cuckoo: Haskell Implementation of Cuckoo Filters

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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.


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Properties

Versions 0.1.0.0, 0.1.0.0, 0.2.0.0, 0.2.0.1, 0.2.1, 0.2.2, 0.3.0, 0.3.1
Change log CHANGELOG.md
Dependencies base (>=4.11 && <4.15), memory (>=0.14), primitive (>=0.7), random (>=1.1), 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 2019-08-17T05:34:02Z

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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.