When Probably is Good Enough
06-19, 12:00–12:40 (Europe/Berlin), Frannz Salon

Probabilistic data structures give developers room to massively cut down on space requirements while sacrificing a bit of accuracy, so when is probably good enough?


Examining the probabilistic data structures that come built into Redis Stack will allow us to fully understand how, why and when they work best. We'll examine each of: count min sketch, top k, and bloom and cuckoo filters. Each of these has a distinct structure that we'll start with so we can see how they work. We'll then look at why each one is probabilistic and what the consequences are for that. Then we'll look at use cases for each to see when they would best be used in the wild. We'll wrap up with a demonstration of the space saving capabilities, for example the size difference between a bloom filter and a set with the same items added to each.

Currently a Developer Advocate at Redis, Savannah has a love for talking about all that technology can (and can't) do for people. When she's not live stream coding, or working on examples to help others get answers faster, she's either crafting, gardening, or hanging out with her husband and their cats.