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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

Using compression to improve performance

Data compression involves encoding information using fewer bits than the original representation. Compression has an important role to play in big data technologies. It makes both storage and transport of data more efficient.

When data is compressed, it becomes smaller, so both disk I/O and network I/O become faster. It also saves storage space. Every optimization has a cost, and the cost of compression comes in the form of added CPU cycles to compress and decompress data.

Hadoop needs to split data to put them into blocks, irrespective of whether the data is compressed or not. Only a few compression formats are splittable.

The two most popular compression formats for big data loads are Lempel-Ziv-Oberhumer (LZO) and Snappy. Snappy is not splittable, while LZO is. Snappy, on the other hand, is a much faster format.

If the compression format is splittable like LZO, the...

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