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Lance Dacey commented on ARROW-12358: ------------------------------------- kDeleteMatchingPartitions - So this only deletes the individual partitions and not the entire dataset correct? So if I save a dataset made up of hundreds of partitions but only 4 of them are written to, then only those 4 partitions will have their existing files cleared? If so, then yes that should work for me. > [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. -- This message was sent by Atlassian Jira (v8.3.4#803005)