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Scala for Data Science

You're reading from  Scala for Data Science

Product type Book
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Pages 416 pages
Edition 1st Edition
Languages
Author (1):
Pascal Bugnion Pascal Bugnion
Profile icon Pascal Bugnion

Table of Contents (22) Chapters

Scala for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Scala and Data Science 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 Pattern Matching and Extractors Index

Extraction using case classes


In the previous sections, we extracted specific fields from the JSON response using Scala extractors. We can do one better and extract full case classes.

When moving beyond the REPL, programming best practice dictates that we move from json4s types to Scala objects as soon as possible rather than passing json4s types around the program. Converting from json4s types to Scala types (or case classes representing domain objects) is good practice because:

  • It decouples the program from the structure of the data that we receive from the API, something we have little control over.

  • It improves type safety: a JObject is, as far as the compiler is concerned, always a JObject, whatever fields it contains. By contrast, the compiler will never mistake a User for a Repository.

Json4s lets us extract case classes directly from JObject instances, making writing the layer converting JObject instances to custom types easy.

Let's define a case class representing a GitHub user:

scala...
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