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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.System requirementsUbuntu OS Installed.Minimum of 8 GB RAM.At least 20 GB free space.PrerequisitesJava8 should be installed in your Machine.Hadoop should be installed in your Machine.Installation ProcedureMaking 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-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 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.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.
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How to Install Spark on Ubuntu

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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.

Prerequisites

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

System requirements

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

Prerequisites

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

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

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.

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

  • 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

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.

  • 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

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

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.

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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Apache Spark Pros and Cons

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This open-source distributed computing platform offers more powerful advantages than any other proprietary solutions. The diverse advantages of Apache Spark make it a very attractive big data framework. Apache Spark has huge potential to contribute to the big data-related business in the industry. Let’s now have a look at some of the common benefits of Apache Spark:Benefits of Apache Spark:SpeedEase of UseAdvanced AnalyticsDynamic in NatureMultilingualApache Spark is powerfulIncreased access to Big dataDemand for Spark DevelopersOpen-source community1. Speed:When comes to Big Data, processing speed always matters. Apache Spark is wildly popular with data scientists because of its speed. Spark is 100x faster than Hadoop for large scale data processing. Apache Spark uses in-memory(RAM) computing system whereas Hadoop uses local memory space to store data. Spark can handle multiple petabytes of clustered data of more than 8000 nodes at a time. 2. Ease of Use:Apache Spark carries easy-to-use APIs for operating on large datasets. It offers over 80 high-level operators that make it easy to build parallel apps.The below pictorial representation will help you understand the importance of Apache Spark.3. Advanced Analytics:Spark not only supports ‘MAP’ and ‘reduce’. It also supports Machine learning (ML), Graph algorithms, Streaming data, SQL queries, etc.4. Dynamic in Nature:With Apache Spark, you can easily develop parallel applications. Spark offers you over 80 high-level operators.5. Multilingual:Apache Spark supports many languages for code writing such as Python, Java, Scala, etc.6. Apache Spark is powerful:Apache Spark can handle many analytics challenges because of its low-latency in-memory data processing capability. It has well-built libraries for graph analytics algorithms and machine learning.7. Increased access to Big data:Apache Spark is opening up various opportunities for big data and making As per the recent survey conducted by IBM’s announced that it will educate more than 1 million data engineers and data scientists on Apache Spark. 8. Demand for Spark Developers:Apache Spark not only benefits your organization but you as well. Spark developers are so in-demand that companies offering attractive benefits and providing flexible work timings just to hire experts skilled in Apache Spark. As per PayScale the average salary for  Data Engineer with Apache Spark skills is $100,362. For people who want to make a career in the big data, technology can learn Apache Spark. You will find various ways to bridge the skills gap for getting data-related jobs, but the best way is to take formal training which will provide you hands-on work experience and also learn through hands-on projects.9. 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Apache Spark Pros and Cons

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