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Home > Data > Big Data > Apache Spark 2.x for Java Developers
Apache Spark 2.x for Java Developers
Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers: Explore big data at scale using Apache Spark 2.x Java APIs

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Apache Spark 2.x for Java Developers

Revisiting Java

This chapter is added as the refresher course of Java. In this chapter, we will discuss some concepts of Java that will be useful while creating applications in Apache Spark.

This book assumes that the reader is comfortable with the basics of Java. We will discuss useful Java concepts and mainly focus on what is new in Java 8? More importantly, we will touch upon on topics such as:

  • Generics
  • Interfaces
  • Lambda expressions
  • Streams

Why use Java for Spark?

With the rise in multi-core CPUs, Java could not keep up with the change in its design to utilize that extra power available to its disposal because of the complexity surrounding concurrency and immutability. We will discuss this in detail, later. First let's understand the importance and usability of Java in the Hadoop ecosystem. As MapReduce was gaining popularity, Google introduced a framework called Flume Java that helped in pipelining multiple MapReduce jobs. Flume Java consists of immutable parallel collections capable of performing lazily evaluated optimized chained operations. That might sound eerily similar to what Apache Spark does, but then even before Apache Spark and Java Flume, there was Cascading, which built an abstraction over MapReduce to simplify the way MapReduce tasks are developed, tested, and run. All these frameworks were majorly...

Generics

Generics were introduced in Java 1.5. Generics help the user to create the general purpose code that has abstract type in its definition. That abstract type can be replaced with any concrete type in the implementation.

For example, the list interface or its implementations, such as ArrayList, LinkedList and so on, are defined with generic type. Users can provide the concrete type such as Integer, Long, or String while implementing the list:

List<Integer> list1 =new ArrayList<Integer>(); 
List<String> list2 =new ArrayList<String>();

Here, list1 is the list of integers and list2 is the list of strings. With Java 7, the compiler can infer the type. So the preceding code can also be written as follows:

List<Integer> list1 =new ArrayList<>(); 
List<String> list2 =new ArrayList<>();

Another huge benefit of generic type is...

Interfaces

Interfaces are the reference types in Java. They are used in Java to define contracts among classes. Any class that implements that interface has to adhere to the contract that the interface defines.

For example, we have an interface car as follows that consists of three abstract methods:

public interface Car { 
   void shape(); 
   void price(); 
   void color(); 
}

Any class that implements this interface has to implement all the abstract methods of this interface unless it is an abstract class. Interfaces can only be implemented or extended by other interfaces, they cannot be instantiated.

Prior to Java 8, interfaces consisted only of abstract methods and final variables. In Java 8, interfaces may contain default and static methods as well.

Static method in an interface...

Lambda expressions

Lambda expressions are the brand new feature of Java. Lambda expressions are introduced in Java 8 and it is a step towards facilitating functional programming in Java.

Lambda expressions help you to define a method without declaring it. So, you do not need a name for the method, return-type, and so on. Lambda expressions, like anonymous inner classes, provide the way to pass behaviors to functions. Lambda, however, is a much more concise way of writing the code.

For example, the preceding example of an anonymous inner class can be converted to Lambda as follows:

public class MyFilterImpl { 
   public static void main(String[] args) { 
      File dir = new File("src/main/java"); 
      dir.list((dirname,name)->name.endsWith("java")); //Lambda Expression 
     } 
}

Note that the signature of the Lambda expression is exactly matching the...

Lexical scoping

Lexical scoping is also referred to as Static scoping. As per lexical scoping, a variable will be accessible in the scope in which it is defined. Here, the scope of the variable is determined at compile time.

Let us consider the following example:

public class LexicalScoping { 
   int a = 1; 
   // a has class level scope. So It will be available to be accessed 
   // throughout the class 
 
   public void sumAndPrint() { 
      int b = 1; 
      int c = a + b; 
      // b and c are local variables of method. These will be accessible 
      // inside the method only 
   } 
   // b and c are no longer accessible 
}

Variable a will be available throughout the class (let's not consider the difference of static and non-static as of now). However, variables b and c will be available inside the sumAndPrint method only.

Similarly, a variable given inside lambda...

Why use Java for Spark?

With the rise in multi-core CPUs, Java could not keep up with the change in its design to utilize that extra power available to its disposal because of the complexity surrounding concurrency and immutability. We will discuss this in detail, later. First let's understand the importance and usability of Java in the Hadoop ecosystem. As MapReduce was gaining popularity, Google introduced a framework called Flume Java that helped in pipelining multiple MapReduce jobs. Flume Java consists of immutable parallel collections capable of performing lazily evaluated optimized chained operations. That might sound eerily similar to what Apache Spark does, but then even before Apache Spark and Java Flume, there was Cascading, which built an abstraction over MapReduce to simplify the way MapReduce tasks are developed, tested, and run. All these frameworks were majorly a Java implementation to simplify MapReduce pipelines among other things.

These abstractions were simple in fact...

Generics

Generics were introduced in Java 1.5. Generics help the user to create the general purpose code that has abstract type in its definition. That abstract type can be replaced with any concrete type in the implementation.

For example, the list interface or its implementations, such as ArrayList, LinkedList and so on, are defined with generic type. Users can provide the concrete type such as Integer, Long, or String while implementing the list:

List<Integer> list1 =new ArrayList<Integer>(); 
List<String> list2 =new ArrayList<String>();

Here, list1 is the list of integers and list2 is the list of strings. With Java 7, the compiler can infer the type. So the preceding code can also be written as follows:

List<Integer> list1 =new ArrayList<>(); 
List<String> list2 =new ArrayList<>();

Another huge benefit of generic type is that it brings compile-time safety. Let's create a list without the use of generics:

List list =new ArrayList<>();...

Interfaces

Interfaces are the reference types in Java. They are used in Java to define contracts among classes. Any class that implements that interface has to adhere to the contract that the interface defines.

For example, we have an interface car as follows that consists of three abstract methods:

public interface Car { 
   void shape(); 
   void price(); 
   void color(); 
}

Any class that implements this interface has to implement all the abstract methods of this interface unless it is an abstract class. Interfaces can only be implemented or extended by other interfaces, they cannot be instantiated.

Prior to Java 8, interfaces consisted only of abstract methods and final variables. In Java 8, interfaces may contain default and static methods as well.

Static method in an interface

The static method in an interface is similar to the static method in a class. Users cannot override them. So even if a class implements an interface, it cannot override a static method of an interface.

Like a static...

Lambda expressions

Lambda expressions are the brand new feature of Java. Lambda expressions are introduced in Java 8 and it is a step towards facilitating functional programming in Java.

Lambda expressions help you to define a method without declaring it. So, you do not need a name for the method, return-type, and so on. Lambda expressions, like anonymous inner classes, provide the way to pass behaviors to functions. Lambda, however, is a much more concise way of writing the code.

For example, the preceding example of an anonymous inner class can be converted to Lambda as follows:

public class MyFilterImpl { 
   public static void main(String[] args) { 
      File dir = new File("src/main/java"); 
      dir.list((dirname,name)->name.endsWith("java")); //Lambda Expression 
     } 
}

Note that the signature of the Lambda expression is exactly matching the signature of the accept method in the FilenameFilter interface.

Note

One of the huge differences between Lambda and anonymous inner classes...

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Key benefits

  • Perform big data processing with Spark—without having to learn Scala!
  • Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics
  • Go beyond mainstream data processing by adding querying capability, Machine Learning, and graph processing using Spark

Description

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications.

Who is this book for?

If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful.

What you will learn

  • Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library.
  • Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library
  • Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library
  • Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems
  • Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark
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Publication date : Jul 26, 2017
Length: 350 pages
Edition : 1st
Language : English
ISBN-13 : 9781787126497
Vendor :
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Category :
Data
Languages :
Java
Concepts :
Big Data
Tools :
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Publication date : Jul 26, 2017
Length: 350 pages
Edition : 1st
Language : English
ISBN-13 : 9781787126497
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Big Data
Tools :
Apache Spark

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Table of Contents

11 Chapters
Introduction to Spark Chevron down icon Chevron up icon
Introduction to Spark
Dimensions of big data
What makes Hadoop so revolutionary?
Why Apache Spark?
RDD - the first citizen of Spark
Exploring the Spark ecosystem
What's new in Spark 2.X?
References
Summary
Revisiting Java Chevron down icon Chevron up icon
Revisiting Java
Why use Java for Spark?
Generics
Interfaces
Lambda expressions
Lexical scoping
Streams
Intermediate operations
Terminal operations
Summary
Let Us Spark Chevron down icon Chevron up icon
Let Us Spark
Getting started with Spark
Spark REPL also known as CLI
Some basic exercises using Spark shell
Spark components
Spark Driver Web UI
Spark job configuration and submission
Spark REST APIs
Summary
Understanding the Spark Programming Model Chevron down icon Chevron up icon
Understanding the Spark Programming Model
Hello Spark
Common RDD transformations
Common RDD actions
RDD persistence and cache
Summary
Working with Data and Storage Chevron down icon Chevron up icon
Working with Data and Storage
Interaction with external storage systems
Working with different data formats
References
Summary
Spark on Cluster Chevron down icon Chevron up icon
Spark on Cluster
Spark application in distributed-mode
Cluster managers
Yet Another Resource Negotiator (YARN)
Summary
Spark Programming Model - Advanced Chevron down icon Chevron up icon
Spark Programming Model - Advanced
RDD partitioning
Advanced transformations
Advanced actions
Shared variable
Broadcast variable
Summary
Working with Spark SQL Chevron down icon Chevron up icon
Working with Spark SQL
SQLContext and HiveContext
Dataframe and dataset
Spark SQL operations
Hive integration
Summary
Near Real-Time Processing with Spark Streaming Chevron down icon Chevron up icon
Near Real-Time Processing with Spark Streaming
Introducing Spark Streaming
Understanding micro batching
Streaming sources
Kafka
Streaming transformations
Fault tolerance and reliability
Structured Streaming
Summary
Machine Learning Analytics with Spark MLlib Chevron down icon Chevron up icon
Machine Learning Analytics with Spark MLlib
Introduction to machine learning
Concepts of machine learning
Machine learning work flow
Operations on feature vectors
Summary
Learning Spark GraphX Chevron down icon Chevron up icon
Learning Spark GraphX
Introduction to GraphX
Introduction to Property Graph
Getting started with the GraphX API
Graph operations
Graph algorithms
Summary

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Customer reviews

Rating distribution
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
(4 Ratings)
5 star 0%
4 star 0%
3 star 25%
2 star 50%
1 star 25%
Ray Brown Apr 07, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
The index needs a lot of help. I don't know if this is a packt publisher problem. The book has a few typos, but only annoying. Spark is a huge subject and this text -- used as a notebook so you can add your own material, combined with a course on Spark can get you started in the right direction. I've not seen any great texts that cover Spark thoroughly and do not require some research on your own. Spark is a changing product that can provide significant throughput increases with Machine Learning and Extract Transform and Load (ETL) systems. Regardless of which text you purchase you will be doing research on the web to find all your answers.
Amazon Verified review Amazon
Amazon Customer Oct 19, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
content not upto the mark
Amazon Verified review Amazon
mark berman Dec 21, 2017
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Lots of grammatical and spelling mistakes. Detracts from quality of this book. Suggest the authors engage a professional proof reader next time.
Amazon Verified review Amazon
phani kumar yadavilli Mar 31, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Some of the chapters are staggered and they are completely unreadable. Please check the screenshots for more details.
Amazon Verified review Amazon

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About the authors

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Profile icon Kumar
Kumar
Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.
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Profile icon Gulati
Gulati
Shekhar Gulati is a developer and OpenShift evangelist working with Red Hat. He has been evangelizing about OpenShift for the last 2 years. He regularly speaks at various conferences and user groups around the world to spread the goodness of OpenShift. He regularly blogs on the OpenShift official blog and has written more than 50 blogs on OpenShift. Shekhar has also written many technical articles for IBM developerWorks, Developer.com, and Javalobby.
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  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela