Guy Royse
Redis
Guy works for Redis as a Developer Advocate. Combining his decades of experience in writing software with a passion for learning—and for sharing what he has learned—Guy explores interesting topics and spreads the knowledge he has gained around developer communities worldwide.Teaching and community have long been a focus for Guy. He runs his local JavaScript meetup in Ohio and has served on the selection committees of numerous conferences. He'll happily speak anywhere that will have him and has even has helped teach programming at a prison in central Ohio.In his personal life, Guy is a hard-boiled geek interested in role-playing games, science fiction, and technology. He also has a slightly less geeky interest in history and linguistics. In his spare time he likes to camp and studies history and linguistics.Guy lives in Ohio with his wife, his sons, and an entire wall of board and role-playing games.
If you’re like most developers, you probably have no idea what probabilistic data structures are. In fact, I did a super-scientific poll on Twitter and found that out of 119 participants, 58% had never heard of them and 22% had heard the term but nothing more. That’s a lot of folks that need to be educated. So, let’s do that.
A probabilistic data structure is, well, they’re sort of like the TARDIS—bigger on the inside—and JPEG compression—a bit lossy. And, like both, they are fast, accurate enough, and can take you to interesting places of adventure. That last one might not be something a JPEG does.
More technically speaking, most probabilistic data structures use hashes to give you faster and smaller data structures in exchange for precision. If you’ve got a mountain of data to process, this is super useful. In this talk, we’ll briefly go over some common probabilistic data structures; dive deep into a couple (Bloom Filter, MinHash, and Top-K); and show a running application that makes use of Top-K to analyze the most commonly used words in all 112,092 of my UFO sightings.
When we’re done, you’ll be ready to start using some of these structures in your own applications. And, if you use the UFO data, maybe you’ll discover that the truth really is out there.
More A probabilistic data structure is, well, they’re sort of like the TARDIS—bigger on the inside—and JPEG compression—a bit lossy. And, like both, they are fast, accurate enough, and can take you to interesting places of adventure. That last one might not be something a JPEG does.
More technically speaking, most probabilistic data structures use hashes to give you faster and smaller data structures in exchange for precision. If you’ve got a mountain of data to process, this is super useful. In this talk, we’ll briefly go over some common probabilistic data structures; dive deep into a couple (Bloom Filter, MinHash, and Top-K); and show a running application that makes use of Top-K to analyze the most commonly used words in all 112,092 of my UFO sightings.
When we’re done, you’ll be ready to start using some of these structures in your own applications. And, if you use the UFO data, maybe you’ll discover that the truth really is out there.