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Apache Spark 2.x for Java Developers

You're reading from  Apache Spark 2.x for Java Developers

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Pages 350 pages
Edition 1st Edition
Languages
Authors (2):
Sourav Gulati Sourav Gulati
Profile icon Sourav Gulati
Sumit Kumar Sumit Kumar
Profile icon Sumit Kumar
View More author details

Table of Contents (19) Chapters

Title Page
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Spark 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

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...

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