mutable - Scala Standard Library 2.11.6 - scala.collection.parallel.mutable
Keywords: mutable Scala Standard Library 2.11.6 scala.collection.parallel.mutable

scala.collection.parallel

# mutable

#### package mutable

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  package.scala

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### Type Members

1. #### trait LazyCombiner[Elem, +To, Buff <: Growable[Elem] with Sizing] extends Combiner[Elem, To]

   Implements combining contents of two combiners by postponing the operation until `result` method is called.

   Implements combining contents of two combiners by postponing the operation until `result` method is called. It chains the leaf results together instead of evaluating the actual collection.

   Elem

     the type of the elements in the combiner

   To

     the type of the collection the combiner produces

   Buff

     the type of the buffers that contain leaf results and this combiner chains together

2. #### class ParArray[T] extends ParSeq[T] with GenericParTemplate[T, ParArray] with ParSeqLike[T, ParArray[T], ArraySeq[T]] with Serializable

   Parallel sequence holding elements in a linear array.

   Parallel sequence holding elements in a linear array.

   `ParArray` is a parallel sequence with a predefined size. The size of the array cannot be changed after it's been created.

   `ParArray` internally keeps an array containing the elements. This means that bulk operations based on traversal ensure fast access to elements. `ParArray` uses lazy builders that create the internal data array only after the size of the array is known. In the meantime, they keep the result set fragmented. The fragments are copied into the resulting data array in parallel using fast array copy operations once all the combiners are populated in parallel.

   T

     type of the elements in the array

   Annotations
     @SerialVersionUID()
   Since

     2.9

   See also

     Scala's Parallel Collections Library overview section on `ParArray` for more information.

3. #### type ParArrayCombiner[T] = ResizableParArrayCombiner[T]

4. #### trait ParFlatHashTable[T] extends FlatHashTable[T]

   Parallel flat hash table.

   Parallel flat hash table.

   T

     type of the elements in the table.

5. #### class ParHashMap[K, V] extends ParMap[K, V] with GenericParMapTemplate[K, V, ParHashMap] with ParMapLike[K, V, ParHashMap[K, V], HashMap[K, V]] with ParHashTable[K, DefaultEntry[K, V]] with Serializable

   A parallel hash map.

   A parallel hash map.

   `ParHashMap` is a parallel map which internally keeps elements within a hash table. It uses chaining to resolve collisions.

   K

     type of the keys in the parallel hash map

   V

     type of the values in the parallel hash map

   Annotations
     @SerialVersionUID()

6. #### class ParHashSet[T] extends ParSet[T] with GenericParTemplate[T, ParHashSet] with ParSetLike[T, ParHashSet[T], HashSet[T]] with ParFlatHashTable[T] with Serializable

   A parallel hash set.

   A parallel hash set.

   `ParHashSet` is a parallel set which internally keeps elements within a hash table. It uses linear probing to resolve collisions.

   T

     type of the elements in the parallel hash set .

   Annotations
     @SerialVersionUID()

7. #### trait ParHashTable[K, Entry >: Null <: HashEntry[K, Entry]] extends HashTable[K, Entry]

   Provides functionality for hash tables with linked list buckets, enriching the data structure by fulfilling certain requirements for their parallel construction and iteration.

8. #### trait ParIterable[T] extends GenIterable[T] with parallel.ParIterable[T] with GenericParTemplate[T, ParIterable] with ParIterableLike[T, ParIterable[T], Iterable[T]] with Mutable

   A template trait for mutable parallel iterable collections.

   A template trait for mutable parallel iterable collections.

   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

   Since

     2.9

9. #### trait ParMap[K, V] extends GenMap[K, V] with parallel.ParMap[K, V] with ParIterable[(K, V)] with GenericParMapTemplate[K, V, ParMap] with ParMapLike[K, V, ParMap[K, V], mutable.Map[K, V]]

   A template trait for mutable parallel maps.

   A template trait for mutable parallel maps.

   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.

   K

     the key type of the map

   V

     the value type of the map

   Since

     2.9

10. #### trait ParMapLike[K, V, +Repr <: ParMapLike[K, V, Repr, Sequential] with ParMap[K, V], +Sequential <: mutable.Map[K, V] with mutable.MapLike[K, V, Sequential]] extends GenMapLike[K, V, Repr] with parallel.ParMapLike[K, V, Repr, Sequential] with Growable[(K, V)] with Shrinkable[K] with mutable.Cloneable[Repr]

    A template trait for mutable parallel maps.

    A template trait for mutable parallel maps. This trait is to be mixed in with concrete parallel maps to override the representation type.

    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.

    K

      the key type of the map

    V

      the value type of the map

11. #### trait ParSeq[T] extends GenSeq[T] with ParIterable[T] with parallel.ParSeq[T] with GenericParTemplate[T, ParSeq] with ParSeqLike[T, ParSeq[T], mutable.Seq[T]]

    A mutable variant of `ParSeq`.

12. #### trait ParSet[T] extends GenSet[T] with ParIterable[T] with parallel.ParSet[T] with GenericParTemplate[T, ParSet] with ParSetLike[T, ParSet[T], mutable.Set[T]]

    A mutable variant of `ParSet`.

13. #### trait ParSetLike[T, +Repr <: ParSetLike[T, Repr, Sequential] with ParSet[T], +Sequential <: mutable.Set[T] with mutable.SetLike[T, Sequential]] extends GenSetLike[T, Repr] with ParIterableLike[T, Repr, Sequential] with parallel.ParSetLike[T, Repr, Sequential] with Growable[T] with Shrinkable[T] with mutable.Cloneable[Repr]

    A template trait for mutable parallel sets.

    A template trait for mutable parallel sets. This trait is mixed in with concrete parallel sets to override the representation type.

    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 set

14. #### final class ParTrieMap[K, V] extends ParMap[K, V] with GenericParMapTemplate[K, V, ParTrieMap] with ParMapLike[K, V, ParTrieMap[K, V], TrieMap[K, V]] with ParTrieMapCombiner[K, V] with Serializable

    Parallel TrieMap collection.

    Parallel TrieMap collection.

    It has its bulk operations parallelized, but uses the snapshot operation to create the splitter. This means that parallel bulk operations can be called concurrently with the modifications.

    Since

      2.10

    See also

      Scala's Parallel Collections Library overview section on `ParTrieMap` for more information.

15. #### trait ResizableParArrayCombiner[T] extends LazyCombiner[T, ParArray[T], ExposedArrayBuffer[T]]

    An array combiner that uses a chain of arraybuffers to store elements.

16. #### trait UnrolledParArrayCombiner[T] extends Combiner[T, ParArray[T]]

    An array combiner that uses doubling unrolled buffers to store elements.

### Value Members

1. #### object ParArray extends ParFactory[ParArray] with Serializable

   This object provides a set of operations to create ``mutable.ParArray`` values.

2. #### val ParArrayCombiner: ResizableParArrayCombiner.type

3. #### object ParHashMap extends ParMapFactory[ParHashMap] with Serializable

   This object provides a set of operations needed to create ``mutable.ParHashMap`` values.

4. #### object ParHashSet extends ParSetFactory[ParHashSet] with Serializable

   This object provides a set of operations needed to create ``mutable.ParHashSet`` values.

5. #### object ParIterable extends ParFactory[ParIterable]

   This object provides a set of operations to create ``ParIterable`` values.

6. #### object ParMap extends ParMapFactory[ParMap]

7. #### object ParSeq extends ParFactory[ParSeq]

   This object provides a set of operations to create ``mutable.ParSeq`` values.

8. #### object ParSet extends ParSetFactory[ParSet]

   This object provides a set of operations needed to create ``mutable.ParSet`` values.

9. #### object ParTrieMap extends ParMapFactory[ParTrieMap] with Serializable

10. #### object ResizableParArrayCombiner

11. #### object UnrolledParArrayCombiner

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