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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Toc

Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Fetcher actors


The workhorse of our application is the fetcher, the actor responsible for fetching the follower details from GitHub. In the first instance, our actor will accept a single message, Fetch(user). It will fetch the followers corresponding to user and log the response to screen. We will use the recipes developed in Chapter 7, Web APIs, to query the GitHub API with an OAuth token. We will inject the token through the actor constructor.

Let's start with the companion object. This will contain the definition of the Fetch(user) message and two factory methods to create the Props instances. You can find the code examples for this section in the chap09/fetchers_alone directory in the sample code provided with this book (https://github.com/pbugnion/s4ds):

// Fetcher.scala
import akka.actor._
import scalaj.http._
import scala.concurrent.Future

object Fetcher {
  // message definitions
  case class Fetch(login:String)

  // Props factory definitions
  def props(token:Option[String]):Props...
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