This is a standalone Heap 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
Min Heap
Max Heap
import {MinHeap, MaxHeap} from 'data-structure-typed';
// /* or if you prefer */ import {MinHeap, MaxHeap} from 'heap-typed';
const minNumHeap = new MinHeap<number>([1, 6, 2, 0, 5]);
minNumHeap.add(9);
minNumHeap.has(1) // true
minNumHeap.has(2) // true
minNumHeap.poll() // 0
minNumHeap.poll() // 1
minNumHeap.peek() // 2
minNumHeap.has(1); // false
minNumHeap.has(2); // true
const arrFromHeap = minNumHeap.toArray();
arrFromHeap.length // 4
arrFromHeap[0] // 2
arrFromHeap[1] // 5
arrFromHeap[2] // 9
arrFromHeap[3] // 6
minNumHeap.sort() // [2, 5, 6, 9]
const maxHeap = new MaxHeap<{ keyA: string }>([], {comparator: (a, b) => b.keyA - a.keyA});
const obj1 = {keyA: 'a1'}, obj6 = {keyA: 'a6'}, obj5 = {keyA: 'a5'}, obj2 = {keyA: 'a2'},
obj0 = {keyA: 'a0'}, obj9 = {keyA: 'a9'};
maxHeap.add(obj1);
maxHeap.has(obj1) // true
maxHeap.has(obj9) // false
maxHeap.add(obj6);
maxHeap.has(obj6) // true
maxHeap.add(obj5);
maxHeap.add(obj2);
maxHeap.add(obj0);
maxHeap.add(obj9);
maxHeap.has(obj9) // true
const peek9 = maxHeap.peek();
console.log(peek9.keyA) // 'a9'
const heapToArr = maxHeap.toArray();
console.log(heapToArr.map(ele => ele?.keyA)); // ['a9', 'a2', 'a6', 'a1', 'a0', 'a5']
const values = ['a9', 'a6', 'a5', 'a2', 'a1', 'a0'];
let i = 0;
while (maxHeap.size > 0) {
const polled = maxHeap.poll();
console.log(polled.keyA) // values[i]
i++;
}
const {MinHeap, MaxHeap} = require('data-structure-typed');
// /* or if you prefer */ const {MinHeap, MaxHeap} = require('heap-typed');
const minNumHeap = new MinHeap([1, 6, 2, 0, 5]);
minNumHeap.add(9);
minNumHeap.has(1) // true
minNumHeap.has(2) // true
minNumHeap.poll() // 0
minNumHeap.poll() // 1
minNumHeap.peek() // 2
minNumHeap.has(1); // false
minNumHeap.has(2); // true
const arrFromHeap = minNumHeap.toArray();
arrFromHeap.length // 4
arrFromHeap[0] // 2
arrFromHeap[1] // 5
arrFromHeap[2] // 9
arrFromHeap[3] // 6
minNumHeap.sort() // [2, 5, 6, 9]
const maxHeap = new MaxHeap([], {comparator: (a, b) => b.keyA - a.keyA});
const obj1 = {keyA: 'a1'}, obj6 = {keyA: 'a6'}, obj5 = {keyA: 'a5'}, obj2 = {keyA: 'a2'},
obj0 = {keyA: 'a0'}, obj9 = {keyA: 'a9'};
maxHeap.add(obj1);
maxHeap.has(obj1) // true
maxHeap.has(obj9) // false
maxHeap.add(obj6);
maxHeap.has(obj6) // true
maxHeap.add(obj5);
maxHeap.add(obj2);
maxHeap.add(obj0);
maxHeap.add(obj9);
maxHeap.has(obj9) // true
const peek9 = maxHeap.peek();
console.log(peek9.keyA) // 'a9'
const heapToArr = maxHeap.toArray();
console.log(heapToArr.map(ele => ele?.keyA)); // ['a9', 'a2', 'a6', 'a1', 'a0', 'a5']
const values = ['a9', 'a6', 'a5', 'a2', 'a1', 'a0'];
let i = 0;
while (maxHeap.size > 0) {
const polled = maxHeap.poll();
console.log(polled.keyA) // values[i]
i++;
}
API Docs
Live Examples
Examples Repository
Data Structure |
Unit Test |
Performance Test |
API Docs |
Heap |
|
|
Heap |
Standard library data structure comparison
Data Structure Typed |
C++ STL |
java.util |
Python collections |
Heap<E> |
priority_queue<T> |
PriorityQueue<E> |
heapq |
heap
test name |
time taken (ms) |
executions per sec |
sample deviation |
10,000 add & pop |
5.80 |
172.35 |
8.78e-5 |
10,000 fib add & pop |
357.92 |
2.79 |
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. |