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Data Structure Typed

npm npm npm package minimized gzipped size (select exports) GitHub top language eslint NPM

Data Structures of Javascript & TypeScript.

Do you envy C++ with STL, 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)

Installation and Usage

npm

npm i data-structure-typed

yarn

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

CDN

Copy the line below into the head tag in an HTML document.

development

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

production

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

Copy the code below into the script tag of your HTML, and you're good to go with your development work.

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

Code Snippets

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.getNode(6);   // BSTNode
bst.getHeight(6) === 2;         // true
bst.getHeight() === 5;          // true
bst.getDepth(6) === 3;          // true

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

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

const objBST = new BST<{height: number, age: number}>();

objBST.add(11, { "name": "Pablo", "age": 15 });
objBST.add(3, { "name": "Kirk", "age": 1 });

objBST.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5], [
    { "name": "Alice", "age": 15 },
    { "name": "Bob", "age": 1 },
    { "name": "Charlie", "age": 8 },
    { "name": "David", "age": 13 },
    { "name": "Emma", "age": 16 },
    { "name": "Frank", "age": 2 },
    { "name": "Grace", "age": 6 },
    { "name": "Hannah", "age": 9 },
    { "name": "Isaac", "age": 12 },
    { "name": "Jack", "age": 14 },
    { "name": "Katie", "age": 4 },
    { "name": "Liam", "age": 7 },
    { "name": "Mia", "age": 10 },
    { "name": "Noah", "age": 5 }
  ]
);

objBST.delete(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.getNode(6);
bst.getHeight(6) === 2;         // true
bst.getHeight() === 5;          // true
bst.getDepth(6) === 3;          // true
const leftMost = bst.getLeftMost();
leftMost?.key === 1;            // true

bst.delete(6);
bst.get(6);                     // undefined
bst.isAVLBalanced();            // true or false
const bfsIDs = bst.bfs();
bfsIDs[0] === 11;               // 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.delete(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.delete(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.deleteEdgeSrcToDest('A', 'B');
graph.hasEdge('A', 'B');    // false

graph.addVertex('C');

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

const topologicalOrderKeys = 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.deleteVertex('C');
graph.addEdge('A', 'B');
graph.addEdge('B', 'D');

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

Built-in classic algorithms

Algorithm Function Description Iteration Type
Binary Tree DFS Traverse a binary tree in a depth-first manner, starting from the root node, first visiting the left subtree, and then the right subtree, using recursion. Recursion + Iteration
Binary Tree BFS Traverse a binary tree in a breadth-first manner, starting from the root node, visiting nodes level by level from left to right. Iteration
Graph DFS Traverse a graph in a depth-first manner, starting from a given node, exploring along one path as deeply as possible, and backtracking to explore other paths. Used for finding connected components, paths, etc. Recursion + Iteration
Binary Tree Morris Morris traversal is an in-order traversal algorithm for binary trees with O(1) space complexity. It allows tree traversal without additional stack or recursion. Iteration
Graph BFS Traverse a graph in a breadth-first manner, starting from a given node, first visiting nodes directly connected to the starting node, and then expanding level by level. Used for finding shortest paths, etc. Recursion + Iteration
Graph Tarjan's Algorithm Find strongly connected components in a graph, typically implemented using depth-first search. Recursion
Graph Bellman-Ford Algorithm Finding the shortest paths from a single source, can handle negative weight edges Iteration
Graph Dijkstra's Algorithm Finding the shortest paths from a single source, cannot handle negative weight edges Iteration
Graph Floyd-Warshall Algorithm Finding the shortest paths between all pairs of nodes Iteration
Graph getCycles Find all cycles in a graph or detect the presence of cycles. Recursion
Graph getCutVertexes Find cut vertices in a graph, which are nodes that, when removed, increase the number of connected components in the graph. Recursion
Graph getSCCs Find strongly connected components in a graph, which are subgraphs where any two nodes can reach each other. Recursion
Graph getBridges Find bridges in a graph, which are edges that, when removed, increase the number of connected components in the graph. Recursion
Graph topologicalSort Perform topological sorting on a directed acyclic graph (DAG) to find a linear order of nodes such that all directed edges go from earlier nodes to later nodes. Recursion

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data Structure Unit Test Performance Test API Documentation Implemented
Binary Tree Binary Tree
Binary Search Tree (BST) BST
AVL Tree AVLTree
Red Black Tree RedBlackTree
Tree Multiset TreeMultimap
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 Typed C++ STL java.util Python collections
DoublyLinkedList<E> list<T> LinkedList<E> deque
SinglyLinkedList<E> - - -
Array<E> vector<T> ArrayList<E> list
Queue<E> queue<T> Queue<E> -
Deque<E> deque<T> - -
PriorityQueue<E> priority_queue<T> PriorityQueue<E> -
Heap<E> priority_queue<T> PriorityQueue<E> heapq
Stack<E> stack<T> Stack<E> -
Set<E> set<T> HashSet<E> set
Map<K, V> map<K, V> HashMap<K, V> dict
- unordered_set<T> HashSet<E> -
HashMap<K, V> unordered_map<K, V> HashMap<K, V> defaultdict
Map<K, V> - - OrderedDict
BinaryTree<K, V> - - -
BST<K, V> - - -
TreeMultimap<K, V> multimap<K, V> - -
AVLTree<E> - TreeSet<E> -
AVLTree<K, V> - TreeMap<K, V> -
AVLTree<E> set TreeSet<E> -
Trie - - -
- multiset<T> - -
DirectedGraph<V, E> - - -
UndirectedGraph<V, E> - - -
- unordered_multiset - Counter
- - LinkedHashSet<E> -
- - LinkedHashMap<K, V> -
- unordered_multimap<K, V> - -
- bitset<N> - -

Code design

Adhere to ES6 standard naming conventions for APIs.

Standardize API conventions by using 'add' and 'delete' for element manipulation methods in all data structures.

Opt for concise and clear method names, avoiding excessive length while ensuring explicit intent.

Object-oriented programming(OOP)

By strictly adhering to object-oriented design (BinaryTree -> BST -> AVLTree -> TreeMultimap), 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.

Benchmark

avl-tree
test nametime taken (ms)executions per secsample deviation
10,000 add randomly35.3828.260.00
10,000 add & delete randomly80.6512.400.01
10,000 addMany46.4521.539.97e-4
10,000 get31.5931.660.00
binary-tree
test nametime taken (ms)executions per secsample deviation
1,000 add randomly13.0176.847.71e-4
1,000 add & delete randomly16.8259.460.00
1,000 addMany10.4695.622.13e-4
1,000 get18.4454.222.49e-4
1,000 dfs163.316.120.01
1,000 bfs58.0717.220.00
1,000 morris273.113.660.00
bst
test nametime taken (ms)executions per secsample deviation
10,000 add randomly33.4429.915.09e-4
10,000 add & delete randomly76.0313.150.00
10,000 addMany32.2631.000.00
10,000 get32.7530.530.01
rb-tree
test nametime taken (ms)executions per secsample deviation
100,000 add99.2710.070.01
100,000 add & delete randomly233.224.290.01
100,000 getNode148.416.740.00
directed-graph
test nametime taken (ms)executions per secsample deviation
1,000 addVertex0.119381.523.21e-6
1,000 addEdge6.36157.147.19e-4
1,000 getVertex0.052.09e+42.72e-6
1,000 getEdge24.6640.550.00
tarjan219.654.550.01
tarjan all239.684.170.04
topologicalSort216.144.630.07
hash-map
test nametime taken (ms)executions per secsample deviation
10,000 set19.5051.270.01
10,000 set & get38.2026.180.01
heap
test nametime taken (ms)executions per secsample deviation
10,000 add & pop4.86205.697.04e-4
10,000 fib add & pop386.502.590.03
doubly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 unshift243.464.110.05
1,000,000 unshift & shift178.495.600.02
1,000,000 insertBefore326.383.060.07
singly-linked-list
test nametime taken (ms)executions per secsample deviation
10,000 push & pop232.854.290.01
10,000 insertBefore255.903.910.01
max-priority-queue
test nametime taken (ms)executions per secsample deviation
10,000 refill & poll11.8884.150.00
priority-queue
test nametime taken (ms)executions per secsample deviation
10,000 add & pop12.6079.351.40e-4
deque
test nametime taken (ms)executions per secsample deviation
1,000,000 push226.324.420.03
1,000,000 shift26.0938.330.00
queue
test nametime taken (ms)executions per secsample deviation
1,000,000 push45.4522.000.01
1,000,000 push & shift82.8712.070.00
stack
test nametime taken (ms)executions per secsample deviation
1,000,000 push43.9522.750.01
1,000,000 push & pop50.9119.640.00
trie
test nametime taken (ms)executions per secsample deviation
100,000 push50.2019.920.00
100,000 getWords110.089.080.01