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1.51.9 • Public • Published

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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




npm i queue-typed --save


yarn add queue-typed


Queue LinkedListQueue



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++) {
for (let i = 0; i < magnitude; i++) {

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


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++) {
for (let i = 0; i < magnitude; i++) {

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

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> -


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.

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npm i queue-typed

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