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Scala and Spark for Big Data Analytics

You're reading from  Scala and Spark for Big Data Analytics

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Pages 796 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Sridhar Alla Sridhar Alla
Profile icon Sridhar Alla
View More author details

Table of Contents (19) Chapters

Preface Introduction to Scala Object-Oriented Scala Functional Programming Concepts Collection APIs Tackle Big Data – Spark Comes to the Party Start Working with Spark – REPL and RDDs Special RDD Operations Introduce a Little Structure - Spark SQL Stream Me Up, Scotty - Spark Streaming Everything is Connected - GraphX Learning Machine Learning - Spark MLlib and Spark ML My Name is Bayes, Naive Bayes Time to Put Some Order - Cluster Your Data with Spark MLlib Text Analytics Using Spark ML Spark Tuning Time to Go to ClusterLand - Deploying Spark on a Cluster Testing and Debugging Spark PySpark and SparkR

Partitioning and shuffling

We have seen how Apache Spark can handle distributed computing much better than Hadoop. We also saw the inner workings, mainly the fundamental data structure known as Resilient Distributed Dataset (RDD). RDDs are immutable collections representing datasets and have the inbuilt capability of reliability and failure recovery. RDDs operate on data not as a single blob of data, rather RDDs manage and operate data in partitions spread across the cluster. Hence, the concept of data partitioning is critical to the proper functioning of Apache Spark Jobs and can have a big effect on the performance as well as how the resources are utilized.

RDD consists of partitions of data and all operations are performed on the partitions of data in the RDD. Several operations like transformations are functions executed by an executor on the specific partition of data being...

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