This talk was presented at PyBay2020 Online Edition. It’s the 5th annual Bay Area Regional Python conference. See pybay.com for more details about PyBay and click SHOW MORE for information about this talk.
DESCRIPTION
There are three reactions to the title of this talk:
– What the heck’s a probabilistic data structure?
– UFO Sightings… wha?
– 112,092 is an oddly specific number.
This is a talk about the first bullet point with the second thrown in just for fun. I like weird stuff—UFOs, Bigfoot, peanut butter and bologna on toast—maybe you do too? As far as the third bullet point, well, that’s how many sightings I have.
Now, 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. I wonder what percentage of that 22% heard the term for the first time in the poll. We’re a literal-minded lot at times.
Anyhow. That’s 4 out of 5 developers or, as I like to call it, the Trident dentist ratio. (It’s actually a manifestation of the Pareto principle but I’m a 70s kid). 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.
SPEAKER BIO
Guy works for Redis Labs as a Developer Advocate. Combining his decades of experience in writing software with a passion for sharing what he has learned, Guy goes out into developer communities and helps others build great software.
Teaching and community have long been a focus for Guy. He is President of the Columbus JavaScript Users Group, an organizer for the Columbus Machine Learners, 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 volunteers for his local Cub Scout Pack, goes camping, and studies history and linguistics.
Guy lives in Ohio with his wife, his three teenage sons, and an entire wall of games.
SPONSOR ACKNOWLEDGEMENT
The post production of this and other PyBay2020 videos are made possible by our sponsors:
– IBM https://community.ibm.com/community/user/datascience/home
– Rookout https://rookout.com/
– Anyscale https://anyscale.com
– Twilio https://twilio.com/
– JetBrains https://www.jetbrains.com
– Linode https://linode.com
– Pearson Informit https://www.informit.com/
EVENT PRODUCER ACKNOWLEDGEMENT
This community conference is produced by organizers of SF Python meetup and volunteers from around the SF Bay Area and beyond. See upcoming events here: https://sfpythonmeetup.com
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