Data Structure Typed

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;

Software Engineering Design Standards

Principle	Description
Practicality	Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.
Extensibility	Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.
Modularization	Includes data structure modularization and independent NPM packages.
Efficiency	All methods provide time and space complexity, comparable to native JS performance.
Maintainability	Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.
Testability	Automated and customized unit testing, performance testing, and integration testing.
Portability	Plans for porting to Java, Python, and C++, currently achieved to 80%.
Reusability	Fully decoupled, minimized side effects, and adheres to OOP.
Security	Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.
Scalability	Data structure software does not involve load issues.

Vivid Examples

Binary Tree

Try it out, or you can execute your own code using our visual tool

Binary Tree DFS

Try it out, or you can execute your own code using our visual tool

AVL Tree

Try it out, or you can execute your own code using our visual tool

Tree Multi Map

Try it out, or you can execute your own code using our visual tool

Matrix

Try it out, or you can execute your own code using our visual tool

Directed Graph

Try it out, or you can execute your own code using our visual tool

Map Graph

Try it out, or you can execute your own code using our visual tool

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>	-	-

Benchmark

comparison

test name	time taken (ms)	executions per sec	sample deviation
SRC 10,000 add	0.57	1745.23	4.83e-6
CJS 10,000 add	0.57	1752.47	4.64e-6
MJS 10,000 add	0.59	1687.89	1.40e-4
SRC PQ 10,000 add & pop	3.41	293.00	2.65e-5
CJS PQ 10,000 add & pop	4.92	203.31	3.60e-5
MJS PQ 10,000 add & pop	4.88	204.72	4.35e-5

avl-tree

test name	time taken (ms)	executions per sec	sample deviation
10,000 add randomly	31.02	32.24	2.83e-4
10,000 add & delete randomly	71.45	14.00	0.00
10,000 addMany	40.21	24.87	4.47e-4
10,000 get	28.34	35.29	5.15e-4

binary-tree

test name	time taken (ms)	executions per sec	sample deviation
1,000 add randomly	13.04	76.71	1.56e-4
1,000 add & delete randomly	15.79	63.33	1.40e-4
1,000 addMany	10.25	97.60	1.08e-4
1,000 get	18.31	54.60	1.50e-4
1,000 dfs	155.22	6.44	8.11e-4
1,000 bfs	56.66	17.65	6.70e-4
1,000 morris	254.79	3.92	5.27e-4

bst

test name	time taken (ms)	executions per sec	sample deviation
10,000 add randomly	28.23	35.42	3.20e-4
10,000 add & delete randomly	68.35	14.63	8.46e-4
10,000 addMany	28.94	34.56	0.00
10,000 get	28.97	34.51	4.57e-4

rb-tree

test name	time taken (ms)	executions per sec	sample deviation
100,000 add	89.12	11.22	0.00
100,000 add & delete randomly	219.00	4.57	0.00
100,000 getNode	105.74	9.46	6.09e-4

directed-graph

test name	time taken (ms)	executions per sec	sample deviation
1,000 addVertex	0.10	9902.14	9.12e-7
1,000 addEdge	6.11	163.67	1.29e-4
1,000 getVertex	0.05	2.16e+4	3.51e-7
1,000 getEdge	23.63	42.33	0.00
tarjan	222.85	4.49	0.01
tarjan all	223.79	4.47	0.00
topologicalSort	181.77	5.50	0.00

hash-map

test name	time taken (ms)	executions per sec	sample deviation
10,000 set	1.00	1001.93	2.06e-5
10,000 set & get	1.51	661.53	2.65e-5

heap

test name	time taken (ms)	executions per sec	sample deviation
10,000 add & pop	5.82	171.79	5.13e-5
10,000 fib add & pop	359.24	2.78	0.00

doubly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	214.50	4.66	0.05
1,000,000 unshift	205.89	4.86	0.05
1,000,000 unshift & shift	168.93	5.92	0.04
1,000,000 insertBefore	291.32	3.43	0.05

singly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
10,000 push & pop	215.82	4.63	0.02
10,000 insertBefore	248.56	4.02	0.01

max-priority-queue

test name	time taken (ms)	executions per sec	sample deviation
10,000 refill & poll	8.85	113.01	1.90e-4

priority-queue

test name	time taken (ms)	executions per sec	sample deviation
100,000 add & pop	104.23	9.59	0.00

deque

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	14.25	70.18	1.63e-4
1,000,000 push & pop	23.15	43.21	2.07e-4
1,000,000 push & shift	24.16	41.40	1.78e-4
1,000,000 unshift & shift	22.28	44.88	1.56e-4

queue

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	45.72	21.87	0.01
1,000,000 push & shift	81.75	12.23	0.00

stack

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	44.30	22.57	0.01
1,000,000 push & pop	49.69	20.12	0.01

trie

test name	time taken (ms)	executions per sec	sample deviation
100,000 push	43.10	23.20	6.46e-4
100,000 getWords	85.45	11.70	0.00

Codebase design

Adhere to ES6 and ESNext 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.