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Windows

Here we'll show you how to install Spark 3.3.2 for Windows. We tested it on Windows 10 and 11 Home edition, but it should work for other versions distros as well

In this tutorial, we'll use MINGW/Gitbash for command line

If you use WSL, follow the instructions from linux.md

Installing Java

Spark needs Java 11. Download it from here: https://www.oracle.com/de/java/technologies/javase/jdk11-archive-downloads.html. Select “Windows x64 Compressed Archive” (you may have to create an oracle account for that)

Unpack it to a folder with no space in the path. We use C:/tools - so the full path to JDK is /c/tools/jdk-11.0.13

Now let’s configure it and add it to PATH:

export JAVA_HOME="/c/tools/jdk-11.0.13"
export PATH="${JAVA_HOME}/bin:${PATH}"

Check that Java works correctly:

java --version

Output:

java 11.0.13 2021-10-19 LTS
Java(TM) SE Runtime Environment 18.9 (build 11.0.13+10-LTS-370)
Java HotSpot(TM) 64-Bit Server VM 18.9 (build 11.0.13+10-LTS-370, mixed mode)

Hadoop

Next, we need to have Hadoop binaries.

We'll need Hadoop 3.2 which we'll get from here.

Create a folder (/c/tools/hadoop-3.2.0) and put the files there

HADOOP_VERSION="3.2.0"
PREFIX="https://raw.githubusercontent.com/cdarlint/winutils/master/hadoop-${HADOOP_VERSION}/bin/"

FILES="hadoop.dll hadoop.exp hadoop.lib hadoop.pdb libwinutils.lib winutils.exe winutils.pdb"

for FILE in ${FILES}; do
  wget "${PREFIX}/${FILE}"
done

Add it to PATH:

export HADOOP_HOME="/c/tools/hadoop-3.2.0"
export PATH="${HADOOP_HOME}/bin:${PATH}"

Spark

Now download Spark. Select version 3.3.2

wget https://dlcdn.apache.org/spark/spark-3.3.2/spark-3.3.2-bin-hadoop3.tgz

Unpack it in some location without spaces, e.g. c:/tools/:

tar xzfv spark-3.3.2-bin-hadoop3.tgz

Let's also add it to PATH:

export SPARK_HOME="/c/tools/spark-3.3.2-bin-hadoop3"
export PATH="${SPARK_HOME}/bin:${PATH}"

Testing it

Go to this directory

cd spark-3.3.2-bin-hadoop3

And run spark-shell:

./bin/spark-shell.cmd

At this point you may get a message from windows firewall — allow it.

There could be some warnings (like this):

WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/C:/tools/spark-3.3.2-bin-hadoop3/jars/spark-unsafe_2.12-3.3.2.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release

You can safely ignore them.

Now let's run this:

val data = 1 to 10000
val distData = sc.parallelize(data)
distData.filter(_ < 10).collect()

PySpark

It's the same for all platforms. Go to pyspark.md.