data-structure-typed

Data Structure Typed

Data Structures of Javascript & TypeScript.

Do you envy languages like 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)

License Language GitHub release (latest by date) Branches npm eslint

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

CDN


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

const {AVLTree} = dataStructureTyped;
const {
  Heap,
  MinHeap,
  SinglyLinkedList,
  Stack,
  AVLTreeNode,
  BST,
  Trie,
  DirectedGraph,
  DirectedVertex,
  TreeMultiset
} = 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 C++ std Data Structure Typed java.util Python collections
Dynamic Array std::vector<T> Array<E> ArrayList<E> list
Linked List std::list<T> DoublyLinkedList<E> LinkedList<E> deque
Set std::set<T> Set HashSet<E> set
Map std::map<K, V> Map HashMap<K, V> dict
Unordered Map std::unordered_map<K, V> N/A HashMap<K, V> defaultdict
Unordered Set std::unordered_set<T> N/A HashSet<E> N/A
Queue std::queue<T> Queue Queue<E> N/A
Priority Queue std::priority_queue<T> PriorityQueue PriorityQueue<E> N/A
Stack std::stack<T> Stack Stack<E> N/A
Bitset std::bitset<N> N/A N/A N/A
Deque std::deque<T> Deque N/A N/A
Multiset std::multiset<T> N/A N/A N/A
Multimap std::multimap<K, V> N/A N/A N/A
Unordered Multiset std::unordered_multiset N/A Counter N/A
Ordered Dictionary N/A Map N/A OrderedDict
Double-Ended Queue (Deque) std::deque<T> Deque N/A N/A
Linked Hash Set N/A N/A LinkedHashSet<E> N/A
Linked Hash Map N/A N/A LinkedHashMap<K, V> N/A
Sorted Set N/A AVLTree, RBTree TreeSet<E> N/A
Sorted Map N/A AVLTree, RBTree TreeMap<K, V> N/A
Tree Set std::set AVLTree, RBTree TreeSet<E> N/A
Persistent Collections N/A N/A N/A N/A
unordered multiset unordered multiset<T> N/A N/A N/A
Unordered Multimap std::unordered_multimap<K, V> N/A N/A N/A

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

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