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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

Dynamic routing

Routing, as we saw, is the mapping of HTTP requests to Scala handlers. Routes are stored in conf/routes. A route is defined by an HTTP verb, followed by the end-point, followed by a Scala function:

// verb   // end-point              // Scala handler
GET       /                         controllers.Application.index

We learnt to add new routes by just adding lines to the routes file. We are not limited to static routes, however. The Play framework lets us include wild cards in routes. The value of the wild card can be passed as an argument to the controller. To see how this works, let's create a controller that takes the name of a person as argument. In the Application object in app.controllers, add:

// app/controllers/Application.scala

class Application extends Controller {

  ...

  def hello(name:String) = Action {
    Ok(s"hello, $name")
  }
}

We can now define a route handled by this controller:

// conf/routes
GET  /hello/:name             controllers.Application...
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