The Architecture of Scala 2.13’s Collections


Julien Richard-Foy

This document describes the architecture of the Scala collections framework in detail. Compared to the Collections Introduction you will find out more about the internal workings of the framework. You will also learn how this architecture helps you define your own collections in a few lines of code, while reusing the overwhelming part of collection functionality from the framework.

The Collections API contains a large number of collection operations, which exist uniformly on many different collection implementations. Implementing every collection operation anew for every collection type would lead to an enormous amount of code, most of which would be copied from somewhere else. Such code duplication could lead to inconsistencies over time, when an operation is added or modified in one part of the collection library but not in others. The principal design objective of the collections framework is to avoid any duplication, defining every operation in as few places as possible. (Ideally, everything should be defined in one place only, but there are a few exceptions where things needed to be redefined.) The design approach was to implement most operations in collection “templates” that can be flexibly inherited from individual base classes and implementations.

More precisely, these templates address the following challenges:

  • some transformation operations return the same concrete collection type (e.g. filter, called on a List[Int] returns a List[Int]),
  • some transformation operations return the same concrete collection type with a different type of elements (e.g. map, called on a List[Int], can return a List[String]),
  • some collections have a single element type (e.g. List[A]), while some others have two (e.g. Map[K, V]),
  • some operations on collections return a different concrete collection depending on the element type. For example, map called on a Map returns a Map if the mapping function returns a key-value pair, but otherwise returns an Iterable,
  • transformation operations on some collections require additional implicit parameters (e.g. map on SortedSet takes an implicit Ordering),
  • some collections are strict (e.g. List), while some others are non-strict (e.g. View and LazyList).

The following sections explain how the templates address these challenges.

Factoring out common operations

This section presents the variability found in collections, which has to be abstracted over to define reusable operation implementations.

We can group collection operations into two categories:

  • transformation operations, which return another collection (e.g. map, filter, zip, …),
  • reduction operations, which return a single value (e.g. isEmpty, foldLeft, find, …).

Transformation operations are harder to implement in template traits because we want them to return collection types that are unknown yet. For instance, consider the signature of the map operation on List[A] and Vector[A]:

trait List[A] {
  def map[B](f: A => B): List[B]

trait Vector[A] {
  def map[B](f: A => B): Vector[B]

To generalize the type signature of map we have to abstract over the resulting collection type constructor.

A slightly different example is filter. Consider its type signature on List[A] and Map[K, V]:

trait List[A] {
  def filter(p: A => Boolean): List[A]

trait Map[K, V] {
  def filter(p: ((K, V)) => Boolean): Map[K, V]

To generalize the type signature of filter we have to abstract over the resulting collection type.

In summary, operations that change the elements type (map, flatMap, collect, etc.) need to abstract over the resulting collection type constructor, and operations that keep the same elements type (filter, take, drop, etc.) need to abstract over the resulting collection type.

Abstracting over collection types

The template trait IterableOps implements the operations available on the Iterable[A] collection type.

Here is the header of trait IterableOps:

trait IterableOps[+A, +CC[_], +C] {  }

The type parameter A stands for the element type of the iterable, the type parameter CC stands for the collection type constructor and the type parameter C stands for the collection type.

This allows us to define the signature of filter and map like so:

trait IterableOps[+A, +CC[_], +C] {
  def filter(p: A => Boolean): C = 
  def map[B](f: A => B): CC[B] = 

Leaf collection types appropriately instantiate the type parameters. For instance, in the case of List[A] we want CC to be List and C to be List[A]:

trait List[+A] extends Iterable[A]
  with IterableOps[A, List, List[A]]

Four branches of templates traits

The astute reader might have noticed that the given type signature for the map operation doesn’t work with Map collections because the CC[_] type parameter of the IterableOps trait takes one type parameter whereas Map[K, V] takes two type parameters.

To support collection types constructors with two types parameters we have another template trait named MapOps:

trait MapOps[K, +V, +CC[_, _], +C] extends IterableOps[(K, V), Iterable, C] {
  def map[K2, V2](f: ((K, V)) => (K2, V2)): CC[K2, V2] = 

And then Map[K, V] can extend this trait and appropriately instantiate its type parameters:

trait Map[K, V] extends Iterable[(K, V)]
  with MapOps[K, V, Map, Map[K, V]]

Note that the MapOps trait inherits from IterableOps so that operations defined in IterableOps are also available in MapOps. Also note that the collection type constructor passed to the IterableOps trait is Iterable. This means that Map[K, V] inherits two overloads of the map operation:

// from MapOps
def map[K2, V2](f: ((K, V)) => (K2, V2)): Map[K2, V2]

// from IterableOps
def map[B](f: ((K, V)) => B): Iterable[B]

At use-site, when you call the map operation, the compiler selects one of the two overloads. If the function passed as argument to map returns a pair, both functions are applicable. In this case, the version from MapOps is used because it is more specific by the rules of overloading resolution, so the resulting collection is a Map. If the argument function does not return a pair, only the version defined in IterableOps is applicable. In this case, the resulting collection is an Iterable. This is how we follow the “same-result-type” principle: wherever possible a transformation method on a collection yields a collection of the same type.

In summary, the fact that Map collection types take two type parameters makes it impossible to unify their transformation operations with the ones from IterableOps, hence the specialized MapOps template trait.

There is another situation where the type signatures of the transformation operations defined in IterableOps don’t match the type signature of a more concrete collection type: SortedSet[A]. In that case the type signature of the map operation is the following:

def map[B](f: A => B)(implicit ord: Ordering[B]): SortedSet[B]

The difference with the signature we have in IterableOps is that here we need an implicit Ordering instance for the type of elements.

Like for Map, SortedSet needs a specialized template trait with overloads for transformation operations:

trait SortedSetOps[A, +CC[_], +C] extends IterableOps[A, Set, C] {

  def map[B](f: A => B)(implicit ord: Ordering[B]): CC[B] = 


And then collection types that inherit the SortedSetOps trait appropriately instantiate its type parameters:

trait SortedSet[A] extends SortedSetOps[A, SortedSet, SortedSet[A]]

Last, there is a fourth kind of collection that requires a specialized template trait: SortedMap[K, V]. This type of collection has two type parameters and needs an implicit ordering instance on the type of keys. Therefore we have a SortedMapOps template trait that provides the appropriate overloads.

In total, we’ve seen that we have four branches of template traits:

kind not sorted sorted
CC[_] IterableOps SortedSetOps
CC[_, _] MapOps SortedMapOps

Here is a diagram illustrating the architecture:

Template traits are in grey whereas collection types are in white.

Strict and non-strict collections

Another difference that has been taken into account in the design of the collections framework is the fact that some collection types eagerly evaluate their elements (e.g. List, Set, etc.), whereas others delay their evaluation until the element is effectively accessed (e.g. LazyList and View). The former category of collections is said to be “strict”, whereas the latter is said to be “non-strict”.

Thus, the default implementation of transformation operations must preserve the “strictness” of the concrete collection type that inherits these implementations. For instance, we want the default map implementation to be non-strict when inherited by a View, and strict when inherited by a List.

To achieve that, operations are, by default, implemented in terms of a non-strict View. For the record, a View “describes” an operation applied to a collection but does not evaluate its result until the View is effectively traversed. Here is the (simplified) definition of View:

trait View[+A] extends Iterable[A] with IterableOps[A, View, View[A]] {
  def iterator: Iterator[A]

A View is an Iterable that has only one abstract method returning an Iterator for traversing its elements. The View elements are evaluated only when its Iterator is traversed.

Operations implementation

Now that we are more familiar with the hierarchy of the template traits, we can have a look at the actual implementation of some operations. Consider for instance the implementations of filter and map:

trait IterableOps[+A, +CC[_], +C] {

  def filter(pred: A => Boolean): C =
    fromSpecific(new View.Filter(this, pred))

  def map[B](f: A => B): CC[B] = 
    from(new View.Map(this, f))

  protected def fromSpecific(coll: IterableOnce[A]): C
  protected def from[E](it: IterableOnce[E]): CC[E]

Let’s detail the implementation of filter, step by step:

  • the instantiation of View.Filter creates a (non-strict) View that filters the elements of the underlying collection ;
  • the call to fromSpecific turns the View into a concrete collection C. The implementation of fromSpecific is left to concrete collections: they can decide to evaluate in a strict or non-strict way the elements resulting from the operation.

The implementation of map is similar, except that instead of using fromSpecific it uses from which takes as parameter an iterable whose element type E is arbitrary.

Actually, the from operation is not defined directly in IterableOps but is accessed via an (abstract) iterableFactory member:

trait IterableOps[+A, +CC[_], +C] {

  def iterableFactory: IterableFactory[CC]
  def map[B](f: A => B): CC[B] = 
    iterableFactory.from(new View.Map(this, f))  


This iterableFactory member is implemented by concrete collections and typically refer to their companion object, which provides factory methods to create concrete collection instances. Here is an excerpt of the definition of IterableFactory:

trait IterableFactory[+CC[_]] {
  def from[A](source: IterableOnce[A]): CC[A]

Last but not least, as explained in the above sections, since we have four branches of template traits, we have four corresponding branches of factories. For instance, here are the relevant parts of code of the map operation implementation in MapOps:

trait MapOps[K, +V, +CC[_, _], +C]
  extends IterableOps[(K, V), Iterable, C] {

  def map[K2, V2](f: ((K, V)) => (K2, V2)): CC[K2, V2] =
    mapFactory.from(new View.Map(this, f))

  // Similar to iterableFactory, but for Map collection types
  def mapFactory: MapFactory[CC]


trait MapFactory[+CC[_, _]] {
  def from[K, V](it: IterableOnce[(K, V)]): CC[K, V]

When a strict evaluation is preferable (or unavoidable)

In the previous sections we explained that the “strictness” of concrete collections should be preserved by default operation implementations. However in some cases this leads to less efficient implementations. For instance, partition has to perform two traversals of the underlying collection. In some other case (e.g. groupBy) it is simply not possible to implement the operation without evaluating the collection elements.

For those cases, we also provide ways to implement operations in a strict mode. The pattern is different: instead of being based on a View, it is based on a Builder. Here is an outline of the Builder trait:

package scala.collection.mutable

trait Builder[-A, +C] {
  def addOne(elem: A): this.type
  def result(): C

Builders are generic in both the element type A and the type of collection they return, C. You can add an element x to a builder b with b.addOne(x) (or b += x). The result() method returns a collection from a builder.

By symmetry with fromSpecificIterable and fromIterable, template traits provide ways to get a builder resulting in a collection with the same type of elements, and to get a builder resulting in a collection of the same type but with a different type of elements. The following code shows the relevant parts of IterableOps and IterableFactory to build collections in both strict and non-strict modes:

trait IterableOps[+A, +CC[_], +C] {
  def iterableFactory: IterableFactory[CC]
  protected def fromSpecific(coll: IterableOnce[A]): C
  protected def newSpecificBuilder: Builder[A, C]

trait IterableFactory[+CC[_]] {
  def from[A](source: IterableOnce[A]): CC[A]
  def newBuilder[A]: Builder[A, CC[A]]

Note that, in general, an operation that doesn’t have to be strict should be implemented in a non-strict mode, otherwise it would lead to surprising behaviour when used on a non-strict concrete collection (you can read more about that statement in this article). That being said, the strict mode is often more efficient. This is why we provide template traits whose operation implementations have been overridden to take advantage of strict builders. The name of these template traits always starts with StrictOptimized. You should use such a template trait for your custom collection if it is a strict collection.


This document explains that:

  • collection operations are implemented in template traits suffixed with Ops (e.g. IterableOps[A, CC[_], C]),
  • these template traits abstract over the type of collection elements (A), the type constructor of returned collections (CC) and the type of returned collections (C),
  • there are four branches of template traits (IterableOps, MapOps, SortedSetOps and SortedMapOps),
  • some transformation operations (e.g. map) are overloaded to return different result types according to their arguments type,
  • the logic of transformation operations is primarily implemented in views but there are specialized versions of template traits (prefixed with StrictOptimized) that override these operations to use a builder based approach.

You now have all the required knowledge to implement custom collection types.


This page contains material adapted from the book Programming in Scala by Odersky, Spoon and Venners. We thank Artima for graciously agreeing to its publication.

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