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https://issues.apache.org/jira/browse/ARROW-11390?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17272708#comment-17272708
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Lance Dacey commented on ARROW-11390:
-------------------------------------
That makes sense. I checked further and the base image I was using is this:
https://github.com/jupyter/docker-stacks/blob/master/pyspark-notebook/Dockerfile
Which pins pyarrow at 2.0:
{code:java}
RUN conda install --quiet --yes --satisfied-skip-solve \
'pyarrow=2.0.*' && \
{code}
I'll try again now that 3.0 is on conda-forge
> [Python] pyarrow 3.0 issues with turbodbc
> -----------------------------------------
>
> Key: ARROW-11390
> URL: https://issues.apache.org/jira/browse/ARROW-11390
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 3.0.0
> Environment: pyarrow 3.0.0
> fsspec 0.8.4
> adlfs v0.5.9
> pandas 1.2.1
> numpy 1.19.5
> turbodbc 4.1.1
> Reporter: Lance Dacey
> Priority: Major
> Labels: python, turbodbc
>
> This is more of a turbodbc issue I think, but perhaps someone here would have
> some idea of what changed to cause potential issues.
> {code:java}
> cursor = connection.cursor()
> cursor.execute("select top 10 * from dbo.tickets")
> table = cursor.fetchallarrow(){code}
> I am able to run table.num_rows and it will print out 10.
> If I run table.to_pandas() or table.schema or try to write the table to a
> dataset, my kernel dies with no explanation. I reverted back to pyarrow 2.0
> and the same code works again.
> [https://github.com/blue-yonder/turbodbc/issues/289]
>
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