Probabilistic data structures for processing continuous, unbounded streams.
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Updated
Mar 15, 2021 - Go
Probabilistic data structures for processing continuous, unbounded streams.
JS implementation of probabilistic data structures: Bloom Filter (and its derived), HyperLogLog, Count-Min Sketch, Top-K and MinHash
Cuckoo Filter implementation in Go, better than Bloom Filters (unmaintained)
Cuckoo Filter go implement, better than Bloom Filter, configurable and space optimized 布谷鸟过滤器的Go实现,优于布隆过滤器,可以定制化过滤器参数,并进行了空间优化
高性能的分布式的专门空间优化的 Bitmap 服务, 高效检查数据是否存在,日活统计,签到,打点等等
Hashing-function agnostic Cuckoo filters for Redis
Cuckoo Index: A Lightweight Secondary Index Structure
A compressed, sparse cuckoo filter (see https://www.vldb.org/pvldb/vol11/p1041-breslow.pdf)
A probabilistic data structures library for C#
Optimized implementation of Cuckoo Filter: Practically Better Than Bloom.
Python implementation of Cuckoo Filter data structure
Probabilistic data structures in python http://pyprobables.readthedocs.io/en/latest/index.html
A variant of Cuckoo Filter whose size automatically scales as necessary
Cuckoo Filter in golang for set membership approximation
Probabilistic data structures for Guava.
🎛️ Use RedisBloom in PHP!
Prefix Filter: Practically and Theoretically Better Than Bloom.
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