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
yarn
methods
Queue
LinkedListQueue
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, ...
}
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. |