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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

Deploying the Spark application on a cluster

In this section, we will discuss how to deploy Spark jobs on a computing cluster. We will see how to deploy clusters in three deploy modes: standalone, YARN, and Mesos. The following figure summarizes terms that are needed to refer to cluster concepts in this chapter:

Figure 8: Terms that are needed to refer to cluster concepts (source: http://spark.apache.org/docs/latest/cluster-overview.html#glossary)

However, before diving onto deeper, we need to know how to submit a Spark job in general.

Submitting Spark jobs

Once a Spark application is bundled as either a jar file (written in Scala or Java) or a Python file, it can be submitted using the Spark-submit script located under the...

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