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[SPARK-32180][PYTHON][DOCS] Installation page of Getting Started in PySpark documentation #29640

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5 changes: 4 additions & 1 deletion python/docs/source/getting_started/index.rst
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Getting Started
===============

This page lists an overview of the basic steps required to setup & get started with PySpark.

.. toctree::
:maxdepth: 2

quickstart
installation
quickstart
119 changes: 119 additions & 0 deletions python/docs/source/getting_started/installation.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.

============
Installation
============

The official release channel is to download it from `the Apache Spark website <https://spark.apache.org/downloads.html>`_.
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"Official releases are available from ..."

Alternatively, you can also install it via pip from PyPI. PyPI installation is usually to use
standalone locally or as a client to connect to a cluster instead of setting a cluster up.

This page includes the instructions for installing PySpark by using pip, Conda, downloading manually, and building it from the source.

Python Version Supported
------------------------

Python 3.6 and above.

Using PyPI
----------

PySpark installation using `PyPI <https://pypi.org/project/pyspark/>`_

.. code-block:: bash

pip install pyspark

Using Conda
-----------

Conda is an open-source package management and environment management system which is a part of `Anaconda <https://docs.continuum.io/anaconda/>`_ distribution. It is both cross-platform and language agnostic.
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part of the


Conda can be used to create a virtual environment from terminal as shown below:

.. code-block:: bash

conda create -n pyspark_env

After the virtual environment is created, it should be visible under the list of conda environments which can be seen using the following command:

.. code-block:: bash

conda env list

The newly created environment can be accessed using the following command:

.. code-block:: bash

conda activate pyspark_env

In lower Conda version, the following command might be used:
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In earlier Conda versions ... should be used:
(earlier than what?)

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Hi @srowen,

Based on the conda documentation the answer is "Conda version before 4.4".
The Source for this info is

conda activate: The logic and mechanisms underlying environment activation have been reworked. With conda 4.4, conda activate and conda deactivate are now the preferred commands for activating and deactivating environments. You'll find they are much more snappy than the source activate and source deactivate commands from previous conda versions. The conda activate command also has advantages of (1) being universal across all OSes, shells, and platforms, and (2) not having path collisions with scripts from other packages like Python virtualenv's activate script.

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Right, I'm suggesting saying that here.


.. code-block:: bash

source activate pyspark_env

PySpark installation using ``pip`` under Conda environment is official.
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What does this mean?

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@rohitmishr1484 rohitmishr1484 Sep 5, 2020

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@srowen, The information this sentence conveys is -

  • Using Conda to install PySpark via conda-forge is not the official way to do it.
  • But If we create a Conda environment using conda and then install PySpark in the new environment then although it's an indirect way of installation but meets the official implementation guideline.

I am fine with removing this line if you and @HyukjinKwon think it's implicit and not required to be mentioned explicitly. Thanks.

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What do you mean 'official' here?

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@srowen,

As per my understanding "official" implies the most updated source used to setup PySpark. Based on this understanding- PyPI is considered official whereas Conda-forge is not. Please feel free to correct me if there is a caveat in my understanding. Thanks.

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Let's remove this line


PySpark can be installed in this newly created environment using PyPI as shown before:
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Why repeat this?

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@rohitmishr1484 rohitmishr1484 Sep 5, 2020

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@srowen, This was added to make sure new users understand the entire flow under one section but we can certainly remove this statement and link the step to the "Using PyPI" section. Is that fine?

Proposed change: PySpark can be installed in this newly created environment using the command specified in Using PyPI <#using-pypi>_


.. code-block:: bash

pip install pyspark

`PySpark at Conda <https://anaconda.org/conda-forge/pyspark>`_ is not the official release.
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Same, I don't think this matters

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I would just say, for example:

Note that PySpark at Conda <https://anaconda.org/conda-forge/pyspark>_ is available but not necessarily synced with PySpark release cycle because it is maintained by the community separately.


Official Release Channel
------------------------

Different flavor of PySpark is available in `the official release channel <https://spark.apache.org/downloads.html>`_.
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flavor -> flavors
What do flavors mean here?

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@rohitmishr1484 rohitmishr1484 Sep 5, 2020

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@srowen,
image

In the above image Step 1 & 2 allows us to choose different combinations and the file to be downloaded in Step 3 changes accordingly. Depending on the user's requirement any combination can be selected and thus by "flavor" I mean to denote this.

Would you suggest something else or based on the above explanation, "flavor" word can be used and it doesn't mislead the reader?

Any suitable version can be downloaded and extracted as below:

.. code-block:: bash

tar xzvf spark-3.0.0-bin-hadoop2.7.tgz

An important step is to ensure ``SPARK_HOME`` environment variable points to the directory where the code has been extracted.
The next step is to properly define ``PYTHONPATH`` such that it can find the PySpark and
Py4J under ``$SPARK_HOME/python/lib``, one example of doing this is shown below:
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(By the way I think you need just single back-ticks?)
Start a new sentence at "One example"

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oh fyi double backticks here make it like a code block. single backtick makes italic for some reasons.


.. code-block:: bash

cd spark-3.0.0-bin-hadoop2.7
export SPARK_HOME=`pwd`
export PYTHONPATH=$(ZIPS=("$SPARK_HOME"/python/lib/*.zip); IFS=:; echo "${ZIPS[*]}"):$PYTHONPATH

Installing from Source
----------------------

To install PySpark from source, refer `Building Spark <https://spark.apache.org/docs/latest/building-spark.html>`_.

Steps for defining ``PYTHONPATH`` is same as described in `Official Release Channel <#official-release-channel>`_.

Dependencies
------------
============= ========================= ====================
Package Minimum supported version Note
============= ========================= ====================
`pandas` 0.23.2 Optional
`NumPy` 1.7 Optional
`pyarrow` 0.15.1 Optional
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pyarrow is also currently only Optional for SQL.

`Py4J` 0.10.9 Required
============= ========================= ====================

**Note**: A prerequisite for PySpark installation is the availability of ``JAVA 8 or 11`` and ``JAVA_HOME`` properly set.