Blog

Special Data Types: Bitmaps, HyperLogLog, Geospatial and Probabilistic Structures (Bloom, Cuckoo, Top-K)

For unique counting, a bitmap gives the exact result with about a fiftieth of a set's memory when identities are dense; HyperLogLog counts any kind of identity in 12 KB with roughly 1 % error. With real output: set, bitmap and HLL on the same visitor data, the 14 MB trap of a sparse bitmap, BITFIELD, Geo for the nearest warehouse, a Bloom filter's measured memory and false positive rate, Cuckoo, Top-K, Count-Min Sketch, t-digest, Redis 8.8's Array and the Valkey differences.