data-structure-typed

Why

Do you envy C++ with STL (std::), Python with collections, and Java with java.util ? Well, no need to envy anymore! JavaScript and TypeScript now have data-structure-typed.Benchmark compared with C++ STL. API standards aligned with ES6 and Java. Usability is comparable to Python

Data structures available

We provide data structures that are not available in JS/TS Heap, Binary Tree, Red Black Tree, Linked List, Deque, Trie, Directed Graph, Undirected Graph, BST, AVL Tree, Priority Queue, Queue, Tree Multiset.

Performance

Performance surpasses that of native JS/TS

Method	Time Taken	Data Scale	Belongs To	big O
Queue.push & shift	5.83 ms	100K	Ours	O(1)
Array.push & shift	2829.59 ms	100K	Native JS	O(n)
Deque.unshift & shift	2.44 ms	100K	Ours	O(1)
Array.unshift & shift	4750.37 ms	100K	Native JS	O(n)
HashMap.set	122.51 ms	1M	Ours	O(1)
Map.set	223.80 ms	1M	Native JS	O(1)
Set.add	185.06 ms	1M	Native JS	O(1)

Conciseness and uniformity

In java.utils, you need to memorize a table for all sequential data structures(Queue, Deque, LinkedList),

Java ArrayList	Java Queue	Java ArrayDeque	Java LinkedList
add	offer	push	push
remove	poll	removeLast	removeLast
remove	poll	removeFirst	removeFirst
add(0, element)	offerFirst	unshift	unshift

whereas in our data-structure-typed, you only need to remember four methods: push, pop, shift, and unshift for all sequential data structures(Queue, Deque, DoublyLinkedList, SinglyLinkedList and Array).

Installation and Usage

npm

npm i data-structure-typed --save

yarn

yarn add data-structure-typed

import {
  Heap, Graph, Queue, Deque, PriorityQueue, BST, Trie, DoublyLinkedList,
  AVLTree, SinglyLinkedList, DirectedGraph, RedBlackTree, TreeMultiMap,
  DirectedVertex, Stack, AVLTreeNode
} from 'data-structure-typed';

Vivid Examples

AVL Tree

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

Tree Multi Map

Try it out

Directed Graph

Try it out

Map Graph

Try it out

Code Snippets

Red Black Tree snippet

TS

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

const rbTree = new RedBlackTree<number>();
rbTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5])
rbTree.isAVLBalanced();    // true
rbTree.delete(10);
rbTree.isAVLBalanced();    // true
rbTree.print()
//         ___6________
//        /            \
//      ___4_       ___11________
//     /     \     /             \
//    _2_    5    _8_       ____14__
//   /   \       /   \     /        \
//   1   3       7   9    12__     15__
//                            \        \
//                           13       16

JS

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

const rbTree = new RedBlackTree();
rbTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5])
rbTree.isAVLBalanced();    // true
rbTree.delete(10);
rbTree.isAVLBalanced();    // true
rbTree.print()
//         ___6________
//        /            \
//      ___4_       ___11________
//     /     \     /             \
//    _2_    5    _8_       ____14__
//   /   \       /   \     /        \
//   1   3       7   9    12__     15__
//                            \        \
//                           13       16

Free conversion between data structures.

const orgArr = [6, 1, 2, 7, 5, 3, 4, 9, 8];
const orgStrArr = ["trie", "trial", "trick", "trip", "tree", "trend", "triangle", "track", "trace", "transmit"];
const entries = [[6, "6"], [1, "1"], [2, "2"], [7, "7"], [5, "5"], [3, "3"], [4, "4"], [9, "9"], [8, "8"]];

const queue = new Queue(orgArr);
queue.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const deque = new Deque(orgArr);
deque.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const sList = new SinglyLinkedList(orgArr);
sList.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const dList = new DoublyLinkedList(orgArr);
dList.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const stack = new Stack(orgArr);
stack.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]

const minHeap = new MinHeap(orgArr);
minHeap.print();
// [1, 5, 2, 7, 6, 3, 4, 9, 8]

const maxPQ = new MaxPriorityQueue(orgArr);
maxPQ.print();
// [9, 8, 4, 7, 5, 2, 3, 1, 6]

const biTree = new BinaryTree(entries);
biTree.print();
//         ___6___
//        /       \
//     ___1_     _2_
//    /     \   /   \
//   _7_    5   3   4
//  /   \
//  9   8

const bst = new BST(entries);
bst.print();
//     _____5___
//    /         \
//   _2_       _7_
//  /   \     /   \
//  1   3_    6   8_
//        \         \
//        4         9


const rbTree = new RedBlackTree(entries);
rbTree.print();
//     ___4___
//    /       \
//   _2_     _6___
//  /   \   /     \
//  1   3   5    _8_
//              /   \
//              7   9


const avl = new AVLTree(entries);
avl.print();
//     ___4___
//    /       \
//   _2_     _6___
//  /   \   /     \
//  1   3   5    _8_
//              /   \
//              7   9

const treeMulti = new TreeMultiMap(entries);
treeMulti.print();
//     ___4___
//    /       \
//   _2_     _6___
//  /   \   /     \
//  1   3   5    _8_
//              /   \
//              7   9

const hm = new HashMap(entries);
hm.print()
// [[6, "6"], [1, "1"], [2, "2"], [7, "7"], [5, "5"], [3, "3"], [4, "4"], [9, "9"], [8, "8"]]

const rbTreeH = new RedBlackTree(hm);
rbTreeH.print();
//     ___4___
//    /       \
//   _2_     _6___
//  /   \   /     \
//  1   3   5    _8_
//              /   \
//              7   9

const pq = new MinPriorityQueue(orgArr);
pq.print();
// [1, 5, 2, 7, 6, 3, 4, 9, 8]

const bst1 = new BST(pq);
bst1.print();
//     _____5___
//    /         \
//   _2_       _7_
//  /   \     /   \
//  1   3_    6   8_
//        \         \
//        4         9

const dq1 = new Deque(orgArr);
dq1.print();
// [6, 1, 2, 7, 5, 3, 4, 9, 8]
const rbTree1 = new RedBlackTree(dq1);
rbTree1.print();
//    _____5___
//   /         \
//  _2___     _7___
// /     \   /     \
// 1    _4   6    _9
//      /         /
//      3         8


const trie2 = new Trie(orgStrArr);
trie2.print();
// ['trie', 'trial', 'triangle', 'trick', 'trip', 'tree', 'trend', 'track', 'trace', 'transmit']
const heap2 = new Heap(trie2, { comparator: (a, b) => Number(a) - Number(b) });
heap2.print();
// ['transmit', 'trace', 'tree', 'trend', 'track', 'trial', 'trip', 'trie', 'trick', 'triangle']
const dq2 = new Deque(heap2);
dq2.print();
// ['transmit', 'trace', 'tree', 'trend', 'track', 'trial', 'trip', 'trie', 'trick', 'triangle']
const entries2 = dq2.map((el, i) => [i, el]);
const avl2 = new AVLTree(entries2);
avl2.print();
//     ___3_______
//    /           \
//   _1_       ___7_
//  /   \     /     \
//  0   2    _5_    8_
//          /   \     \
//          4   6     9

Binary Search Tree (BST) snippet

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

const bst = new BST<number>();
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
bst.print()
//       ______________11_____           
//      /                     \          
//   ___3_______            _13_____
//  /           \          /        \    
//  1_     _____8____     12      _15__
//    \   /          \           /     \ 
//    2   4_       _10          14    16
//          \     /                      
//          5_    9
//            \                          
//            7

const objBST = new BST<number, { 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);

AVLTree snippet

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

const avlTree = new AVLTree<number>();
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

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

const graph = new DirectedGraph<string>();

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

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

const graph = new UndirectedGraph<string>();
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']

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data Structure	Unit Test	Performance Test	API Docs
Binary Tree			View
Binary Search Tree (BST)			View
AVL Tree			View
Red Black Tree			View
Tree Multimap			View
Heap			View
Priority Queue			View
Max Priority Queue			View
Min Priority Queue			View
Trie			View
Graph			View
Directed Graph			View
Undirected Graph			View
Queue			View
Deque			View
Hash Map			View
Linked List			View
Singly Linked List			View
Doubly Linked List			View
Stack			View
Segment Tree			View
Binary Indexed Tree			View

Benchmark

MacBook Pro (15-inch, 2018)

Processor 2.2 GHz 6-Core Intel Core i7

Memory 16 GB 2400 MHz DDR4

Graphics Radeon Pro 555X 4 GB

Intel UHD Graphics 630 1536 MB

macOS Big Sur

Version 11.7.9

heap

test name	time taken (ms)	executions per sec	sample deviation
100,000 add	6.44	155.39	1.89e-4
100,000 add & poll	31.54	31.71	7.91e-4

rb-tree

test name	time taken (ms)	executions per sec	sample deviation
100,000 add	55.64	17.97	3.93e-4
100,000 add randomly	70.35	14.21	0.00
100,000 get	115.51	8.66	0.00
100,000 iterator	27.64	36.18	0.01
100,000 add & delete orderly	120.73	8.28	0.00
100,000 add & delete randomly	223.37	4.48	0.00

queue

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	42.87	23.33	0.01
100,000 push & shift	4.87	205.17	6.94e-4
Native JS Array 100,000 push & shift	2196.84	0.46	0.19

deque

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	23.68	42.22	0.00
1,000,000 push & pop	30.68	32.60	0.00
1,000,000 push & shift	30.49	32.80	0.00
100,000 push & shift	3.21	311.51	2.41e-4
Native JS Array 100,000 push & shift	2510.08	0.40	0.34
100,000 unshift & shift	2.89	346.57	2.98e-4
Native JS Array 100,000 unshift & shift	4581.65	0.22	0.40

hash-map

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 set	120.66	8.29	0.03
Native JS Map 1,000,000 set	202.57	4.94	0.01
Native JS Set 1,000,000 add	167.46	5.97	0.01
1,000,000 set & get	115.60	8.65	0.01
Native JS Map 1,000,000 set & get	265.34	3.77	0.01
Native JS Set 1,000,000 add & has	167.85	5.96	0.01
1,000,000 ObjKey set & get	308.73	3.24	0.03
Native JS Map 1,000,000 ObjKey set & get	300.60	3.33	0.03
Native JS Set 1,000,000 ObjKey add & has	270.49	3.70	0.04

trie

test name	time taken (ms)	executions per sec	sample deviation
100,000 push	45.79	21.84	7.32e-4
100,000 getWords	87.85	11.38	0.00

avl-tree

test name	time taken (ms)	executions per sec	sample deviation
100,000 add	260.78	3.83	0.00
100,000 add randomly	306.61	3.26	0.00
100,000 get	140.27	7.13	0.00
100,000 iterator	29.90	33.45	0.01
100,000 add & delete orderly	428.76	2.33	0.00
100,000 add & delete randomly	580.74	1.72	0.00

binary-tree-overall

test name	time taken (ms)	executions per sec	sample deviation
10,000 RBTree add	5.74	174.10	9.29e-5
10,000 RBTree add & delete randomly	18.83	53.10	1.49e-4
10,000 RBTree get	0.77	1290.55	7.33e-6
10,000 AVLTree add	22.60	44.25	2.14e-4
10,000 AVLTree get	10.63	94.08	1.02e-4
10,000 AVLTree add & delete randomly	44.17	22.64	3.52e-4

directed-graph

test name	time taken (ms)	executions per sec	sample deviation
1,000 addVertex	0.11	9501.69	1.02e-6
1,000 addEdge	6.18	161.81	4.27e-4
1,000 getVertex	0.05	2.16e+4	3.23e-7
1,000 getEdge	23.31	42.90	0.00
tarjan	206.06	4.85	0.01
topologicalSort	181.65	5.51	0.01

doubly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	207.88	4.81	0.04
1,000,000 unshift	214.33	4.67	0.06
1,000,000 unshift & shift	185.54	5.39	0.04
1,000,000 addBefore	308.66	3.24	0.08

singly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push & shift	202.61	4.94	0.04
10,000 push & pop	219.69	4.55	0.02
10,000 addBefore	247.13	4.05	0.01

priority-queue

test name	time taken (ms)	executions per sec	sample deviation
100,000 add	27.36	36.55	9.92e-4
100,000 add & poll	146.72	6.82	6.84e-4

stack

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	39.36	25.41	0.01
1,000,000 push & pop	47.86	20.89	0.01

The corresponding relationships between data structures in different language standard libraries.

Data Structure Typed	C++ STL	java.util	Python collections
Heap<E>	-	-	heapq
PriorityQueue<E>	priority_queue<T>	PriorityQueue<E>	-
Deque<E>	deque<T>	ArrayDeque<E>	deque
Queue<E>	queue<T>	Queue<E>	-
HashMap<K, V>	unordered_map<K, V>	HashMap<K, V>	defaultdict
DoublyLinkedList<E>	list<T>	LinkedList<E>	-
SinglyLinkedList<E>	-	-	-
BinaryTree<K, V>	-	-	-
BST<K, V>	-	-	-
RedBlackTree<E>	set<T>	TreeSet<E>	-
RedBlackTree<K, V>	map<K, V>	TreeMap<K, V>	-
TreeMultiMap<K, V>	multimap<K, V>	-	-
TreeMultiMap<E>	multiset<T>	-	-
Trie	-	-	-
DirectedGraph<V, E>	-	-	-
UndirectedGraph<V, E>	-	-	-
PriorityQueue<E>	priority_queue<T>	PriorityQueue<E>	-
Array<E>	vector<T>	ArrayList<E>	list
Stack<E>	stack<T>	Stack<E>	-
HashMap<E>	unordered_set<T>	HashSet<E>	set
-	unordered_multiset	-	Counter
LinkedHashMap<K, V>	-	LinkedHashMap<K, V>	OrderedDict
-	unordered_multimap<K, V>	-	-
-	bitset<N>	-	-

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 getCutVertices	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

Software Engineering Design Standards

We strictly adhere to computer science theory and software development standards. Our LinkedList is designed in the traditional sense of the LinkedList data structure, and we refrain from substituting it with a Deque solely for the purpose of showcasing performance test data. However, we have also implemented a Deque based on a dynamic array concurrently.

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.

supported module system

Now you can use it in Node.js and browser environments

CommonJS:require export.modules =

ESModule:   import export

Typescript:   import export

UMD:           var Deque = dataStructureTyped.Deque

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.

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