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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

Table of Contents (21) Chapters

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

Overview


A graph can be considered to be a data structure that consists of a group of vertices and edges connecting them. The vertices or nodes in the graph can be anything as long it is an object (so people for example), and the edges are the relationships between them. The edges can be un-directional or directional, meaning that the relationship operates from one node to another. For instance, node A is the parent of node B.

In the following diagram, the circles represent the vertices or nodes (A to D), while the thick lines represent the edges or relationships between them (E1 to E6). Each node or edge may have properties, and these values are represented by the associated gray squares (P1 to P7):

So, if a graph represents a physical route map, the edges might represent minor roads or motorways. The nodes would be motorway junctions or road intersections. Node and edge properties might be road types, speed limits, distance, cost, and grid location.

There are many types of graph implementation...

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