[jira] [Closed] (ARROW-7584) [Python] Improve ergonomics of new FileSystem API

["Fabian Höring (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-7584?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Fabian Höring closed ARROW-7584.
--------------------------------
    Resolution: Duplicate

> [Python] Improve ergonomics of new FileSystem API
> -------------------------------------------------
>
>                 Key: ARROW-7584
>                 URL: https://issues.apache.org/jira/browse/ARROW-7584
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Python
>            Reporter: Fabian Höring
>            Priority: Major
>              Labels: FileSystem
>
> The [new Python FileSystem API |https://github.com/apache/arrow/blob/master/python/pyarrow/_fs.pyx#L185] is nice but seems to be very verbose to use.
> The documentation of the old FS API is [here|https://arrow.apache.org/docs/python/filesystems.html]
> h2. Here are some examples
> *Filesystem access:*
> Before:
> fs.ls()
> fs.mkdir()
> fs.rmdir()
> Now:
> fs.get_target_stats()
> fs.create_dir()
> fs.delete_dir()
> What is the advantage of having a longer method ? The short ones seem clear and are much easier to use. Seems like an easy change.  Also this is consistent with what is doing hdfs in the [fs api| https://arrow.apache.org/docs/python/filesystems.html] and works naturally with a local filesystem.
> *File opening:*
> Before:
> with fs.open(self, path, mode=u'rb', buffer_size=None)
> Now:
> fs.open_input_file()
> fs.open_input_stream()
> fs.open_output_stream()
> It seems more natural to fit to Python standard open function which works for local file access as well. Not sure if this is possible to do easily as there is `_wrap_output_stream` method.
> h2. Possible solutions
> - If the current Python API is still unused we could just rename the methods
> - We could keep everything as is and add some alias methods, it would make the FileSystem class a bit messy I think becasue there would be always 2 methods to do the work
> - Make everything compatible to FSSpec and reference the Spec, see https://issues.apache.org/jira/browse/ARROW-7102, 
>     I like the idea of a https://github.com/intake/filesystem_spec repo. Some comments on the proposed solutions there:
>     Make a fsspec wrapper for pyarrow.fs => seems strange to me, it would be having to wrap again a FileSystem that is not good enough in yet another repo
>     Make a pyarrow.fs wrapper for fsspec => if the wrapper becomes the documented "official" pyarow FileSystem it is fine I think, otherwise I would be yet another wrapper on top of the pyarrow "official" fs
> h2. Tensorflow RFC on FileSystems
> Tensorflow is also doing some standardization work on their FileSystem:
> https://github.com/tensorflow/community/blob/master/rfcs/20190506-filesystem-plugin-modular-tensorflow.md#python-considerations
> Not clear (to me) what they will do with Python file API though. it seems like they will also just wrap the C code back to [tf.Gfile|https://www.tensorflow.org/api_docs/python/tf/io/gfile/GFile]
> h2. Other considerations on FS ergonomics
> In the long run I would also like to enhance the FileSystem API and add more methods that use the basic ones to provide new features for example:
> - introduce put and get on top of the streams that directly upload/download files
> - introduce [touch|https://github.com/dask/hdfs3/blob/master/hdfs3/core.py#L601] from dask/hdfs3
> - introduce [du|https://github.com/dask/hdfs3/blob/master/hdfs3/core.py#L252] from dask/hdfs3
> - check if selector works with globs or add https://github.com/dask/hdfs3/blob/master/hdfs3/core.py#L349
> - be able to write strings to the file streams (instead of only bytes, already implemented by https://github.com/dask/hdfs3/blob/master/hdfs3/utils.py#L96), it would permit to directly use some Python API's like json.dump
> {code}
> with fs.open(path, "wb") as fd:
>   res = {"a": "bc"}
>   json.dump(res, fd)
> {code}
> instead of
> {code}
> with fs.open(path, "wb") as fd:
>   res = {"a": "bc"}
>   fd.write(json.dumps(res))
> {code}
> or like currently (with old API, which required encore each time, untested with new one)
> {code}with fs.open(path, "wb") as fd:
>   res = {"a": "bc"}
>   fd.write(json.dumps(res).encode())
> {code}
> - implementing [readline|https://github.com/dask/hdfs3/blob/master/hdfs3/core.py#L809], needed for:
>  {code}
> with hdfs.open("file", 'wb') as outfile:
>   pickle.dump({"a": "b"}, outfile)
> with hdfs.open("file", 'wb') as infile:
>   pickle.load(infile) 
> {code}
> - not clear how to make this also work when reading from files 



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