park is Hadoop’s sub-project. Therefore, it is better to install Spark into a Linux based system. The following steps show how to install Apache Spark.
Step 1: Verifying Java Installation
Java installation is one of the mandatory things in installing Spark. Try the following command to verify the JAVA version.
$java -version
If Java is already, installed on your system, you get to see the following response −
java version "1.7.0_71" Java(TM) SE Runtime Environment (build 1.7.0_71-b13) Java HotSpot(TM) Client VM (build 25.0-b02, mixed mode)
In case you do not have Java installed on your system, then Install Java before proceeding to next step.
Step 2: Verifying Scala installation
You should Scala language to implement Spark. So let us verify Scala installation using following command.
$scala -version
If Scala is already installed on your system, you get to see the following response −
Scala code runner version 2.11.6 -- Copyright 2002-2013, LAMP/EPFL
In case you don’t have Scala installed on your system, then proceed to next step for Scala installation.
Step 3: Downloading Scala
Download the latest version of Scala by visit the following link Download Scala. For this tutorial, we are using scala-2.11.6 version. After downloading, you will find the Scala tar file in the download folder.
Step 4: Installing Scala
Follow the below given steps for installing Scala.
Extract the Scala tar file
Type the following command for extracting the Scala tar file.
$ tar xvf scala-2.11.6.tgz
Move Scala software files
Use the following commands for moving the Scala software files, to respective directory (/usr/local/scala).
$ su – Password: # cd /home/Hadoop/Downloads/ # mv scala-2.11.6 /usr/local/scala # exit
Set PATH for Scala
Use the following command for setting PATH for Scala.
$ export PATH = $PATH:/usr/local/scala/bin
Verifying Scala Installation
After installation, it is better to verify it. Use the following command for verifying Scala installation.
$scala -version
If Scala is already installed on your system, you get to see the following response −
Scala code runner version 2.11.6 -- Copyright 2002-2013, LAMP/EPFL
Step 5: Downloading Apache Spark
Download the latest version of Spark by visiting the following link Download Spark. For this tutorial, we are using spark-1.3.1-bin-hadoop2.6 version. After downloading it, you will find the Spark tar file in the download folder.
Step 6: Installing Spark
Follow the steps given below for installing Spark.
Extracting Spark tar
The following command for extracting the spark tar file.
$ tar xvf spark-1.3.1-bin-hadoop2.6.tgz
Moving Spark software files
The following commands for moving the Spark software files to respective directory (/usr/local/spark).
$ su – Password: # cd /home/Hadoop/Downloads/ # mv spark-1.3.1-bin-hadoop2.6 /usr/local/spark # exit
Setting up the environment for Spark
Add the following line to ~/.bashrc file. It means adding the location, where the spark software file are located to the PATH variable.
export PATH = $PATH:/usr/local/spark/bin
Use the following command for sourcing the ~/.bashrc file.
$ source ~/.bashrc
Step 7: Verifying the Spark Installation
Write the following command for opening Spark shell.
$spark-shell
If spark is installed successfully then you will find the following output.
Spark assembly has been built with Hive, including Datanucleus jars on classpath Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties 15/06/04 15:25:22 INFO SecurityManager: Changing view acls to: hadoop 15/06/04 15:25:22 INFO SecurityManager: Changing modify acls to: hadoop 15/06/04 15:25:22 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop); users with modify permissions: Set(hadoop) 15/06/04 15:25:22 INFO HttpServer: Starting HTTP Server 15/06/04 15:25:23 INFO Utils: Successfully started service 'HTTP class server' on port 43292. Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\ version 1.4.0 /_/ Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_71) Type in expressions to have them evaluated. Spark context available as sc scala>
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