Book Image

Learning Spark SQL

By : Aurobindo Sarkar
Book Image

Learning Spark SQL

By: Aurobindo Sarkar

Overview of this book

In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

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...