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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

Interacting with JSON

JSON, as we discovered in previous chapters, is becoming the de-facto language for communicating structured data over HTTP. If you develop a web application or a web API, it is likely that you will have to consume or emit JSON, or both.

In Chapter 7, Web APIs, we learned how to parse JSON through json4s. The Play framework includes its own JSON parser and emitter. Fortunately, it behaves in much the same way as json4s.

Let's imagine that we are building an API that summarizes information about GitHub repositories. Our API will emit a JSON array listing a user's repositories when queried about a specific user (much like the GitHub API, but with just a subset of fields).

Let's start by defining a model for the repository. In Play applications, models are normally stored in the folder app/models, in the models package:

// app/models/Repo.scala

package models

case class Repo (
  val name:String,
  val language:String,
  val isFork: Boolean,
  val size: Long...
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