Algorithm Visualizer

Number of bits (m):

Number of hash functions (k):

Number of elements (n):

Filter size (m): -

Hash functions (k): -

Elements (n): -

False positive rate (p): -

Enable step-by-step execution:

A compact overview of what Bloom Filters are, how they work, and where they shine

Introduction
Key Concepts
Advantages
Disadvantages
Applications

Note:

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Key Concepts

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About this project

This is a Bloom Filter algorithm visualizer built using JavaScript, HTML, and CSS. It is designed to help users understand how the Bloom Filter data structure works through interactive simulations and visual representations.

This project was developed by

Skalnark

Spell Checker

m = ceil((n * log(p)) / log(1 / pow(2, log(2)))) <-- 2588 bits k = round((m / n) * log(2)) <-- 20

pow(1 - exp(-k / (m / n)), k) <-- ~0.0000009999%

n = ceil(m / (-k / log(1 - exp(log(p) / k)))) // n = ceil(2588 / (-20 / log(1 - exp(log(0.000001) / 20)))) <-- 91 elements // this means that we can keep the p value for one more element (:

Jabberwocky, by Lewis Carroll

Filter size: -

Hash functions: -

Elements: -

False positive rate: -

> Bloom Filter