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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
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (13) Chapters Close

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

Understanding project Tungsten


Project Tungsten, starting with Spark Version 1.4, was the initiative to bring Spark closer to bare metal, which has become a first-class integral feature now. The goal of this project is to substantially improve the memory and CPU efficiency of the Spark applications and push the limits of the underlying hardware.

In distributed systems, conventional wisdom has been to always optimize network I/O as that has been the most scarce and bottlenecked resource. This trend has changed in the last few years. Network bandwidth in the last 5 years has changed from 1 gigabit per second to 10 gigabit per second. In fact, Amazon Web Services is poised to make 40 Gbps standard, and there are already instances available at 20 Gbps. 

On similar lines, the disk bandwidth has increased from 50 MB/s to 500 MB/s, and solid state drives (SSDs) are being deployed more and more. Pruning unneeded input data and predicate push-down have made the speed gains even larger effectively....

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