BoomFilters – Probabilistic data structures for processing streams
Probabilistic data structures for processing continuous, unbounded streams. - tylertreat/BoomFilters
BoomFilters – Probabilistic data structures for processing streams launched on April 11, 2015 with 70 votes, #789 of 3,600 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 11 years on.
In plain words
BoomFilters is a collection of probabilistic data structures designed for processing continuous, unbounded data streams. It provides efficient algorithms for handling large-scale streaming data where memory constraints are a concern. The software is suitable for developers working with real-time data processing, analytics, or systems that need to track patterns in continuous information flows. Its probabilistic approach allows approximate answers with minimal resource overhead, making it practical for resource-constrained environments.
written from the facts on this page · September 2026
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Cuckoo Filter Implementation in Go, Better Than Bloom Filters2016 · github.com · ▲181Cuckoo Filter implementation in Go, better than Bloom Filters (unmaintained) - irfansharif/cfilter
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PDD – Probabilistic De-Duplication of Streams with Bloom Filters2016 · github.com · ▲86Advanced Bloom Filter Based Algorithms for Efficient Approximate Data De-Duplication in Streams - jparkie/PDD
Cuckoo Filter: Practically better than Bloom filter implemented in Go2017 · github.com · ▲16Cuckoo Filter: Practically better than bloom filter - vedhavyas/cuckoo-filter
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