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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

Resilient distributed datasets


Spark expresses all computations as a sequence of transformations and actions on distributed collections, called Resilient Distributed Datasets (RDD). Let's explore how RDDs work with the Spark shell. Navigate to the examples directory and open a Spark shell as follows:

$ spark-shell
scala> 

Let's start by loading an email in an RDD:

scala> val email = sc.textFile("ham/9-463msg1.txt")
email: rdd.RDD[String] = MapPartitionsRDD[1] at textFile

email is an RDD, with each element corresponding to a line in the input file. Notice how we created the RDD by calling the textFile method on an object called sc:

scala> sc
spark.SparkContext = org.apache.spark.SparkContext@459bf87c

sc is a SparkContext instance, an object representing the entry point to the Spark cluster (for now, just our local machine). When we start a Spark shell, a context is created and bound to the variable sc automatically.

Let's split the email into words using flatMap:

scala> val words...
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