There are 5 repositories under hyperloglog topic.
MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW
HyperLogLog with lots of sugar (Sparse, LogLog-Beta bias correction and TailCut space reduction) brought to you by Axiom
JS implementation of probabilistic data structures: Bloom Filter (and its derived), HyperLogLog, Count-Min Sketch, Top-K and MinHash
Yet another distributed fault-tolerant key-value database Compatible with Redis written in Golang.
Distributed caching based on StackExchange.Redis and Redis. Includes support for tagging and is cluster-compatible.
Hyper LogLog (native and sliding) cardinality counters
C++ Implementations of sketch data structures with SIMD Parallelism, including Python bindings
Sketching Algorithms for Clojure (bloom filter, min-hash, hyper-loglog, count-min sketch)
Dynatrace hash library for Java
Fast HyperLogLog for Python.
Performant implementations of various streaming algorithms, including Count–min sketch, Top k, HyperLogLog, Reservoir sampling.
A probabilistic data structures library for C#
Yet another distributed fault-tolerant key-value database Compatible with Redis written in Golang.
Paper about the estimation of cardinalities from HyperLogLog sketches
HyperLogLog cardinality estimation algorithm in go/golang!
Union, intersection, and set cardinality in loglog space
The dream accurate approximate set cardinality estimator based on 3-bit HyperLogLog. More accurate than Redis HyperLogLog.
SetSketch: Filling the Gap between MinHash and HyperLogLog
Lightning fast concurrent HyperLogLog for Rust.
Probabilistic data structures for OCaml
Integrates DuckDB with the high-performance Apache DataSketches library. This extension enables users to perform approximate analytics on large-scale datasets using state-of-the-art streaming algorithms, all from within DuckDB.
Rust implementation of probminhash, superminhash and hyperloglog sketching algorithms
A crate for estimating the cardinality of distinct elements in a stream or dataset.
A Ruby implementation of the HyperLogLog algorithm for efficient cardinality estimation with minimal memory footprint. Count millions of distinct elements using only kilobytes of memory.
Fast and Memory Efficient Genome Sketching via HyperLogLog, HyperMinHash and UltraLogLog