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Bloom Filter - probability and benchmarks
Bloom filters are a data structure which allows you to test whether an element exists in a set, with lower memory usage and better access times than other hash table implementations. It is probabilistic, and while it can guarantee negative matches, there is a slight chance it returns a false positive match. Through clever mathematical assumptions, we can produce constraints to minimise the chance of a false positive.
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Bloom Filter - probability and benchmarks
Bloom filters are a data structure which allows you to test whether an element exists in a set, with lower memory usage and better access times than other hash table implementations. It is probabilistic, and while it can guarantee negative matches, there is a slight chance it returns a false positive match. Through clever mathematical assumptions, we can produce constraints to minimise the chance of a false positive.
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Bloom Filter - probability and benchmarks
Bloom filters are a data structure which allows you to test whether an element exists in a set, with lower memory usage and better access times than other hash table implementations. It is probabilistic, and while it can guarantee negative matches, there is a slight chance it returns a false positive match. Through clever mathematical assumptions, we can produce constraints to minimise the chance of a false positive.
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13- titleBloom Filter - probability and benchmarks – Andy Bui – Blog
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- authorAndy Bui
- descriptionBloom filters are a data structure which allows you to test whether an element exists in a set, with lower memory usage and better access times than other hash table implementations. It is probabilistic, and while it can guarantee negative matches, there is a slight chance it returns a false positive match. Through clever mathematical assumptions, we can produce constraints to minimise the chance of a false positive.
- article:published_time2020-12-26T00:00:00+00:00
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en_US- og:descriptionBloom filters are a data structure which allows you to test whether an element exists in a set, with lower memory usage and better access times than other hash table implementations. It is probabilistic, and while it can guarantee negative matches, there is a slight chance it returns a false positive match. Through clever mathematical assumptions, we can produce constraints to minimise the chance of a false positive.
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- og:site_nameAndy Bui
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