This library implements several of the more commonly useful immutable data structures for Rust. They rely on structural sharing to keep most operations fast without needing to mutate the underlying data store, leading to more predictable code without necessarily sacrificing performance.

Because Rust is not a garbage collected language, and immutable data structures generally rely on some sort of garbage collection, values inside these data structures are kept inside Arc s. Methods will generally accept either owned values or Arc s and perform conversion as needed, but you'll have to expect to receive Arc s when iterating or performing lookup operations. All caveats about using reference counted values apply in general (eg. reference counting is simplistic and doesn't detect loops).

A design goal of this library is to make using immutable data structures as easy as it is in higher level languages, but obviously there's only so much you can do. Methods will generally attempt to coerce argument values where they can: where an Arc is called for, it will be able to figure out how to convert whatever is provided into an Arc if it isn't already.

It's also been a design goal to provide as complete an API as possible, which in a practical sense has meant going over the equivalent implementations for Haskell to ensure the API covers the same set of use cases. This obviously doesn't include things like Foldable and Functor which aren't yet expressible in Rust, but in these cases we've tried to make sure Rust iterators are able to perform the same tasks.

Care has been taken to use method names similar to those in Rust over those used in the source material (largely Haskell) where possible (eg. Vector::new() rather than Vector::empty() , HashMap::get() rather than HashMap::lookup() ). Where Rust equivalents don't exist, terminology tends to follow Haskell where the Haskell isn't too confusing, or, when it is, we provide more readily understandable aliases (because we wouldn't want to deprive the user of their enjoyment of the word ' snoc ,' even though it's reportedly not an obviously intuitive term).

Programming with immutable values, meaning that references to values can be relied on to always point to the same value, means that you can stop worrying about other parts of your code tampering with a value you're working on unexpectedly, or from unexpected parts of your code, making it a lot easier to keep track of what your code is actually doing.

Mutable values are, generally, a huge source of unexpected behaviour that a lot of languages, like Haskell, Elm and Clojure, have been designed to avoid altogether. Rust, being what it is, does a good job of discouraging this kind of behaviour, and keeping it strictly controlled when it's necessary, but the standard library doesn't provide collection data structures which are optimised for immutable operations. This means, for instance, that if you want to add an item to a Vec without modifying it in place, you first need to clone the whole thing before making your change.

Data structures exist which are designed to be able to make these copies much cheaper, usually by sharing structure between them, which, because this structure is also immutable, is both cheap and safe. The most basic example of this kind of data structure is the ConsList , where, if you have a list L and you want to push an item I to the front of it, you'll get back a new list which literally contains the data 'item I followed by list L.' This operation is extremely inexpensive, but of course this also means that certain other operations which would be inexpensive for a Vec are much more costly for a ConsList —index lookup is an example of this, where for a Vec it's just a matter of going to memory location index times item size inside the Vec 's memory buffer, but for a ConsList you'd have to walk through the entire list from the start, following references through to other list nodes, until you get to the right item.

While all immutable data structures tend to be less efficient than their mutable counterparts, when chosen carefully they can perform just as well for the operations you need, and there are some, like Vector and HashMap , which have performance characteristics good enough for most operations that you can safely choose them without worrying too much about whether they're going to be the right choice for any given use case. Better yet, most of them can even be safely mutated in place when they aren't sharing any structure with other instances, making them nearly as performant as their mutable counterparts.

We'll attempt to provide a comprehensive guide to the available data structures below.

If you're not familiar with big O notation, here's a quick cheat sheet:

O(1) means an operation runs in constant time: it will take the same time to complete regardless of the size of the data structure.

O(n) means an operation runs in linear time: if you double the size of your data structure, the operation will take twice as long to complete; if you quadruple the size, it will take four times as long, etc.

O(log n) means an operation runs in logarithmic time: for log 2 , if you double the size of your data structure, the operation will take one step longer to complete; if you quadruple the size, it will need two steps more; and so on. However, the data structures in this library generally run in log 16 time, meaning you have to make your data structure 16 times bigger to need one extra step, and 256 times bigger to need two steps. This means that, while they still count as O(log n), operations on all but really large data sets will run at near enough to O(1) that you won't usually notice.

O(1)* means 'amortised O(1),' which means that an operation usually runs in constant time but will occasionally be more expensive, often O(n). Please note that this is not a common notation; it's just a convention I've used in these docs to save myself from having to type 'amortised' everywhere.

Lists are ordered sequences of single elements, usually with cheap push/pop operations, and index lookup tends to be O(n). Lists are for collections of items where you expect to iterate rather than lookup.

Type Constraints Order Push Front Pop Front Push Back Pop Back Append Lookup Vector<A> insertion O(log n) O(log n) O(log n) O(log n) O(n) O(log n) CatList<A> insertion O(1) O(1)* O(1) O(1)* O(1) O(n) ConsList<A> insertion O(1) O(1) O(n) O(n) O(n) O(n)

Maps are mappings of keys to values, where the most common read operation is to find the value associated with a given key. Maps may or may not have a defined order. Any given key can only occur once inside a map, and setting a key to a different value will overwrite the previous value.

Sets are collections of unique values, and may or may not have a defined order. Their crucial property is that any given value can only exist once in a given set.

Values in sets, in practice, behave exactly like keys in maps, and are therefore normally implemented as maps from type A to type () .

Type Constraints Order Insert Remove Lookup HashSet<A> Hash + Eq undefined O(log n) O(log n) O(log n) OrdSet<A> Ord sorted O(log n) O(log n) O(log n)

Most of these data structures support in-place copy-on-write mutation, which means that if you're the sole user of a data structure, you can update it in place with a huge performance benefit (about an order of magnitude faster than immutable operations, almost as fast as std::collections 's mutable data structures).