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Scala and Spark for Big Data Analytics

You're reading from  Scala and Spark for Big Data Analytics

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Pages 796 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Sridhar Alla Sridhar Alla
Profile icon Sridhar Alla
View More author details
Toc

Table of Contents (19) Chapters close

Preface 1. Introduction to Scala 2. Object-Oriented Scala 3. Functional Programming Concepts 4. Collection APIs 5. Tackle Big Data – Spark Comes to the Party 6. Start Working with Spark – REPL and RDDs 7. Special RDD Operations 8. Introduce a Little Structure - Spark SQL 9. Stream Me Up, Scotty - Spark Streaming 10. Everything is Connected - GraphX 11. Learning Machine Learning - Spark MLlib and Spark ML 12. My Name is Bayes, Naive Bayes 13. Time to Put Some Order - Cluster Your Data with Spark MLlib 14. Text Analytics Using Spark ML 15. Spark Tuning 16. Time to Go to ClusterLand - Deploying Spark on a Cluster 17. Testing and Debugging Spark 18. PySpark and SparkR

Using Scala implicits

We have addressed implicits in the previous chapters, but here we are going to see more examples. Implicit parameters are very similar to default parameters but they use different mechanisms in order to find the default value.

An implicit parameter is one that is passed to a constructor or a method and is marked as implicit, which means that the compiler will search for an implicit value within the scope if you don't provide a value for this parameter. For example:

scala> def func(implicit x:Int) = print(x) 
func: (implicit x: Int)Unit
scala> func
<console>:9: error: could not find implicit value for parameter x: Int
func
^
scala> implicit val defVal = 2
defVal: Int = 2
scala> func(3)
3

Implicits are very useful for the collection API. For example, the collections API use implicit parameters to supply CanBuildFrom...

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