Installation¶

PySpark is included in the official releases of Spark available in the Apache Spark website. For Python users, PySpark also provides pip installation from PyPI. This is usually for local usage or as a client to connect to a cluster instead of setting up a cluster itself.

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

Python Version Supported¶

Python 3.6 and above.

Using PyPI¶

PySpark installation using PyPI is as follows:

pip install pyspark

If you want to install extra dependencies for a specific component, you can install it as below:

pip install pyspark[sql]

For PySpark with/without a specific Hadoop version, you can install it by using PYSPARK_HADOOP_VERSION environment variables as below:

PYSPARK_HADOOP_VERSION=2.7 pip install pyspark

The default distribution uses Hadoop 3.2 and Hive 2.3. If users specify different versions of Hadoop, the pip installation automatically downloads a different version and use it in PySpark. Downloading it can take a while depending on the network and the mirror chosen. PYSPARK_RELEASE_MIRROR can be set to manually choose the mirror for faster downloading.

PYSPARK_RELEASE_MIRROR=http://mirror.apache-kr.org PYSPARK_HADOOP_VERSION=2.7 pip install

It is recommended to use -v option in pip to track the installation and download status.

PYSPARK_HADOOP_VERSION=2.7 pip install pyspark -v

Supported values in PYSPARK_HADOOP_VERSION are:

without: Spark pre-built with user-provided Apache Hadoop
2.7: Spark pre-built for Apache Hadoop 2.7
3.2: Spark pre-built for Apache Hadoop 3.2 and later (default)

Note that this installation way of PySpark with/without a specific Hadoop version is experimental. It can change or be removed between minor releases.

Using Conda¶

Conda is an open-source package management and environment management system which is a part of the Anaconda distribution. It is both cross-platform and language agnostic. In practice, Conda can replace both pip and virtualenv.

Create new virtual environment from your terminal as shown below:

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:

conda env list

Now activate the newly created environment with the following command:

conda activate pyspark_env

You can install pyspark by Using PyPI to install PySpark in the newly created environment, for example as below. It will install PySpark under the new virtual environment pyspark_env created above.

pip install pyspark

Alternatively, you can install PySpark from Conda itself as below:

conda install pyspark

However, note that PySpark at Conda is not necessarily synced with PySpark release cycle because it is maintained by the community separately.

Manually Downloading¶

PySpark is included in the distributions available at the Apache Spark website. You can download a distribution you want from the site. After that, uncompress the tar file into the directory where you want to install Spark, for example, as below:

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

Ensure the SPARK_HOME environment variable points to the directory where the tar file has been extracted. Update PYTHONPATH environment variable such that it can find the PySpark and Py4J under SPARK_HOME/python/lib. One example of doing this is shown below:

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 to Building Spark.

Dependencies¶

Package	Minimum supported version	Note
pandas	0.23.2	Optional for SQL
NumPy	1.7	Required for ML
pyarrow	1.0.0	Optional for SQL
Py4J	0.10.9	Required

Note that PySpark requires Java 8 or later with JAVA_HOME properly set. If using JDK 11, set -Dio.netty.tryReflectionSetAccessible=true for Arrow related features and refer to Downloading.

Getting Started Quickstart