probabilistic_data_structures

README

Probabilistic-data-structures

Probabilistic data structures are data structures that use randomized algorithms to provide an approximate solution to a problem with a certain level of error or probability. They are designed to optimize memory usage and improve query performance.

They are often used for large datasets where exact results are not required or are too expensive to compute, and approximate results with a certain error rate are acceptable.

Here are some popular types of probabilistic data structures, accompanied by their simplified implementations

• BloomFilter
• Count-Min Sketch
• HyperLogLog
• Cuckoo Filter
• Quotient Filter
• T-Digest
• Lossy Counting
• Skip List