Bloom filters are probabilistic data structures that allow for efficient testing of an element's membership in a set. They effectively filter out unwanted items from extensive data sets while maintaining a small probability of false positives. Since their invention in 1970 by Burton H. Bloom, these data structures have found applications in various fields such as databases, caching, networking, and more. In this article, we will delve into the concept of Bloom filters, their functioning, explore a contemporary real-world application, and illustrate their workings with a practical example.
Understanding Bloom Filters
A Bloom filter consists of an array of m bits, initially set to 0. It employs k independent hash functions, each mapping an element to one of the m positions in the array. To add an element to the filter, it is hashed using each of the k hash functions, and the corresponding positions in the array are set to 1. To verify if an element is present in the filter, the element is hashed again using the same k hash functions, and if all the corresponding positions are set to 1, the element is considered present.