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https://issues.apache.org/jira/browse/ARROW-12358?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17403461#comment-17403461
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Weston Pace commented on ARROW-12358:
-------------------------------------
I've added some customization here in
https://github.com/apache/arrow/pull/10955 via "existing_data_behavior". This
will provide the options...
kError - Raise an error if there are any files or directories in `base_dir`
(the new default)
kOverwriteOrIgnore - Existing files will be ignored unless the filename is
one of those chosen by the dataset writer in which case they will be
overwritten (the old default)
kDeleteMatchingPartitions - This is similar to the dynamic partition
overwrite mode in parquet. The first time a directory is written to it will
delete any existing data.
This was based partially on discussion in ARROW-7706. I think
kDeleteMatchingPartitions might simplify your step 4 but I'm not entirely sure.
Feedback welcome!
> [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: 6.0.0
>
>
> 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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