proxy.golang.org : github.com/bits-and-blooms/bloom

Package bloom provides data structures and methods for creating Bloom filters. A Bloom filter is a representation of a set of _n_ items, where the main requirement is to make membership queries; _i.e._, whether an item is a member of a set. A Bloom filter has two parameters: _m_, a maximum size (typically a reasonably large multiple of the cardinality of the set to represent) and _k_, the number of hashing functions on elements of the set. (The actual hashing functions are important, too, but this is not a parameter for this implementation). A Bloom filter is backed by a BitSet; a key is represented in the filter by setting the bits at each value of the hashing functions (modulo _m_). Set membership is done by _testing_ whether the bits at each value of the hashing functions (again, modulo _m_) are set. If so, the item is in the set. If the item is actually in the set, a Bloom filter will never fail (the true positive rate is 1.0); but it is susceptible to false positives. The art is to choose _k_ and _m_ correctly. In this implementation, the hashing functions used is murmurhash, a non-cryptographic hashing function. This implementation accepts keys for setting as testing as []byte. Thus, to add a string item, "Love": Similarly, to test if "Love" is in bloom: For numeric data, I recommend that you look into the binary/encoding library. But, for example, to add a uint32 to the filter: Finally, there is a method to estimate the false positive rate of a particular Bloom filter for a set of size _n_: Given the particular hashing scheme, it's best to be empirical about this. Note that estimating the FP rate will clear the Bloom filter.

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purl: pkg:golang/github.com/bits-and-blooms/bloom
Keywords: bloom , bloom-filters , go
License: BSD-2-Clause
Latest release: over 8 years ago
First release: over 11 years ago
Namespace: github.com/bits-and-blooms
Dependent packages: 17
Dependent repositories: 5
Stars: 2,657 on GitHub
Forks: 252 on GitHub
Docker dependents: 14
Docker downloads: 2,172
Total Commits: 126
Committers: 30
Average commits per author: 4.2
Development Distribution Score (DDS): 0.802
More commit stats: commits.ecosyste.ms
See more repository details: repos.ecosyste.ms
Funding links: https://donate.mcc.org/
Last synced: about 3 hours ago

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