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Apache Spark 2.x for Java Developers

You're reading from   Apache Spark 2.x for Java Developers Explore big data at scale using Apache Spark 2.x Java APIs

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Length 350 pages
Edition 1st Edition
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Authors (2):
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Sourav Gulati Sourav Gulati
Author Profile Icon Sourav Gulati
Sourav Gulati
Sumit Kumar Sumit Kumar
Author Profile Icon Sumit Kumar
Sumit Kumar
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Spark FREE CHAPTER 2. Revisiting Java 3. Let Us Spark 4. Understanding the Spark Programming Model 5. Working with Data and Storage 6. Spark on Cluster 7. Spark Programming Model - Advanced 8. Working with Spark SQL 9. Near Real-Time Processing with Spark Streaming 10. Machine Learning Analytics with Spark MLlib 11. Learning Spark GraphX

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<>();...
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