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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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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

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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