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

Follower network crawler


We are now ready to code up the remaining pieces of our network crawler. The largest missing piece is the fetcher manager. Let's start with the companion object. As with the worker actors, this just contains the definitions of the messages that the actor can receive and a factory to create the Props instance:

// FetcherManager.scala
import scala.collection.mutable
import akka.actor._

object FetcherManager {
  case class AddToQueue(login:String)
  case object GiveMeWork

  def props(token:Option[String], nFetchers:Int) = 
    Props(classOf[FetcherManager], token, nFetchers)
}

The manager can receive two messages: AddToQueue, which tells it to add a username to the queue of users whose followers need to be fetched, and GiveMeWork, emitted by the fetchers when they are unemployed.

The manager will be responsible for launching the fetchers, response interpreter, and follower extractor, as well as maintaining an internal queue of usernames and a set of usernames that we...