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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

Single page applications

The client-server duality adds a degree of complication to the elegant MVC architecture. Where should the model reside? What about the controller? Traditionally, the model and the controller ran almost entirely on the server, which just pushed the relevant HTML view to the client.

The growth in client-side JavaScript frameworks, such AngularJS, has resulted in a gradual shift to putting more code in the client. Both the controller and a temporary version of the model typically run client-side. The server just functions as a web API: if, for instance, the user updates the model, the controller will send an HTTP request to the server informing it of the change.

It then makes sense to think of the program running server-side and the one running client-side as two separate applications: the server persists data in databases, for instance, and provides a programmatic interface to this data, usually as a web service returning JSON or XML data. The client-side program maintains...

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