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How to Install Spark on Ubuntu

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.In this article, we will cover the installation procedure of Apache Spark on the Ubuntu operating system.PrerequisitesThis guide assumes that you are using Ubuntu and Hadoop 2.7 is installed in your system.Java8 should be installed in your Machine.Hadoop should be installed in your Machine.System requirementsUbuntu OS Installed.Minimum of 8 GB RAM.At least 20 GB free space.Installation ProcedureMaking system readyBefore installing Spark ensure that you have installed Java8 in your Ubuntu Machine. If not installed, please follow below process to install java8 in your Ubuntu System.a. Install java8 using below command.sudo apt-get install oracle-java8-installerAbove command creates java-8-oracle Directory in /usr/lib/jvm/ directory in your machine. It looks like belowNow we need to configure the JAVA_HOME path in .bashrc file..bashrc file executes whenever we open the terminal.b. Configure JAVA_HOME and PATH  in .bashrc file and save. To edit/modify .bashrc file, use below command.vi .bashrc Then press i(for insert) -> then Enter below line at the bottom of the file.export JAVA_HOME= /usr/lib/jvm/java-8-oracle/ export PATH=$PATH:$JAVA_HOME/binBelow is the screen shot of that.Then Press Esc -> wq! (For save the changes) -> Enter.c. Now test Java installed properly or not by checking the version of Java. Below command should show the java version.java -versionBelow is the screenshotInstalling Spark on the SystemGo to the below official download page of Apache Spark and choose the latest release. For the package type, choose ‘Pre-built for Apache Hadoop’.https://spark.apache.org/downloads.htmlThe page will look like belowOr You can use a direct link to download.https://www.apache.org/dyn/closer.lua/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgzCreating Spark directoryCreate a directory called spark under /usr/ directory. Use below command to create spark directorysudo mkdir /usr/sparkAbove command asks password to create spark directory under the /usr directory, you can give the password. Then check spark directory is created or not in the /usr directory using below commandll /usr/It should give the below results with ‘spark’ directoryGo to /usr/spark directory. Use below command to go spark directory.cd /usr/sparkDownload Spark versionDownload spark2.3.3 in spark directory using below commandwget https://www.apache.org/dyn/closer.lua/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgzIf use ll or ls command, you can see spark-2.4.0-bin-hadoop2.7.tgz in spark directory.Extract Spark fileThen extract spark-2.4.0-bin-hadoop2.7.tgz using below command.sudo tar xvzf spark-2.4.0-bin-hadoop2.7Now spark-2.4.0-bin-hadoop2.7.tgz file is extracted as spark-2.4.0-bin-hadoop2.7Check whether it extracted or not using ll command. It should give the below results.ConfigurationConfigure SPARK_HOME path in the .bashrc file by following below steps.Go to the home directory using below commandcd ~Open the .bashrc file using below commandvi .bashrcNow we will configure SPARK_HOME and PATHpress i for insert the enter SPARK_HOME and PATH  like belowSPARK_HOME=/usr/spark/spark-2.4.0-bin-hadoop2.7PATH=$PATH:$SPARK_HOME/binIt looks like belowThen save and exit by entering below commands.Press Esc -> wq! -> EnterTest Installation:Now we can verify spark is successfully installed in our Ubuntu Machine or not. To verify use below command then enter.spark-shell Above command should show below screenNow we have successfully installed spark on Ubuntu System. Let’s create RDD and Dataframe then we will end up.a. We can create RDD in 3 ways, we will use one way to create RDD.Define any list then parallelize it. It will create RDD. Below are the codes. Copy paste it one by one on the command line.val nums = Array(1,2,3,5,6) val rdd = sc.parallelize(nums)Above will create RDD.b. Now we will create a Data frame from RDD. Follow the below steps to create Dataframe.import spark.implicits._ val df = rdd.toDF("num")Above code will create Dataframe with num as a column.To display the data in Dataframe use below commanddf.show()Below is the screenshot of the above code.How to uninstall Spark from Ubuntu System: You can follow the below steps to uninstall spark on Windows 10.Remove SPARK_HOME from the .bashrc file.To remove SPARK_HOME variable from the .bashrc please follow below stepsGo to the home directory. To go to home directory use below command.cd ~Open .bashrc file. To open .bashrc file use below command.vi .bashrcPress i for edit/delete SPARK_HOME from .bashrc file. Then find SPARK_HOME the delete SPARK_HOME=/usr/spark/spark-2.4.0-bin-hadoop2.7 line from .bashrc file and save. To do follow below commandsThen press Esc -> wq! -> Press EnterWe will also delete downloaded and extracted spark installers from the system. Please do follow below command.rm -r ~/sparkAbove command will delete spark directory from the system.Open Command Line Interface then type spark-shell,  then press enter, now we get an error.Now we can confirm that Spark is successfully uninstalled from the Ubuntu System. You can also learn more about Apache Spark and Scala here.

How to Install Spark on Ubuntu

11K
How to Install Spark on Ubuntu

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

In this article, we will cover the installation procedure of Apache Spark on the Ubuntu operating system.

Prerequisites

This guide assumes that you are using Ubuntu and Hadoop 2.7 is installed in your system.

  1. Java8 should be installed in your Machine.
  2. Hadoop should be installed in your Machine.

System requirementsSystem Requirements of Install Spark on Ubuntu

  • Ubuntu OS Installed.
  • Minimum of 8 GB RAM.
  • At least 20 GB free space.

Installation Procedure

Making system ready

Before installing Spark ensure that you have installed Java8 in your Ubuntu Machine. If not installed, please follow below process to install java8 in your Ubuntu System.

a. Install java8 using below command.

sudo apt-get install oracle-java8-installer

Above command creates java-8-oracle Directory in /usr/lib/jvm/ directory in your machine. It looks like below

Installation Procedure in Spark on ubuntu

Now we need to configure the JAVA_HOME path in .bashrc file.

.bashrc file executes whenever we open the terminal.

b. Configure JAVA_HOME and PATH  in .bashrc file and save. To edit/modify .bashrc file, use below command.

vi .bashrc 

Then press i(for insert) -> then Enter below line at the bottom of the file.

export JAVA_HOME= /usr/lib/jvm/java-8-oracle/
export PATH=$PATH:$JAVA_HOME/bin

Below is the screen shot of that.

Installation Procedure in Spark on ubuntu

Then Press Esc -> wq! (For save the changes) -> Enter.

c. Now test Java installed properly or not by checking the version of Java. Below command should show the java version.

java -version

Below is the screenshot

Installation Procedure in Spark on ubuntu

Installing Spark on the System

Go to the below official download page of Apache Spark and choose the latest release. For the package type, choose ‘Pre-built for Apache Hadoop’.

https://spark.apache.org/downloads.html

The page will look like below

Installation Procedure in Spark on ubuntu

Or You can use a direct link to download.

https://www.apache.org/dyn/closer.lua/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz

Creating Spark directory

Create a directory called spark under /usr/ directory. Use below command to create spark directory

sudo mkdir /usr/spark

Above command asks password to create spark directory under the /usr directory, you can give the password. Then check spark directory is created or not in the /usr directory using below command

ll /usr/

It should give the below results with ‘spark’ directory

Go to /usr/spark directory. Use below command to go spark directory.

cd /usr/spark

Download Spark version

Download spark2.3.3 in spark directory using below command

wget https://www.apache.org/dyn/closer.lua/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz

If use ll or ls command, you can see spark-2.4.0-bin-hadoop2.7.tgz in spark directory.

Extract Spark file

Then extract spark-2.4.0-bin-hadoop2.7.tgz using below command.

sudo tar xvzf spark-2.4.0-bin-hadoop2.7

Now spark-2.4.0-bin-hadoop2.7.tgz file is extracted as spark-2.4.0-bin-hadoop2.7

Check whether it extracted or not using ll command. It should give the below results.

Installation Procedure in Spark on ubuntu

Configuration

Configure SPARK_HOME path in the .bashrc file by following below steps.

Go to the home directory using below command

cd ~

Open the .bashrc file using below command

vi .bashrc

Now we will configure SPARK_HOME and PATH

press i for insert the enter SPARK_HOME and PATH  like below

SPARK_HOME=/usr/spark/spark-2.4.0-bin-hadoop2.7

PATH=$PATH:$SPARK_HOME/bin

It looks like below

Installation Procedure in Spark on ubuntu

Then save and exit by entering below commands.

Press Esc -> wq! -> Enter

Test Installation:

Now we can verify spark is successfully installed in our Ubuntu Machine or not. To verify use below command then enter.

spark-shell 

Above command should show below screen

Test Installation in Spark on Ubuntu

Now we have successfully installed spark on Ubuntu System. Let’s create RDD and Dataframe then we will end up.

a. We can create RDD in 3 ways, we will use one way to create RDD.

Define any list then parallelize it. It will create RDD. Below are the codes. Copy paste it one by one on the command line.

val nums = Array(1,2,3,5,6)
val rdd = sc.parallelize(nums)

Above will create RDD.

b. Now we will create a Data frame from RDD. Follow the below steps to create Dataframe.

import spark.implicits._
val df = rdd.toDF("num")

Above code will create Dataframe with num as a column.

To display the data in Dataframe use below command

df.show()

Below is the screenshot of the above code.

Test Installation in Spark on Ubuntu

How to uninstall Spark from Ubuntu System: 

You can follow the below steps to uninstall spark on Windows 10.

  1. Remove SPARK_HOME from the .bashrc file.

To remove SPARK_HOME variable from the .bashrc please follow below steps

Go to the home directory. To go to home directory use below command.

cd ~

Open .bashrc file. To open .bashrc file use below command.

vi .bashrc

Press i for edit/delete SPARK_HOME from .bashrc file. Then find SPARK_HOME the delete SPARK_HOME=/usr/spark/spark-2.4.0-bin-hadoop2.7 line from .bashrc file and save. To do follow below commands

Then press Esc -> wq! -> Press Enter

We will also delete downloaded and extracted spark installers from the system. Please do follow below command.

rm -r ~/spark

Above command will delete spark directory from the system.

Open Command Line Interface then type spark-shell,  then press enter, now we get an error.

Now we can confirm that Spark is successfully uninstalled from the Ubuntu System. You can also learn more about Apache Spark and Scala here.

Ravichandra

Ravichandra Reddy Maramreddy

Blog Author

Ravichandra is a developer and specialized in Spark and Hadoop Ecosystems, HDFS and MapReduce which includes estimations, requirement analysis, design development, coordination, validation in-depth understanding of game design practices. Having extensive experience in Spark, Spark Streaming, Pyspark, Scala, Shell, Oozie, Hive, HBase, Hue, Java, SparkSQL, Kafka, WSO2. Having extensive experience in using Data structures and algorithms.

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The use of Big Data over a network cluster has become a major application in multiple industries. The wide use of MapReduce and Hadoop technologies is proof of this evolving technology, along with the recent rise of Apache Spark, a data processing engine written in Scala programming language. Introduction to Scala Scala is a general purpose object-oriented programming language, similar to Java programming. Scala is an acronym for “Scalable language” meaning its capabilities can grow along the lines of your requirements & also there are more technologies built on scala. The capabilities of Scala programming can range from a simple scripting language to the preferred language for mission-critical applications. Scala has the following capabilities: Support for functional programming, with features including currying, type interference, immutability, lazy evaluation, and pattern matching. An advanced type system including algebraic data types and anonymous types. Features that are not available in Java, like operator overloading, named parameters, raw strings, and no checked exceptions. Scala can run seamlessly on a Java Virtual Machine (JVM), and Scala and Java classes can be freely interchanged or can refer to each other. Scala also supports cluster computing, with the most popular framework solution, Spark, which was written using Scala. Introduction to Apache Spark Apache Spark is an open-source Big Data processing framework that provides an interface for programming data clusters using data parallelism and fault tolerance. Apache Spark is widely used for fast processing of large datasets. Apache Spark is an open-source platform, built by a wide set of software developers from over 200 companies. Since 2009, more than 1000 developers have contributed to Apache Spark. Apache Spark provides better capabilities for Big Data applications, as compared to other Big Data technologies such as Hadoop or MapReduce. Listed below are some features of Apache Spark: 1. Comprehensive framework Spark provides a comprehensive and unified framework to manage Big Data processing, and supports a diverse range of data sets including text data, graphical data, batch data, and real-time streaming data. 2. Speed Spark can run programs up to 100 times faster than Hadoop clusters in memory, and 10 times faster when running on disk. Spark has an advanced DAG (directed acrylic graph) execution engine that provides support for cyclic data flow and in-memory data sharing across DAGs to execute different jobs with the same data. 3. Easy to use With a built-in set of over 80 high-level operators, Spark allows programmers to write Java, Scala, or Python applications in quick time. 4. Enhanced support In addition to Map and Reduce operations, Spark provides support for SQL queries, streaming data, machine learning, and graphic data processing. 5. Can be run on any platform Apache Spark applications can be run on a standalone cluster mode or in the cloud. Spark provides access to diverse data structures including HDFS, Cassandra, HBase, Hive, Tachyon, and any Hadoop data source. Spark can be deployed as a standalone server or on a distributed framework such as Mesos or YARN. 6. Flexibility In addition to Scala programming language, programmers can use Java, Python, Clojure, and R to build applications using Spark. Comprehensive library support As a Spark programmer, you can combine additional libraries within the same application, and provide Big Data analytical and Machine learning capabilities. The supported libraries include: Spark Streaming, used for processing of real-time streaming data. Spark SQL, used for exposing Spark datasets over JDBC APIs and for executing SQL-like queries on Spark datasets. Spark MLib, which is the machine learning library, consisting of common algorithms and utilities. Spark GraphX, which is the Spark API for graphs and graphical computation . BlinkDB, a query engine library used for running interactive SQL queries on large data volumes. Tachyon, which is a memory-centric distributed file system to enable file sharing across cluster frameworks. Spark Cassandra Connector and Spark R, which are integration adapters. With Cassandra Connector, Spark can access data from the Cassandra database and perform data analytics. Compatibility with Hadoop and MapReduce Apache Spark can be much faster as compared to other Big Data technologies. Apache Spark can run on an existing Hadoop Distributed File System (HDFS) to provide compatibility along with enhanced functionality. It is easy to deploy Spark applications on existing Hadoop v1 and v2 cluster. Spark uses the HDFS for data storage, and can work with Hadoop-compatible data sources including HBase and Cassandra. Apache Spark is compatible with MapReduce and enhances its capabilities with features such as in-memory data storage and real-time processing. Conclusion The standard API set of Apache Spark framework makes it the right choice for Big Data processing and data analytics. For client installation setups of MapReduce implementation with Hadoop, Spark and MapReduce can be used together for better results. Apache Spark is the right alternative to MapReduce for installations that involve large amounts of data that require low latency processing
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Analysis Of Big Data Using Spark And Scala

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