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 metadata about file entries, such as the file size and modification time, is made available.

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

Usage

A FileSystem object can be created with one of the constructors (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 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 FileSystem.get_file_info() method. To list the contents of a directory, use the 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 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 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'

See also

See the AWS docs 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 HadoopFileSystem constructor:

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:

    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 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 (PyFileSystem) using 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))