Steps to Install Apache Spark
Step 1: Ensure if Java is installed on your system
Before installing Spark, Java is a must-have for your system. The following command will verify the version of Java installed on your system:
If Java is already installed on your system, you get to see the following output:
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)
You have to install Java if it is not installed on your system.
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Step 2: Now, ensure if Scala is installed on your system
Installing the Scala programming language is mandatory before installing Spark as it is important for Spark’s implementation. The following command will verify the version of Scala used in your system:
If the Scala application is already installed on your system, you get to see the following response on the screen:
Scala code runner version 2.11.6 -- Copyright 2002-2013, LAMP/EPFL
If you don’t have Scala, then you have to install it on your system. Let’s see how to install Scala.
Step 3: First, download Scala
You need to download the latest version of Scala. Here, you will see the scala-2.11.6 version being used. After downloading, you will be able to find the Scala tar file in the Downloads folder.
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Step 4: Now, install Scala
You must follow the given steps to install Scala on your system:
- Extract the Scala tar file using the following command:
$ tar xvf scala-2.11.6.tgz
- Move Scala software files to the directory (/usr/local/scala) using the following commands:
$ su –
# cd /home/Hadoop/Downloads/
# mv scala-2.11.6 /usr/local/scala
- Set PATH for Scala using the following command:
$ export PATH = $PATH:/usr/local/scala/bin
- Now, verify the installation of Scala by checking the version of it
If your Scala installation is successful, then you will get the following output:
Scala code runner version 2.11.6 — Copyright 2002-2013, LAMP/EPFL
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Now, you are welcome to the core of this tutorial section on ‘Download Apache Spark.’ Once, you are ready with Java and Scala on your systems, go to Step 5.
Step 5: Download Apache Spark
After finishing with the installation of Java and Scala, now, in this step, you need to download the latest version of Spark by using the following command:
After this, you can find a Spark tar file in the Downloads folder.
Step 6: Install Spark
Follow the below steps for installing Apache Spark.
- Extract the Spark tar file using the following command:
$ tar xvf spark-1.3.1-bin-hadoop2.6.tgz
- Move Spark software files to the directory using the following commands:
$ su –
# cd /home/Hadoop/Downloads/
# mv spark-1.3.1-bin-hadoop2.6 /usr/local/spark
- Now, configure the environment for Spark
For this, you need to add the following path to ~/.bashrc file which will add the location, where the Spark software files are located to the PATH variable type.
export PATH = $PATH:/usr/local/spark/bin
- Use the below command for sourcing the ~/.bashrc file
$ source ~/.bashrc
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With this, you have successfully installed Apache Spark on your system. Now, you need to verify it.
Step 7: Verify the Installation of Spark on your system
The following command will open the Spark shell application version:
If Spark is installed successfully, then you will be getting 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 naming 'HTTP class server' on port 43292.
Welcome to the Spark World!
Using the Scala version 2.10.4 (Java HotSpot™ 64-Bit Server VM, Java 1.7.0_71), type in the expressions to have them evaluated as and when the requirement is raised. The Spark context will be available as Scala.
Initializing Spark in Python
from pyspark import SparkConf, SparkContext
conf = SparkConf().setMaster("local").setAppName("My App")
sc = SparkContext(conf = conf)
Initializing Spark in Scala
val conf = new SparkConf().setMaster("local").setAppName("My App")
val sc = new SparkContext(conf)
Initializing Spark in Java
SparkConf conf = new SparkConf().setMaster("local").setAppName("My App");
JavaSparkContext sc = new JavaSparkContext(conf);
The above examples show the minimal way to initialize a SparkContext, in Python, Scala, and Java, respectively, where you pass two parameters:
- A cluster URL, namely, ‘local’ in these examples, which tells Spark how to connect to a cluster. This ‘local’ is a special value that runs Spark on one thread on the local machine, without connecting to a cluster.
- An application name, namely, ‘My App’ in these examples. This will identify your application on the cluster manager’s UI if you connect to a cluster.
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