[jira] [Updated] (ARROW-9938) [Python] Add filesystem capabilities to other IO formats (feather, csv, json, ..)?

["Joris Van den Bossche (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-9938?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joris Van den Bossche updated ARROW-9938:
-----------------------------------------
    Fix Version/s: 3.0.0

> [Python] Add filesystem capabilities to other IO formats (feather, csv, json, ..)?
> ----------------------------------------------------------------------------------
>
>                 Key: ARROW-9938
>                 URL: https://issues.apache.org/jira/browse/ARROW-9938
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Python
>            Reporter: Joris Van den Bossche
>            Priority: Major
>              Labels: filesystem
>             Fix For: 3.0.0
>
>
> In the parquet IO functions, we support reading/writing files from non-local filesystems directly (in addition to passing a buffer) by:
> - passing a URI (eg {{pq.read_parquet("s3://bucket/data.parquet")}})
> - specifying the filesystem keyword (eg {{pq.read_parquet("bucket/data.parquet", filesystem=S3FileSystem(...))}}) 
> On the other hand, for other file formats such as feather, we only support local files or buffers. So for those, you need to do the more manual (I _suppose_ this works?):
> {code:python}
> from pyarrow import fs, feather
> s3 = fs.S3FileSystem()
> with s3.open_input_file("bucket/data.arrow") as file:
>     table = feather.read_table(file)
> {code}
> So I think the question comes up: do we want to extend this filesystem support to other file formats (feather, csv, json) and make this more uniform across pyarrow, or do we prefer to keep the plain readers more low-level (and people can use the datasets API for more convenience)?
> cc [~apitrou] [~kszucs]



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