35
Bloom filters: real-world applications
(llimllib.github.io)
Welcome to the main community in programming.dev! Feel free to post anything relating to programming here!
Cross posting is strongly encouraged in the instance. If you feel your post or another person's post makes sense in another community cross post into it.
Hope you enjoy the instance!
Rules
Follow the wormhole through a path of communities !webdev@programming.dev
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
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