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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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 Yadav Yadav
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Yadav
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Toc

Table of Contents (19) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Chapter 12. Optimizations and Performance Tuning

This chapter covers various optimization and performance tuning best practices when working with Spark.

The chapter is divided into the following recipes:

  • Optimizing memory
  • Leveraging speculation
  • Optimizing joins
  • Using compression to improve performance
  • Using serialization to improve performance
  • Optimizing level of parallelism
  • Understanding project Tungsten
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