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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

Client-side program architecture

The basic idea is simple: the user searches for the name of someone on GitHub in the input box. When he enters a name, we fire a request to the API designed earlier in this chapter. When the response from the API returns, the program binds that response to a model and emits an event notifying that the model has been changed. The views listen for this event and refresh from the model in response.

Designing the model

Let's start by defining the client-side model. The model holds information regarding the repos of the user currently displayed. It gets filled in after the first search.

// public/javascripts/model.js

define([], function(){
   return {
    ghubUser: "", // last name that was searched for
    exists: true, // does that person exist on github?
    repos: [] // list of repos
  } ;
});

To see a populated value of the model, head to the complete application example on app.scala4datascience.com, open a JavaScript console in your browser...

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