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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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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

First steps with JDBC


Let's start by connecting to JDBC from the command line. To follow with the examples, you will need access to a running MySQL server. If you added the MySQL connector to the list of dependencies, open a Scala console by typing the following command:

$ sbt console

Let's import JDBC:

scala> import java.sql._
import java.sql._

We then need to tell JDBC to use a specific connector. This is normally done using reflection, loading the driver at runtime:

scala> Class.forName("com.mysql.jdbc.Driver")
Class[_] = class com.mysql.jdbc.Driver

This loads the appropriate driver into the namespace at runtime. If this seems somewhat magical to you, it's probably not worth worrying about exactly how this works. This is the only example of reflection that we will consider in this book, and it is not particularly idiomatic Scala.

Connecting to a database server

Having specified the SQL connector, we can now connect to a database. Let's assume that we have a database called test on...

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