Probabilistic Data Structures with Redis Bloom

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Probabilistic Data Structures are the big data, cloud era, and streaming solution to efficiently storing counts. Especially when you are paying somebody else for disk space! Let me introduce you to Redis Bloom, a Redis Module that natively implements the most useful PDS. PDS is all about calculated trade-offs in certainty or knowing when you can tolerate being slightly off. The RedisBloom module provides four data structures: a scalable Bloom filter, a cuckoo filter, a count-min sketch, and a top-k. that can help you tackle “counting at scale”.

In this session, we will explore the most common applications of these data structures in the context of a Spring RESTful Web Services application.

Speaker:
Brian Sam-Bodden is a developer advocate at Redis Labs as well as an author, instructor, speaker, and hacker who has spent over twenty years crafting software systems. He holds dual bachelor’s degrees from Ohio Wesleyan University in computer science and physics. Brian is a frequent speaker at user groups and conferences nationally and abroad and is the author of “Beginning POJOs: Spring, Hibernate, JBoss and Tapestry”, co-author of the “Enterprise Java Development on a Budget: Leveraging Java Open Source Technologies” and a contributor to O'Reilly's “97 Things Every Project Manager Should Know”.