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SECURITY.md test coverage report supported. Code quality enhanced and support multiple environments such as ES6 (ESModule), ES5 (CommonJS), and a single file for both browser and Node.js environments (UMD). Supported for source maps. CODE-OF-CONDUCT.md, COMMANDS.md, SECURITY.md, .gitattributes added. 2023-09-22 00:53:34 +08:00
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Data Structure Typed

npm npm npm package minimized gzipped size (select exports) GitHub top language eslint NPM

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

Installation and Usage

npm

npm i data-structure-typed --save

yarn

yarn add data-structure-typed
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

<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;

Vivid Examples

Binary Tree

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

Binary Tree DFS

Try it out

AVL Tree

Try it out

Tree Multi Map

Try it out

Matrix

Try it out

Directed Graph

Try it out

Map Graph

Try it out

Code Snippets

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<{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

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

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

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

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

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

Live Examples

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

avl-tree
test nametime taken (ms)executions per secsample deviation
10,000 add randomly31.3231.933.67e-4
10,000 add & delete randomly70.9014.100.00
10,000 addMany40.5824.644.87e-4
10,000 get27.3136.622.00e-4
binary-tree
test nametime taken (ms)executions per secsample deviation
1,000 add randomly12.3580.997.17e-5
1,000 add & delete randomly15.9862.587.98e-4
1,000 addMany10.9691.270.00
1,000 get18.6153.730.00
1,000 dfs164.206.090.04
1,000 bfs58.8417.000.01
1,000 morris256.663.907.70e-4
bst
test nametime taken (ms)executions per secsample deviation
10,000 add randomly31.5931.662.74e-4
10,000 add & delete randomly74.5613.418.32e-4
10,000 addMany29.1634.300.00
10,000 get29.2434.210.00
rb-tree
test nametime taken (ms)executions per secsample deviation
100,000 add85.8511.650.00
100,000 add & delete randomly211.544.730.00
100,000 getNode37.9226.371.65e-4
comparison
test nametime taken (ms)executions per secsample deviation
SRC PQ 10,000 add0.571748.734.96e-6
CJS PQ 10,000 add0.571746.694.91e-6
MJS PQ 10,000 add0.571749.684.43e-6
SRC PQ 10,000 add & pop3.47288.146.38e-4
CJS PQ 10,000 add & pop3.39295.363.90e-5
MJS PQ 10,000 add & pop3.37297.173.03e-5
directed-graph
test nametime taken (ms)executions per secsample deviation
1,000 addVertex0.109534.938.72e-7
1,000 addEdge6.30158.670.00
1,000 getVertex0.052.16e+43.03e-7
1,000 getEdge22.3144.820.00
tarjan210.904.740.01
tarjan all214.724.660.01
topologicalSort172.525.800.00
hash-map
test nametime taken (ms)executions per secsample deviation
1,000,000 set275.883.620.12
1,000,000 Map set211.664.720.01
1,000,000 Set add177.725.630.02
1,000,000 set & get317.603.150.02
1,000,000 Map set & get274.993.640.03
1,000,000 Set add & has172.235.810.02
1,000,000 ObjKey set & get929.401.080.07
1,000,000 Map ObjKey set & get310.023.230.05
1,000,000 Set ObjKey add & has283.283.530.04
heap
test nametime taken (ms)executions per secsample deviation
10,000 add & pop5.80172.358.78e-5
10,000 fib add & pop357.922.790.00
doubly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push221.574.510.03
1,000,000 unshift229.024.370.07
1,000,000 unshift & shift169.215.910.02
1,000,000 insertBefore314.483.180.07
singly-linked-list
test nametime taken (ms)executions per secsample deviation
10,000 push & pop212.984.700.01
10,000 insertBefore250.683.990.01
max-priority-queue
test nametime taken (ms)executions per secsample deviation
10,000 refill & poll8.91112.292.26e-4
priority-queue
test nametime taken (ms)executions per secsample deviation
100,000 add & pop103.599.650.00
deque
test nametime taken (ms)executions per secsample deviation
1,000,000 push14.5568.726.91e-4
1,000,000 push & pop23.4042.735.94e-4
1,000,000 push & shift24.4140.971.45e-4
1,000,000 unshift & shift22.5644.321.30e-4
queue
test nametime taken (ms)executions per secsample deviation
1,000,000 push39.9025.070.01
1,000,000 push & shift81.7912.230.00
stack
test nametime taken (ms)executions per secsample deviation
1,000,000 push37.6026.600.00
1,000,000 push & pop47.0121.270.00
trie
test nametime taken (ms)executions per secsample deviation
100,000 push45.9721.760.00
100,000 getWords66.2015.110.00

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.