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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

Accessing database metadata


Commonly, especially during development, you might start the script by dropping the table if it exists, then recreating it. We can find if a table is defined by accessing the database metadata through the MTable object. To get a list of tables with name matching a certain pattern, we can run MTable.getTables(pattern):

scala> import slick.jdbc.meta.MTable
import slick.jdbc.meta.MTable

scala> db.withSession { implicit session =>
  MTable.getTables("transactions").list
}
List[scala.slick.jdbc.meta.MTable] = List(MTable(MQName(fec.transactions),TABLE,,None,None,None) ...)

Thus, to drop the transactions table if it exists, we can run the following:

scala> db.withSession { implicit session =>
  if(MTable.getTables("transactions").list.nonEmpty) {
    Tables.transactions.ddl.drop
  }
}

The MTable instance contains a lot of metadata about the table. Go ahead and recreate the transactions table if you dropped it in the previous example. Then, to find information...

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