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.. _filesystem:

.. currentmodule:: pyarrow.fs

Filesystem Interface
====================

PyArrow comes with an abstract filesystem interface, as well as concrete
implementations for various storage types.

The filesystem interface provides input and output streams as well as
directory operations.  A simplified view of the underlying data
storage is exposed.  Data paths are represented as *abstract paths*, which
are ``/``-separated, even on Windows, and shouldn't include special path
components such as ``.`` and ``..``.  Symbolic links, if supported by the
underlying storage, are automatically dereferenced.  Only basic
:class:`metadata <FileInfo>` about file entries, such as the file size
and modification time, is made available.

The core interface is represented by the base class :class:`FileSystem`.
Concrete subclasses are available for various kinds of storage, such as local
filesystem access (:class:`LocalFileSystem`), HDFS (:class:`HadoopFileSystem`)
and Amazon S3-compatible storage (:class:`S3FileSystem`).


Usage
-----

A FileSystem object can be created with one of the constuctors (and check the
respective constructor for its options)::

   >>> from pyarrow import fs
   >>> local = fs.LocalFileSystem()

or alternatively inferred from a URI::

   >>> s3, path = fs.FileSystem.from_uri("s3://my-bucket")
   >>> s3
   <pyarrow._s3fs.S3FileSystem at 0x7f6760cbf4f0>
   >>> path
   'my-bucket'


Reading and writing files
~~~~~~~~~~~~~~~~~~~~~~~~~

Several of the IO-related functions in PyArrow accept either a URI (and infer
the filesystem) or an explicit ``filesystem`` argument to specify the filesystem
to read or write from. For example, the :meth:`pyarrow.parquet.read_table`
function can be used in the following ways::

   # using a URI -> filesystem is inferred
   pq.read_table("s3://my-bucket/data.parquet")
   # using a path and filesystem
   s3 = fs.S3FileSystem(..)
   pq.read_table("my-bucket/data.parquet", filesystem=s3)

The filesystem interface further allows to open files for reading (input) or
writing (output) directly, which can be combined with functions that work with
file-like objects. For example::

   local = fs.LocalFileSystem()

   with local.open_output_stream("test.arrow") as file:
      with pa.RecordBatchFileWriter(file, table.schema) as writer:
         writer.write_table(table)


Listing files
~~~~~~~~~~~~~

Inspecting the directories and files on a filesystem can be done with the
:meth:`FileSystem.get_file_info` method. To list the contents of a directory,
use the :class:`FileSelector` object to specify the selection::

   >>> local.get_file_info(fs.FileSelector("dataset/", recursive=True))
   [<FileInfo for 'dataset/part=B': type=FileType.Directory>,
    <FileInfo for 'dataset/part=B/data0.parquet': type=FileType.File, size=1564>,
    <FileInfo for 'dataset/part=A': type=FileType.Directory>,
    <FileInfo for 'dataset/part=A/data0.parquet': type=FileType.File, size=1564>]

This returns a list of :class:`FileInfo` objects, containing information about
the type (file or directory), the size, the date last modified, etc.

You can also get this information for a single explicit path (or list of
paths)::

   >>> local.get_file_info('test.arrow')
   <FileInfo for 'test.arrow': type=FileType.File, size=3250>

   >>> local.get_file_info('non_existent')
   <FileInfo for 'non_existent': type=FileType.NotFound>

S3
--

The :class:`S3FileSystem` constructor has several options to configure the S3
connection (e.g. credentials, the region, an endpoint override, etc). In
addition, the constructor will also inspect configured S3 credentials as
supported by AWS (for example the ``AWS_ACCESS_KEY_ID`` and
``AWS_SECRET_ACCESS_KEY`` environment variables).

Example how you can read contents from a S3 bucket::

   >>> from pyarrow import fs
   >>> s3 = fs.S3FileSystem(region='eu-west-3')

   # List all contents in a bucket, recursively
   >>> s3.get_file_info(fs.FileSelector('my-test-bucket', recursive=True))
   [<FileInfo for 'my-test-bucket/File1': type=FileType.File, size=10>,
    <FileInfo for 'my-test-bucket/File5': type=FileType.File, size=10>,
    <FileInfo for 'my-test-bucket/Dir1': type=FileType.Directory>,
    <FileInfo for 'my-test-bucket/Dir2': type=FileType.Directory>,
    <FileInfo for 'my-test-bucket/EmptyDir': type=FileType.Directory>,
    <FileInfo for 'my-test-bucket/Dir1/File2': type=FileType.File, size=11>,
    <FileInfo for 'my-test-bucket/Dir1/Subdir': type=FileType.Directory>,
    <FileInfo for 'my-test-bucket/Dir2/Subdir': type=FileType.Directory>,
    <FileInfo for 'my-test-bucket/Dir2/Subdir/File3': type=FileType.File, size=10>]

   # Open a file for reading and download its contents
   >>> f = s3.open_input_stream('my-test-bucket/Dir1/File2')
   >>> f.readall()
   b'some data'

.. seealso::

   See the `AWS docs <https://docs.aws.amazon.com/sdk-for-cpp/v1/developer-guide/credentials.html>`__
   for the different ways to configure the AWS credentials.


Hadoop File System (HDFS)
-------------------------

PyArrow comes with bindings to the Hadoop File System (based on C++ bindings
using ``libhdfs``, a JNI-based interface to the Java Hadoop client). You connect
using the :class:`HadoopFileSystem` constructor::

.. code-block:: python

   from pyarrow import fs
   hdfs = fs.HadoopFileSystem(host, port, user=user, kerb_ticket=ticket_cache_path)

The ``libhdfs`` library is loaded **at runtime** (rather than at link / library
load time, since the library may not be in your LD_LIBRARY_PATH), and relies on
some environment variables.

* ``HADOOP_HOME``: the root of your installed Hadoop distribution. Often has
  `lib/native/libhdfs.so`.

* ``JAVA_HOME``: the location of your Java SDK installation.

* ``ARROW_LIBHDFS_DIR`` (optional): explicit location of ``libhdfs.so`` if it is
  installed somewhere other than ``$HADOOP_HOME/lib/native``.

* ``CLASSPATH``: must contain the Hadoop jars. You can set these using:

  .. code-block:: shell

      export CLASSPATH=`$HADOOP_HOME/bin/hdfs classpath --glob`

  If ``CLASSPATH`` is not set, then it will be set automatically if the
  ``hadoop`` executable is in your system path, or if ``HADOOP_HOME`` is set.


Using fsspec-compatible filesystems
-----------------------------------

The filesystems mentioned above are natively supported by Arrow C++ / PyArrow.
The Python ecosystem, however, also has several filesystem packages. Those
packages following the
`fsspec <https://filesystem-spec.readthedocs.io/en/latest/>`__ interface can be
used in PyArrow as well.

Functions accepting a filesystem object will also accept an fsspec subclass.
For example::

   # creating an fsspec-based filesystem object for Google Cloud Storage
   import gcsfs
   fs = gcsfs.GCSFileSystem(project='my-google-project')

   # using this to read a partitioned dataset
   import pyarrow.dataset as ds
   ds.dataset("data/", filesystem=fs)

Under the hood, the fsspec filesystem object is wrapped into a python-based
PyArrow filesystem (:class:`PyFileSystem`) using :class:`FSSpecHandler`.
You can also manually do this to get an object with the PyArrow FileSystem
interface::

   from pyarrow.fs import PyFileSystem, FSSpecHandler
   pa_fs = PyFileSystem(FSSpecHandler(fs))