Alternatives

Products that do what BoomFilters – Probabilistic data structures for processing streams does

1. 1PP
   PDD – Probabilistic De-Duplication of Streams with Bloom Filters▲86

   2016 · github.com

2. 2RA
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3. 3SH
   Skizze – High-throughput probabilistic data structure service▲53

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4. 4JI
   Jet – in-memory, fault-tolerant, distributed stream processing▲129

   2020 · github.com

5. 5BA
   Brancher – A user-friendly PyTorch module for probabilistic inference▲96

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6. 6AF
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7. 7AR
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8. 8GT
   Gazette: towards unification of batch and stream processing▲12

   2019 · github.com

9. 9CF
   Cuckoo Filter Implementation in Go, Better Than Bloom Filters▲181

   2016 · github.com

10. 10LL
    Liftbridge – Lightweight, fault-tolerant message streams▲147

    2020 · liftbridge.io

11. 11MB
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    2021 · github.com

12. 12MP
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13. 13MA
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14. 14AG
    A Go implementation of TensorFlow's streaming quantiles estimator▲98

    2018 · github.com

15. 15KA
    Knuckleball – An in-memory data structure server▲100

    2016 · github.com

16. 16IP
    InBloom – Portable Bloom Filters in Java, Python and Go▲45

    2015 · github.com

17. 17CF
    Cuckoo Filter: Practically better than Bloom filter implemented in Go▲16

    2017 · github.com

18. 18GD
    Goka – distributed stream processing library for Kafka written in Go▲105

    2017 · github.com

19. 19ED
    Efficient data loading using zeta distributions▲34

    2018 · github.com

20. 20BS
    Bistro Streams – A light-weight column-oriented stream analytics server▲26

    2018 · github.com

21. 21SJ
    Stochastic – JavaScript library for simulating stochastic processes▲55

    2014 · github.com

22. 22PD
    Prolly – DSL to express and query probabilities in code▲47

    2015 · github.com

23. 23CA
    Condenser – A database subsetting project▲26

    2018 · github.com

24. 24CH
    Concord – High Performance Stream Processing with C++ and Apache Mesos▲7

    2016 · concord.io

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