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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 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 (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Creating vectors


Before understanding Vectors, let's focus on what is a point. A point is just a set of numbers. This set of numbers or coordinates defines the point's position in space. The numbers of coordinates determine dimensions of the space.

We can visualize space with up to three dimensions. Space with more than three dimensions is called hyperspace. Let's put this spatial metaphor to use.

Let's start with a person. A person has the following dimensions:

  • Weight

  • Height

  • Age

We are working in three-dimensional space here. Thus, the interpretation of point (160,69,24) would be 160 lb weight, 69 inches height, and 24 years age.

Note

Points and vectors are same thing. Dimensions in vectors are called features. In another way, we can define a feature as an individual measurable property of a phenomenon being observed.

Spark has local vectors and matrices and also distributed matrices. Distributed matrix is backed by one or more RDDs. A local vector has numeric indices and double values, and is stored...

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