Getting started with PySpark on Windows and PyCharm

Table Of Contents

• Pre-Requisites
• Getting started with Spark on Windows
• PyCharm Configuration

Pre-Requisites

Both Java and Python are installed in your system.

Getting started with Spark on Windows

Download Apache Spark by choosing a Spark release (e.g. 2.2.0) and package type (e.g. Pre-built for Apache Hadoop 2.7 and later).

Extract the Spark tar file to a directory e.g. C:\Spark\spark-2.2.0-bin-hadoop2.7

GIT clone winutils to your system e.g. cloned to directory C:\winutils

Add below system environment variables where HADOOP_HOME is set to the winutils hadoop binary location (depending on the version of pre-built chosen earlier) and SPARK_HOME is set to the Spark location which we had extracted in step 2.

HADOOP_HOME=C:\winutils\hadoop-2.7.1
SPARK_HOME=C:\Spark\spark-2.2.0-bin-hadoop2.7

Create a new folder tmp/hive in your C: drive.

Provide permissions for the folder tmp/hive using winutils.exe by running below command in your command prompt

C:>C:\winutils\hadoop-2.7.1\bin\winutils.exe chmod 777 C:\tmp\hive

Now validate the setup by running spark-shell from your SPARK_HOME directory in your command prompt

C:\Spark\spark-2.2.0-bin-hadoop2.7>bin\spark-shell

PyCharm Configuration

Configure the python interpreter to support pyspark by following the below steps

1. Create a new virtual environment (File -> Settings -> Project Interpreter -> select Create Virtual Environment in the settings option)
2. In the Project Interpreter dialog, select More in the settings option and then select the new virtual environment. Now select Show paths for the selected interpreter option.
3. Add the paths for Spark Python and Spark Py4j to this virtual environment as shown in the screenshot below.

Create a new run configuration for Python in the dialog Run\Debug Configurations.

In the Python interpreter option select the interpreter which we had created in the first step. Also in the Environment variables option make sure Include parent environment variables is checked.

You can now add your pyspark script to the project and use this run configuration to execute it in a Spark context.