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Hacker news loves upvoting articles about bloom filters (and Bayesian probability - its on the front page again this evening.) Personally I've never found a use for either of them in practice.
Hacker news loves upvoting articles about bloom filters (and Bayesian probability - its on the front page again this evening.) Personally I've never found a use for either of them in practice.
Hacker news loves upvoting articles about bloom filters (and Bayesian probability - its on the front page again this evening.) Personally I've never found a use for either of them in practice.
E.g. Google Chrome’s safe browsing feature used Bloom filters to check whether a site might be flagged for malware, and if necessary query Google to check if it is actually flagged. It would have been impractical to download the full safe browsing dataset to every client, and also impractical to query Google for every single site visited. Bloom filters were a good solution (note: they’ve since moved to a custom data structure[1] which is also interesting.)
[1] https://bugs.chromium.org/p/chromium/issues/detail?id=71832
Hacker news loves upvoting articles about bloom filters (and Bayesian probability - its on the front page again this evening.) Personally I've never found a use for either of them in practice.
Hacker news loves upvoting articles about bloom filters (and Bayesian probability - its on the front page again this evening.) Personally I've never found a use for either of them in practice.
Hacker news loves upvoting articles about bloom filters (and Bayesian probability - its on the front page again this evening.) Personally I've never found a use for either of them in practice.
One of my servers batch downloads approximately 550GB of data per day. I use a bloom filter to deduplicate the file significantly faster than naive Unix sort or awk ‘!X[$0]++’. It also has superior asymptotic complexity.
Hacker news loves upvoting articles about bloom filters (and Bayesian probability - its on the front page again this evening.) Personally I've never found a use for either of them in practice.
Hacker news loves upvoting articles about bloom filters (and Bayesian probability - its on the front page again this evening.) Personally I've never found a use for either of them in practice.
They have one use: as a fast check for whether you should bother with a slower check. Bloom filters are basically a sort of a pre-cache.
but to me it seems that research papers _must_ be, for lack of a better term, runnnable. i would, and hopefully others as well, like to, replicate all these wonderful results that are advertised in these papers. without that, they are all just advertisements of scholarship rather than scholarship themselves. a set of instructions + environment which generated these figures would be very welcome.
on the subject of bloom filters, have a look at this one: https://www3.cs.stonybrook.edu/~ppandey/files/p775-pandey.pd... (A General-Purpose Counting Filter: Making Every Bit Count)
This should also be marked with a year. A cursory google search has StackOverflow answers from 2013. A lack of a year label implicitly suggests that it’s new, e.g. Hacker News. Please add a tag with the appropriate year.
Looks like it's from 2007: https://www.sciencedirect.com/science/article/pii/S002001900...