[jira] [Updated] (ARROW-12358) [C++][Python][R][Dataset] Control overwriting vs appending when writing to existing dataset

["Alessandro Molina (Jira)" <ji...@apache.org>]

     [ 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:     (was: 6.0.0)
                   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: 7.0.0
>
>
> 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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