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Mastering Apache Spark 2.x - Second Edition

You're reading from Mastering Apache Spark 2.x - Second Edition

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Pages 354 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (21) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. A First Taste and What’s New in Apache Spark V2

2. Apache Spark SQL

3. The Catalyst Optimizer

4. Project Tungsten

5. Apache Spark Streaming

6. Structured Streaming

7. Apache Spark MLlib

8. Apache SparkML

9. Apache SystemML

10. Deep Learning on Apache Spark with DeepLearning4j and H2O

11. Apache Spark GraphX

12. Apache Spark GraphFrames

13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience

14. Apache Spark on Kubernetes

Importing and saving data

We wanted to add this section about importing and saving data here, even though it is not purely about Spark SQL, so that concepts such as Parquet and JSON file formats could be introduced. This section also allows us to cover how to access saved data in loose text as well as the CSV, Parquet, and JSON formats conveniently in one place.

Processing the text files

Using SparkContext, it is possible to load a text file in RDD using the textFile method. Additionally, the wholeTextFile method can read the contents of a directory to RDD. The following examples show you how a file, based on the local filesystem (file://) or HDFS (hdfs://), can be read to a Spark RDD. These examples show you that the data will be divided into six partitions for increased performance. The first two examples are the same as they both load a file from the Linux filesystem, whereas the last one resides in HDFS:

sc.textFile("/data/spark/tweets.txt",6)
sc.textFile("file:///data/spark/tweets.txt...

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