queue-typed
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Queue data structure
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Markdown
# 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](https://medium.com/@ariel.salem1989/an-easy-to-use-guide-to-big-o-time-complexity-5dcf4be8a444#:~:text=O(N)%E2%80%94Linear%20Time)
*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](https://medium.com/@ariel.salem1989/an-easy-to-use-guide-to-big-o-time-complexity-5dcf4be8a444#:~:text=O(1)%20%E2%80%94%20Constant%20Time)
O(1) Queue.enqueue(), a snappy O(1) Queue.dequeue(), and a lightning-fast O(1)
Queue.getAt(). Yep, it's Queue-tastic!
<table>
<thead>
<tr><th>Data Structure</th><th>Enqueue</th><th>Dequeue</th><th>Access</th><th>Enqueue & Dequeue 100000</th><th>Access 100000</th></tr>
</thead>
<tbody>
<tr><td>Queue Typed</td><td>O(1)</td><td>O(1)</td><td>O(1)</td><td>22.60ms</td><td>10.60ms</td></tr>
<tr><td>JavaScript Native Array</td><td>O(1)</td><td>O(n)</td><td>O(1)</td><td>931.10ms</td><td>8.60ms</td></tr>
<tr><td>Other Queue</td><td>O(1)</td><td>O(1)</td><td>O(n)</td><td>28.90ms</td><td>17175.90ms</td></tr>
</tbody>
</table>
## 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](https://www.npmjs.com/package/data-structure-typed) package
# How
## install
### npm
```bash
npm i queue-typed --save
```
### yarn
```bash
yarn add queue-typed
```
### snippet
#### TS
```typescript
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
```javascript
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, ...
}
```
[//]: # (No deletion!!! Start of Example Replace Section)
### Sliding Window using Queue
```typescript
const nums = [2, 3, 4, 1, 5];
const k = 2;
const queue = new Queue<number>();
let maxSum = 0;
let currentSum = 0;
nums.forEach(num => {
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
```typescript
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]
```
[//]: # (No deletion!!! End of Example Replace Section)
## API docs & Examples
[API Docs](https://data-structure-typed-docs.vercel.app)
[Live Examples](https://vivid-algorithm.vercel.app)
<a href="https://github.com/zrwusa/vivid-algorithm" target="_blank">Examples Repository</a>
## Data Structures
<table>
<thead>
<tr>
<th>Data Structure</th>
<th>Unit Test</th>
<th>Performance Test</th>
<th>API Docs</th>
</tr>
</thead>
<tbody>
<tr>
<td>Queue</td>
<td><img src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/tick.svg" alt=""></td>
<td><img src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/tick.svg" alt=""></td>
<td><a href="https://data-structure-typed-docs.vercel.app/classes/Queue.html"><span>Queue</span></a></td>
</tr>
</tbody>
</table>
## Standard library data structure comparison
<table>
<thead>
<tr>
<th>Data Structure Typed</th>
<th>C++ STL</th>
<th>java.util</th>
<th>Python collections</th>
</tr>
</thead>
<tbody>
<tr>
<td>Queue<E></td>
<td>queue<T></td>
<td>Queue<E></td>
<td>-</td>
</tr>
</tbody>
</table>
## Benchmark
[//]: # (No deletion!!! Start of Replace Section)
<div class="json-to-html-collapse clearfix 0">
<div class='collapsible level0' ><span class='json-to-html-label'>queue</span></div>
<div class="content"><table style="display: table; width:100%; table-layout: fixed;"><tr><th>test name</th><th>time taken (ms)</th><th>executions per sec</th><th>sample deviation</th></tr><tr><td>1,000,000 push</td><td>39.90</td><td>25.07</td><td>0.01</td></tr><tr><td>1,000,000 push & shift</td><td>81.79</td><td>12.23</td><td>0.00</td></tr></table></div>
</div>
[//]: # (No deletion!!! End of Replace Section)
## Built-in classic algorithms
<table>
<thead>
<tr>
<th>Algorithm</th>
<th>Function Description</th>
<th>Iteration Type</th>
</tr>
</thead>
<tbody>
</tbody>
</table>
## Software Engineering Design Standards
<table>
<tr>
<th>Principle</th>
<th>Description</th>
</tr>
<tr>
<td>Practicality</td>
<td>Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.</td>
</tr>
<tr>
<td>Extensibility</td>
<td>Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.</td>
</tr>
<tr>
<td>Modularization</td>
<td>Includes data structure modularization and independent NPM packages.</td>
</tr>
<tr>
<td>Efficiency</td>
<td>All methods provide time and space complexity, comparable to native JS performance.</td>
</tr>
<tr>
<td>Maintainability</td>
<td>Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.</td>
</tr>
<tr>
<td>Testability</td>
<td>Automated and customized unit testing, performance testing, and integration testing.</td>
</tr>
<tr>
<td>Portability</td>
<td>Plans for porting to Java, Python, and C++, currently achieved to 80%.</td>
</tr>
<tr>
<td>Reusability</td>
<td>Fully decoupled, minimized side effects, and adheres to OOP.</td>
</tr>
<tr>
<td>Security</td>
<td>Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.</td>
</tr>
<tr>
<td>Scalability</td>
<td>Data structure software does not involve load issues.</td>
</tr>
</table>