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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

Custom type serialization


So far, we have only tried to serialize and deserialize simple types. What if we wanted to decode the language field in the repository array to an enumeration rather than a string? We might, for instance, define the following enumeration:

scala> object Language extends Enumeration {
  val Scala, Java, JavaScript = Value
}
defined object Language

Casbah lets us define custom serializers tied to a specific Scala type: we can inform Casbah that whenever it encounters an instance of the Language.Value type in a DBObject, the instance should be passed through a custom transformer that will convert it to, for instance, a string, before writing it to the database.

To define a custom serializer, we need to define a class that extends the Transformer trait. This trait exposes a single method, transform(o:AnyRef):AnyRef. Let's define a LanguageTransformer trait that transforms from Language.Value to String:

scala> import org.bson.{BSON, Transformer}
import org.bson...
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