Bloom filters - Eli Bendersky's website
The original motivation for the creation of Bloom filters is efficient set membership, using a probabilistic approach to significantly reduce the time and space required to reject items that are not members in a certain set. The data structure was proposed by Burton Bloom in a 1970 paper titled "Space/Time Trade-offs in Hash Coding with Allowable Errors". It's a good paper that's worth reading. Wh
Why and How Does Python Use Bloom Filters in String Processing?
In our last discussion we learned all about bloom filters. It’s a unique data structure that provides membership queries in constant time while using a minimal quantity of memory. Primarily, you will find them being used in large scale and streaming applications where it is infeasible to keep all the data in memory. Examples include NoSQL databases, CDNs, load balancers, etc. However, it also has
Ribbon filter: practically smaller than Bloom and Xor
Filter data structures over-approximate a set of hashable keys, i.e. set membership queries may incorrectly come out positive. A filter with false positive rate $f \in (0,1]$ is known to require $\ge \log_2(1/f)$ bits per key. At least for larger $f \ge 2^{-4}$, existing practical filters require a space overhead of at least 20% with respect to this information-theoretic bound. We introduce the Ri
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Curtis Lassam - Hash Functions and You: Partners in Freedom - PyCon 2015
"Speaker: Curtis Lassam Our trusty friend, the hash function, is as crucial to programming as linked lists or recursion, but it doesn't always get the press that it deserves. We're going to talk about hash functions, some data structures involving hash functions, the stately bloom filter, and the security implications of password hashing. Slides can be found at: https://speakerdeck.com/pycon2
Bloom Filters
Everyone is always raving about bloom filters. But what exactly are they, and what are they useful for? Operations The basic bloom filter supports two operations: test and add. Test is used to check whether a given element is in the set or not. If it returns: false then the element is definitely not in the set. true then the element is probably in the set. The false positive rate is a function of
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Using Bloom filters to efficiently synchronise hash graphs — Martin Kleppmann’s blog
https://amy.dev/?p=853
