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## 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 [//]: # (![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)) ### We provide data structures that are not available in JS/TS Heap, Binary Tree, RedBlack Tree, Linked List, Deque, Trie, Directed Graph, Undirected Graph, BST, AVL Tree, Priority Queue, Queue, Tree Multiset. ### Performance surpasses that of native JS/TS
Method Time Taken (ms) Scale Belongs To
Queue.push & shift 5.83 100,000 data-structure-typed
Array.push & shift 2829.59 100,000 Native JS
Deque.unshift & shift 2.44 100,000 data-structure-typed
Array.unshift & shift 4750.37 100,000 Native JS
HashMap.set 122.51 1,000,000 data-structure-typed
Map.set 223.80 1,000,000 Native JS
Set.add 185.06 1,000,000 Native JS
## 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 ### RedBlackTree snippet #### TS ```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 ``` #### JS ```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 ``` ### 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 bst.print() // ______________11_____ // / \ // ___3_______ _13_____ // / \ / \ // 1_ _____8____ 12 _15__ // \ / \ / \ // 2 4_ _10 14 16 // \ / // 5_ 9 // \ // 7 const objBST = new BST(); 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 ```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 ``` ### 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 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
## 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 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.
## Benchmark [//]: # (No deletion!!! Start of Replace Section)
avl-tree
test nametime taken (ms)executions per secsample deviation
10,000 add randomly126.827.880.01
10,000 add & delete randomly186.365.370.00
10,000 addMany133.157.510.00
10,000 get50.6519.745.59e-4
binary-tree-overall
test nametime taken (ms)executions per secsample deviation
10,000 RBTree add6.12163.481.94e-4
10,000 RBTree add & delete randomly15.4664.703.64e-4
10,000 RBTree get19.9850.060.00
10,000 AVLTree add130.407.670.02
10,000 AVLTree add & delete randomly193.645.160.01
10,000 AVLTree get0.991005.443.95e-5
rb-tree
test nametime taken (ms)executions per secsample deviation
100,000 add90.7211.020.03
100,000 add & delete randomly228.774.370.02
100,000 getNode192.255.205.16e-4
100,000 add & iterator112.498.890.01
directed-graph
test nametime taken (ms)executions per secsample deviation
1,000 addVertex0.119250.941.22e-5
1,000 addEdge6.35157.512.88e-4
1,000 getVertex0.052.06e+48.69e-6
1,000 getEdge23.0243.430.00
tarjan213.854.680.01
tarjan all6674.110.150.28
topologicalSort179.095.580.00
hash-map
test nametime taken (ms)executions per secsample deviation
1,000,000 set131.277.620.05
Native Map 1,000,000 set267.343.740.04
Native Set 1,000,000 add207.034.830.06
1,000,000 set & get132.197.560.03
Native Map 1,000,000 set & get276.303.620.01
Native Set 1,000,000 add & has187.745.330.02
1,000,000 ObjKey set & get336.392.970.03
Native Map 1,000,000 ObjKey set & get394.472.540.09
Native Set 1,000,000 ObjKey add & has295.483.380.04
heap
test nametime taken (ms)executions per secsample deviation
100,000 add & poll24.1841.356.43e-4
100,000 add & dfs33.6429.720.00
10,000 fib add & pop363.382.750.00
doubly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push220.164.540.03
1,000,000 unshift210.844.740.05
1,000,000 unshift & shift189.595.270.07
1,000,000 addBefore412.742.420.17
singly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push & shift252.063.970.09
10,000 push & pop230.294.340.01
10,000 addBefore261.573.820.01
priority-queue
test nametime taken (ms)executions per secsample deviation
100,000 add & poll75.7113.218.95e-4
deque
test nametime taken (ms)executions per secsample deviation
1,000,000 push25.1839.710.01
1,000,000 push & pop33.5229.830.01
100,000 push & shift3.61276.965.50e-4
Native Array 100,000 push & shift2703.160.370.11
100,000 unshift & shift3.73268.148.29e-4
Native Array 100,000 unshift & shift4767.610.210.40
queue
test nametime taken (ms)executions per secsample deviation
1,000,000 push50.5619.780.01
100,000 push & shift5.99166.850.00
Native Array 100,000 push & shift2962.430.340.29
Native Array 100,000 push & pop4.49222.693.01e-4
stack
test nametime taken (ms)executions per secsample deviation
1,000,000 push59.6816.760.03
1,000,000 push & pop52.0419.220.01
trie
test nametime taken (ms)executions per secsample deviation
100,000 push47.7020.960.00
100,000 getWords66.5315.030.00
[//]: # (No deletion!!! End of Replace Section)