Heap.js

Efficient Binary heap (priority queue, binary tree) data structure for JavaScript / TypeScript.

Now with support for async comparators with the new HeapAsync class!

Includes JavaScript methods, Python's heapq module methods, and Java's PriorityQueue methods.

Easy to use, known interfaces, tested, and well-documented JavaScript binary heap library.

Instances are integer min heap by default.

Is it faster than sorting an array?

It depends on your usage, but for some scenarios, it is much faster:

heap vs array: push + pop/unshift 50
 heap  x 72,130 ops/sec ±0.50% (93 runs sampled)
 array x 121 ops/sec ±78.09% (5 runs sampled)

heap vs array: push + peek 20
 heap  x 622,332 ops/sec ±27.93% (63 runs sampled)
 array x 207 ops/sec ±78.18% (5 runs sampled)

heap vs array: push + top(1) of 100
 heap  x 124,835 ops/sec ±40.37% (61 runs sampled)
 array x 123 ops/sec ±78.49% (5 runs sampled)

heap vs array: push + top(50) of 100
 heap  x 59,210 ops/sec ±17.66% (82 runs sampled)
 array x 125 ops/sec ±78.79% (5 runs sampled)

Star History

Changelog

2.5

• Improves the limit property to support negative, Infinity, and NaN values. They will be set as 0 and the heap will not be limited.
• Adds the setLimit method to set the limit of the heap. It returns NaN if the limit is negative, or NaN. This method is useful to check if the limit was set as expected.
• Improves tests and documentation.

2.4

• Adds the indexOf method to find the first index of an element in the heap.
• Adds the indexOfEvery method to find all indexes of an element in the heap.
• Changes the remove method to use the indexOf method.
• Changes the contains method to use the indexOf method.
• Improves documentation.

2.3

• Adds the HeapAsync class, with async methods and supporting async comparators. It is a drop-in replacement for the Heap class with Promises.

2.2

• Fixes .iterator() method to follow Java's PriorityQueue implementation: > The Iterator provided in method iterator() is not guaranteed to traverse the elements of the priority queue in any particular order.

Notice that using the heap directly as an iterator will consume the heap, as Python's heapq implementation does.

2.1

• Adds Heap.nlargest as in heapq.
• Adds Heap.nsmallest as in heapq.
• Sanitizes top / bottom input to force an integer.
• Linted with Eslint.

2.0

The main breaking change is that now top(N) does NOT sort the output, because sorting should not be part of the spec for a priority queue. The output is the top N elements, and they will be partially ordered with the peek at index 0 by definition.

• top(N) is unordered, only the first element is guaranteed to be the top priority element.
• Fixes custom heap issue #31.
• Performance improvements.
• More tests, including those for custom heaps.
• Auxiliary experimental top(N) algorithms.
• (WIP) Benchmarks.

1.5

• Adds the Iterator interface and iterator() method.

Examples

Basic usage

Min Heap

A heap where the smallest element is always at the top. It is the default heap.

import { Heap } from 'heap-js';

// Min Heap by default
const minHeap = new Heap();

// Initialize the heap with an array
minHeap.init([5, 18, 1]);
// Push a new value
minHeap.push(2);

console.log(minHeap.peek()); //> 1
console.log(minHeap.pop()); //> 1
console.log(minHeap.peek()); //> 2

Max Heap

A heap where the largest element is always at the top.

import { Heap } from 'heap-js';

// Max Heap
const maxHeap = new Heap(Heap.maxComparator);

// Initialize the heap with an array
maxHeap.init([3, 4, 1, 12, 8]);
// Push a new value
maxHeap.push(2);

console.log(maxHeap.peek()); //> 12
console.log(maxHeap.pop()); //> 12
console.log(maxHeap.peek()); //> 8

Custom Heap

A heap where the most important element is always at the top, but the elements are objects with a priority property.

import { Heap } from 'heap-js';

const customPriorityComparator = (a, b) => a.priority - b.priority;
// Custom Heap
const customHeap = new Heap(customPriorityComparator);

// Initialize the heap with an array
customHeap.init([{ priority: 5 }, { priority: 18 }, { priority: 1 }]);
// Push a new value
customHeap.push({ priority: 2 });

console.log(customHeap.peek()); //> { priority: 1 }
console.log(customHeap.pop()); //> { priority: 1 }
console.log(customHeap.peek()); //> { priority: 2 }

Min HeapAsync

A heap where the most important element is always at the top, the elements are objects with a priority property, and the comparator function is asynchronous. Implements the same interface as Heap, but almost all methods return a Promise.

import { HeapAsync } from 'heap-js';

const customPriorityComparator = (a, b) => Promise.resolve(a.priority - b.priority);
// Custom HeapAsync
const customHeap = new HeapAsync(customPriorityComparator);

// Initialize the heap with an array
await customHeap.init([{ priority: 5 }, { priority: 18 }, { priority: 1 }]);
// Push a new value
await customHeap.push({ priority: 2 });

console.log(customHeap.peek()); //> { priority: 1 }
console.log(await customHeap.pop()); //> { priority: 1 }
console.log(await customHeap.peek()); //> { priority: 2 }

Priority Queue usage

JavaScript / Python-style iterator (recommended)

Iterates over the heap consuming it, and guarantees to traverse the elements of the heap in the order of priority. Useful.

const { Heap } = require('heap-js');

// Get all tasks from the database
const tasks = db.collection.find().toArray();
// The most important task has the lowest priority value
const customPriorityComparator = (a, b) => a.priority - b.priority;

// Create the priority queue
const priorityQueue = new Heap(customPriorityComparator);
// Initialize the priority queue with the tasks
priorityQueue.init(tasks);

// Iterator that will consume the heap while traversing it in the order of priority
for (const task of priorityQueue) {
  console.log(task);
}

Java-style iterator (not recommended)

Iterates over the heap without consuming it, but does not guarantee to traverse the elements of the heap in any particular order. Barely useful.

const { Heap } = require('heap-js');

// Get all tasks from the database
const tasks = db.collection.find().toArray();
// The most important task has the lowest priority value
const customPriorityComparator = (a, b) => a.priority - b.priority;

const priorityQueue = new Heap(customPriorityComparator);
// Initialize the priority queue with the tasks
priorityQueue.init(tasks);

// Iterator, the Java way, that will not consume the heap BUT does not guarantee to traverse the elements of the heap in any particular order. Barely useful.
for (const task of priorityQueue.iterator()) {
  console.log(task);
}

Python-like static methods

import { Heap } from 'heap-js';
const numbers = [2, 3, 7, 5];

// Changes the array elements order into a heap in-place
Heap.heapify(numbers);
console.log(numbers); //> [ 2, 3, 5, 7 ]

// Pushes a new value to the heap
Heap.heappush(numbers, 1);
console.log(numbers); //> [ 1, 2, 5, 7, 3 ]

Installation

yarn add heap-js # if you use yarn

npm install --save heap-js # if you use npm

Constructor

Heap

new Heap([comparator]);

HeapAsync

new HeapAsync([asyncComparator]);

Comparators already included

• Heap.minComparator: Uses less-than operator to compare elements. It is the default comparator.
• Heap.maxComparator: Uses greater-than operator to compare elements.
• Heap.minComparatorNumber: Uses subtraction a - b to compare elements.
• Heap.maxComparatorNumber: Uses subtraction b - a to compare elements.

Implements JavaScript-style methods

• for (const value of heap) directly usable as an Iterator, consumes the heap.
• length of the heap.
• limit the number of elements in the heap.
• pop() the top element.
• push(...elements) one or more elements to the heap.
• pushpop(element) faster than push & pop.
• replace(element) faster than pop & push.
• top(number?) most valuable elements from the heap.
• bottom(number?) least valuable elements from the heap.
• indexOf(element, fn?) returns the internal index of the first occurrence of the element in the heap.
• indexOfEvery(element, fn?) returns an array with the internal indexes of all occurrences of the element in the heap.

Implements Java's PriorityQueue interface

• add(element) to the heap.
• addAll([element, element, ... ]) to the heap, faster than loop add.
• clear()
• clone()
• comparator()
• contains(element, fn?)
• element() alias of peek()
• isEmpty()
• iterator() returns the same as toArray() because it is iterable and follows Java's implementation. Barely useful. Use for (const value of heap) instead.
• offer(element) alias of add(element)
• peek()
• poll() alias of pop()
• remove(element?)
• removeAll() alias of clear()
• size() alias of length
• toArray()
• toString()

To do:

• containsAll
• equals
• retainAll

Implements static Python's heapq interface

• Heap.heapify(array, comparator?) that converts an array to an array-heap.
• Heap.heappop(heapArray, comparator?) that takes the peek of the array-heap.
• Heap.heappush(heapArray, item, comparator?) that appends elements to the array-heap.
• Heap.heappushpop(heapArray, item, comparator?) faster than heappush & heappop.
• Heap.heapreplace(heapArray, item, comparator?) faster than heappop & heappush.
• Heap.nlargest(n, iterable, comparator?) that gets the n most valuable elements of an iterable.
• Heap.nsmallest(n, iterable, comparator?) that gets the n least valuable elements of an iterable.

Extras:

• Heap.heaptop(n, heapArray, comparator?) that returns the n most valuable elements of the array-heap
• Heap.heapbottom(n, heapArray, comparator?) that returns the n least valuable elements of the array-heap

To do:

• merge(...iterables, comparator?)

Documentation

Contributing

Development of Heap.js happens in the open on GitHub, and I am grateful to the community for contributing bug fixes and improvements.

Dev setup

yarn

Tests

npm run test

Benchmarks

npm run benchmarks

License

Heap.js is BSD licensed.