RBTree

Key-value map implemented as a red-black tree (RBTree) with nodes storing key-value pairs.

A red-black tree is a balanced binary search tree ordered by the keys.

The tree data structure internally colors each of its nodes either red or black, and uses this information to balance the tree during the modifying operations.

Creation: Instantiate class RBTree<K, V> that provides a map from keys of type K to values of type V.

Example:

import RBTree "mo:base/RBTree";
import Nat "mo:base/Nat";
import Debug "mo:base/Debug";

let tree = RBTree.RBTree<Nat, Text>(Nat.compare); // Create a new red-black tree mapping Nat to Text
tree.put(1, "one");
tree.put(2, "two");
tree.put(3, "tree");
for (entry in tree.entries()) {
  Debug.print("Entry key=" # debug_show(entry.0) # " value=\"" # entry.1 #"\"");
}

Performance:

Runtime: O(log(n)) worst case cost per insertion, removal, and retrieval operation.
Heap space: O(n) for storing the entire tree.
Stack space: O(log(n)) for storing the entire tree. n` denotes the number of key-value entries (i.e. nodes) stored in the tree.

Note:

Tree insertion, replacement, and removal produce O(log(n)) garbage objects.

Credits:

The core of this implementation is derived from:

Ken Friis Larsen's RedBlackMap.sml, which itself is based on:
Stefan Kahrs, "Red-black trees with types", Journal of Functional Programming, 11(4): 425-432 (2001), version 1 in web appendix.

Type `Color`

type Color = {#R; #B}

Node color: Either red (#R) or black (#B).

Type `Tree`

type Tree<K, V> = {#node : (Color, Tree<K, V>, (K, ?V), Tree<K, V>); #leaf}

Red-black tree of nodes with key-value entries, ordered by the keys. The keys have the generic type K and the values the generic type V. Leaves are considered implicitly black.

Class `RBTree<K, V>`

class RBTree<K, V>(compare : (K, K) -> O.Order)

A map from keys of type K to values of type V implemented as a red-black tree. The entries of key-value pairs are ordered by compare function applied to the keys.

The class enables imperative usage in object-oriented-style. However, internally, the class uses a functional implementation.

The compare function should implement a consistent total order among all possible values of K and for efficiency, only involves O(1) runtime costs without space allocation.

Example:

import RBTree "mo:base/RBTree";
import Nat "mo:base/Nat";

let tree = RBTree.RBTree<Nat, Text>(Nat.compare); // Create a map of `Nat` to `Text` using the `Nat.compare` order

Costs of instantiation (only empty tree): Runtime: O(1). Heap space: O(1). Stack space: O(1).

Function `share`

func share() : Tree<K, V>

Return a snapshot of the internal functional tree representation as sharable data. The returned tree representation is not affected by subsequent changes of the RBTree instance.

Example:

tree.put(1, "one");
let treeSnapshot = tree.share();
tree.put(2, "second");
RBTree.size(treeSnapshot) // => 1 (Only the first insertion is part of the snapshot.)

Useful for storing the state of a tree object as a stable variable, determining its size, pretty-printing, and sharing it across async function calls, i.e. passing it in async arguments or async results.

Runtime: O(1). Heap space: O(1). Stack space: O(1).

Function `unshare`

func unshare(t : Tree<K, V>) : ()

Reset the current state of the tree object from a functional tree representation.

Example:

import Iter "mo:base/Iter";

tree.put(1, "one");
let snapshot = tree.share(); // save the current state of the tree object in a snapshot
tree.put(2, "two");
tree.unshare(snapshot); // restore the tree object from the snapshot
Iter.toArray(tree.entries()) // => [(1, "one")]

Useful for restoring the state of a tree object from stable data, saved, for example, in a stable variable.

Runtime: O(1). Heap space: O(1). Stack space: O(1).

Function `get`

func get(key : K) : ?V

Retrieve the value associated with a given key, if present. Returns null, if the key is absent. The key is searched according to the compare function defined on the class instantiation.

Example:

tree.put(1, "one");
tree.put(2, "two");

tree.get(1) // => ?"one"

Runtime: O(log(n)). Heap space: O(1). Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree and assuming that the compare function implements an O(1) comparison.

Function `replace`

func replace(key : K, value : V) : ?V

Replace the value associated with a given key, if the key is present. Otherwise, if the key does not yet exist, insert the key-value entry.

Returns the previous value of the key, if the key already existed. Otherwise, null, if the key did not yet exist before.

Example:

import Iter "mo:base/Iter";

tree.put(1, "old one");
tree.put(2, "two");

ignore tree.replace(1, "new one");
Iter.toArray(tree.entries()) // => [(1, "new one"), (2, "two")]

Runtime: O(log(n)). Heap space: O(1) retained memory plus garbage, see the note below. Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree and assuming that the compare function implements an O(1) comparison.

Note: Creates O(log(n)) garbage objects.

Function `put`

func put(key : K, value : V)

Insert a key-value entry in the tree. If the key already exists, it overwrites the associated value.

Example:

import Iter "mo:base/Iter";

tree.put(1, "one");
tree.put(2, "two");
tree.put(3, "three");
Iter.toArray(tree.entries()) // now contains three entries

Runtime: O(log(n)). Heap space: O(1) retained memory plus garbage, see the note below. Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree and assuming that the compare function implements an O(1) comparison.

Note: Creates O(log(n)) garbage objects.

Function `delete`

func delete(key : K)

Delete the entry associated with a given key, if the key exists. No effect if the key is absent. Same as remove(key) except that it does not have a return value.

Example:

import Iter "mo:base/Iter";

tree.put(1, "one");
tree.put(2, "two");

tree.delete(1);
Iter.toArray(tree.entries()) // => [(2, "two")].

Runtime: O(log(n)). Heap space: O(1) retained memory plus garbage, see the note below. Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree and assuming that the compare function implements an O(1) comparison.

Note: Creates O(log(n)) temporary objects that will be collected as garbage.

Function `remove`

func remove(key : K) : ?V

Remove the entry associated with a given key, if the key exists, and return the associated value. Returns null without any other effect if the key is absent.

Example:

import Iter "mo:base/Iter";

tree.put(1, "one");
tree.put(2, "two");

ignore tree.remove(1);
Iter.toArray(tree.entries()) // => [(2, "two")].

Runtime: O(log(n)). Heap space: O(1) retained memory plus garbage, see the note below. Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree and assuming that the compare function implements an O(1) comparison.

Note: Creates O(log(n)) garbage objects.

Function `entries`

func entries() : I.Iter<(K, V)>

An iterator for the key-value entries of the map, in ascending key order. The iterator takes a snapshot view of the tree and is not affected by concurrent modifications.

Example:

import Debug "mo:base/Debug";

tree.put(1, "one");
tree.put(2, "two");
tree.put(3, "two");

for (entry in tree.entries()) {
  Debug.print("Entry key=" # debug_show(entry.0) # " value=\"" # entry.1 #"\"");
}

// Entry key=1 value="one"
// Entry key=2 value="two"
// Entry key=3 value="three"

Cost of iteration over all elements: Runtime: O(n). Heap space: O(log(n)) retained memory plus garbage, see the note below. Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree.

Note: Full tree iteration creates O(n) temporary objects that will be collected as garbage.

Function `entriesRev`

func entriesRev() : I.Iter<(K, V)>

An iterator for the key-value entries of the map, in descending key order. The iterator takes a snapshot view of the tree and is not affected by concurrent modifications.

Example:

import Debug "mo:base/Debug";

let tree = RBTree.RBTree<Nat, Text>(Nat.compare);
tree.put(1, "one");
tree.put(2, "two");
tree.put(3, "two");

for (entry in tree.entriesRev()) {
  Debug.print("Entry key=" # debug_show(entry.0) # " value=\"" # entry.1 #"\"");
}

// Entry key=3 value="three"
// Entry key=2 value="two"
// Entry key=1 value="one"

Cost of iteration over all elements: Runtime: O(n). Heap space: O(log(n)) retained memory plus garbage, see the note below. Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree.

Note: Full tree iteration creates O(n) temporary objects that will be collected as garbage.

Function `iter`

func iter<X, Y>(tree : Tree<X, Y>, direction : {#fwd; #bwd}) : I.Iter<(X, Y)>

Get an iterator for the entries of the tree, in ascending (#fwd) or descending (#bwd) order as specified by direction. The iterator takes a snapshot view of the tree and is not affected by concurrent modifications.

Example:

import RBTree "mo:base/RBTree";
import Nat "mo:base/Nat";
import Debug "mo:base/Debug";

let tree = RBTree.RBTree<Nat, Text>(Nat.compare);
tree.put(1, "one");
tree.put(2, "two");
tree.put(3, "two");

for (entry in RBTree.iter(tree.share(), #bwd)) { // backward iteration
  Debug.print("Entry key=" # debug_show(entry.0) # " value=\"" # entry.1 #"\"");
}

// Entry key=3 value="three"
// Entry key=2 value="two"
// Entry key=1 value="one"

Cost of iteration over all elements: Runtime: O(n). Heap space: O(log(n)) retained memory plus garbage, see the note below. Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree.

Note: Full tree iteration creates O(n) temporary objects that will be collected as garbage.

Function `size`

func size<X, Y>(t : Tree<X, Y>) : Nat

Determine the size of the tree as the number of key-value entries.

Example:

import RBTree "mo:base/RBTree";
import Nat "mo:base/Nat";

let tree = RBTree.RBTree<Nat, Text>(Nat.compare);
tree.put(1, "one");
tree.put(2, "two");
tree.put(3, "three");

RBTree.size(tree.share()) // 3 entries

Runtime: O(log(n)). Heap space: O(1). Stack space: O(log(n)). where n denotes the number of key-value entries stored in the tree.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

RBTree.md

RBTree.md

RBTree

Type `Color`

Type `Tree`

Class `RBTree<K, V>`

Function `share`

Function `unshare`

Function `get`

Function `replace`

Function `put`

Function `delete`

Function `remove`

Function `entries`

Function `entriesRev`

Function `iter`

Function `size`

Files

RBTree.md

Latest commit

History

RBTree.md

File metadata and controls

RBTree

Type Color

Type Tree

Class RBTree<K, V>

Function share

Function unshare

Function get

Function replace

Function put

Function delete

Function remove

Function entries

Function entriesRev

Function iter

Function size

Type `Color`

Type `Tree`

Class `RBTree<K, V>`

Function `share`

Function `unshare`

Function `get`

Function `replace`

Function `put`

Function `delete`

Function `remove`

Function `entries`

Function `entriesRev`

Function `iter`

Function `size`