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Scala Functional Programming Patterns

You're reading from   Scala Functional Programming Patterns Grok and perform effective functional programming in Scala

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Product type Paperback
Published in Dec 2015
Publisher
ISBN-13 9781783985845
Length 298 pages
Edition 1st Edition
Languages
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Author (1):
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Atul S. Khot Atul S. Khot
Author Profile Icon Atul S. Khot
Atul S. Khot
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Table of Contents (13) Chapters Close

Preface 1. Grokking the Functional Way 2. Singletons, Factories, and Builders FREE CHAPTER 3. Recursion and Chasing your Own Tail 4. Lazy Sequences – Being Lazy, Being Good 5. Taming Multiple Inheritance with Traits 6. Currying Favors with Your Code 7. Of Visitors and Chains of Responsibilities 8. Traversals – Mapping/Filtering/Folding/Reducing 9. Higher Order Functions 10. Actors and Message Passing 11. It's a Paradigm Shift Index

Streams are collections


Scala collections shine as we almost never need to explicitly loop over them. Using functional combinators like map, flatMap, and filter we get things done declaratively. This comes very handy, as we will soon see.

Streams are lazy lists. You guessed right—these are collections all right:

scala> def succ(n: Int):Stream[Int] = n #:: succ(n+1)
succ: (n: Int)Stream[Int]

scala> lazy val r = succ(0)
r: Stream[Int] = <lazy>
scala> println(r.take(10).mkString(" + "))   
0 + 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 
scala> val evenNums = r filter { x => x %2 == 0 } // 1
evenNums: scala.collection.immutable.Stream[Int] = Stream(0, ?) 

We are jumping the gun a bit here. We are using filter, a functional combinator. We will be taking a detailed look at combinators in a next chapter. Refer to the following information box for a quick introduction.

Note

scala> val p = List(1,2,3,4) 
p: List[Int] = List(1, 2, 3, 4) 
scala> p filter { x => x > 2 } 
res40...
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