Fabian Höring commented on ARROW-7584:

I agree for transactions. Something lightweight would be better.

> [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}
> - not clear how to make this also work when reading from files 

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