arrow/docs/source/python/orc.rst

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.. currentmodule:: pyarrow
.. _orc:

Reading and Writing the Apache ORC Format
=============================================

The `Apache ORC <http://orc.apache.org/>`_ project provides a
standardized open-source columnar storage format for use in data analysis
systems. It was created originally for use in `Apache Hadoop
<http://hadoop.apache.org/>`_ with systems like `Apache Drill
<http://drill.apache.org>`_, `Apache Hive <http://hive.apache.org>`_, `Apache
Impala <http://impala.apache.org>`_, and `Apache Spark
<http://spark.apache.org>`_ adopting it as a shared standard for high
performance data IO.

Apache Arrow is an ideal in-memory representation layer for data that is being read
or written with ORC files.

Obtaining pyarrow with ORC Support
--------------------------------------

If you installed ``pyarrow`` with pip or conda, it should be built with ORC
support bundled:

.. code-block:: python

   >>> from pyarrow import orc

If you are building ``pyarrow`` from source, you must use
``-DARROW_ORC=ON`` when compiling the C++ libraries and enable the ORC
extensions when building ``pyarrow``. See the :ref:`Python Development
<python-development>` page for more details.

Reading and Writing Single Files
--------------------------------

The functions :func:`~.orc.read_table` and :func:`~.orc.write_table`
read and write the :ref:`pyarrow.Table <data.table>` object, respectively.

Let's look at a simple table:

.. code-block:: python

   >>> import numpy as np
   >>> import pyarrow as pa

   >>> table = pa.table(
   ...     {
   ...         'one': [-1, np.nan, 2.5],
   ...         'two': ['foo', 'bar', 'baz'],
   ...         'three': [True, False, True]
   ...     }
   ... )

We write this to ORC format with ``write_table``:

.. code-block:: python

   >>> from pyarrow import orc
   >>> orc.write_table(table, 'example.orc')

This creates a single ORC file. In practice, an ORC dataset may consist
of many files in many directories. We can read a single file back with
``read_table``:

.. code-block:: python

   >>> table2 = orc.read_table('example.orc')

You can pass a subset of columns to read, which can be much faster than reading
the whole file (due to the columnar layout):

.. code-block:: python

   >>> orc.read_table('example.orc', columns=['one', 'three'])
   pyarrow.Table
   one: double
   three: bool
   ----
   one: [[-1,nan,2.5]]
   three: [[true,false,true]]

We need not use a string to specify the origin of the file. It can be any of:

* A file path as a string
* A Python file object
* A pathlib.Path object
* A :ref:`NativeFile <io.native_file>` from PyArrow

In general, a Python file object will have the worst read performance, while a
string file path or an instance of :class:`~.NativeFile` (especially memory
maps) will perform the best.

We can also read partitioned datasets with multiple ORC files through the
:mod:`pyarrow.dataset <dataset>` interface.

.. seealso::
   :ref:`Documentation for datasets <dataset>`.

ORC file writing options
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

:func:`~pyarrow.orc.write_table()` has a number of options to
control various settings when writing an ORC file.

* ``file_version``, the ORC format version to use.  ``'0.11'`` ensures
  compatibility with older readers, while ``'0.12'`` is the newer one.
* ``stripe_size``, to control the approximate size of data within a column
  stripe. This currently defaults to 64MB.

See the :func:`~pyarrow.orc.write_table()` docstring for more details.

Finer-grained Reading and Writing
---------------------------------

``read_table`` uses the :class:`~.ORCFile` class, which has other features:

.. code-block:: python

   >>> orc_file = orc.ORCFile('example.orc')
   >>> orc_file.metadata
   <BLANKLINE>
   -- metadata --
   >>> orc_file.schema
   one: double
   two: string
   three: bool
   >>> orc_file.nrows
   3

See the :class:`~pyarrow.orc.ORCFile` docstring for more details.

As you can learn more in the `Apache ORC format
<https://orc.apache.org/specification/>`_, an ORC file consists of
multiple stripes. ``read_table`` will read all of the stripes and
concatenate them into a single table. You can read individual stripes with
``read_stripe``:

.. code-block:: python

   >>> orc_file.nstripes
   1
   >>> orc_file.read_stripe(0)
   pyarrow.RecordBatch
   one: double
   two: string
   three: bool
   ----
   one: [-1,nan,2.5]
   two: ["foo","bar","baz"]
   three: [true,false,true]

We can write an ORC file using ``ORCWriter``:

.. code-block:: python

   >>> with orc.ORCWriter('example2.orc') as writer:
   ...     writer.write(table)

Compression
---------------------------------------------

The data pages within a column in a row group can be compressed after the
encoding passes (dictionary, RLE encoding). In PyArrow we don't use compression
by default, but Snappy, ZSTD, Zlib, and LZ4 are also supported:

.. code-block:: python

   >>> orc.write_table(table, 'example.orc', compression='uncompressed')
   >>> orc.write_table(table, 'example.orc', compression='zlib')
   >>> orc.write_table(table, 'example.orc', compression='zstd')
   >>> orc.write_table(table, 'example.orc', compression='snappy')

Snappy generally results in better performance, while Zlib may yield smaller
files.

Reading from cloud storage
--------------------------

In addition to local files, pyarrow supports other filesystems, such as cloud
filesystems, through the ``filesystem`` keyword:

.. code-block:: python

   >>> from pyarrow import fs

   >>> s3  = fs.S3FileSystem(region="us-east-2")  # doctest: +SKIP
   >>> table = orc.read_table("bucket/object/key/prefix", filesystem=s3)  # doctest: +SKIP

.. seealso::
   :ref:`Documentation for filesystems <filesystem>`.