SequencesAndCollections.rst

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Sequences And Collections in Swift

Unlike many languages, Swift provides a rich taxonomy of abstractions for processing series of elements. This document explains why that taxonomy exists and how it is structured.

Sequences

It all begins with Swift's for…in loop:

for x in s {
  doSomethingWith(x)
}

Because this construct is generic, s could be

• an array
• a set
• a linked list
• a series of UI events
• a file on disk
• a stream of incoming network packets
• an infinite series of random numbers
• a user-defined data structure
• etc.

In Swift, all of the above are called sequences, an abstraction represented by the SequenceType protocol:

protocol SequenceType {
  typealias Generator : GeneratorType
  func generate() -> Generator
}

Hiding Generator Type Details

A sequence's generator is an associated type—rather than something like AnyGenerator<T> that depends only on the element type—for performance reasons. Although the alternative design has significant usability benefits, it requires one dynamic allocation/deallocation pair and N dynamic dispatches to traverse a sequence of length N. That said, our optimizer has improved to the point where it can sometimes remove these overheads completely, and we are considering changing the design accordingly.

As you can see, sequence does nothing more than deliver a generator. To understand the need for generators, it's important to distinguish the two kinds of sequences.

• Volatile sequences like “stream of network packets,” carry their own traversal state, and are expected to be “consumed” as they are traversed.
• Stable sequences, like arrays, should not be mutated by for…in, and thus require separate traversal state.

To get an initial traversal state for an arbitrary sequence x, Swift calls x.generate(). The sequence delivers that state, along with traversal logic, in the form of a generator.

Generators

for…in needs three operations from the generator:

• get the current element
• advance to the next element
• detect whether there are more elements

If we literally translate the above into protocol requirements, we get something like this:

protocol NaiveGeneratorType {
  typealias Element
  var current() -> Element      // get the current element
  mutating func advance()       // advance to the next element
  var isExhausted: Bool         // detect whether there are more elements
}

Such a protocol, though, places a burden on implementors of volatile sequences: either the generator must buffer the current element internally so that current can repeatedly return the same value, or it must trap when current is called twice without an intervening call to moveToNext. Both semantics have a performance cost, and the latter unnecessarily adds the possibility of incorrect usage.

NSEnumerator

You might recognize the influence on generators of the NSEnumerator API:

class NSEnumerator : NSObject {
  func nextObject() -> AnyObject?
}

Therefore, Swift's GeneratorType merges the three operations into one, returning nil when the generator is exhausted:

protocol GeneratorType {
  typealias Element
  mutating func next() -> Element?
}

Combined with SequenceType, we now have everything we need to implement a generic for…in loop.

Adding a Buffer

The use-cases for singly-buffered generators are rare enough that it is not worth complicating GeneratorType, [1] but support for buffering would fit nicely into the scheme, should it prove important:

public protocol BufferedGeneratorType
  : GeneratorType {
  var latest: Element? {get}
}

The library could easily offer a generic wrapper that adapts any GeneratorType to create a BufferedGeneratorType:

/// Add buffering to any GeneratorType G
struct BufferedGenerator<G: GeneratorType>
  : BufferedGeneratorType {

  public init(_ baseGenerator: G) {
    self._baseGenerator = baseGenerator
  }
  public func next() -> Element? {
    latest = _baseGenerator.next() ?? latest
    return latest
  }
  public private(set) var
    latest: G.Element? = nil
  private var _baseGenerator: G
}

Operating on Sequences Generically

Given an arbitrary SequenceType, aside from a simple for…in loop, you can do anything that requires reading elements from beginning to end. For example:

// Return an array containing the elements of `source`, with
// `separator` interposed between each consecutive pair.
func array<S: SequenceType>(
  source: S,
  withSeparator separator: S.Generator.Element
) -> [S.Generator.Element] {
  var result: [S.Generator.Element] = []
  var g = source.generate()
  if let start = g.next() {
    result.append(start)
    while let next = g.next() {
      result.append(separator)
      result.append(next)
    }
  }
  return result
}

let s = String(array("Swift", withSeparator: "|"))
print(s)        // "S|w|i|f|t"

Because sequences may be volatile, though, you can—in general—only make a single traversal. This capability is quite enough for many languages: the iteration abstractions of Java, C#, Python, and Ruby all go about as far as SequenceType, and no further. In Swift, though, we want to do much more generically. All of the following depend on stability that an arbitrary sequence can't provide:

• Finding a sub-sequence
• Finding the element that occurs most often
• Meaningful in-place element mutation (including sorting, partitioning, rotations, etc.)

Generators Should Be Sequences

In principle, every generator is a volatile sequence containing the elements it has yet to return from next(). Therefore, every generator could satisfy the requirements of SequenceType by simply declaring conformance, and returning self from its generate() method. In fact, if it weren't for current language limitations, GeneratorType would refine SequenceType, as follows:

protocol GeneratorType : SequenceType {
  typealias Element
  mutating func next() -> Element?
}

Though we may not currently be able to require that every GeneratorType refines SequenceType, most generators in the standard library do conform to SequenceType.

Fortunately, many real sequences are stable. To take advantage of that stability in generic code, we'll need another protocol.

Collections

A collection is a stable sequence with addressable “positions,” represented by an associated Index type:

protocol CollectionType : SequenceType {
  typealias Index : ForwardIndexType             // a position
  subscript(i: Index) -> Generator.Element {get}

  var startIndex: Index {get}
  var endIndex: Index {get}
}

The way we address positions in a collection is a generalization of how we interact with arrays: we subscript the collection using its Index type:

let ith = c[i]

An index—which must model ForwardIndexType—is a type with a linear series of discrete values that can be compared for equality:

Dictionary Keys

Although dictionaries overload subscript to also operate on keys, a Dictionary's Key type is distinct from its Index type. Subscripting on an index is expected to offer direct access, without introducing overheads like searching or hashing.

protocol ForwardIndexType : Equatable {
  typealias Distance : SignedIntegerType
  func successor() -> Self
}

While one can use successor() to create an incremented index value, indices are more commonly advanced using an in-place increment operator, just as one would when traversing an array: ++i or i++. These operators are defined generically, for all models of ForwardIndexType, in terms of the successor() method.

Every collection has two special indices: a startIndex and an endIndex. In an empty collection, startIndex == endIndex. Otherwise, startIndex addresses the collection's first element, and endIndex is the successor of an index addressing the collection's last element. A collection's startIndex and endIndex form a half-open range containing its elements: while a collection's endIndex is a valid index value for comparison, it is not a valid index for subscripting the collection:

if c.startIndex != c.endIndex { } // OK
c[c.endIndex]                     // Oops! (index out-of-range)

Mutable Collections

A mutable collection is a collection that supports in-place element mutation. The protocol is a simple refinement of CollectionType that adds a subscript setter:

protocol MutableCollectionType : CollectionType {
  subscript(i: Index) -> Generator.Element { get set }
}

The CollectionType protocol does not require collection to support mutation, so it is not possible to tell from the protocol itself whether the order of elements in an instance of a type that conforms to CollectionType has a domain-specific meaning or not. (Note that since elements in collections have stable indices, the element order within the collection itself is stable; the order sometimes does not have a meaning and is not chosen by the code that uses the collection, but by the implementation details of the collection itself.)

MutableCollectionType protocol allows the to replace a specific element, identified by an index, with another one in the same position. This capability essentially allows to rearrange the elements inside the collection in any order, thus types that conform to MutableCollectionType can represent collections with a domain-specific element order (not every instance of a MutableCollectionType has an interesting order, though).

Range Replaceable Collections

The MutableCollectionType protocol implies only mutation of content, not of structure (for example, changing the number of elements). The RangeReplaceableCollectionType protocol adds the capability to perform structural mutation, which in its most general form is expressed as replacing a range of elements, denoted by two indices, by elements from a collection with a different length.

public protocol RangeReplaceableCollectionType : MutableCollectionType {
  mutating func replaceRange<
    C: CollectionType where C.Generator.Element == Self.Generator.Element
  >(
    subRange: Range<Index>, with newElements: C
  )
}

Index Protocols

As a generalization designed to cover diverse data structures, CollectionType provides weaker guarantees than arrays do. In particular, an arbitrary collection does not necessarily offer efficient random access; that property is determined by the protocol conformances of its Index type.

Forward indices are the simplest and most general, capturing the capabilites of indices into a singly-linked list:

1. advance to the next position
2. detect the end position

Bidirectional indices are a refinement of forward indices that additionally support reverse traversal:

protocol BidirectionalIndexType : ForwardIndexType {
  func predecessor() -> Self
}

Indices into a doubly-linked list would be bidirectional, as are the indices that address Characters and UnicodeScalars in a String. Reversing the order of a collection's elements is a simple example of a generic algorithm that depends on bidirectional traversal.

Random access indices have two more requirements: the ability to efficiently measure the number of steps between arbitrary indices addressing the same collection, and the ability to advance an index by a (possibly negative) number of steps:

public protocol RandomAccessIndexType : BidirectionalIndexType {
  func distanceTo(other: Self) -> Distance
  func advancedBy(n: Distance) -> Self
}

From these methods, the standard library derives several other features such as Comparable conformance, index subtraction, and addition/subtraction of integers to/from indices.

The indices of a deque can provide random access, as do the indices into String.UTF16View (when Foundation is loaded) and, of course, array indices. Many common sorting and selection algorithms, among others, depend on these capabilities.

All direct operations on indices are intended to be lightweight, with amortized O(1) complexity. In fact, indices into Dictionary and Set could be bidirectional, but are limited to modeling ForwardIndexType because the APIs of NSDictionary and NSSet—which can act as backing stores of Dictionary and Set—do not efficiently support reverse traversal.

Conclusion

Swift's sequence, collection, and index protocols allow us to write general algorithms that apply to a wide variety of series and data structures. The system has been both easy to extend, and predictably performant. Thanks for taking the tour!

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[1]	This trade-off is not as obvious as it might seem. For example, the C# and C++ analogues for GeneratorType (IEnumerable and input iterator) are saddled with the obligation to provide buffering.