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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
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CHAPTERS
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0:00 Intro
0:54 Sponsored message
1:50 Bloom filter interface
2:21 Why bloom filters?
3:10 How does it work?
4:31 Analyzing the False Positve Rate
6:33 Where to get k hashes?
7:30 Bloom filter implementation
8:44 Bit Arrays
9:32 Running the example code
11:11 Thanks