Book Image

Scala for Data Science

By : Pascal Bugnion
Book Image

Scala for Data Science

By: Pascal Bugnion

Overview of this book

Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. Scala, with its powerful functional libraries for interacting with databases and building scalable frameworks will give you the tools to construct robust data pipelines. This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Packed with real-world examples and interesting data sets, this book will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectures to process and modelling your data, starting from simple concurrency constructs such as parallel collections and futures, through to actor systems and Apache Spark. As well as Scala’s emphasis on functional structures and immutability, you will learn how to use the right parallel construct for the job at hand, minimizing development time without compromising scalability. Finally, you will learn how to build beautiful interactive visualizations using web frameworks. This book gives tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed with building data science and data engineering solutions.
Table of Contents (22 chapters)
Scala for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Lifting the hood


In the last section of this chapter, we will discuss, very briefly, how Spark works internally. For a more detailed discussion, see the References section at the end of the chapter.

When you open a Spark context, either explicitly or by launching the Spark shell, Spark starts a web UI with details of how the current task and past tasks have executed. Let's see this in action for the example mutual information program we wrote in the last section. To prevent the context from shutting down when the program completes, you can insert a call to readLine as the last line of the main method (after the call to takeOrdered). This expects input from the user, and will therefore pause program execution until you press enter.

To access the UI, point your browser to 127.0.0.1:4040. If you have other instances of the Spark shell running, the port may be 4041, or 4042 and so on.

The first page of the UI tells us that our application contains three jobs. A job occurs as the result of an action...