

[jira] [Created] (ARROW-7389) [Python][Packaging] Remove pyarrow.s3fs import check from the recipe

Krisztian Szucs created ARROW-7389: -- Summary: [Python][Packaging] Remove pyarrow.s3fs import check from the recipe Key: ARROW-7389 URL: https://issues.apache.org/jira/browse/ARROW-7389 Project:

[jira] [Created] (ARROW-7388) [Python] Skip HDFS tests if libhdfs cannot be located

Krisztian Szucs created ARROW-7388: -- Summary: [Python] Skip HDFS tests if libhdfs cannot be located Key: ARROW-7388 URL: https://issues.apache.org/jira/browse/ARROW-7388 Project: Apache Arrow

[NIGHTLY] Arrow Build Report for Job nightly-2019-12-13-0

Arrow Build Report for Job nightly-2019-12-13-0 All tasks: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-13-0 Failed Tasks: - centos-7: URL: https://github.com/ursa-labs/crossbow/branches/all?query=nightly-2019-12-13-0-azure-centos-7 - conda-linux-gcc-py27:

Re: [Gandiva] How to optimize per CPU feature

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

[Gandiva] How to optimize per CPU feature

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. [1]