Packages

•  package root

  This is the documentation for the Scala standard library.

  This is the documentation for the Scala standard library.

  Package structure

  The scala package contains core types like Int, Float, Array or Option which are accessible in all Scala compilation units without explicit qualification or imports.

  Notable packages include:

  • scala.collection and its sub-packages contain Scala's collections framework
    • scala.collection.immutable - Immutable, sequential data-structures such as Vector, List, Range, HashMap or HashSet
    • scala.collection.mutable - Mutable, sequential data-structures such as ArrayBuffer, StringBuilder, HashMap or HashSet
    • scala.collection.concurrent - Mutable, concurrent data-structures such as TrieMap
    • scala.collection.parallel.immutable - Immutable, parallel data-structures such as ParVector, ParRange, ParHashMap or ParHashSet
    • scala.collection.parallel.mutable - Mutable, parallel data-structures such as ParArray, ParHashMap, ParTrieMap or ParHashSet
  • scala.concurrent - Primitives for concurrent programming such as Futures and Promises
  • scala.io - Input and output operations
  • scala.math - Basic math functions and additional numeric types like BigInt and BigDecimal
  • scala.sys - Interaction with other processes and the operating system
  • scala.util.matching - Regular expressions

  Other packages exist. See the complete list on the right.

  Additional parts of the standard library are shipped as separate libraries. These include:

  • scala.reflect - Scala's reflection API (scala-reflect.jar)
  • scala.xml - XML parsing, manipulation, and serialization (scala-xml.jar)
  • scala.swing - A convenient wrapper around Java's GUI framework called Swing (scala-swing.jar)
  • scala.util.parsing - Parser combinators (scala-parser-combinators.jar)

  Automatic imports

  Identifiers in the scala package and the scala.Predef object are always in scope by default.

  Some of these identifiers are type aliases provided as shortcuts to commonly used classes. For example, List is an alias for scala.collection.immutable.List.

  Other aliases refer to classes provided by the underlying platform. For example, on the JVM, String is an alias for java.lang.String.

  Definition Classes

  root

•  package scala

  Core Scala types.

  Core Scala types. They are always available without an explicit import.

  Definition Classes

  root

•  package collection

  Contains the base traits and objects needed to use and extend Scala's collection library.

  Contains the base traits and objects needed to use and extend Scala's collection library.

  Guide

  A detailed guide for using the collections library is available at http://docs.scala-lang.org/overviews/collections/introduction.html. Developers looking to extend the collections library can find a description of its architecture at http://docs.scala-lang.org/overviews/core/architecture-of-scala-collections.html.

  Using Collections

  It is convenient to treat all collections as either a scala.collection.Traversable or scala.collection.Iterable, as these traits define the vast majority of operations on a collection.

  Collections can, of course, be treated as specifically as needed, and the library is designed to ensure that the methods that transform collections will return a collection of the same type:

  scala> val array = Array(1,2,3,4,5,6)
  array: Array[Int] = Array(1, 2, 3, 4, 5, 6)
  
  scala> array map { _.toString }
  res0: Array[String] = Array(1, 2, 3, 4, 5, 6)
  
  scala> val list = List(1,2,3,4,5,6)
  list: List[Int] = List(1, 2, 3, 4, 5, 6)
  
  scala> list map { _.toString }
  res1: List[String] = List(1, 2, 3, 4, 5, 6)

  Creating Collections

  The most common way to create a collection is to use its companion object as a factory. The three most commonly used collections are scala.collection.Seq, scala.collection.immutable.Set, and scala.collection.immutable.Map. They can be used directly as shown below since their companion objects are all available as type aliases in either the scala package or in scala.Predef. New collections are created like this:

  scala> val seq = Seq(1,2,3,4,1)
  seq: Seq[Int] = List(1, 2, 3, 4, 1)
  
  scala> val set = Set(1,2,3,4,1)
  set: scala.collection.immutable.Set[Int] = Set(1, 2, 3, 4)
  
  scala> val map = Map(1 -> "one", 2 -> "two", 3 -> "three", 2 -> "too")
  map: scala.collection.immutable.Map[Int,String] = Map(1 -> one, 2 -> too, 3 -> three)

  It is also typical to prefer the scala.collection.immutable collections over those in scala.collection.mutable; the types aliased in the scala.Predef object are the immutable versions.

  Also note that the collections library was carefully designed to include several implementations of each of the three basic collection types. These implementations have specific performance characteristics which are described in the guide.

  The concrete parallel collections also have specific performance characteristics which are described in the parallel collections guide

  Converting to and from Java Collections

  The scala.collection.JavaConverters object provides a collection of decorators that allow converting between Scala and Java collections using asScala and asJava methods.

  Definition Classes

  scala

•  package parallel

  Package object for parallel collections.

  Package object for parallel collections.

  Definition Classes

  collection

•  trait ParIterableLike[+T, +Repr <: ParIterable[T], +Sequential <: scala.Iterable[T] with IterableLike[T, Sequential]] extends GenIterableLike[T, Repr] with CustomParallelizable[T, Repr] with Parallel with HasNewCombiner[T, Repr]

  A template trait for parallel collections of type ParIterable[T].

  A template trait for parallel collections of type ParIterable[T].

  This is a base trait for Scala parallel collections. It defines behaviour common to all parallel collections. Concrete parallel collections should inherit this trait and ParIterable if they want to define specific combiner factories.

  Parallel operations are implemented with divide and conquer style algorithms that parallelize well. The basic idea is to split the collection into smaller parts until they are small enough to be operated on sequentially.

  All of the parallel operations are implemented as tasks within this trait. Tasks rely on the concept of splitters, which extend iterators. Every parallel collection defines:

  def splitter: IterableSplitter[T]

  which returns an instance of IterableSplitter[T], which is a subtype of Splitter[T]. Splitters have a method remaining to check the remaining number of elements, and method split which is defined by splitters. Method split divides the splitters iterate over into disjunct subsets:

  def split: Seq[Splitter]

  which splits the splitter into a sequence of disjunct subsplitters. This is typically a very fast operation which simply creates wrappers around the receiver collection. This can be repeated recursively.

  Tasks are scheduled for execution through a scala.collection.parallel.TaskSupport object, which can be changed through the tasksupport setter of the collection.

  Method newCombiner produces a new combiner. Combiners are an extension of builders. They provide a method combine which combines two combiners and returns a combiner containing elements of both combiners. This method can be implemented by aggressively copying all the elements into the new combiner or by lazily binding their results. It is recommended to avoid copying all of the elements for performance reasons, although that cost might be negligible depending on the use case. Standard parallel collection combiners avoid copying when merging results, relying either on a two-step lazy construction or specific data-structure properties.

  Methods:

  def seq: Sequential
  def par: Repr

  produce the sequential or parallel implementation of the collection, respectively. Method par just returns a reference to this parallel collection. Method seq is efficient - it will not copy the elements. Instead, it will create a sequential version of the collection using the same underlying data structure. Note that this is not the case for sequential collections in general - they may copy the elements and produce a different underlying data structure.

  The combination of methods toMap, toSeq or toSet along with par and seq is a flexible way to change between different collection types.

  Since this trait extends the GenIterable trait, methods like size must also be implemented in concrete collections, while iterator forwards to splitter by default.

  Each parallel collection is bound to a specific fork/join pool, on which dormant worker threads are kept. The fork/join pool contains other information such as the parallelism level, that is, the number of processors used. When a collection is created, it is assigned the default fork/join pool found in the scala.parallel package object.

  Parallel collections are not necessarily ordered in terms of the foreach operation (see Traversable). Parallel sequences have a well defined order for iterators - creating an iterator and traversing the elements linearly will always yield the same order. However, bulk operations such as foreach, map or filter always occur in undefined orders for all parallel collections.

  Existing parallel collection implementations provide strict parallel iterators. Strict parallel iterators are aware of the number of elements they have yet to traverse. It's also possible to provide non-strict parallel iterators, which do not know the number of elements remaining. To do this, the new collection implementation must override isStrictSplitterCollection to false. This will make some operations unavailable.

  To create a new parallel collection, extend the ParIterable trait, and implement size, splitter, newCombiner and seq. Having an implicit combiner factory requires extending this trait in addition, as well as providing a companion object, as with regular collections.

  Method size is implemented as a constant time operation for parallel collections, and parallel collection operations rely on this assumption.

  The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.

  T

  the element type of the collection

  Repr

  the type of the actual collection containing the elements

  Definition Classes

  parallel

•  trait BuilderOps[Elem, To] extends AnyRef

  Definition Classes

  ParIterableLike

• Otherwise

scala.collection.parallel.ParIterableLike.BuilderOps

Otherwise 