How does the redis bloom filter work?

The Redis Bloom Filter is a data structure that quickly determines if an element exists in a set, using bit arrays and multiple hash functions.

The working principle is as follows:

1. Initialization: The Bloom filter consists of a bit array where all bits are set to 0. Additionally, a suitable number of hash functions and their seeds need to be selected.
2. Add element: When adding an element, calculate multiple hash values using multiple hash functions, then set the corresponding bit array positions to 1.
3. To determine if an element exists: Calculate multiple hash values using multiple hash functions, and then check if the corresponding bit array positions are all 1. If any bit is 0, then the element definitely does not exist; if all bits are 1, then the element may exist (with a probability of false positives).
4. Error rate: Due to the limitations of the hash function and the size of the bit array, Bloom filters will have a certain error rate, meaning it might mistakenly identify a non-existent element as existing.

In a nutshell, Redis Bloom filters efficiently determine and store elements using bit arrays and multiple hash functions, allowing for quick determination of whether a particular element exists in large-scale data.