Scala collections wizardry - Scalapeño

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Scala collections wizardry - Scalapeño

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  Warm up example: Fibonaccisequence val fibs: Stream[Int] = 0 #:: fibs.scanLeft(1)(_ + _) Key concepts: • Recursive values • Streams • Scan • Binary place-holder notation

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  Immutable collections You’ll knowabout • Avoid memory allocation for empty collections • Optimize for small collections • Equal-hashCode contract • Asymptotic behavior of common operations

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  Computing a derivative defderivative(nums: Iterable[Double]) = nums.sliding(2) .map (pair => pair._2 - pair._1) What can be improved in this solution? Bonus question: change a few characters to find the max slope

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  Word n-grams val range= 1 to 3 val text = "hello sweet world" val tokenize = (s: String) => s.split(" ") range flatMap (size => tokenize(text) sliding size) Result: Vector(Array(hello), Array(sweet), Array(world), Array(hello, sweet), Array(sweet, world), Array(hello, sweet, world))

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  Are all membersof a greater than corresponding members of b val a = List(2,3,4) val b = List(1,2,3) // O(n^2) and not very elegant. (0 until a.size) forall (i => a(i) > b(i)) // O(n) but creates tuples and a temporary list. Yet, more elegant. a zip b forall (x=> x._1 > x._2) // same as above but doesn't create a temporary list (lazy) a.view zip b forall (x=> x._1 > x._2) // O(n), without tuple or temporary list creation, and even more elegant. (a corresponds b)(_ > _)

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  Strings are collections.How come? “abc”.max @inline implicit def augmentString(x: String) = new StringOps(x) String <% StringOps <: StringLike <: IndexedSeqOptimized …

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  Complexity of collectionoperations • Linear: – Unary: O(n): • Mappers: map, collect • Reducers: reduce, foldLeft, foldRight • Others: foreach, filter, indexOf, reverse, find, mkString – Binary: O(n+ m): • union, diff, and intersect

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  Immutable Collections timecomplexity head tail apply update prepend append List C C L L C L Stream C C L L C L Vector eC eC eC eC eC eC Stack C C L L C L Queue aC aC L L L C Range C C C - - - String C L C L L L

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  Mutable Collections timecomplexity head tail apply update prepend append insert ArrayBuffer C L C C L aC L ListBuffer C L L L C C L StringBuilde r C L C C L aC L MutableList C L L L C C L Queue C L L L C C L ArraySeq C L C C - - - Stack C L L L C L L ArrayStack C L C C aC L L Array C L C C - - -

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  Equals-hashCode contract (a equalsb)  (a.hashCode == b.hashCode) All Scala collection implement the contract Bad idea: Set[Array[Int]] Good idea: Set[Vector[Int]] Bad Idea: Set[ArrayBuffer[Int]] Bad Idea: Set[collection.mutable._] Good Idea: Set[collection.immutable._]

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  More on collectionsequality val (a, b) = (1 to 3, List(1, 2, 3)) a == b // true Q: Wait, how efficient is Range.hashCode? A: O(n) override def hashCode = util.hashing.MurmurHash3.seqHash(seq) Challenge yourself: Is there a closed (o(1)) formula for a range hashCode?

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  Java interoperability Implicit (lessboilerplate): import collection.javaConversions._ javaCollection.filter(…) Explicit (better control): Import collection.javaConverters._ javaCollection.asScala.filter(…) scalaCollection.asJava

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  The power oftype-level programming graph path-finding in compile time import scala.language.implicitConversions // Vertices case class A(l: List[Char]) case class B(l: List[Char]) case class C(l: List[Char]) case class D(l: List[Char]) case class E(l: List[Char]) // Edges implicit def ad[A1 <% A](x: A1) = D(x.l :+ 'A') implicit def bc[B1 <% B](x: B1) = C(x.l :+ 'B') implicit def ce[C1 <% C](x: C1) = E(x.l :+ 'C') implicit def ea[E1 <% E](x: E1) = A(x.l :+ 'E') def pathFrom(end:D) = end pathFrom(B(Nil)) // res0: D = D(List(B, C, E, A))

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  Want to goPro? • Shapeless (Miles Sabin) – Polytypic programming & Heterogenous lists – github.com/milessabin/shapeless • Scalaxy (Olivier Chafik) – Macros for boosting performance of collections – github.com/ochafik/Scalaxy