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https://issues.apache.org/jira/browse/ARROW-12358?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17346110#comment-17346110
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Lance Dacey commented on ARROW-12358:
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Being able to update and replace specific rows would be very powerful. For my
use case, I am basically overwriting the entire partition in order to update a
(sometimes tiny) subset of rows. That means that I need to read the existing
data for that partition which was saved previously, and the new data with
updated or new rows. Then I need to sort and drop duplicates (I use pandas
because there is no simple .drop_duplicates() for a pyarrow table, but adding a
step with pandas can add some complication sometimes with data types), then I
need to overwrite the partition (I use the partition_filename_cb to guarantee
that the final file for the partition is the same).
> [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: 5.0.0
>
>
> 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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