What

Brief

This is a standalone Min 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

How

install

npm

npm i min-heap-typed --save

yarn

yarn add min-heap-typed

snippet

TS

    import {MinHeap} from 'data-structure-typed';
    // /* or if you prefer */ import {MinHeap} from 'heap-typed';

    const minNumHeap = new MinHeap<number>();
    minNumHeap.add(1).add(6).add(2).add(0).add(5).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]

JS

    const {MinHeap} = require('data-structure-typed');
    // /* or if you prefer */ const {MinHeap} = require('heap-typed');

    const minNumHeap = new MinHeap();
    minNumHeap.add(1).add(6).add(2).add(0).add(5).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]

Merge K sorted arrays

 const arrays = [
      [1, 4, 7],
      [2, 5, 8],
      [3, 6, 9]
    ];

    // Use min heap to merge: track (value, arrayIndex, elementIndex)
    const heap = new MinHeap<[number, number, number]>([], {
      comparator: (a, b) => a[0] - b[0]
    });

    // Initialize with first element of each array
    arrays.forEach((arr, i) => heap.add([arr[0], i, 0]));

    const merged: number[] = [];
    while (heap.size > 0) {
      const [val, arrIdx, elemIdx] = heap.poll()!;
      merged.push(val);
      if (elemIdx + 1 < arrays[arrIdx].length) {
        heap.add([arrays[arrIdx][elemIdx + 1], arrIdx, elemIdx + 1]);
      }
    }

    console.log(merged); // [1, 2, 3, 4, 5, 6, 7, 8, 9];

Dijkstra-style shortest distance tracking

 // Simulating distance updates: (distance, nodeId)
    const heap = new MinHeap<[number, string]>([], {
      comparator: (a, b) => a[0] - b[0]
    });

    heap.add([0, 'start']);
    heap.add([10, 'A']);
    heap.add([5, 'B']);
    heap.add([3, 'C']);

    // Process nearest node first
    console.log(heap.poll()); // [0, 'start'];
    console.log(heap.poll()); // [3, 'C'];
    console.log(heap.poll()); // [5, 'B'];
    console.log(heap.poll()); // [10, 'A'];

Running median with min heap (upper half)

 const upperHalf = new MinHeap<number>();

    // Add larger numbers to min heap
    for (const n of [5, 8, 3, 9, 1]) {
      upperHalf.add(n);
    }

    // Smallest of the upper half is always accessible
    console.log(upperHalf.peek()); // 1;
    console.log(upperHalf.size); // 5;

    // Remove smallest repeatedly
    console.log(upperHalf.poll()); // 1;
    console.log(upperHalf.poll()); // 3;
    console.log(upperHalf.peek()); // 5;

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Standard library data structure comparison

Benchmark

Built-in classic algorithms

Software Engineering Design Standards