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Learning Spark SQL

You're reading from Learning Spark SQL

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781785888359

Pages 452 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Aurobindo Sarkar

Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with Spark SQL

2. Using Spark SQL for Processing Structured and Semistructured Data

3. Using Spark SQL for Data Exploration

4. Using Spark SQL for Data Munging

5. Using Spark SQL in Streaming Applications

6. Using Spark SQL in Machine Learning Applications

7. Using Spark SQL in Graph Applications

8. Using Spark SQL with SparkR

9. Developing Applications with Spark SQL

10. Using Spark SQL in Deep Learning Applications

11. Tuning Spark SQL Components for Performance

12. Spark SQL in Large-Scale Application Architectures

Exploring graphs using GraphFrames

In this section, we explore data, modeled as a graph, using Spark GraphFrames. The vertices and edges of the graph are stored as DataFrames, and Spark SQL and DataFrame-based queries are supported to operate on them. As DataFrames can support a variety of data sources, we can our input vertices edges information from relational tables, files (JSON, Parquet, Avro, and CSV), and so on.

The vertex DataFrame must contain a column called id which specifies unique IDs for each vertex. Similarly, the edges DataFrame must contain two columns named src (source vertex ID) and dst (destination vertex ID). Both the vertices and edges DataFrames can contain additional columns for the attributes.

GraphFrames exposes a concise language-integrated API that unifies graph analytics and relational queries. The system optimizes across the steps based on join plans and performing algebraic optimizations. Machine learning code, external data sources, and UDFs can be integrated...

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Authors (1)

Aurobindo Sarkar

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