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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
Toc

Table of Contents (21) Chapters close

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

Summary


We could have provided streaming examples for other systems as well, but there was no room in this chapter. Twitter streaming has been examined by example in the Checkpointing section. This chapter has provided practical examples of data recovery via checkpointing in Spark Streaming. It has also touched on the performance limitations of checkpointing and shown that the checkpointing interval should be set at five to ten times the Spark stream batch interval.

Checkpointing provides a stream-based recovery mechanism in the case of Spark application failure. This chapter has provided some stream-based worked examples for TCP, File, Flume, and Kafka-based Spark stream coding. All the examples here are based on Scala and compiled with sbt. In case you are more familiar with Maven the following tutorial explains how to set up a Maven based Scala project: http://www.scala-lang.org/old/node/345.

All of the code will be released with this book. Where the example architecture has become overcomplicated...

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