[GitHub] [arrow] jorisvandenbossche commented on a change in pull request #11779: ARROW-13231: [Doc] Add ORC documentation

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jorisvandenbossche commented on a change in pull request #11779:
URL: https://github.com/apache/arrow/pull/11779#discussion_r832367282



##########
File path: docs/source/python/orc.rst
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+.. Licensed to the Apache Software Foundation (ASF) under one
+.. or more contributor license agreements.  See the NOTICE file
+.. distributed with this work for additional information
+.. regarding copyright ownership.  The ASF licenses this file
+.. to you under the Apache License, Version 2.0 (the
+.. "License"); you may not use this file except in compliance
+.. with the License.  You may obtain a copy of the License at
+
+..   http://www.apache.org/licenses/LICENSE-2.0
+
+.. Unless required by applicable law or agreed to in writing,
+.. software distributed under the License is distributed on an
+.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+.. KIND, either express or implied.  See the License for the
+.. specific language governing permissions and limitations
+.. under the License.
+
+.. 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 (incubating) <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:
+
+.. ipython:: python
+
+   import pyarrow.orc as po
+
+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:
+
+.. ipython:: python
+
+   import numpy as np
+   import pandas as pd
+   import pyarrow as pa
+
+   df = pd.DataFrame({'one': [-1, np.nan, 2.5],
+                      'two': ['foo', 'bar', 'baz'],
+                      'three': [True, False, True]},
+                      index=list('abc'))
+   table = pa.Table.from_pandas(df)
+
+We write this to ORC format with ``write_table``:
+
+.. ipython:: python
+
+   import pyarrow.orc as po
+   po.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``:
+
+.. ipython:: python
+
+   table2 = po.read_table('example.orc')
+   table2.to_pandas()
+
+You can pass a subset of columns to read, which can be much faster than reading
+the whole file (due to the columnar layout):
+
+.. ipython:: python
+
+   po.read_table('example.orc', columns=['one', 'three'])
+
+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 :ref:`NativeFile <io.native_file>` from PyArrow
+* A Python file object
+
+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.
+
+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.
+
+There are some additional data type handling-specific options
+described below.
+
+Omitting the DataFrame index
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When using ``pa.Table.from_pandas`` to convert to an Arrow table, by default
+one or more special columns are added to keep track of the index (row
+labels). Storing the index takes extra space, so if your index is not valuable,
+you may choose to omit it by passing ``preserve_index=False``
+
+.. ipython:: python
+
+   df = pd.DataFrame({'one': [-1, np.nan, 2.5],
+                      'two': ['foo', 'bar', 'baz'],
+                      'three': [True, False, True]},
+                      index=list('abc'))
+   df
+   table = pa.Table.from_pandas(df, preserve_index=False)
+
+Then we have:
+
+.. ipython:: python
+
+   po.write_table(table, 'example_noindex.orc')
+   t = po.read_table('example_noindex.orc')
+   t.to_pandas()
+
+Here you see the index did not survive the round trip.
+
+Finer-grained Reading and Writing
+---------------------------------
+
+``read_table`` uses the :class:`~.ORCFile` class, which has other features:
+
+.. ipython:: python
+
+   orc_file = po.ORCFile('example.orc')
+   orc_file.metadata
+   orc_file.schema
+   orc_file.nrows
+
+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``:
+
+.. ipython:: python
+
+   orc_file.nstripes
+   orc_file.read_stripe(0)
+
+We can write an ORC file using ``ORCWriter``:
+
+.. ipython:: python
+
+   with po.ORCWriter('example2.orc') as writer:
+      writer.write_table(table)

Review comment:
       It is this example that segfaults




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