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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

Actor construction


Actor construction is a common source of difficulty for people new to Akka. Unlike (most) ordinary objects, you never instantiate actors explicitly. You would never write, for instance, val echo = new EchoActor. In fact, if you try this, Akka raises an exception.

Creating actors in Akka is a two-step process: you first create a Props object, which encapsulates the properties needed to construct an actor. The way to construct a Props object differs depending on whether the actor takes constructor arguments. If the constructor takes no arguments, we simply pass the actor class as a type parameter to Props:

val echoProps = Props[EchoActor]

If we have an actor whose constructor does take arguments, we must pass these as additional arguments when defining the Props object. Let's consider the following actor, for instance:

class TestActor(a:String, b:Int) extends Actor { ... }

We pass the constructor arguments to the Props object as follows:

val testProps = Props(classOf[TestActor...
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