Installing Spark on Ubuntu in 3 Minutes

One thing I hear often from people starting out with Spark is that it’s too difficult to install. Some guides are for Spark 1.x and others are for 2.x. Some guides get really detailed with Hadoop versions, JAR files, and environment variables.

So here’s yet another guide on how to install Apache Spark, condensed and simplified to get you up and running with Apache Spark 2.3.1 in 3 minutes or less.

All you need is a machine (or instance, server, VPS, etc.) that you can install packages on (e.g. “sudo apt” works). If you need one of those, check out DigitalOcean. It’s much simpler than AWS for small projects.

First, log in to the machine via SSH.

Now, install OpenJDK 8 (Java):

sudo apt update && sudo apt install -y openjdk-8-jdk-headless python

Next, download and extract Apache Spark:

wget && tar xf spark-2.3.1-bin-hadoop2.7.tgz

Set up environment variables to configure Spark:

echo 'SPARK_HOME=$HOME/spark-2.3.1-bin-hadoop2.7' >> ~/.bashrc
echo 'PATH=$PATH:$SPARK_HOME/bin' >> ~/.bashrc
echo 'export PYSPARK_PYTHON=python3' >> ~/.bashrc
source ~/.bashrc

That’s it – you’re all set! You’ve installed Spark and it’s ready to go. Try out “pyspark”, “spark-submit” or “spark-shell”.

Try running this inside “pyspark” to validate that it worked:

spark.createDataFrame([{"hello": x} for x in range(1000)]).count() # hopefully this equals 1000

Apache Spark on Google Colaboratory

Google recently launched a preview of Colaboratory, a new service that lets you edit and run IPython notebooks right from Google Drive – free! It’s similar to Databricks – give that a try if you’re looking for a better-supported way to run Spark in the cloud, launch clusters, and much more.

Google has published some tutorials showing how to use Tensorflow and various other Google APIs and tools on Colaboratory, but I wanted to try installing Apache Spark. It turned out to be much easier than I expected. Download the notebook and import it into Colaboratory or read on…



Jupyter Notebooks with PySpark on AWS EMR

One of the biggest, most time-consuming parts of data science is analysis and experimentation. One of the most popular tools to do so in a graphical, interactive environment is Jupyter.

Combining Jupyter with Apache Spark (through PySpark) merges two extremely powerful tools. AWS EMR lets you set up all of these tools with just a few clicks. In this tutorial I’ll walk through creating a cluster of machines running Spark with a Jupyter notebook sitting on top of it all.