Probabilistic Data Structures
Showing 14 of 14 projects
A Go package implementing Bloom filters, a space-efficient probabilistic data structure for set membership queries.
A Go library of probabilistic data structures for processing continuous, unbounded data streams.
A space-efficient probabilistic data structure for set membership queries that supports dynamic additions and deletions.
An improved HyperLogLog implementation with LogLog-Beta bias correction, sparse representation, and flexible precision for cardinality estimation.
Native and Redis-backed Bloom filter implementations in Ruby for probabilistic set membership testing.
A Go implementation of Cuckoo Filter, a space-optimized probabilistic data structure supporting dynamic additions and deletions.
A high-performance Java implementation of a Cuckoo filter, supporting deletions, counting, and concurrent operations.
A collection of Bloom filter implementations in Go, including standard, partitioned, and scalable variants.
A high-performance, thread-safe Bloom filter implementation in Go.
A Coq library for formally verifying probabilistic properties of hash-based approximate membership query structures like Bloom filters.
A pure Elixir implementation of Scalable Bloom Filters for probabilistic set membership testing.
A probabilistic data structure service and storage for efficient frequency, cardinality, and membership queries on large datasets.
A Java implementation of multidimensional Bloom filters for efficiently searching across many Bloom filters.
Go implementation of Count-Min-Log sketch for improved approximate counting of low-frequency events.
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