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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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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

Safer JDBC connections with the loan pattern


We have already seen how to connect to a JDBC database and send statements to the database for execution. This technique, however, is somewhat error prone: you have to remember to close statements; otherwise, you will quickly run out of memory. In more traditional imperative style, we write the following try-finally block around every connection:

// WARNING: poor Scala code
val connection = DriverManager.getConnection(url, user, password)
try {
  // do something with connection
}
finally {
  connection.close()
}

Scala, with first-class functions, provides us with an alternative: the loan pattern. We write a function that is responsible for opening the connection, loaning it to the client code to do something interesting with it, and then closing it when the client code is done. Thus, the client code is not responsible for closing the connection any more.

Let's create a new SqlUtils object with a usingConnection method that leverages the loan pattern...

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