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Learning Apache Spark 2

You're reading from Learning Apache Spark 2

Product type Book

Published in Mar 2017

Publisher Packt

ISBN-13 9781785885136

Pages 356 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Table of Contents (18) Chapters

Learning Apache Spark 2

Credits

About the Author

About the Reviewers

www.packtpub.com

Customer Feedback

Preface

1. Architecture and Installation

2. Transformations and Actions with Spark RDDs

3. ETL with Spark

4. Spark SQL

5. Spark Streaming

6. Machine Learning with Spark

7. GraphX

8. Operating in Clustered Mode

9. Building a Recommendation System

10. Customer Churn Prediction

1. Theres More with Spark

Transformations

We've used few transformation functions in the examples in this chapter, but I would like to share with you a list of the most commonly used transformation functions in Apache Spark. You can find a complete list of functions in the official documentation http://bit.ly/RDDTransformations.

Most Common Transformations
`map(func)`	coalesce(numPartitions)
`filter(func)`	repartition(numPartitions)
`flatMap(func)`	repartitionAndSortWithinPartitions(partitioner)
`mapPartitions(func)`	join(otherDataset, [numTasks])
`mapPartitionsWithIndex(func)`	cogroup(otherDataset, [numTasks])
`sample(withReplacement, fraction, seed)`	cartesian(otherDataset)

Map(func)

The map transformation is the most commonly used and the simplest of transformations on an RDD. The map transformation applies the function passed in the arguments to each of the elements of the source RDD. In the previous examples, we have seen the usage of map() transformation where we have passed the split() function...

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