Bloom Filters: A Probabilistic Data Structure for Efficient Set Membership

Bloom filters are probabilistic data structures designed for rapid and memory-efficient set membership testing. They use multiple hash functions to map elements to bits in a bit vector. If all corresponding bits are 1, the element *may* be present; otherwise, it's definitely absent. While prone to false positives, their speed and space efficiency make them ideal for large datasets. This article details Bloom filter principles, hash function selection, sizing, applications, and implementation examples across various systems.