Bloom Filter Calculator

Calculate the required bloom filter size and optimal number of hashes from the expected number of items in the collection and acceptable false-positive rate.

You can also see the breakdown of how the bloom filter size and optimal number of hashes are calculated.

All processing happens on the client-side, locally in your browser. No data is uploaded.

Expected number of items in the collection (n)

Acceptable false-positive rate (fpr). 0.01 = 1%

Optimal size (m) - number of elements in the bit array

Optimal number of hash functions (k)

Number of Items (n)Expected number of items in the collection (n): 3,000,000
False Positive Rate (f)Acceptable false-positive rate (fpr). 0.01 = 1%: 0.01
Optimal Size (m)Optimal size (number of elements in the bit array): 28755176 bits (3.43 MB)
Optimal Hash Functions (k)Optimal number of hash functions: 7

Calculations

$⌈ n \cdot \frac{∣ lo g ( f ) ∣}{lo g ( 2 ) ^{2}} ⌉$
$⌈ 3000000 \cdot \frac{∣ lo g ( 0.01 ) ∣}{0.693 1 ^{2}} ⌉$
$⌈ 3000000 \cdot \frac{∣ - 4.6052 ∣}{0.4805} ⌉$
$⌈ 3000000 \cdot \frac{4.6052}{0.4805} ⌉$
$⌈ \frac{13815510.558}{0.4805} ⌉$
$⌈ 28755175.1321 ⌉$
optimalSize = $28, 755, 176$

$r o u n d (\frac{m}{n} \cdot lo g (2))$
$r o u n d (\frac{28755176}{3000000} \cdot 0.6931)$
$r o u n d (9.5851 \cdot 0.6931)$
$r o u n d (6.6439)$
optimalHashFunctions = $7$

History

2019-02-16Calculate Number of Items and False Positive Rate
2018-09-16Calculation formula breakdown
2018-04-25Tool Launched

Background

Bloom Filter is a space-efficient probabilistic data structure which is used to test whether an element is a member of a set or not. We may get false positive matches, but false negatives are not.