How do I choose m and k?

Given n elements and desired false positive rate p: m = -n*ln(p)/(ln(2))^2 bits, k = (m/n)*ln(2) hash functions. For n=1M, p=1%: m ≈ 9.6M bits (1.2MB), k ≈ 7. The filter uses 1.2MB regardless of element size — far less than storing 1M strings.

Can I remove elements from a Bloom filter?

Not from a standard Bloom filter — setting bits to 0 might affect other elements. Use a counting Bloom filter (counter instead of bits) to support deletion, or a Cuckoo filter (supports deletion, better space efficiency). For most use cases (caching, deduplication), not supporting deletion is acceptable.

Utiliser Bloom filters pour définir membership

Bloom filters probabilistically tester définir membership avec aucun faux negatives mais possible faux positives. Space-efficient pour large sets.

1

A Bloom filter is a bit array of m bits and k hash functions. To add an element: set bits at positions h1(x), h2(x), ..., hk(x) to 1. To test membership: check if all k positions are 1. If any is 0, the element is definitely not in the set. If all are 1, it is probably in the set (false positive rate p ≈ (1 - e^(-kn/m))^k).
2

Calculatrice de hash →
Use multiple hash functions (or one hash with different seeds) to compute the k positions. For k=7, m=10000 bits, n=1000 elements: false positive rate ≈ 0.8%. Increase m or k to reduce false positives. Use SHA-256 with different salts for each hash function.
3

Hash Murmur3 →
Use MurmurHash3 (non-cryptographic, fast) for Bloom filter hashing. Bloom filters do not need cryptographic hash functions — they need speed and uniform distribution. MurmurHash3, FNV-1a, and xxHash are good choices. Use k independent hash functions (or double hashing: h1(x) + i*h2(x) for i = 0..k-1).

Questions fréquentes

How do I choose m and k?: Given n elements and desired false positive rate p: m = -n*ln(p)/(ln(2))^2 bits, k = (m/n)*ln(2) hash functions. For n=1M, p=1%: m ≈ 9.6M bits (1.2MB), k ≈ 7. The filter uses 1.2MB regardless of element size — far less than storing 1M strings.
Can I remove elements from a Bloom filter?: Not from a standard Bloom filter — setting bits to 0 might affect other elements. Use a counting Bloom filter (counter instead of bits) to support deletion, or a Cuckoo filter (supports deletion, better space efficiency). For most use cases (caching, deduplication), not supporting deletion is acceptable.