Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources.

Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing.

With this book, you’ll explore:

• How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure
• The choice between data joins in Core Spark and Spark SQL
• Techniques for getting the most out of standard RDD transformations
• How to work around performance issues in Spark’s key/value pair paradigm
• Writing high-performance Spark code without Scala or the JVM
• How to test for functionality and performance when applying suggested improvements
• Using Spark MLlib and Spark ML machine learning libraries
• Spark’s Streaming components and external community packages

Table of Contents

Chapter 1. Introduction to High Performance Spark Chapter 2. How Spark Works

Chapter 3. DataFrames, Datasets, and Spark SQL

Chapter 4. Joins (SQL and Core)

Chapter 5. Effective Transformations

Chapter 6. Working with Key/Value Data

Chapter 7. Going Beyond Scala

Chapter 8. Testing and Validation

Chapter 9. Spark MLlib and ML

Chapter 10. Spark Components and Packages

About the Authors

Holden Karau is transgender Canadian, and an active open source contributor. When not in San Francisco working as a software development engineer at IBM's Spark Technology Center, Holden talks internationally on Apache Spark and holds office hours at coffee shops at home and abroad. She is a Spark committer with frequent contributions, specializing in PySpark and Machine Learning. Prior to IBM she worked on a variety of distributed, search, and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelor of Mathematics in Computer Science. Outside of software she enjoys playing with fire, welding, scooters, poutine, and dancing.

Rachel Warren is a data scientist and software engineer at Alpine Data Labs, where she uses Spark to address real-world data processing challenges. She has experience working as an analyst both in industry and academia. She graduated with a degree in Computer Science from Wesleyan University in Connecticut.