# data-structure-typed ![npm](https://img.shields.io/npm/dm/data-structure-typed) ![GitHub contributors](https://img.shields.io/github/contributors/zrwusa/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) ![GITHUB Star](https://img.shields.io/github/stars/zrwusa/data-structure-typed) ![eslint](https://aleen42.github.io/badges/src/eslint.svg) ![NPM](https://img.shields.io/npm/l/data-structure-typed) ![npm](https://img.shields.io/npm/v/data-structure-typed) [//]: # (![npm bundle size](https://img.shields.io/bundlephobia/min/data-structure-typed)) [//]: # (

English | 简体中文

) ## Installation and Usage ### npm ```bash npm i data-structure-typed --save ``` ### yarn ```bash yarn add data-structure-typed ``` ```js import { Heap, Graph, Queue, Deque, PriorityQueue, BST, Trie, DoublyLinkedList, AVLTree, SinglyLinkedList, DirectedGraph, RedBlackTree, TreeMultiMap, DirectedVertex, Stack, AVLTreeNode } from 'data-structure-typed'; ``` If you only want to use a specific data structure independently, you can install it separately, for example, by running ```bash npm i heap-typed --save ``` ## 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)) ### 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). ### Data structures available We provide data structures that are not available in JS/TS
Data Structure Unit Test Perf Test API Doc NPM
Binary Tree View View
Binary Search Tree (BST) View View
AVL Tree View View
Red Black Tree View View
Tree Multimap View View
Heap View View
Priority Queue View View
Max Priority Queue View View
Min Priority Queue View View
Trie View View
Graph View View
Directed Graph View View
Undirected Graph View View
Queue View View
Deque View View
Hash Map View
Linked List View View
Singly Linked List View View
Doubly Linked List View View
Stack View View
Segment Tree View
Binary Indexed Tree View
## Vivid Examples ### AVL 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/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) ### 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 ### Red Black Tree 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 ``` ### 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 ``` ### 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'] ``` ## API docs & Examples [API Docs](https://data-structure-typed-docs.vercel.app) [Live Examples](https://vivid-algorithm.vercel.app) Examples Repository ## 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 [//]: # (No deletion!!! Start of Replace Section)
heap
test nametime taken (ms)executions per secsample deviation
100,000 add6.44155.391.89e-4
100,000 add & poll31.5431.717.91e-4
rb-tree
test nametime taken (ms)executions per secsample deviation
100,000 add55.6417.973.93e-4
100,000 add randomly70.3514.210.00
100,000 get115.518.660.00
100,000 iterator27.6436.180.01
100,000 add & delete orderly120.738.280.00
100,000 add & delete randomly223.374.480.00
queue
test nametime taken (ms)executions per secsample deviation
1,000,000 push42.8723.330.01
100,000 push & shift4.87205.176.94e-4
Native JS Array 100,000 push & shift2196.840.460.19
deque
test nametime taken (ms)executions per secsample deviation
1,000,000 push23.6842.220.00
1,000,000 push & pop30.6832.600.00
1,000,000 push & shift30.4932.800.00
100,000 push & shift3.21311.512.41e-4
Native JS Array 100,000 push & shift2510.080.400.34
100,000 unshift & shift2.89346.572.98e-4
Native JS Array 100,000 unshift & shift4581.650.220.40
hash-map
test nametime taken (ms)executions per secsample deviation
1,000,000 set120.668.290.03
Native JS Map 1,000,000 set202.574.940.01
Native JS Set 1,000,000 add167.465.970.01
1,000,000 set & get115.608.650.01
Native JS Map 1,000,000 set & get265.343.770.01
Native JS Set 1,000,000 add & has167.855.960.01
1,000,000 ObjKey set & get308.733.240.03
Native JS Map 1,000,000 ObjKey set & get300.603.330.03
Native JS Set 1,000,000 ObjKey add & has270.493.700.04
trie
test nametime taken (ms)executions per secsample deviation
100,000 push45.7921.847.32e-4
100,000 getWords87.8511.380.00
avl-tree
test nametime taken (ms)executions per secsample deviation
100,000 add260.783.830.00
100,000 add randomly306.613.260.00
100,000 get140.277.130.00
100,000 iterator29.9033.450.01
100,000 add & delete orderly428.762.330.00
100,000 add & delete randomly580.741.720.00
binary-tree-overall
test nametime taken (ms)executions per secsample deviation
10,000 RBTree add5.74174.109.29e-5
10,000 RBTree add & delete randomly18.8353.101.49e-4
10,000 RBTree get0.771290.557.33e-6
10,000 AVLTree add22.6044.252.14e-4
10,000 AVLTree get10.6394.081.02e-4
10,000 AVLTree add & delete randomly44.1722.643.52e-4
directed-graph
test nametime taken (ms)executions per secsample deviation
1,000 addVertex0.119501.691.02e-6
1,000 addEdge6.18161.814.27e-4
1,000 getVertex0.052.16e+43.23e-7
1,000 getEdge23.3142.900.00
tarjan206.064.850.01
topologicalSort181.655.510.01
doubly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push207.884.810.04
1,000,000 unshift214.334.670.06
1,000,000 unshift & shift185.545.390.04
1,000,000 addBefore308.663.240.08
singly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push & shift202.614.940.04
10,000 push & pop219.694.550.02
10,000 addBefore247.134.050.01
priority-queue
test nametime taken (ms)executions per secsample deviation
100,000 add27.3636.559.92e-4
100,000 add & poll146.726.826.84e-4
stack
test nametime taken (ms)executions per secsample deviation
1,000,000 push39.3625.410.01
1,000,000 push & pop47.8620.890.01
[//]: # (No deletion!!! End of Replace Section) ## 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 ```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; ```