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data-structure-typed
Advanced tools
Javascript & TypeScript Data Structure Library, meticulously crafted to empower developers with a versatile set of essential data structures. Our library includes a wide range of data structures, such as Binary Tree, AVL Tree, Binary Search Tree (BST), Tr
Javascript & TypeScript Data Structure collections.
DFS, DFSIterative, BFS, morris, Bellman-Ford Algorithm, Dijkstra's Algorithm, Floyd-Warshall Algorithm, Tarjan's Algorithm. Listed only a few out, you can discover more in API docs
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.
npm install data-structure-typed
yarn add data-structure-typed
<script src="https://cdn.jsdelivr.net/npm/data-structure-typed/dist/bundle.js"></script>
const {AVLTree} = dataStructureTyped;
const {Heap, MinHeap, SinglyLinkedList, Stack, AVLTreeNode, BST, Trie, DirectedGraph, DirectedVertex, TreeMultiset} = dataStructureTyped;
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);
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<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);
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
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
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']
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 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 |
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 | 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 |
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 |
FAQs
Javascript Data Structure. Heap, Binary Tree, Red Black Tree, Linked List, Deque, Trie, HashMap, Directed Graph, Undirected Graph, Binary Search Tree(BST), AVL Tree, Priority Queue, Graph, Queue, Tree Multiset, Singly Linked List, Doubly Linked List, Max
The npm package data-structure-typed receives a total of 6,808 weekly downloads. As such, data-structure-typed popularity was classified as popular.
We found that data-structure-typed demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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