# Data Structure Typed ![npm](https://img.shields.io/npm/v/data-structure-typed) ![npm](https://img.shields.io/npm/dm/data-structure-typed) ![npm package minimized gzipped size (select exports)](https://img.shields.io/bundlejs/size/data-structure-typed) ![GitHub top language](https://img.shields.io/github/languages/top/zrwusa/data-structure-typed) ![eslint](https://aleen42.github.io/badges/src/eslint.svg) ![NPM](https://img.shields.io/npm/l/data-structure-typed) [//]: # (![npm bundle size](https://img.shields.io/bundlephobia/min/data-structure-typed)) Data Structures of Javascript & TypeScript. 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 [//]: # (![Branches](https://img.shields.io/badge/branches-55.47%25-red.svg?style=flat)) [//]: # (![Statements](https://img.shields.io/badge/statements-67%25-red.svg?style=flat)) [//]: # (![Functions](https://img.shields.io/badge/functions-66.38%25-red.svg?style=flat)) [//]: # (![Lines](https://img.shields.io/badge/lines-68.6%25-red.svg?style=flat)) ## Installation and Usage 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`** ### npm ```bash npm i data-structure-typed --save ``` ### yarn ```bash yarn add data-structure-typed ``` ```js 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 ```html ``` #### production ```html ``` Copy the code below into the script tag of your HTML, and you're good to go with your development. ```js const {Heap} = dataStructureTyped; const { BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList, AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultimap, DirectedVertex, AVLTreeNode } = dataStructureTyped; ``` ## Vivid Examples ### Binary Tree [Try it out](https://vivid-algorithm.vercel.app/), or you can run your own code using our [visual tool](https://github.com/zrwusa/vivid-algorithm) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/binary-tree-array-to-binary-tree.webp) ### Binary Tree DFS [Try it out](https://vivid-algorithm.vercel.app/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/binary-tree-dfs-in-order.webp) ### AVL Tree [Try it out](https://vivid-algorithm.vercel.app/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/avl-tree-test.webp) ### Tree Multi Map [Try it out](https://vivid-algorithm.vercel.app/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/tree-multiset-test.webp) ### Matrix [Try it out](https://vivid-algorithm.vercel.app/algorithm/graph/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/matrix-cut-off-tree-for-golf.webp) ### Directed Graph [Try it out](https://vivid-algorithm.vercel.app/algorithm/graph/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/directed-graph-test.webp) ### Map Graph [Try it out](https://vivid-algorithm.vercel.app/algorithm/graph/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/map-graph-test.webp) ## Code Snippets ### Binary Search Tree (BST) snippet #### TS ```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 bst.print() // ______________11_____ // / \ // ___3_______ _13_____ // / \ / \ // 1_ _____8____ 12 _15__ // \ / \ / \ // 2 4_ _10 14 16 // \ / // 5_ 9 // \ // 7 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 ```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 ``` ### RedBlackTree snippet ```ts 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 ``` ### Directed Graph simple snippet ```ts 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 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'] ``` ### Free conversion between data structures. ```js 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 ``` ## API docs & Examples [API Docs](https://data-structure-typed-docs.vercel.app) [Live Examples](https://vivid-algorithm.vercel.app) 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 Multiset			View
Segment Tree			View
Binary Indexed Tree			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

## Standard library data structure comparison

Data Structure Typed	C++ STL	java.util	Python collections
Heap<E>	priority_queue<T>	PriorityQueue<E>	heapq
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>	-	-
-	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>	-
Set<E>	-	HashSet<E>	set
HashMap<K, V>	-	HashMap<K, V>	dict
-	unordered_set<T>	HashSet<E>	-
Map<K, V>	-	-	OrderedDict
-	unordered_multiset	-	Counter
-	-	LinkedHashSet<E>	-
LinkedHashMap<K, V>	-	LinkedHashMap<K, V>	-
-	unordered_multimap<K, V>	-	-
-	bitset<N>	-	-

## Benchmark [//]: # (No deletion!!! Start of Replace Section)

avl-tree

test name	time taken (ms)	executions per sec	sample deviation
10,000 add randomly	34.57	28.93	0.01
10,000 add & delete randomly	80.91	12.36	0.02
10,000 addMany	38.81	25.76	0.01
10,000 get	32.58	30.69	0.01

binary-tree

test name	time taken (ms)	executions per sec	sample deviation
1,000 add randomly	14.61	68.46	0.00
1,000 add & delete randomly	21.50	46.51	0.01
1,000 addMany	15.57	64.22	0.01
1,000 get	32.93	30.36	0.02
1,000 has	26.55	37.67	0.02
1,000 dfs	181.64	5.51	0.03
1,000 bfs	94.85	10.54	0.05
1,000 morris	316.07	3.16	0.04

bst

test name	time taken (ms)	executions per sec	sample deviation
10,000 add randomly	27.63	36.19	0.01
10,000 add & delete randomly	73.15	13.67	0.02
10,000 addMany	27.44	36.44	0.01
10,000 get	37.03	27.01	0.02

rb-tree

test name	time taken (ms)	executions per sec	sample deviation
100,000 add	113.44	8.82	0.02
100,000 add & delete randomly	283.31	3.53	0.03
100,000 getNode	89.35	11.19	0.01
100,000 add & iterator	143.95	6.95	0.03

comparison

test name	time taken (ms)	executions per sec	sample deviation
SRC PQ 10,000 add	0.16	6180.55	3.37e-5
CJS PQ 10,000 add	0.17	6004.31	4.09e-5
MJS PQ 10,000 add	0.66	1512.95	1.66e-4
SRC PQ 10,000 add & pop	3.89	256.83	7.53e-4
CJS PQ 10,000 add & pop	4.02	248.66	0.00
MJS PQ 10,000 add & pop	3.78	264.62	8.38e-4

directed-graph

test name	time taken (ms)	executions per sec	sample deviation
1,000 addVertex	0.12	8625.55	2.88e-5
1,000 addEdge	8.52	117.35	0.00
1,000 getVertex	0.05	1.92e+4	1.11e-5
1,000 getEdge	20.83	48.02	0.00
tarjan	195.71	5.11	0.02
tarjan all	191.98	5.21	0.02
topologicalSort	155.83	6.42	0.03

hash-map

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 set	129.78	7.71	0.05
1,000,000 Map set	276.67	3.61	0.03
1,000,000 Set add	239.70	4.17	0.04
1,000,000 set & get	143.96	6.95	0.04
1,000,000 Map set & get	371.31	2.69	0.10
1,000,000 Set add & has	247.24	4.04	0.05
1,000,000 ObjKey set & get	396.42	2.52	0.06
1,000,000 Map ObjKey set & get	411.40	2.43	0.10
1,000,000 Set ObjKey add & has	357.45	2.80	0.05

heap

test name	time taken (ms)	executions per sec	sample deviation
100,000 add & pop	88.96	11.24	0.02
100,000 add & dfs	38.81	25.77	0.01
10,000 fib add & pop	399.77	2.50	0.01

doubly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	264.62	3.78	0.08
1,000,000 unshift	264.00	3.79	0.05
1,000,000 unshift & shift	211.89	4.72	0.07
1,000,000 insertBefore	467.62	2.14	0.22

singly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
10,000 push & pop	260.65	3.84	0.04
10,000 insertBefore	283.61	3.53	0.02

max-priority-queue

test name	time taken (ms)	executions per sec	sample deviation
10,000 refill & poll	10.29	97.16	0.00

priority-queue

test name	time taken (ms)	executions per sec	sample deviation
100,000 add & pop	117.05	8.54	0.02

deque

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	15.96	62.68	0.00
1,000,000 push & pop	25.90	38.61	0.01
1,000,000 push & shift	26.33	37.98	0.00
1,000,000 unshift & shift	24.90	40.16	0.00

queue

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	44.53	22.46	0.01
1,000,000 push & shift	92.57	10.80	0.02

stack

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	59.07	16.93	0.02
1,000,000 push & pop	52.68	18.98	0.02

trie

test name	time taken (ms)	executions per sec	sample deviation
100,000 push	68.45	14.61	0.02
100,000 getWords	134.22	7.45	0.04

[//]: # (No deletion!!! End of Replace Section) ## 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

## 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.