test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 6.58 | 152.07 | 3.13e-4 |
100,000 add & poll | 35.67 | 28.03 | 0.01 |
# 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)) [//]: # (
) ## 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)) ### Data structures available We provide data structures that are not available in JS/TS Heap, Binary Tree, Red Black Tree, Linked List, Deque, Trie, Directed Graph, Undirected Graph, BST, AVL Tree, Priority Queue, Queue, Tree Multiset. ### 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 | 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 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 6.58 | 152.07 | 3.13e-4 |
100,000 add & poll | 35.67 | 28.03 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 55.91 | 17.89 | 0.01 |
100,000 get | 125.11 | 7.99 | 0.01 |
100,000 iterator | 27.97 | 35.76 | 0.01 |
100,000 add & delete orderly | 125.06 | 8.00 | 0.00 |
100,000 add & delete randomly | 242.78 | 4.12 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 44.81 | 22.32 | 0.01 |
100,000 push & shift | 4.99 | 200.39 | 7.40e-4 |
Native JS Array 100,000 push & shift | 2234.97 | 0.45 | 0.19 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 24.34 | 41.08 | 0.01 |
1,000,000 push & pop | 31.41 | 31.83 | 0.00 |
1,000,000 push & shift | 31.12 | 32.13 | 0.00 |
100,000 push & shift | 3.27 | 306.17 | 2.62e-4 |
Native JS Array 100,000 push & shift | 2362.95 | 0.42 | 0.12 |
100,000 unshift & shift | 2.89 | 345.46 | 3.23e-4 |
Native JS Array 100,000 unshift & shift | 4313.81 | 0.23 | 0.13 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 set | 118.81 | 8.42 | 0.02 |
Native JS Map 1,000,000 set | 218.24 | 4.58 | 0.02 |
Native JS Set 1,000,000 add | 180.64 | 5.54 | 0.03 |
1,000,000 set & get | 121.23 | 8.25 | 0.01 |
Native JS Map 1,000,000 set & get | 273.71 | 3.65 | 0.01 |
Native JS Set 1,000,000 add & has | 173.69 | 5.76 | 0.01 |
1,000,000 ObjKey set & get | 331.89 | 3.01 | 0.04 |
Native JS Map 1,000,000 ObjKey set & get | 331.49 | 3.02 | 0.05 |
Native JS Set 1,000,000 ObjKey add & has | 308.15 | 3.25 | 0.04 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 push | 58.78 | 17.01 | 0.02 |
100,000 getWords | 95.99 | 10.42 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
10,000 add randomly | 50.17 | 19.93 | 0.01 |
10,000 get | 20.86 | 47.94 | 0.00 |
10,000 add & delete randomly | 79.18 | 12.63 | 0.00 |
10,000 addMany | 52.92 | 18.90 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
10,000 RBTree add | 5.69 | 175.82 | 5.70e-4 |
10,000 RBTree add & delete randomly | 19.94 | 50.16 | 0.00 |
10,000 RBTree get | 0.65 | 1543.77 | 1.81e-5 |
10,000 AVLTree add | 44.46 | 22.49 | 0.00 |
10,000 AVLTree get | 20.07 | 49.84 | 0.00 |
10,000 AVLTree add & delete randomly | 77.29 | 12.94 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000 addVertex | 0.10 | 9883.75 | 9.48e-7 |
1,000 addEdge | 6.05 | 165.15 | 1.08e-4 |
1,000 getVertex | 0.05 | 2.15e+4 | 5.74e-7 |
1,000 getEdge | 23.58 | 42.41 | 0.00 |
tarjan | 208.84 | 4.79 | 0.01 |
topologicalSort | 180.27 | 5.55 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 208.91 | 4.79 | 0.07 |
1,000,000 unshift | 202.73 | 4.93 | 0.03 |
1,000,000 unshift & shift | 182.70 | 5.47 | 0.06 |
1,000,000 addBefore | 314.16 | 3.18 | 0.06 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push & shift | 207.94 | 4.81 | 0.05 |
10,000 push & pop | 216.07 | 4.63 | 0.01 |
10,000 addBefore | 246.19 | 4.06 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 27.00 | 37.04 | 2.43e-4 |
100,000 add & poll | 77.16 | 12.96 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 39.29 | 25.45 | 0.00 |
1,000,000 push & pop | 48.05 | 20.81 | 0.00 |
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. |