Bloom Filters

From the creator

Thanks to Hostinger: https://hostinger.com/mcoding Use coupon code MCODING at checkout to get an additional 10% off! Bloom filters are a simple probabilistic kind of set that exemplify how using probability to your advantage can result in huge performance wins at scale. By giving up some of the flexibility of a full set interface and even getting the wrong answer to "is this element in the set?" on a small percentage of elements, we can reduce memory usage in our example web service from over 1 GB down to 10 MB to keep a "set" of 10M links, which in turn makes it practical to store the data on-server instead of calling out to an (imaginary) costly third party API, allowing us to respond to clients immediately 98% of the time, resulting in much faster response times and a lower API bill. ― mCoding with James Murphy (https://mcoding.io) Source code: https://github.com/mCodingLLC/VideosSampleCode Bloom filters: https://en.wikipedia.org/wiki/Bloom_filter Confused about new Python 3.12 syntax? https://youtu.be/8l4UWz48Elc Made with Manim: https://docs.manim.community/ Sponsored by Hostinger: https://hostinger.com/mcoding SUPPORT ME ⭐ --------------------------------------------------- Sign up on Patreon to get your donor role and early access to videos! https://patreon.com/mCoding Feeling generous but don't have a Patreon? Donate via PayPal! (No sign up needed.) https://www.paypal.com/donate/?hosted_button_id=VJY5SLZ8BJHEE Want to donate crypto? Check out the rest of my supp

Published 2023-11-24 · Watch on YouTube ↗
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