queue-typed

What

Brief

In the usual gig, we make do with Array.push and Array.shift to play Queue in JavaScript, but here's the kicker – native JavaScript Array isn't exactly Queue VIP. That shift move? It's a bit of a slow dance with a time complexity of linear time complexity O(n). When you're working with big data, you don't want to be caught slow-shifting. So, we roll up our sleeves and craft a Queue that's got a speedy constant time complexity O(1) Queue.enqueue(), a snappy O(1) Queue.dequeue(), and a lightning-fast O(1) Queue.getAt(). Yep, it's Queue-tastic!

Data Structure	Enqueue	Dequeue	Access	Enqueue & Dequeue 100000	Access 100000
Queue Typed	O(1)	O(1)	O(1)	22.60ms	10.60ms
JavaScript Native Array	O(1)	O(n)	O(1)	931.10ms	8.60ms
Other Queue	O(1)	O(1)	O(n)	28.90ms	17175.90ms

more data structures

This is a standalone Queue data structure from the data-structure-typed collection. If you wish to access more data structures or advanced features, you can transition to directly installing the complete data-structure-typed package

How

install

npm

npm i queue-typed --save

yarn

yarn add queue-typed

snippet

TS

import {Queue} from 'queue-typed';
// /* or if you prefer */ import {Queue} from 'queue-typed';

const queue = new Queue<number>();
for (let i = 0; i < magnitude; i++) {
    queue.enqueue(i);
}
for (let i = 0; i < magnitude; i++) {
    queue.dequeue();
}

for (let i = 0; i < magnitude; i++) {
    console.log(queue.getAt(i));        // 0, 1, 2, 3, ...
}

JS

const {Queue} = require('queue-typed');
// /* or if you prefer */ const {Queue} = require('queue-typed');

const queue = new Queue();
for (let i = 0; i < magnitude; i++) {
    queue.enqueue(i);
}
for (let i = 0; i < magnitude; i++) {
    queue.dequeue();
}

for (let i = 0; i < magnitude; i++) {
    console.log(queue.getAt(i));        // 0, 1, 2, 3, ...
}

Sliding Window using Queue

    const nums = [2, 3, 4, 1, 5];
    const k = 2;
    const queue = new Queue<number>();

    let maxSum = 0;
    let currentSum = 0;

    nums.forEach((num, i) => {
      queue.push(num);
      currentSum += num;

      if (queue.length > k) {
        currentSum -= queue.shift()!;
      }

      if (queue.length === k) {
        maxSum = Math.max(maxSum, currentSum);
      }
    });

    console.log(maxSum); // 7

Breadth-First Search (BFS) using Queue

    const graph: { [key in number]: number[] } = {
      1: [2, 3],
      2: [4, 5],
      3: [],
      4: [],
      5: []
    };

    const queue = new Queue<number>();
    const visited: number[] = [];

    queue.push(1);

    while (!queue.isEmpty()) {
      const node = queue.shift()!;
      if (!visited.includes(node)) {
        visited.push(node);
        graph[node].forEach(neighbor => queue.push(neighbor));
      }
    }

    console.log(visited); // [1, 2, 3, 4, 5]

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data Structure	Unit Test	Performance Test	API Docs
Queue			Queue

Standard library data structure comparison

Data Structure Typed	C++ STL	java.util	Python collections
Queue<E>	queue<T>	Queue<E>	-

Benchmark

queue

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	39.90	25.07	0.01
1,000,000 push & shift	81.79	12.23	0.00

Built-in classic algorithms

Algorithm	Function Description	Iteration Type

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.