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

Understanding Catalyst optimizations


We briefly explored the optimizer in Chapter 1, Getting Started with Spark SQL. Basically, Catalyst has an internal representation of the user's program, called the query plan. A set of transformations is executed on the initial query plan to yield the optimized query plan. Finally, through Spark SQL's generation mechanism, the optimized query plan gets converted to a DAG of RDDs, ready for execution. At its core, the Catalyst optimizer defines the abstractions of users' programs as trees and also the transformations from one tree to another.

In order to take advantage of optimization opportunities, we need an optimizer that automatically finds the most efficient plan to execute data operations (specified in the user's program). In the context of this chapter, Spark SQL's Catalyst optimizer acts as the interface between the user's high-level programming constructs and the low-level execution plans.

Understanding the Dataset/DataFrame API

A Dataset or a...