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.

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.

Parallel flat hash table.

A parallel hash map.

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

A parallel hash set.

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

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.

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.

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.

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.

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.