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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

Case classes as messages


In our "hello world" example, we constructed an actor that is expected to receive a string as message. Any object can be passed as a message, provided it is immutable. It is very common to use case classes to represent messages. This is better than using strings because of the additional type safety: the compiler will catch a typo in a case class but not in a string.

Let's rewrite our EchoActor to accept instances of case classes as messages. We will make it accept two different messages: EchoMessage(message) and EchoHello, which just echoes a default message. The examples for this section and the next are in the chap09/hello_akka_case_classes directory in the sample code provided with this book (https://github.com/pbugnion/s4ds).

A common Akka pattern is to define the messages that an actor can receive in the actor's companion object:

// EchoActor.scala

object EchoActor { 
  case object EchoHello
  case class EchoMessage(msg:String)
}

Let's change the actor definition...

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