pitrou commented on a change in pull request #11779: URL: https://github.com/apache/arrow/pull/11779#discussion_r815842436
########## File path: docs/source/cpp/orc.rst ########## @@ -0,0 +1,126 @@ +.. 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. + +.. default-domain:: cpp +.. highlight:: cpp + +.. cpp:namespace:: arrow::adapters::orc + +============================= +Reading and Writing ORC files +============================= + +The `ORC format <https://orc.apache.org/docs/>`__ +is a space-efficient columnar storage format for complex data. +Arrow provides a fast ORC reader and a fast ORC writer allowing +ingestion of external data as Arrow tables. + +.. seealso:: + :ref:`ORC reader/writer API reference <cpp-api-orc>`. + +Supported ORC features +========================== + +The ORC format has many features, and we support a subset of them. Review comment: Also mention which datatypes are supported? ########## File path: docs/source/python/orc.rst ########## @@ -0,0 +1,261 @@ +.. 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 Review comment: The Python and C++ doc pages should probably give the same description of the ORC format, it seems a bit gratuitous to have two different ones. ########## File path: docs/source/python/orc.rst ########## @@ -0,0 +1,261 @@ +.. 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 transport 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) + +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, Gzip/Zlib, and LZ4 are also supported: + +.. code-block:: python + + po.write_table(table, where, compression='uncompressed') + po.write_table(table, where, compression='gzip') + po.write_table(table, where, compression='zstd') + po.write_table(table, where, compression='snappy') + +Snappy generally results in better performance, while Gzip may yield smaller +files. + +Partitioned Datasets (Multiple Files) +------------------------------------------------ + +Multiple ORC files constitute a ORC *dataset*. These may present in a +number of ways: + +* A list of ORC absolute file paths +* A directory name containing nested directories defining a partitioned dataset + +A dataset partitioned by year and month may look like on disk: + +.. code-block:: text + + dataset_name/ + year=2021/ + month=01/ + 0.orc + 1.orc + ... + month=02/ + 0.orc + 1.orc + ... + month=03/ + ... + year=2022/ + month=01/ + ... + ... + + +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") + table = po.read_table("bucket/object/key/prefix", filesystem=s3) + +Currently, :class:`HDFS <pyarrow.fs.HadoopFileSystem>` and Review comment: I think we could stop this section here and just redirect the user to the filesystem docs for details. ########## File path: docs/source/cpp/orc.rst ########## @@ -0,0 +1,126 @@ +.. 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. + +.. default-domain:: cpp +.. highlight:: cpp + +.. cpp:namespace:: arrow::adapters::orc + +============================= +Reading and Writing ORC files +============================= + +The `ORC format <https://orc.apache.org/docs/>`__ +is a space-efficient columnar storage format for complex data. +Arrow provides a fast ORC reader and a fast ORC writer allowing +ingestion of external data as Arrow tables. + +.. seealso:: + :ref:`ORC reader/writer API reference <cpp-api-orc>`. + +Supported ORC features +========================== + +The ORC format has many features, and we support a subset of them. + +Compression +----------- + ++-------------------+---------+ +| Compression codec | Notes | Review comment: No need for a notes column if there aren't any notes. ########## File path: docs/source/python/orc.rst ########## @@ -0,0 +1,261 @@ +.. 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 transport 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) + +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, Gzip/Zlib, and LZ4 are also supported: + +.. code-block:: python + + po.write_table(table, where, compression='uncompressed') + po.write_table(table, where, compression='gzip') + po.write_table(table, where, compression='zstd') + po.write_table(table, where, compression='snappy') + +Snappy generally results in better performance, while Gzip may yield smaller +files. + +Partitioned Datasets (Multiple Files) +------------------------------------------------ Review comment: This section doesn't showcase any example or API, is it useful to have it? ########## File path: docs/source/python/orc.rst ########## @@ -0,0 +1,261 @@ +.. 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 transport layer for data that is being read Review comment: Rather a representation layer if you're using ORC for transport or storage... ########## File path: docs/source/python/orc.rst ########## @@ -0,0 +1,261 @@ +.. 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 transport 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 Review comment: We would like to cut down on `ipython` code blocks actually, since they make building the docs slower and more fragile. AFAIU we would like to use [doctest](https://www.sphinx-doc.org/en/master/usage/extensions/doctest.html) instead: https://www.sphinx-doc.org/en/master/usage/extensions/doctest.html @jorisvandenbossche or @amol- may want to elaborate on this. ########## File path: docs/source/python/orc.rst ########## @@ -0,0 +1,261 @@ +.. 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 transport 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 Review comment: I get the impression you're copying and adapting large chunks of the Parquet docs. I'm not sure it makes sense to do this (also, I don't know why the Parquet docs talk about `preserve_index` specifically). -- 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]
