iajoiner commented on a change in pull request #11779: URL: https://github.com/apache/arrow/pull/11779#discussion_r834815617
########## File path: docs/source/python/orc.rst ########## @@ -0,0 +1,180 @@ +.. 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 + + 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: + +.. ipython:: 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``: + +.. ipython:: 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``: + +.. ipython:: 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): + +.. ipython:: python + + orc.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. + Review comment: Thanks! Added! -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
