You may need to restart your terminal to be able to run PySpark. Let’s check if PySpark is properly installed without using Jupyter Notebook first. You can run a regular jupyter notebook by typing: $ jupyter notebook Your first Python program on Spark Install Jupyter notebook: $ pip install jupyter To do so, configure your $PATH variables by adding the following lines in your ~/.bashrc (or ~/.zshrc) file: export SPARK_HOME=/opt/spark export PATH=$SPARK_HOME/bin:$PATH Install Jupyter Notebook This way, you will be able to download and use multiple Spark versions.įinally, tell your bash (or zsh, etc.) where to find Spark. Unzip it and move it to your /opt folder: $ tar -xzf spark-1.2.0-bin-hadoop2.4.tgz $ mv spark-1.2.0-bin-hadoop2.4 /opt/spark-1.2.0Ĭreate a symbolic link: $ ln -s /opt/spark-1.2.0 /opt/spark̀ Select the latest Spark release, a prebuilt package for Hadoop, and download it directly. To install Spark, make sure you have Java 8 or higher installed on your computer. I also encourage you to set up a virtualenv Go to the Python official website to install it. I am using Python 3 in the following examples but you can easily adapt them to Python 2. Python for Apache Spark.īefore installing pySpark, you must have Python and Spark installed. Scala pro and cons for Spark context, please refer to this interesting article: Scala vs. If you prefer to develop in Scala, you will find many alternatives on the following github repository: alexarchambault/jupyter-scala In my opinion, Python is the perfect language for prototyping in Big Data/Machine Learning fields. However like many developers, I love Python because it’s flexible, robust, easy to learn, and benefits from all my favorites libraries. Python for Spark is obviously slower than Scala. While using Spark, most data engineers recommends to develop either in Scala (which is the “native” Spark language) or in Python through complete PySpark API. I wrote this article for Linux users but I am sure Mac OS users can benefit from it too. That’s why Jupyter is a great tool to test and prototype programs. It allows you to modify and re-execute parts of your code in a very flexible way. Jupyter Notebook is a popular application that enables you to edit, run and share Python code into a web view. In a few words, Spark is a fast and powerful framework that provides an API to perform massive distributed processing over resilient sets of data. Spark with JupyterĪpache Spark is a must for Big data’s lovers.
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