test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 6.09 | 164.12 | 1.35e-4 |
100,000 add & poll | 34.55 | 28.94 | 6.43e-4 |
# 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)) [//]: # (
) ## 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/TSMethod | 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) |
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 |
Data Structure | Unit Test | Perf Test | API Doc | NPM | Downloads |
---|---|---|---|---|---|
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 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 6.09 | 164.12 | 1.35e-4 |
100,000 add & poll | 34.55 | 28.94 | 6.43e-4 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 76.73 | 13.03 | 0.00 |
100,000 add randomly | 80.67 | 12.40 | 0.00 |
100,000 get | 110.86 | 9.02 | 0.00 |
100,000 iterator | 24.99 | 40.02 | 0.00 |
100,000 add & delete orderly | 152.66 | 6.55 | 0.00 |
100,000 add & delete randomly | 230.75 | 4.33 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 39.27 | 25.46 | 0.01 |
100,000 push & shift | 4.53 | 220.81 | 4.84e-4 |
Native JS Array 100,000 push & shift | 1948.05 | 0.51 | 0.02 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 23.22 | 43.06 | 0.00 |
1,000,000 push & pop | 29.68 | 33.69 | 0.00 |
1,000,000 push & shift | 29.33 | 34.09 | 0.00 |
100,000 push & shift | 3.10 | 323.01 | 2.47e-4 |
Native JS Array 100,000 push & shift | 1942.12 | 0.51 | 0.02 |
100,000 unshift & shift | 2.77 | 360.50 | 2.43e-4 |
Native JS Array 100,000 unshift & shift | 3835.21 | 0.26 | 0.03 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 set | 112.38 | 8.90 | 0.02 |
Native JS Map 1,000,000 set | 199.97 | 5.00 | 0.01 |
Native JS Set 1,000,000 add | 163.34 | 6.12 | 0.01 |
1,000,000 set & get | 109.86 | 9.10 | 0.02 |
Native JS Map 1,000,000 set & get | 255.33 | 3.92 | 0.00 |
Native JS Set 1,000,000 add & has | 163.91 | 6.10 | 0.00 |
1,000,000 ObjKey set & get | 317.89 | 3.15 | 0.04 |
Native JS Map 1,000,000 ObjKey set & get | 282.99 | 3.53 | 0.03 |
Native JS Set 1,000,000 ObjKey add & has | 253.93 | 3.94 | 0.03 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 push | 43.71 | 22.88 | 7.33e-4 |
100,000 getWords | 83.63 | 11.96 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 271.93 | 3.68 | 0.01 |
100,000 add randomly | 318.27 | 3.14 | 0.00 |
100,000 get | 128.85 | 7.76 | 0.00 |
100,000 iterator | 29.09 | 34.38 | 0.00 |
100,000 add & delete orderly | 435.48 | 2.30 | 7.44e-4 |
100,000 add & delete randomly | 578.70 | 1.73 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
10,000 RBTree add randomly | 6.69 | 149.54 | 1.06e-4 |
10,000 RBTree get randomly | 9.19 | 108.82 | 1.43e-4 |
10,000 RBTree add & delete randomly | 18.54 | 53.94 | 1.73e-4 |
10,000 AVLTree add randomly | 23.70 | 42.20 | 1.88e-4 |
10,000 AVLTree get randomly | 9.89 | 101.11 | 0.00 |
10,000 AVLTree add & delete randomly | 44.44 | 22.50 | 4.30e-4 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000 addVertex | 0.10 | 9766.65 | 9.83e-7 |
1,000 addEdge | 6.15 | 162.57 | 7.99e-4 |
1,000 getVertex | 0.05 | 2.18e+4 | 4.52e-7 |
1,000 getEdge | 22.70 | 44.06 | 0.00 |
tarjan | 203.00 | 4.93 | 0.01 |
topologicalSort | 176.40 | 5.67 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 222.02 | 4.50 | 0.07 |
1,000,000 unshift | 220.41 | 4.54 | 0.05 |
1,000,000 unshift & shift | 185.31 | 5.40 | 0.01 |
1,000,000 addBefore | 317.20 | 3.15 | 0.07 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push & shift | 204.82 | 4.88 | 0.09 |
10,000 push & pop | 221.88 | 4.51 | 0.03 |
10,000 addBefore | 247.28 | 4.04 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 26.97 | 37.08 | 7.97e-4 |
100,000 add & poll | 74.55 | 13.41 | 5.19e-4 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 35.54 | 28.14 | 0.00 |
1,000,000 push & pop | 44.89 | 22.27 | 0.01 |
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> | - | - |
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 |
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. |