this post was submitted on 06 Sep 2023
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Bloom filters: real-world applications (llimllib.github.io)
submitted 1 year ago by [email protected] to c/[email protected]
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What are your real-world applications of this versatile data structure?

They are useful for optimization in databases like sqlite and query engines like apache spark. Application developers can use them as concise representations of user data for filtering previously seen items.

The linked site gives a short introduction to bloom filters along with some links to further reading:

A Bloom filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set. The price paid for this efficiency is that a Bloom filter is a probabilistic data structure: it tells us that the element either definitely is not in the set or may be in the set.

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[–] [email protected] 2 points 1 year ago (1 children)

Interesting. Do I understand it correctly if I say it's a bloom filter where instead of setting a bit to 1 for each of the hashes, you increment a counter for that hash?

How do you infer the count then, take the minimum of all matching hashes? Because intuitively it seems to me like you would need a lot more space to avoid counts being too high

[–] [email protected] 2 points 1 year ago* (last edited 1 year ago)

This data structure uses a 2-dimensional array to store data, documented in this scala implementation: https://github.com/twitter/algebird/blob/develop/algebird-core/src/main/scala/com/twitter/algebird/CountMinSketch.scala. I’m still trying to understand it as well.

Similar to your idea, I had thought that by using k bloom filters, each with their own hash function and bit array, one could store an approximate count up to k for each key, which also might be wasteful or a naïve solution.

PDF link: http://www.eecs.harvard.edu/~michaelm/CS222/countmin.pdf