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Posted to jira@arrow.apache.org by "Alessandro Molina (Jira)" <ji...@apache.org> on 2022/01/04 14:05:00 UTC
[jira] [Updated] (ARROW-12358) [C++][Python][R][Dataset] Control overwriting vs appending when writing to existing dataset
[ https://issues.apache.org/jira/browse/ARROW-12358?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Alessandro Molina updated ARROW-12358:
--------------------------------------
Fix Version/s: 8.0.0
(was: 7.0.0)
> [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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