3 Rules of thumb for Bloom Filters
But see also http://stackoverflow.com/a/9554448 , http://www.eecs.harvard.edu/~kirsch/pubs/bbbf/esa06.pdf (thanks Tony Finch!)
bloom-filters
algorithm
probabilistic
rules
reasoning
via:norman-maurer
false-positives
hashing
coding
august 2014 by jm
I often need to do rough back-of-the-envelope reasoning about things, and I find that doing a bit of work to develop an intuition for how a new technique performs is usually worthwhile. So, here are three broad rules of thumb to remember when discussing Bloom filters down the pub:
One byte per item in the input set gives about a 2% false positive rate.
The optimal number of hash functions is about 0.7 times the number of bits per item.
3 - The number of hashes dominates performance.
But see also http://stackoverflow.com/a/9554448 , http://www.eecs.harvard.edu/~kirsch/pubs/bbbf/esa06.pdf (thanks Tony Finch!)
august 2014 by jm
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