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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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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

JDBC summary


We now have an overview of JDBC. The rest of this chapter will concentrate on writing abstractions that sit above JDBC, making database accesses feel more natural. Before we do this, let's summarize what we have seen so far.

We have used three JDBC classes:

  • The Connection class represents a connection to a specific SQL database. Instantiate a connection as follows:

    import java.sql._
    Class.forName("com.mysql.jdbc.Driver")val connection = DriverManager.getConnection(
      "jdbc:mysql://127.0.0.1:3306/test",
      "root", // username when connecting
      "" // password
    )

    Our main use of Connection instances has been to generate PreparedStatement objects:

    connection.prepareStatement("SELECT * FROM physicists")
  • A PreparedStatement instance represents a SQL statement about to be sent to the database. It also represents the template for a SQL statement with placeholders for values yet to be filled in. The class exposes the following methods:

    statement.executeUpdate

    This sends the statement to the...

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