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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 | ||
============ | ||
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The official release channel is to download it from `the Apache Spark website <https://spark.apache.org/downloads.html>`_. | ||
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. | ||
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This page includes the instructions for installing PySpark by using pip, Conda, downloading manually, and building it from the source. | ||
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Python Version Supported | ||
------------------------ | ||
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Python 3.6 and above. | ||
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Using PyPI | ||
---------- | ||
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PySpark installation using `PyPI <https://pypi.org/project/pyspark/>`_ | ||
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.. code-block:: bash | ||
pip install pyspark | ||
Using Conda | ||
----------- | ||
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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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Conda can be used to create a virtual environment from terminal as shown below: | ||
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.. 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: | ||
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.. code-block:: bash | ||
conda env list | ||
The newly created environment can be accessed using the following command: | ||
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.. code-block:: bash | ||
conda activate pyspark_env | ||
In lower Conda version, the following command might be used: | ||
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.. code-block:: bash | ||
source activate pyspark_env | ||
PySpark installation using ``pip`` under Conda environment is official. | ||
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PySpark can be installed in this newly created environment using PyPI as shown before: | ||
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.. code-block:: bash | ||
pip install pyspark | ||
`PySpark at Conda <https://anaconda.org/conda-forge/pyspark>`_ is not the official release. | ||
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Official Release Channel | ||
------------------------ | ||
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Different flavor of PySpark is available in `the official release channel <https://spark.apache.org/downloads.html>`_. | ||
Any suitable version can be downloaded and extracted as below: | ||
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.. 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 ``$SPARK_HOME/python/lib``, one example of doing this is shown below: | ||
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.. code-block:: bash | ||
cd spark-3.0.0-bin-hadoop2.7 | ||
export SPARK_HOME=`pwd` | ||
export PYTHONPATH=$(ZIPS=("$SPARK_HOME"/python/lib/*.zip); IFS=:; echo "${ZIPS[*]}"):$PYTHONPATH | ||
Installing from Source | ||
---------------------- | ||
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To install PySpark from source, refer `Building Spark <https://spark.apache.org/docs/latest/building-spark.html>`_. | ||
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Steps for defining ``PYTHONPATH`` is same as described in `Official Release Channel <#official-release-channel>`_. | ||
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Dependencies | ||
------------ | ||
============= ========================= ==================== | ||
Package Minimum supported version Note | ||
============= ========================= ==================== | ||
`pandas` 0.23.2 Optional | ||
`NumPy` 1.7 Optional | ||
`pyarrow` 0.15.1 Optional | ||
`Py4J` 0.10.9 Required | ||
============= ========================= ==================== | ||
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**Note**: A prerequisite for PySpark installation is the availability of ``JAVA 8 or 11`` and ``JAVA_HOME`` properly set. |