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https://issues.apache.org/jira/browse/ARROW-12358?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17475774#comment-17475774
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Weston Pace commented on ARROW-12358:
-------------------------------------
Thanks for checking in. I did some testing on this today. I might not be
understanding what you are after. I just tested with the following:
{code}
import shutil
import pyarrow as pa
import pyarrow.dataset as ds
# Make sure the /tmp/newdataset directory does not exist
shutil.rmtree('/tmp/newdataset', ignore_errors=True)
tab = pa.Table.from_pydict({ 'part': [0, 0, 1, 1], 'value': [0, 1, 2, 3] })
ds.write_dataset(tab,
'/tmp/newdataset',
partitioning_flavor='hive',
partitioning=['part'],
existing_data_behavior='delete_matching',
format='parquet')
{code}
I used the 6.0.1 release and did not run into any issues. Am I
misunderstanding the use case? Or is it possible you are using a certain
filesystem? Or maybe you are on a particular OS?
> [C++][Python][R][Dataset] Control overwriting vs appending when writing to
> existing dataset
> -------------------------------------------------------------------------------------------
>
> Key: ARROW-12358
> URL: https://issues.apache.org/jira/browse/ARROW-12358
> Project: Apache Arrow
> Issue Type: Improvement
> Components: C++
> Reporter: Joris Van den Bossche
> Priority: Major
> Labels: dataset
> Fix For: 8.0.0
>
>
> Currently, the dataset writing (eg with {{pyarrow.dataset.write_dataset}})
> uses a fixed filename template ({{"part\{i\}.ext"}}). This means that when
> you are writing to an existing dataset, you de facto overwrite previous data
> when using this default template.
> There is some discussion in ARROW-10695 about how the user can avoid this by
> ensuring the file names are unique (the user can specify the
> {{basename_template}} to be something unique). There is also ARROW-7706 about
> silently doubling data (so _not_ overwriting existing data) with the legacy
> {{parquet.write_to_dataset}} implementation.
> It could be good to have a "mode" when writing datasets that controls the
> different possible behaviours. And erroring when there is pre-existing data
> in the target directory is maybe the safest default, because both appending
> vs overwriting silently can be surprising behaviour depending on your
> expectations.
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