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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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 Bugnion Bugnion
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Bugnion
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Table of Contents (22) Chapters Close

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

Enriching JDBC statements with the "pimp my library" pattern


In the previous section, we saw how to create self-closing connections with the loan pattern. This allows us to open connections to the database without having to remember to close them. However, we still have to remember to close any ResultSet and PreparedStatement that we open:

// WARNING: Poor Scala code
SqlUtils.usingConnection("test") { connection =>
  val statement = connection.prepareStatement(
    "SELECT * FROM physicists")
  val results = statement.executeQuery
  // do something useful with the results
  results.close
  statement.close
}

Having to open and close the statement is somewhat ugly and error prone. This is another natural use case for the loan pattern. Ideally, we would like to write the following:

usingConnection("test") { connection =>
  connection.withQuery("SELECT * FROM physicists") {
    resultSet => // process results
  }
}

How can we define a .withQuery method on the Connection class? We do not...

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