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Fast Data Processing with Spark 2

You're reading from   Fast Data Processing with Spark 2 Accelerate your data for rapid insight

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
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Authors (2):
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Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
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Toc

Table of Contents (13) Chapters Close

Preface 1. Installing Spark and Setting Up Your Cluster 2. Using the Spark Shell FREE CHAPTER 3. Building and Running a Spark Application 4. Creating a SparkSession Object 5. Loading and Saving Data in Spark 6. Manipulating Your RDD 7. Spark 2.0 Concepts 8. Spark SQL 9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists 10. Spark with Big Data 11. Machine Learning with Spark ML Pipelines 12. GraphX

Shared Java and Scala APIs


Once you have a SparkSession object created, it will serve as your main entry point. In the next chapter, you will learn how to use the SparkSession object to load and save data. You can also use SparkSession.SparkContext to launch more Spark jobs and add or remove dependencies. Some of the non-data-driven methods you can use on the SparkSession.SparkContext object are shown here:

Method

Use

addJar(path)

This method adds the JAR file for all the future jobs that would run through the SparkContext object.

addFile(path)

This method downloads the file to all the nodes on the cluster.

listFiles/listJars

This method shows the list of all the currently added files/JARs.

stop()

This method shuts down SparkContext.

clearFiles()

This method removes the files so that new nodes will not download them.

clearJars()

This method removes the JARs from being required for future jobs.

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