Probabilistic data structures for processing continuous, unbounded streams.

In-memory nucleotide sequence k-mer counting, filtering, graph traversal and more

JS implementation of probabilistic data structures: Bloom Filter (and its derived), HyperLogLog, Count-Min Sketch, Top-K and MinHash

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)

Probabilistic data structures in python http://pyprobables.readthedocs.io/en/latest/index.html

A probabilistic data structures library for C#

A class library implementing probabilistic data structures in .NET

Pond is a high performance object-pooling library for Python

Count-Min Sketch Implementation in C

go patterns

An implementation of Count-Min Sketch in Golang

A compressed adaptive optimizer for training large-scale deep learning models using PyTorch

🎛️ Use RedisBloom in PHP!

PHP client for RedisBloom module

an implementation of Count-Min Sketch, an approximate counting data structure for summarizing data streams, in golang

LFU-based in-memory cache in Rust

High performance approximate algorithms in Go (e.g. morris counter, count min, etc.)

Thread-safe and persistent Golang implementations of probabilistic data structures: Bloom Filter, Cuckoo Filter, HyperLogLog, Count-Min Sketch and Top-K

Implementation and experimental tests of various algorithms.