Probabilistic Data Structures
summary
Overview of probabilistic data structures (like Bloom filters, HyperLogLog, Count-Min Sketch) for reducing memory use and improving performance when datasets are too large to fit in memory. Explains how these structures trade exactness for efficiency and shows concrete ways to apply them to common business problems to get faster, more memory‑efficient analytics.
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chapters (11)
- 0:00Introduction
- 3:43What are probabilistic data structures
- 12:23Bloom filters theory and practice
- 16:17Bloom filter code implementation
- 18:45When to use Bloom filters
- 20:32Count-Min Sketch counting problems
- 24:21Count-Min Sketch implementation and tests
- 27:39HyperLogLog unique value counting
- 33:26HyperLogLog comparison and efficiency
- 38:13Memory comparison of structures
- 38:37Bonus: string segmentation
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