They work as window functions, and they also support creating roll-up tables, e.g. you have a table storing a sketch of daily active users and can use that to get weekly and monthly active users.
Same for quantiles (e.g. median and tail latencies) and next up top-K (e.g. most popular content, highest spenders, etc).
donatj 2 hours ago [-]
I'm kind of sad there's no mention of Cuckoo filters. I've generally found them preferable to Bloom filters
jpitz 7 hours ago [-]
My introduction to probabilistic data structures was Pugh's skip list, which is probabilistic in structure and performance, but precise with answers.
https://pkg.go.dev/github.com/ncruces/go-sqlite3/ext/approx
They work as window functions, and they also support creating roll-up tables, e.g. you have a table storing a sketch of daily active users and can use that to get weekly and monthly active users.
Same for quantiles (e.g. median and tail latencies) and next up top-K (e.g. most popular content, highest spenders, etc).