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Spark Cookbook
Spark Cookbook

Spark Cookbook: With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Spark Cookbook

Chapter 2. Developing Applications with Spark

In this chapter, we will cover:

  • Exploring the Spark shell
  • Developing a Spark application in Eclipse with Maven
  • Developing Spark applications in Eclipse with SBT
  • Developing a Spark application in Intellij IDEA with Maven
  • Developing a Spark application in Intellij IDEA with SBT

Introduction

To create production quality Spark jobs/application, it is useful to use various integrated development environments (IDEs) and build tools. This chapter will cover various IDEs and build tools.

Exploring the Spark shell

Spark comes bundled with a REPL shell, which is a wrapper around the Scala shell. Though the Spark shell looks like a command line for simple things, in reality a lot of complex queries can also be executed using it. This chapter explores different development environments in which Spark applications can be developed.

How to do it...

Hadoop MapReduce's word count becomes very simple with the Spark shell. In this recipe, we are going to create a simple 1-line text file, upload it to the Hadoop distributed file system (HDFS), and use Spark to count occurrences of words. Let's see how:

  1. Create the words directory by using the following command:
    $ mkdir words
    
  2. Get into the words directory:
    $ cd words
    
  3. Create a sh.txt text file and enter "to be or not to be" in it:
    $ echo "to be or not to be" > sh.txt
    
  4. Start the Spark shell:
    $ spark-shell
    
  5. Load the words directory as RDD:
    Scala> val words = sc.textFile("hdfs://localhost:9000/user/hduser/words...

Developing Spark applications in Eclipse with Maven

Maven as a build tool has become the de-facto standard over the years. It's not surprising if we look little deeper into the promise Maven brings. Maven has two primary features and they are:

  • Convention over configuration: Build tools prior to Maven gave developers freedom about where to put source files, where to put test files, where to put compiled files, and so on. Maven takes away that freedom. With this freedom, all the confusion about locations also goes. In Maven, there is a specific directory structure for everything. The following table shows a few of the most common locations:

    /src/main/scala

    Source code in Scala

    /src/main/java

    Source code in Java

    /src/main/resources

    Resources to be used by source code such as configuration files

    /src/test/scala

    Test code in Scala

    /src/test/java

    Test code in Java

    /src/test/resources

    Resources to be used by test code such as configuration files

  • Declarative dependency management...

Developing Spark applications in Eclipse with SBT

Simple Build Tool (SBT) is a build tool made especially for Scala-based development. SBT follows Maven-based naming conventions and declarative dependency management.

SBT provides the following enhancements over Maven:

  • Dependencies are in the form of key-value pairs in the build.sbt file as opposed to pom.xml in Maven
  • It provides a shell that makes it very handy to perform build operations
  • For simple projects without dependencies, you do not even need the build.sbt file

In build.sbt, the first line is the project definition:

lazy val root = (project in file("."))

Each project has an immutable map of key-value pairs. This map is changed by settings in SBT like so:

lazy val root = (project in file("."))
  settings(
    name := "wordcount"
  )

Every change in the settings leads to a new map, as it's an immutable map.

How to do it...

Here's how we go about adding the sbteclipse plugin:

  1. Add this to the global plugin...

Developing a Spark application in IntelliJ IDEA with Maven

IntelliJ IDEA comes bundled with support for Maven. We will see how to create a new Maven project in this recipe.

How to do it...

Perform the following steps to develop a Spark application on IntelliJ IDEA with Maven:

  1. Select Maven in new project window and click on Next:
    How to do it...
  2. Enter three dimensions of the project:
    How to do it...
  3. Enter the project's name and location:
    How to do it...
  4. Click on Finish and the Maven project is ready.

Developing a Spark application in IntelliJ IDEA with SBT

Before Eclipse became famous, IntelliJ IDEA was considered best of the breed in IDEs. IDEA has not shed its former glory yet and a lot of developers love IDEA. IDEA also has a community edition, which is free. IDEA provides native support for SBT, which makes it ideal for SBT and Scala development.

How to do it...

Perform the following steps to develop a Spark application on IntelliJ IDEA with SBT:

  1. Add the sbt-idea plugin.
  2. Add to the global plugin file:
    $mkdir /home/hduser/.sbt/0.13/plugins
    $echo addSbtPlugin("com.github.mpeltone" % "sbt-idea" % "1.6.0" )  > /home/hduser/.sbt/0.12/plugins/plugin.sbt
    

    Alternatively, you can add to your project as well:

    $cd <project-home>
    $ echo addSbtPlugin("com.github.mpeltone" % "sbt-idea" % "1.6.0" ) > plugin.sbt
    

IDEA is ready to use with SBT.

Now you can develop Spark code using Scala and build using SBT.

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Description

If you are a data engineer, an application developer, or a data scientist who would like to leverage the power of Apache Spark to get better insights from big data, then this is the book for you.

Who is this book for?

If you are a data engineer, an application developer, or a data scientist who would like to leverage the power of Apache Spark to get better insights from big data, then this is the book for you.

What you will learn

  • Install and configure Apache Spark with various cluster managers
  • Set up development environments
  • Perform interactive queries using Spark SQL
  • Get to grips with realtime streaming analytics using Spark Streaming
  • Master supervised learning and unsupervised learning using MLlib
  • Build a recommendation engine using MLlib
  • Develop a set of common applications or project types, and solutions that solve complex big data problems
  • Use Apache Spark as your single big data compute platform and master its libraries
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 27, 2015
Length: 226 pages
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Language : English
ISBN-13 : 9781783987061
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Table of Contents

13 Chapters
1. Getting Started with Apache Spark Chevron down icon Chevron up icon
2. Developing Applications with Spark Chevron down icon Chevron up icon
3. External Data Sources Chevron down icon Chevron up icon
4. Spark SQL Chevron down icon Chevron up icon
5. Spark Streaming Chevron down icon Chevron up icon
6. Getting Started with Machine Learning Using MLlib Chevron down icon Chevron up icon
7. Supervised Learning with MLlib – Regression Chevron down icon Chevron up icon
8. Supervised Learning with MLlib – Classification Chevron down icon Chevron up icon
9. Unsupervised Learning with MLlib Chevron down icon Chevron up icon
10. Recommender Systems Chevron down icon Chevron up icon
11. Graph Processing Using GraphX Chevron down icon Chevron up icon
12. Optimizations and Performance Tuning Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(18 Ratings)
5 star 66.7%
4 star 16.7%
3 star 11.1%
2 star 0%
1 star 5.6%
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Amazon Customer Nov 03, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A simple book to get you started on Spark. Way to go for students. Well organized.
Amazon Verified review Amazon
CP95 Nov 02, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Accurate Concepts and IdeasConciseVery Easy to understandVery Helpful Real Time examplesWell-WrittenWell-FormattedAttractive to ReadI totally recommend this book. I tried to solve a programming problem for a week and with this book I understood how to fix it in about half an hour. I read the whole book and practice all the examples which are mention it is very easy to understand and the examples are also easy to implement. I Loved it. One of the most recommended book to be a expert in spark.
Amazon Verified review Amazon
Sanjay Nov 20, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book. Very Clear information for beginner & advanced developer. Information is to the point & practical. I like the way the chapters are laid out , progressing along from a hadoop developer, then transitioning to a Spark developer.....I hope the author enhances the content to include Spark 1.5 & 1.6 with details on EMR & EC2 usage for Spark
Amazon Verified review Amazon
Venkat M Jul 26, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very comprehensive book on Spark. Well organized and covers a wide range of topics. Must read book for anyone using Spark.
Amazon Verified review Amazon
Amazon Customer Nov 01, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I listened to this entire book in just a few days. it is interesting and informative I have new motivation to exercise! I never thought about how exercise could affect my mind and happiness. I highly recommend. Wonderful!
Amazon Verified review Amazon
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