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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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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

Accessing the sender of a message


When our fetcher manager receives a GiveMeWork request, we will need to send work back to the correct fetcher. We can access the actor who sent a message using the sender method, which is a method of Actor that returns the ActorRef corresponding to the actor who sent the message currently being processed. The case statement corresponding to GiveMeWork in the fetcher manager is therefore:

def receive = {
  case GiveMeWork =>
    login = // get next login to fetch
    sender ! Fetcher.Fetch(login)
  ...
}

As sender is a method, its return value will change for every new incoming message. It should therefore only be used synchronously with the receive method. In particular, using it in a future is dangerous:

def receive = {
  case DoSomeWork =>
    val work = Future { Thread.sleep(20000) ; 5 }
    work.onComplete { result => 
      sender ! Complete(result) // NO!
    }
}

The problem is that when the future is completed 20 seconds after the message is...

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