High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark

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

• Learning Spark: Lightning-Fast Big Data Analysis
• Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
• Programming in Scala: Updated for Scala 2.12
• Advanced Analytics with Spark: Patterns for Learning from Data at Scale
• Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale
• Spark: The Definitive Guide: Big Data Processing Made Simple
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
• Functional Programming in Scala
• Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing
• Doing Data Science: Straight Talk from the Frontline