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

Analyzing nested structures


There is a reason why the nested structures recipe is right after that of joins. Nested structures have traditionally been associated with web-based applications and hyper-scale companies. The most common format of nested structures is JSON. JSON inherited nested structures from XML, which JSON made irrelevant. 

Getting ready

The power of nested structures goes far beyond traditional use cases, though. It has been very difficult to represent hierarchical data in highly normalized databases. Data needs to be joined across tables as needed. This does provide us with flexibility. Let's understand it with the example we covered in the previous recipe. In the Yelp dataset, a user reviews a business, which is represented by yelp_academic_dataset_review.json. In reality, a user reviews multiple businesses and a business is reviewed by multiple users. One would argue that it represents standard NxN relationships between entities, so what's the big deal here? The challenges...