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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
Languages
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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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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

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...

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