Joris Van den Bossche created ARROW-6823:
Summary: [C++][Python][R] Support metadata in the feather format?
Key: ARROW-6823
URL: https://issues.apache.org/jira/browse/ARROW-6823
Project: Apache
Hi Lucas,
Do you have a small code example?
Trying the following worked in pyarrow 0.14, and still seems to work now:
In [1]: table = pa.table({'a': [1, 2, 3]})
In [2]: table
Out[2]:
pyarrow.Table
a: int64
In [3]: table.cast(pa.schema([('a', pa.int32())]))
Out[3]:
pyarrow.Table
a: int32
In [4
On Wed, Oct 9, 2019 at 12:11 PM Andy Grove wrote:
> I'm very interested in helping to find a solution to this because we really
> do need integration tests for Rust to make sure we're compatible with other
> implementations... there is also the ongoing CI dockerization work that I
> feel is relat
I'm very interested in helping to find a solution to this because we really
do need integration tests for Rust to make sure we're compatible with other
implementations... there is also the ongoing CI dockerization work that I
feel is related.
I haven't looked at the current integration tests yet a
Hi:
I'm developing rust version of reader which reads parquet into arrow array.
To verify the correct of this reader, I use the following approach:
1. Define schema with protobuf.
2. Generate json data of this schema using other language with more
sophisticated implementation (e.g. java
Kouhei Sutou created ARROW-6822:
---
Summary: [Website] merge_pr.py is published
Key: ARROW-6822
URL: https://issues.apache.org/jira/browse/ARROW-6822
Project: Apache Arrow
Issue Type: Improvement
So it seems in 'pyarrow==0.15.0' `Table.columns` now returns ChunkedArray
instead of Column. This has broken `Table.cast()` as it just calls
`Table.itercolumns` and expects the yielded values to have a `.cast()` method,
which ChunkedArray doesn't.
Was `Table.cast()` missed in cleaning up after
Thanks Micah, I've been following the discussion. I encourage others
to participate as well
On Mon, Oct 7, 2019 at 3:11 PM Micah Kornfield wrote:
>
> The proposal is in PR form at: [1]. I thought I'd mention it here in case
> people are interested but haven't seen it yet.
>
> [1] https://github.c
Wes McKinney created ARROW-6821:
---
Summary: [C++][Parquet] Do not require Thrift compiler when
building (but still require library)
Key: ARROW-6821
URL: https://issues.apache.org/jira/browse/ARROW-6821
P
On Tue, Oct 8, 2019 at 3:34 PM Wes McKinney wrote:
>
> hi Jacques,
>
> On Tue, Oct 8, 2019 at 1:54 PM Jacques Nadeau wrote:
> >
> > I removing all my objections to this work.
> >
> > I wish there was more feedback from additional community members. I
> > continue to be concerned about fragmentat
hi Jacques,
On Tue, Oct 8, 2019 at 1:54 PM Jacques Nadeau wrote:
>
> I removing all my objections to this work.
>
> I wish there was more feedback from additional community members. I continue
> to be concerned about fragmentation. I don't agree with the arguments here
> that we need to add a n
I'm not sure whether flatbuffers is actually an issue in the end but keeping it
out of the C-API definitely simplifies it a bit adoption-wise. I don't think
that though that using protobuf would make a difference here.
In general, I really like the C-interface work as sadly C-APIs are still the
I removing all my objections to this work.
I wish there was more feedback from additional community members. I
continue to be concerned about fragmentation. I don't agree with the
arguments here that we need to add a new api to make it easy for people to
*not* use Arrow codebase. It seems like a p
Antoine Pitrou created ARROW-6820:
-
Summary: [C++] [Doc] Map specification and implementation
inconsistent
Key: ARROW-6820
URL: https://issues.apache.org/jira/browse/ARROW-6820
Project: Apache Arrow
Ryan Patrick Kyle created ARROW-6819:
Summary: arrow::read_parquet ignores as_data_frame when sparklyr
package is attached
Key: ARROW-6819
URL: https://issues.apache.org/jira/browse/ARROW-6819
Pro
Antoine Pitrou created ARROW-6818:
-
Summary: [Doc] Format docs confusing
Key: ARROW-6818
URL: https://issues.apache.org/jira/browse/ARROW-6818
Project: Apache Arrow
Issue Type: Bug
Henri Gough created ARROW-6817:
--
Summary: dynamic_cast fails on Mac C++
Key: ARROW-6817
URL: https://issues.apache.org/jira/browse/ARROW-6817
Project: Apache Arrow
Issue Type: Bug
Com
Francois Saint-Jacques created ARROW-6816:
-
Summary: [Archery] Cleanup integration module to use companion
classes
Key: ARROW-6816
URL: https://issues.apache.org/jira/browse/ARROW-6816
Project
New draft
## Description:
The mission of Apache Arrow is the creation and maintenance of software related
to columnar in-memory processing and data interchange
## Issues:
* We are struggling with Continuous Integration scalability as the project has
definitely outgrown what Travis CI and Appve
Yes, I agree with raising the issue to the board.
On Tue, Oct 8, 2019 at 8:31 AM Antoine Pitrou wrote:
>
>
> I agree. Especially given that the constraints imposed by Infra don't
> help solving the problem.
>
> Regards
>
> Antoine.
>
>
> Le 08/10/2019 à 15:02, Uwe L. Korn a écrit :
> > I'm not s
I agree. Especially given that the constraints imposed by Infra don't
help solving the problem.
Regards
Antoine.
Le 08/10/2019 à 15:02, Uwe L. Korn a écrit :
> I'm not sure what qualifies for "board attention" but it seems that CI is a
> critical problem in Apache projects, not just Arrow.
I'm not sure what qualifies for "board attention" but it seems that CI is a
critical problem in Apache projects, not just Arrow. Should we raise that?
Uwe
On Tue, Oct 8, 2019, at 12:00 AM, Wes McKinney wrote:
> Here is a start for our Q3 board report
>
> ## Description:
> The mission of Apache
Mark Litwintschik created ARROW-6815:
Summary: Timestamps saved via Pandas and PyArrow unreadable in
Hive and Presto
Key: ARROW-6815
URL: https://issues.apache.org/jira/browse/ARROW-6815
Project:
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