Book Image

Apache Spark 2.x Cookbook

By : Rishi Yadav
Book Image

Apache Spark 2.x Cookbook

By: Rishi Yadav

Overview of this book

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Understanding the Catalyst optimizer


Most of the power of Spark SQL comes from the Catalyst optimizer, so it makes sense to spend some time understanding it. The following diagram shows where exactly the optimization occurs along with the queries:

The Catalyst optimizer primarily leverages functional programming constructs of Scala, such as pattern matching. It offers a general framework for transforming trees, which we use to perform analysis, optimization, planning, and runtime code generation.

This optimizer has two primarilly goals:

  • To make adding new optimization techniques easy
  • To enable external developers to extend the optimizer

Spark SQL uses Catalyst's transformation framework in four phases:

  1. Analyzing a logical plan to resolve references.
  2. Logical plan optimization.
  3. Physical planning.
  4. Code generation, to compile the parts of the query to Java byte-code.

Analysis

The analysis phase involves two parts, the first part being:

  1. Looking at a SQL query or a DataFrame/Dataset
  2. Making sure there are no...