cuckoo: Haskell Implementation of Cuckoo Filters

[ bsd3, data, library ] [ Propose Tags ]

Haskell implementation of Cuckoo filters as described in

B. Fan, D.G. Anderson, M. Kaminsky, M.D. Mitzenmacher. Cuckoo Filter: Practically Better Than Bloom. In Proc. CoNEXT, 2014.

Cuckoo filters are a data structure for probabilistic set membership. They support insertion, deletion, and membership queries for set elements.

Membership queries may return false positive results. But queries don't return false negative results.

Unlike Bloom filters, Cuckoo filters maintain an upper bound on the false positive rate that is independent of the load of the filter. However, insertion of new elements in the filter can fail. For typical configurations this probability is very small for load factors smaller than 90 percent.


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Versions [faq] 0.1.0.0, 0.2.0.0, 0.2.0.1
Change log CHANGELOG.md
Dependencies base (>=4.11 && <4.15), memory (>=0.14), primitive (>=0.7), random-internal, vector (>=0.12) [details]
License BSD-3-Clause
Copyright Copyright (c) 2019, Lars Kuhtz <lakuhtz@gmail.com>
Author Lars Kuhtz
Maintainer lakuhtz@gmail.com
Category Data
Home page https://github.com/larskuhtz/cuckoo
Bug tracker https://github.com/larskuhtz/cuckoo/issues
Source repo head: git clone https://github.com/larskuhtz/cuckoo.git
Uploaded by larsk at Sat Aug 17 06:07:43 UTC 2019
Distributions NixOS:0.2.0.1
Downloads 126 total (51 in the last 30 days)
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Status Hackage Matrix CI
Docs available [build log]
Last success reported on 2019-08-17 [all 1 reports]

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NameDescriptionDefaultType
mwc-random

Use mwc-random instead of the random package

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pcg-random

Use pcg-random instead of the random package

DisabledManual

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Readme for cuckoo-0.1.0.0

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Haskell implementation of Cuckoo filters as described in

B. Fan, D.G. Anderson, M. Kaminsky, M.D. Mitzenmacher. Cuckoo Filter: Practically Better Than Bloom. In Proc. CoNEXT, 2014.

Cuckoo filters are a data structure for probabilistic set membership. They support insertion, deletion, and membership queries for set elements.

Membership queries may return false positive results. But queries don't return false negative results.

Unlike Bloom filters, Cuckoo filters maintain an upper bound on the false positive rate that is independent of the load of the filter. However, insertion of new elements in the filter can fail. For typical configurations this probability is very small for load factors smaller than 90 percent.

The implementation allows the user to specify the bucket size and the fingerprint size in addition to the capacity of the filter. The user can also provide custom functions for computing the primary hash and fingerprint.