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Probabilistic Data Structures with Redis Bloom
Originally aired:
About the Session
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 are all about calculated trade-offs in certainty or knowing when you can tolerate to be 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.
Probabilistic Data Structures with Redis Bloom
Originally aired:
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