Metadata-Version: 1.2
Name: HLL
Version: 2.1.5
Summary: Fast HyperLogLog
Home-page: https://github.com/ascv/HyperLogLog
Author: Joshua Andersen
Author-email: josh.h.andersen@gmail.com
Maintainer: Joshua Andersen
License: MIT
Description: 
        Fast HyperLogLog for Python.
        
        The HyperLogLog algorithm [1] is a space efficient method to estimate the
        cardinality of extraordinarily large data sets. This library implements a 64
        bit variant [2] for Python, written in C, that uses a MurmurHash64A hash
        function.
        
        [1] Flajolet, Philippe; Fusy, Eric; Gandouet, Olivier; Meunier, Frederic
        (2007). "Hyperloglog: The analysis of a near-optimal cardinality estimation
        algorithm" (PDF). Disc. Math. and Theor. Comp. Sci. Proceedings. AH: 127146.
        CiteSeerX 10.1.1.76.4286.
        
        [2] Omar Ertl, "New cardinality estimation algorithms for HyperLogLog Sketches"
        arXiv:1702.01284 [cs] Feb. 2017.
        
Keywords: algorithm,approximate counting,big data,big data,cardinality,cardinality estimate,counting,data analysis,data processing,data science,data sketching,efficient computation,estimating cardinality,fast,frequency estimation,hyper log log,hyper loglog,hyperloglog,large-scale data,log log,loglog,memory efficient,probability estimate,probability sketch,probablistic counting,probablistic data structures,real-time analytics,scalable,set cardinality,set operations,sketch,statistical analysis,streaming algorithms,streaming algorithms,unique count,unique element counting
Platform: UNKNOWN
