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Mastering Apache Spark 2.x

You're reading from   Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (21) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. A First Taste and What’s New in Apache Spark V2 FREE CHAPTER 2. Apache Spark SQL 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

User-defined functions


In order to create user-defined functions in Scala, we need to examine our data in the previous Dataset. We will use the age property on the client entries in the previously introduced client.json. We plan to create an UDF that will enumerate the age column. This will be useful if we need to use the data for machine learning as a lesser number of different values is sometimes useful. This process is also called binning or categorization. This is the JSON file with the age property added:

Now let's define a Scala enumeration that converts ages into age range codes. If we use this enumeration among all our relations, we can ensure consistent and proper coding of these ranges:

 object AgeRange extends Enumeration {
    val Zero, Ten, Twenty, Thirty, Fourty, Fifty, Sixty, Seventy, Eighty, Ninety, HundretPlus = Value
    def getAgeRange(age: Integer) = {
      age match {
        case age if 0 until 10 contains age => Zero
        case age if 11 until 20 contains age ...
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