A Bloom filter is a data structure that provides an efficient way to verify whether an entry exists in a set by using probabilistic methods. It was first conceived in 1970 and has been widely used in various applications such as ad serving, content recommendation systems, databases, and more. The Bloom filter works by hashing an item through a quick hashing function and sampling bits from that hash to determine the presence of the item in the set. While it can produce false positives, the likelihood of this occurrence is controllable. To mitigate collisions, multiple hashes are often used with different seeds for each iteration. The accuracy of a Bloom filter is affected by its fill ratio, which determines how many bits in the filter are actually set. Variants such as counting filters and Cuckoo filters offer additional features and can be more space-efficient than traditional Bloom filters. A good use case for a Bloom filter is to check for an already used username, allowing developers to quickly verify existence without querying a database, reducing latency and improving performance. The ReBloom module for Redis provides a powerful implementation of the Bloom filter, which has been benchmarked against other implementations and performs well in terms of speed and efficiency.