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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

Building and running standalone programs


So far, we have interacted exclusively with Spark through the Spark shell. In the section that follows, we will build a standalone application and launch a Spark program either locally or on an EC2 cluster.

Running Spark applications locally

The first step is to write the build.sbt file, as you would if you were running a standard Scala script. The Spark binary that we downloaded needs to be run against Scala 2.10 (You need to compile Spark from source to run against Scala 2.11. This is not difficult to do, just follow the instructions on http://spark.apache.org/docs/latest/building-spark.html#building-for-scala-211).

// build.sbt file

name := "spam_mi"

scalaVersion := "2.10.5"

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % "1.4.1"
)

We then run sbt package to compile and build a jar of our program. The jar will be built in target/scala-2.10/, and called spam_mi_2.10-0.1-SNAPSHOT.jar. You can try this with the example code provided...

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