Data Structure Typed

Data Structures of Javascript & TypeScript.

Do you envy C++ with std, Python with collections, and Java with java.util ? Well, no need to envy anymore! JavaScript and TypeScript now have data-structure-typed.

Now you can use this library in Node.js and browser environments in CommonJS(require export.modules = ), ESModule(import export), Typescript(import export), UMD(var Queue = dataStructureTyped.Queue)

Built-in classic algorithms

DFS(Depth-First Search), DFSIterative, BFS(Breadth-First Search), morris, Bellman-Ford Algorithm, Dijkstra's Algorithm, Floyd-Warshall Algorithm, Tarjan's Algorithm.

Installation and Usage

npm

npm i data-structure-typed --save

yarn

yarn add data-structure-typed

import {
  BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList,
  AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultiset,
  DirectedVertex, AVLTreeNode
} from 'data-structure-typed';

CDN

<script src='https://cdn.jsdelivr.net/npm/data-structure-typed/umd/bundle.min.js'></script>

const {Heap} = dataStructureTyped;
const {
  BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList,
  AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultiset,
  DirectedVertex, AVLTreeNode
} = dataStructureTyped;

API docs & Examples

API Docs

Live Examples

Examples Repository

Code Snippet

Binary Search Tree (BST) snippet

TS

import {BST, BSTNode} from 'data-structure-typed';

const bst = new BST();
bst.add(11);
bst.add(3);
bst.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]);
bst.size === 16;                // true
bst.has(6);                     // true
const node6 = bst.get(6);       // BSTNode
bst.getHeight(6) === 2;         // true
bst.getHeight() === 5;          // true
bst.getDepth(6) === 3;          // true

bst.getLeftMost()?.id === 1;    // true

bst.remove(6);
bst.get(6);                     // null
bst.isAVLBalanced();            // true
bst.bfs()[0] === 11;            // true

const objBST = new BST<BSTNode<{id: number, keyA: number}>>();
objBST.add(11, {id: 11, keyA: 11});
objBST.add(3, {id: 3, keyA: 3});

objBST.addMany([{id: 15, keyA: 15}, {id: 1, keyA: 1}, {id: 8, keyA: 8},
  {id: 13, keyA: 13}, {id: 16, keyA: 16}, {id: 2, keyA: 2},
  {id: 6, keyA: 6}, {id: 9, keyA: 9}, {id: 12, keyA: 12},
  {id: 14, keyA: 14}, {id: 4, keyA: 4}, {id: 7, keyA: 7},
  {id: 10, keyA: 10}, {id: 5, keyA: 5}]);

objBST.remove(11);

JS

const {BST, BSTNode} = require('data-structure-typed');

const bst = new BST();
bst.add(11);
bst.add(3);
bst.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]);
bst.size === 16;        // true
bst.has(6);             // true
const node6 = bst.get(6);
bst.getHeight(6) === 2; // true
bst.getHeight() === 5;  // true
bst.getDepth(6) === 3;  // true
const leftMost = bst.getLeftMost();
leftMost?.id === 1;     // true
expect(leftMost?.id).toBe(1);
bst.remove(6);
bst.get(6);             // null
bst.isAVLBalanced();    // true or false
const bfsIDs = bst.bfs();
bfsIDs[0] === 11;       // true
expect(bfsIDs[0]).toBe(11);

const objBST = new BST();
objBST.add(11, {id: 11, keyA: 11});
objBST.add(3, {id: 3, keyA: 3});

objBST.addMany([{id: 15, keyA: 15}, {id: 1, keyA: 1}, {id: 8, keyA: 8},
  {id: 13, keyA: 13}, {id: 16, keyA: 16}, {id: 2, keyA: 2},
  {id: 6, keyA: 6}, {id: 9, keyA: 9}, {id: 12, keyA: 12},
  {id: 14, keyA: 14}, {id: 4, keyA: 4}, {id: 7, keyA: 7},
  {id: 10, keyA: 10}, {id: 5, keyA: 5}]);

objBST.remove(11);

const avlTree = new AVLTree();
avlTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5])
avlTree.isAVLBalanced();    // true
avlTree.remove(10);
avlTree.isAVLBalanced();    // true

AVLTree snippet

TS

import {AVLTree} from 'data-structure-typed';

const avlTree = new AVLTree();
avlTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5])
avlTree.isAVLBalanced();    // true
avlTree.remove(10);
avlTree.isAVLBalanced();    // true

JS

const {AVLTree} = require('data-structure-typed');

const avlTree = new AVLTree();
avlTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5])
avlTree.isAVLBalanced();    // true
avlTree.remove(10);
avlTree.isAVLBalanced();    // true

Directed Graph simple snippet

TS or JS

import {DirectedGraph} from 'data-structure-typed';

const graph = new DirectedGraph();

graph.addVertex('A');
graph.addVertex('B');

graph.hasVertex('A');       // true
graph.hasVertex('B');       // true
graph.hasVertex('C');       // false

graph.addEdge('A', 'B');
graph.hasEdge('A', 'B');    // true
graph.hasEdge('B', 'A');    // false

graph.removeEdgeSrcToDest('A', 'B');
graph.hasEdge('A', 'B');    // false

graph.addVertex('C');

graph.addEdge('A', 'B');
graph.addEdge('B', 'C');

const topologicalOrderIds = graph.topologicalSort(); // ['A', 'B', 'C']

Undirected Graph snippet

TS or JS

import {UndirectedGraph} from 'data-structure-typed';

const graph = new UndirectedGraph();
graph.addVertex('A');
graph.addVertex('B');
graph.addVertex('C');
graph.addVertex('D');
graph.removeVertex('C');
graph.addEdge('A', 'B');
graph.addEdge('B', 'D');

const dijkstraResult = graph.dijkstra('A');
Array.from(dijkstraResult?.seen ?? []).map(vertex => vertex.id) // ['A', 'B', 'D']

Data Structures

Data Structure	Unit Test	Performance Test	API Documentation	Implemented
Binary Tree			Binary Tree
Binary Search Tree (BST)			BST
AVL Tree			AVLTree
Tree Multiset			TreeMultiset
Segment Tree			SegmentTree
Binary Indexed Tree			BinaryIndexedTree
Graph			AbstractGraph
Directed Graph			DirectedGraph
Undirected Graph			UndirectedGraph
Linked List			SinglyLinkedList
Singly Linked List			SinglyLinkedList
Doubly Linked List			DoublyLinkedList
Queue			Queue
Object Deque			ObjectDeque
Array Deque			ArrayDeque
Stack			Stack
Coordinate Set			CoordinateSet
Coordinate Map			CoordinateMap
Heap			Heap
Priority Queue			PriorityQueue
Max Priority Queue			MaxPriorityQueue
Min Priority Queue			MinPriorityQueue
Trie			Trie

Standard library data structure comparison

Data Structure	Data Structure Typed	C++ std	java.util	Python collections
Dynamic Array	Array<E>	vector<T>	ArrayList<E>	list
Linked List	DoublyLinkedList<E>	list<T>	LinkedList<E>	deque
Singly Linked List	SinglyLinkedList<E>	-	-	-
Set	Set<E>	set<T>	HashSet<E>	set
Map	Map<K, V>	map<K, V>	HashMap<K, V>	dict
Ordered Dictionary	Map<K, V>	-	-	OrderedDict
Queue	Queue<E>	queue<T>	Queue<E>	-
Priority Queue	PriorityQueue<E>	priority_queue<T>	PriorityQueue<E>	-
Heap	Heap<V>	priority_queue<T>	PriorityQueue<E>	heapq
Stack	Stack<E>	stack<T>	Stack<E>	-
Deque	Deque<E>	deque<T>	-	-
Trie	Trie	-	-	-
Unordered Map	HashMap<K, V>	unordered_map<K, V>	HashMap<K, V>	defaultdict
Multiset	-	multiset<T>	-	-
Multimap	-	multimap<K, V>	-	-
Binary Tree	BinaryTree<K, V>	-	-	-
Binary Search Tree	BST<K, V>	-	-	-
Directed Graph	DirectedGraph<V, E>	-	-	-
Undirected Graph	UndirectedGraph<V, E>	-	-	-
Unordered Multiset	-	unordered_multiset	-	Counter
Linked Hash Set	-	-	LinkedHashSet<E>	-
Linked Hash Map	-	-	LinkedHashMap<K, V>	-
Sorted Set	AVLTree<E>	-	TreeSet<E>	-
Sorted Map	AVLTree<K, V>	-	TreeMap<K, V>	-
Tree Set	AVLTree<E>	set	TreeSet<E>	-
Unordered Multimap	-	unordered_multimap<K, V>	-	-
Bitset	-	bitset<N>	-	-
Unordered Set	-	unordered_set<T>	HashSet<E>	-

Code design

By strictly adhering to object-oriented design (BinaryTree -> BST -> AVLTree -> TreeMultiset), you can seamlessly inherit the existing data structures to implement the customized ones you need. Object-oriented design stands as the optimal approach to data structure design.

Complexities

performance of Big O

Big O Notation	Type	Computations for 10 elements	Computations for 100 elements	Computations for 1000 elements
O(1)	Constant	1	1	1
O(log N)	Logarithmic	3	6	9
O(N)	Linear	10	100	1000
O(N log N)	n log(n)	30	600	9000
O(N^2)	Quadratic	100	10000	1000000
O(2^N)	Exponential	1024	1.26e+29	1.07e+301
O(N!)	Factorial	3628800	9.3e+157	4.02e+2567

Data Structure Complexity

Data Structure	Access	Search	Insertion	Deletion	Comments
Array	1	n	n	n
Stack	n	n	1	1
Queue	n	n	1	1
Linked List	n	n	1	n
Hash Table	-	n	n	n	In case of perfect hash function costs would be O(1)
Binary Search Tree	n	n	n	n	In case of balanced tree costs would be O(log(n))
B-Tree	log(n)	log(n)	log(n)	log(n)
Red-Black Tree	log(n)	log(n)	log(n)	log(n)
AVL Tree	log(n)	log(n)	log(n)	log(n)
Bloom Filter	-	1	1	-	False positives are possible while searching

Sorting Complexity

Name	Best	Average	Worst	Memory	Stable	Comments
Bubble sort	n	n2	n2	1	Yes
Insertion sort	n	n2	n2	1	Yes
Selection sort	n2	n2	n2	1	No
Heap sort	n log(n)	n log(n)	n log(n)	1	No
Merge sort	n log(n)	n log(n)	n log(n)	n	Yes
Quick sort	n log(n)	n log(n)	n2	log(n)	No	Quicksort is usually done in-place with O(log(n)) stack space
Shell sort	n log(n)	depends on gap sequence	n (log(n))2	1	No
Counting sort	n + r	n + r	n + r	n + r	Yes	r - biggest number in array
Radix sort	n * k	n * k	n * k	n + k	Yes	k - length of longest key