Understanding Probabilistic Data Structures
The probabilistic data structures of Redis Stack are packed into a set of capabilities also known as Bloom filters. Such structures owe their name to Burton Howard Bloom, a computer scientist who introduced the concept of a probabilistic data structure in 1970 to resolve the problem of verifying whether an item belongs to a set. By using hash data representations, it is possible to achieve a sufficient approximation to the problem under analysis, allowing false positives (the item may belong to the set), but without false negatives (the item definitely does not belong to the set).
The Bloom filter has since become a widely used data structure in computer science. It is used in various applications, such as spell-checking, network routing, content filtering, and DNA sequence analysis.
Probabilistic data structures process large volumes of data in real time with minimal memory requirements. This chapter covers several types of probabilistic...
