# 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](). Now you can use this in Node.js and browser environments CommonJS:**`require export.modules =`** ESModule:   **`import export`** Typescript:   **`import export`** UMD:           **`var Deque = dataStructureTyped.Deque`** [//]: # (![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 ### 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'] ``` ## 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 Binary Tree
Binary Search Tree (BST) BST
AVL Tree AVLTree
Red Black Tree RedBlackTree
Tree Multiset TreeMultimap
Segment Tree SegmentTree
Binary Indexed Tree BinaryIndexedTree
Heap Heap
Priority Queue PriorityQueue
Max Priority Queue MaxPriorityQueue
Min Priority Queue MinPriorityQueue
Trie Trie
Graph AbstractGraph
Directed Graph DirectedGraph
Undirected Graph UndirectedGraph
Queue Queue
Deque Deque
Linked List SinglyLinkedList
Singly Linked List SinglyLinkedList
Doubly Linked List DoublyLinkedList
Stack Stack
## 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
Map<K, V> - HashMap<K, V> dict
- unordered_set<T> HashSet<E> -
Map<K, V> - - OrderedDict
- unordered_multiset - Counter
- - LinkedHashSet<E> -
HashMap<K, V> - LinkedHashMap<K, V> -
- unordered_multimap<K, V> - -
- bitset<N> - -
## Benchmark [//]: # (No deletion!!! Start of Replace Section)
avl-tree
test nametime taken (ms)executions per secsample deviation
10,000 add randomly33.0930.224.32e-4
10,000 add & delete randomly74.1213.490.00
10,000 addMany41.7123.970.00
10,000 get28.3735.252.37e-4
binary-tree
test nametime taken (ms)executions per secsample deviation
1,000 add randomly14.5068.961.33e-4
1,000 add & delete randomly16.2061.722.03e-4
1,000 addMany10.5195.128.76e-5
1,000 get18.2854.691.82e-4
1,000 dfs157.236.367.06e-4
1,000 bfs58.0617.220.01
1,000 morris256.363.900.00
bst
test nametime taken (ms)executions per secsample deviation
10,000 add randomly30.4832.814.13e-4
10,000 add & delete randomly71.8413.920.00
10,000 addMany29.5433.855.25e-4
10,000 get30.5332.750.01
rb-tree
test nametime taken (ms)executions per secsample deviation
100,000 add90.8911.000.00
100,000 CPT add50.6519.740.00
100,000 add & delete randomly230.084.350.02
100,000 getNode38.9725.665.82e-4
100,000 add & iterator118.328.450.01
comparison
test nametime taken (ms)executions per secsample deviation
SRC PQ 10,000 add0.146939.331.74e-6
CJS PQ 10,000 add0.156881.641.91e-6
MJS PQ 10,000 add0.571745.921.60e-5
CPT PQ 10,000 add0.571744.711.01e-5
SRC PQ 10,000 add & pop3.51284.936.79e-4
CJS PQ 10,000 add & pop3.42292.554.04e-5
MJS PQ 10,000 add & pop3.41293.385.11e-5
CPT PQ 10,000 add & pop2.09478.762.28e-5
CPT OM 100,000 add43.2223.140.00
CPT HM 10,000 set0.581721.251.85e-5
CPT HM 10,000 set & get0.681477.311.26e-5
CPT LL 1,000,000 unshift81.3812.290.02
CPT PQ 10,000 add & pop2.10476.501.60e-4
CPT DQ 1,000,000 push22.5144.420.00
CPT Q 1,000,000 push47.8520.900.01
CPT ST 1,000,000 push42.5423.510.01
CPT ST 1,000,000 push & pop50.0819.970.00
directed-graph
test nametime taken (ms)executions per secsample deviation
1,000 addVertex0.119501.346.10e-6
1,000 addEdge6.35157.556.69e-4
1,000 getVertex0.052.14e+42.50e-6
1,000 getEdge25.0039.990.01
tarjan219.464.560.01
tarjan all218.154.580.00
topologicalSort176.835.660.00
hash-map
test nametime taken (ms)executions per secsample deviation
1,000,000 set254.463.930.04
1,000,000 CPT set251.213.980.03
1,000,000 Map set211.274.730.01
1,000,000 Set add175.155.710.02
1,000,000 set & get370.542.700.11
1,000,000 CPT set & get283.343.530.07
1,000,000 Map set & get287.093.480.04
1,000,000 Set add & has190.505.250.01
1,000,000 ObjKey set & get880.471.140.10
1,000,000 Map ObjKey set & get334.472.990.05
1,000,000 Set ObjKey add & has310.123.220.06
heap
test nametime taken (ms)executions per secsample deviation
100,000 add & pop80.1312.480.00
100,000 add & dfs35.0828.500.00
10,000 fib add & pop367.842.720.01
doubly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push237.284.210.07
1,000,000 CPT push75.6613.220.03
1,000,000 unshift226.384.420.05
1,000,000 CPT unshift93.3410.710.07
1,000,000 unshift & shift188.345.310.05
1,000,000 insertBefore329.603.030.05
singly-linked-list
test nametime taken (ms)executions per secsample deviation
10,000 push & pop221.324.520.01
10,000 insertBefore255.523.910.01
max-priority-queue
test nametime taken (ms)executions per secsample deviation
10,000 refill & poll9.07110.242.71e-4
priority-queue
test nametime taken (ms)executions per secsample deviation
100,000 add & pop101.939.817.95e-4
100,000 CPT add & pop28.5435.040.00
deque
test nametime taken (ms)executions per secsample deviation
1,000,000 push14.4369.292.36e-4
1,000,000 CPT push25.0839.870.01
1,000,000 push & pop22.8743.726.05e-4
1,000,000 push & shift25.2839.550.01
1,000,000 unshift & shift21.8845.712.05e-4
queue
test nametime taken (ms)executions per secsample deviation
1,000,000 push38.4925.989.08e-4
1,000,000 CPT push43.9322.760.01
1,000,000 push & shift82.8512.070.00
stack
test nametime taken (ms)executions per secsample deviation
1,000,000 push40.1924.880.01
1,000,000 CPT push39.8725.080.00
1,000,000 push & pop41.6724.000.01
1,000,000 CPT push & pop46.6521.440.00
trie
test nametime taken (ms)executions per secsample deviation
100,000 push43.4223.037.57e-4
100,000 getWords93.4110.710.00
[//]: # (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.