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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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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

Wrapping result sets in a stream


The JDBC ResultSet object plays very badly with Scala collections. The only real way of doing anything useful with it is to loop through it directly with a while loop. For instance, to get a list of the names of physicists in our database, we could write the following code:

// WARNING: poor Scala code
import Implicits._ // import implicit conversions

SqlUtils.usingConnection("test") { connection =>
  connection.withQuery("SELECT * FROM physicists") { resultSet =>
    var names = List.empty[String]
    while(resultSet.next) {
      val name = resultSet.getString("name")
      names = name :: names
    }
    names
  }
}
//=> List[String] = List(Paul Dirac, Albert Einstein, Marie Curie, Richard Feynman, Isaac Newton)

The ResultSet interface feels unnatural because it behaves very differently from Scala collections. In particular, it does not support the higher-order functions that we take for granted in Scala: no map, filter, fold, or for comprehensions...

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