bloomfilter-rb

Offers native C in-memory filters and Redis-backed versions, providing flexibility for both standalone and distributed use cases, as detailed in the README examples.

Redis non-counting filter uses minimal memory, e.g., only 2.5 MB for 1 million items at a 1% false positive rate, making it scalable for large datasets.

Includes Redis counting filters with time-based expiration (TTL), enabling automatic data cleanup and deletion capabilities, as shown in the code snippets.

Allows fine-tuning of parameters like bit array size, hash functions, and seed values to optimize false positive rates and performance for specific needs.