Wes McKinney created ARROW-7394:
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Summary: [C++] Implement zero-copy optimizations when performing
Filter on ChunkedArray
Key: ARROW-7394
URL: https://issues.apache.org/jira/browse/ARROW-7394
Project: A
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 bu
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, 2
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 dep
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 need
Donatien Criaud created ARROW-7393:
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Summary: Fix plasma build for Kava
Key: ARROW-7393
URL: https://issues.apache.org/jira/browse/ARROW-7393
Project: Apache Arrow
Issue Type: Bug
Krisztian Szucs created ARROW-7392:
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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
Ben Kietzman created ARROW-7391:
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Summary: [Python] Remove unnecessary classes from the binding layer
Key: ARROW-7391
URL: https://issues.apache.org/jira/browse/ARROW-7391
Project: Apache Arrow
Francois Saint-Jacques created ARROW-7390:
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Summary: [C++][Dataset] Concurrency race in Projector::Project
Key: ARROW-7390
URL: https://issues.apache.org/jira/browse/ARROW-7390
Project: Apache
Krisztian Szucs created ARROW-7389:
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Summary: [Python][Packaging] Remove pyarrow.s3fs import check from
the recipe
Key: ARROW-7389
URL: https://issues.apache.org/jira/browse/ARROW-7389
Project: Apach
Krisztian Szucs created ARROW-7388:
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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
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:
URL:
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
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] https
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